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PMC10001176

Konstantin A. Lusta,Anastasia V. Poznyak,Vasily N. Sukhorukov,Ilya I. Eremin,Irina I. Nadelyaeva,Alexander N. Orekhov

Hypotheses on Atherogenesis Triggering: Does the Infectious Nature of Atherosclerosis Development Have a Substruction?

23-02-2023

atherosclerosis,cardiovascular disease,inflammation,infections,lipoprotein modification,endothelial dysfunction,pathogen-associated molecular patterns,cytokines

Since the end of the 20th century, it has been clear that atherosclerosis is an inflammatory disease. However, the main triggering mechanism of the inflammatory process in the vascular walls is still unclear. To date, many different hypotheses have been put forward to explain the causes of atherogenesis, and all of them are supported by strong evidence. Among the main causes of atherosclerosis, which underlies these hypotheses, the following can be mentioned: lipoprotein modification, oxidative transformation, shear stress, endothelial dysfunction, free radicals’ action, homocysteinemia, diabetes mellitus, and decreased nitric oxide level. One of the latest hypotheses concerns the infectious nature of atherogenesis. The currently available data indicate that pathogen-associated molecular patterns from bacteria or viruses may be an etiological factor in atherosclerosis. This paper is devoted to the analysis of existing hypotheses for atherogenesis triggering, and special attention is paid to the contribution of bacterial and viral infections to the pathogenesis of atherosclerosis and cardiovascular disease.

Hypotheses on Atherogenesis Triggering: Does the Infectious Nature of Atherosclerosis Development Have a Substruction? Since the end of the 20th century, it has been clear that atherosclerosis is an inflammatory disease. However, the main triggering mechanism of the inflammatory process in the vascular walls is still unclear. To date, many different hypotheses have been put forward to explain the causes of atherogenesis, and all of them are supported by strong evidence. Among the main causes of atherosclerosis, which underlies these hypotheses, the following can be mentioned: lipoprotein modification, oxidative transformation, shear stress, endothelial dysfunction, free radicals’ action, homocysteinemia, diabetes mellitus, and decreased nitric oxide level. One of the latest hypotheses concerns the infectious nature of atherogenesis. The currently available data indicate that pathogen-associated molecular patterns from bacteria or viruses may be an etiological factor in atherosclerosis. This paper is devoted to the analysis of existing hypotheses for atherogenesis triggering, and special attention is paid to the contribution of bacterial and viral infections to the pathogenesis of atherosclerosis and cardiovascular disease. The most common heart diseases are prompted by atherosclerosis (AS). Thus, ischemia is caused by insufficient blood supply to the heart muscle. Oxygen starvation occurs when the lumen narrows as a result of plaque formation on the arteries’ inner walls. Lipids, cholesterol, calcium, and fibrin are deposited in atherosclerotic plaques [1]. These deposits block the blood flow, leading to thrombosis, heart attack, or stroke. Cardiovascular diseases (CVD) are the leading cause of death worldwide [2], and the main CVD contributor is AS of the coronary and other large arteries [3]. The main AS manifestations are damage to the vascular endothelial cells (VECs) of the vessel walls; recruitment of leukocytes and monocytes, with their subsequent transformation into macrophages in the vascular subendothelium; absorption of lipids by macrophages; the formation of fatty streaks; and calcification and fibrinization of the intimal layer. All these pathological changes in blood vessels lead to myocardial infarction and stroke [4]. AS is a chronic inflammatory disease, suggesting that various infections play an important role in its development. Many studies provide pieces of evidence that many different pathogens detected in atherosclerotic lesions initiate a cascade of inflammatory processes and accelerate plaque growth in the blood vessel walls [5]. The stressor effect of bacterial and viral infection on the vascular wall is the most substantial known risk factor [6]. The suggestions of the possibility of pathogenic bacteria and/or viruses’ involvement in AS pathogenesis are encouraged by coincidences between the CVD morbidity and infection markers; moreover, another piece of evidence is an enhanced rate of atherogenesis at pathogen infection [7]. The leading cause behind AS triggering has not yet been clearly established. There are many hypotheses on the possible causes of pathological process development. First of all, inflammation plays a key role in atherogenesis and is involved in every stage of this pathogenesis. The inflammatory nature of AS is now firmly established and is quite well studied. The driving forces behind inflammation are endothelial dysfunction, altered lipoprotein metabolism, hemodynamic shear stress, free radicals, hypertension, diabetes mellitus, genetic alterations, elevated level of homocysteine, infectious microorganisms, and viruses. Notably, the most probable scenario could be the combination of all these or other factors [4,8]. Inflammation operates as a common basis for atherogenesis and progression. In the blood vessel walls, inflammation is accompanied by the release of pro-inflammatory cytokines and chemokines, bioactive lipoproteins, adhesion molecules, and the involvement of signaling pathways [9,10]. After injury, VECs become activated and produce inflammatory molecules: in particular, monocyte chemoattractant protein-1 (MCP-1), IL-8, intercellular adhesion molecule-1 (ICAM-1), vascular adhesion molecule-1 (VCAM-1), E-selectin, P-selectin, and other inflammatory factors. Via these molecules, the recruiting of immune cells, such as lymphocytes and monocytes, is implemented. They attach to VECs, enter the vessel wall, and initialize inflammation. A wide range of inflammatory mediators are released by T and B cells, dendritic cells, vascular smooth muscle cells (VSMCs), and VECs, which results in the activation of cytokines, chemokines, bioactive lipids, and adhesion molecules that initiate the local inflammation and progression of focal AS damages [11]. Lipoprotein accumulation in the intima layer of the vessel wall and massive migration of immune–inflammatory cells such as lymphocytes and monocytes correlate with major histocompatibility complex (MHC) class II molecule expression in diffuse intimal thickening (DIT), which is the earliest pre-lesional stage in AS development [12]. The monocytes then differentiate into macrophages (MPhs). Among all the immune cells, monocytic MPhs are the major contributors to AS lesions, as they promote arterial inflammation, produce reactive oxygen and nitrogen, secrete a variety of pro-inflammatory mediators in response to stimulation, phagocytize mLDL-C, and then develop into foam cells [13]. Monocytes recruited into vessel walls can differentiate into MPh of various subsets with different phenotypes and functions depending on the specific stimuli. MPhs differentiate into pro-inflammatory (M1) and anti-inflammatory (M2) cell populations emitting the corresponding pro- and anti-inflammatory cytokines. M1 macrophages are pro-atherogenic, while M2 macrophages may promote tissue repair and have anti-inflammatory properties [14]. Under normal conditions, MPhs respond to damage-associated molecular patterns (DAMPs), pathogen-associated molecular patterns (PAMPs), or mLDL-C, which results in a pro-inflammatory immune response and the release of appropriate cytokines. Following this, immuno-tolerance is developed, and upon that development, MPhs do not respond to previous stimuli, meaning that inflammation does not progress. However, in the case of mutations in the MPhs’ DNA, the cells lose the immuno-tolerance, as a result of which they react to external stimuli and begin to intensively secrete pro-inflammatory cytokines, leading to chronic inflammation and CVD [15]. M1 macrophages are stimulated by Th1 cytokines, such as GM-CSF and IFNγ, as well as lipopolysaccharide (LPS), fatty acids, and HMGB1 (a chromatin protein that interacts with transcription factors, organizing DNA and regulating transcription). M1 MPhs are regarded as pro-atherogenic, since they produce high levels of TNF-α, NO, IL-1β, IL-6, IL-12, and IL-23, which play a crucial role in tissue destruction. M1 expresses pro-inflammatory transcription factors, such as nuclear factor-κB, signal transducer, and activator of transcription (STAT) 1. On the contrary, M2 MPhs are polarized by Th2 cytokines such as IL-4, IL-10, IL-13, and M-CSF. M2 MPhs produce anti-inflammatory cytokines such as IL-10 and TGF-β to restrain inflammation and accomplish tissue repair [16]. MPhs have bilateral capabilities to overthrow pathogens even as they repair inflammation-generated vessel disturbance. Through M1/M2 MPhs differentiation, balance manipulates the conditions of the vessel wall in inflammation or injury [17]. The different MPh subsets are intimately involved throughout atherosclerosis progression and in models of regression. Elucidation of the role of the balance between pro-inflammatory and anti-inflammatory factors in atherogenesis will allow the development of new pharmacological and gene treatments for AS and CVD [18]. VSMCs also play an important role at all stages of AS development. During atherogenesis, they migrate from the media into the intima, wherein they undergo phenotypic conversion and restructuring into several cell types, such as Mphs, mesenchymal stem cells, osteochondrogenic-like cells, myofibroblast-like cells, and proliferative synthetic cells, that synthesize extracellular fibrous matrix and thus provide added value to AS progression [19]. Upon mLDL-C consumption by VSMC, they transform into MPh-like cells, expressing the scavenger receptor CD68 and other specific MPh markers, such as CD11b and galectin-3. VSMCs can switch to different phenotypes, demonstrating their role in AS progression. Transformed VSMCs are characterized by increased secretion of pro-inflammatory cytokines and exosomes that can prompt osteopontin expression and release calcium sediments. VSMCs can transubstantiate into endothelial-like cells characterized by CD31 expression influenced by shear stress. The main function of VSMC in the formation of AS plaques is the production of fibronectin, collagen 1 alpha 1, and proteoglycans [20]. The accumulation of somatic mutations with age is a direct consequence of the constant effects of stress on cells, as well as exposure to DNA-damaging chemical agents. Mutations, called “driver mutations”, provide cells with a selective advantage. Chronic inflammation or, for example, constant exposure to certain stimuli allow cells carrying the corresponding driver mutations to become dominant in the population. Therefore, when a single ancestor cell of a positively expanded clone receives additional driver mutations, cancer cells appear. Remarkably, clonal expansion occurs even in tissues that appear normal. Among the different forms of clonal expansion, clonal hematopoiesis (CH) has been most intensively studied. The term CHIP (CH of indeterminate potential) refers to the presence of at least one driver mutation in peripheral blood hematopoietic cells without hematological malignancy. The precursors of these cells are hematopoietic stem cells (HSCs), which have acquired somatic driver mutations. As in stem cells of other tissues, the accumulation of somatic mutations in HSCs occurs in an age-dependent manner. CHIP is a significant risk factor for the development of several different pathologies, such as acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasms (MPN), as well as cardiovascular diseases. According to available data, diabetes, hypercholesterolemia, and other metabolic diseases can provoke changes in HSPC function and monocytosis. The differentiation of inflammatory monocytes is one of the central events in the pathogenesis of atherosclerosis. In 2014, there was evidence that CHIP-associated mutations are also associated with the risk of cardiovascular disease. A 2017 study showed that CHIP, together with mutations in the DNMT3A, TET2, ASXL1, or JAK2 genes, was associated with an increased risk of cardiovascular disease. Several studies in LDLR knockout mice suggest a causal relationship between the activation of inflammation in macrophages due to CHIP-associated mutations and the pathogenesis of cardiovascular disease [21]. CRP is an important indicator of the inflammation process and AS progression in the vessel walls [22]. An increase in the CRP level is also a significant risk factor for CVD [23]. With inflammation, pentameric CRP dissociates into five subunits, and the monomeric CRP (mCRP) acquires proatherogenic properties. mCRP activity enhances up to 1000-fold in response to acute inflammation. Under these circumstances, CRP can be considered a biomarker of inflammation and CVD. It participates in recruiting lymphocytes and monocytes, releases a variety of cytokines, such as Interleikin-6 (IL-6) and TNF-α, and also takes part in switching monocytes and T cells to pro-inflammatory phenotypes. Moreover, it is implicated in modified LDL (mLDL) ingestion by macrophages. Thus, CRP is closely associated with many factors involved in the atherogenesis process. mCRPs were found in atherosclerotic lesions from the human aorta, carotid, and coronary arteries. Thus, CRP involvement in atherogenesis has been established [24]. The bloodstream CRP grade of concentration is a superb instrument for diagnosis and controlling administration of patients with atherosclerosis [25]. CRP is absent in vessel walls unaffected by AS, but the CRP content rises in atheromatous tissues and increases progressively as AS develops. Accordingly, it possesses the ability to predict the risk of AS and CVD [26]. Cytokines and chemokines are the soluble factors that can activate various cells involved in AS pathogenesis. Pro-inflammatory cytokines stimulate atherogenesis progression. On the other hand, anti-inflammatory cytokines inhibit inflammation and have a beneficial effect on the disease. Cytokines could be divided into several classes: interleukins (ILs), tumor necrosis factors (TNFs), interferons (IFNs), transforming growth factors (TGFs), colony-stimulating factors (CSFs), and various chemokines. They are produced by different cells, such as T helper cells, monocytes, MPhs, and B cells. T helper cells localized in vascular walls were subdivided into two categories: Th1, producing pro-inflammatory cytokines, such as TNF-α, IL-1β, IL-6, IL-12, IL-18, and IL-23; and Th2, producing anti-inflammatory cytokines, such as IL-4, IL-10, IL-13, IL-19, IL-33, IL-35, and M-CSF. During AS progression, the array of cytokines produced by Th cells may be dynamically changed. An aggravated pro-inflammatory cytokine production leads to AS progression and promotes CVD [27]. Immunohistochemical localization of the inflammation markers in the human aortic wall during atherogenesis revealed the differences in localization of pro-inflammatory cytokines (TNF-α) and anti-inflammatory chemokines (CCL 18). At the initial stages, the association of TNF-α with immune and smooth muscle cells and diffuse distribution of CCL 18 chemokine, as well as a significantly increased expression of CCL 18 marker in atherosclerotic lesions and their translocation from the upper zone of the intima to tunica media, were demonstrated. Simultaneously, the reduction in the level of the pro-inflammatory cytokine TNF-α was recorded with the development of atherosclerosis [28]. This inflammatory cytokine synthesized by activated macrophages and T cells represents an important component of the immune system. It functions as a B cell stimulatory factor and causes terminal differentiation of B-lymphocytes to plasma cells. IL-6 controls the course of the inflammatory process. Consequently, IL-6 could be used as a marker to predict CVD [29]. MHC class II—HLA-DR antigen is normally expressed only by cells of the immune system. These antigens can be specifically recognized by CD4+ Th cells that facilitate activation of MPhs and B cells releasing cytokines, such as interleukins and interferon (IFN)-γ, at the site of infection of AS lesions [30]. The atherosclerotic plaques, however, are composed of a heterogeneous population of cells, which includes VECs, VSMC, MPhs, and T cells. In atherosclerosis-transformed tissue, the majority of all these cell types express MHC class II—HLA-DR. In contrast, very few HLA-DR-positive cells were found in normal human arteries, so MHC may be presented as an AS marker [31]. CAMs are a subset of cell surface proteins involved in the binding of cells with other cells or with the extracellular matrix. They include isoforms such as ICAM-1, VCAM-1, E-selectin, and P-selectin. CAMs play a significant role in AS development [32]. They are involved in numerous processes in the organism, including cell identification, mobilization, signal transduction, and differentiation. They also intermediate inflammation and immune responses and participate in the AS plaque progression [33]. The expression of CAMs is accomplished in VECs, and leukocytes in the bloodstream reciprocate inflammatory stimuli at the beginning of AS development. With that, CAMs mediate the recruitment of inflammatory cells from the circulation and their transendothelial migration [34]. All isoforms of cell adhesion molecules were identified in areas of atheromatous lesions [35]. All these results suggest that CAMs can be used for therapeutic purposes [36]. One of the most common and well-known causes of AS development is an increased and modified low-density lipoprotein cholesterol (mLDL-C) infiltrating the vessel endothelium, which triggers intimal accumulation and retention of apolipoprotein B (apoB)-containing LDL in focal areas of arteries, followed by inflammatory response [37,38,39]. Among the several subclasses of LDL particles, which have various dimensions and compactions, the small dense LDL (sdLDL) particles present a maximal atherogenic capability compared to the rest of the LDL subcategories, so they are superior markers for diagnosis of cardiovascular disease [40]. mLDL, such as oxLDL, tends to aggregate in the intima of blood vessels. These aggregates stimulate leukocyte recruitment and phagocytosis of macrophages and contribute to the secretion of pro-inflammatory cytokines, accumulation of intracellular cholesterol, and foam cell formation [8]. Several genes affinitive with signaling pathways, in particular F2RL1, EIF2AK3, and IL15, are responsible for encoding inflammatory molecules and regulating the interaction of mLDL with macrophages [41,42]. There is much evidence concerning the relationship between CRP and mLDL in the atherosclerotic process [43]. Elevation of both CRP and mLDL levels predicts the risk of CVD. Notably, the CRP level is a major indicator of cardiovascular events compared to the LDL-C level in predicting CVD [44]. It was established that CRP is capable of specifically binding to mLDL through calcium participation [45]. These two associated compounds are present in AS plaques and indicate the excessive risk of CVD [46]. The stages of AS progression upon LDL-C modification are as follows: after modification and oxidation of LDL-C in blood, plasma mLDL (oxLDL) is converted into atherogenic lipoprotein. A cytotoxic effect of mLDL on endothelial cells, the stimulation of chemotaxis, and the migration of monocytes into the intima with their subsequent differentiation into macrophages occur. Then, macrophages ingest m-LDL (by scavenger receptors) and transform into foam cells. m-LDL accumulates in fatty streaks, cytokines and chemokines are released, and the unfolding of the inflammatory response cascade occurs. Hyperplasia of smooth muscles occurs, as well as extracellular fibrous matrix production and the formation of lipofibrous plaque [47]. Many factors contributing to LDL modification in the bloodstream and vessel walls exist. Upon modification, LDL transforms into mLDL, changing size and physicochemical characteristics. Among these changes, LDL oxidation is believed to be the primary modification in AS development. The risk factors contributing to oxLDL formation include, for example, genetic predisposition, smoking, infection, hypertension, and diabetes. The pertinent enzymes that participate in LDL oxidative modification include NADPH oxidase, lipoxygenases, xanthine oxidase, myeloperoxidase, reactive oxygen species (ROS), and endothelial nitric oxide synthase (eNOS) [48]. OxLDL can bind to scavenger receptors (SR), including SR-A1, SR-A2, and LOX-1. OxLDL also upregulates the expression of the LOX-1 receptor on VECs and manages the activation of these cells. Moreover, oxLDL gives rise to the growth and displacement of VSMCs, monocytes, and fibroblasts. Additionally, oxLDL promotes ROS production, which, when in excess, triggers oxidative stress [49]. OxLDL can initialize the inflammation process through the activation of macrophages and other immune cells. As a result of oxLDL focal infiltration, foam cell formation and fatty streaks development occur, followed by atherosclerotic plaque formation after endothelial dysfunction, monocyte transmigration to the vessel wall, proliferation and displacement of VSMCs, and platelet activation [50]. One of the most important factors in maintaining endothelial homeostasis under normal physiological conditions is the friction force acting on endothelial cells, otherwise known as hemodynamic shear stress. Considering endothelial dysfunction and the effect of blood flow on it, one can speak of two types of blood flow, stable laminar flow and disturbed flow, since endothelial cells respond differently to these types of flow both in vivo and in vitro. Laminar flow, which is exerted by steady laminar shear stress, is atheroprotective, while disturbed flow promotes atherosclerosis. Emerging data have provided new insights into the cellular mechanisms of flow-dependent regulation of vascular function, which leads to cardiovascular events such as atherosclerosis, atherothrombosis, and myocardial infarction [51]. Shear stress eventuates in vascular endothelium sites because frictional force arises in blood circulation predominantly in areas of artery curves, branching, and bends due to the mechanical impression. It exerts a significant impact on inflammation triggering, vascular dysfunction, and AS. Immune cells swoop down to these damaged endothelium areas and infiltrate into the intima, transforming into macrophages that actively swallow up mLDL [52]. The mechanisms behind shear stress functioning are not completely unraveled. For a better understanding of these mechanosensitive signaling pathways (MSP) in arteries, systems biology approaches are employed, including transcriptome, proteome profiling, and functional screening platforms [53]. The vascular endothelium has specialized receptors that specify blood flow and convey signals with the aid of MSP to responding compounds that result in atherogenic development. In many clinical observations, it has been established that atherosclerotic distinctive features revealed themselves predominantly at arterial inflections that represent the areas shown to have altered blood flow. In these endothelium areas, the inflammatory process is initiated, the release of nitric oxide is ramped down, barrier function is reduced, and adhesive power and proliferative efficiency are increased. These endothelial peculiar features may elucidate the preferred advent of atherogenesis in arterial sites with disturbed flow [54]. Endothelial dysfunction is caused by a wide range of risk factors, such as high levels of mLDL (oxLDL), dyslipidemia, insulin resistance syndrome, diabetes, arterial hyperglycemia, genetic defects, dysregulated renin–angiotensin system, hypertension, and shear stress [55]. There are several pathological occurrences of endothelial dysfunction, such as nitric oxide synthase dysfunction and activation of inflammatory mediators, including oxLDL, cytokines, and other molecular patterns. Pro-inflammatory cytokines are the key figures in atherogenesis, imparting the AS plaque formation in affected vessels. The switching-in of multiple signaling pathways, NF-kβ in particular, which controls the DNA transcription, triggers the overproduction of adhesion molecules, selectins, and chemokines that promote monocyte migration to the vascular intima, VECs apoptosis, flow-sensitive microRNA regulation, activation of coagulation pathways, and downregulation of thrombomodulin, leading to AS progression [56]. This hypothesis proposes that the main causes of AS development are endothelial dysfunction, which is accompanied by insufficient NO production, and substances such as prostacyclin, hyperpolarizing factor, and endothelin [57]. Taking together, VECs play an ultimate role in the process of AS and CVD development through their regulatory functions. Nitric oxide (NO) is a product of transforming L-arginine to L-citrulline by the enzymatic action of NO synthase (eNOS) in the endothelium [58]. In the case of atherogenesis, one of the main NO functions is vascular endothelium-dependent relaxations (EDR), which is accomplished during NO liberation from the vascular endothelium, and so this agent acts as an endothelium-derived relaxing factor (EDRF) [59]. NO, as EDRF, inspires guanylate cyclase of the vascular smooth muscle that leads to an escalation in cGMP activating relaxation. eNOS represents an anti-atherogenic substance, and insufficiency of eNOS leads up to atherogenesis triggering. AS is concerned with an EDR malfunction, which comprises the decrease in NO bioavailability. The main cause of this event in AS is superoxide excreted through the dysfunction of eNOS as a result of all sorts of pathological conditions. Consequently, EDRF activity is blocked by superoxide. In addition, defective eNOS may disable EDR, and it can also damage atherosclerotic vessels [60]. Homocysteine is a derivative of the amino acid methionine resulting from the metabolic process. The increase in blood homocysteine proceeds from B-vitamin limitation, genetic factors, and some kinds of drugs. Hyperhomocysteinemia initiates oxidative stress, endothelial dysfunction, an increase in arterial pressure, AS, and thrombosis. Homocysteine is the AS causative agent that acts as an independent risk factor. An elevated homocysteine level in circulation increases CVD risk nearly two-fold [61,62]. Homocysteine contributes to vascular inflammation and atherosclerosis acceleration driven by excessive emission of inflammatory factors, especially IL-1β [63]. Diabetes mellitus is triggered by disorders of carbohydrate metabolism. The disease is characterized by a high blood glucose level (hyperglycemia) due to either the lowering of insulin production by the pancreas or the insulin resistance of target cells. Cardiovascular disease and AS associated with diabetes are dependent not only on hyperglycemia but also on alterations in lipids, changes in hormones in addition to insulin, and a pro-inflammatory state [64]. Diabetes-associated dyslipidemia is a key element of influence on atherogenesis. Alteration of the blood lipid composition in diabetes is linked to the increased formation of atherogenic lipoproteins [65]. Elevated glucose levels, insulin resistance syndrome, dyslipidemia, oxidative stress, inflammation, and other alterations have a great impact on atherogenesis. Concordantly, diabetes mellitus exerts an influence on the atherogenic process through its relationship with chronic inflammation [66]. An idea proposed by William Osler at the beginning of the 20th century that infections also play a role in atherosclerosis [67] has been backed up by an increasing body of evidence at the end of the 20th and beginning of the 21st century. Since the end of the 1970s, the possible involvement of microorganisms and viruses in AS development has been widely discussed in the literature. In several studies, it has been demonstrated that animals infected with various pathogens showed significant arterial changes, compared with the control groups of animals. Microscopically, these lesions were characterized by intimal thickening, which formed fibrous caps that overspread the atherosclerotic change loci [68]. Infectious agents also influence lipids metabolism and promote cholesterol accumulation in the arterial walls [69]. At that time, there were no direct pieces of evidence for infectious agents involved in the atherogenic process. Since then, much research has been carried out suggesting that bacteria and viruses entail cellular and molecular changes in vessel walls. The serological evidence of antibodies to some groups of bacterial and virus antigens present in patients with chronic coronary heart disease and acute myocardial infarction, which have been absent in the control group of patients, was demonstrated. Moreover, a co-relation of IgG antibodies against these pathogens with AS of large arteries has been elicited [69]. Beyond that, the pathogens, such as Chlamydia pneumonia, Helicobacter pylori, human cytomegalovirus (HCMV), Epstein–Barr virus (EBV), and herpes simplex virus (HSV), have been identified in human arterial lesion samples through the use of histopathological, immunocytochemical, and ultrasonographic imaging studies, apart from seroepidemiological assays. Pathogens can transform VSMC, followed by migration of these cells in AS lesions. Various vascular cell types, such as VSMC, VECs, and leukocytes, presented in AS lesions can produce and react to cytokines [70]. The hypothesis on the variety of atherogenic-associated pathogens’ existence gradually became more and more popular [71]. Several studies have been carried out to identify bacterial DNA in biopsy samples from AS lesions of patients using various methods. The list of identified microorganisms can be found in Table 1. However, the microbiome composition in atheromatous regions may significantly differ from patient to patient. Therefore, it seems difficult to determine which of the identified bacterial species are involved in atherosclerotic plaque development. Further, more precise research methods are needed to solve this problem. The presence of a wide range of bacterial species in atheromatous lesions may appear to have a greater effect on vascular tissues than individual species. The detection of numerous types of bacteria in vascular atheromas by different methods does not allow us to make an unambiguous conclusion that pathogens can be an etiological factor that triggers atherogenesis in the vessels. However, the microbiome may be a concomitant factor capable of exacerbating and/or accelerating inflammation and disease progression [76]. A large number and variety of bacterial types in areas of atherosclerotic vascular lesions may be associated with increased resistance of the microbiome to antibiotics, immune cells, and other factors due to the biofilm formation and buildup of an insoluble exopolymer matrix [77]. Thereby, a bacterial biofilm structure may complementarily be responsible for inflammation in the pathogenesis vessel walls and cause the AS development [74]. Risk of infection-induced AS development depends at least in part on the amount of atherogenic pathogens in the infected patient, as well as on their responsivity to the lesion effects of pathogens; in other words, whether the host is able to create an immune response and thus control the infectious process and a pathogen-induced atherogenesis [78]. It was also established that infection was associated with an increase in the content of fibrinogen, leukocytes, clotting factor, cytokines, and elevated levels of CRP in the blood, as well as a clear change in the functioning of the vascular endothelium, monocytes, and macrophages. Moreover, it is accompanied by CVD and acute ischemic symptoms [79]. “Infection hypothesis” does not disable the contribution of all other risk factors for atherosclerosis. As a matter of fact, infections may functionalize through the mediation of or in consort with all other factors, such as mLDL-C, homocysteinemia, shear stress, endothelial dysfunction, action of free radicals, diabetes mellitus, and decreased nitric oxide level. Infection caused by pathogenic bacteria or viruses may be responsible for inflammatory reaction cascades in areas of atherosclerotic lesions in arteries, and, simultaneously, any of the risk factors may also lie behind this inflammation and atherogenesis. In Table 2, we summarize the involvement of selected pathogens in atherosclerosis development. A huge variety of bacteria inhabit the oral cavity [80]. It has been shown that some of the oral microbiota bacterial species demonstrate a pronounced correlation with atherosclerotic vascular lesions and an increase in blood cholesterol levels. In particular, Koren et al. noted bacteria from the genera Chryseomonas, Veillonella, and Streptococcus. These bacteria were highly numerous both in the oral cavity and in the AS plaques simultaneously in the same patient [89]. A number of studies have presented data on the close relationship between chronic periodontitis (CP) and CVD as well as CP’s correlation with blood cholesterol levels [90]. Detection of CP-causing bacteria in vessel wall areas impaired by AS was performed particularly by using PCR procedure. Moreover, DNA–DNA hybridization was also administered to estimate the periodontal pathogens in subgingival areas from CP patients and control group. The number of bacteria colonizing areas of the vessel walls affected by AS in patients with periodontitis significantly exceeded those in patients without CP. These data suggest the implication of periodontal bacteria in atherogenesis. A great number of bacterial species were identified in atherosclerotic lesion samples isolated from CP patients. Among them, there are many established periodontopathogen bacteria, such as Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Aggregatibacter actinomycetemcomitans, Prevotella intermedia, and Fusobacterium necrophorum [91]. Comparison of results obtained from patients with and without CP led to the conclusion that these bacteria are implicated in AS pathogenesis [92]. The comparative quantification of periodontal pathogens (such as Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Campylobacter rectus, and Tannerella forsythia) in subgingival samples, blood, and vessel walls from patients with AS disorders was performed using two molecular identification methods (nested PCR and quantitative PCR). The results confirm the assumption that bacteria are able to move from the periodontal area into the vessel walls with AS lesions [93]. Some recent research has contributed to understanding the relationship of gut microbiota with inflammation and atherogenesis. Thus, Brandsma et al. (2019) conducted experiments on the transplantation of fecal microflora from Caspase1-/-(Casp1-/-) mice to Ldlr-/-mice and revealed AS acceleration, an elevated level of blood leukocytes, pro-inflammatory cytokines, and neutrophil accumulation in atherosclerotic plaques of Ldlr-/-mice, compared to the control group [94]. AS progression and CVD are caused specifically by disarrangements in the intestinal microflora and gut dysbiosis [95]. An increase in the gut bacteria transmigration into the circulating blood, as well as bacterial metabolic products, leads to the emergence and intensification of the inflammatory process in the vessel wall. Some of the bacterial metabolites released into the bloodstream, such as trimethylamine and trimethylamine N-oxide, short-chain fatty acids, and secondary bile acids, are involved in AS and CVD progression [96]. In several studies, an obvious relationship was noted between abnormal metabolism of the gut microbiome and the progression of inflammation and AS. The mechanisms of gut microbiota’s impact on atherogenesis appear to comprise the initiating of inflammation processes via alterations of lipid metabolism in adipocytes, macrophages, and VECs, stimulating insulin resistance, and producing trimethylamine-N-oxide. The triggering factor of lipid metabolism abnormalities could be bacterial LPS delivered from the intestine to the bloodstream via chylomicron transfer [97]. It has been established that LPS can increase the morbidity and mortality of AS-related CVD. Particularly, LPS enlarges the intimal layer and facilitates lipid accumulation through the mobilization of the TLR4-NF-κβ pathway. Moreover, LPS promotes MCP-1 generation from activated adventitial fibroblasts, followed by monocyte capture to the vessel wall, accumulation of lipids in macrophages, and foam cell formation [98]. Concerning changes in the bacterial composition of the gut microbiome affecting CVD development, it has been observed that a decrease in Bacteroidetes and an increase in Firmicutes, the most representative groups of bacteria in the gut, have an appreciable impact on atherogenesis and CVD occurrence [99]. In the first stage, infectious agents directly damage VECs, which leads to increased ROS production. Elevated ROS production by polymorphonuclear neutrophils at the inflammation area causes oxidation of cellular signaling proteins, such as tyrosine phosphatases, and promotes the migration of inflammatory cells through the endothelium [100]. This not only destroys bacterial and viral pathogens but leads to tissue damage. Moreover, ROS can promptly bind to NO, forming reactive nitrogen species (RNS). RNS causes nitrosative stress, which supplements the pro-inflammatory charge of ROS [101]. Pathogen-induced oxidative stress is one of the most important factors causing CVD. ROS, localized in VECs, plays a key role in provoking an inflammatory response, cell apoptosis, and promoting NF-κb signaling [102]. Inflammation in vascular walls generated by infection is the first stage of AS. Inflammation causes VECs to induce heat shock protein 60 (Hsp60) secretion. T-cell-mediated autoimmune reaction against Hsp60 is the initiating episode in atherogenesis, as suggested by Wick G. et al. in 1995. The inflammatory stage that follows progresses with atherosclerotic lesions and goes through all the classical pathological effects [103]. Hsp60 is a molecular chaperone, participating in protein folding and impeding the magnification of misfolded proteins. In the vascular wall, Hsp60 has a preserving function under physiologic conditions. In the case of any pathology, though, Hsp60, localized on the VECs surface, triggers autoimmune processes [104]. The protein GroEL is also a member of the chaperonin family and is found in many bacteria. It is very close in structure and function to Hsp60. GroEL can enhance monocyte adhesion by elevating the expression of ICAM-1 and VCAM-1 in VECs. Moreover, GroEL forced an increase in oxLDL uptake, which depends on the elevated LOX-1 expression. Furthermore, GroEL can interrelate with TLR4 and thus trigger atherogenic events in vessel walls. Accordingly, bacterial GroEL may contribute to CVD by influencing TLR4 expression [105]. TLRs are representative of pattern recognition receptors (PRRs) that are involved in immune processes by identification of pathogenic bacteria and viruses that come across the blood vessel walls. Accordingly, TLRs perform the function of host cells protecting from pathogens [106]. Apart from pathogen recognition, TLRs have numerous other functions, such as coordination between innate and adaptive immunity, regulation of cytokine production, cell proliferation, and supporting survival of the organism in adverse conditions [107]. After TLRs’ sensitization, they are also directly involved in the AS progression caused by infectious agents. The participation of these receptors in the atherogenesis-associated processes suggests the feasibility of using them as targets for the therapeutic approaches developed to prevent CVD [108]. A significant contribution to the stress state of VECs is made by the overproduction of pro-inflammatory cytokines and adhesion molecules induced by infectious pathogens that promote leukocyte and monocyte migration and infiltration into the vessel wall intima [109]. Cytokines comprise several classes, such as interleukins (IL), chemokines, colony-stimulating factors (CSF), tumor necrosis factors (TNF), interferons (IFN), and transforming growth factors (TGF). Cytokines are involved in all stages of atherosclerosis, and all cells implicated in atherogenesis are capable of cytokine production [110]. These molecules perform a dual function. Pro-inflammatory cytokines facilitate AS development, while anti-inflammatory cytokines (IL-4, IL-10, IL-13, TGF-β) display antiatherogenic properties. Pro-inflammatory cytokines, such as TNF-α, IL-1, IL-12, IL-18, CD40L, M-CSF, and IFN-γ, modify the endothelium’s functioning, which leads to the loss of barrier function and intensification of the leukocytes and monocytes’ influx into the vascular walls. Leukocytes that arrive at the intima of the vascular wall transform into macrophages under the influence of local cytokines. IFN-γ can promote foam cell formation via initiation of scavenger receptors that mediate uptake of mLDL and subsequent conversion of macrophages to foam cells. Then and there, IFN-γ binds the two global body functions: immunity and lipid metabolism [111]. An NF-κβ signaling pathway is one of the highest relevance in the regulation of innate immunity and inflammation processes. NF-κβ is an ancient protein complex operating as a transcription factor that is upregulated in response to a variety of detrimental stimuli, such as inflammation [112]. It was established that NF-κβ is forced into lipid metabolism and atherogenesis processes, including foam cell formation, vascular inflammation, the proliferation of VSMCs, vessel wall calcification, and plaque development [113]. There is a growing body of evidence allowing consideration that NF-κβ plays a substantial role in all stages of atherogenesis through engagement genes, membranes, protein complexes, cytokines, chemokines, and hormones. Therefore, the NF-κβ signaling pathway may serve as a target for therapeutic approaches to discourage an inflammatory process in the blood vessel walls [114]. This pathway is upregulated in response to pro-inflammatory cytokines generation, which results in the TLRs’ activation by the pattern recognition of pathogen-associated molecular patterns (PAMPs). Activation of the NF-κβ pathway plays a fundamental role in the process of inflammation, which is carried out by regulating the expression of genes encoding growth factors, VCAM-1, E-selectin, IL-1, IL-6, IL-8, tissue factor, plasminogen activator inhibitor (PAI)-1, cyclooxygenase (COX)-2, and iNOS. Further development of atherogenesis processes leads to damage of the vessel walls and vascular cell dysfunction [57]. It was also shown that MCP-1 heightened production is induced by many pathogens. This chemoattractant is designated for the transmigration of monocytes [115]. The elucidation of molecular mechanisms behind the cascade of inflammatory responses associated with chronic infection provides more and more evidence on behalf of the infection hypothesis and the interdependency between infections and CVD [5,72]. Numerous hypotheses about the causes of triggering the atherogenesis mechanism indicate the extraordinary complexity of this phenomenon. Therefore, it is not reasonable to accentuate any single main contributor to initiating AS progression. However, based on growing evidence, a close relationship has been established between atherogenic CVD and various infections. Thus, the paradigm of the infectious nature of atherogenesis may become prevalent. The assumptions that pathogens become involved in the etiology of atherosclerosis are built on the following factual knowledge: (1) the occurrence of antigens to the particular pathogens in atheromatous degeneration; (2) the presence of DNA or RNA sequences of bacteria and/or viruses in atherosclerotic lesions; (3) the relationship between concrete pathogenic infection and accelerated AS plaque growth; (4) the significant effects on cholesterol metabolism in vascular smooth muscle cells infected with pathogens, resulting in cholesteryl ester accumulation; (5) vascular endothelial cells losing anticoagulant capabilities when infected by a pathogen; (6) increased recruiting of inflammatory cells to a pathogen-infected vascular wall; (7) the ability of infectious agents to stimulate the production of cytokines and other pro-inflammatory factors by vascular and inflammatory cells. Thus, the involvement of pathogenic bacteria and viruses in atherogenesis triggering, considering all the evidence presented, is quite evident. However, two approaches to the pathogen’s action are plausible: either through the direct infection of vascular cells or via the indirect effects of cytokines or any other proatherogenic factors induced by infection.

PMC10001180

Natalia Landeros,Iván Castillo,Ramón Pérez-Castro

Preclinical and Clinical Trials of New Treatment Strategies Targeting Cancer Stem Cells in Subtypes of Breast Cancer

24-02-2023

breast cancer,biomarker,metastasis,breast cancer stem cells,stemness

Breast cancer (BC) can be classified into various histological subtypes, each associated with different prognoses and treatment options, including surgery, radiation, chemotherapy, and endocrine therapy. Despite advances in this area, many patients still face treatment failure, the risk of metastasis, and disease recurrence, which can ultimately lead to death. Mammary tumors, like other solid tumors, contain a population of small cells known as cancer stem-like cells (CSCs) that have high tumorigenic potential and are involved in cancer initiation, progression, metastasis, tumor recurrence, and resistance to therapy. Therefore, designing therapies specifically targeting at CSCs could help to control the growth of this cell population, leading to increased survival rates for BC patients. In this review, we discuss the characteristics of CSCs, their surface biomarkers, and the active signaling pathways associated with the acquisition of stemness in BC. We also cover preclinical and clinical studies that focus on evaluating new therapy systems targeted at CSCs in BC through various combinations of treatments, targeted delivery systems, and potential new drugs that inhibit the properties that allow these cells to survive and proliferate.

Preclinical and Clinical Trials of New Treatment Strategies Targeting Cancer Stem Cells in Subtypes of Breast Cancer Breast cancer (BC) can be classified into various histological subtypes, each associated with different prognoses and treatment options, including surgery, radiation, chemotherapy, and endocrine therapy. Despite advances in this area, many patients still face treatment failure, the risk of metastasis, and disease recurrence, which can ultimately lead to death. Mammary tumors, like other solid tumors, contain a population of small cells known as cancer stem-like cells (CSCs) that have high tumorigenic potential and are involved in cancer initiation, progression, metastasis, tumor recurrence, and resistance to therapy. Therefore, designing therapies specifically targeting at CSCs could help to control the growth of this cell population, leading to increased survival rates for BC patients. In this review, we discuss the characteristics of CSCs, their surface biomarkers, and the active signaling pathways associated with the acquisition of stemness in BC. We also cover preclinical and clinical studies that focus on evaluating new therapy systems targeted at CSCs in BC through various combinations of treatments, targeted delivery systems, and potential new drugs that inhibit the properties that allow these cells to survive and proliferate. Breast cancer (BC) is the most diagnosed cancer and the leading cause of cancer death in women worldwide [1]. BC has a worldwide incidence of 2,261,419 new cases per year (11.7% of all cancers) and 684,996 deaths (6.9% of all cancer-related deaths) in 2020 [2]. Rates of BC incidence and mortality rates in women are primarily influenced by geographic location and socioeconomic status. High-income North America, Oceania, and Western Europe countries have higher BC incidence rates, while lower-middle-income countries such as South America, East Africa, and Central Asia report fewer women diagnosed with BC but higher mortality, mainly due to late diagnosis and lack of health care resources [3]. BC is considered a complex and multifactorial disease, that arises from the accumulation of multiple genetic alterations and environmental factors. Six of the most relevant risk factors that can increase the possibility of developing BC are: (i) gender, most BC occur in women, only 1% of all BC cases occur in men [4]; (ii) age, the incidence rises sharply as people aged; (iii) reproductive factors such as early menarche, late menopause, late age at first pregnancy and low parity [5,6]; (iv) estrogen, both endogenous and exogenous are associated with the risk of this cancer; (v) modern lifestyles including excessive alcohol consumption and excessive fat intake in the diet, can increase the risk of breast cancer [7], and (vi) family history, almost a quarter of all cases of BC are related to family history, where women, whose mother or sister have BC, are prone to this disease [7,8]. The mammary gland is a highly dynamic organ that undergoes multiple phases of remodeling [3]. It is composed of an epithelium containing luminal and basal epithelial cells. The luminal cells form the ducts of the mammary gland, while the basal cells surround the luminal cells, and are in contact with the basal membrane [9]. This apparently hierarchical organization depends on a variety of stem and progenitor cells that populate the mammary gland [3]. The World Health Organization has defined 21 histological types of BC [10]. Breast adenocarcinomas more common are of two types: one that begins in the cells of the ducts (ductal adenocarcinoma) or in the lobules (lobular adenocarcinoma) (Figure 1), which can be in situ, if it has not spread, or invasive (infiltrating) if it has invaded other surrounding breast tissue. In addition, invasive carcinomas are designated by their architecture, secretion (mucinous/colloid), or structural form (medullary, tubular, papillary). Infiltrating ductal carcinoma is the most frequent, representing between 70 to 80% of invasive breast tumors [11]. These subtypes differ in their rate of relapse, metastasis, response to therapies, and composition of cancer cells. BC is a heterogeneous disease that presents considerable diversity at the molecular, histological, and clinical levels. The first immunohistochemical biomarkers which have laid the foundation for the classification of the BC subtypes mainly include the expression of the estrogen receptor (ER), the progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). The evaluation of the expression of ER, PR, and HER2 is a routine procedure in clinical practice, and it is essential for the determination of these subtypes, and so to guide hormonal and anti-HER2 treatments, additional to predict the prognosis of patients with BC [12]. About 75% of BC tumors are ER and PR positive and are associated with a more favorable prognosis as they respond to endocrine therapies (estrogen receptor modulators, aromatase inhibitors, or estrogen receptor inhibitors) [13]. Tumors are interpreted as ER or PR positive when more than 1% of the tumor cell nuclei are stained (Figure 1) [14]. Approximately 20% of BC tumors have ERBB2 gene amplification and/or HER2 protein overexpression identified by fluorescent in situ hybridization or immunohistochemistry (Figure 1), respectively. These tumors are aggressive and have a poor prognosis and response to chemotherapy [11]. Tumors that do not express ER, PR, and HER2 are referred to as triple-negative (TNBC), and they account for 10-15% of the total [15]. This subtype is a clinical challenge, as it does not respond to standard endocrine therapies, such as tamoxifen (an anti-estrogen agent against the estrogen receptor) and trastuzumab (a monoclonal antibody against HER2), and is, therefore, associated with worse prognosis and survival, increased metastases, and higher risk of recurrence compared to the other BC subtypes [15]. Hence, it is necessary to develop new effective therapies for patients with BC, especially for the subtype TNBC. More than 15 years ago, the analysis of the gene expression profiling of BC tumors suggested a new molecular classification that divides breast carcinoma into different subtypes according to their expression: (i) luminal (expression of ER, ER regulatory genes, and/or normal luminal epithelial cells), (ii) HER2-positive (amplification and/or overexpression of the ERBB2 gene), (iii) basal (ER, PR, and HER2-negative, but with an expression of genes from normal mammary myoepithelial and basal cells), and (iv) normal-like subtype [16,17,18]. These intrinsic subtypes differ according to the etiology of the cancer, the age of onset, and the predictive prognosis of the patient. Currently, BC characterization includes the recently mentioned immunohistochemical markers (ER, RP, and HER2), proliferation marker proteins such as Ki67, genomic markers (BRCA1, BRCA2, PIK3CA), immunomarkers such as PD-L1, among others [19,20]. These different subtypes and the intratumoral complexity highlight the great heterogeneity of BC, which needs to be understood in order to develop targeted therapies for each subtype. Conventional treatments for BC include surgery, hormone-blocking therapy, radiation therapy, and chemotherapy. However, 30–50% of patients diagnosed with this disease at an early stage will progress to metastatic recurrence, which may occur months or decades after the initial diagnosis despite the treatment administered [21,22]. Although the largest cell burden of a tumor is formed by the so-called bulk tumor cells, a small subpopulation of cells within the tumor has recently been identified, which presents a stem cell phenotype, due to the similarities to these cells have been called “cancer stem-like cells” (CSCs) or “tumor-initiating cells” (TICs) (Figure 2) [23]. This cell population generally has the characteristic of unlimited self-renew, a hallmark of stem cells. Thus, these cells can divide symmetrically, producing two daughter cells with stem cell properties, or divide asymmetrically, producing a daughter cell with stem cell properties and a second cell that integrates with the tumor mass through differentiation mechanisms [24,25]. The symmetrical division allows excessively increased tumor growth in response to stress conditions, such as cell loss during treatments [26]. The CSCs present high tumorigenicity, several studies have proposed that they participate in all stages of cancer development, and would be responsible for the initiation, maintenance, and progression of tumors. In addition, they would participate in the expansion of the tumor to distant organs during metastasis and recurrence [27]. The lethal behavior of CSCs is mainly due to their tumorigenic capacity just described, but these cells have also been shown to be more resistant to chemotherapy, endocrine therapy, and radiotherapy compared to bulk tumor cells [28,29,30]. Accumulated evidence shows that there is an increase in the CSC ratio after conventional treatment [31]. Different mechanisms are involved in resistance to therapies, including the overexpression of membrane transporter genes of the ATP-binding cassette (ABC) family, which encode proteins responsible for pumping drugs out of the cell. These cells are more resistant to chemotherapeutic agents; have increased DNA repair; increased aldehyde dehydrogenase (ALDH) activity, and can reduce intracellular reactive oxygen species levels [32]. Induction of epithelial–mesenchymal transition (EMT) has been shown to result in the acquisition of stem cell-like properties. EMT is characterized by decreased epithelial markers such as E-cadherin and upregulation of mesenchymal proteins (Vimentin, N-cadherin, and Fibronectin). These molecular alterations cause loss of apical polarity, loss of cell-cell epithelial junctions, and promote a reorganization of the cytoskeleton which allows cancer cells to migrate, invade, and metastasize [33]. Cells exhibiting the stemness properties in breast tumors are known as “breast cancer stem-like cells” (BCSCs) [3,27,34,35]. Different BC subtypes exhibit different proportions of BCSCs, subtypes TNBC and BRCA1 hereditary are enriched for cells with the stem cells phenotype (CD44+/CD24−), while HER2+ tumors contain very few cells with this phenotype [36,37]. Similar results have been obtained by evaluating ALDH1, another CSC marker, finding that basal-type breast tumors are enriched for BCSC cells [38]. It has been proposed that CSCs originate from normal adult stem cells or from differentiated progenitor cells undergoing transformation. In BC, this idea is well supported, since the breast, unlike other organs, develops after birth, therefore, it requires a reservoir of adult stem cells that fulfill homeostatic and developmental functions [39]. Tumor cells and the tumor microenvironment are in a dynamic process that plays an imperative role in driving these pathways for BCSC enrichment and maintenance [40]. There are many different non-malignant cell types (such as mesenchymal stem cells, cancer-associated fibroblasts, adipocytes, endothelial cells, and immune cells) that communicate with CSCs. Besides, there is an extracellular matrix, with secreted factors, and other microenvironmental components, that are helping to induce non-CSC cancer cells to acquire a stem cell phenotype and influence the subsequent fate of these cells [40,41,42]. CSCs are plastic and dynamic and can acquire different characteristics after interactions with the tumor microenvironment [43]. In the same way, CSCs can shape the microenvironment to support both primary tumor and metastatic growth [44,45,46]. Different strategies have been used to study, isolate and/or characterize BCSCs, including (i) analysis of cell surface markers by flow cytometry; (ii) in vitro functional detection assays such as side population [47,48], the activity of aldehyde dehydrogenases ALDH1, tumorigenesis assays by soft agar colony formation, evaluation of self-renewal with sphere formation assay (mammosphere in case of breast cancer) [48,49], analysis of the expression of EMT markers and drug resistance markers (mainly ABCG1, ABCC1, and ABCG2); (iii) in vivo tumorigenesis assays by heterotopic and/or orthotopic xenograft models derived from cell lines or from patient tumors, among others [50,51]. The identification of biomarkers of BCSCs has had a growing interest in the BC field. Several studies have associated the expression of the biomarkers with diagnosis, therapy, and/or cancer prognosis, however, the clinical use of these CSC-specific biomarkers has been very limited. Thus, these markers could have tremendous potential not only in a better understanding of the clinical diagnosis, and tumor biology understanding but also most importantly, as new therapeutic strategies directed mainly against the BCSC population in tumors. Currently, conventional therapeutic agents used to treat BC primarily target the tumor bulk but are less effective in killing CSCs. Therefore, it is necessary to develop better therapeutic agents focused on molecular targets expressed in BCSC, and through this strategy, target the cell population that survives conventional treatments, and is the cause of tumor recurrence, promoting the successful BC eradication. In this way, multitarget inhibitors are promising methods to overcome drug resistance and chemoresistance of BCSCs [9]. In this review, we focus on compounds that target biomarker membrane proteins of BCSCs and compounds that regulate CSC-associated signaling pathways in BC. The proteins are listed below and summarized in Table 1. The increased understanding of CSC surface biomarkers has enabled significant progress in the development of CSC-targeted antibodies, and it has become an emerging technology for cancer therapy. This population is characterized by a signature of stem cell gene expression, and cell surface proteins such as CD133, CD44, EpCAM, CD49f, CD47, ABCG2, and CD24 among others that have been associated with the BCSCs phenotype (Figure 2) [52,53]. CD44 is a receptor for hyaluronic acid and participates in the formation of complexes between extracellular components and elements of the cytoskeleton [54,55]. This receptor regulates critical cell processes including cell adhesion, migration, survival, invasion, and epithelial–mesenchymal transition through signaling pathways such as Rho GTPases, Ras-MAPK, and PI3K/AKT [54,56]. Accumulating evidence indicates that CD44 is not only a marker for CSC in BC but also in other types of cancer [57,58,59,60]. BC cells with a CD44+/CD24−/low phenotype have a high tumorigenic capacity compared to those cells with a different phenotype that fail to form tumors [56,61]. CD44 has several isoforms as a result of alternative splicing and N- and O-glycosylation post-translational modifications. The isoform switching from CD44v to CD44s is necessary for cells to undergo EMT. Furthermore, Zhang et al., demonstrated that CD44s activate the PDGFRβ/STAT3 cascade which promotes the acquisition of BCSC characteristics. Additionally, they found that the TNBC subtype has a higher number of CSCs that mainly overexpress the CD44s isoform [58]. CD44 plays a pivotal role in CSCs in communicating with the microenvironment and regulating CSC stemness properties [62]. Hyaluronic acid (HA) has the inherent ability to target CD44 receptors, which is why it has been used to develop CSC-targeted drug delivery systems. Lapatinib (LPT) is a dual EGFR and HER2 inhibitor used as a treatment for advanced BC of the HER2 subtype, however, it has low oral bioavailability and several side effects. Agrawal et al., designed a delivery system for this inhibitor. LPT-HA-NCs are LPT nanocrystals coated with hyaluronic acid to actively target CD44 receptors on BC cells. In vitro and in vivo studies showed that LPT-HA-NCs have several cellular effects including anticancer activity of LPT-HA-NCs, increase of the intracellular concentration of LPT, promote apoptosis, and delay significantly the tumor growth [63]. Han and colleagues, developed a hyaluronan-conjugated liposome delivery system that encapsulates gemcitabine, targeting the BCSC population. This system improves the stability of gemcitabine in the bloodstream, prolonging its half-life. In vitro experiments showed a significant improvement in cytotoxicity compared to free gemcitabine. In addition, gemcitabine induces inhibition of colony-forming and cell migration. In a xenograft mouse model, a significant antitumor effect of the system was found [64]. Mitoxantrone (MTX), is an anthraquinone derivative and is used in the treatment of a wide spectrum of tumors including BC, but it has significant side effects. For this reason, Sargazi et al., developed a more effective form for administration MTX through hyaluronic acid/polyethylene glycol nanoparticles. The authors propose this delivery form as an effective nanomedicine to eliminate CD44-positive breast cancer stem cells [65]. In order to study the efficacy of nanomedicines directed against CSCs, Gener et al., developed a new model of the fluorescent label for CSCs that allows efficient detection of this subpopulation. Using this CSC reporter system, the efficacy of polymeric micelles functionalized with anti-CD44 antibodies and loaded with paclitaxel (PTX) was evaluated. The data showed that antibody-mediated targeting increases the efficacy of PTX, as well as that the mammary CSC population was sensitized to PTX treatment only when these specific micelles were used. The experiments were replicated in colon cancer cell lines, obtaining similar results [66]. CD133 is a transmembrane glycoprotein also known as Prominin 1. It is often used as a biomarker for CSC enrichment in cancer, as it targets a subset of cancer cells that show a drug-resistant phenotype and an increased tumor-initiating ability in xenotransplantation assays [67]. In BC, high levels of CD133 mRNA are associated with basal, estrogen receptor-negative tumors, and higher-grade tumors (grade 3 versus 1–2) [68]. In addition, it is considered a factor of poor prognosis for metastasis-free survival in all the histological subtypes of BC analyzed to date [69]. The precise molecular mechanisms by which CD133 acts in cancer remain unclear [70]. CD133 is critical for the survival and growth of BCSCs, and antibodies against CD133 can reduce BCSC growth. Ohlfest et al., developed an immunotoxin against CD133 by fusing a gene fragment encoding the scFv portion of an anti-CD133 antibody to a gene fragment encoding deimmunized PE38KDEL. This fusion protein dCD133KDEL was shown to represent a new biological assessment tool that can be used to determine the clinical importance of eradicating CD133-positive cells since selectively inhibited CD133+ ductal breast carcinoma cells, and cause regression of tumor growth in mice [71]. Swaminathan et al. designed polymeric nanoparticles conjugated with an anti-CD133 monoclonal antibody (CD133NP) which were loaded with PTX (microtubule-stabilizing anticancer agent). The effect of CD133NPs on tumor-initiating cells was measured in vitro, finding that the treatment with CD133NPs significantly reduces the number of mammospheres. The anticancer effectiveness of CD133NPs in vivo in an orthotopic mouse model of BC was also investigated. This treatment effectively reduced tumor volume compared to treatment with free PTX [72]. Yin et al., designed a therapy with nanoparticles carrying anti-miR21 and the CD133 aptamer to target the stem cell marker as therapy against TNBC. Treatment with these nanoparticles had high efficacy in inhibiting tumor growth in the MDA-MB-231 xenograft model compared to the control group. Furthermore, it showed high specificity in targeting the TNBC tumor [73]. The compound 3,3′-diindolylmethane (DIM) has been studied as a chemo-preventive agent in BC. However, after the treatment with this compound, the CD133+/NANOG+ subpopulation increased, and the AKT signaling pathway was significantly activated. When using DIM in combination with the AKT inhibitor AZD5363, DIM-induced CD133 expression was decreased, further suppressing stemness and promoting anticancer activity [74]. EpCAM also designated CD326, is a membrane glycoprotein located mainly in the basolateral membrane of the cells, that participates in the adhesion of epithelial cells. EpCAM is recognized as one of the cell surface markers used for the identification and isolation of CSCs from various cancer types [75]. In vitro BC studies reported that the overexpression of EpCAM promotes proliferation, invasion, and metastasis in cancer cells. In addition, it has been associated with clinicopathological characteristics such as poor prognosis, larger tumor size, high histological grade, and bone metastases in BC [76]. Recent studies carried out by Dionisio et al., identified that EpCAM, along with other BCSC markers including CD44, and ALDH1, were significantly enriched in BC brain metastases compared with primary tumors. Furthermore, these patients had a worse prognosis, poor overall survival, and positive lymph nodes [77]. Catumaxomab (anti-EpCAM × anti-CD3) is a bispecific monoclonal antibody that binds to both EpCAM (on tumor cells) and CD3 (on T cells). Kubo’s group conducted an in vitro study testing the combination of catumaxomab and activated T cells in BC cell lines, finding that this combination can eliminate chemoresistant EpCAM-positive TNBC cell lines. The authors proposed that catumaxomab combined with activated T cells might act as a strong treatment to eliminate this type of cells [78]. Another immunotherapy approach developed an EpCAM aptamer-linked to small-interfering RNA chimeras to selectively knock down genes in mouse BC. This therapy was applied subcutaneously in orthotopic mouse models of HER2+ and aggressive TNBC tumors. The results obtained with this therapy showed a notable delay in tumor growth, and consequently, the function of tumor-infiltrating immune cells was enhanced [79]. CD47 is an important protein in immune control as it can bind to its SIRPα ligand on macrophages, and inhibit their phagocytic capacity [80]. CD47 antibody B6H12 inhibits BCSC proliferation, asymmetric division, and expression of the mammary CSCs transcription factor KLF4 [81]. The co-expression of CD47 and HER2 markers are detected in BC patients with a poor prognosis [82]. Therefore, Candas-Green et al., generated a B6H12.2 antibody with a dual activity that blocks CD47 and HER2, this enhancing macrophage-mediated phagocytosis, and also suppressing the aggressive phenotype associated with HER2 by killing radioresistant BCSCs [82]. Although HER2 is not a marker of BCSCs by itself, different strategies directed against HER2-positive BC have been developed, including monoclonal antibodies that bind to the extracellular domain of HER2, EGFR-HER2 small-molecule kinase inhibitors, and antibody-drug conjugates. Yet, most patients with metastatic disease eventually progress to anti-HER2 therapy due to resistance to the traditional treatments [83]. Therefore, developing therapeutic strategies against the population of CSCs in HER2-positive tumors is essential. Metformin (MET) is an anti-type 2 diabetic agent effective against BC, and it has become of high therapeutical interest due to its ability to target BCSC [84]. The anticancer effect of MET in Herceptin-Conjugated Liposome (Her-LP-MET) has been evaluated in vitro and in vivo. This treatment produced greater inhibition of BCSC proliferation in vitro compared to free MET. The anti-migratory effect of Her-LP-MET on BCSC was enhanced when used in conjunction with doxorubicin (DOX). In the same way, in a mouse model, Her-LP-MET combined with free DOX was more effective, reducing tumor mass and prolonging tumor remission. This Her-LP-MET formulation is proposed as a new therapy to efficiently targets BCSCs [85]. Other therapeutic strategies have also been tested for cells that express the HER2 receptor. The combined treatment of a carbon-ion beam and LPT, effectively destroys the proportion of HER2-positive BC stem cells (ESA+/CD24−), decreasing cell viability, the formation of spheroids, and promoting apoptosis [86]. In recent years, various strategies have been proposed to eliminate BCSCs. These include the blockade of signaling pathways associated with the acquisition of stem cell characteristics [87]. BCSCs exhibit dysregulation of critical signaling pathways such as Notch [88], Hedgehog (Hh) [89], transforming growth factor-β (TGF-β) [90], Wnt/β-catenin [27,91], STAT3 [92,93], PI3K/AKT/FOXO [94], and NF-κB [95]. These cellular signalings allow BCSCs to have a greater capacity to proliferate and tolerate hostile environments. BCSCs also express metabolic markers of stem cells, such as aldehyde dehydrogenase (ALDH1) [96], transcription factors such as OCT4 (octamer-binding factor 4), SOX2 (SRY-box transcription factor 2), NANOG, KLF4, and MYC, that act as key regulators of pluripotency and self-renewal [97] (Figure 2). Importantly, these pathways also play a fundamental role in the normal development of the mammary glands. The Wnt/β-catenin pathway is a critical pathway in embryonic development and tissue homeostasis. In cancer, it plays a fundamental role in the functioning of the CSC, orchestrating self-renewal and cell differentiation [98]. In BC, more than half of the tumors have this active pathway, which is associated with decreased patient survival of these patients. Given the importance of this signaling pathway in cancer, a large number of inhibitory compounds targeting proteins of this pathway have been designed [99]. The group of Jang et al., designed a selective small-molecule inhibitor called CWP232228 (US Patent 8,101,751 B2), which prevents β-catenin from binding to TCF in the nucleus. In vitro and in vivo studies demonstrated that treatment with CWP232228 targets BCSC populations, blocking the formation of secondary spheres in cells resistant to conventional chemotherapy. Besides, the inhibitor suppresses tumor formation in a murine xenograft model and metastasis by inhibiting the growth of both, bulk tumor cells and BCSCs. It is proposed that this inhibitor would have significant therapeutic potential for the treatment of BC [100]. The TGF-β signaling pathway has biological activity capable of inducing the oncogenic transformation of non-cancerous cells. TGF-β promotes cell survival and proliferation, in addition to stimulates the maintenance of stemness in the BCSC population [101]. Several studies suggest that TGF-β-induced CSC accumulation in TNBC is a mechanism of drug resistance in TNBC [37]. Park et al., reported that blockade of TGF-β signaling with the inhibitor EW-7197, in combination with PTX, reduce the EMT process and BCSC population (induced by PTX treatment) by suppressing Snail expression. Besides, a reduction of mammosphere formation efficiency, a decrease of ALDH activity and CD44+/CD24− phenotype, and diminish of pluripotency regulators (OCT4, NANOG, KLF4, MYC, and SOX2) was observed. This combined treatment enhances the therapeutic effect of PTX by decreasing lung metastasis, and increasing survival time in vivo [102]. Another study also shows that PTX increases the BCSC population through TGF-β signaling. The inhibition of this pathway with the inhibitor LY2157299 reduced the population of BCSCs resistant to PTX in vivo, and abolished the tumor-initiating potential of BCSCs after chemotherapy [103]. In the Notch signaling pathway, Notch is a ligand-activated transmembrane receptor (delta-like (DLS) and Jagged), which undergoes serial cleavage by γ-secretases, leading to the intracellular portion of Notch translocating to the nucleus where activates the expression of downstream transcription factors [104]. It has been reported that this pathway is over-activated in BC, and would be promoting chemo/radioresistance, both in BC cells and in BCSC [105]. Several research groups hypothesize that Notch inhibition will allow BCSC to be eliminated, controlling the progression of BC [106]. Currently, there are three main clinical methods used to inhibit Notch signaling. These include γ-secretase inhibition (GSI), antibodies against Notch receptor or ligand antibodies, and a combination therapy with other pathways [107]. A study conducted by Li et al., evaluated the in vitro and in vivo effect of five inhibitors directed to BCSC in TNBC. The tested inhibitors were DAPT, GDC-0449, Salinomycin, Ruxolitinib, and Stattic, that target Notch (γ-secretase), Hedgehog (SMO), Wnt (β-catenin), JAK/STAT, and JAK (STAT3) pathways respectively. The inhibitors were found to have antiproliferative and proapoptotic functions, along with suppressing invasion and decreased self-renewal (markedly diminished size and number of mammospheres) in BCSCs, in TNBC cells. Also, these inhibitors reduced the expression or phosphorylation of their downstream signaling target molecules in a dose-dependent manner. Furthermore, these five inhibitors suppress the tumor-forming ability of the TNBC stem cell line HCC1806 when pretreated with the inhibitors in xenograft mouse models [15]. In another study, mesoporous silica nanoparticles (MSNPs) have been designed as vehicles for the targeted delivery of GSI to block Notch signaling. In vivo analysis, showed that MSNP-GSI nanoparticles enhanced Notch signaling inhibition compared to the free drug. These data suggest that MSNP-GSI is an attractive platform for the targeted delivery of anticancer drugs and, specifically, against the population of CSCs [108]. The FK506 binding protein (FKBPL) is considered a prognostic and predictive biomarker of BC since it has antitumor and antiangiogenic activity. FKBPL has been shown to have the ability to target CD44-positive cells and reducing their population. Also, FK506-derived peptides (ALM201 and AD-01) inhibit the BCSC resistant to endocrine therapy. In addition, downregulation of DLL4 and Notch4 has been reported to reduce migration, invasion, and pulmonary metastasis of BC [109,110]. The Hedgehog (Hh) signaling pathway includes three secreted ligands, of which Sonic Hedgehog (SHH) is the most widely expressed, followed by transmembrane receptor/coreceptor Patched (PTCH) and Smoothened (SMO). Activation of this pathway occurs when the Hh ligands bind to the PTCH transmembrane receptor, which regulates the SMO transmembrane protein The binding induces the activation of the GLI oncogene, which is a transcriptional effector of the Hh pathway [111]. This pathway regulates the maintenance, self-renewal, survival, and proliferation of BCSC [37,112]. In TNBC, Hh signaling has been associated with high-grade, highly proliferative cancer, increased metastasis, and poorer disease-free survival [113]. GANT61 is a non-canonical Hh pathway inhibitor. GANT61 was able to decrease E2-induced GLI1/2 activation accompanied by a decrease in the proportion of CSCs in ER-positive BC cells [89]. Another study evaluated the effect of GANT61 on TNBC cells and found that this inhibitor significantly decreased the proportion of BCSCs in all TNBC cell lines analyzed. The combined treatments of GANT61 and PTX greatly enhanced anti-cell growth and/or anti-CSC activities. These results suggest that GANT61 has the potential as a therapeutic agent in the treatment of patients with TNBC [114]. In the STAT3 signaling pathway, the tyrosine kinase Janus-activated kinase 2 (JAK2) is hyperactive in HER2-positive and TNBC tumors [115]. JAK2 phosphorylates and activates STAT3 to induce its nuclear translocation, increasing the expression of genes that promote CSC turnover [116]. Dysregulation of the JAK2-STAT3 pathway is associated with poor clinical outcomes, and it has been investigated as a possible therapeutic target for BCSC [92,93]. The effect of combined inhibition of the JAK2-STAT3 (by Ruxolitinib and Pacritinib) and SMO-GLI1/tGLI1 (by Vismodegib and Sonidegib) signaling pathways on the BCSC population has been evaluated. The combined treatment with these inhibitors suppressed the high CD44high/CD24low BC stem cell population compared with vehicle or either every agent alone. Also, the simultaneous inhibition of the JAK2-STAT3 and SMO signaling pathways suppresses the orthotopic growth of TNBC tumors and reduces metastasis in vivo [93]. Tamoxifen (TAM) is the first-line treatment for ER+ receptor-positive BC. However, resistance to this drug is the main obstacle in clinical practice. Therefore, new therapies are needed to treat TAM resistance. Liu et al., evaluated the effect of Napabucasin, a small STAT3 inhibitor, and found that it attenuates BC cell resistance to TAM by reducing the population of BCSCs, which was evidenced by the decrease in stem markers (OCT4, NANOG, and SOX2), in combination with a reduction in the ability to form spheroids and a decrease of the ALDH1 activity [91]. Aldehyde dehydrogenase-1 (ALDH1) is a detoxification enzyme that catalyzes the oxidation of intracellular aldehyde substrates. Often, ALDH1 is used to isolate and identify normal cell populations enriched in stem and progenitor cells, as well as BCSCs in cancerous tissues [96]. In BC, ALDH1 positivity has been correlated with high histological-grade tumors, HER2 overexpression, and the absence of the expression of estrogen and progesterone receptors [117,118]. In addition, it was reported that the expression of this marker is related to a poor prognosis [118]. ALDH1+ BCSCs are enriched in basal-like and HER2-overexpressed tumors [38]. Furthermore, the expression of ALDH1 contributes to both chemotherapy and radiation resistance [119]. Analyses of gene expression profiles have shown that there are two different subpopulations of BCSCs, those that display a CD44+/CD24− phenotype, which is a mesenchymal marker, located in the ductal structures frequently associated with ductal branch points [120]. Conversely, ALDH+ BCSCs, are epithelial and highly proliferative cells located in an abluminal location within lobules. These two BCSC subgroups can transit through these two dynamic states [120,121,122]. Indeed, BCSC CD44+/CD24−/low/ALDH1+ phenotype is a very small population within the tumor [38]. This population is more tumorigenic, and it is able to recapitulate the tumor the after reduction of the cell population sensitive to therapy, which leads to relapse of the disease [123]. Disulfiram (DSF) is a drug used as a treatment for alcoholism that produces an irreversible inhibition of ALDH [124]. Its effect on the CSC population in BC was evaluated and it was found that DSF eradicates BCSC, inhibits CSC marker expression such as SOX2 and OCT4, and reverses PTX and cisplatin resistance in MDA-MB-231 PAC10 cells (resistant to cytotoxicity) [125]. A subsequent study investigated the mechanism of action of DSF on BCSCs. They found that DSF eliminated BCSCs and inhibits tumor development in vivo through suppression of HER2/AKT signaling. [126]. Another study showed, for the first time, that DSF suppresses stem properties in TNBC by targeting the STAT3 signaling pathway [127]. New drug development is a significantly expensive multi-step process, involving drug design and synthesis, as well as re-testing in animal models, to ensure precise safety and efficacy. An alternative strategy to de novo drug development is drug repurposing, which uses molecules already approved by the FDA for new therapeutic indications that can effectively eradicate BCSCs and control metastatic disease in BC. The latter represents an effective strategy to rapidly improve the prognosis of BC patients [128]. Salinomycin and pyrvinium pamoate are 2 FDA-approved drugs that have been redirected to the treatment of BCSC [129]. Salinomycin (SLM) is an antibiotic identified as an effective anti-BCSC compound. SLM reduces the proportion of CSC by >100-fold compared to PTX and inhibits mammary tumor growth in vivo. Besides, it decreases BCSC-associated gene expression through modulation of WNT and Hh signaling pathways [130,131,132]. SLM has also been tested in conjunction with LBH589 (Panobinostat, a histone deacetylase inhibitor) as a new therapy against TNBC. The combined treatment of these drugs showed effective and synergistic inhibition of tumor growth of an ALDH1+ TNBC xenograft mouse model, by inducing apoptosis, cell cycle arrest, and EMT regulation, with no apparent associated severe toxicity. Therefore, this drug combination could offer a novel therapeutic strategy for patients with TNBC [133]. A recent study demonstrated that SLM and its C20-propargylamine derivative (Ironomycin 2) eliminate BCSCs by ROS production, induced after iron accumulation in lysosomes [134]. Another delivery system for SLM was designed by Muntimadugu et al., They prepared nanoparticles loaded with SLM or PTX and coated them with hyaluronic acid to target BCSC cells that overexpress the CD44 receptor. The results obtained in this study showed a co-eradication of CD44+ BCSCs as well as bulk tumor cells. Combination therapy of HA-coated SLM nanoparticles and PTX nanoparticles is a promising approach to overcome cancer recurrence due to resistant BCSCs [135]. Pyrvinium pamoate (PP) is an anthelmintic drug and a suppressor of the WNT pathway. Xu’s group evaluated the effect of the pharmacological blockade of this pathway with PP on the BCSC population as a possible therapy to treat BC. They found that this drug inhibits the in vitro proliferation and self-renewal of BCSCs in BC cell lines. Moreover, PP decreases the content of CD44+/CD24−/low and ALDH+ BCSCs in a panel of BC cell lines. In a xenograft model of BC cells, it was shown that cells pretreated with PP strongly delayed tumor development. Furthermore, an in-depth analysis revealed that PP inhibits the activity of the WNT pathway and the expression of stem regulators (NANOG, SOX2, and OCT4) [136]. A related study identified the metabolic consequences of PP. This drug inhibits the anabolic metabolism of fatty acids and cholesterol, both are essential for the survival of BCSC in TNBC [129]. Cui et al. screened a drug library of FDA-approved compounds (Prestwick Library) to identify inhibitors of mammary CSCs. They found that the treatment with benztropine mesylate decreases the cell subpopulation that shows both ALDH1 activity and CD44+/CD24− phenotype, in addition to the suppression in the formation of mammosphere and the decrease of the self-renewal properties of BCSC. Additionally, in vivo studies showed that benztropine mesylate inhibited the potential for tumor initiation of the 4T1 cell line in a mouse model. Their findings show that benztropine mesylate could be a good BCSC inhibitor both in vitro and in vivo [137]. CD49f (α6 integrin; ITGA6) is expressed on breast CSCs and functions in the maintenance of stemness. Pranlukast, a drug used to treat asthma, works as an antagonist of CD49f. Pranlukast treatment decreased BC cell clonogenicity in the mammosphere formation assay, and also diminished CD44 and SOX2 expression, and tumorigenicity in vivo, showing that this drug reduces the CSC population in TNBC cells [138]. Wedelolactone is a promising anticancer drug [139]. To enhance its biological activity, wedelolactone-encapsulated PLGA nanoparticles (nWdl) were formulated. In vitro assays showed that nWdl delayed migration, invasion, and EMT in MDA-MB-231 cells compared to the free drug. Wedelolactone significantly upregulates mesenchymal markers, such as N-cadherin, Vimentin, Twist, Snail, and Slug. Moreover, it also upregulated epithelial markers E-cadherin and cytokeratin-19. Regarding the population of BCSCs, it was observed that the expression of ALDH+ cells was significantly decreased when treated with nWdl combined with PTX relative to PTX alone. Also, nWdl significantly inhibited the formation of mammospheroids, and reduced the expression of pluripotency and chemoresistance markers such as ABCG2, SOX2, and ALDH1. In vivo studies showed that nWdl effectively reduced tumor volume and BCSC population (CD44+/CD24−/low) in xenograft models [140]. One of the main challenges in BCSC research is at the translational level, moving from laboratory experiments toward appropriate clinical trials to test new compounds targeting BCSCs. The challenge includes to assessing their efficacy in killing the BCSC population of patient tumor cells [141]. In the last decade, a wide variety of treatments have been developed in clinical trials based on the classification of BC molecular subtypes. Current therapies for BC (chemotherapy and hormone therapy) are effective in killing cancer cells and controlling tumor growth. Most anticancer compounds only affect tumor cells that are in a proliferative state, and they leave behind a small population of dormant CSCs. These cells exhibit increased tumorigenic potential, and often acquire an EMT phenotype, leading to subsequent relapse and therapy-resistant metastases [142]. It has been reported that almost all patients with metastatic BC, and a quarter of those with early BC will relapse despite the initial response [106]. Nevertheless, only a few clinical trials evaluate the effectiveness of conventional treatments against this specific population of cancer cells, even when targeting BCSCs seems to lead to promising therapies [107]. Antitumor therapies specifically targeting BCSCs have long been advised to be administered in conjunction with the traditional chemotherapeutic regimen with the goal of preventing relapse. Interestingly, there are several clinical trials being developed to determine the efficacy of specific therapies against the BCSC population [143]. In addition, these clinical trials have helped to understand the biology, and regulatory mechanisms of BCSCs, and how they respond to new therapies. To identify new compounds or treatments that might be evaluated in a clinical trial setting against the BCSCs population, a search on the website ClinicalTrials.gov was performed using the keywords “breast cancer stem cells”. Almost 150 clinical trials were found to date, but only a small part of them were actually therapies directed toward BCSCs. The vast majority were peripheral stem cell transplants as a treatment for patients with BC. Clinical trials targeting BCSCs will be detailed below and are summarized in Table 2. One of the clinical trials with therapeutic agents targeting CSC in BC is the AVASTEM NCT01190345 trial, which evaluated the anticancer capacity of preoperative Bevacizumab, a monoclonal antibody targeting the vascular endothelial growth factor (VEGF) receptor, along with conventional therapy in 75 participants with BC. The proportion of BCSCs was measured by the number of cells positive for the ALDH1 marker after 4 cycles of treatment. This trial does not confirm the impact of Bevacizumab on breast CSC cells, as it did not change BCSC rates compared to standard neoadjuvant chemotherapy [144]. Therefore, this antibody still has a controversial role in the treatment of BC [145]. In the clinical trial, NCT01424865, the expression of the cancer stem cell biomarker CD44+/CD24− y ALDH1 was evaluated as a predictor of response to Trastuzumab in 1874 samples from BC patients previously treated in the NSABP-B-31 trial. The results of this clinical trial suggest that the CD44+/CD24− phenotype may be used as a predictor of clinical outcome, and as a predictor of response to Trastuzumab treatment in patients with HER2-positive primary BC [146]. Schmidt et al. conducted a randomized phase II study to investigate the efficacy of adecatumumab (anti-EpCAM) as monotherapy in 117 patients with metastatic BC. They found that the probability of tumor progression was significantly lower in patients who received high doses of adecatumumab, and who expressed high levels of EpCAM. But the use of this antibody in BC requires more research [147]. Subsequently, the same group evaluated the combined effect of adecatumumab with Docetaxel (DTX) in a phase IB trial in 31 women with advanced-stage BC, especially for high EpCAM-expressing tumors. They declare that this combination therapy is safe, tolerable, and potentially active in advanced-stage BC [148]. Another clinical trial directed at EpCAM is NCT02915445, in which the safety of EpCAM CAR-T cells was determined in 30 patients with nasopharyngeal carcinoma or BC expressing high levels of EpCAM. Chimeric antigen receptor-modified T cells (CAR-T) have the ability to target tumor antigens such as EpCAM, and can specifically recognize, bind to, and kill antigen-positive tumor cells [149]. In these patients, the potential to inhibit tumor progression will be measured. The results of this clinical trial are expected to provide a new treatment strategy for patients with BC, but nevertheless, the results of this study are not yet known. The clinical trial NCT02254005 evaluated the maximum tolerable dose, safety, pharmacokinetics, and efficacy of a single dose of the Bivatuzumab mertansine antibody in female patients with CD44v6-positive metastatic BC. The disease was stabilized in 50% of the treated BC patients regardless of the dose level [150]. The currently running clinical study NCT02776917 is evaluating the combined treatment of Cirmtuzumab, a monoclonal antibody directed against the ROR1 (receptor-tyrosine-kinase like orphan receptor 1), with PTX, in 22 patients with unresectable BC, metastatic or locally advanced. After 4 weeks of treatment, ROR1 expression levels will be measured and the proportion of CSCs will be evaluated through ALDH1 and CD133 markers. Additionally, primary tumor samples will be compared before and after treatment. Several research groups have hypothesized that inhibition of the Notch pathway is effective to eliminate BCSCs, and thus, controlling advanced BC [106]. In a first phase I clinical trial (NCT00106145) conducted in 103 patients with metastatic or advanced BC, the safety/tolerability and efficacy of the Notch signaling pathway inhibitor, MK-0752, were determined. The study shows a clinical benefit of the γ-secretase inhibition, which led to further trials in combination with other therapies to maximize the clinical benefit of the strategy [151]. Schott et al., suggest that the combination of cytotoxic chemotherapy together with MK-0752 would give better results to efficiently eliminate BCSC, and control the disease. Therefore, preclinical and clinical studies were carried out in parallel [106]. Using human breast tumor graft studies, they evaluated the impact of this inhibitor on the BCSC population, and the efficacy of a combination treatment of MK-0752 with DTX. The study demonstrates that MK-0752 targets the BCSC population. In parallel, a clinical trial (NCT00645333) was carried out in 30 patients with advanced BC, treated with increasing doses of MK-0752 together with DTX. This work was designed to determine the maximum tolerated dose of the inhibitor, administered sequentially with DTX, and to evaluate BCSC biomarkers in tumor biopsies. A decrease in CD44+/ CD24−, and ALDH+ was demonstrated in tumors from patients with this combination therapy [106]. The effect of MK-0752 in combination with TAM or Letrozole to treat early-stage BC patients was also tested in the clinical trial NCT00756717, but its effect on BCSC markers was not evaluated. Another γ-secretase inhibitor is RO4929097, in preclinical studies, this inhibitor showed a significant reduction in anchorage-independent growth and sensitized BC cells to ionizing radiation, both characteristics of CSCs [152]. Additionally, its antitumor activity in patients with advanced, metastatic, or recurrent triple-negative invasive BC has been studied in clinical trials (NCT01151449). The effect of RO4929097 in combination with other drugs like PTX and Carboplatin in TNBC [153] or in combination with Exemestane in patients with advanced or metastatic BC (clinical trial NCT01149356 [154], has been also evaluated, but its anti-BCSC effect has not yet been addressed. PF-03084014 is another selective, reversible, non-competitive γ-secretase inhibitor that blocks the Notch signaling pathway. In the phase I trial NCT01876251, they evaluated the maximum tolerated dose, safety, pharmacokinetics, and antitumor activity of PF-03084014 in combination with DTX, in 30 patients with advanced TNBC [155]. Subsequently, in a phase II trial NCT02299635, the effect of PF-03084014 was evaluated in patients with advanced triple-negative breast cancer with or without genomic alterations in Notch receptors, however, its specific anti-CSC activity was not evaluated. Zhang et al., studied the antitumor efficacy of PF-03084014 alone and in combination with DTX against TNBC in vitro. They found that PF-03084014 enhanced the antitumor efficacy of DTX in TNBC xenograft models, through impairment of the Notch Pathway. Furthermore, PF-03084014 significantly reduced tumor-initiating cells or CSCs in the BC xenograft model [156]. Reparixin is a small-molecule inhibitor of the CXCR1 receptor, identified as an investigational drug targeting CSCs. It has been reported that this inhibitor selectively reduced the BCSC population in BC cell lines, and also was able to specifically target the CSC population in human BC xenografts. The latter delays tumor growth rate, and reduces metastasis [157]. A pilot study (NCT01861054) evaluated the effect of reparixin (administered orally) on BCSCs in primary tumors in a population of 20 patients with early BC. BCSCs were measured in tissue samples using CD44, C24, and ALDH1 biomarkers, as well as EMT markers (Snail, Twist, and Notch), among other proteins. ALDH+ and CD44+/CD24− markers measured by flow cytometry decreased by >20%. However, these results could not be confirmed by immunofluorescence due to the very low number of BCSCs [158]. Subsequently, this same group conducted a phase 2 FRIDA NCT02370238 trial where they evaluated progression-free survival in 123 patients with triple-negative metastatic BC treated with PTX in combination with reparixin. In this study, the BCSCs positive for CD44+/CD24−, and ALDH+ markers were measured. However, the combined treatment did not improve progression-free survival in these patients [159]. LGK974 is an inhibitor of Wnt signaling [160]. The phase I clinical trial (NCT01351103) will evaluate its effectiveness and safe administration as a single agent, and in combination with PDR001 (anti-PD-1 immunotherapy) in adult patients with solid malignancies, including TNBC. The results would support the promising use of Wnt inhibitors as candidates to target BCSCs. Previous studies found that this inhibitor can influence the recruitment of immune cells to tumors and can enhance checkpoint inhibitor activity [161]. Preclinical studies together with a phase I/II clinical trial (NCT01118975) determined the effects of the combination of vorinostat (inhibitor of histone deacetylase, which induces epigenetic changes) and LPT (a dual EGFR and HER2 inhibitor) in BCSCs population. In these studies, the combination of vorinostat and LPT was shown to reduce the BCSC population, measured through the surface markers CD49f and CD44+/CD24−/low. Additionally, the ALDH1 activity was evaluated in three BC cell lines. A reduction in the formation of mammospheres, decreased self-renewal proteins (BMI-1 and β-catenin), diminished EMT markers (Twist1 and Vimentin), and the inhibition of cell migration were also observed. The phase I/II clinical trial conducted in patients with BC positive for advanced HER2 demonstrated that the combined treatment of vorinostat and LPT is feasible and safe, with controllable side effects. Interestingly, patients who continued on vorinostat and LPT did not develop any new metastatic sites, supporting the preclinical results that show that this combination targets the BCSC population and prevents metastases. Additional studies are needed to further validate these results [162]. Another phase II clinical trial with LPT is NCT01868503 in which the combination of LPT ditosylate and radiation therapy work in treating patients with locally advanced or locally recurrent BC was tested. This study evaluated the change in BCSC proportion, viability by flow cytometry, and gene expression profiling after combined therapy. The results of this trial are not yet reported. The clinical trial NCT00524303 conducted in 100 women with invasive BC overexpressing HER2, evaluated the effect of LPT in combination with standard neoadjuvant chemotherapy (5FU, Epirubicin, Cyclophosphamide, and PTX). However, the data obtained from BCSCs were of poor quality, therefore, they could not be analyzed. Ruxolitinib is a recently discovered drug that has been shown to block the IL6/JAK/STAT3 pathway by targeting JAK1 and JAK2 [163]. This inhibitor is being tested in a phase II clinical trial (NCT02876302) as a possible treatment for inflammatory breast cancer in 23 patients in combination with PTX. The distribution of the CD44+/CD24− stem cell population and the inhibition of JAK and pSTAT3 expression in BC tumors before and after treatment with ruxolotinib will be evaluated. This trial has not yet finished. The retrospective study S9313B (NCT00949013) included 1600 tumor tissue samples recruited from women with early-stage BC. In this trial, the expression of the BCSC marker, ALDH1, was evaluated as a predictor of response to adjuvant chemotherapy (DOX and Cyclophosphamide), together with other biomarkers such as HER2, ER, and PR, but the data has not been published. The great heterogeneity and complexity of BC represents a challenge in the search for new treatments that are effective in reducing the risk of metastasis and recurrence. The accumulated evidence indicates that the current subtype classification of breast cancer tumors is not sufficient to choose appropriate treatments. Instead, the representation and behavior of existing BCSCs in the tumor mass need to be also considered. This finding suggests the need to include the analysis of a variety of specific markers expressed throughout the natural history of the tumor, and in response to conventional treatments in this cell population. This evaluation will allow the development of treatments that combine targeting the inhibition of the mechanisms associated with the proliferation and survival of BCSCs. As described here, there are several preclinical studies evaluating new therapeutic alternatives directed toward BCSCs. The strategies that have shown the most promising results not only consider the use of inhibitory compounds directed at the most representative signaling pathways of this type of cell (BCSCs), but also consider the use of innovative nanoparticles, and explore targeted delivery by coating these formulations with molecules that recognize surface markers expressed by BCSCs. These approaches have shown better effectiveness and specificity in the treatment of chemoresistant breast cancer than the use of these compounds as free formulations or standard treatments or conventional therapies alone. In order to obtain more comprehensive evidence to guide the design of clinical studies evaluating the effectiveness of treatments for BC patients, it is essential to use not only mammosphere models, but also patient-derived xenograft (PDX) models that accurately recapitulate the tumor microenvironment, including the presence of BCSCs in the tumor. Many of the clinical studies primarily evaluate the effectiveness of treatments targeting this type of cells alone, or in combination with standard treatments. However, the variability in the results obtained highlights the need to improve the translation of findings from preclinical studies to their evaluation in patients. Therefore, the available evidence suggests that the most promising treatments should consider the different subtypes of BC, the expression of specific BCSC markers, the use of nanoparticles targeted toward these markers, and the combination of these new treatments with the most successful current standard treatments or conventional therapies.

PMC10001185

Mallika Sengupta,Riya Sarkar,Soma Sarkar,Manideepa Sengupta,Sougata Ghosh,Parthajit Banerjee

Vancomycin and Linezolid-Resistant Enterococcus Isolates from a Tertiary Care Center in India

02-03-2023

VRE,LRE,resistant,Enterococcus

Introduction: There is increasing development of antibiotic resistance among the Enterococcus species. Objectives: This study was performed to determine prevalence and characterize the vancomycin-resistant and linezolid-resistant enterococcus isolates from a tertiary care center. Moreover, the antimicrobial susceptibility pattern of these isolates was also determined. Materials and Methods: A prospective study was performed in Medical College, Kolkata, India, over a period of two years (from January 2018 to December 2019). After obtaining clearance from the Institutional Ethics Committee, Enterococcus isolates from various samples were included in the present investigation. In addition to the various conventional biochemical tests, the VITEK 2 Compact system was used to identify the Enterococcus species. The isolates were tested for antimicrobial susceptibility to different antibiotics using the Kirby–Bauer disk diffusion method and VITEK 2 Compact to determine the minimum inhibitory concentration (MIC). The Clinical and Laboratory Standards Institute (CLSI) 2017 guidelines were used to interpret susceptibility. Multiplex PCR was performed for genetic characterization of the vancomycin-resistant Enterococcus isolates and sequencing was performed for characterization of the linezolid-resistant Enterococcus isolates. Results: During the period of two years, 371 isolates of Enterococcus spp. were obtained from 4934 clinical isolates showing a prevalence of 7.52%. Among these isolates, 239 (64.42%) were Enterococcus faecalis, 114 (30.72%) Enterococcus faecium, and others were Enterococcus durans, Enterococcus casseliflavus, Enterococcus gallinarum, and Enterococcus avium. Among these, 24 (6.47%) were VRE (Vancomycin-Resistant Enterococcus) of which 18 isolates were Van A type and six isolates of Enterococcus casseliflavus and Enterococcus gallinarum were resistant VanC type. There were two linezolid-resistant Enterococcus, and they were found to have the G2576T mutation. Among the 371 isolates, 252 (67.92%) were multi-drug resistant. Conclusion: This study found an increasing prevalence of vancomycin-resistant Enterococcus isolates. There is also an alarming prevalence of multidrug resistance among these isolates.

Vancomycin and Linezolid-Resistant Enterococcus Isolates from a Tertiary Care Center in India Introduction: There is increasing development of antibiotic resistance among the Enterococcus species. Objectives: This study was performed to determine prevalence and characterize the vancomycin-resistant and linezolid-resistant enterococcus isolates from a tertiary care center. Moreover, the antimicrobial susceptibility pattern of these isolates was also determined. Materials and Methods: A prospective study was performed in Medical College, Kolkata, India, over a period of two years (from January 2018 to December 2019). After obtaining clearance from the Institutional Ethics Committee, Enterococcus isolates from various samples were included in the present investigation. In addition to the various conventional biochemical tests, the VITEK 2 Compact system was used to identify the Enterococcus species. The isolates were tested for antimicrobial susceptibility to different antibiotics using the Kirby–Bauer disk diffusion method and VITEK 2 Compact to determine the minimum inhibitory concentration (MIC). The Clinical and Laboratory Standards Institute (CLSI) 2017 guidelines were used to interpret susceptibility. Multiplex PCR was performed for genetic characterization of the vancomycin-resistant Enterococcus isolates and sequencing was performed for characterization of the linezolid-resistant Enterococcus isolates. Results: During the period of two years, 371 isolates of Enterococcus spp. were obtained from 4934 clinical isolates showing a prevalence of 7.52%. Among these isolates, 239 (64.42%) were Enterococcus faecalis, 114 (30.72%) Enterococcus faecium, and others were Enterococcus durans, Enterococcus casseliflavus, Enterococcus gallinarum, and Enterococcus avium. Among these, 24 (6.47%) were VRE (Vancomycin-Resistant Enterococcus) of which 18 isolates were Van A type and six isolates of Enterococcus casseliflavus and Enterococcus gallinarum were resistant VanC type. There were two linezolid-resistant Enterococcus, and they were found to have the G2576T mutation. Among the 371 isolates, 252 (67.92%) were multi-drug resistant. Conclusion: This study found an increasing prevalence of vancomycin-resistant Enterococcus isolates. There is also an alarming prevalence of multidrug resistance among these isolates. Enterococcus is a genus of facultatively anaerobic, Gram-positive organisms of ovoid shape found in pairs or short chains. Previously, they were classified as Streptococcus Group D [1]. Nosocomial infections are often caused by Enterococci, which are known as opportunistic pathogens. Enterococcus faecalis and Enterococcus faecium are two of the most common Enterococcus species that are associated with human diseases. Infections caused by them include bacteremia, endocarditis, urinary tract infections, surgical wound infections, and intra-abdominal and intra-pelvic infections. Vancomycin-resistant Enterococci have been on the rise over the last two decades [2]. There has been an increase in resistance to the most common anti-Enterococcal antibiotics, including ampicillin and aminoglycosides, and they are inherently resistant to many other antibiotics, such as cephalosporins and clindamycin, making these infections difficult to treat [3]. Infections caused by Enterococcus can be treated with glycopeptide antibiotics. However, glycopeptide resistance is also on the rise. There are six types of glycopeptide resistance described in Enterococci, based on the sequence of the structural gene for the resistance ligase (vanA, vanB, vanC, vanD, vanE, and vanG). The VanA type of resistance is characterized by a high level of resistance to vancomycin and teicoplanin. In contrast, the VanB type is characterized by variable levels of resistance to vancomycin and teicoplanin. VanD strains are resistant to moderate levels of vancomycin and teicoplanin. VanC, VanE, and VanG isolates exhibit low-level resistance to vancomycin only [4]. Vancomycin resistance was found in 24% of Enterococcus isolates in a study by Phukan et al. [5]. The combination of penicillin and gentamicin is no longer effective due to high-level resistance to aminoglycosides. A study performed in Assam, India, found that 53.76% of patients had a high level of gentamicin resistance and 33.33% had a high level of streptomycin resistance [6]. The first report of a linezolid-resistant Enterococcus in India came from Kolkata, but there have been very few reports since then. A G2576T mutation in domain V of 23S ribosomal ribonucleic acid (rRNA) genes of Enterococcus causes clinical resistance to linezolid [7]. Both intrinsic and acquired resistance to many antimicrobials is known to exist in Enterococcus species. There are many resistance genes present that act against various antimicrobials, and this is the most common mechanism responsible for intrinsic resistance. The acquired resistance among Enterococci is caused by DNA mutation or by acquiring new genes through gene transfer. The result is the development of resistance to a variety of antibiotics, including vancomycin, tetracycline, macrolides, fluoroquinolones, etc. Multidrug-resistant isolates are those that are resistant to three or more antimicrobial classes [8]. There has been an increase in multidrug-resistant bacteria (MDR) in clinical and environmental specimens over the last 50 years. Multidrug-resistant organisms are also known as superbugs. Among the most dreaded multidrug-resistant organisms are Gram negative bacilli such as Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. In contrast, Gram-positive bacteria such as Staphylococcus aureus and Enterococcus faecium have also been reported to display multidrug resistance [9]. To develop resistance to antimicrobials, bacteria have developed a variety of mechanisms. Resistance is caused by several mechanisms. The most significant of these is horizontal gene transfer. Biofilms are also produced by some bacteria. Biofilms remain adherent to the surface and help the bacteria to evade the attack of different antimicrobials [10]. Infections due to vancomycin-resistant Enterococci (VRE) have gained prominence as the leading cause of healthcare-associated infections. It is essential to understand the epidemiology of VRE infections, transmission modes in health care settings, and risk factors for colonization and infection. This is essential to prevent and control VRE infections. A hospital’s infection control strategy should be tailored to meet the needs of patients as well as the available resources to effectively manage VRE infections. To decrease the risk of VRE acquisition, it is essential to maintain proper hand hygiene. Prevention and control of VREs include cleaning environment, bathing with chlorhexidine, and adhering to antimicrobial stewardship [11]. There are studies showing that a chlorohexidine gluconate bathing regimen may reduce the incidence of vancomycin-resistant Enterococci (VRE) and methicillin-resistant Staphylococcus aureus (MRSA) hospital-acquired infections [12]. The objective of this study was to determine the prevalence of Enterococcus isolates and characterize those that were resistant to vancomycin and linezolid in a tertiary health care facility. A pattern of antimicrobial susceptibility was also studied in these cases. Sample selection: A prospective study was performed in Medical College, Kolkata, India over a period of two years (from January 2018 to December 2019). After obtaining clearance from the Institutional Ethics Committee, non-repetitive clinical samples, such as blood, cerebrospinal fluid (CSF), pus, tissue, urine etc. from locations where isolates of Enterococcus have clinical significance, were included in the study. The clinical samples were received, and a direct Gram stain and culture was made using standard microbiological techniques. A semi-quantitative urine culture was made for all samples as per standard criteria. After the culture was made, a colony count was performed wherever applicable, and samples with significant growth of Enterococcus were included in the study. Enterococcus genus identification was made by Gram stain, non-fastidious growth, and conventional biochemical tests including catalase test, growth on 6.5% NaCl, MacConkey agar, bile esculin agar, and arginine hydrolysis. Fermentation of mannitol, arabinose, sorbitol, growth on tellurite agar and identification by VITEK 2 Compact (BioMerieux Inc., France) was used to identify species. Antimicrobial susceptibility testing: These isolates were tested for antimicrobial susceptibility to different antibiotics such as ampicillin (10 µg), tetracycline (30 µg), ciprofloxacin (5 µg), levofloxacin (5 µg), vancomycin (30 µg), teicoplanin (30 µg), and linezolid (30 µg) for all isolates, fosfomycin (200 µg) and nitrofurantoin (300 µg) for urinary isolates, and erythromycin (15 µg) and chloramphenicol (30 µg) for non-urinary isolates. The Kirby–Bauer disk diffusion method was used to test the antimicrobial susceptibility of bacteria on Mueller Hinton agar plates using standard microbiological techniques as per Clinical and Laboratory Standards Institute (CLSI) guidelines. VITEK 2 Compact (BioMerieux Inc., France) was used to determine minimum inhibitory concentrations (MICs) for penicillin, tetracycline, ciprofloxacin, levofloxacin, vancomycin, and teicoplanin, linezolid, and nitrofurantoin. An interpretation of susceptibility was performed according to the CLSI 2017 guidelines [13]. Disk diffusion for fosfomycin was carried out on Mueller Hinton agar supplemented with 25 µg/mL G6P with 200 µg disks. The minimum inhibitory concentration (MIC) for vancomycin, teicoplanin and linezolid was assessed by microbroth dilution method. The MIC for fosfomycin was performed by the agar dilution method. All interpretations of susceptibility pattern were assessed according to the Clinical and Laboratory Standards Institute (CLSI) guidelines 2017. The susceptibility to high-level gentamicin (120 µg) was tested using the Kirby–Bauer disc diffusion method and interpreted using the 2016 European Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines. Antimicrobial susceptibility testing was conducted with Staphylococcus aureus ATCC 25923 and Enterococcus faecalis ATCC 29212 for quality control. Genotypic characterization: The isolates resistant to vancomycin were taken for DNA isolation and amplification. A few colonies were picked from a freshly streaked blood agar plate and inoculated in a 3 mL nutrient broth where they were grown at 37 °C for 2–3 h. The DNA extraction was performed using a Qiagen kit (DNeasy PowerLyzer Microbial Kit, Qiagen, Germany). Multiplex PCR was carried out to detect the presence of genes encoding for vancomycin resistance in the Enterococcus isolates as per the protocol given by Bhatt et al. It was attempted to identify the most common vancomycin-resistant genotypes in Enterococci, namely vanA, vanB, and vanC (vanC1 or vanC2/C3 genes), vanD, vanE, and vanG. For amplification, the following thermal cycling profile was used: 3 min at 94 °C for denaturation, 1 min at 94 °C, 1 min at 45 °C, and 1 min at 72 °C, followed by 7 min at 72 °C for extension. Analyses of DNA fragments were conducted by electrophoresis in 0.5× Tris-borate-EDTA on a 1% agarose gel stained with ethidium bromide [14]. The genomic DNA that was isolated was used for 23S rRNA sequencing. The sequencing was performed on an Illumina sequencer (Lifecell sequencing services Pvt Ltd., India) and the data were obtained. G2576T mutation was noted as a marker for linezolid resistance. Data analysis: All data were entered in the excel spreadsheet (Microsoft Office, Redmond, WA, USA). The geometric mean (GM) and the standard deviation (SD) were calculated using excel spreadsheet. The statistical analysis of the data was performed using Statistical Package for Social Sciences (SPSS) version 23 (IBM, SPSS Inc., USA). The data were summarized using mean along with standard deviation for continuous variables, and frequency along with percentages for categorical variable. The Chi square test was used to check the categorical variables association and p value < 0.05 was taken as significant. During a period of two years, 371 isolates of Enterococcus spp. were obtained from 4934 clinical isolates showing a prevalence of 7.52%. Among these 371 samples, there were 208 (56%) males and 163 (44%) females. Among these 371 isolates, 58 (15.63%) were over 18 years, 94 (25.33%) were aged 18–40 years, 132 (35.58%) were aged 41-64 years, and 87 (23.45%) were over 65 years of age. The isolated Enterococcus samples were obtained from different departments. Most of the isolates were obtained from the Medicine Department (144 (38.81%)), followed by Surgery (112 (30.19%)), Pediatrics (56 (15.09%)), Orthopedics (53 (14.28%)) and Obstetrics and Gynecology (6 (1.62%)). Out of these 371 isolates, the most common sample was urine (223 (60.11%)), which made up more than half of the samples, followed by blood (76 (20.48%)), pus (68 (18.33%)), and tissue, as shown in Table 1. Among these 371 isolates of Enterococcus spp., 239 (64.42%) were Enterococcus faecalis, 114 (30.72%) Enterococcus faecium and the others were Enterococcus durans, Enterococcus casseliflavus, Enterococcus gallinarum, and Enterococcus avium, as shown in Table 2. Among these, 24 (6.47%) were VRE (Vancomycin-resistant Enterococcus) of which 18 isolates were Van A type consisting of 14 (5.86%) isolates of Enterococcus faecalis and 4 (3.51%) isolates of Enterococcus faecium. The other six isolates of VRE consisted of four Enterococcus casseliflavus and two Enterococcus gallinarum which were intrinsically resistant and known to be of VanC type as given in Table 2. Among these 371 isolates, Enterococcus species were highly susceptible to vancomycin, teicoplanin, and linezolid. The isolates from urine samples were also highly susceptible to fosfomycin and nitrofurantoin. The susceptibility pattern of these Enterococcus isolates is shown in Table 3. Among the 371 isolates, 252 (67.92%) were multi-drug-resistant, i.e., resistant to three different classes of antibiotics. There were two linezolid-resistant Enterococcus, and it was found to have the G2576T mutation in 23S rRNA. The susceptibility of E. faecalis and E. faecium was compared, and there was a significant difference in the susceptibility toward ampicillin and nitrofurantoin only, as shown in Table 4. According to a study performed by Chakraborty et al. in Kolkata in 2011, there was a prevalence of 7.3% Enterococcus isolates from all clinical samples [15]. According to this study, there is a prevalence of 7.52% among all samples, which is very similar to that found in the previous study. In a similar study performed by Phukan et al., the prevalence of Enterococcus isolates was found to be 7.4% [5]. The Vitek 2 automated system was found to be as accurate in speciating 93 Enterococcus species as conventional biochemical tests performed in Assam. E. faecalis was the most common isolated species (81.72%), followed by E. faecium (12.9%), E. raffinosus (3.23%, n = 3), E. avium (1.08%, n = 1) and E. gallinarum (1.08%, n = 1) [6]. In this study, among these 371 isolates there were 239 (64.42%) Enterococcus faecalis, 114 (30.72%) Enterococcus faecium, 4 (1.08%) Enterococcus casseliflavus, 2 (0.54%) Enterococcus gallinarum, 4 (1.08%) Enterococcus durans and 8 (2.16%) Enterococcus avium. Another study in Uttar Pradesh found that out of the included Enterococcus strains, 47 were E. faecalis, 51 were E. faecium, two were E. gallinarum and one was E. casseliflavus [16]. Glycopeptide resistance is associated with a variety of phenotypes in Enterococci. Enterococcus gallinarum and Enterococcus casseliflavus/flavescens exhibit intrinsic low-level vancomycin resistance. The VanC-1 ligase is specific for E. gallinarum, and the VanC-2/3 ligase is specific for E. casseliflavus/flavescens. During pentapeptide peptidoglycan synthesis, VanC enzymes are involved in the formation of D-alanyl-D-serine peptidoglycan precursors that have reduced affinity for vancomycin. Teicoplanin remains effective against organisms resistant to VanC. In these species, vancomycin resistance is a naturally occurring trait, and the associated genes are chromosomal encoded [17]. This study also found that E.gallinarum and E.casseliflavus isolates were resistant to vancomycin but susceptible to teicoplanin. Enterococcus gallinarum and Enterococcus casseliflavus/flavescens are Enterococci intrinsically resistant to vancomycin that belong to the E. gallinarum group. They are responsible mainly for healthcare-associated infections, such as those associated with blood, urinary tract, and surgical wounds. Globally, these bacteria are causing a significant increase in diseases because they are prone to causing infection in patients with concurrent hepatobiliary or onco-hematologic conditions. Furthermore, their intrinsic vancomycin resistance poses a different infection control problem from that of Enterococcus faecalis and Enterococcus faecium, which are spread by transmissible plasmids [18]. The prevalence of multidrug-resistant Enterococcus in this study was 67.92%. Similarly, Bhatt et al. found a prevalence of multidrug resistance of 63% among 200 clinical isolates of Enterococcus [19]. According to Praharaj and colleagues, out of 367 isolates of Enterococcus, 32 (8.7%) were vancomycin-resistant. There was no resistance to linezolid among the Enterococcus isolates [20]. Vancomycin-resistant Enterococci were isolated in 24 (6.47%) of the isolates in this study. There were more vancomycin-resistant isolates in Mangalore, where 13 (8.6%) of 150 isolates tested showed vancomycin resistance, 11 (7.3%) of which had an MIC over 8 g/mL [21]. It has been historically proven difficult to treat serious infections due to vancomycin-resistant Enterococci (VRE), require combination therapy and management of treatment-related toxicity. Even though new antibiotics with VRE activity have been introduced to the therapeutic arsenal, significant challenges remain. It is easier for clinicians to tackle these challenging hospital-associated pathogens if they understand the factors driving the emergence of resistance to VRE, the dynamics of gastrointestinal colonization, microbiota-mediated colonization resistance, and mechanisms of resistance to currently available therapeutic options. Daptomycin and linezolid antibiotics inhibit VRE; however, understanding their clinical role and mechanisms of resistance is critical to maximizing their effectiveness [22]. In a meta-analysis performed in Iran among culture-positive Enterococcus species cases, VRE infections were found to be 9.4%. In Germany, the United Kingdom (UK), and Italy, VRE prevalence was 11.2%, 8.5–12.5%, and 9%, respectively. Moreover, the rate of vancomycin resistance among E. faecalis isolates was higher than for E. faecium [23]. In the present study there were 6.47% cases of vancomycin-resistant Enterococcus. The prevalence of VRE was more in E.faecalis (5.86%) compared to E.faecium (3.51%). In another meta-analysis performed in Ethiopia, the analysis included 831 Enterococci and 71 VRE isolates. VRE prevalence was 14.8%. Enterococci were more resistant to penicillin (60.7%), amoxicillin (56.5%), doxycycline (55.1%), and tetracycline (53.7%) than vancomycin. Daptomycin and linezolid showed relatively low resistance rates with a pooled estimate of 3.2%. Multidrug resistance (MDR) was 60.0% for enterococci [24]. In the present study, there were 23.18% isolates susceptible to ampicillin, 26.14% susceptible to ciprofloxacin, and 26.68% susceptible to levofloxacin. In this study, it was found that 67.92% isolates were multi-drug resistant. The emerging problem of multidrug resistance poses a problem for clinicians as there are fewer therapeutic options. Different methods have been documented for detecting linezolid resistance in Enterococcus strains. The Vitek 2 system showed poor correlation between MICs in the susceptible and intermediate range and G2576T mutation status, likely reflecting the lack of validation of the Vitek AST GP-61 card for LR Enterococcus strains. The use of disk diffusion testing appears to be less sensitive than dilution methods for detecting reduced linezolid susceptibility due to the G2576T mutation. However, it is more specific for detecting fully susceptible strains. Variability in E-test results may be due to the difficulty in interpreting 80% growth inhibition end points. Agar and broth dilution methods were in agreement with polymerase chain reaction detection of the mutation, and disk diffusion was somewhat less sensitive, but equally specific. Although the first report of a strain of linezolid-resistant enterococcus was reported from Kolkata [6], we detected two strains that were linezolid-resistant. Both isolates were found to have found to have G2576T mutation in 23S rRNA. According to a study in Katihar, 2.8% of enterococcus isolates were resistant to linezolid [25]. In the current study, 99.46% isolates were susceptible to linezolid. However, the development of linezolid resistance is an alarming feature as linezolid is one of the last resorts for management of VRE. Hence, screening for linezolid resistance and understanding the mechanism of linezolid resistance are essential for proper management of infections caused by linezolid-resistant Enterococcus species. By both disk diffusion and agar-screen methods, greatest resistance to aminoglycoside was observed among E. faecium, followed by E. durans, E. faecalis, and E. casseliflavus. The high-level aminoglycoside resistance (HLAR) was significantly (p < 0.05) more prevalent in E. faecium [26]. Two-hundred and fifteen (57.95%) of the Enterococcus in this study were susceptible to high-level gentamicin. However, there was no significant difference in the prevalence of susceptibility to high-level gentamicin among the different species. According to a study performed in China, among 1157 clinical isolates of Enterococcus species, there were 679 E. faecium isolates (58.7%), 382 E. faecalis isolates (33%), 26 E. casseliflavus isolates (2.2%), 24 E. avium isolates (2.1%), and 46 isolates of other Enterococcus species (4%). Significantly different prevalence of antimicrobial resistance between Enterococcus faecium and Enterococcus faecalis was observed, and ≤ 1.1% of these Enterococcus species were resistant to vancomycin, teicoplanin, or linezolid. Moreover, different Enterococcus species isolated from different departments of the hospital exhibited different levels of resistance to the same antimicrobial agent, while reserpine treatment significantly reduced resistance to ciprofloxacin, gatifloxacin, and levofloxacin [27]. The susceptibility rates of vancomycin-resistant E. faecium urinary isolates were 100% for linezolid, 81% for fosfomycin, 68% for tetracycline, 6% for ampicillin, and 3% for penicillin [28]. In a study performed to assess fosfomycin susceptibility among the VRE isolates, it was found that 26.6% of bacteria were susceptible to fosfomycin [29]. Researchers found that fosfomycin combined with daptomycin or daptomycin monotherapy had bactericidal effects against VRE at 24 h in an in vitro study [30]. There was a study performed in China in which 19 VRE isolates were resistant to fosfomycin, 18 of which had conjugative fosfomycin resistance and were positive for the fosB gene [31]. In the present study, 97.76% of the Enterococcus isolates were susceptible to fosfomycin. However, genotypic characterization of the fosfomycin resistance was beyond the scope of this study. In a study performed in India among 514 isolates of Enterococci with 46.5% Enterococcus faecalis and 53.5% Enterococcus faecium, E. faecalis was seen to be significantly more resistant (p < 0.05) to ciprofloxacin, and high strength gentamicin. 7.2% isolates were resistant to vancomycin. Among these, 114 (22.18%) isolates were MDR [32]. One study, conducted in Iran, found high-level gentamicin resistance in 50.9% of isolates, though all isolates were multidrug-resistant (100%) [33]. According to another study, 93% of isolates studied were resistant to one or more antimicrobial agents, including tetracycline (86%), ciprofloxacin (73%), and quinupristin-dalfopristin (53%). High-level gentamicin and high-level streptomycin resistance were detected in 50% and 34% of isolates, respectively [34]. In the current study, 57.95% of the Enterococcus were susceptible to high-level gentamicin. The limitations of the study were that genotypic characterization of the fosfomycin resistance was beyond the scope of this study. Moreover, follow-up of the patients was not performed to look for the clinical improvement of the infection. This study showed a prevalence of 24 (6.47%) vancomycin-resistant Enterococcus, 2 (0.54%) linezolid-resistant Enterococcus, and 252 (67.92%) multi-drug-resistant Enterococcus. In the current era of developing resistance, it is essential to characterize the different resistant isolates for proper management of these cases. The intrinsic resistance of different Enterococcus species to vancomycin should also be noted.

PMC10001186

Angelos Yfantis,Ilias Mylonis,Georgia Chachami,Marios Nikolaidis,Grigorios D. Amoutzias,Efrosyni Paraskeva,George Simos

Transcriptional Response to Hypoxia: The Role of HIF-1-Associated Co-Regulators

03-03-2023

hypoxia,HIF-1,transcriptional regulation,chromatin,cancer

The Hypoxia Inducible Factor 1 (HIF-1) plays a major role in the cellular response to hypoxia by regulating the expression of many genes involved in adaptive processes that allow cell survival under low oxygen conditions. Adaptation to the hypoxic tumor micro-environment is also critical for cancer cell proliferation and therefore HIF-1 is also considered a valid therapeutical target. Despite the huge progress in understanding regulation of HIF-1 expression and activity by oxygen levels or oncogenic pathways, the way HIF-1 interacts with chromatin and the transcriptional machinery in order to activate its target genes is still a matter of intense investigation. Recent studies have identified several different HIF-1- and chromatin-associated co-regulators that play important roles in the general transcriptional activity of HIF-1, independent of its expression levels, as well as in the selection of binding sites, promoters and target genes, which, however, often depends on cellular context. We review here these co-regulators and examine their effect on the expression of a compilation of well-characterized HIF-1 direct target genes in order to assess the range of their involvement in the transcriptional response to hypoxia. Delineating the mode and the significance of the interaction between HIF-1 and its associated co-regulators may offer new attractive and specific targets for anticancer therapy.

Transcriptional Response to Hypoxia: The Role of HIF-1-Associated Co-Regulators The Hypoxia Inducible Factor 1 (HIF-1) plays a major role in the cellular response to hypoxia by regulating the expression of many genes involved in adaptive processes that allow cell survival under low oxygen conditions. Adaptation to the hypoxic tumor micro-environment is also critical for cancer cell proliferation and therefore HIF-1 is also considered a valid therapeutical target. Despite the huge progress in understanding regulation of HIF-1 expression and activity by oxygen levels or oncogenic pathways, the way HIF-1 interacts with chromatin and the transcriptional machinery in order to activate its target genes is still a matter of intense investigation. Recent studies have identified several different HIF-1- and chromatin-associated co-regulators that play important roles in the general transcriptional activity of HIF-1, independent of its expression levels, as well as in the selection of binding sites, promoters and target genes, which, however, often depends on cellular context. We review here these co-regulators and examine their effect on the expression of a compilation of well-characterized HIF-1 direct target genes in order to assess the range of their involvement in the transcriptional response to hypoxia. Delineating the mode and the significance of the interaction between HIF-1 and its associated co-regulators may offer new attractive and specific targets for anticancer therapy. Hypoxia or lack of sufficient oxygen can occur under either physiological or pathological conditions such as intense muscular exercise or ischemic diseases, respectively. Hypoxia also characterizes the micro-environment of solid tumors and potentiates the aggressiveness and resistance of cancer cells to therapy. A key element in the cellular response to hypoxia is the stabilization of the alpha subunits of the hypoxia inducible factors (HIFα) and the subsequent activation of the HIF heterodimers, that upregulate the transcription of many genes required for adaptation at low oxygen conditions. The HIF family of heterodimeric transcription factors comprises three HIFα members (HIF-1α, HIF-2α, and HIF-3α) and one HIFβ member (HIF-1β, also known as aryl hydrocarbon receptor nuclear translocator, ARNT). HIF-1 is the most widely expressed and best studied form and it will be the subject of this review. The breakthrough work by G. Semenza, Sir P. Ratcliffe and W. Kaelin (2019 Nobel prize in Physiology or Medicine) led to the characterization of the cellular oxygen sensing mechanism that controls the expression levels of HIFα [1,2,3,4,5]. Briefly, under atmospheric oxygen concentrations (normoxia), oxygen sensitive enzymes hydroxylate HIFα and cause its degradation and/or block its binding to transcriptional co-activators. The inactivation of these enzymes under hypoxia leads to stabilization of HIFα, its translocation into the nucleus, the formation of functional HIF heterodimer with ARNT, through their Per-Arnt-Sim (PAS) homology domains, and binding to specific DNA sequences called hypoxia response elements (HRE), through their basic helix-loop-helix (bHLH) domains. Thus, the transactivation domains (TAD) of HIFα can then interact with transcriptional coactivator proteins such as CREB-binding protein (CBP) and stimulate expression of genes containing HREs in the promoter or enhancer regions. The way HIF-1 selects the HREs it binds to, the means of its interaction with chromatin and chromatin-associated regulators and how these interactions may be controlled by oxygen-dependent or independent mechanisms are questions addressed in the following sections. Early analysis of several different individual validated hypoxia-responsive and HIF-dependent target genes, revealed that the HRE comprises the short core consensus sequence 5′-RCGTG-3′, as originally determined in the erythropoietin enhancer, which led to the first purification and identification of HIF-1 [6,7]. In addition, early transcriptomic analyses using microarrays in different cell lines identified 500–4000 genes that changed their expression after exposure to hypoxia, while studies using chromatin immunoprecipitation (ChIP) coupled with analysis on microarrays (ChIP-chip) identified a much smaller number (approx. 300–500) of HIF-1 binding sites [8,9,10,11,12,13]. Several important conclusions were drawn from these studies. First, a surprisingly small overlap between genes deregulated by hypoxia was detected among different cell types, suggesting that the transcriptional response to hypoxia depends a lot on cellular context [9,11,13]. Second, the majority of hypoxia responsive genes did not contain a detectable HIF-binding site in their proximal promoter, although the majority of HIF-1-binding sites were localized in close proximity to genes [9,10,12]. This indicates that a significant part of the transcriptional response to hypoxia is only indirectly regulated by HIF-1 through induction of other transcriptional regulators, in agreement with the observed large difference between the number of deregulated genes and the number of true HIF-1 binding sites. Furthermore, HIF-1-binding sites were mostly absent from genes down-regulated by hypoxia, suggesting that HIF-1 functions predominantly or even solely as a transcriptional activator [10,12]. Therefore, any transcriptional repression observed under hypoxia must be a result of HIF-1-dependent induction of repressor proteins and/or non-coding RNAs. Third, less than 1% of the DNA promoter sequences containing the core RCGTG motif bound HIF-1 or HIF-2 [10] and extended sequence preferences beyond the core motif could not explain the lower than-predicted number of observed HIF-1-bound sites [12], raising the issue of how productive HREs are selected. In relation to this, although many loci containing the core motif bound both HIF-isoforms, substantially more bound HIF-1 than HIF-2 [10]. This was in agreement with the considerably smaller contribution of HIF-2 to the transcriptional responses to acute hypoxia [8], at least under the conditions and cell lines studied, further underlining the question of selectivity. Subsequent and more detailed studies utilizing RNA-seq and/or Chip-Seq [14] in combination with analysis of the non-coding transcriptome [15] and the role of HIF-α hydroxylases [16] or HIF-α isoforms [17] in many different cell lines [18,19] largely corroborated and extended the previous conclusions. These studies confirmed that only a relatively small set of genes (less than 50) are upregulated consistently and substantially by hypoxia or hydroxylase inhibitors in different human cell types, which may form the core of a hypoxia responsive gene signature [16,18,19]. It was also shown that, at genome-wide level, HIF-binding sites were enriched in the vicinity of gene promoters and their majority overlapped with DNAse1-hypersensitive peaks, i.e., open chromatin, although only approx. 1% of hypersensitive RCGTG motifs were bound by HIFs, indicating again that functional HREs may be defined by epigenetic mechanisms [14]. Interestingly, despite the fact that HIF-1 and HIF-2 share a common consensus DNA-binding motif, they were shown to bind different but overlapping sets of sites in chromatin and transactivate only partially overlapping sets of genes, in accordance with their distinct physiological functions and roles in disease [17]. HIF-1 binding sites were more often close to transcription start sites than those of HIF-2 and the binding site distribution was suggested to be caused by inherent properties of each isoform rather than by the severity or the duration of the hypoxic stimulus itself [17]. Concomitant analysis of RNA Pol II binding and histone H3 modification suggested that both HIFs may act predominantly through release of pre-bound promoter-paused RNA Pol II [15]. However, HIF-1 associated more strongly with histone H3 modifications (H3K4me3 and H3K9ac) that mark primarily promoters and proximal regulatory elements while HIF-2 interacted more strongly with H3 modifications (H3K4me1 and H3K27ac) often found in enhancers and other distal regulatory elements [17]. These studies suggested that functional HREs may be largely defined by preformed chromatin structures (i.e., present also under normoxia) which are not affected by HIF binding. Overall, the genome-wide transcriptomic studies support the idea that HIFs do not alter the chromatin accessibility by their binding but rather associate with already defined and partially active promoters or enhancers, as also suggested by the fact that most HIF-target genes display normoxic expression which is further enhanced by hypoxia [20,21]. However, this is not an absolute rule as recent studies utilizing other than ChIP-seq methodology such as Micrococcal Nuclease (MNase) protection assays [22] and Assay for Transposase-Accessible Chromatin (ATAC)-seq [21] suggest that HIF binding at certain genes can also have a significant effect on nucleosome organization and chromatin accessibility. In either case, isoform specificity, gene selection and cell-type differences cannot be explained by a simple HIF-HRE association and must be conferred through interactions between HIFs and distinct transcriptional and chromatin-associated cofactors. Indeed, recent single-gene studies have identified a significant number of HIF-1α physical partners, several of which are involved in transactivation and act as HIF-co-regulators [23]. A compilation of proteins identified in physical association with HIF-1α and affecting the activity of HIF-1, by modulating its transactivation ability and not the expression levels of HIF-1α, is shown in Table 1, together with any known effectors, and schematically in Figure 1. Table 1 also includes the HIF-1α protein domains, regions or amino acid residues involved in the association with co-regulators or effectors (whenever this information is available, see also Figure 1) as well as the cell lines or types in which these associations were detected, so this information will not be repeated in the following sections. Although this review is focused on HIF-1-interacting co-activators, HIF-2 will also be mentioned in cases of common interactors. The list of co-regulators includes acetyl-transferases, such as p300/CBP and Tat-interactive protein (TIP60), enzymes introducing or removing methylation, other epigenetic enzymes or readers, basic components of the transcriptional machinery, chromatin remodeling factors and other proteins with miscellaneous functions which will be briefly discussed in the following sections. Bioinformatic analysis of a comparison between the genes affected by these co-activators (in cases with available transcriptomic data) and a compilation of validated direct HIF-1 gene targets is also presented in the last section of this review. The involvement of other hypoxia-activated transcription factors that interact with HIF-1 to mediate context-specific gene activation will not be examined here as it has been previously reviewed [24]. Very early studies [25] implicated one of the most important coactivator proteins, p300/CBP [67], in the regulation of HIF transcriptional activity (Table 1). Highly homologous E1A-binding protein p300 and CREB-binding protein CREBBP or CBP (often referred as a single p300/CBP moiety) regulate chromatin structure through histone and other protein acetylation in the transcriptional machinery. It has been shown that p300/CBP forms a protein complex with HIF-1α that is induced under low oxygen availability, it is recruited to chromatin via binding to HIF-1α and acts as adaptor protein in order to induce transcription of hypoxia-responsive genes [25]. p300/CBP can also function as a protein scaffold that binds simultaneously different transcription factors and thus receive multiple input information and signals. Specifically under hypoxia, CBP, which co-localises with HIF-1α in intranuclear foci, was shown to mediate formation of HIF-1α complexes containing Steroid Receptor Coactivator-1 (SRC-1), another coactivator (see also below) [28]. HIF-1α has two distinct transactivation domains, termed N-terminal and C-terminal TADs (N-TAD and C-TAD respectively; Figure 1). HIF-1 C-TAD (amino acids 786–826) interacts with the CH1 domain of CBP/p300 in a hypoxia-dependent manner [26]. HIF-1 N-TAD (amino acids 531–575) also associates with endogenous CBP/p300 through its CH3 domain and although this interaction is essential for transactivation, it is weaker compared to the C-TAD/CH1 interaction [26]. Post-translational modifications of HIF-1 C-TAD, including S-nitrosation of HIF-1α Cys800 (Cys848 for HIF-2α) [68], phosphorylation of HIF-2α Thr844 [69] and hydroxylation of HIF-1α Asn803 [29,70] affect the ability of this domain to recruit CBP/p300. In general, CBP/p300 appears to play a vital role in the formation of a HIF “co-activator-some” by recruiting secondary molecular players in order to assist the HIF-dependent transcription initiation [27]. As already mentioned, CBP/p300 is responsible for an orchestrated cooperation with a broad variety of proteins, which in turn facilitate the HIF-1α-CBP/p300 interaction, regulate the assembly of the transcriptional apparatus and, consequently, stimulate transcription initiation. The HIF-1α-CBP/p300 interaction and, therefore, HIF-1-dependent gene expression can be regulated by various different effectors, a representative compilation of which are also briefly presented below and shown in Table 1 and Figure 1. Instability and degradation of HIF-1α under normoxia is mediated by binding of the tumour suppressor protein von Hippel Lindau (pVHL) to HIF-1α. This binding is triggered via hydroxylation of two Pro residues in the oxygen dependent degradation (ODD) domain of HIF-1α by a family of HIF-α specific prolyl-hydroxylases or PHDs. Another hydroxylase, targeting Asn-803 in HIF-1α (Asn-847 in HIF-2α), was originally identified as protein interacting with HIF-1α and termed Factor Inhibiting HIF-1 (FIH-1) [30]. Modification of HIF-1α C-TAD by FIH-1 under normoxia abrogates the HIF-1α/p300 interaction and blocks the transactivation activity of HIF-1, even in the case that HIF-1α escapes pVHL-mediated ubiquitination and degradation [29,31]. Thus, FIH-1 together with the PHDs comprise the oxygen sensing system that regulates both stability and activity of HIF-α [71]. In addition to hydroxylation, the interaction of HIF-1α with p300 has also been suggested to be regulated by acetylation at Lys-674, which lies N-terminally and outside the C-TAD. Sirtuin 1 (SIRT1) has been shown to physically interact with HIF-1α and reverse the lysyl acetylation introduced by the p300/CBP-associated factor (PCAF) [32]. Interaction/deacetylation by SIRT1 represses HIF-1α activity by blocking p300 recruitment facilitated by PCAF. Interestingly, although SIRT1 also interacts with HIF-2α, it enhances rather than represses HIF-2 transcriptional activity. It has been suggested that SIRT1 may be part of a HIF-1-specific positive feedback loop in which stimulation of glycolysis by HIF-1 and cytoplasmic NAD+ reduction leads to transcriptional downregulation of SIRT1 and further activation of HIF-1 [32]. SRC-1 as well as transcriptional mediators/intermediary factor 2 (TIF2) are transcriptional co-activators of the p160 protein family. They can interact with various members of the nuclear hormone receptor family to promote activation of transcription [72] and associate with co-activators [73,74,75], in order to bridge receptor activation to the basal transcriptional apparatus and enhance transcription initiation. SRC-1 has also been shown to interact with HIF-1α in a hypoxia-dependent manner. Both SRC-1 and TIF2, can boost HIF-1α mediated transcriptional activity, acting synergistically with CBP [33]. Redox factor 1 (Ref-1), a dual-function protein harbouring both DNA repair endonuclease activity and cysteine reducing activity, potentiated the functional and physical interaction of HIF-1α with SRC-1 and CBP, suggesting that in hypoxic cells Ref-1 facilitates the recruitment of the CBP–SRC-1 coactivator complex by HIF-1. Mucin 1 (MUC-1) is a transmembrane protein mainly expressed in epithelial and hematopoietic cells with aberrant expression in various types of cancer [76,77]. Its small cytoplasmic tail (MUC1-CT) can be released under certain stimulatory conditions and translocate into the nucleus where it can affect gene expression via its interaction with transcription factors. MUC1-CT was shown to physically associate with HIF-1α and enhance HIF-1 activity independent of its effect on HIF-1α expression levels [34]. Furthermore, MUC1 also interacted with p300, occupied promoters of hypoxia-target genes and, when overexpressed, stimulated hypoxia-dependent recruitment of both HIF-1α and p300 to glycolytic gene promoters. Interestingly, MUC1 is itself a target of HIF-1α [78], suggesting the operation of a positive feedback loop in cancer cells under hypoxia. cAMP-dependent protein kinase A (PKA) was shown to interact with HIF-1α and phosphorylate Thr-63 and Ser-692, thereby inhibiting the proteasomal degradation of HIF-1α, independently of prolyl hydroxylation, and promoting HIF-1 target gene expression [35]. Furthermore, PKA could also stimulate binding of the C-TAD of HIF-1α to p300 by counteracting the inhibitory effect of Asn803 hydroxylation. However, the mechanism of this stimulation was not clarified. Interestingly, a catalytic subunit of PKA could be induced by hypoxia in a HIF-dependent manner in A540 lung carcinoma cells [79], which may indicate the operation of yet another positive feedback loop in certain cell types. Finally, proteomic analysis of HIF-1-binding partners led to the identification of fatty acid-binding protein 5 (FABP5), the cytosolic transporter of oleic acid, as an interactor and positive effector of HIF-1α [36]. FABP5 is shown to upregulate HIF-1α mRNA translation while it can also associate physically with HIF-1α and activate HIF-1 transcriptional activity by inhibiting FIH-dependent hydroxylation and promoting p300 binding to the HIF-1α C-TAD. Therefore, induction of FABP5 by oleic acid can promote HIF-1 activity and reinforces its role in lipid biogenesis and storage under hypoxia [80]. CBP/p300-interacting transactivator 2 (p35srj/CITED2), a 30-kDa protein, was discovered as an interactor of the CH1 domain of CBP/p300. This interaction inhibited binding of HIF-1α C-TAD to the same site and blocked the transactivation potential of HIF-1 [37] p35srj/CITED2 can be transcriptionally induced by hypoxia in a HIF-1-dependent way suggesting that p35srj is part of a negative-feedback loop which can finetune the availability of p300 not only for HIF-1α, but also for other p300-CH1 interacting transcription factors. FHL2, a member of the four-and-a-half LIM domain (FHL) protein family was shown to associate with HIF-1α and inhibit HIF-1 (but not HIF-2) transcriptional activity without affecting HIF-1α expression levels [39]. Interestingly, two more members of the same protein family, FHL1 and FHL3, inhibited the transcriptional activity of both HIF-1 and HIF-2, the former by binding to p300 and blocking the HIF-α/p300 interaction (mimicking the action of p35srj/CITED2) and the latter via an unidentified mechanism. The expression of all three FHL proteins was induced by hypoxia in a HIF-dependent manner, suggesting that they may be part of a negative feedback loop. Fibroblast growth factor receptor 2 (FGFR2) was shown to physically associate with both HIF-1α and HIF-2α and to inhibit HIF-1 transcriptional activity without affecting protein expression levels of either HIF-1α and HIF-2α [40]. Furthermore, FGFR2 could bind to the HIF-1α C-TAD and cause dissociation of p300, thereby inhibiting recruitment of HIF-1α and p300 to a HIF-1 target promoter. As the interaction of FGFR2 with HIF-1α and HIF-2α was stronger under normoxia, it may act as a means of ensuring low transcriptional activity of HIFs in normal oxygen conditions. EAF2 (ELL-associated factor 2) is a potential tumour suppressor that binds to and stabilizes pVHL, thereby supressing HIF-1 activity [81]. In a subsequent study by the same team, EAF2 was shown to associate with HIF-1α, but not HIF-2α, and suppress the transcriptional activity of HIF-1, but not that of HIF-2 [41]. This suppression was attributed to the disruption of the interaction between the C-TAD of HIF-1α and p300 independently of FIH-1 and Sirt1. Moreover, the same study revealed that expression of EAF2 is directly induced by HIF-1 in response to hypoxia, suggesting yet another negative feedback regulation loop. Although the lysine acetyl-transferases p300/CBP are usually considered as the main HIF transcriptional coactivators, abrogation of the interaction between HIF-1α and p300/CBP was shown to affect the expression of only a subset of HIF target genes [82]. It appears that the human Tip60 or nucleosome acetyltransferase of histone H4 (NuA4) chromatin-remodelling complex, a multiprotein complex that consists of at least 16 subunits [83], also plays a significant role as HIF-1 co-activator. Two of the subunits of the Tip60 complex are Pontin and Reptin that belong to the family of AAA+ helicases (ATPases associated with diverse cellular activities) and participate in the control of transcription both as members of the Tip60 complex and independently through their association with a variety of transcription factors [84,85]. Both reptin and pontin as well as other Tip60 subunits act as HIF-1 co-regulators (Table 1 and Figure 1). Reptin was initially reported to physically associate with HIF-1α (but not HIF-2α) and repress the expression of a significant number of HIF-1 target genes [42]. This activity required methylation of Reptin by lysine methyltransferase G9a, the expression of which is upregulated under hypoxia [86], suggesting the operation of a negative feedback loop. Methylated Reptin is recruited to HIF-1 target and Reptin-dependent promoters via its binding to HIF-1α and, in turn, recruits Histone Deacetylase 1 (HDAC1) and blocks association of RNA Pol II, thereby repressing transcription [42]. More recently, Reptin has been shown to also associate with HIF-2α, under ERK1/2 inactivating conditions that increase the cytosolic pool of HIF-2α. This association also results in lower HIF-2 activity, albeit, via a different mechanism that involves PHD/VHL-independent HIF-2α destabilization [87]. Like Reptin, methylation of Pontin by G9a and G9a-like protein (GLP) is stimulated under hypoxia and methylated Pontin physically interacts with HIF-1α [43]. However, unlike Reptin, methylation-dependent association of Pontin with HIF-1α resulted in transcriptional activation of a subset of hypoxia inducible genes. This involves interaction of Pontin with p300 and stimulation of its association with chromatin-bound HIF-1α. Pontin depletion affected a significant number of HIF-1 target genes, which did not overlap with the set of genes affected by Reptin. These opposing and Tip60-independent roles of Pontin and Reptin on different sets of HIF-1-target genes have been suggested to reflect the flexible ability of HIF-1α to interact with distinct co-activators/repressors under different cellular/environmental contexts. Partly in contrast to the above, genetic experiments in Drosophila suggested that both Reptin and Pontin, as well as other subunits of the Tip60 complex are required for the transcriptional activity of Sima, the Drosophila homolog of HIF1A [44]. Experiments with human cancer cells confirmed that a significant proportion of HIF-1-dependent genes relied on Tip60 for their induction by hypoxia and demonstrated physical association between HIF-1α and components of the Tip60 complex, including Tip60 itself, the catalytic subunit with lysine acetyl transferase (KAT) activity, Reptin and Pontin. Recruitment of the Tip60 complex by HIF-1α promoted acetylation of histone H3 lysine 9 (H3K9) and histone H4 as well as activation of RNA Pol II by phosphorylation of its C-terminal domain (CTD) in Tip60-dependent promoters [44]. In addition to acetylation, histone and DNA methylation also play crucial roles in epigenetic regulation of gene expression and can severely affect the activity of transcription factors, including HIF-1 (Table 1 and Figure 1). Methylation of lysine residues on histone proteins is mediated by the histone lysine methyltransferases (KMTs) while the removal of methyl groups is accomplished by lysine demethylases (KDMs). An important KDM family are the Jumonji C (JmjC) domain-containing (JMJD) demethylases. A member of this family, JMJD2C, encoded by the KDM4C gene, was shown to interact via its catalytic domain with HIF-1α but not HIF-2α [45]. JMJD2C stimulated the transcriptional activity of HIF-1, but not HIF-2, and this effect depended on its histone demethylase activity. HIF-1 could mediate recruitment of JMJD2C specifically to HIF-1 target genes in hypoxic cells and JMJD2C could in turn enhance HIF-1 binding to the HREs of target genes, demethylate H3K9me3 at these sites and stimulate their expression. The KDM4C gene is a HIF-1 target gene [88]; therefore the interaction of JMJD2C with HIF-1α may provide a positive feedback mechanism in cancer cells by amplifying HIF-1–mediated transactivation. In addition to JMJD2C, HIF-1 is directly involved in the hypoxic induction of another two members of the JMJD family of demethylases, JMJD1A (KDM3A) and JMJD2B [88,89]. Similarly to JMJD2C, JMJD1A was shown to associate with HIF-1α, be recruited by HIF-1α to the enhancer of SLC2A3, a direct target of HIF-1 encoding for glucose transporter 3 (GLUT3), and demethylate the repressive histone H3K9me2 [46]. Similar observations were also made for the promoter of PGK1, the gene encoding for the glycolytic enzyme phosphoglycerate kinase 1 [47]. Both JMJD1A studies suggested that the cooperation between HIF-1 and JMJD1A was especially important for the hypoxic induction of glycolytic genes and the subsequent upregulation of glycolysis in both normal and cancer cells. Demethylation of DNA involves the ten-eleven-translocation (TET) family of 5-methylcytosine dioxygenases that catalyze the conversion of 5-methylcytosine (5-mC) to 5-hydroxymethylcytosine (5-hmC) and can regulate gene expression both dependent and independent of their catalytic activity [90]. Both HIF-1 and HIF-2 have been shown to mediate the hypoxic induction of TET1 in various cell lines [49]. Furthermore, TET1 as well as an enzymatically inactive TET1 mutant were shown to associate physically with both HIF-1α and HIF-2α and to enhance their transactivation activities. Knockdown of TET1 alleviated the hypoxia induced epithelial-mesenchymal transition, a phenotype rescued by the catalytically inactive TET1 mutant. Therefore, the HIF-co-activator function of TET1 was independent of 5-hmC formation, although TET1 could demethylate hypoxia-inducible promoters. Indeed, a concurrent study could demonstrate that TET1 expression was essential for the global 5-hmC gains and the subsequent upregulation of HIF-1 target genes observed under hypoxic conditions [48]. A major family of KMTs are the SET (Su(var)3-9, Enhancer of Zeste, Trithorax) domain containing histone methyltransferases. A member of this family, SET9, was shown to associate with HIF-1α, but not HIF-2α [50]. SET9 could stabilize HIF-1α in various cells lines by inhibiting its proteasomal degradation. In addition, SET9 was enriched and was required for HIF-1α binding, H3K4 monomethylation and transactivation at a subset of HIF-1 target gene promoters regulating mostly expression of glycolytic genes, suggesting a selective role of the HIF-1α/SET9 interaction in the upregulation of glycolysis under hypoxia [50]. Another example of differential HIF-1-target gene regulation is provided by a second histone H3K4 methyltransferase, SET1B [51]. Unlike SET9, SET1B could associate with both HIF-1α and HIF-2α and its expression was required for the upregulation of a subset of hypoxia-inducible genes, which included both HIF-1 and HIF-2 targets but encompassed preferentially genes involved in angiogenesis and less in glycolysis. SET1B was recruited to HIF target sites by the HIF complex and mediated H3K4me3 deposition across the gene bodies, which subsequently also increased H3K27 acetylation. Therefore, it appears that association of HIF-1α with different KMTs may dictate gene selectivity, which may also relate to the fact that certain HIF-1 gene targets, such as glycolytic ones, are expressed to a certain degree also under normoxia, while others, such as angiogenic ones, are significantly expressed only upon exposure to low oxygen. In addition to acetylation and methylation, histone citrullination can also affect regulation of gene expression by modulating chromatin binding of transcription factors and co-factors in conjunction with other epigenetic marks [91]. Citrullination is the hydrolysis of arginine (Arg) residues to citrulline (Cit), which is catalyzed by the small family of peptidylarginine deiminase (PADI or PAD) enzymes, and can affect histone-histone, histone-DNA or histone-protein interactions. A member of this family, PADI4, was shown to be upregulated by as well as associate with HIF-1α and/or HIF-2α [52]. Furthermore, in a positive feedback way, PADI4 could be recruited to HREs by HIFs, stabilize HIF occupancy of HREs and activate HIF target gene transcription (Table 1 and Figure 1). Recruitment of PADI4 to HIF target genes was required for hypoxia-induced citrullination of histones H3 and H4 as well as for deposition of marks of actively transcribed chromatin such as H3K4me3, H3K36me3, H3K4ac, and H4K5ac. There was a very high overlap between hypoxia-inducible genes regulated by HIFs and those that depended on PADI4 expression, suggesting that, at least in cancer cells, PADI4 is a global co-activator of HIFs. Modified histones are recognized by epigenetic readers containing protein domains that bind to acetylated or methylated histone residues. ZMYND8 (Zinc finger MYND-type containing 8), a core chromatin reader/effector with affinity for acetyl and methyl lysine residues of histones H3 and H4 [92], was shown to physically interact with HIF-1α and HIF-2α, while its hypoxic expression was also regulated by HIF-1 and HIF-2 [53] (Table 1 and Figure 1). Moreover, ZMYND8 was required for the hypoxic upregulation of the majority of HIF-controlled genes without affecting protein levels of HIF-1α and HIF-2α, constituting yet another positive feedback mechanism that amplifies HIF mediated transactivation and subsequent breast cancer progression and metastasis. ZMYND8 colocalizes with HIFs on HREs and its positive effect on their activity requires its acetylation by p300 and its association with Bromodomain-containing protein 4 (BRD4), another bromodomain acetyl lysine reader [93], which can in turn lead to release of paused RNA Pol II and transcriptional elongation of HIF target genes. The HIF-1 co-regulators described so far include epigenetic writers, erasers, and readers that can modify chromatin around HREs and/or upon HIF-1 binding and make it more accessible to the basic components of the transcriptional machinery. However, this may not necessarily suffice for activation of RNA Pol II and transcription initiation and elongation. As already mentioned, HIFs predominantly bind to open chromatin regions and activate promoters with pre-bound, paused RNA Pol II. [15]. A functional and physical connection between transcription factors and the basal transcriptional machinery, including RNA Pol II, is provided by the Mediator, a large conserved multi-subunit and modular complex, that can stimulate phosphorylation of the Pol II CTD and trigger Pol II release from promoters and transition from transcription initiation to productive elongation [94]. The Mediator comprises three core modules while a fourth, the cyclin-dependent kinase 8 (CDK8) module transiently associates with the rest of the complex. Productive elongation is also controlled by the super elongation complex (SEC) comprising the RNA Pol II elongation factors, including the positive transcription elongation factor (P-TEFb) and its catalytic subunit cyclin-dependent kinase 9 (CDK9), which also targets the CTD of Pol II [95]. CDK8 as well as other components of the mediator CDK8-module were shown to interact with the C-TAD of HIF-1α and to be recruited by HIF-1 to HIF-1-target promoters [54] (Table 1 and Figure 1). CDK8 was required for the upregulation of the majority of the hypoxia-inducible genes, suggesting that the Mediator is an important, and probably global, HIF-1 co-activator. Mechanistically, although HIF-1α binding to chromatin was independent of CDK8, the interaction between HIF-1α and CDK8 was required to attract AFF4, the scaffold subunit of the SEC, and CDK9, thereby triggering release of paused RNA Pol II and robust induction of HIF-1-dependent genes. Release of paused RNA Pol II at signal-regulated genes can also be controlled by the tripartite motif containing protein 28 (TRIM28), which stabilizes paused Pol II but also triggers Pol II release when phosphorylated on S824 by either DNA-dependent protein kinase (DNA-PK) or the kinase ataxia telangiectasia mutated (ATM) [96]. TRIM28 as well as the three subunits of DNA-PK were shown to associate with HIF-1α and HIF-2α [55] (Table 1 and Figure 1). Both TRIM28 and the catalytic subunit of DNA-PK (DNA-PKcs), which is activated by hypoxia through phosphorylation, were required for HIF-1 transcriptional activity, hypoxia-induced expression of known HIF-1-target genes, and stable HIF occupancy of their HREs. Furthermore, most of the genes induced in HIF-dependent manner were also TRIM- and DNA-PKcs-dependent and the corresponding mRNAs were enriched for mediators of glycolysis and angiogenesis. Recruitment of TRIM28 and DNA-PK by HIF-1 led to phosphorylation of TRIM28 by DNA-PK, which could subsequently promote interaction with CDK9, release of paused Pol II, and productive transcriptional elongation of HIF target genes in response to hypoxia. In addition to DNA and histone modifications, chromatin topology, DNA-accessibility and, therefore, gene activation are also controlled by ATP-depended remodeling. The mammalian Switch/Sucrose-Nonfermentable (mSWI/SNF) chromatin-remodeling complexes contain the Brahma-related gene 1 (BRG1) or Brahma (BRM) proteins that have DNA-stimulated ATPase activity and can remodel chromatin through nucleosome sliding and eviction [97]. BRG1 was shown to associate with HIF-1α and HIF-2α and be recruited by HIF to a subset of HIF-target genes causing nucleosome remodeling and stimulation of transcription in hypoxic cells [56] (Table 1 and Figure 1). Interestingly, in the same study, evidence was presented suggesting that the BRG1 complex may also be involved in expression of the genes coding for HIF-1α and HIF-2α themselves. Chromatin remodeling ATPase activities and histone deacetylation are coupled in the nucleosome remodeling and deacetylase (NuRD) complex, which is defined by the presence of one of the chromodomain helicase DNA-binding proteins 3–5 (CHD3–5) [98]. CHD4 was shown to form a complex with both HIF-α isoforms and could potentiate HIF-dependent transactivation in a subset of HIF target genes [57] (Table 1 and Figure 1). Interestingly, the effect of CHD4 on HIF-mediated transcription was independent of its helicase activity and other subunits of the NuRD complex. Nevertheless, HIFs mediated recruitment of CHD4 to HIF target genes and CHD4 itself also assisted HIF-1 occupancy on HREs. CHD4 could also associate with RNA Pol II through p300 under both normoxia and hypoxia, suggesting that CHD4 may facilitate loading of paused RNA Pol II on HIF targets under normoxia and its subsequent release upon HIF binding under hypoxia. Nucleophosmin (NPM1) is a nuclear protein usually associated with nucleolar ribosomal biogenesis [99]. However, NPM1 is also involved in transcriptional activation, as NPM1 interacts both with chromatin components, through its histone chaperone ability, and transcription factors such as the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) [100]. NPM1 was recently identified as a HeLa cell protein binding, through its C-terminal aromatic domain, to a C-terminal HIF-1α region containing the ERK1/2 modification sites at Ser641/643 [101], termed ETD (ERK-target domain) and conserved in HIF-1α but not HIF-2α (Table 1 and Figure 1). This interaction was direct, as shown by in vitro binding of purified recombinant protein fragments, and stimulated inside cells by ERK1/2-mediated phosphorylation of HIF-1α or the phosphomimetic mutation Ser641Glu [58]. NPM1 apparently participates in a HIF-1 specific positive feedback mechanism as its hypoxic expression is mediated by HIF-1 [102] and its association with the ETD domain of HIF-1α enhances HIF-1 transcriptional activity and upregulates the hypoxic expression of several HIF-1, but not HIF-2, gene targets. NPM1 occupied HRE-containing and HIF-1-dependent promoters under normoxia and its presence was essential for subsequent recruitment and stable binding of HIF-1 under hypoxia. HIF-1α- and NPM1-regulated genes significantly overlapped and disruption of the HIF-1/NPM1 association with cell penetrating peptides derived from the ETD sequence of HIF-1α inhibited cancer cell proliferation and survival under hypoxia by triggering apoptosis [103]. The interaction between HIF-1 and NPM1 is the only known so far to be directly stimulated by an oncogenic pathway such as the MAPK/ERK that controls cellular proliferation. Oligomeric NPM1 is known to undergo liquid-liquid phase separation [99], so its association with HIF-1 target genes and HIF-1 itself (the C-terminal part of which is predicted to be largely intrinsically disordered) may contribute to the formation of a phase-separated multi-molecular assembly of transcriptional co-activators [104,105] that maintains hypoxia-inducible promoters in an open and rapidly activated state. Pyruvate kinase (PK) catalyzes the last and irreversible step in glycolysis, the conversion of phosphoenolpyruvate to pyruvate with the simultaneous production of ATP. The PKM gene encodes two distinct isoforms, PKM1 and PKM2, through alternative splicing. Expression of PKM2 is associated with rapidly proliferating cells and is thought to promote the Warburg effect of cancer cells and tumorigenesis [106]. PKM2, but not PKM1, as well as a catalytically inactive PKM2 mutant were shown to associate physically with HIF-1α and HIF-2α and to promote HIF-1 and HIF-2 transcriptional activity, without affecting their protein expression levels [59] (Table 1 and Figure 1). The HIF-1α/PKM2 interaction and PKM2-mediated HIF-1α transactivation required prolyl hydroxylation of PKM2 by PHD3. PKM2 and PHD3 both colocalized with HIF-1α and enhanced its binding at the HREs of HIF-1 target genes. PKM2 also interacted with p300 and promoted its recruitment and H3K9 acetylation in the same HIF-1 target genes. Both the genes encoding for PKM2 and PHD3 are directly regulated by HIF-1 [59,107] suggesting their participation in a positive feedback loop, which may be especially important in cancer cells expressing HIF-1α under well-oxygenated conditions, i.e., when PHD3 is catalytically active. PKM2 was also shown to associate with JMJD5, a JMJD protein with lysine demethylation and hydroxylation activity, and their complex promoted HIF-1 transcriptional activity in breast cancer cells, while JMJD5 and PKM2 were co-recruited to HREs of two known HIF-1 target genes enhancing HIF-1α binding [60]. The interaction of PKM2 with HIF-1 and its positive effect on HIF-1-dependent transcription of glycolytic genes or the IL-1β gene were also demonstrated in LPS-stimulated macrophages [62,63] and binding of digoxin, a cardiac glycoside, to PKM2 resulted in chromatin remodeling and downregulation of HIF-1α transactivation, independently of PKM2 kinase activity, in the same type of cells [61], which may have therapeutic implications in inflammatory diseases. Another metabolic enzyme involved in HIF regulation is FBP1 (fructose 1,6, bisphosphatase), that catalyzes the hydrolysis of fructose 1,6, bisphosphatase to fructose 6-phosphate in the penultimate irreversible and regulatory step of gluconeogenesis. FBP1 was shown to bind directly to the C-terminal regions of HIF-1α and HIF-2α [64] (Table 1 & Figure 1), with the interaction site on HIF-1α mapped using purified recombinant proteins in the, so called, inhibitory domain (ID; [108]) that lies between the N-TAD and the C-TAD and contains the nuclear localization signal [109], the nuclear export signal [110], the ERK1/2 phosphorylation sites [101] discussed above and many other modification and regulatory sites [111]. This interaction did not require the catalytic activity of FBP1, could take place on the HRE-containing promoters of HIF-1-target genes and repressed HIF activity, expression of HIF-target genes, glucose catabolism and hypoxic cellular proliferation, explaining the tumor suppressive functions of FBP1 [64]. Poly (ADP-ribose) polymerase 1 (PARP1) belongs to a large family of enzymes that can synthesize long and branched polymers of ADP-ribose onto acceptor proteins. PARP1 has DNA-binding and catalytic activities important for DNA repair, histone modification, chromatin remodeling and gene expression [112]. PARP1 was shown to associate with HIF-1α, bind in recombinant form directly to HIF-1α and HIF-2α, and promote HIF-dependent transcription, which required PARP1 catalytic activity but without affecting HIF-1 expression or its binding to DNA [65] (Table 1 and Figure 1). Filamins are large actin-binding proteins that stabilize the actin cytoskeleton and connect it to the plasma membrane. Filamin A (FLNA), the most abundant of the three filamin isoforms, interacts with a wide range of proteins involved in signal transduction, including transcription factors. It has been known that cleavage of FLNA by calpain, generates a C-terminal fragment that enters the nucleus and can modulate the activity of transcription factors [113]. The C-terminal fragment of FLNA, the release of which was enhanced under hypoxia, was also shown to interact with HIF-1α, but not HIF-2α [66] (Table 1 and Figure 1). Furthermore, FLNA promoted HIF-dependent transcriptional activity and its C-terminal fragment was recruited to the VEGF-A promoter, enhancing binding of HIF-1 and its transactivation function. As mentioned above, in several cases of the aforementioned co-regulators, transcriptomic analysis demonstrated a significant overlap between the co-regulator-dependent genes and the HIF-1-dependent genes, the latter usually being defined as the hypoxia-inducible genes that are de-regulated upon depletion of HIF-1α. However, as also explained in Section 2 above, only a fraction of these genes are true direct HIF-1 targets, with the majority of them being only indirectly regulated by HIF-1 through HIF-1-dependent induction of other transcriptional regulators. Therefore, to get a better idea of the extent of co-regulator involvement in HIF-1-driven transcriptional regulation, we first screened the literature and defined a group of 83 highly validated HIF-1-dependent genes as List A (Table S1A). This list contains genes that were characterized as HIF-1 direct targets in single gene studies and satisfied the following four stringent criteria: a. they were inducible under hypoxia at both mRNA and protein levels; b. they were down-regulated by silencing HIF-1α under hypoxia; c. their promoters were activated by HIF-1 under hypoxia or HIF-1α stabilization conditions in gene reporter assays; d. HIF-1 binding to their promoters could be detected by ChIP assays or the functionality of the HREs present in their promoter were verified by mutational analysis. In addition, we also re-analyzed publicly available transcriptomic data from eight genome-wide studies which used microarrays, ChIP to chip, ChIP-Seq, RNA-seq or bioinformatic analysis to define HIF-1-dependent genes [8,9,10,11,12,13,14,114] and compiled a list of 433 potential HIF-1 targets, 49 of which were also among the genes comprising List A. Of the rest, 109 genes that were identified in at least two of the aforementioned eight genome-wide studies (but not in any of the single-gene studies) were included in List B as likely, but not verified, HIF-1 direct targets (Table S1B). Next, List A and List B gene datasets were analyzed in comparison with differentially expressed genes found to be controlled by HIF-1 co-regulators in studies with available and processible transcriptomic data (Table 2 and Table S1C–F). Gene datasets were compared in pairs (co-activator-dependent genes vs. List A or List B) using DeepVenn [115] to identify common genes. As shown in Table 2, datasets of genes regulated by five HIF-1 co-activators, namely ZMYND8, CDK8, TRIM28, NPM1 and JMJD1A, were found to have significant overlap with both List A and List B genes. As revealed by KEGG pathway analysis performed with ShinyGO [116] and SciPy [117], the common genes between each of these five HIF-1 co-activator-dependent gene sets and List A were enriched for HIF-1 signaling, cancer-associated pathways and carbohydrate metabolism (Figure 2; see also Figure S1A–E, left panels, for single co-regulator analysis). On the other hand, co-activator-dependent genes overlapping with List B were involved in a wider spectrum of pathways including metabolism, p53 signaling, and autophagy (Figure 2; see also Figure S1A–E, right panels, for single co-regulator analysis), suggesting that List B genes may not be part of a core hypoxia signature, but rather displaying context specific expression. The overlap between List A or List B genes and all five of the aforementioned co-activator-dependent gene sets are shown in the Venn diagrams of Figure 3A,B. There is high degree of overlap between List A and co-activator-dependent genes, with the majority of the List A genes (58/83, 70%) also found in at least one of the co-activator-dependent gene sets and more than half of List A genes (42/83, 51%) being common with two or more of the co-activator-dependent genes (Figure 3A and Table S1G). Although only four genes of List A (namely ANGPTL4, BHLHE40, BNIP3L and SLC2A1) are co-dependent on all five co-activators, it is very likely that these five factors comprise the core of a HIF-1-dependent transcriptional activator complex involved in the regulation of many HIF-1 direct gene targets. KEGG pathway analysis of the List A genes not found in any of the co-activator gene sets (25/83, 30%), shows, in addition to hypoxic or cancer signaling, their involvement in other, apparently unrelated, pathways (Figure 3C), indicating limited expression that may explain their non-detection in the co-activator transcriptomic analyses. The degree of overlap between List B and co-activator-dependent genes is similar but somewhat lower than List A, with the majority of the List B genes (80/109, 73%) also found in at least one of the co-activator-dependent gene sets but less than half of List A genes (48/109, 44%) being common with two or more of the co-activator-dependent genes (Figure 3B and Table S1H). List B genes not found in any of the co-activator gene sets (29/109, 27%) are associated with pathways not directly related to hypoxic signaling (Figure 3D), suggesting either limited expression or indirect regulation by HIF-1 and its associated co-activators The repertoire of transcription co-regulators that escort HIF-1α and consolidate its activity and functions is wide and still expanding. Besides revisiting the important role of HIF-1, the knowledge acquired from the studies reviewed in this article helps to draw a number of conclusions and better appreciate the complexity of transcriptional regulation by HIF-1. First, proteins with diverse functions, including histone modification enzymes, epigenetic readers, components of the transcriptional machinery as well as chromatin remodeling factors have been identified as HIF-1α interactors and co-regulators. By collectively looking at them (Table 1 and Figure 1), one can easily see that the vast majority of HIF-1α-interacting co-regulators enhance or are required for HIF-1-dependent transcriptional activation. This is probably to be expected, as cell survival under hypoxia depends on continuous transcription of HIF-1-target genes. If and when HIF-1 down-regulation is necessary, e.g., upon re-oxygenation or after chronic hypoxia exposure, it can easily be achieved by the oxygen-mediated activation or HIF-1-mediated transcriptional upregulation of PHDs, respectively, i.e., through the elegant system that regulates HIF-1α protein stability [5]. A second interesting point is that many of the HIF-1α co-activators are themselves direct targets of HIF-1 (Table 1 and Figure 1). This suggests the operation of several positive feedback loops that promote the activity of HIF-1, thus enhancing the robustness of the transcriptional response to hypoxia. On the other hand, a few cases of negative feedback regulation are mediated by HIF-1 direct targets that inhibit the interaction between HIF-1α and CBP/p300, probably in response to specific signaling pathways and in order to balance the co-activator function of CBP/p300 between different transcription factors. A third important observation is that the C-terminal part of HIF-1α harbors the binding sites for significantly more co-regulators than does the N-terminal part (Table 1 and Figure 1). This is not surprising as the conserved and highly structured N-terminal part of HIF-1α mediates both DNA binding (through the bHLH domain) and heterodimerization with ARNT (through the PAS domain), so it is hard to accommodate additional interactions without disturbing the vital associations with DNA and ARNT. Furthermore, the C-terminal part HIF-1α contains, in addition to the two TADs that by definition interact with co-regulators, the ID. This domain, even though it was originally described as TAD repressor [108], it apparently acts as a regulatory hub since it controls the subcellular localization of HIF-1α and harbors many modification sites [111] that facilitate fine-tuning of HIF-1 activity in response to signaling pathways, as exemplified by the ERK1/2 mediated control of the HIF-1α/NPM1 interaction [58]. Finally, the C-terminal part of HIF-1α, the overall three-dimensional structure of which remains unknown, is predicted to be largely intrinsically unstructured (https://alphafold.ebi.ac.uk/entry/Q16665 accessed on 31 January 2023), which could allow flexibility and multiple interactions through conformational adaptation elicited by residue modifications or physical contact with different partners. A question arising in light of the large number of HIF-1α co-regulators, is whether and how these factors collaborate/interact with each other. So far, most co-regulators have been studied on their own for their effect on HIF-1, so, although this is not an easy task, it is important to study them in combination in order to, eventually, join the snapshots collected so far into a whole picture of HIF-1 at work on the target gene promoters. This is, probably, more achievable by analyzing the HIF-1α interactome on the promoters of single genes rather than trying to draw conclusions from genome-wide transcriptomic data that are often incomplete or cell-type specific. On the other hand, a question that also needs to be addressed is whether a HIF-1α co-regulator acts globally or is specific for a particular set of genes, which would require detailed genome-wide chromatin co-occupancy studies. Last but not least, it is urgent to map precisely and structurally characterize the physical interactions between HIF-1α and its co-regulators, in order to not only gain mechanistic insight but to also allow the design of drugs that can specifically target these interactions for therapeutic purposes.

PMC10001188

Stamatia C. Vorri,Ilias Christodoulou,Styliani Karanika,Theodoros Karantanos

Human Immunodeficiency Virus and Clonal Hematopoiesis

22-02-2023

HIV,human immunodeficiency virus,clonal hematopoiesis,non-AIDS related comorbidities

The evolution of antiretroviral therapies (ART) has tremendously improved the life expectancy of people living with human immunodeficiency virus (HIV) (PLWH), which is currently similar to the general population. However, as PLWH are now living longer, they exhibit various comorbidities such as a higher risk of cardiovascular disease (CVD) and non-acquired immunodeficiency syndrome (AIDS)-defined malignancies. Clonal hematopoiesis (CH) is the acquisition of somatic mutations by the hematopoietic stem cells, rendering them survival and growth benefit, thus leading to their clonal dominance in the bone marrow. Recent epidemiologic studies have highlighted that PLWH have a higher prevalence of CH, which in turn is associated with increased CVD risk. Thus, a link between HIV infection and a higher risk for CVD might be explained through the induction of inflammatory signaling in the monocytes carrying CH mutations. Among the PLWH, CH is associated with an overall poorer control of HIV infection; an association that requires further mechanistic evaluation. Finally, CH is linked to an increased risk of progression to myeloid neoplasms including myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), which are associated with particularly poor outcomes among patients with HIV infection. These bidirectional associations require further molecular-level understanding, highlighting the need for more preclinical and prospective clinical studies. This review summarizes the current literature on the association between CH and HIV infection.

Human Immunodeficiency Virus and Clonal Hematopoiesis The evolution of antiretroviral therapies (ART) has tremendously improved the life expectancy of people living with human immunodeficiency virus (HIV) (PLWH), which is currently similar to the general population. However, as PLWH are now living longer, they exhibit various comorbidities such as a higher risk of cardiovascular disease (CVD) and non-acquired immunodeficiency syndrome (AIDS)-defined malignancies. Clonal hematopoiesis (CH) is the acquisition of somatic mutations by the hematopoietic stem cells, rendering them survival and growth benefit, thus leading to their clonal dominance in the bone marrow. Recent epidemiologic studies have highlighted that PLWH have a higher prevalence of CH, which in turn is associated with increased CVD risk. Thus, a link between HIV infection and a higher risk for CVD might be explained through the induction of inflammatory signaling in the monocytes carrying CH mutations. Among the PLWH, CH is associated with an overall poorer control of HIV infection; an association that requires further mechanistic evaluation. Finally, CH is linked to an increased risk of progression to myeloid neoplasms including myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), which are associated with particularly poor outcomes among patients with HIV infection. These bidirectional associations require further molecular-level understanding, highlighting the need for more preclinical and prospective clinical studies. This review summarizes the current literature on the association between CH and HIV infection. Although the life expectancy of people living with HIV (PLWH) has improved steeply in the past decades with the introduction of safe and effective antiretroviral therapy (ART), HIV infection continues to be associated with various comorbidities [1,2], including but not limited to cardiovascular disease (CVD), accelerated aging [3,4] and non-acquired immunodeficiency syndrome (AIDS)-related neoplasms [5]. Given that ART suppresses the HIV viral load to an undetectable level, the majority of these comorbidities have been largely attributed to chronic low-level inflammation [6,7], altered T-cell biology [8], ART adverse effects [9,10] and the prothrombotic phenotype of PLWH [10,11]. The current research focuses on early risk assessment, identifying very high-risk individuals, and preventing these comorbidities. Preclinical and clinical research aims to understand the mechanisms mediating the association of HIV infection with these comorbidities. Clonal hematopoiesis (CH) is the process of acquiring somatic mutations in the genes that affect the function of the hematopoietic stem cells (HSC) by providing a growth advantage to the mutated cells over the unmutated counterparts, resulting in the dominance of the malignant clone in the bone marrow [12]. CH without evidence of hematologic malignancy, dysplasia, or cytopenia with a variant allele frequency (VAF) of at least 2% is defined as clonal hematopoiesis of indeterminate potential (CHIP) [12,13]. In contrast, the CH associated with low counts in at least one cell lineage is defined as clonal cytopenia of unknown significance (CCUS) [13]. The prevalence of CH increases with aging and is estimated to be 15% among individuals older than 70 years [14]. CH has been associated with worse overall outcomes, such as a higher incidence of atherosclerosis and CVD, which is mediated by the induction of innate immune signaling and chronic low-level inflammation [15] and the increased risk of progression to myeloid neoplasms such as myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML) [16,17]. The most common CH-associated mutations are in the DNMT3A and TET2 genes, which encode the epigenetic regulators and cause altered transcriptional profiles in differentiated monocytes and macrophages, leading to the upregulation of the inflammatory cytokines such as IL-1β [18]. Less common mutations associated with CH are mutations in the genes such as ASXL1 and TP53, which provide more prominent survival and growth advantage to the mutated hematopoietic cells, introducing a higher risk of progression to myeloid neoplasms [12,14]. Patients with HIV previously presented with severe cytopenias in the setting of uncontrolled viremia and chronic infections, but the introduction of efficacious ART has dramatically decreased the incidence of these abnormalities. However, mild cytopenias, particularly anemias, continue to be commonly described in PLWH [19,20,21]. Recent studies highlight that CH is more common among PLWH compared to the general population [18,22,23] and is associated with altered viral control [24] and increased risk of CVD [25], providing a novel link between HIV infection and chronic inflammation in PLWH via CH. Lastly, it has been demonstrated that HIV infection alters the outcomes of patients with advanced myeloid stem cell neoplasms [26]. Thus, a bidirectional effect between HIV infection and abnormal hematopoiesis exists. This review summarizes the current findings on the prevalence of CH among PLWH and the implication of CH in the control of HIV. Dharan et al. analyzed the prevalence and the characteristics of CH in 220 PLWH and 226 HIV-negative Australian adults over the age of 55 enrolled in the ARCHIVE study by performing targeted sequencing of genomic DNA extracted from the peripheral blood [18]. PLWH had a significantly higher prevalence of CH than the non-HIV controls (28.2% vs. 16.8%), with most of the mutations observed in the DNMT3A, TET2, and ASXL1 genes. The difference in the prevalence of the higher-risk ASXL1 mutations between the PLWH and the non-HIV controls was more prominent [18]. It was noted that the sub-analysis for VAF, gender and sexual orientation, ancestry, BMI, the extent of smoking exposure and alcohol use, recreational and injection drug use, annual household income, type of health insurance coverage, and HIV-specific characteristics showed no significant correlation [18]. These findings suggest that PLWH have a higher prevalence of CH and its associated mutations than non-HIV infected individuals. These observational conclusions are limited by the relatively small number of patients studied. Interestingly, the authors showed no correlation between the duration of HIV and the VAF or the presence of more than one mutation [18]. In this study population, the prevalence of cardiovascular comorbidities was similar between the PLWH and the controls (64.1 vs. 65.5%). The authors evaluated the prevalence of a mutation in the IL-6 receptor (IL6R p.Asp358Ala), suggested by a different study to play a role in CH-related cardiovascular risk, but found no difference between the two groups [18]. No other correlation or sub-analysis of CVD was made in this study. It is important to note that while the ARCHIVE study was designed and powered as a prospective cohort study with a 10-year follow-up of participants, these results were published in a single-time-point cross-sectional manner [18]. In another study, Bick et al. assessed the prevalence of CH in a multi-ethnic, randomly selected sample of 600 PLWH enrolled in the Swiss HIV Cohort Study (SHCS), aged between 21 and 83, and 8111 individuals with available exome sequences enrolled in the Atherosclerotic risk in the Community study (ARIC), aged between 45 and 84, as the population controls [22]. A significant association between HIV case status and CHIP was observed [22]. To account for the demographic imbalance of the cases and controls, the authors performed a 1:5 propensity matching strategy to select a subgroup of 230 cases and 1002 controls with similar baseline characteristics. This subset analysis detected CHIP in 7% of the exomes from the PLWH but only in 3% of the controls [22]. It is noted that the authors demonstrated a positive correlation between the prevalence of CH and ART duration but not with HIV infection duration [22]. It is possible that this correlation still underlies the importance of the chronicity of low-level inflammation in PLWH, even under ART, as a mechanism of chronic stress in the hematopoietic stem cells. This correlation may reflect that the most critical risk factor for the development of CH is advanced age. Van der Heijden et al. performed a cross-sectional cohort study comparing the prevalence of CH in 217 individuals; primarily men, PLWH on stable ART from the 200 HIV cohort (between 24 and 74 years) with the prevalence of CH in a cohort of overweight individuals and a cohort of age- and sex-matched population controls [24]. The authors confirmed that the probability of CHIP was significantly higher in the PLWH compared to the HIV-uninfected overweight controls [24]. Regarding mutations in specific genes, the proportion of the CH mutations in genes other than DNMT3A, and specifically JAK2, STAT3, and TP53, was larger in the PLWH [24]. Furthermore, the authors performed a mutational signature analysis showing that the clock-like and the reactive oxygen species signatures contributed uniquely to CH in the PLWH. In contrast, DNA mismatch repair signatures contributed uniquely to CH in the HIV-uninfected controls [24]. C>A mutations were identified as contributing to CH in the PLWH with prior exposure to zidovudine (AZT) but not in the unexposed individuals [24]. These results highlighted that a different mutational process potentially drives CH in PLWH and that the underlying biology of CH pathogenesis is different between PLWH and non-infected individuals. Finally, this study highlighted that within the PLWH group, CH mutations carriers had elevated coagulation markers (D-dimer and von Willebrand Factor) compared to the PLWH without the CH mutation [24], which further suggests that CH in PLWH is associated with an increased risk of thrombosis and potentially worse cardiovascular outcomes. In a more recent cross-sectional study, Wang et al. examined the differences in CHIP and coronary artery disease prevalence between PLWH and non-HIV-infected individuals [23]. The authors performed next-generation sequencing in genomic DNA extracted from the peripheral blood mononuclear cells of 118 men (86 PLWH and 32 non-HIV-infected individuals) aged between 42 and 70 from the Baltimore-Washington D.C. center of the Multicenter AIDS Cohort Study (MACS) cohort who had coronary computed tomography angiography (CTA) and a measurement of multiple serologic inflammatory biomarkers [23]. Using a VAF cut-off of 1% and excluding germline variants, the PLWH were almost two-fold more likely to have CH than the non-infected controls (64% vs. 38%) [23]. Using a VAF of less than 1% in the same population, the effect size was increased (23% vs. 6%) [23]. The use of the lower cut-off VAFs allowed for the detection of a significant number of mutations that would have been missed in the studies mentioned above. Thirty-five of 86 (40%) of the PLWH and 10 of 32 (31%) of the non-HIV-infected individuals carried mutations with VAF between 0.5 and 1%, which may explain the identification of mutations less commonly associated with CH in this study, such as somatic TP53 and ARID1A, which were only found in the PLWH [23]. Contrary to the studies by Dharan et al. and Bick et al., which identified ASXL1 as one of the most commonly mutated genes in PLWH participants, Wang et al., did not detect mutations in this gene despite the lower VAF cut-off [23]. Moreover, the authors demonstrated that moderate-to-severe coronary artery stenosis was significantly more common in PLWH with CH than those without CH (30% vs. 9%) even after adjustment for the ACC-AHA Pooled Cohort Equation [23]. This finding is essential as it links CH and CVD among PLWH, supporting the hypothesis that CH promotes atherosclerotic disease in these individuals, likely through the induction of inflammatory alterations in the monocytes and migrated macrophages. The robustness of this study is limited, though, by the relatively small sample size, especially for the uninfected controls, and the significant differences in baseline demographics such as race, BMI, and lack of female participants. Overall, there is robust epidemiological evidence that clonal hematopoiesis is more prevalent in PLWH than in non-HIV-infected individuals. Chronic inflammation can potentially mediate these associations. Numerous studies have highlighted that various inflammatory markers such as IL-6, soluble CD14, and CD163 are elevated among the PLWH compared to the uninfected individuals [27,28]. It is well described that sub-acute or chronic inflammation drives CH, likely by the increased resistance of mutated clones to inflammatory signals [29,30,31]. Thus, it is possible that induced inflammation in PLWH, potentially in combination with patient-related factors such as age and comorbidities, could increase the risk of CH development, further promoting inflammation and negatively affecting innate immunity [32] (Figure 1). Finally, the combined effect of CH, persistent chronic inflammation, and patient-related factors increase the risk of CVD [33,34,35] (Figure 1). However, the causality of these associations should be established with prospective studies adequately controlled for all possible confounders and adequately powered to detect a relationship between HIV infection duration, ART duration, viral load, or inflammatory markers. When designing such studies in the future, it is important to consider ways to decrease biases and maintain the generalizability of the results with broad participant eligibility criteria. Furthermore, technical details such as the sequencing starting material (whole blood vs. peripheral blood mononuclear cells), the sequencing ‘modalities’ sensitivity to detect low VAF, the variety of screened mutations, and the VAF cut-offs with the exclusion of germline mutations should be predetermined after weighing sensitivity with clinical meaningfulness of the findings. A large prospective study with long-term follow-up would be ideal to identify which aspect(s) of HIV infection drives this relationship with CH and the time course of the mutational landscape. Finally, this would allow the investigation of a possible impact of CH in developing CVD in PLWH as a mediator or inducer of inflammatory changes in the monocytes in these individuals, causing high-risk atherosclerotic lesions. ART can be categorized into different major classes and the most commonly used include non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs), protease inhibitors (PI) and integrase inhibitors (INSTI) [36]. The effect in hematopoiesis of some ART regimens has initially been explored in a preclinical murine model. The combinations of tenofovir disoproxil fumarate (TDF) plus lamivudine (3TC) and efavirenz (EFV) plus zidovudine (AZT) and 3TC, resulted in genotoxic and mitogenic effects in the bone marrow of mice that could have the potential for carcinogenesis [37]. However, no studies have explored those effects in humans. Focusing on AZT as an agent alone, an older NRTI used for the treatment of HIV, and the prevention of HIV transmission from actively viremic HIV+ mothers to their offspring during labor has been associated with myelotoxicity in humans [36]. A study by Shah et al. in 1996 showed that in vitro exposure of human hematopoietic progenitors in AZT could impact the erythroid progenitors but not the multipotent progenitors [38]. Indeed, AZT has been identified as a cause of pure red cell aplasia in a case report [39]. In a more recent study, Lin et al. investigated whether AZT could result in CH after in-utero exposure by examining 1-week-old neonate peripheral blood mononuclear cells for single nucleotide variants, small insertions and deletions, and large somatic copy number alterations [40]. The authors did not identify any significant difference in their mutational signature of AZT-exposed vs. AZT-unexposed infants [40].Therefore, since CH is linked to mutagenic events, the authors concluded that AZT exposure in-utero was not associated with CH and more studies on AZT and other ART regimens are imperative to safely determine their effect on CH [40]. More recently, Van der Heijden et al. found in his cross-sectional study that SBS18, a signature predominantly characterized by C>A mutations, was similarly identified as contributing to CH mutations in PLWH with prior exposure to AZT, whereas it was absent in unexposed individuals [24]. In the same study, a similar trend was noted for PIs which did not reach statistical significance [24]. A previous study published in the 2000s has shown that PIs may overcome hematopoiesis inhibition by inhibiting the caspase-dependent apoptotic pathway [41], which can theoretically increases the risk of CH. Additionally, a number of preclinical studies have shown that NRTIs can induce mitochondrial stress though the depletion of mitochondrial DNA [42,43,44,45]. It was recently highlighted that mitochondrial stress could negatively affect the function of hematopoietic stem cells and expedite their aging potentially via the upregulation of the inflammatory pathways [46,47]. Thus, it is possible that CH may develop as a protective mechanism against increased mitochondrial stress in the hematopoietic stem cells of individuals who are on NRTIs. However, as the epidemiologic evidence is lacking, further mechanistic studies are required to confirm this hypothesis. While several recent studies have investigated the correlation between HIV infection and the development of CH [18,22,23,24], the associations of CH with the characteristics and the course of HIV infection and the risk for other infections in PLWH are less well known. Van der Heijden explored the clinical correlates of PLWH with their CH mutation carrier status and found that the CH carriers were older with a longer duration of HIV infection and lower CD4 nadir [24]. On the contrary, no significant difference in CH prevalence was found concerning CD4/CD8 T-cell ratio and the most recent CD4 T-cell count [24]. Older age, lower CD4 nadir, and increased CD4/CD8 ratio were independently associated with the CH mutation prevalence, while HIV duration and the most recent CD4 T cell count were not [24]. The same study demonstrated that the PLWH with a CH mutation had at least once a detectable HIV viral load within a year before a visit supporting an existing HIV reservoir [24]. Similarly, the ratio of HIV cell-associated (CA)-RNA to CA-DNA, which represents the relative viral transcription level, was found to be increased in the CH-mutation carriers compared to the PLWH without the CH mutation [24]. Overall, these observations support that CH can negatively affect the course of HIV infection in PLWH. Recent studies provided evidence that CH increases the risk of several other infections. Specifically, Bolton et al. found that CH increased the risk of Streptococcal and Clostridium difficile infections in patients with solid malignancies after adjusting for age, race, smoking, gender, cumulative exposure to cytotoxic therapy before the blood draw, cumulative exposure to cytotoxic therapy after the blood draw, and primary tumor site [48]. In the same study, the authors found that among COVID-19 positive patients, individuals with CH had a significantly higher risk for severe disease in a multivariable analysis showing a positive association between the VAF and the severity of illness [48]. The exact mechanisms underlying these associations remain unclear. Somatic alterations in the hematopoietic stem cells lead to altered inflammatory signaling in differentiated white blood cells, including the monocytes, migrated macrophages, and other cells participating in the innate immune reactions [49,50]. Thus, CH may affect the regulation of the innate immunity in the setting of infections leading to induced inflammatory responses with poor antimicrobial effect. Further preclinical studies and prospective clinical trials are required to shed light on the underlying biologic mechanisms linking CH to poor infection outcomes. Identifying the subset of the PLWH who eventually develop a myeloid malignancy and analyzing their mutational status is an indirect way to examine the role of CH in the development of hematopoietic malignancy in these patients. While some of the molecular abnormalities associated with CH seem to be enriched in the PLWH who develop MDS compared with the HIV-negative counterparts (mainly ASXL1, DNMT3A and TP53 mutations, and higher risk cytogenetics) [26], progression of CH from >2% VAF and correlation with hematologic malignancy is not well defined [51]. The common mutations associated with CH (epigenetic factors DNMT3A, ASXL1, TET2, DNA damage repair genes such as PPM1D, TP53, signaling genes such as JAK2, and spliceosome components SF3B1, SRSF2) can act as drivers, more often in the PLWH who develop MDS than those who develop AML, where the most common alterations are adverse/intermediate karyotype or chromosome 7 abnormalities [52]. The role of other quantitative rather than qualitative factors, such as the role of the VAF percentage and the accumulation of more than one driver mutations, or the presence of CH without driver mutations in the PLWH who eventually develop a malignancy, should be taken into account when planning prospective follow-up studies of PLWH to understand the pathophysiology and need for surveillance of these patients. The outcomes of PLWH who develop myeloid neoplasms such as MDS and AML are worse than the non-HIV patients [26,53]. It has been reported that patients with HIV and MDS (HIV+/MDS) exhibited more prominent cytopenias and a higher percentage of marrow blasts, a marker indicative of a higher risk for AML transformation, than the HIV-/MDS patients [26,53]. Similarly, despite being younger and potentially better candidates for chemotherapy and allogeneic bone marrow transplantation, the HIV+/AML patients have worse overall survival outcomes compared to the HIV-/AML patients [54,55]. Since these findings suggest that the development of myeloid neoplasms among PLWH is associated with particularly poor outcomes and CH in its turn predisposes to myeloid neoplasms, the need for better understanding of the biology of these associations and the early detection of PLWH with CH to facilitate close monitoring and potentially early treatment to prevent the progression to advanced myeloid neoplasms is warranted. The optimization of ART has tremendously improved the outcomes of PLWH, who have now similar survival to the general population. However, PLWH have a higher prevalence of CVD and non-AIDS associated malignancies, with the underlying biology remaining unclear. The recent data support that PLWH have a higher prevalence of CH, with a potentially different pattern of acquisition of somatic mutations in their hematopoietic stem cells, which appears to be associated with a higher risk of CVD. The underlying biologic mechanism remains unclear, however HIV infection may increase the age-related replication stress to hematopoietic cells leading to a higher risk of acquisition of somatic mutations. Interestingly, CH among the PLWH is associated with lower CD4 nadir and worse HIV-related outcomes, potentially associated with worse infectious outcomes among individuals with CH. Finally, CH carries a significant risk of progression to myeloid neoplasms such as MDS and AML, which are strongly associated with worse outcomes among patients with HIV infection. Further basic science, translational studies, and prospective clinical trials are required to confirm these associations and elucidate the underlying biological mechanisms.

PMC10001189

Siqingaowa Caidengbate,Yuichi Akama,Anik Banerjee,Khwanchanok Mokmued,Eiji Kawamoto,Arong Gaowa,Louise D. McCullough,Motomu Shimaoka,Juneyoung Lee,Eun Jeong Park

MicroRNA Profiles in Intestinal Epithelial Cells in a Mouse Model of Sepsis

24-02-2023

sepsis,cecal slurry injection,inflammation,intestinal epithelial cell,miRNA

Sepsis is a systemic inflammatory disorder that leads to the dysfunction of multiple organs. In the intestine, the deregulation of the epithelial barrier contributes to the development of sepsis by triggering continuous exposure to harmful factors. However, sepsis-induced epigenetic changes in gene-regulation networks within intestinal epithelial cells (IECs) remain unexplored. In this study, we analyzed the expression profile of microRNAs (miRNAs) in IECs isolated from a mouse model of sepsis generated via cecal slurry injection. Among 239 miRNAs, 14 miRNAs were upregulated, and 9 miRNAs were downregulated in the IECs by sepsis. Upregulated miRNAs in IECs from septic mice, particularly miR-149-5p, miR-466q, miR-495, and miR-511-3p, were seen to exhibit complex and global effects on gene regulation networks. Interestingly, miR-511-3p has emerged as a diagnostic marker in this sepsis model due to its increase in blood in addition to IECs. As expected, mRNAs in the IECs were remarkably altered by sepsis; specifically, 2248 mRNAs were decreased, while 612 mRNAs were increased. This quantitative bias may be possibly derived, at least partly, from the direct effects of the sepsis-increased miRNAs on the comprehensive expression of mRNAs. Thus, current in silico data indicate that there are dynamic regulatory responses of miRNAs to sepsis in IECs. In addition, the miRNAs that were increased with sepsis had enriched downstream pathways including Wnt signaling, which is associated with wound healing, and FGF/FGFR signaling, which has been linked to chronic inflammation and fibrosis. These modifications in miRNA networks in IECs may lead to both pro- and anti-inflammatory effects in sepsis. The four miRNAs discovered above were shown to putatively target LOX, PTCH1, COL22A1, FOXO1, or HMGA2, via in silico analysis, which were associated with Wnt or inflammatory pathways and selected for further study. The expressions of these target genes were downregulated in sepsis IECs, possibly through posttranscriptional modifications of these miRNAs. Taken together, our study suggests that IECs display a distinctive miRNA profile which is capable of comprehensively and functionally reshaping the IEC-specific mRNA landscape in a sepsis model.

MicroRNA Profiles in Intestinal Epithelial Cells in a Mouse Model of Sepsis Sepsis is a systemic inflammatory disorder that leads to the dysfunction of multiple organs. In the intestine, the deregulation of the epithelial barrier contributes to the development of sepsis by triggering continuous exposure to harmful factors. However, sepsis-induced epigenetic changes in gene-regulation networks within intestinal epithelial cells (IECs) remain unexplored. In this study, we analyzed the expression profile of microRNAs (miRNAs) in IECs isolated from a mouse model of sepsis generated via cecal slurry injection. Among 239 miRNAs, 14 miRNAs were upregulated, and 9 miRNAs were downregulated in the IECs by sepsis. Upregulated miRNAs in IECs from septic mice, particularly miR-149-5p, miR-466q, miR-495, and miR-511-3p, were seen to exhibit complex and global effects on gene regulation networks. Interestingly, miR-511-3p has emerged as a diagnostic marker in this sepsis model due to its increase in blood in addition to IECs. As expected, mRNAs in the IECs were remarkably altered by sepsis; specifically, 2248 mRNAs were decreased, while 612 mRNAs were increased. This quantitative bias may be possibly derived, at least partly, from the direct effects of the sepsis-increased miRNAs on the comprehensive expression of mRNAs. Thus, current in silico data indicate that there are dynamic regulatory responses of miRNAs to sepsis in IECs. In addition, the miRNAs that were increased with sepsis had enriched downstream pathways including Wnt signaling, which is associated with wound healing, and FGF/FGFR signaling, which has been linked to chronic inflammation and fibrosis. These modifications in miRNA networks in IECs may lead to both pro- and anti-inflammatory effects in sepsis. The four miRNAs discovered above were shown to putatively target LOX, PTCH1, COL22A1, FOXO1, or HMGA2, via in silico analysis, which were associated with Wnt or inflammatory pathways and selected for further study. The expressions of these target genes were downregulated in sepsis IECs, possibly through posttranscriptional modifications of these miRNAs. Taken together, our study suggests that IECs display a distinctive miRNA profile which is capable of comprehensively and functionally reshaping the IEC-specific mRNA landscape in a sepsis model. Sepsis is a leading cause of global mortality. Epidemiological studies have shown that the mortality of the patients in intensive care units with sepsis is higher than 40% [1]. Among the 49 million people who are affected annually worldwide, approximately 11 million individuals die [2]. Multiple organ dysfunction (MOD) is a pathologic condition which contributes to the increase in morbidity and mortality in sepsis [3,4,5]. The aberrant host response to polymicrobial infection and inflammation is the leading cause of MOD [6,7]. Sepsis has become increasingly recognized as a condition that promotes an overactive host immune response followed by MOD [6,8]. The pathophysiology of sepsis development is immunologically and spatiotemporally complex. Upregulation of both pro- and anti-inflammatory responses occurs during initial stages of infection, followed by morbid outcomes which are associated with hyperinflammation or immune paralysis [9,10]. The intestines are sensitive to sepsis-induced inflammation. Splanchnic ischemia and mucosal injury occur in the intestines upon onset of sepsis [11]. The injured mucosa upregulates and secretes pro-inflammatory mediators into the systemic vasculature, inducing systemic inflammation in multiple organs, including the brain [12]. Intestinal epithelial cells (IECs) constitute a single-layered lining that plays an important role in host defense by providing a physical barrier between the luminal surface containing microbe-derived factors and the host. These cells transfer signals bidirectionally between the host and microbes to mount appropriate immune responses [13,14,15,16,17]. Intrinsic and extrinsic inflammatory stimuli induced by sepsis disrupt the intestinal barrier and enhance epithelial permeability, resulting in the development of systemic inflammatory response and MOD [18,19]. IECs are one of the key players that regulate immune pathophysiology in sepsis [20,21]. Thus, understanding the IEC response to sepsis is highly significant. We investigated changes in the expression profiles of epigenetic regulators in septic IECs, such as small regulatory RNAs (e.g., microRNAs; miRNAs). Such approaches may be helpful to better understand how IECs reshape their post-sepsis gene expression and mediate changes in downstream signaling pathways. Cecal slurry (CS) injection is a widely accepted model to induce chronic polymicrobial sepsis. In this model, cecal contents of other animals are administered into the peritoneal cavity of recipient mice, as described previously [22,23,24,25,26]. This model has been used to establish experimental sepsis in neonatal mice using freshly prepared samples [22,24]. The CS-injection model can induce differential mortality in a dose-dependent manner and is dependent more on bacterial infection than an endotoxin effect [27,28]. Starr et al. have also improved the method of CS preservation via maintaining bacterial viability in samples for at least several months [27]. In this study, we used the CS-injection model to induce chronic sepsis. We analyzed miRNA profiles specifically in IECs. IECs isolated from CS-injected groups dynamically responded to sepsis by altering their miRNA profiles. Subsequent in silico analysis showed that the miRNAs upregulated in IECs after sepsis regulate both pro- and anti-inflammatory downstream pathways, activating pathways related to protective and detrimental effects of epithelial inflammation. C57BL/6J (13–15w-old male) mice were purchased from Japan SLC (Shizuoka, Japan). The mice were maintained in the Mie University Experimental Animal Facility at a specific pathogen-free condition under a 12-h light–dark cycle. The mice were given water and food ad libitum. All the experiments were conducted according to protocols approved by the Ethics Review Committee for Animal Experimentation of Mie University (approval number: #2019-41-1). Polymicrobial sepsis was induced by intraperitoneally injecting CS, as previously described [25,27]. In brief, 0.25 mL of CS resuspended in 10% glycerol/PBS was injected into each mouse. Mice in the sham cohort were given the same volume of 10% glycerol intraperitoneally. Twelve hours after CS injection, mice in both CS and sham cohorts were intraperitoneally injected with antibiotics of 3 mg meropenem (Wako, Osaka, Japan) and 3 mg cilastatin (Wako) per mouse, seven times at twelve-hour intervals. All mice were subcutaneously injected with 0.7 mL 0.9% saline. Previously, the survival rate observation and sampling were conducted from days 14 to 30 [25] and from days 15 to 17 after CS injection (an unpublished report by Akama et al.), respectively. Thus, we used day 17 after CS injection in the current model, as previously described, with slight modifications to provide the mice within same cohorts with similar sepsis conditions. IECs were isolated as previously described [29,30,31] with slight modifications. In brief, small intestines were collected from mice after euthanasia and Peyer’s patches, mesentery, and fats were removed prior to further processing. The tissues were opened and washed with ice-cold RPMI-1640 (Nacalai, Kyoto, Japan). The rinsed tissues were cut into small pieces at 1-cm length and incubated in RPMI-1640 containing 10% FBS (Equitech-Bio, Kerrville, TX, USA) and 2 mM ethylenediaminetetraacetic acid (EDTA) (Wako) for 30 min at 37 °C. The digested tissues were filtered using a 70-μm cell strainers (Corning, Glendale, AZ, USA). The filtered cell suspension was resuspended in 40% Percoll (GE Healthcare Life Sciences, Chicago, IL, USA) and applied to gradients of 25, 40, and 75% Percoll. After centrifugation in AX-511 (Tomy, Tokyo, Japan) at 780× g for 20 min at 22 °C, the interface between 25 and 40% gradients was saved to collect IECs. IECs were further enriched using EpCAM microbeads (Miltenyi Biotec, Gaithersburg, MD, USA) and magnetic-activated cell sorting (MACS) cell separation columns (Miltenyi Biotec). The enriched IECs were tested for EpCAM expression using a monoclonal antibody to EpCAM (G8.8) (eBioscience, San Diego, CA, USA), the rat IgG2a isotype control antibody (BioLegend, San Diego, CA, USA), and a BD Accuri C6 Flow Cytometer and BD Accuri C6 Software (BD Biosciences, San Jose, CA, USA). RNA was extracted from IECs using a miRNeasy Mini Kit (Qiagen, Germantown, MD, USA). Library construction and sequencing of small RNAs (including miRNAs) were achieved by using an Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific, Waltham, MA, USA) and the Ion Personal Genome machine (PGM) system (Thermo Fisher Scientific) according to the manufacturer’s instructions at the Mie University Center for Molecular Biology and Genetics (Tsu, Japan) as previously described [30]. Data collection was performed with Torrent Suite v4.0.1 software. The assessment of miRNA profiling was conducted as previously described [30]. In brief, detectable miRNAs (>0, RPKM) across all samples were chosen for differential expression and downstream pathway analysis [32]. Individual fold changes (RPKM in CS-injected sepsis mouse/RPKM in sham mouse) were calculated by taking the ratio of the candidate miRNA expression values with one sham control. Those miRNAs and mRNAs with a fold change of 2 or greater (FC > 2) were classified as upregulated miRNAs and mRNAs in the sepsis group compared to sham, while those with (FC < −2) were classified as downregulated miRNAs and mRNAs in the sepsis group compared to sham. Human IEC lines (C2Bbe1, HUTU80, and H747) were obtained from ATCC (Manassas, VA, USA). The cells were cultured in RPMI 1640 supplemented with 10% fetal bovine serum (FBS) (Equitech-Bio, Kerrville, TX, USA) and penicillin (100 U/mL)/streptomycin (100 μg/mL) (Nacalai) in 5% CO2 at 37 °C. The cells of 70 to 80% confluency on a 6-well plate (Corning, Glendale, AZ, USA) were treated with lipopolysaccharide (LPS) (L3880, Sigma, St. Louis, MO, USA) at a concentration of 1 μg/mL and incubated for 24 h for further study, as described previously [33,34,35,36]. RT-qPCR was performed as previously described [29,30,31] with slight modifications. Briefly, RNA was extracted from the cells and blood using a miRNeasy Kit (Qiagen, Hilden, Germany) and TRIzol reagent (Thermo Fisher Scientific) according to the manufacturers’ instructions. Approximately 1 μg RNA was subjected to a reaction of a reverse transcription using a Mir-X miRNA First-Strand Synthesis Kit (Takara Bio, Shiga, Japan) and a Prime Script RT Kit (Takara Bio), to detect the expressions of miRNAs and mRNAs, respectively. To examine relative gene expression, qPCR was conducted using a PowerUp SYBR Green Master Mix PCR kit (Applied Biosystems, Foster City, CA, USA) and the StepOne Real-Time PCR System (Applied Biosystems) according to manufacturer’s instructions. For endogenous controls, U6 and β-actin were used to normalize expressions of miRNAs and mRNAs, respectively. For miRNAs, the universal primer (Thermo Fisher Scientific) was utilized as the reverse primer for miRNA validation runs. All the PCR-primer sequences for RT-qPCR used in this study are listed in Supplementary Table S2. Relative expression was calculated using the comparative threshold (CT) method (2−dCT) normalized to endogenous control genes and expressed between two cohorts. The miRNAs of the IECs were applied to miRNet 2.0 [37], which incorporates miRBase [38], and miRTarBase v8.0 [39], to construct the networks. The Reactome, Gene Ontology, and KEGG analyses were performed in miRNet 2.0 [37]. Data are presented as the mean ± standard error of the mean (SEM). Results were analyzed using two-tailed Student’s t test for comparison of two groups. p-values < 0.05 were considered significant. Statistical analysis was completed using Prism 8 software (GraphPad, San Diego, CA, USA). To examine the role of IEC-specific miRNAs and its downstream regulatory networks in sepsis, the CS injection model was used to induce sepsis in mice [27] and miRNA expression within IECs was investigated. The CS (25 mg of cecal contents resuspended in 10% glycerol in PBS) was injected intraperitoneally into each mouse, followed by antibiotic treatment (3 mg meropenem plus 3 mg cilastatin per dose; 7 doses). Sham mice received the same volume of 10% glycerol in PBS (0.25 mL per mouse) followed by treatment with antibiotics (Figure 1A). The survival rate was approximately 67% (12/18 mice) at day 17 after CS injection, while 100% survival (10/10 mice) was seen in shams. At post-injection day 17, all mice were euthanized, the small intestines were removed, and IECs were isolated using Percoll density gradients [29,31] and further enriched using magnetic sorting with CD326 (epithelial cell adhesion molecule; EpCAM) microbeads (Figure 1B). After isolation of IECs, their exclusive expression of EpCAM was validated using flow cytometry (Supplementary Figure S1). The whole transcriptome of small RNAs including miRNAs of the isolated IECs was sequenced using the high-throughput Ion Xpress™ RNA-Seq platform. Among 1076 miRNAs initially detected using the sequencing platform, 239 miRNAs that had any expression (i.e., threshold detection hit of >0) for all analyzed samples (1 sham and 3 septic mice) were analyzed and listed in Supplementary Table S1. The average miRNA expression level in IECs after sepsis was compared with a sham counterpart with a threshold cutoff of fold change (FC) of 2 or greater. As shown in Figure 2A, the mean expression of 35 miRNAs was upregulated in IECs after sepsis compared with sham IECs, while 15 miRNAs were downregulated following sepsis. Figure 2A depicts miRNA candidates plotted across both mean fold change and mean expression (RPKM values). We further compared miRNA reads of each sample with the respective sham control for a more robust identification of miRNAs differentially expressed after sepsis. Following this filtering criteria, our data indicated that 14 miRNAs (miR-669o, miR-3096, miR-466q, miR-511, miR-495, miR-467e, miR-434, miR-154, miR-669a-4, miR-127, miR-328, miR-669a-5, miR-378c, and miR-149; ordered by FC) were upregulated in IECs after sepsis. Nine miRNAs (miR-6238, miR-1258, miR-124-2hg, miR-17hg, miR-5125, miR-6240, miR-351, miR-717, and miR-1983; ordered by FC) were downregulated in IECs after sepsis (Figure 2B). To investigate any downstream regulating effects of the identified miRNAs, we analyzed the whole transcriptome and acquired comprehensive expression signatures of mRNAs using the same IEC samples of sham and sepsis mice as used in the miRNA analysis. We found that, among a total of 14,316 mRNAs detected, 2248 mRNAs were downregulated with sepsis, while 612 mRNAs in the IECs were upregulated (Figure 2C). Both up- and down-regulated mRNAs, shown as dots, were determined by the same criteria used in examining miRNAs for the mean RPKM values across the sepsis IECs. The number of downregulated mRNAs was approximately 3.6-times higher than that of the mRNAs upregulated by sepsis. Thus, this suggests that the miRNAs upregulated in IECs after sepsis contribute, at least partly, to the downregulation of the comprehensive mRNA profile. We next sought to further identify the upstream regulating factors that affect the changes in gene expression of RNAs (including both mRNAs and miRNAs). DNA methylation is an epigenetic marker that effectively silences transcription [40] and requires enzymatic activity of DNA methyltransferases (e.g., DNMT1 and DNMT3A) [41,42]. We thus examined the expression levels of DNMT1 and DNMT3A and found that both genes were significantly upregulated within the IECs of CS-injected sepsis mice compared to those of sham mice (Supplementary Figure S2). Thus, these results suggest that sepsis induces an alteration of the overall transcriptome that may be related to epigenetic modifications. This can be achieved by the enzymatic activity of DNMT1 and DNMT3A in DNA methylations and/or by the posttranscriptional regulation of the miRNAs (such as miR-149-5p, miR-466q, miR-495, and miR-511-3p) upregulated in the sepsis IECs. To further elucidate the functional role of these IEC miRNAs in the pathophysiology of sepsis, we constructed miRNA–mRNA target interaction networks using an analytic platform, miRNet 2.0 [37]. As shown in Figure 3A, upregulated miRNAs in IECs after sepsis showed complex networks with putative targets. A large continent network encompassing multiple miRNAs, including miR-149-5p (423 targets), miR-495-3p (207 targets), miR-511-3p (130 targets), and miR-466q (105 targets), was identified as pivotal nodes. In a separate island network, miR-127-5p potentially targeted only one gene transcript. Taken together, these results indicated that IECs dynamically respond to sepsis by altering miRNA profiles and downstream sepsis-induced regulatory gene networks. In contrast to the networks containing upregulated miRNAs, only two networks (one continent and one island) were identified which were regulated by the pool of downregulated miRNAs in IECs after sepsis (Figure 3B). The continent network incorporated 3 miRNAs including miR-5125 (194 targets), miR-717 (101 targets), and miR-1983 (20 targets). In contrast, miR-351-5p created 1 island network with 20 targets. However, 5 other miRNAs (i.e., miR-6238, miR-1258, miR-124-2hg, miR-17hg, and miR-6240) did not show any network interactions in the miRNet analysis platform. To examine underlying pathways regulated by the candidate miRNAs in sepsis, we used Reactome [43], a high-performance bioinformatics tool, within miRNet 2.0. Following Reactome analysis, we identified a total of 100 pathways potentially regulated by the 14 upregulated miRNAs in IECs after sepsis, filtered by the significance level (adjusted p < 0.05) and then ranked by the number of hits (the number of gene targets involved in the given pathway; Supplementary Table S3). The top 20 pathways were listed by a 3-way bubble plot depicted using the number of hits, the significance level, and the gene ratio (Figure 4A). Interestingly, 6 out of the 20 pathways shown to be altered with sepsis were related to the fibroblast growth factor receptor (FGFR) signaling. Although not identified within the top 20 pathways, apoptosis and programmed cell death pathways were significantly enriched, indicating that sepsis may trigger epithelial cell death in the small intestine. In addition, gene ontology (GO) analysis revealed that a total of 99 pathways (adjusted p < 0.05) were identified (Supplementary Table S4) and the top 20 pathways (ranked by the number of hits) were shown in Figure 4B, depicted by the same criteria used in Reactome analysis. To further identify candidate pathways, we performed systemic enrichment analysis on the identified upregulated pool of IEC miRNAs with sepsis, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) platform, a downstream pathway analysis tool within miRNet 2.0. The KEGG analysis showed a total of 57 pathways, filtered by the significance level (adjusted p < 0.05) and ranked by the number of hits (Supplementary Table S5). The top 20 pathways potentially regulated by miRNAs following sepsis were listed by a 3-way depiction, as described above (Figure 4C). Many of the notable pathways involved pathways in cancer. In addition, epithelial Wnt signaling in the small intestine is potentially altered by sepsis. For the downregulated miRNAs seen in IECs after sepsis, we also performed Reactome, KEGG, and GO analyses. However, both the Reactome and GO analyses did not show any pathway. Only KEGG analysis revealed that Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling and ERBB signaling are putatively involved in the pathways regulated by the miRNAs downregulated in sepsis (e.g., miR-351-5p, miR-717, miR-1983, and miR-5125) (Supplementary Figure S3). In conclusion, upregulated miRNAs may potentially be actively involved in cell-specific responses in the IECs after sepsis. We next performed validation of the identified miRNA candidates. Most of the miRNAs upregulated in sepsis, including miR-149-5p, miR-154-3p, miR-328-3p, miR-378c, miR-434-5p, miR-466q, miR-467e-5p, miR-495-5p, miR-511-3p&-5p, and miR-699o-3p&-5p, were validated for their increased expression via quantitative PCR using RNA and complementary DNA of sepsis and control (sham) IECs (Figure 5 and Supplementary Figure S4). These results suggest that sepsis produces a miRNA signature that is specific to the mucosal compartment and has the potential for the discovery of novel epithelium-specific biomarkers in sepsis. To confirm that there were no off-target effects in sham mice that were injected with antibiotics (Figure 1), which were used as the control in this study, we included an additional cohort of mice which were not given antibiotics. To examine whether the levels of miRNAs increased in sepsis IECs are affected by the antibiotic injections alone, we assessed miRNA expression in IECs of the control sham mice in comparison with those of the mice with no injection (namely, normal mice). None of the 16 miRNAs that were upregulated in sepsis IECs showed any significant alterations in sham (control) mice injected with antibiotics when compared with those in uninjected normal mice (Supplementary Figure S5). Consequently, these data indicate that injections with antibiotics did not affect the expression level of any of the sepsis-increased 16 miRNAs tested. To examine if human cells exhibit a similar expression pattern of the miRNAs (including miR-149-5p, miR-466q, miR-495, and miR-511-3p) increased in the IECs of sepsis mice, we validated the levels of these miRNAs in human IEC lines, such as C2Bbe1, HUTU80, and H747. To provide an in vitro sepsis environment, we treated LPS to the cells, as described previously [33,34,35,36], and examined their miRNA expression levels. Cells treated with LPS did not show a significant change in the expression of the miRNAs upregulated in the IECs of sepsis mice (Supplementary Figure S6). Thus, these results suggest that human IECs from cell lines may differ from sepsis-mouse IECs in miRNA expression in the currently used in vitro model of experimental sepsis. The profile of miRNAs in human IECs with sepsis requires further assessment using IECs isolated from the patients diagnosed with sepsis. Next, we investigated the expression of the downregulated miRNAs (including miR-351-5p, miR-717, miR-1258-3p, miR-1983, and miR-5125) for validation and confirmed the reduction in miR-351-5p, miR-717, and miR-5125 expression levels in sepsis IECs by RT-qPCR. The expression of miR-1258-3p and miR-1983 did not show a significant downregulation in the IECs of the sepsis mice, compared to sham mice (Figure 6). To ask if the expression patterns of up- and down-regulated miRNAs are restricted to sepsis IECs, we next investigated expression of the miRNAs in blood samples. The upregulated miRNAs selected for further study (miR-149-5p, miR-466q, miR-495-3p, miR-495-5p, and miR-511-3p) and the downregulated miRNAs (miR-351-5p, miR-717, miR-1258-3p, miR-1983, and miR-5125) were tested for their levels in blood of sham and sepsis mice. Among the 10 miRNAs (as shown in Figure 5 and Figure 6), only the miR-511-3p exhibited a significant augmentation, whereas the other miRNAs did not show significant changes (Figure 7). This suggests that this miRNA may be useful as a diagnostic marker in this sepsis model and should be examined in the blood of patients with sepsis in the future. We then explored gene expressions of specific mRNAs, such as LOX, PTCH1, COL22A1, FOXO1, and HMGA2, which were found to be bioinformatically targeted by one or more of the four identified miRNAs (miR-149-5p, miR-466q, miR-495, and miR-511-3p), as their gene products have been reported to associate with Wnt or inflammatory pathways. Specifically, the mRNA of the lysyl oxidase (LOX) is a putative target for miR-149-5p and miR-511-3p, and its downregulation activates the Wnt/β-catenin pathway [44,45]. The mRNA of the patched1 (PTCH1) is a putative target of miR-466q and miR-511-3p, and PTCH1 targeting by miR-511-3p can activate the hedgehog pathway to trigger hepatic sinusoidal obstruction syndrome [46], which is promoted by inflammatory and fibrinolytic pathways. The mRNA of collagen type XXII α1 (COL22A1) is a putative target of miR-149-5p and miR-466q, and targeting of COL22A1 by miR-149-5p regulates inflammation and fibrosis of cardiomyocytes [47]. The mRNA of Forkhead Box O1 (FOXO1) is a putative target for miR-466q, miR-495-5p, and miR-511-3p, and functional inhibition of FOXO1 is associated with the Wnt/β-catenin pathway [48]. The mRNA of high mobility group A2 (HMGA2) was shown to be a target for miR-495 [49,50], and the HMGA2 mediates the secretion of pro-inflammatory cytokines, while its downregulation induces hypermethylation [51]. Based on the literature and our current data that demonstrate a significant reduction in LOX, PTCH1, COL22A1, FOXO1, and HMGA2 in the IECs of sepsis mice, compared to sham mice (Figure 8), the sepsis-augmented miRNAs in the IECs may have the potential for regulating both anti- and pro-inflammatory responses, possibly through posttranscriptional modification of their functional target genes. The proposed model illustrating miRNA-mediated target-gene expression regulations for anti- and pro-inflammatory responses in the IECs of sepsis mice is shown in Figure 9. To analyze expression of the target genes such as LOX, PTCH1, FOXO1, and HMGA2 in the human IEC lines subjected to LPS treatment, compared to control (no treatment), RT-qPCR was performed. Human cell lines (C2BBe1, HUTU80, and H747) treated with LPS showed no significant change in expression levels of the target genes (Supplementary Figure S7) that were decreased in the IECs of the sepsis mice (Figure 8). Human IEC lines were expected to be largely different from those in mouse cells, with regards to expression of the miRNAs and their regulatory pathways, at least under the currently used in vitro model of experimental sepsis. Future studies are needed to evaluate miRNAs from human IECs in vivo and in the blood from septic patients. Here we have shown that sepsis induces distinctively expressed miRNA profiles in IECs in a mouse model of sepsis. The upregulated miRNAs exhibited more complex and broader effects on comprehensive gene regulations in silico related to both Wnt signaling and inflammatory pathways. These upregulated miRNAs in sepsis IECs may contribute to quantitative downregulation of overall mRNAs. Intriguingly, several distinct miRNAs (miR-149-5p, miR-466q, miR-495, and miR-511-3p) may suppress expressions of LOX, PTCH1, COL22A1, FOXO1, or HMGA2. Current findings could provide us insight into the miRNA–mRNA crosstalk in IECs that may contribute to pro- and anti-inflammatory responses in sepsis. IECs are pivotal for the surveillance of intestinal environment to protect the host from both local and systemic challenges [52]. Disruption of the IEC barrier’s integrity caused by intestinal infection and inflammation has been shown to significantly shift the transcriptomic patterns within IECs [53]. Alterations of cell-specific epigenetic factors have received much attention as a potential contributor to the regulation of host mucosal immunosurveillance and IEC barrier function [54,55]. In this study, we aimed to uncover the epigenetic alterations to impact the disruption of the intestinal epithelium during sepsis by investigating the miRNA signature profiles. miRNAs have been proposed as potential biomarkers for diagnosing sepsis [56]. Our data showed that sepsis upregulated the expression levels of 14 miRNAs in IECs, compared with sham counterparts. Bioinformatics analysis further revealed that miR-149-5p, miR-495, miR-511-3p, and miR-466q might be key epithelial miRNAs in the small intestines, following sepsis. Hūbner et al. found that miR-149-5p plays an important role in TLR-mediated inflammation of bronchial epithelial cells by directly regulating chitinase-3-like 1 (CHI3L1), which has been known to regulate the bacterial infection [57]. CHI3L1 is highly expressed in IECs and can contribute to bacterial adhesion and invasion in intestinal inflammation [58]. Further, Heinsbroek et al. demonstrated that miR-511-3p expressed by immune cells regulates microbiota-associated intestinal inflammation [59]. Thus, further investigation on the roles of miRNAs and biomarker discovery in the context of sepsis-induced IEC disruption is warranted. In silico analysis further revealed that the upregulated miRNAs can regulate several pathways, including FGFR signaling. Al Alam et al. found that FGF and FGFR are expressed in both human and mouse small intestines [60]. In addition, FGF is significantly involved in cell differentiation of goblet cells and Paneth cells that are pivotal for epithelial protection in the intestines. Huang et al. also showed that inhibition of FGFR by a selective inhibitor, AZD4547, protected septic mice from pulmonary inflammation [61]. Song et al. suggested a protective role of FGF in a mouse model of intestinal inflammation [62]. More specifically, they found that FGF2 expressed by regulatory T cells cooperates with the cytokine IL-17 derived from Th17 cells to promote epithelial repair in a mouse model of intestinal inflammation [62]. Therefore, the interaction of FGFs and FGFRs in sepsis-induced intestinal epithelial inflammation and how miRNAs play a role as a mitigator of sepsis-induced inflammation are warranted for further investigation. In the intestines, Wnt signaling is fundamental for epithelial homeostasis [63]. Wnt signaling regulates several cellular functions of IECs, such as intestinal stem cells, related to their capacities for self-renewal and differentiation [64]. Interestingly, our KEGG analysis suggests that epithelial Wnt signaling might be significantly regulated by miRNAs after sepsis. In addition, recent studies have demonstrated that Wnt signaling can be a therapeutic target for the regeneration of intestinal epithelium. Xie et al. showed that Wnt mimetics (molecules mimicking endogenous Wnt) can regenerate the damaged epithelial tissues and reduce inflammation in a mouse model of colitis [65]. Xu et al. also demonstrated that miRNAs are significantly involved in the activation of the Wnt pathway [66]. Indeed, a target prediction tool, TargetScanMouse 7.1, revealed that most of the upregulated miRNAs in IECs after sepsis can bind to the 3′-UTR region of the mRNAs of multiple Wnt genes. Thus, the role of intestinal epithelial miRNAs regulating Wnt signaling in tissue regeneration after sepsis is warranted. Among downregulated miRNAs following sepsis, four miRNAs (i.e., miR-5125, miR-351-5p, miR-717, and miR-1983) displayed potential miRNA–mRNA networks in our analysis. There is little information on the pathways affected by the downregulated miRNAs seen in IECs after sepsis; one possibility is that those miRNAs are still comparatively novel and less documented in the literature. Although there has been a limited number of studies that demonstrate the role of those miRNAs, our KEGG analysis revealed that the JAK-STAT signaling pathway and ERBB signaling pathway might putatively be altered in SIECs after sepsis. Notably, our data showed that ERBB signaling is a common pathway which can be regulated by both upregulated and downregulated miRNAs. It has been shown that ErbB receptors and their ligands are crucial in epithelial cell recovery in mucosal tissues [67,68]. Therefore, further studies will investigate the involvement of ErbB signaling in the regulation of intestinal epithelial injury. Dysregulation of IEC remodeling may lead to a sustained mucosal inflammatory response in sepsis. Impaired wound-healing and remodeling capacities in the IECs disrupt their barrier integrity and further lead to bacterial translocation and subsequent inflammation in the intestine and other systemic compartments. IECs dynamically respond to sepsis by altering their miRNA profiles to regulate both epithelial injury and regeneration. Collectively, our data suggest that sepsis-induced inflammation is centralized toward persistent inflammation and immunosuppression. These findings are reminiscent of the spectrum of host responses typically seen in sepsis [69,70]. Further extensive understanding of sepsis-induced epigenetic alterations in different cell types, such as human IECs or leukocytes, would provide insight into the identification of therapeutic targets for sepsis.

PMC10001195

Duhita Sengupta,Asima Mukhopadhyay,Kaushik Sengupta

Elevated Levels of Lamin A Promote HR and NHEJ-Mediated Repair Mechanisms in High-Grade Ovarian Serous Carcinoma Cell Line

27-02-2023

lamin A,ovarian cancer,genomic instability,etoposide,chemoresistance

Extensive research for the last two decades has significantly contributed to understanding the roles of lamins in the maintenance of nuclear architecture and genome organization which is drastically modified in neoplasia. It must be emphasized that alteration in lamin A/C expression and distribution is a consistent event during tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (high-grade ovarian serous carcinoma cell line) in comparison to IOSE (immortalised ovarian surface epithelial cells) and, consequently, altered damage repair machinery in OVCAR3. We have analysed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We hereby establish the role of elevated lamin A in neoplastic transformation in the context of high-grade ovarian serous cancer through a combination of HR and NHEJ mechanisms.

Elevated Levels of Lamin A Promote HR and NHEJ-Mediated Repair Mechanisms in High-Grade Ovarian Serous Carcinoma Cell Line Extensive research for the last two decades has significantly contributed to understanding the roles of lamins in the maintenance of nuclear architecture and genome organization which is drastically modified in neoplasia. It must be emphasized that alteration in lamin A/C expression and distribution is a consistent event during tumorigenesis of almost all tissues of human bodies. One of the important signatures of a cancer cell is its inability to repair DNA damage which befalls several genomic events that transform the cells to be sensitive to chemotherapeutic agents. This genomic and chromosomal instability is the most common feature found in cases of high-grade ovarian serous carcinoma. Here, we report elevated levels of lamins in OVCAR3 cells (high-grade ovarian serous carcinoma cell line) in comparison to IOSE (immortalised ovarian surface epithelial cells) and, consequently, altered damage repair machinery in OVCAR3. We have analysed the changes in global gene expression as a sequel to DNA damage induced by etoposide in ovarian carcinoma where lamin A is particularly elevated in expression and reported some differentially expressed genes associated with pathways conferring cellular proliferation and chemoresistance. We hereby establish the role of elevated lamin A in neoplastic transformation in the context of high-grade ovarian serous cancer through a combination of HR and NHEJ mechanisms. The nuclear lamina is a nuclear peripheral meshwork composed of lamins which are type V intermediate filament proteins [1,2]. Findings over the past few years have established that lamins not only impart mechanical stability and facilitate the binding of proteins and chromatin but also serve a wide range of nuclear functions such as genome organization [3,4], maintenance of genome stability by tethering chromatin [5], chromatin regulation, DNA replication-transcription damage, and repair [6,7,8]. Mutations in the LMNA gene encoding lamin A/C or its altered expression are found to be coupled with defects in DNA replication, transcription, and repair [8,9,10,11]. These observations, together with the fact that defects in lamin A are associated with various degenerative disorders, premature aging syndromes, and cancer, validate the notion that this protein serves as the “caretaker of the genome” [12]. Lamin A/C plays a role as a complex regulatory machine in various cancers but whether its expression alteration is a cause or a consequence of a particular type and stage of cancer calls for deeper investigation. Ovarian cancer is the 7th leading cause of cancer mortality among women and 8th leading cause of cancer diagnosis worldwide [13]. It is the leading cause of death from gynaecological malignancies after cervical and uterine cancer [14]. High-grade serous ovarian cancer (HGSOC) is the most regular histological subtype of epithelial ovarian cancer (EOC) which is characterized by functional p53 loss in 96% of cases along with a high frequency of copy number alterations (CNAs) [15]. Interestingly, most carcinoma tissues and cell lines exhibit a heterogeneous expression pattern of lamin A/C [16,17,18]. Some studies have reported that lower lamin A levels in cancer cells are associated with a higher ability to migrate; whereas, migration was impeded by introducing lamin A ectopically in the system [19]. Another group has observed that increased levels of lamin A facilitate migration in colorectal cancer cells through an elevated level of T-plastin which on the other hand, downregulates E cadherin [20]. Taken together, these findings suggest that lamin A/C expression, whether low or high, has a chance of causing metastasis and invasion, given that low levels are associated with invasion through nuclear deformability, while high levels protect against mechanical forces and, thus, resist DNA damage-induced cell cycle arrest by assisting the recruitment of DNA damage repair proteins [21,22,23,24]. In ovarian cancer tissue specimens, high-density protein microarrays revealed enhanced expression of lamin A with unaltered cellular location [17]. A proteomic investigation was recently conducted in patients with polycystic ovarian disease to identify biomarkers for ovarian cancer [25]. Calreticulin, fibrinogen, superoxide dismutase, vimentin, malate dehydrogenase, and lamin B2 were found to be overexpressed in women with both ovarian cancer and PCOD in the study [26]. Therefore, this study was another hint for predicting lamins as probable regulators of cancer progression in the ovary. In this work, etoposide, which is a semi-synthetic derivative of podophyllotoxin extracted from Podophyllum peltatum or Podophyllum emodi, has been used as a DNA-damaging agent [27]. It works by inhibiting the action of topoisomerase II resulting in DNA strand breaks and the induction of cytotoxic and apoptotic cell death [28]. The principal chemotherapeutic effect of etoposide lies in the fact that the permanent double-strand breaks overwhelm the cells thereby initiating cell deaths (Supplementary information file S1: Supplementary Figure S3A). Thus, etoposide changes topoisomerase II from an important enzyme to a strong cellular toxin that induces breaks in the genome facilitating mutagenesis and cell death pathways [29]. However, other mechanisms, such as chromosomal aberrations, aneuploidy, and endoreplication, may, nevertheless, play a role in cancer cells’ sensitivity to etoposide [30,31]. The roles of etoposide in mitotic catastrophe, senescence, neosis, and cell survival are yet to be fully determined and are currently being debated [32]. Oral etoposide is a common candidate as a single or combination drug for second-line and/or maintenance treatment, as it does not display cross resistance to platinum or paclitaxel, which are utilized as first-line chemotherapeutic agents in the treatment of ovarian cancer [33]. Various studies in mutant LMNA and HGPS models have established fascinating cues regarding the active regulatory roles of a functional lamin A in the maintenance of genomic stability and subsequent response to radiation and chemotherapy [34,35]. Studies suggest Zmpste24-deficient mouse embryonic fibroblasts (MEFs) have higher tendencies to acquire chromosome abnormalities and are more vulnerable to DNA-damaging chemicals [36]. Upon exposure to ionizing radiation, these cells demonstrate delayed recruitment of 53BP1 to H2AX-labeled DNA repair foci, as well as delayed resolution of these foci. In addition, progeroid fibroblasts have an abnormal build-up of the xeroderma pigmentosum group A (XPA) protein, which is partly responsible for DNA repair abnormalities, as well as poor recruitment of the double-strand break (DSB) repair proteins Rad50 and Rad51 to sites of DNA damage [37]. It was also reported that DNA breakage decreases the mobility of nucleoplasmic GFP lamin A [8,38]. Furthermore, lamin A was reported to engage chromatin through phosphorylated H2AX, induced by genome damage which was elevated following irradiation [8,39]. It was also established that LMNA inactivation affects the positional stability of DNA repair foci which is recovered by the stable expression of GFP-lamin A [8,40]. The early response of cells expressing disease-causing lamin A has been studied by Parnaik and colleagues [7]. Several mutants were exposed to DNA damage induced by cisplatin or UV, which primarily caused replicational stress due to stalled replication forks in the S phase, and it was discovered that several mutants inhibited the formation of phosphorylated H2AX at DNA repair foci and the recruitment of 53BP1 to the repair sites [7]. In untreated cells, these mutations impaired emerin localization and, more importantly, mislocalized ATR kinase [7]. These data also pose an intriguing question—whether lamin A is a critical component of the DNA damage response, such that its loss causes radiation and chemosensitivity as well as an abnormal DNA damage response, or does an abnormal DNA damage response result from lamin-induced changes in the nuclear substructure [41]. To investigate this, the DNA damage repair status in a high-grade ovarian serous cancer cell line with a high endogenous lamin A (OVCAR3) level was studied compared to a normal immortalized ovarian surface epithelial cell line with a low level of endogenous lamin A (IOSE). The global gene expression was studied upon the introduction of etoposide as a DNA damaging reagent in a high endogenous lamin A background. RNA sequencing analysis reported many genes to be differentially regulated which overlapped with some previously discovered genes in studies with etoposide treatment in breast and lung adenocarcinoma cell lines. Interestingly, the list of differentially expressed genes (DEGs) in the current study also included the expression of some signature genes, directly or indirectly linked to pathways associated with cellular proliferation, apoptosis evasion, and chemoresistance. Studies in lamin A knockdown background further validated the potential of high endogenous lamin A to directly or indirectly contribute to chemoprotection in the OVCAR3 cell line. NIH-OVCAR3 cell line was obtained from Dr. Asima Mukhopadhyay at Tata Medical Centre and maintained in ATCC-formulated RPMI 1640 supplemented with penicillin streptomycin to a final concentration of 1% and fetal bovine serum (FBS) to a final concentration of 20%. IOSE (immortalised human ovarian surface epithelial cell line) was a kind gift from Dr. Nelly Auersperg (Canadian Ovarian Tissue Bank, BC) maintained in a 1:1 ratio by volume of Media 199 and MCDB 105 supplemented with penicillin streptomycin to a final concentration of 1% and fetal bovine serum (FBS) to a final concentration of 10%. Cells were cultured using a standard protocol and are maintained at 37 °C under 5% CO2. OVCAR3 cells were treated with 100 ng/mL nocodazole for 16 h and washed with PBS thrice and incubated in the fresh media for 3 h before treatment with etoposide (final concentration:1.7 µM) for 24 h. DMSO was used as the vehicle. For the knockdown experiments, cells in 35 mm culture dishes were transfected with 60 nM of siRNA complex, and 5 µL lipofectamine 2000 was prepared as per manufacturer’s instructions and incubated for 24 h per dose for up to three doses [42]. siRNA sequences for LMNA are mentioned in Supplementary Materials file S1 (Supplementary Table S1). Cells were allowed to grow to a maximum of 75–90% confluency followed by pelleting. The pellets were washed with sterile 1X PBS and stored at −80 °C until further use. For lysis, the pellets were treated with mammalian protein extraction reagent (M-PER) along with the 1X protease inhibitor cocktail. The lysates were stored at −80 °C. Protein concentrations in cell lysates were measured using the Bradford assay. Proteins were separated using 10% SDS gel electrophoresis and transferred onto a nitrocellulose membrane. Primary antibodies along with the dilutions used in this study are mentioned in the Supplementary Materials file S1 (Supplementary Table S2). Antibodies were diluted in the blocking buffer and membranes were incubated overnight at 4 °C with primary antibodies. Secondary antibody dilution was 1:400 for both antimouse and antirabbit IgG conjugated to horse radish peroxidase. Membranes were incubated with secondary antibodies for 2 h at room temperature. Blots were analysed using ImageJ (ImageJ bundled with 64-bit Java 1.8.0_112) for densitometric analysis. Experiments were performed at least 6 times. Cells were grown on sterile coverslips and on reaching 70–80% confluency, they were processed for immunostaining. Cells were fixed with 4% paraformaldehyde for 15 min, permeabilized with 0.5% Triton × 100 for 5 min, blocked, washed, and incubated with primary antibody solution containing blocking agent (5% normal goat serum and primary antibody as per dilution recommended in 1× PBS) for 2 h in a humidified environment at room temperature. Following the primary antibody, cells were incubated with a secondary antibody diluted in 1× PBS in a humidified environment for 2 h in dark at room temperature. Following similar washing steps (3 min twice) with 0.05% Tween-20 and 1× PBS, cells were mounted on glass slides with Vectashield mounting medium with an antifade agent and DAPI or PI to stain the nucleus. Propidium iodide was used at a concentration of 10 μg/mL for 40 min and, then, coverslips were mounted on glass slides with a mounting medium containing an antioxidizing agent (PPD). The slides were stored in dark at 4 °C. Primary antibodies along with the dilutions used in this study are mentioned in the Supplementary Materials file S1 (Supplementary Table S2). Secondary antibodies were conjugated with Alexa Fluor 488 and Alexa Fluor 546. For confocal imaging, the slides were visualized under 63× oil immersion objectives in Zeiss LSM 710 Meta, 60× (water immersion), and 100× oil DIC N2 objective/1.40 NA/1.515 RI in NIKON TiE inverted research microscope. The images were captured in resonant mode. The excitation filters used were 450/50, 525/50, and 595/50, the first dichroic mirror used was 405/488/561. The lasers used were multi line argon–krypton mixed gas laser (λex: 488 nm), a solid state laser 100 λex—405 nm, and a solid state laser (λex—561 nm). Images were processed using Ni elements analysis AR Ver 4.13 and ImageJ software (ImageJ bundled with 64-bit Java 1.8.0_112). Immunofluorescence experiments were performed at least 6 times. Around 250 nuclei were used for quantification of fluorescence intensities. OVCAR3 cells were grown in 100 mm dishes. Cells were treated with etoposide and DMSO (vehicle) following synchronization with nocodazole. The lysis solution for comet assay was chilled at 4 °C for at least 20 min before use. LM agarose was melted in a microwave and cooled in a water bath for at least 20 min before use. 105/mL cells were combined with molten LMagarose (at 37 °C) at a ratio of 1:10 (v/v) and immediately pipetted 50 µL onto two well CometSlides™ (Trevigen, Minneapolis, MN, USA). Agarose mixed cells were spread over the sample area using the side of the pipette tips. Slides were placed at 4 °C in the dark for 15–30 min. Next, the slides were immersed in lysis solution at 4 °C for 30 min. After removing the slide from lysis buffer, excess buffers were tapped and the slides were gently immersed in 50 mL of 1X TBE buffer for 5 min, two times. Neutral electrophoresis was performed at room temperature with 1X TBE at 21 V. After electrophoresis, the slides were gently removed from the tray and washed with neutralizing buffer (0.4 M Tris-HCl, pH 7.5) for 5 min followed by washing in distilled H2O for 5 min. Finally, the slides were stained with propidium iodide (20 µg/mL) and visualized under 20X DIC N2 objective in NIKON TiE inverted research microscope. Tail lengths and intensities were measured in a semiautomated manner using ImageJ software (ImageJ bundled with 64-bit Java 1.8.0_112.15 nuclei) from each field and a total of 500 nuclei were analysed. Ten such fields were used for quantification. Each is selected as representative independent experiment. Experiments were performed in triplicates. Ten to fifteen cells were analysed in each field. Twenty such fields were studied for each sample. OVCAR3 cells were treated with 100 ng/mL nocodazole for 16 h and washed with PBS thrice and incubated in the fresh media for 3 h before treatment with etoposide for 24 h. DMSO was used as the vehicle. Ten µM of BrdU was introduced in culture media after treatment. After 2 h of BrdU treatment, cells were fixed with methanol and incubated in 2N hydrochloric acid for 30 min followed by 10 min of neutralization in 0.1 M phosphate buffer. It was then washed and stained with anti BrdU antibody (Santa Cruz) following the immunocytochemistry protocol described above. Images were grey scaled and thresholded to count the BrdU positive cells using the cell counter plugin in ImageJ (ImageJ bundled with 64-bit Java 1.8.0_112). Ten fields from each sample for all independent experiments were analysed. An equal number of cells were seeded in each well of 96 well plates and incubated at 37 °C for 24 h. OVCAR3 cells were treated with siRNA only, and siRNA with etoposide and DMSO, respectively. Cells without any treatment were used as control. Ten µL of MTT solution (Roche, Boston, MA, USA, 11465007001) was added to each well and the whole experiment was performed according to the manufacturer’s protocol. The absorbance was recorded at 570 nm with a reference wavelength of 630 nm by an ELISA reader. Experiments were performed in triplicates. The data was analysed in MS Excel. RNA was synthesized using QIAGEN RNAeasy mini kit. Two µg of RNA was run on FA gel to analyse the purity and integrity of the sample. Five µg of RNA was reverse transcribed using a cDNA synthesis kit (Thermo Fischer Scientific, Waltham, MA, USA) with oligodt primers according to the manufacturer’s instructions. Real-time PCR with SYBR green detection was using applied biosystem 7500 real-time PCR system with fluorescence detection. Sequences of the primers used for qPCR are mentioned in the Supplementary Materials file S1 (Supplementary Table S3). Experiments were conducted in three biological replicates every time for at least 6 times. Fold changes were measured from cycle threshold values using 2−ΔΔCT method. Transfected cells were washed twice in ice-cold PBS, trypsinized, and transferred to a 15 mL conical tube and centrifuged for 2 min at 1000 rpm. The supernatant was discarded and the pellet was stored at −80 °C until further use. RNA was isolated using a Pure Link mini RNA isolation kit (Thermo Fisher Scientific, USA). mRNA library preparation was performed using Kapa Hyperprep stranded library preparation kit (Roche, Basel, Switzerland) followed by sequencing on Novaseq 6000 (Illumina, Inc., San Diego, CA, USA) using 2X100 bp read length targeting 50 million paired-end reads. The raw sequence dataset was mapped to the reference genome homo sapiens (human) [assembly GRCh38.p13] using the HISAT2 package. The mapped sequences were then converted from SAM file format to BAM file format and sorted using the SAMTOOLS package. Following that, the FPKM value was calculated using the StringTie package. Following that, the DESeq2 package [43] was used to perform differential expression analysis between the control and mutant in replicates. The genes with adj. p values less than 0.05 and log2 fold change ± 1.5 from the differential expression analysis results between C (C1 and C2 denoting DMSO treated OVCAR3 cells in replicates) and D (D1 and D2 denoting etoposide treated OVCAR3 cells in replicates) were chosen for further downstream comparison analysis. GO and pathway enrichment [44] were studied using the DAVID tool. For each given gene list, pathway and process enrichment analysis was carried out with the following ontology sources: GO biological processes [44], KEGG pathway [45], reactome gene sets [46,47], TRRUST [47], PaGenBase [48], WikiPathways [49], and PANTHER [50] pathway. All genes in the genome have been used as the enrichment background. The network was visualized with Cytoscape (v3.1.2) with a “force-directed” layout and with edges bundled for clarity. MCODE algorithm was then applied to this network to identify neighbourhoods where proteins are densely connected. Lamin A interactome was studied in BioGRID which is the biological general repository for interaction datasets and a database that archives genetic and protein interaction data from model organisms and humans [51]. The functional association networks were generated with the help of the GeneMANIA tool, whereupon inserting a specific gene list, it returns connections between the genes within the selected datasets and finds a dataset where the gene list under the experiment is highly connected [52]. The transcription factor identification was performed with the help of the ORegAnno database (Open REGulatory ANNOtation database) which is an open database for the curation of known regulatory elements from the literature. Annotations are gathered from users worldwide for various biological experiments and automatically cross-referenced against common databases such as PubMed, Entrez Gene, etc., with information regarding the original experimentation performed [53]. Promoter sequences for putative transcription factor binding analysis were collected from EPD (biological database and web resource of eukaryotic RNA polymerase II promoters with experimentally defined transcription start sites) and ChipBase V2 software [54,55]. All experiments were performed in replicates. All the graphical diagrams have been plotted in GraphPad Prism 9.5.0 (730) software. Statistical significance was determined by multiple t tests (unpaired, using parametric test, assuming both samples from each row are from populations with the same SD) for densitometric analyses of western blot, immunofluorescence data, and fold change analyses of qPCR data. Paired t test (parametric) was used to analyse comet assay results. Ordinary one-way ANOVA test was performed by comparing the mean of each column by the mean of a control column in analysing the counts of BrdU positive cells. Two-way ANOVA (fitting a full model and using the Geiser Greenhouse correction) test was performed by comparing each cell mean with every other cell mean on that row in analysing the results of cell viability assay. For all the experiments, error bar indicates standard error of mean. p value outputs are in GP style [0.1234 (ns), 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.0001 (****)]. Statistical analyses of RNA sequencing data were carried out using R language packages (version 4.0.2). Differences with p value < 0.05 were considered to be statistically significant. For pathway and process enrichment analysis, terms with a p value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) were collected and grouped into clusters based on their membership similarities. More specifically, p values were calculated based on the cumulative hypergeometric distribution [56] and q values were calculated using the Benjamini–Hochberg procedure to account for multiple testings [57]. Kappa scores [58] were used as the similarity metric when performing hierarchical clustering on the enriched terms where subtrees with a similarity of >0.3 were considered a cluster. The most statistically significant term within a cluster was chosen to represent the cluster. The expression of lamins in healthy human individuals is heterogenous and very tissue specific [21]. According to the report on the differential distribution of lamins in normal human tissues, ovarian stromal cells exhibited moderate positive expression in immunostaining with lamin antibodies [59]. At the same time, the altered expression of lamins in different cancer subtypes is now evident from research worldwide [21]. The cancer cell line under this study (NIH-OVCAR3) is well characterized as a high-grade ovarian serous carcinoma cell line with a missense mutation in the TP53 gene (R248Q) [60] and an appropriate model system to study drug resistance in ovarian cancer. On the other hand, the ovarian surface epithelium (OSE) is a modified mesothelium that converts to most human ovarian carcinomas [61]. Hence, the control cell line used for comparative analysis is the immortalized ovarian surface epithelial cells (IOSE). Expression of A- and B-type lamin proteins was upregulated in OVCAR3 cells which were inferred from the western blot and immunofluorescence data (Figure 1). A similar phenomenon was also reflected in mRNA levels by qRT-PCR (Figure 1). Elevation in lamin A and lamin B levels are visibly clear both in the nuclear rim and nucleoplasm in OVCAR3 nuclei with respect to IOSE, although an abundance of nucleoplasmic lamin A is more prominent in OVCAR3 nuclei. This is even more intriguing as interaction with the transcription factors is mostly carried out by the nucleoplasmic lamin A [62] (Supplementary Materials file S1: Supplementary Figure S1). However, the extent of lamin A overexpression was significant and consistent in all the experimental and biological replicates, unlike lamin B. In addition, there is a prominent elevation in the size of the OVCAR3 nuclei as observed in the immunofluorescence data which corroborates the previous study from our lab in patients diagnosed with ovarian cancer [63]. Prioritizing the extent of upregulation and statistical significance, this study further focuses on the elevated endogenous expression of lamin A in OVCAR3 cells compared to IOSE cells and its effect. In the studies on laminopathies, especially on progeria that is associated with deficiency of a functional lamin A, evidence of DNA repair abnormalities first appeared along with baseline DNA damage, increased sensitivity to DNA damaging agents, chromosomal aberrations, and in some cases, a constitutively activated repair mechanism [36,64,65]. Given the function of A-type lamins in the processing of long-range NHEJ processes, such as deprotected telomeres and the stabilization of 53BP1, we anticipated that lamin A expressional changes might also modulate the overall repair apparatus [66]. Therefore, we checked the damage repair status in both cell lines. As the phosphorylation of the H2AX (γH2AX) in the surrounding chromatin is the first sign of genome damage, we checked the expression of γH2AX in both the cell lines with western blot and immunofluorescence (Figure 2). To begin with, the distribution of γH2AX was similar in both the cell lines in the absence of any DNA damaging agent. We further selected some proteins from HR (homologous recombination) and some from NHEJ (nonhomologous end joining) to check the expression of the commonly studied damage repair mediators in the cell lines. Interestingly, HR proteins (BRCA1, Rad51) exhibited elevated expression in OVCAR3 cells (Figure 2, Supplementary Materials File S1: Supplementary Figure S2). The expression of Ku70 was similar in both cell lines which goes in line with another study by Langland et al., 2010 [67]. Although a recent transcriptomic study reports high NHEJ activity in OVCAR3 cells, specifically XRCC6 (Ku70) expression was found to be low in the cell line (OVCAR3) compared to the other 12 ovarian cancer cell lines carrying various mutations. It was speculated that Ku70 expression may not be the appropriate determinant of a repair mode due to the other complex genotypic and phenotypic alterations in ovarian cancers [68]. As reported by Gonzalo et al., a deficiency in A-type lamins would result in increased activity of the p130/E2F4 repressor complex and repression of the RAD51 and BRCA1 genes [66]. Our observation also followed a similar trend and we could find a higher expression of HR mediators in OVCAR3 cells with a high endogenous expression of lamin A without exposure to any form of genome damage (Figure 2, Supplementary Materials File S1: Supplementary Figure S2). Next, we aimed to study the cells’ response to chemically induced DNA damage. For this, we have used a well-studied topoisomerase inhibitor (Supplementary Materials File S1: Supplementary Figure S3A) etoposide as the chemical DNA damaging agent, which is also a routinely used drug in the conventional treatment of ovarian cancer [69]. The dose and duration of etoposide treatment in ovarian cancer cell lines were optimized by different groups for clinical studies in patients or in vitro [70,71,72]. The specific dose and duration for this study were 1.7 µM in DMSO for 24 h. It was selected in a way so that it brings about a significant amount of DNA damage, which was validated by comet assay and quantification of the change in γH2AX expression by immunoblot and immunofluorescence (Supplementary Materials File S1: Supplementary Figure S3). As observed from the increased tail lengths of the comets in the etoposide treated OVCAR3 cells, a significant amount of genome damage was confirmed (Supplementary Materials Information File S1: Supplementary Figure S3B). DMSO alone was used as a sham for the study. Expression of γH2AX in OVCAR3 increased by 50% after treatment with etoposide which was another indication of significant DNA damage (Supplementary Materials File S1: Supplementary Figure S3C). We also checked the expression of PCNA which was elevated after treatment with the anticancer drug etoposide (Supplementary Materials File S1: Supplementary Figure S3D) which strongly affects the functional organization of S-phase nuclei, leading to the disassembly of replication factories and the redistribution of replicative factors resulting in the formation of DNA repair foci [73]. Elevation in PCNA level was maximum in 24 h and decreased slightly after 48 h (Supplementary Materials File S1: Supplementary Figure S3D). This experiment was performed initially to optimize the duration of etoposide treatment. All the experiments afterward have been performed with 24 h of etoposide treatment. The expressions of γH2AX and PCNA were analysed by western blot as well (Supplementary Materials File S1: Supplementary Figure S3E). Since OVCAR3 cells are isolated from the highly aggressive ovarian carcinoma background and are one of the desired model cells to study chemoresistance, the dose and duration of etoposide were specifically optimized to elaborately study the cells’ response to combating the damage. We also studied the expression of the common repair mediators in OVCAR3 cells after treatment with etoposide. Interestingly, both HR (BRCA1, Rad51) and NHEJ (Ku70) mediators exhibited an increase in expression following treatment with etoposide, indicating successful activation of damage-induced repair (Supplementary Materials File S1: Supplementary Figure S4). High throughput next generation sequencing data from this study have been deposited in NCBI’s gene expression omnibus and are accessible through GEO series accession number GSE211529 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211529 (accessed on 23 August 2022)). The raw sequence dataset is mapped to the reference genome using the HISAT2 package and a high mapping rate of 91–92% and 92% of reads mapped to the reference genome were obtained for DMSO and etoposide treated OVCAR3 cell samples, respectively (Supplementary Materials File S1: Supplementary Figure S5). As part of the analytical pipeline, most sequencers routinely provide a QC report. In this case, FastQC generated a quality control report that could identify issues that originate in the sequencer or the starting library material. Different quality values of each sample are summarized in the table provided in Supplementary Materials File S1: Supplementary Figure S5. Out of the total number of genes identified from the DeSeq2 gene count analysis (supplementary information file S2), the ones with adj. p values of less than 0.05 and a fold change of ±1.5 from the differential expression analysis results between the DMSO treated (control) and etoposide treated cell samples were chosen for further downstream comparison analysis. The heatmap and volcano plot for the top 205 selected genes are shown in Figure 3A,B. Interestingly, gene ontology revealed that most of the differentially expressed genes were primarily involved in DNA damage/telomere stress induced senescence, DNA double-strand break repair, cell cycle regulation, DNA packaging, DNA replication, transcriptional misregulation in cancer, telomere organization, negative regulation of DNA recombination, Rap1 signalling pathway, negative regulation of DNA metabolic processes, etc. Differentially expressed genes from various pathways were further validated by qRT-PCR (Figure 3C,D). The genes and the corresponding pathways are given below(Table 1). Interestingly, along with the upregulated expression of genes associated with cell cycle regulation telomere maintenance DNA replication packaging, we also encountered increased expression in PI3K-Akt signalling mediators (FGF2, THBS1, TLR2), TGF-beta signalling mediators (THBS1), and genes such as BIRC3 which points to chemoresistance and apoptotic evasion [74,75,76,77,78,79,80,81]. Genes from the heatmap (Figure 3A) were selected for further analysis. A gene ontology (GO) analysis revealed that cellular functions associated with DNA damage/telomere stress induced senescence, DNA double-strand break repair, cell cycle regulation, DNA packaging, DNA replication, transcriptional misregulation in cancer, telomere organization, negative regulation of DNA recombination, Rap1 signalling pathway, and negative regulation of DNA metabolic processes were primarily affected (Figure 4A). All genes in the genome have been used as the enrichment background. The most statistically significant term within a cluster is chosen to represent the cluster (Figure 4B). The terms within each cluster are exported to a table in supporting information file S3. To further elucidate the relationships between the terms, a subset of enriched terms has been selected and rendered as a network plot, where terms with a similarity >0.3 are connected by edges. We selected the terms with the best p values from each of the 20 clusters, with the constraint that there were no more than 15 terms per cluster and no more than 250 terms in total (Figure 4B). To better understand the network, PPI enrichment analysis was performed for each given gene list using the following databases: STRING [82], BioGrid [51], OmniPath [83], and InWeb_IM [83]. Only physical interactions in STRING (physical score > 0.132) and BioGrid were used. The resultant network contains the subset of proteins that form physical interactions with at least one other member in the list. If the network contains between three and five hundred proteins, the molecular complex detection (MCODE) algorithm [84] has been applied to identify densely connected network components. The MCODE networks identified for individual gene lists have been collated and are shown in Figure 4C,D. Pathway and process enrichment analysis has been applied to each MCODE component independently, and the three best-scoring terms by p value have been retained as the functional description of the corresponding components, shown in the tables underneath corresponding network plots within Figure 4C,D. Here, also, we observed that the genes in the PPI network were distinctly enriched in DNA damage and telomere stress-induced senescence, meiotic recombination, HDACs, and GPCR signalling events. The study so far generated cues on the influence of etoposide as a chemical inducer of double-strand breaks in OVCAR3 cells with a high endogenous expression of lamin A. As established from the previous results, normal epithelial cells have a significantly low amount of lamin A compared to ovarian cancer cells. Therefore, to study the specific role of lamin A in this context, an siRNA-mediated knockdown of lamin A was performed in OVCAR3 cells (Figure 5A). Simultaneously, the replication assay and cell viability assay were performed after exposure to etoposide in lamin A deficient OVCAR3 cells. It was previously reported that etoposide induces S-phase accumulation, through a p53-related pathway in the mouse foetal brain [85]. Pursuing a similar observation, a greater number of BrdU-positive cells were found in etoposide-treated OVCAR3 cells (Figure 5B). Interestingly, the number decreased in the LMNA knockdown cells and the difference in the number of BrdU-positive cells was insignificant between the etoposide-treated and untreated counterparts of LMNA-deficient OVCAR3 cells (Figure 5B). Similarly, cell viability was also hindered in the knockdown condition which further deteriorated following treatment according to the data obtained from the MTT assay (Figure 5C). Genes from the majorly affected pathways were selected to be studied further in knockdown conditions to check whether lamin A has specific roles in the modulation of their mRNA expression. Interestingly enough, most of the genes which were upregulated in etoposide-treated OVCAR3 cells were downregulated in knockdown conditions except PLK1 (Figure 5D). We further checked the expression of those genes in LMNA knockdown cells both before and after treatment with etoposide. The genes which were upregulated in etoposide-treated OVCAR3 cells exhibited decreased mRNA expression after etoposide treatment in LMNA knockdown conditions (Figure 5E) indicating potential cues of direct or indirect association with lamin A in those pathways conferring cellular proliferation, apoptosis evasion, and chemoresistance. BrdU assay, MTT assay, and qPCR experiments were performed at least three times to consider results from independent biological and experimental replicates for statistical analysis. To correlate how the lamin A level is connected to the regulation, lamin A interactome analysis was performed to investigate whether there is any direct or indirect association of lamin A with the mediators under study or the proteins capable of regulating their expressions. BioGrid [51] was used to investigate lamin A interactome. The excel file containing information on 1601 interactions reported to date is provided in supporting information file S4. We started with the search for the known transcription factors (Human) which belong to the interactome of lamin A. We also searched for published reports regarding the interaction of lamin A with each of the 205 genes present in the heatmap which were differentially expressed in etoposide treated OVCAR3 cells. Thirty-three such genes were found to be members of the lamin A interactome. One such gene, HES1 was a known human transcription factor which is also reported to be overexpressed in advanced ovarian serous adenocarcinoma, contributing to its stemness, metastasis, and drug resistance [86,87] (Figure 6A). As confirmed from the biochemical studies, the representative mediators (PIF1, RIF1, BRCA2, FGF2, MCM10, BIRC3, THBS1, PLK1, XRCC2, POLQ, TLR2, and BRIP1) from the RNA seq data had shown severely altered expression patterns in presence and the absence of lamin A following etoposide treatment. Therefore, these genes were primarily used for this detailed survey. Firstly, we generated a functional association network of LMNA with this set of 12 genes using GeneMANIA [52]. This association data includes protein and genetic interactions, pathways, coexpression, colocalization, and domain similarity encoded by different colours. GeneMANIA also finds other genes that are related to the set of input genes, using a very large set of functional association data. Some additional genes were found as the new members to define the pathway or the specific function completely (Figure 6B). We could find direct physical interactions of lamin A with some of the factors under study and some of them were indirectly connected. We identified 20 additional interacting proteins in this network to give rise to a complete functional association. Interestingly, we could find five of the twelve factors (BIRC3, BRCA2, POLQ, BRIP1, RIF1) under study that were direct interactors of lamin A (Figure 6C). Next, we searched for the interactors of lamin A which are reported to interact with or bind to the promoters of those 12 genes. This helped us discover 12 such lamin A interactors (TP53, MYC, TEAD4, TEAD1, CTCF, YWHAQ, NCOR2, SMAD1, RUNX1, ZBTB7A, MECP2, IGFBP5) which are reported to bind the promoters of most of the genes under study (Figure 6D). The search for the regulatory elements of the genes was performed by the ORegAnno tool and ChipBase v.2 software [53,55]. This Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. This has information about regulatory regions, transcription factor binding sites, RNA binding sites, and regulatory elements (supporting information file S5). Therefore, it was evident from this exercise that either lamin A level has a direct impact on the altered expression of some of the genes or it might have regulatory roles over the expression of the transcription factors regulating the genes under study. To address this, we selected the first four transcription factors which regulate the maximum number of the genes under study and generated a separate functional association network of LMNA with this set of four transcription factors using GeneMANIA (Figure 7A). We checked the mRNA expressions of the transcription factors and found that the levels are decreased in etoposide treated OVCAR3 cells in an LMNA knockdown background with respect to etoposide treated OVCAR3 cells with a high endogenous LA (Figure 7B). With this set of information, we could speculate the possible nodes of association of lamin A in this complex regulatory mechanism. In addition, we could find additional players in the scenario. This would require confirmatory biochemical experiments to further approve the mechanistic trajectories. Genomic instability is one of the fundamental signatures of carcinogenicity [88], which originates from defects in DNA damage response (DDR) pathways. DDR defects can be inflicted either by genotoxic damage induced by radiation/chemotherapy in a DDR defective background or by targeting a complimentary repair pathway leading to “synthetic lethality” [89]. In this study, we have worked on NIH-OVCAR3, one of the most extensively studied ovarian cancer cell lines which was developed in 1982 from ascites of a progressive ovarian adenocarcinoma that was resistant to cyclophosphamide, cisplatin, and doxorubicin [90]. Numerous studies [91,92,93] have indicated that the cell line is typical of HGSOC (high-grade serous ovarian carcinoma). In retrospect, NIH-OVCAR3 was incorporated in the NIH NCI-60 cell line panel study (https://dtp.cancer.gov/ (accessed on 10 September 2022)), as well as extensive genomic and drug sensitivity studies, such as the Cancer Cell Line Encyclopaedia (CCLE) study conducted by the Broad Institute and funded by Novartis (https://portals.broadinstitute.org/ccle (accessed on 10 September 2022)) and the Genomics of Drug Sensitivity in Cancer (GDSC) study organized by the UK Wellcome Sanger Institute (https://www.cancerrxgene.org/ (accessed on 10 September 2022)). Our study confirmed that OVCAR3 has significantly high endogenous lamin A levels compared to normal immortalized ovarian surface epithelial cell lines and other ovarian cancer cell lines as shown earlier [93]. It must be emphasized that the regulatory roles of lamin A in maintaining genomic stability and subsequently ionizing radiation-induced responses have been documented by many researchers, especially in mutant LMNA and HGPS models [34,35,66]. In a parallel observation, it was found that HR mediators such as RAD51, BRCA1, etc. were elevated in OVCAR3 cells both as mRNA and protein levels, which supported a previous study in the lmna-/-MEF model demonstrating a compromised HR in an lmna-/-background [66]. We analysed global gene expression in OVCAR3 following treatment with etoposide as a DNA-damaging reagent in this high endogenous lamin A background. RNA sequencing analysis revealed multiple genes to be differentially regulated that overlapped with the previously reported genes in etoposide-directed therapy in breast and lung cancer cell lines [32,94]. However, no change in the gene expression of LMNA, LMNB1, and LMNB2 was observed. Interestingly, along with genes associated with double-strand break repair and telomere maintenance (BRCA2, BRIP1, RIF1, XRCC2, POLQ), the current study found some differentially expressed genes which are either directly or indirectly associated with cellular proliferation (FGF2), positive regulation of cell cycle (BRIP1, BRCA2, MCM10), apoptosis evasion (BIRC3), and chemoresistance genes linked to PI3-Akt signalling and TGFβ signalling pathways (THBS1, TLR2, BIRC3, FGF2). We have selected 12 such genes (PIF1, RIF1, BRCA2, FGF2, MCM10, BIRC3, THBS1, PLK1, XRCC2, POLQ, TLR2, and BRIP1) from the majorly affected pathways for validation. All the genes except PIF1 and PLK1 were upregulated in etoposide treated OVCAR3 cells. Further to exploring the specific role of lamin A, lamin A was knocked down in OVCAR3 cells and subsequent qRT-PCR analysis of these genes reversed the trend, except for PLK1. We further performed qRT-PCR experiments with this set of genes in a lamin A knockdown background before and after etoposide treatment which exhibited similarly decreased mRNA expressions for all the genes even after etoposide treatment. Therefore, we can conclude that an elevated expression of lamin A might lead to increased chemoresistance and aggressive metastasis of OVCAR3 directly or indirectly. However, the complex circuitry still eluded us and we searched for ways through which lamin A may affect the expression of these factors. It is well established that lamins can affect gene expression in many ways such as controlling chromatin shape, organization at the nuclear periphery, and regulating transcriptional activity [95]. Lamins also serve as a scaffold for transcription factors such as RNA polymerase II [96,97]. Simultaneously, studies also suggest that the importance of promoter-lamina contacts in gene suppression is emphasized by the frequent exclusion of transcriptionally active gene promoters from nuclear lamina association [98]. We set sail to search for factors with reported association (direct and indirect) with lamin A via all possible modes of interaction (physical/chemical/high throughput/genetic) using bioinformatic tools to investigate the following routes: (a) Whether some of the factors have direct interaction with lamin A; (b) Whether any transcription factors of the differentially regulated genes are interacting partners of lamin A. We explored five among the twelve factors (BIRC3, BRCA2, POLQ, BRIP1, RIF1) under study to be direct interactors of lamin A. We could also find 12 lamin A interactors (TP53, MYC, TEAD4, TEAD1, CTCF, YWHAQ, NCOR2, SMAD1, RUNX1, ZBTB7A, MECP2, IGFBP5) which are reported to bind the promoters of most of the genes under study. We further found decreased expression of some of the transcription factors in etoposide treated OVCAR3 cells under LMNA knockdown background. Therefore, we could identify the potential nodes of connection of lamin A in this intricate regulatory system. The gene set under study was comprised of genes that have direct connections to cellular proliferation, apoptotic evasion, and chemoresistance. Most of these genes were upregulated in etoposide treated OVCAR3. Interestingly, their expressions reversed with a decrease in lamin A level. However, the lamin A level had been associated with anti-apoptosis, proliferation, and resistance to chemotherapy in various independent studies pertaining to different cancer models and therapies. Earlier studies suggest that lamins may prevent or postpone apoptosis in some cancers depending on their quantity and accessibility to caspases [99]. A dense lamina can stop or at least postpone the onset of apoptosis and prevents chromatin condensation and fragmentation. For instance, a study by Rao et al. showed that lamin A might postpone the initiation of apoptosis for 12 h by making the VEVD/VEID lamin cleavage site uncleavable by caspases. Therefore, the indirect strategy for evasion of apoptosis might be the reduction in the functional caspases [99] and/or an elevation in the level of antiapoptotic protein such as BIRC3, which is further strengthened in this current study in ovarian cancer background. Another study in PTEN-positive DU145 and the PTEN-negative LNCaP and PC3 prostate cancer cells exhibited that the protein levels of the PI3K subunits (p110 and p85), phosphor-AKT, and PTEN are directly proportional with lamin A levels. These indicate a possible role for lamin A/C proteins in prostate malignancy via the PI3K/AKT/PTEN pathway [100]. Researchers also suggest lamin A/C knockdown results are in a pause in cellular proliferation as well as a decrease in p130 levels in the nucleus. The proliferation deficit induced by lamin A/C depletion was not reversed by shRNA against p130 [101]. Studies have also shown resistance to paclitaxel-induced nuclear breakage in lamin A/C over-expressing cancer cells. It was found that decreased nuclear lamin A/C protein levels correlate with nuclear shape deformation and are a critical predictor of cancer cells’ susceptibility to paclitaxel [102]. We have encountered different players of similar processes being significantly affected by the abundance or deficiency of lamin A. We were also able to speculate possible nodes in this complex regulatory system and identified additional participants in this network. BRCA1/2 are among the genes that are essential for homologous recombination repair. BRCA2 operates almost solely in homologous recombination, in contrast to BRCA1, which has multiple activities [103,104]. Studies have found that ovarian cancer tissues have higher transcriptome-level BRCA1/2 expression which is in line with our finding [105]. The faster rate of proliferation in malignant tissues, along with genetic instability, needs a greater requirement for DNA damage repair which may be the root cause of these changes [105]. Accordingly, high-grade cancers also showed greater BRCA1/2 expression in earlier studies [105]. Gudas et al. provide credence to this idea by proposing that the proliferation of breast cancer cells is indirectly responsible for the elevation of BRCA1 expression by steroid hormones [105]. It is to be noted that OVCAR3 cells did not have any mutations in known HR repair genes, but deletions were reported in several HRR-related genes including BRCA2. However, in BRCA2 wild-type cases of ovarian cancer, a high level of BRCA2 mRNA expression is one of the determinants of chemoresistance [68,105]. Similarly, studies have demonstrated that NHEJ defects, which are independent of HR function and linked to resistance to PARP inhibitors in ex vivo primary cultures, are present in 40% of ovarian malignancies [106]. The DNA-dependent protein kinase (DNA-PK) containing Ku70, Ku80, and DNA-PK catalytic subunit (DNA-PKcs), as well as the heteromultimeric XRCC4/Ligase IV, form the base of the nonhomologous end-joining machinery [107]. Researchers have performed CGH profiling on the panel of ovarian cell lines to see if gross amplifications or deletions in these locations associated with relative X-ray sensitivity exist, because Ku70 and DNA-PK exist on chromosomes that are frequently found to be abnormal in ovarian malignancies [67]. However, no significant correlations were observed. Interestingly, in accordance with our finding, this study also could find no difference in Ku70 protein expression between OVCAR3 and IOSE cells [67]. Although, there were changes in the copy number of DNA-PKs, Ku70, and Ku80 which did not correlate with radiosensitivity [67]. Finally, the network of the interaction of lamin A has been further analysed using the miRWalk database and tools. Simultaneously, we searched for miRNAs that act as regulatory elements of the other genes as well; hsa-miR-30a-3p was one such miRNA that has putative binding affinities for LMNA 5′UTR. Not only that, but it also has tendencies to bind CDS of BRCA2, THBS1, MCM10, POLQ, BRIP1, 3′UTR of XRCC2, and 5′UTR of TLR2. Interestingly, this miRNA was also found as a regulatory element for genes such as PTEN, SMAD4, ESR1, NRG1, etc., in the miRWalk database for ovarian carcinoma. Although this was intriguing to study, it complicates the scenario with an entirely new approach. However, it opens up avenues for future studies to look into the molecular details behind the regulation. In summary, this study has provided for the first time the potential molecular cues (Figure 8) behind the role of lamin A in the maintenance of genomic stability, cellular viability, apoptotic evasion, and, more interestingly, resistance to DNA damaging agents or chemotherapeutic substances in the context of high-grade ovarian serous carcinoma.

PMC10001200

Meng Dong,Kathrin Böpple,Julia Thiel,Bernd Winkler,Chunguang Liang,Julia Schueler,Emma J. Davies,Simon T. Barry,Tauno Metsalu,Thomas E. Mürdter,Georg Sauer,German Ott,Matthias Schwab,Walter E. Aulitzky

Perfusion Air Culture of Precision-Cut Tumor Slices: An Ex Vivo System to Evaluate Individual Drug Response under Controlled Culture Conditions

04-03-2023

precision-cut tumor slices,perfusion culture,tumor microenvironment,ovarian tumor,individual drug responses,mouse xenografts,preclinical model,personalized medicine

Precision-cut tumor slices (PCTS) maintain tissue heterogeneity concerning different cell types and preserve the tumor microenvironment (TME). Typically, PCTS are cultured statically on a filter support at an air–liquid interface, which gives rise to intra-slice gradients during culture. To overcome this problem, we developed a perfusion air culture (PAC) system that can provide a continuous and controlled oxygen medium, and drug supply. This makes it an adaptable ex vivo system for evaluating drug responses in a tissue-specific microenvironment. PCTS from mouse xenografts (MCF-7, H1437) and primary human ovarian tumors (primary OV) cultured in the PAC system maintained the morphology, proliferation, and TME for more than 7 days, and no intra-slice gradients were observed. Cultured PCTS were analyzed for DNA damage, apoptosis, and transcriptional biomarkers for the cellular stress response. For the primary OV slices, cisplatin treatment induced a diverse increase in the cleavage of caspase-3 and PD-L1 expression, indicating a heterogeneous response to drug treatment between patients. Immune cells were preserved throughout the culturing period, indicating that immune therapy can be analyzed. The novel PAC system is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy responses.

Perfusion Air Culture of Precision-Cut Tumor Slices: An Ex Vivo System to Evaluate Individual Drug Response under Controlled Culture Conditions Precision-cut tumor slices (PCTS) maintain tissue heterogeneity concerning different cell types and preserve the tumor microenvironment (TME). Typically, PCTS are cultured statically on a filter support at an air–liquid interface, which gives rise to intra-slice gradients during culture. To overcome this problem, we developed a perfusion air culture (PAC) system that can provide a continuous and controlled oxygen medium, and drug supply. This makes it an adaptable ex vivo system for evaluating drug responses in a tissue-specific microenvironment. PCTS from mouse xenografts (MCF-7, H1437) and primary human ovarian tumors (primary OV) cultured in the PAC system maintained the morphology, proliferation, and TME for more than 7 days, and no intra-slice gradients were observed. Cultured PCTS were analyzed for DNA damage, apoptosis, and transcriptional biomarkers for the cellular stress response. For the primary OV slices, cisplatin treatment induced a diverse increase in the cleavage of caspase-3 and PD-L1 expression, indicating a heterogeneous response to drug treatment between patients. Immune cells were preserved throughout the culturing period, indicating that immune therapy can be analyzed. The novel PAC system is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy responses. Solid tumors are often considered as abnormal organs not only composed of the tumor cells but also the surrounding tumor microenvironment which mainly contains fibroblasts, immune cells, blood vessels, lymphatic vessels, and the extracellular matrix [1,2]. The tumor microenvironment plays a very important role in tumor development and resistance to drug treatment. The functional and physical interaction of the tumor microenvironment with cancer cells plus the variations of the vascular networks within the tumor contribute to inter- and intratumoral heterogeneity and ultimately influence clinical outcomes [3,4]. Therefore, for personalized medicine in precision oncology, it is crucial that a preclinical model captures the complexity and heterogeneity of tumor biology ex vivo to individually predict in vivo therapy of tumors. Precision-cut tumor slices (PCTS) of 200–300 µm thickness maintain both the three-dimensional architecture and tumor heterogeneity, in addition to preserving the native microenvironment concerning different cell types and the extracellular matrix [2]. There have been a growing number of publications using tumor slices as a model to study the tumor microenvironment and address the response of drug treatment [5,6,7,8,9,10,11]. Davies et al. [12] described a standardized workflow for systematic comparison of different tumor slice cultivation methods and showed that the cultivation of tumor slices requires organotypic support materials and atmospheric oxygen to maintain the viability and structure of the tumor slices. The Millipore filter (MF) support culture system under atmosphere was found as one of the promising systems, but it is still associated with significant temporal and loco-regional changes in protein expression such as estrogen receptor (ER) and hypoxia-inducible factor α (HIF1α) in slices of the MCF-7 cell line-derived xenograft (CDX). The development of loco-regional heterogeneity during slice culture makes the data interpretation more complicated in the studies especially with pharmacological perturbation [12,13]. Due to the lack of functional vasculature in the tumor slices, the diffusion of oxygen, nutrients, and drugs are influenced by several variables. Still, it is not yet established in common tumor slice cultures to combine possible perfusion systems to mimic the vasculature on both sides of the tumor slice and further optimize oxygen and nutrients supply and drug distribution [12,14]. Recently, we have developed a perfusion air culture (PAC) system which integrates a perfusion system to partially mimic the vasculature in tumor slices with a continuous and controlled oxygen, medium, and drug supply. In the PAC system, precision-cut tumor slices (PCTS) were kept in between two organotypic supports fixed in a special chamber and placed inside of a 50 mL tube with air exchange capacity housed in a standard CO2-incubator. Cotton meshes were used as organotypic supports to cover both sides of the tumor slice in the system. Due to their material structure, the cotton fibers can function as capillaries to supply medium containing nutrients and drugs to the tumor slice. Meanwhile, the relatively big open pores of the cotton mesh allow the oxygen to be easily diffused into the tumor slice from both sides during cultivation. It has been stated that the key parameters governing tissue viability are organotypic filter supports and oxygen levels [12]. The PAC system fulfills these criteria and can supply the oxygen, nutrients, and drugs from the same direction to the tumor slice imitating the in vivo situation. For the in vivo tumor, viable tumor cells were not observed at distances greater than 160 μm from blood vessels [15]. In addition, it was demonstrated that the oxygen can only be diffused to a distance of about 100 µm from a blood vessel [16,17]. In the PAC system, the two cotton meshes supported on both sides of the tumor slice work to mimic the vasculature in a tumor and guarantee the equal distribution and diffusion of oxygen, nutrients, and drugs from both sides of the tumor slices into the deeper cell layers. Tumor tissue is complex and dynamic, and there are interactions between cancer cells and immune cells in the tumor [1]. In recent years, there has been an increasing interest in using tumor slice cultures as a model to study the tumor immune microenvironment and the immune response of the tumor to the drug treatment [10,18]. Immune cell survival after slice culture in pancreatic cancer has been reported [10]. The immune cells in the slices responded predictably to an immuno-modulator and anti-programmed death- ligand 1 (PD-L1) checkpoint inhibitor blockade [18]. To test whether the PAC system is also suitable to assess functional immune response in tumor slices, we analyzed the immune cells of tumor slices before and after cultivation. The patient-specific immune cells and their composition were preserved throughout the culture period in the PAC system. Increased expression of PD-L1 was observed in the tumor slices after cisplatin treatment. In this study, we introduce a novel PAC system to culture the tumor slices. It overcomes the problems of the static MF culture system, better represents the complexities of tumor biology, and facilitates the homogenous and controlled supply of oxygen, nutrients, and drugs. It allows the long-term culture of tumor tissue and the analysis of therapy response, including immune therapy, and is thus suitable for individual testing of drug efficacy to predict patient response and enhance drug selection for clinical trials. Perfusion air culture system construction: The designed chambers (Figure 1) were printed by a 3D printer (Printrbot Simple Metal, Printbot, Lincoln, CA, USA) with polylactic acid (PLA) using the stock thermoplastic extruder. The software used for printing the STL file was Cura Ultimaker. The printed chambers were immersed in 70% ethanol for 15 min, followed by drying out under the cell culture hood for sterilization. The chamber consists of two main components (a and b, Supplementary Figure S1a), which can be assembled very easily under sterile conditions using a click system. Both components together form the holder for the organotypic supports (component c in Supplementary Figure S1a) for the tumor slices. The filter papers (Whatman™ Cellulose Blotting Papers, Grade GB003, #10426892 Cytiva, Marlborough, MA, USA), which are shown as component d in Supplementary Figure S1a, with the property of diffusion were fixed in the chambers (components a and b) and served as a reservoir for the nutrient fluid. The tumor slices were kept in between two organotypic supports and fixed in the chamber (Supplementary Figure S1b). The organotypic supports used in this study were cotton mesh (ES-Kompressen, #2050040 HARTMANN, Heidenheim, Germany, for xenograft tumor slice cultivation and aluderm® Kompressen #KR03029, SÖHNGEN, Taunusstein, Germany, for human ovarian tumor slice cultivation). The decellularized porcine intestine scaffold which was kindly provided by Prof. Heike Walles (Otto-von-Guericke-University, Magdeburg, Germany) was used for the long-term cultivation of human ovarian tumor slices in the PAC system. The chamber was settled vertically inside of a 50 mL tube with air exchange capacity (TubeSpin® Bioreactor 50 with Septum, TPP, Trasadingen, Switzerland). A needle went through the lid of the tube and was inserted in the top of the chamber. The needle was connected to a silicone tube with an inside diameter (ID) of 0.5 mm and an outside diameter (OD) of 2.5 mm. The 1 m-long gas-permeable silicone tube was further connected to a syringe pump for delivery of the medium (Figure 1). The commercially available syringe pump allows precise, low-speed perfusion that can be matched to blood perfusion rates in capillaries (e.g., 50–100 µL/hour; Figure 1). Cultivation was performed in a regular cell culture incubator at 37 °C and 5% CO2 under atmospheric oxygen (21% oxygen) conditions. Cell-line-derived xenograft (CDX) tumor samples: The local committees approved all of the animal facilities and handling protocols on the ethics of animal experiments, as required in each country, and adhered to the European Convention for Protection of Vertebrate Animals used for Experimental Purposes (Directive 2010/63/EU). Experiments with mice performed at AstraZeneca were compliant with the UK Animals (Scientific Procedures) Act, which is consistent with EU Directive 2010/63/EU and had undergone internal ethical review. At Charles River Germany GmbH, experiments carried out with mice were scrutinized by the Committee on the Ethics of Animal Experiments of the regional council (Regierungspräsidium Freiburg, Abt. Landwirtschaft, Ländlicher Raum, Veterinär- und Lebensmittelwesen). The breast CDX MCF-7 was derived by subcutaneous injection of 5 × 106 MCF-7 cells (ATCC-HTB-22) per 0.1 mL in 50:50 basal media Matrigel (#356234, BD Biosciences, San Jose, CA, USA) into the left flank of male SCID mice (SCID/CB17). The animals were implanted with 0.5 mg/21 day 17β oestradiol pellets (Innovative Research of America) one day before cell implant. The lung CDX NCI-H1437 was derived by subcutaneous injection of 5 × 106 NCI-H1437 cells (ATCC-CRL-5872) into the flank of 4–6-week-old NMRI nu/nu mice. All of the cell lines were routinely (every 3 months) checked for mycoplasma contamination and the master stock of the cells was authenticated using STR analysis before injected being into the mice. Tumors from mouse xenografts were harvested when the volume reached between 0.4–1 cm3. The animals were euthanized by cervical dislocation and tumors were excised. A small sample of the tumor was either snap-frozen using liquid nitrogen or fixed in 10% neutral buffered formalin immediately after resection. Tumors were placed into ice-cold MACS Tissue Storage Solution (#130-100-008, Miltenyi Biotec, Bergisch Gladbach, Germany) before tissue slicing. Primary human ovarian tumors: Sterile fresh tissue was obtained during debulking surgery in case of ovarian cancer first detected by a frozen section and confirmed by final histological examination at the Robert Bosch Hospital, Stuttgart. Immediately after surgical resection, the tumor tissue was maintained in ice-cold MACS Tissue Storage Solution (#130-100-008, Miltenyi Biotec) until use. The procedure had been approved by the local ethics committee (397/2016BO1) and informed consent from all participating subjects was obtained. Tumor slice preparation and cultivation: Preparation of tumor slices was performed as described previously [12]. The tumors were mounted onto the magnetic specimen holder of a Leica VT1200S vibrating blade microtome using cyanoacrylate adhesive. The tumor slices were prepared at a thickness of 250 μm (MCF-7 CDX and H1437 CDX) and 280 μm (primary OV) using the precision-cut vibratome. The slices were visually inspected whilst being cut to ensure the tissue was not compressed or torn, resulting in an inconsistent slice thickness. Generally, 15–25 slices can be obtained from one tumor depending on the size and condition of the tumor tissue. MCF-7 CDX slices were cultivated in DMEM (#31053-028, Gibco, Grand Island, NY, USA); H1437 CDX and primary OV slices were cultivated in RPMI 1640 (# F1215, Biochrom AG, Berlin, Germany). The medium was supplemented with glutamine (2 mM; Gibco), penicillin (100 U/mL) and streptomycin (100 μg/mL; Gibco), and 10% fetal bovine serum (FBS; #10082, Gibco). Cultivation was performed at 37 °C and 5% CO2 in a humidified atmosphere under atmospheric oxygen (21% oxygen) conditions. The tumor slices were maintained on a Millipore filter (Millicell Cell Culture inserts, #PICM ORG 50, pore size 0.4 μm, Merck Millipore, PTFE, Merck KGaA, Darmstadt, Germany) with an air-liquid interface in a six-well plate with 1.5 mL medium under the filter in each well and one drop of medium on the top of each slice. The tumor slices were alternatively cultured on the self-made perfusion air culture (PAC) system illustrated in Figure 1. The slices were harvested at different time points. The snap-frozen samples were either collected for RNA isolation (three to four slices per condition) or fixed in 10% neutral buffered formalin for immunohistochemistry (IHC) (one slice per condition). Overall, at least 12–15 slices were analyzed per tumor. The tissue fixed immediately after surgical resection was defined as the in vivo sample. The tissue after the slicing process and before tumor slice cultivation was defined as the d0 sample. The fixed slices were embedded in paraffin in vertical orientation as published in Davies et al., 2015 [12]. Drug treatment: Tumor slices from both xenografts and primary human ovarian tumors were treated with cisplatin (Teva®, Ulm, Germany) in both MF and PAC systems. With a final concentration of 13 µM, cisplatin was applied in 1.5 mL medium in the MF system with a drop of medium containing cisplatin on the top of the tumor slices. In the PAC system, the cisplatin in the medium was prepared in a 10 mL syringe and continually applied to the tumor slices through a silicone tube. In order to minimize the influence of tumor heterogeneity, the control and treated slices were always adjacent slices in the tumor. Immunohistochemical staining: The fixed tumor slices were cut into 4 μm serial sections by Rotary Microtome (Leica RM2255, Wetzlar, Germany). The paraffin sections were stained with hematoxylin (#1.09253.0500, Merck KGaA, Darmstadt, Germany) and eosin (R03040, Merck KGaA) (H&E) for histopathological examination. IHC was carried out by standard protocols as previously described. Briefly, the sections were deparaffinized in Microclear and rehydrated in graded ethanol followed by use of a Dako Envision Kit (#K5007, Agilent Technologies, Glostrup, Denmark) according to the manufacturer’s manual. Epitope retrieval was achieved in a steam heater for 30 min with either citric acid buffer pH 6 or Tris/EDTA buffer pH9 (Agilent Technologies, Dako). The primary antibodies were as follows: Ki67 (Clone MIB-1, Dako), hypoxia-inducible actor 1 α (HIF1α, #610959, BD Biosciences), cleaved-caspase 3 (CC3, #9661, Cell Signaling Technology, Danvers, MA, USA), an estrogen receptor (ER, Clone 6F11, #PA1051, LeicaBond), phospho-histone H2A.X (γH2AX, #2577, Cell Signaling Technology), CD8 (SP16, Cell Marque, Rocklin, CA, USA), CD4 (SP35, Cell Marque), CD68 (Kp-1, Cell Marque), FOXP3 (236A/E7, eBioscience), PD-1 (NAT105, Cell Marque), and PD-L1 (E1L3N, Cell Signaling Technology). The antibodies were visualized using 3,3′-diaminobenzidine (DAB) chromogen and counterstained with hematoxylin. Images were taken by an Olympus slide scanner VS120. Multiplex immunohistochemical staining: The tissue sections were prepared, deparaffinized, rehydrated, subjected to heat-induced epitope retrieval, and incubated with primary and secondary antibodies as described for immunohistochemical staining. The antibodies were visualized using fluorescent tyramide. The process of epitope retrieval and staining was repeated sequentially for different primary antibody and fluorescent tyramide combinations. The following tyramide dyes were used: CF®488 (#92171, Biotium, Fremont, CA, USA), CF®555 (#96021, Biotium), and CF®640R (#92175, Biotium). After all of the staining steps, the sections were mounted using a DAPI-containing mounting medium (EverBrite™, Biotium). Images were acquired using a Leica TCS SP8 confocal microscope. Quantification of immunohistochemical staining: The quantification of immunohistochemical staining images was performed in whole tissue sections using the computer-aided image analysis software Tissue Studio from Definiens and QuPath. These programs allow quantification of the positively stained cell numbers in user-defined regions of interest (ROIs). Within these ROIs, algorithms were used that detect nuclei, membranous, and cytoplasmic staining. The mean percentages of positively stained tumor cells from IHC quantification were calculated across tumor types from at least three independent experiments. The Wilcoxon matched-pairs test was used for statistical analysis in GraphPad Prism (GraphPad Software, San Diego, CA, USA). Loco-regional changes in biomarker expression across different areas of the tumor slices were quantified by splitting the tumor slice longitudinally into three layers using ROIs (MF: Filter side, Middle, Air side; PAC: Air side-1, Middle, Air side-2). The percentage difference (|Difference|/Average × 100%) in the positively stained cells between the two outer layers was calculated (MF: Filter side and Air side; PAC: Air side-1 and Air side-2). The average was defined as the average of positively stained cells of the three layers in the tumor slice. Tumor slices with a percentage difference of >20% were defined as having a gradient for this biomarker. The detailed workflow for determining the biomarker expression gradient is shown in Supplementary Figure S2. High-throughput TaqMan-based qPCR Fluidigm: Snap-frozen tumor slices were used for total RNA extraction. Lysing matrix D tubes were used to prepare tissue lysates in the FastPrep sample preparation system (MP Biomedicals, Santa Ana, CA, USA). Subsequently, RNA extraction was carried out using an RNeasy Mini Kit (Qiagen, Hilden, Germany) and on-column DNase (Qiagen) digestion was performed to eliminate genomic DNA contamination. M-MLV reverse transcriptase (Promega, Madison, WI, USA) was used to generate the cDNA. Expression analysis was performed on the BioMark HD System (Fluidigm, South San Francisco, CA, USA) according to the manufacturer´s instructions. The TaqMan assays were purchased from Applied Biosystems as previously described [12]. Gene expression analysis: Gene expression qPCR data were normalized to a housekeeping gene and converted to log2 values. Differentially expressed genes were found using the limma package in R statistics environment [19]. Tumor number was included in the linear model to take heterogeneity between the tumors into account. Genes with an FDR-corrected p-value less than 0.05 and a log-fold change of at least 1 were considered as significant. Euclidean distance (square root of the sum of square differences) was used to measure the difference of the slice models from the in vivo situation. The function removeBatchEffect from limma R package was used to remove the effect of the tumor number before calculating Euclidean distances and completing principal component analysis (PCA) [20]. PCA was completed using the prcomp function in base R. Genes containing any missing values were removed before calculating principal components. The scatterplot showed the first two components (those with the largest and second-largest variance). In the perfusion air culture (PAC) system, the tumor slices were kept in between two organotypic supports and fixed in a special chamber allowing continuous perfusion with the medium and drugs. The printed PLA chambers for the PAC system were all used once for each experiment. The PLA material and the 15 min in 70% ethanol sterilization process for the chambers did not show toxicity to the tumor cells (data not shown). The chamber was settled vertically inside a 50 mL tube with air exchange capacity and connected to a syringe pump via a silicone tube (Figure 1). The system was placed in a cell culture incubator at 37 °C and 5% CO2 under atmospheric oxygen conditions. The medium pumped out from the syringe went through a 1 m-long highly gas-permeable silicone tube with a 0.5 mm inner and 2.5 mm outer diameter to allow optimal diffusion of oxygen to the culture medium. The flow rate can be adjusted according to the culture conditions or experimental purpose. A low-speed flow rate of 2 mL per day (83.3 µL/h) was used in this study. With such a low perfusion rate, the medium in the highly gas-permeable silicone tube can be well saturated with 21% oxygen conditions at 37 °C in the cell culture incubator before it reached the tumor slices. Therefore, the oxygen concentration of the medium was maintained at the same level of 21% oxygen independent of the samples and experiments. The medium flows through the needle and passes the organotypic supports on both sides of the tumor slices, which offers an air-liquid interface on both sides of the tumor slices. This allows constant exposure to drugs via the medium. The organotypic supports can also be replaced with other materials such as scaffolds from a porcine intestine according to the tissue type and experimental purpose. As a comparison, the tumor slices were also cultured with the Millipore filter (MF) system. In this system, the tumor slices were placed on the filter membrane in the six-well plate with 1.5 mL medium under the filter in each well (Figure 1b). Two or three tumor slices can be cultured together on one MF. One drop of medium was added on the top of each slice to keep it moist without drying it out. In the PAC system, different organotypic supports can be used. After testing several different materials, we used a cotton mesh and scaffold from a porcine intestine for the tumor slice culture (Figure 1d) in this study. The cotton mesh is a highly absorbent gauze. The gauze was first cut into strips to fit the PAC system before use. The scaffold from the porcine intestine is a biological vascularized scaffold which can be used as a dynamic 3D matrix system. The structure of the scaffold is shown in Figure 1d as H&E staining. Mouse xenografts (MCF-7, H1437) and primary human ovarian tumor (primary OV) tissues were cultured in the PAC system and compared to the commonly used static Millipore filter (MF) culture system. After 3 days of cultivation in the PAC system, tumor slices of MCF-7 xenografts, a breast cancer model, showed similar morphology and biomarker expression (ER, HIF1α, Ki67, and γH2AX) to the original tissues (Figure 2b). The ER expression of the MCF-7 tumor slices was significantly higher in the PAC system compared to the MF system, and more close to the in vivo situation (Figure 2d). The MCF7 xenografts tumor slices were cultured for up to 7 days with only minor changes in viability and morphology (Figure 3a,c). The tumor slices of H1437 xenografts, a lung cancer model, were stained for Ki67, HIF1α, γH2AX, and cleaved-caspase 3 (CC3) to investigate cell proliferation, oxygen supply, DNA damage and apoptosis. The H1437 slices showed similar morphology and biomarker expression after 3 days of culture compared to the day 0 (d0) non-cultivated tumor slices (Figure 4). Primary OV tissues cultured with decellularized scaffolds from porcine intestines together with cotton meshes as organotypic supports maintained their morphology and biomarker expression for up to 8 days (Figure 5a). In order to evaluate loco-regional changes across slices during culture, formalin-fixed tumor slices were vertically embedded in paraffin blocks as previously published [12]. The tumor slices were objectively divided into three layers longitudinally according to the shape of the tumor slices (MF: Air side, Middle, Filter side; PAC: Air side-1, Middle, Air side-2) using the QuPath software (Figure 2a). Spatial quantification of biomarker expression was performed separately on the three layers of each tumor slice. Different biomarkers showed different levels of loco-regional changes. The heterogeneity of ovarian patient samples was higher than the CDX. After 3 days of culture, tumor slices from MCF-7 xenografts showed a biomarker expression gradient with loco-regional change for the ER and HIF1α expression for all the tumor slices from six different xenograft tumors in the MF system but not in the PAC system (Figure 2c). It remained the same after 7 days of cultivation (Figure 3b). The high expression of hormone receptor ER at the air-interface and the reduction of ER-positive staining in the filter region of the slices in the MF system were not observed in the PAC system after both 3 days and 7 days of culture. Detailed quantified data of the percentage difference in biomarker expression between the two outer layers of the tumor slices are shown in Supplementary Tables S1 and S2. The proliferation marker Ki67 showed the same expression pattern. As can be seen in Figure 2b,d, HIF1α and γH2AX showed an inverse expression pattern to ER and Ki67 expression, accumulating at the filter interface of the slices. In contrast, in the PAC system, the expression of the biomarkers was more homogeneously distributed and closer to the in vivo situation even after 7 days of culture. The quantification data, as shown in Figure 2d, indicate that the overall ER expression of MCF-7 xenograft tumor slice cultures in the PAC system for 3 days had significantly higher expression compared to the MF culture system and were closer to the in vivo situation. Similar results were obtained using slices from H1437 xenografts. The slices cultured in the PAC system presented similar HIF1α expression patterns to the day 0 tissues. The induced expression of Ki67 and the reduced expression of γH2AX at the air-interface of the tumor slices in the MF system was not observed in the PAC system (Figure 4a,b). All of the analyzed tumor slices from three different xenograft tumors in the MF system showed a gradient for HIF1 α, γH2AX, and CC3 (Figure 4b). In Figure 4c, IHC quantification showed a clear trend of lower Ki67 expression and higher CC3 expression after 3 days of culture in the MF system compared to the PAC. The biomarker expression of primary human OV was highly heterogeneous between different patients. Not all of the human ovarian tumor slices showed loco-regional changes of biomarker expression with the MF culture system. Although the patient ovarian tumor slices have a high heterogeneity of biomarker expression, we can still observe the highly loco-regional changes induced in the MF culture system for some patient tumors (Figure 5b). In 9 out of 15 primary OV tumors, a gradient of biomarkers expression in the MF system was observed after 3 days cultivation, especially regarding Ki67 and HIF1α expression. The strong loco-regional changes from the air-liquid side to the filter side were not observed in the PAC system after 3 days or even after 8 days of cultivation (Figure 5). The detailed quantified data of the percentage differences are shown in Supplementary Table S3. To have a broader view of the impact of slice cultivation on tissue viability, we analyzed changes in the expression of genes that were described as being biomarkers related to cellular stress in tumor slices using Fluidigm microfluidic dynamic qPCR arrays (48.48 and 96.96 chip formats), in combination with 134 TaqMan® assays according to the methods previously published [12]. The tumor slices were analyzed 3 days after culture initiation (MCF-7, H1437, and primary OV tissues) in the MF and PAC systems. The day 0 (d0) samples that had been sliced but not cultivated were also included if the in vivo samples were not available. To compare the overall impact of changes in stress-related biomarkers for the two different culture conditions, we used principal component analysis (PCA) and Euclidian distance scores (Figure 6). In accordance with the histological observations (Figure 2a), the MCF-7 xenografts’ tumor slices cultured in the MF system or the PAC system showed similar cluster patterns to the in vivo and day 0 non-cultivated samples (Figure 6a). This was confirmed by Euclidean distance scores which represent the relationship between the cultivated slice samples and the in vivo samples (Figure 6d). The absolute number of significantly changed transcripts was slightly less in the PAC system compared to the MF system, especially with regards to the upregulated genes (Figure 7a). The fold-changes of individual biomarker transcripts are represented in Supplementary Table S4. For the H1437 xenografts tumor slices, the PAC system clustered closest to the day 0 samples (Figure 6b). This was confirmed by Euclidean distance scores (Figure 6e). The absolute number of significantly changed transcripts was lower in the PAC system compared to the MF system for both the upregulated and downregulated genes (Figure 7a). The primary OV tissues showed a high heterogeneity of cluster patterns already for the in vivo samples (Figure 6c), while the MF and PAC systems displayed similar cluster patterns. The MF system had smaller Euclidean distance scores compared to the PAC system (Figure 6f). The PAC system showed a higher absolute number of significantly changed transcripts compared to the MF system for both the upregulated and downregulated genes (Figure 7a). However, due to the high heterogeneity of the primary OV, it is hard to draw a conclusion from the data of primary OV tissues. Classification of stress-related transcripts according to their involvement in cellular processes (Supplementary Table S4) revealed that the majority of transcripts were downregulated during slice culture with both the MF and PAC systems (Figure 7). Upregulation of transcripts was predominantly observed for apoptosis (H1437 MF system, primary OV PAC system), p38/JNK (MCF-7 MF system), and ROS (primary OV PAC system) (Figure 7). Downregulation of transcripts was predominantly observed for cell cycle (H1437 MF and PAC systems), apoptosis (MCF-7 MF and PAC systems) (Figure 7c), and cell cycle and apoptosis (primary OV in PAC system) (Figure 7d). Overall, the data indicated that the PAC and MF systems have similar impact on stress gene expression. To analyze responses to therapy, tumor slices from lung model H1437 xenografts and primary OV tumor slices were cultured in the MF and PAC systems and treated with 13 µM cisplatin, a DNA-crosslinking drug which represents a key treatment option for both lung and ovarian cancer [7,21,22]. For H1437 xenografts, cisplatin treatment led to a general enhancement of γH2Ax, Ki67, and CC3 expression in both MF and PAC systems. The gradients from the air-liquid side to the filter side of Ki67 and γH2AX expression were maintained after cisplatin treatment. The gradients were not observed in the PAC system in both the control and treated groups (Figure 8a). For primary OV slices, the expression of γH2AX and CC3 was induced after 24 h cisplatin treatment and increased further up to 72 h (Supplementary Figure S3). Cisplatin treatment did not change Ki67 expression of the tumor slices in the primary OV slices (Supplementary Figure S4). CC3 showed slightly higher expression in the PAC system after 3 days of treatment (Figure 8b). The different primary OV showed distinct responses to cisplatin treatment (Figure 9c). The treatment induced a similar pattern of significantly increased γH2AX expression in both MF and PAC systems (Figure 9b) while strongly enhanced CC3 was observed only in the PAC system in some patients. In Figure 9c, orange and green circles mark the corresponding data of the patients shown in Figure 9a with orange and green frames. The patient marked with orange had a strong CC3 induction after cisplatin treatment compared to the patient marked with green, although the γH2AX expression pattern did not show a big difference between these two patients. After cisplatin treatment, the overall induction of CC3 was higher in the PAC system than in the MF system (Figure 9b). This might reflect a higher aerobic metabolism in the PAC system. Overall, the individual drug response can be evaluated in the PAC system. The heterogeneous cell composition of solid tumors makes the tumor microenvironment important to identify clinically relevant drugs and patient responses to specific therapeutics. The tumor slices cultured in the PAC system have a preserved tumor microenvironment. Different cell types can be detected in the tumor slices after 3 days of culture, also after cisplatin treatment (Figure 10a). The heterogeneous cell composition observed in the in vivo tissue before cultivation is sustained. To test whether the PAC system is suitable to detect the immune response in tumor slices, we analyzed the immune microenvironment in tumor slices before and after cultivation. Using histopathological methods, the biomarkers of CD8+ for cytotoxic T cells, CD4+ for T-helper cells, CD68+ for macrophages, FOXP3+ for regulatory T cells, programmed cell death protein 1 (PD-1), and programmed cell death-ligand 1 (PD-L1) were analyzed in primary OV tumor slices for in vivo and in 3 day cultured samples. The patient-specific immune cells and their composition were preserved throughout the culture period in the PAC system after 3 days of culture and cisplatin treatment (Figure 10b). PD-1 had low expression levels in the OV tumors and did not change after tumor slice cultivation (Figure 9c,d). Patients showed an individual induction of PD-L1 after cisplatin treatment. Compared to the untreated control group, PD-L1 expression was increased after 3 days of cisplatin treatment in the PAC system for the patients with relative high PD-L1 expression (Figure 10c–e). Patients with very low PD-L1 expression did not show PD-L1 induction after cisplatin treatment. The patient with the highest PD-L1 expression level in the in vivo tumor showed the highest induction after cisplatin treatment (Figure 10e). This indicates that the tumor slices cultured in the PAC system can be used to analyze not only the individual drug response but also the immune response of the patient. We further proved that the multiplex imaging technology can be applied to the tumor slice samples and we can observe the spatial relationship between the tumor, stromal, and immune cell components in the tumor slices. As shown in Figure 10f, after 8 days of culture, the structure of the OV tissue was preserved and the EpCAM+ tumor cells showed proliferation with Ki67 expression (Figure 10f). After cisplatin treatment, not only did EpCAM+ cells displayed PD-L1 induction but other cells did as well (Figure 10g). Except for the EpCAM+ cells, which expressed γH2AX and CC3, some CD3+ cells also showed positive expression of γH2AX and CC3. This indicates that the T cells in the tumor microenvironment may also react to cisplatin treatment (Figure 10g). Further analysis should be carried out with more samples to draw clearer conclusions about it. Precision-cut tumor slice culture has the ability to closely recapitulate the architecture and heterogeneity of the original tumors. Therefore, it can be used as a platform to study the tumor microenvironment and evaluate the preclinical efficacy of drug treatment. As a model, the tumor slice culture also has limitations that cannot be overcome with the currently available culture methods. A major limitation is the lack of functional vasculature in the tumor slice [14]. Due to the loss of the intact circulatory network in the tumor slice, the availability of oxygen, nutrients, or drugs in tumor slices is strictly limited to diffusion [12,23]. In in vivo tumors, oxygen is provided by hemoglobin in red blood cells. Hemoglobin can deliver large amounts of oxygen to cells at relatively low oxygen tension. This unique property simply cannot be replicated in vitro [24]. In the cell culture medium, an efficient oxygen carrier such as hemoglobin does not exist. It was shown that the oxygen concentration in the medium is about 2% of the oxygen content in the ambient atmosphere [25]. The total amount of oxygen that can be supplied from the medium to the tumor slice is very limited. Studies have tried to increase the amount of dissolved oxygen in culture medium by using up to 95% oxygen in the culture. However, the increase in the oxygen concentration is still limited and may induce hyperoxia problems in the tissue [24,26]. Because of hemoglobin, blood as a liquid has the ability to offer comparable amounts of oxygen as the atmosphere, which has approximately 10 times higher oxygen content (delta of artery and venous blood oxygen content) than the medium (saturated with atmospheric oxygen) even under lower oxygen partial pressure with 100 mmHg [27,28]. Therefore, in vivo, the oxygen can be quickly transported and rapidly released to the tissue even in a low oxygen partial pressure environment. The special ability of hemoglobin ensures sufficient oxygen supply to the tissue without the problem of hyperoxia. The tumor slices do not have a functional blood supply; the oxygen must be diffused from ambient gases into the medium and then into the tumor slices. The PAC system has two sides of air-liquid interfaces, which can minimize the diffusion distance from the ambient gases to the tumor slice; therefore, it can provide a sufficient oxygen supply to the tumor slices during cultivation under a low oxygen tension. For the experiments, a normal tumor slice with 200–300 µm thickness and 5–10 mm diameter contains more than 5 million cells, which is about the same cell number of a confluent T75 cell culture flask. Normally, up to 20 mL medium is used for the 2D monolayer cell culture with the T75 flask. The commonly used Millipore filter (MF) system for tumor slice culture can only apply about 1.5 mL medium under static conditions, which is much less than the 2D monolayer cell culture system. This also explains the gradients from the air to the filter interface of the tumor slice cultured in the MF system. The tumor slice cultivated in the MF system has two different sides, the air-interface side and filter-liquid side. In the in vivo tumor, the oxygen, nutrients, or drugs can diffuse from the blood vessels to the cells [1]. In the MF system, they are mainly provided from two different directions to the tumor slices. The oxygen can only diffuse mainly from the air-liquid interface side of the tumor slice, and the nutrients and drugs are mostly supplied from the filter-liquid side and have to first pass through the filter to reach the tumor slices. Considering that the MF system is a static culture, the exchange of nutrients has low efficiency. It cannot faithfully recapitulate the in vivo situation. The development of loco-regional heterogeneity in the MF culture system has been reported for both xenografts and patient tumors [12]. This can also be observed in our data. Although gradients of oxygen tension are a common feature of solid tumors [29], prudence is required when interpreting the data from the MF system culture with the gradients, especially after drug treatment, because the oxygen, nutrients, or drugs may be supplied from two separate sides of the tumor slice. The PAC system overcomes this problem. The oxygen, nutrients, and drugs are supplied from both sides of the tumor slices with the same direction, which is closer to the in vivo situation. The cotton meshes on both sides of the tumor slice work not only as a support structure but also as vasculature in tumor slices. With this special structure, the thickness of the tumor slice can be increased and is not limited to 200–300 µm for cultivation. The hypoxic condition can also be created in the middle of the tumor slices when changing the oxygen supply to hypoxia in the PAC system. As one of the key parameters governing tissue viability, organotypic supports must fulfill two functions. One is to mimic the vasculature and supply sufficient oxygen and nutrients to the tumor tissue continuously; the other is to act as the extracellular matrix (ECM) to protect the thin and friable slice from the stress of fluidic flow and keep the stiffness of the tumor slices. It has been well known that tumors are much stiffer than normal tissues because of the changes in their ECM. The relative stiffness can have profound effects on cellular function [1]. We have compared many different materials as organotypic supports for the PAC system. Nylon meshes with different pore sizes (89 µm, 41 µm, and 29 µm), polycarbonate membranes with a 12 µm pore size, and hydrogels did not show good oxygen and nutrients supply because of their small pore sizes and material structures (data not shown). Cotton meshes with 500 µm pore size showed the best oxygen, nutrient, and drug supply. Because of their big pore size, some cells on the surface of the slices were washed away during the culture in the PAC system. Therefore, we mainly used cotton meshes as organotypic supports for 3-day, short-term experiments. The decellularized scaffold from the porcine intestine can better protect the slices from the shear stress of fluidic flow. We used it together with cotton meshes for long-term cultivation of the tumor slices. Thus, the primary OV tumor slices can be cultured for up to 8 days within the PAC system. Although the cotton mesh showed better properties than the other organotypic supports tested, it still has drawbacks. It may wash away cells from tumor slices during culture and it is difficult to section the cotton fibers together with the tissue in the formalin-fixed paraffin-embedded (FFPE) samples for IHC staining. The scaffold from the porcine intestine does not have this problem, but it still requires the addition of a cotton mesh to maintain a better distribution of the medium to the slices. In addition, it was presumed that atmospheric concentrations of oxygen can produce hyperoxic conditions at the air-liquid interface side to allow a supply of just sufficient oxygen to deeper cell layers [12]. In the PAC system, if the oxygen can be supplied from both sides of the tumor slice, the oxygen concentration in the atmosphere can be reduced to avoid hyperoxic damage to the tumor slices. Overall, the PAC system still needs to be further optimized especially with regards to supports and a suitable oxygen concentration surrounding the tumor slices during culture. Cultivation of tumor slices induces similar changes in key stress pathways in the MF and PAC systems. This result can be confirmed with the Euclidean distance of stress gene biomarker expression. The in vivo primary OV samples showed a high heterogeneity of the cluster pattern in the PCA plots, making it hard to draw a conclusion from the primary OV tissue data. Therefore, IHC staining is a better method to evaluate the primary OV tumor slices. Because of the highly inter- and intratumoral heterogeneity of the patient tumors, the tissue structure and the spatial distribution of biomarker expression in tumors are more important. Unlike the CDX tumor slices, the primary OV tumor slices showed gradient patterns in the MF system culture in only 9 out of 15 cases. There could be two reasons for this. One is the heterogeneity of the primary OV of patients; the other is that the proliferation rate of the tumor cells in primary OV is much slower than the tumor cells in CDX. This can also explain why the cisplatin treatment induced Ki67 expression in H1437 CDX but not in primary OV tumor slices. Different primary OV tumors showed different responses to cisplatin treatment. This also reflects the heterogeneity of the primary OV and indicates the necessity of personalized therapy. Cisplatin treatment was accompanied by a minor increase in γH2AX in both MF and PAC systems while strongly enhanced CC3 was observed only in the PAC system. This might reflect higher aerobic metabolism in the PAC system [22]. This indicates that the functional response to drug treatment is more sensitive in the PAC system. Further studies will need to be undertaken which correlate the responses of primary OV tumor slices to cisplatin treatment with clinical outcomes to predict patient response, especially the large difference in CC3 induction between primary OV tumor slices. Several reports have shown that tumor slices can be used to study the immune microenvironment and test the immune responses of drug treatment [10,18]. Here, we provide evidence that the tumor slices cultured in the PAC system are suitable for studying the tumor immune cell environment and the immune response of drug treatment. Interestingly, PD-L1 expression was increased individually after cisplatin treatment compared to the untreated control group in primary OV tumors (Figure 10d,e). The PAC system showed comparable and even more sensitive data in comparison to the MF system (Supplementary Figure S5). Other studies have shown that non-small cell lung cancer patients who received cisplatin-based neoadjuvant chemotherapy followed by surgery have significantly increased PD-L1 expression in both tumor cells and immune cells from the microenvironment [30]. Our data also demonstrated that not only the EpCAM+ cells showed increased PD-L1 expression. It is also reported that cisplatin induces PD-L1 over-expression in hepatoma H22 cells [31]. Cisplatin also upregulates PD-L1 expression in vitro and in vivo in ovarian cancer mouse models [32]. Our results on tumor slices support the evidence from previous observations and indicate that the PAC system is a reliable system for studying drug treatment and predicting in vivo drug response. Cultivation of precision-cut tumor slices in the novel PAC system provides a preclinical model that preserves tumor heterogeneity and the native microenvironment. As the PAC system facilitates the homogenous and controlled supply of oxygen, nutrients, and drugs, it is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy response. Dong M, Schwab M, and van der Kuip H (WO/2019/029947; EP 20180742758) device for cultivating tissue sections.

PMC10001201

David C. Boyd,Emily K. Zboril,Amy L. Olex,Tess J. Leftwich,Nicole S. Hairr,Holly A. Byers,Aaron D. Valentine,Julia E. Altman,Mohammad A. Alzubi,Jacqueline M. Grible,Scott A. Turner,Andrea Ferreira-Gonzalez,Mikhail G. Dozmorov,J. Chuck Harrell

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs

03-03-2023

patient-derived xenograft,precision medicine,basal-like breast cancer,triple-negative breast cancer,synergism,BYL-719 (alpelisib),everolimus,dronedarone,afatinib

Simple Summary Basal-like breast cancers comprise the majority of triple-negative breast cancers (TNBC) and lack effective treatment options that have a sustained response. Part of the reason that they are hard to eliminate is that they exhibit high levels of genomic instability and cellular diversity. Most basal-like tumors have high levels of Phosphoinositide 3-Kinase (PI3K) pathway activity. A key driver of this pathway is PIK3CA. Many compounds have been made to target PIK3CA and have become standard-of-care in some estrogen-dependent patients; however, in TNBC patients, PI3K inhibitors (PI3Ki) as single agents thus far have shown limited duration at tolerable doses. The goal of this study was to identify and/or repurpose drugs that, when combined with PI3Ki, yield a significant inhibition of tumor growth. When treated in conjunction with the PI3Ki BYL-719, which is clinically prescribed as alpelisib, 20 potent drug combinations were identified and formed a basis toward clinical studies with these molecules. Abstract Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways.

Discovering Synergistic Compounds with BYL-719 in PI3K Overactivated Basal-like PDXs Basal-like breast cancers comprise the majority of triple-negative breast cancers (TNBC) and lack effective treatment options that have a sustained response. Part of the reason that they are hard to eliminate is that they exhibit high levels of genomic instability and cellular diversity. Most basal-like tumors have high levels of Phosphoinositide 3-Kinase (PI3K) pathway activity. A key driver of this pathway is PIK3CA. Many compounds have been made to target PIK3CA and have become standard-of-care in some estrogen-dependent patients; however, in TNBC patients, PI3K inhibitors (PI3Ki) as single agents thus far have shown limited duration at tolerable doses. The goal of this study was to identify and/or repurpose drugs that, when combined with PI3Ki, yield a significant inhibition of tumor growth. When treated in conjunction with the PI3Ki BYL-719, which is clinically prescribed as alpelisib, 20 potent drug combinations were identified and formed a basis toward clinical studies with these molecules. Basal-like triple-negative breast cancer (TNBC) tumor cells are difficult to eliminate due to resistance mechanisms that promote survival. While this breast cancer subtype has low PIK3CA mutation rates when compared to estrogen receptor-positive (ER+) breast cancers, most basal-like TNBCs have an overactive PI3K pathway due to gene amplification or high gene expression. BYL-719 is a PIK3CA inhibitor that has been found to have low drug-drug interactions, which increases the likelihood that it could be useful for combinatorial therapy. Alpelisib (BYL-719) with fulvestrant was recently approved for treating ER+ breast cancer patients whose cancer had developed resistance to ER-targeting therapy. In these studies, a set of basal-like patient-derived xenograft (PDX) models was transcriptionally defined with bulk and single-cell RNA-sequencing and clinically actionable mutation profiles defined with Oncomine mutational profiling. This information was overlaid onto therapeutic drug screening results. BYL-719-based, synergistic two-drug combinations were identified with 20 different compounds, including everolimus, afatinib, and dronedarone, which were also found to be effective at minimizing tumor growth. These data support the use of these drug combinations towards cancers with activating PIK3CA mutations/gene amplifications or PTEN deficient/PI3K overactive pathways. In total, 300,590 new breast cancer diagnoses and 43,700 breast cancer-related deaths are predicted for 2023 [1], an increase from the year before [2]. Basal-like tumors have the most limited treatment options of the breast cancer intrinsic subtypes because they lack ER, PR, and HER2, which are susceptible to inhibition with anti-estrogens or HER2-targeted agents. They also have high rates of DNA mutations and amplifications, which increases the heterogeneity of cancer cells and can lead to more genetically diverse subpopulations [3]. Basal-like breast cancer patients have a low 5-year survival rate, partially because of their cancers’ tendency to metastasize to lung, bone, brain, and other organs [4]. Because of these challenges, the basal-like disease has a worse prognosis than breast cancer overall and is most in need of new effective therapeutic options. PIK3CA is the second most commonly aberrant gene in breast cancer, after TP53 [5]. PIK3CA codes for the catalytic subunit p110α that converts the lipid PIP2 into PIP3. This acts as a substrate for AKT and its activating kinases, which have oncogenic downstream effects [6]. PIK3CA often has an activating mutation that drives oncogenic transformation [7,8]. Overexpression of PIK3CA is also pathogenic: a gain of 1 or 2 copies is enough to see a significant increase in PI3K expression [5]. PIK3CA expression is often associated with resistance to therapeutics, such as EGFR inhibitors [9]. The loss of a regulatory protein of p110α, PTEN, often happens concurrently with PIK3CA mutations [10] but can increase PI3K pathway activity without PIK3CA alteration. PTEN can have normal CNV on the DNA level but be lost on the RNA level through gene silencing via methylation of the promoter [11]. Activating mutations of PIK3CA occur in basal-like tumors at a rate of approximately 10%, yet nearly all exhibit robust upregulation of the PI3K pathway. This central network is also activated through mutations or loss of PTEN or INPP4B or changes in RTKs [12] and AKT3. Collectively, PI3K is most highly expressed in basal-like breast cancers compared to other subtypes [13]. Despite the attention of researchers on PIK3CA, alpelisib (BYL-719) is the only p110α inhibitor that is FDA-indicated for use in breast cancer and only in conjunction with fulvestrant, an estrogen receptor antagonist [14]. This combination is only approved for patients with ER+ HER2- breast cancer that are male or post-menopausal female with PIK3CA mutant after disease progression after or while being treated with endocrine-based treatment [15]. A novel combination therapy approach with BYL-719 may provide pharmacokinetic synergism and improve meaningful clinical efficacy, such as disease-free survival, all while lessening the likelihood of severe adverse events through reduced drug exposure. PDXs (patient-derived xenografts) are similar to cancer cell lines but differ in that they are maintained in a physiological setting as soon as they are isolated from the patient and for subsequent passages. These models are valuable for preclinical trials because PDX models have been shown to closely match their patient counterparts [16,17], both in genomic profile and response to treatment [18]. In comparison, some cell lines have been shown to diverge from human patient tumors and lose intratumor heterogeneity and have alterations in protein levels revealed using histopathology [19]. PDX models have unique difficulties that cell lines do not have in establishing; one among them is establishment rates as low as 4% [20], as well as requiring mouse implantation instead of media-based tissue culture. Among other institutions, cohorts of human breast cancer PDXs have been created at Huntsman Cancer Institute [17] (HCI), Baylor College of Medicine [21] (BCM), University of Colorado, Denver [22] (UCD), and Washington University, St. Louis which developed the Washington Human in Mouse (WHIM) [23] PDXs. BYL-719 is approved as part of a multi-drug treatment strategy for ER+ disease, but it is not curative as a single agent due to the development of drug insensitivity. To overcome resistance, an additional agent would be required. In this study, high throughput screening was utilized to identify synergistic candidates. Everolimus, an mTOR inhibitor, afatinib, an Epidermal Growth Factor Receptor (EGFR) inhibitor, and dronedarone, a multi-ion channel inhibitor, were selected and tested in vivo and in vitro in PI3K aberrant and PI3K overactive PDXs. All three combinations proved promising in PIK3CA aberrant basal-like breast cancer in the mouse models. Cells were passaged in tissue culture-treated filter flasks in a 37 °C, 5% CO2 environment. To passage and in assays, the cells were grown in DMEM media with 10% FBS and 2% Pen/Strep for MDA-MB-453. Isogenic MCF10A wild type, E545K, and H1047R PIK3CA, kind gifts from Ben Ho Park [24] were utilized in a similar media that contained horse serum instead of FBS. To identify clinically actionable mutations targeted next-generation sequencing (NGS) was performed by VCU Pathology Molecular Diagnostics laboratory with clinically validated methodology used for diagnostic tumor profiling. PDX tumors were excised from mice, flash frozen, prepared into frozen sections using optimal cutting temperature (OTC) compound, and total nucleic acids extracted. NGS using the Oncomine Comprehensive Assay v3 (ThermoFisher, Waltham, MA, USA) was performed as previously described [25] to identify DNA mutations, DNA copy number variations, and RNA fusions across cancer-related genes. The UCD52, WHIM30, HCI-001, HCI-010, and HCI-013 PDXs were utilized. PDXs were passaged in vivo by injecting single cell suspensions in a 1:1 ratio of HF (Hanks’ Balanced Salt Solution + 2% FBS) and Matrigel into the abdominal mammary fat pad of female NSG mice. At the end of a passage, the tumor-bearing mouse was euthanized, and its primary tumor or tumors were immediately excised and placed in PBS. The tumors were minced with a sterile razor blade and placed in tumor digestion solution (DMEM/F12 with 5% FBS, 0.0533 mg/mL hyaluronidase, and 2.4 mg/mL collagenase) on a thermoregulated tube cycler at 37 °C for 1 h. Solutions were centrifuged, and their supernatants were discarded. Pellets were resuspended in red blood cell lysis buffer, centrifuged, and again supernatants discarded into aspiration bleach traps. Pellets were resuspended in trypsin. Cells were resuspended at 500,000 cells per 100 μL HF; this was mixed 1:1 with Matrigel or Cultrex before injecting into the recipient mouse. Tumor area was calculated using volume = length times width. Cells were plated as described above for secondary cell lines or PDX single cell suspension in 96 or 384 well plates by hand or by automated robotic micropipette (Integra, Hudson, NH, USA, Assist Plus). PDX cells were plated in M87 media [26]. Drugs or control vehicles were pipetted into the wells immediately after plating for PDX single cell suspensions or after allowing cells to adhere, 2 h minimum for adherent cell lines. After the desired timepoint, a Cell Titer Glo luminescence assay was performed using a plate reader (BMG LABTECH, Ortenberg, Germany, POLARstar OPTIMA), and percent viability was calculated relative to vehicle control. All in vivo studies were approved by VCU IACUC protocol approval through Animal Care and Use Program, an AAALAC-accredited program. BYL-719 (50 mg/kg) and afatinib (25 mg/kg) were administered via oral gavage (OG) in 100 μL of 1% methylcellulose 6 times a week. Everolimus (10 mg/kg) was administered via OG 3 times a week. Dronedarone (50 mg/kg) was administered via intraperitoneal (IP) injection in 100 μL of 10% DMSO, 40% PEG300, 5% Tween-80, and 45% Saline solution 6 times a week. Tumors were measured 3 times a week for health checks. Mouse weights were recorded once or twice a week. Mice that reduced in weight by 10% or reached maximum tumor burden by protocol guidelines were euthanized. Dry ice flash frozen tumors were processed using Qiagen Rneasy Kit in conjunction with QIAshredder tubes and RNA Zap. The quality of the sample was tested using Nanodrop. To construct the library, the RNA sample was first quantified with the Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA) and checked for its integrity with TapeStation (Agilent Technologies, Santa Clara, CA, USA) Then, NEBNext Ultra II RNA Library Prep Kit for Illumina was used to prepare the RNA sequencing library according to manufacturer’s instructions (New England Biolabs, Ipswich, MA, USA). Afterward, the mRNAs were briefly enriched with Oligod(T) beads and then fragmented for 15 min at 94 °C. Subsequently, the first- and second-strand cDNA fragments were synthesized, after which their 3’ ends were end-repaired and adenylated. Following this, universal adapters were ligated to the cDNA fragments, and then index addition and library enrichment via PCR were performed with limited cycles. Finally, to validate the finished sequencing library, Agilent TapeStation (Agilent Technologies) was used, and the library was quantified with the Qubit 2.0 Fluorometer (ThermoFisher Scientific) as well as by quantitative PCR (KAPA Biosystems, Wilmington, MA, USA). Sequencing was performed by Azenta. Sequencing libraries were first multiplexed and clustered onto a flow cell, after which the flow cell was inserted into the Illumina HiSeq 4000 instrument according to manufacturer’s instructions. A 2 × 150 bp paired-end (PE) configuration was selected to sequence the samples. The HiSeq Control Software (HCS) was utilized to conduct image analysis and base calling. Once the raw BCL data were generated, it was converted into FASTQ format and, lastly, demultiplexed with the Illumina BCL2FASTQ 2.17 program. For index sequence identification, one mismatch was allowed. Bulk RNA-seq data were preprocessed as previously described in Alzubi et.al. [27] Briefly, FastQC v0.11.8 [28] was used to assess sequencing quality and adapters and low-quality base pairs were removed using CutAdapt v1.15 [29]. High-quality reads were aligned to a merged human/mouse genome using STAR v2.5.2b [30] (see Alzubi et al. for merged genome construction) with the following command line options: “--outSAMtype BAM Unsorted --outSAMorder Paired --outReadsUnmapped Fastx --quantMode TranscriptomeSAM --outFilterMultimapNmax 1. The Salmon v0.8.2” [31] “quant” algorithm was used to obtain read counts from the aligned BAM files using the “IU” library type. Read counts were loaded into R to calculate Log2 TPM (transcript per million) values used for gene signature computations (methods below) and PAM50 subtyping using the genefu v2.11.2 R package [32]. New and previously generated data were analyzed (Supplementary Table S1). A set of 13 previously published gene signatures [33,34,35,36,37,38,39,40] were scored by averaging bulk RNA-seq Log2 TPM expression values over all genes in each signature to create a gene signature profile for each PDX. Morpheus was used to cluster these gene signature PDX profiles, with hierarchical clustering using the one minus Pearson’s correlation coefficient as the distance metric and average linkage method to cluster both rows and columns. PDX gene signatures were then compared using Pearson’s correlation coefficient to generate a similarity matrix. Single-cell RNA-Seq (scRNA-Seq) was performed on four TNBC cell lines (HCC1143, HCC1187, MDA-MB-468, and SUM149) and 13 PDX samples using the Chromium single Cell Gene Expression Kit (10x Genomics) per the manufacturer’s protocol and sequenced in the VCU Genomics core. The TNBC cell line samples were aligned to the GRCh38 version of the human genome and gene expression was calculated using the 10x Genomics CellRanger v6.0 software suite of tools. Dead and poor-quality cell removal was performed using an in-house R v4.1.3 script with the Seurat v4.3.0 package [41]. Briefly, cutoffs for number of genes detected (nFeature), number of molecules detected (nCount), and percent mitochondrial expression (percent.mt) were calculated for each sample individually using 3 median absolute deviations (MADs) above the median value for that sample (the median for the percent.mt attribute was calculated using only cells with ≤50% mitochondrial expression). Cells were deemed poor quality if their nFeature or nCount value was greater than 3 MADs from the median in either direction, and if the percent.mt was above the 3 MAD cutoff, or a hard cutoff of 25%, whichever was lower. PDX samples followed a similar pipeline, however, they were first aligned to the 10x Genomics merged human/mouse genome (human genome version GRCh38 and mouse genome version mm10) to identify and remove mouse cells. Once mouse cells were removed the remaining human cells were re-aligned to the GRCh38 human genome, followed by the dead and poor-quality cell filtering described above. All PDX samples were then normalized using log normalization and merged using Seurat’s merge() function. A principal component analysis (PCA) was performed using a 100-gene PI3K activity signature from Gatza et al. [33], instead of the default most variable features in the Seurat pipeline. This modified PCA matrix was utilized for the generation of UMAP and tSNE visualizations, as well as Seurat’s graph-based clustering. An in-house script was then used to export the Seurat object to a 10x Genomics formatted file, and the CellRanger “reanalyze” function was run to reformat the data so that it could be imported into the 10x Loupe Cell Browser v6. Data analysis scripts are available on GitHub at https://github.com/AmyOlex/Boyd_SynCompounds_PI3K (accessed on 31 January 2023). Note that for this study the raw data were re-processed with a newer version of the 10x CellRanger software than previously reported. Lysates were made using homogenized tumors in RIPA with protease and phosphatase inhibitors, sonicated, and cold centrifuged at 4 °C for 20 min. Lysates were quantified using Bradford reagent using spectrophotometer (ThermoScientific BioMate 160) to quantify absorption. Electrophoresis samples were made from those lysates, Laemmli buffer, and beta-mercaptoethanol, heated to 95 °C. Electrophoresis was run using (BIO-RAD, Hercules, CA, USA, mini protein TGX 4–15% gradient) in using (BIO-RAD PowerPac Basic). Gels were transferred using semi-dry blotter (BIO-RAD Tran-Blot Turbo) onto methanol-activated nitrocellulose membranes using WypAll sheets soaked in transfer buffer. Cell Signaling anti- Vinculin, RPS6 p-RPS6 rabbit primaries were used. LI-COR 680 florescent anti-rabbit donkey were used as secondary antibodies, which were detected using (LI-COR, Lincoln, Nebraska, Odyssey FC) using ImageStudio. Drying the membrane and reimaging were utilized to reduce background. P-RPS6 levels were used as a known metric for PI3K pathway inhibition [42]. Ray Biotech’s C55 arrays were incubated with lysates that were produced from MDA453 and UCD52 cells, which had been treated either with DMSO control or 5 μM BYL-719, which were previously sonicated and cold centrifuged. The membranes were imaged using ImageStudio. Relative development was quantified using ImageStudioLite, each probe was normalized relative to background, those values were normalized relative to positive control probes normalized to their own background. Using the DAKO envision system HRP kit for rabbit primaries, formalin-fixed, paraffin-embedded tissues were cut and sections placed on slides. These sections were melted at 60 °C and rehydrated using stepwise xylene to ethanol to water baths. Antigen retrieval was performed using 9pH EDTA, TRIS Antigen retrieval buffer in a decloaking chamber (DakoCytomation, Glostrup, Denmark, Pascal). Slides were washed in TBST. A solution of 0.3% hydrogen peroxide was used as a peroxidase block. Then, slides were washed in TBST. Anti-PTEN, Anti-RPS6, and Anti-p-RPS6 rabbit primary Cell Signaling antibodies were used for overnight incubation at 4 °C. Then they were washed again in TBST. HRP-conjugated anti-rabbit secondaries were used to incubate the sections, then they were washed again in TBST before DAB incubation. Finally, they were washed in TBST one more time before hematoxylin counterstaining for 1 min, which was rinsed repeatedly with tap water. Dehydrating was performed using stepwise water to ethanol to xylene baths. Permount was used as mounting media. Slides were imaged using ZenBlue software. Normalized values from protein array data were uploaded into Ingenuity Pathway Analysis (IPA) and analyzed to produce Z scores that denote relative activity of known clusters of molecules contributing to pathway activity. IPA was also utilized to predict upstream regulators, which are molecules that it determined were likely to cause the state of uploaded expression values. To assess clinically actionable targets of these models, NGS was utilized to characterize pathogenic genetic profiles of 14 cell lines and 20 PDX samples (Table 1), which revealed pathogenic PIK3CA aberrations in 37% of models tested; 5 cell lines and 7 PDXs. PIK3CA was the second most commonly identified pathogenic driver among these models after TP53 confirmed previous observations [43]. The most common PIK3CA aberrations found in patient tumors were identified in this cohort; this included amplification through copy number gain and two of the activating mutations, E542K and H1047R (Figure 1A). Human-specific bulk RNA sequencing data of 82 samples (45 previously unpublished) from 21 PDXs were used to stratify PDX models based on their proliferation rate (11-gene proliferation signature) [44] and PI3K pathway activity defined through 13 previously published PI3K gene signatures [33,34,35,36,37,38,39,40]. Most basal-like PDXs had low PTEN expression when assessed by IHC, but BCM3887CR (a model that has become carboplatin resistant, CR) was an example of high PTEN expression (Figure 1B). Figure 1C shows the hierarchical clustering of these signatures and PIK3CA and PTEN as individual gene values overlayed for reference. UCD52 overexpresses PIK3CA and scored highly on the signatures despite high PTEN expression, which usually inhibits PI3K activity. The reason UCD52 scored highly may have been that the PTEN it expresses has a deactivating mutation (Table 1). WHIM30 and HCI-010 scored highly despite modest PIK3CA single gene expression and no pathogenic mutation, but these PDXs are PTEN deficient. WHIM2 is an example of a basal-like breast cancer that is not PI3K overactive, having moderate PTEN expression and low PIK3CA expression and scoring relatively lowly on PI3K activity signatures. WHIM2 exhibiting relatively low PI3K gene expression levels was similarly observed in luminal models (BCM15034, BCM5097, HCI-011, HCI-013, HCI-009), clustering more closely to them than to other basal-like models. Two of the ER+, HCI-011 and HCI-013, scored relatively lowly on most of these gene signatures, despite containing an activating mutation of PIK3CA. UCD52 appears to have reduced PI3K activity after treatment with carboplatin (UCD52CR), but this may be because of a reduction in average RNA expression (from 2.17 to 2.01 log2 TPM), whereas HCI-001 and WHIM30 did not have as large of a change (from 2.15 to 2.14 and from 2.16 to 2.20 log2 TPM, respectively). An all-by-all pairwise comparison of the PDX gene signature enrichment scores using Pearson’s correlation coefficient (Figure 1D) revealed these signatures were, overall, highly similar to one another. Only 4 out of 91 signature pairs obtained a negative correlation, with the lowest correlation being −0.12 (REACTOME PI3K CASCADE to Lung 545k DEG Viglietto) and the highest correlation being 0.91 (Scorr PTEN Absent PNAS.2007 to YALE PIK3CA Pathway Ann.Oncol.2017) on a scale of from −1 to 1. Because PI3K inhibitors have not been found to be effective as single agents, we sought to determine if subpopulations of cells existed within PDXs that would be more or less therapeutically targetable for inhibitors of PI3K. Single-cell RNA-seq data containing 37,851 total cells from 18 samples (6 published previously) across 12 models (4 cell lines and 8 PDX) were utilized to compare cells and subpopulations. A 100-gene PI3K activity signature from Gatza et al. [33] was used to map these models, some of which, such as UCD52 and WHIM30, formed distinct subpopulations. (Figure 2A) These populations contained different proportions of the PIK3CA single gene (Figure 2B) and AKT1, AKT2, and AKT3 average expression (Figure 2C). Individual re-cluster analyses of UCD52 or WHIM30 cells identified two or three distinct subpopulations within each PDX (Figure 2D–G). Each model in the cluster was assessed for PIK3CA and AKT1-3 percent cell positivity (>0) (Figure 2H,I). To determine if the PIK3CA aberrations found within the models tested were targetable and resulted in the loss of cell viability, two clinically tested PI3K inhibitors (PI3Ki) were utilized; BYL-719 and GDC-0032 (marketed as taselisib). HCI-013, an ER+ PIK3CAH1047R mutant PDX, was the most responsive to both drugs at higher doses (Figure 3A and Supplemental Figure S1A). When grouped by pathogenic PIK3CA status, those with mutations were significantly (p > 0.01) more responsive on average to the PI3Ki in vitro at 5 μM and 10 μM using two-way ANOVA with Šidák’s correction for multiple comparisons (Figure 3B and Supplemental Figure S1B). We next sought to contrast the downstream effects of PI3K inhibition on TNBC cells from a PDX as compared to a cell line often utilized for PI3K inhibition studies, MDA-MB-453. Protein lysates from two basal-like models containing pathogenic PIK3CA aberrations, UCD52 and MDA-MB-453, were prepared from in vitro cultures, which were treated with BYL-719 or vehicle and applied to antibody arrays for orpheus-proteins relating to the PI3K/mTOR/AKT pathway (Supplemental Figure S2). Phospho-protein levels were quantified and analyzed using IPA, and fold changes (Figure 4A,B) were used to predict upstream regulators. The effects of BYL-719 were predicted by IPA to be seven upstream regulators to be downregulated and three upstream regulators to be upregulated, including PTEN, of the top 15 z-scores (Figure 4C,D). PTEN downstream effects are the same as BYL-719, to reduce PIP3 levels in the plasma membrane, as PI3K activity is to convert PIP2 into PIP3, so a reduction in PIK3CA activity from BYL-719 would be predicted to have a similar or potentially identical downstream effect as an increase in PTEN activity. Since the sensitivity or resistance to drugs that were not intended for targeted treatment can arise from a mutation in another gene, it was important to test the effect of PIK3CA activating mutations in controlled models. MCF10A wild type, E545K, and H1047R PIK3CA variant containing cell lines, when screened, responded to a similar number of drugs at 40% viability or lower (Figure 5A). The two mutant variants responded similarly (R = 0.97) to each drug as one another (Figure 5B), but when the average value of each of those was compared to the wild type containing cell line, viability was more dissimilar (R = 0.76) (Figure 5C); generally, each oncogenic mutant version caused cells to react the same to most drugs, but having one of these mutations changes the response of the cell to far more drugs relative to wild type containing cells. Different viability in parental versus mutation-containing cells was observed in 45 drugs, significant at 0.01 or lower p-value using a two-way ANOVA correcting for multiple comparisons using Tukey’s post-hoc test (Figure 5D). No drugs were significant with the same statistical comparison when comparing the effects of those drugs relative to mutation. Because BYL-719 as a single agent only had modest effects in vivo (Supplemental Figure S3A) while still inhibiting PI3K activity (Supplemental Figure S3B), it was of interest to discover agents that potentiated BYL-719’s effect on these models. High-throughput screening (HTS) with 516 single drugs alone and those agents with BYL-719 was performed on MDA453, UCD52, and HCI-013 (Figure 6A–C). Of drugs that were found to be synergistic using the coefficient of drug interaction (CDI), there were 20 for the two basal-like PIK3CA oncogenic aberration containing model, and half of those were also found to be synergistic with BYL-719 in HCI-013, an ER+ model which also has oncogenic PIK3CA. In total, 20 drugs were identified as synergistic in both basal-like PIK3CA aberrant-containing models, half of which were also synergistic in the ER+ PIK3CAH1047R (Figure 6D). Some drugs were highly effective as single agents, such as YM-155 and Digoxin, so significant synergy was not observed in combination with BYL-719. The PIK3CAWT basal-like PDX HCI-001 had fewer drugs with which BYL-719 was synergistic, and none of those found in the aberrant models were found to be synergistic in HCI-001 (Figure 6E). To investigate these drugs at a higher level of rigor, afatinib, dronedarone, and everolimus, three of the drugs which were discovered in HTS, were assessed with CompuSyn using 5 dose ratios per drug and 5 or more dose escalations per ratio in MDA453 (Supplemental Figure S4). Each of these combinations was predicted to have at least one dose ratio that was synergistic below one combination index (CI) and at a high fraction affected (FA), denoting that each combination had at least one dose ratio that performed better than the expected effects of both drugs alone combined. Each combination was predicted to have dose reduction index (DRI) greater than one at high FA with at least one dose ratio (Figure 7A–C), denoting that each combination could have one or the other of the drug’s concentrations reduced and be predicted to see the same effects. Highlighted in yellow is the range of interest, FA of 0.75 or higher, and CI below one or DRI above one. Those three drug combinations were then tested at two dose ratios each, each at a lower and higher escalation of doses, for four conditions total per drug combination. In total, 11 models were tested in vitro. Two dose ratios showed synergistic to significantly synergistic response in all models tested, the BYL-719 10 μM with afatinib 3.125 μM combination and the same dose of BYL-719 with 6.25 μM dronedarone. No dose ratio with everolimus showed synergism across each model, but the 10 μM BYL-719 with 7.5 μM everolimus had synergistic to significantly synergistic proliferation reduction in HCI-013, UCD52, WHIM30, and WHIM30CR. The only models that these dose ratios that were not synergistic were the ER+ HCI-011, which does contain pathogenic PIK3CA, and the PIK3CAWT, PTENWT basal-like PDX, BCM2147 (Figure 8). Finally, the drugs needed to be tested in living systems. BYL-719 alone, afatinib alone, the combination, and vehicle control were each administered to mice bearing tumors of the PIK3CA overactive UCD52 basal-like PDX. The combinatorial group showed significant (p < 0.0001) and significantly synergistic activity when comparing the final tumor area (CDI = 0.32) (Figure 9A,B). The combination of dronedarone and BYL-719 was significantly more effective than either agent alone (p < 0.0001) (Figure 9C). Everolimus with BYL-719 also showed significant efficacy compared to its single-agent components (p < 0.0001) (Figure 9E). Each of these combinations yielded significant synergism (CDI = 0.69 and 0.18, respectively) (Figure 9D,F). PI3K pathway activity was assessed using IHC and Western blot of p-RPS6 relative to total RPS6. Each group treated with BYL-719 alone does not show the same reduction in p-RPS6 that shorter in vivo trials show, which also occurred after BYL-719 single-agent resistance began. The afatinib + BYL-719 group had reduced total RPS6 (Supplemental Figure S5), as did each group treated with dronedarone on IHC (Supplemental Figure S6). Treatment with everolimus in UCD52 reduced p-RPS6 even at longer time points. (Supplemental Figure S7). These three combinations were tested in the same way on mice with xenografts of WHIM30, a PTEN deficient PIK3CA wild type basal-like PDX, with the everolimus and BYL-719 combination showing a reduction in tumor growth for each treatment group relative to the vehicle and synergistic effects (p < 0.05 and CDI 0.77) (Figure 10A,B) and everolimus treatment also reduced p-RPS6 in HCI-010 (Supplemental Figure S8). The combinations of dronedarone and afatinib with BYL-719 were tested, and while showing a trend, the effectiveness of BYL-719 alone drove the phenotype statistically (Supplemental Figure S9). The BYL-719 and everolimus combination was tested in HCI-010, another PTEN deficient PIK3CA wild type basal-like PDX, yielding significant difference (p < 0.01) and significantly synergistic effects (0.66) (Figure 10C,D). Once again, each everolimus-treated group had a reduction of p-RPS6 (Supplemental Figure S10). BYL-719 is currently approved for some cases of PIK3CA mutated ER+ breast cancer, though such as with HCI-011, targeting PIK3CA in ER+ disease, even with pathogenic PIK3CA, does not always yield an effective treatment. In clinical trials not related to breast cancer, the most common side effects of dronedarone were nausea at about 5% and diarrhea at about 10%, a very manageable adverse event profile, at least as a single agent [45]. Dronedarone, a multi-ion channel inhibitor, and afatinib, an EGFR inhibitor, have been studied in breast cancer previously [46,47] but not in combination with BYL-719. Afatinib has been approved as a single agent in some lung cancers [48], but despite clinical trials so far in the breast cancer setting, it has not been shown to be effective as a single agent in breast cancer [49]. These combinations did show synergistic effects in the PI3K overactive basal-like breast cancers tested. The combination of BYL-719 plus everolimus, an mTOR inhibitor, has been studied in PIK3CA mutant breast cancer cell lines previously [50] and was reconfirmed through this study. Toxicity has been tested in a phase 1b clinical trial for HR+ HER2- breast cancer to test the safety of the drugs, resulting in a manageable safety profile with no observed interactions between the two drugs [51], which is a promising outcome since both drugs have serious, but manageable side effects alone. The efficacy of the drugs was not able to be assessed in that trial because of the sample size. The results of previous research of the BYL-719 and everolimus combination point towards utilizing it on PIK3CA mutant cancers only, but they showed to be synergistic and effective in the PTEN deficient PIK3CA WT PDXs WHIM30 and HCI-010, suggesting that the precision medicine potential of BYL-719 plus everolimus should be considered in the treatment for PTEN lacking basal-like cancers, which are three or more times or more common than PIK3CA mutation containing basal-like breast cancer. Further research is merited, but based on these results, clinical trials could be considered using BYL-719 in combination with everolimus for basal-like tumors with PI3K pathway overactivity, either through PTEN loss or pathogenic PIK3CA and the combinations of either dronedarone or afatinib with BYL-719 should be considered for testing for patients with basal-like breast cancer with pathogenic PIK3CA.

PMC10001213

Cintia Scucuglia Heluany,Anna De Palma,Nicholas James Day,Sandra Helena Poliselli Farsky,Giovanna Nalesso

Hydroquinone, an Environmental Pollutant, Affects Cartilage Homeostasis through the Activation of the Aryl Hydrocarbon Receptor Pathway

22-02-2023

environmental pollutant,smoking,chondrocyte,cartilage,oxidative stress,IL-1β

Exposure to environmental pollutants has a proven detrimental impact on different aspects of human health. Increasing evidence has linked pollution to the degeneration of tissues in the joints, although through vastly uncharacterised mechanisms. We have previously shown that exposure to hydroquinone (HQ), a benzene metabolite that can be found in motor fuels and cigarette smoke, exacerbates synovial hypertrophy and oxidative stress in the synovium. To further understand the impact of the pollutant on joint health, here we investigated the effect of HQ on the articular cartilage. HQ exposure aggravated cartilage damage in rats in which inflammatory arthritis was induced by injection of Collagen type II. Cell viability, cell phenotypic changes and oxidative stress were quantified in primary bovine articular chondrocytes exposed to HQ in the presence or absence of IL-1β. HQ stimulation downregulated phenotypic markers genes SOX-9 and Col2a1, whereas it upregulated the expression of the catabolic enzymes MMP-3 and ADAMTS5 at the mRNA level. HQ also reduced proteoglycan content and promoted oxidative stress alone and in synergy with IL-1β. Finally, we showed that HQ-degenerative effects were mediated by the activation of the Aryl Hydrocarbon Receptor. Together, our findings describe the harmful effects of HQ on articular cartilage health, providing novel evidence surrounding the toxic mechanisms of environmental pollutants underlying the onset of articular diseases.

Hydroquinone, an Environmental Pollutant, Affects Cartilage Homeostasis through the Activation of the Aryl Hydrocarbon Receptor Pathway Exposure to environmental pollutants has a proven detrimental impact on different aspects of human health. Increasing evidence has linked pollution to the degeneration of tissues in the joints, although through vastly uncharacterised mechanisms. We have previously shown that exposure to hydroquinone (HQ), a benzene metabolite that can be found in motor fuels and cigarette smoke, exacerbates synovial hypertrophy and oxidative stress in the synovium. To further understand the impact of the pollutant on joint health, here we investigated the effect of HQ on the articular cartilage. HQ exposure aggravated cartilage damage in rats in which inflammatory arthritis was induced by injection of Collagen type II. Cell viability, cell phenotypic changes and oxidative stress were quantified in primary bovine articular chondrocytes exposed to HQ in the presence or absence of IL-1β. HQ stimulation downregulated phenotypic markers genes SOX-9 and Col2a1, whereas it upregulated the expression of the catabolic enzymes MMP-3 and ADAMTS5 at the mRNA level. HQ also reduced proteoglycan content and promoted oxidative stress alone and in synergy with IL-1β. Finally, we showed that HQ-degenerative effects were mediated by the activation of the Aryl Hydrocarbon Receptor. Together, our findings describe the harmful effects of HQ on articular cartilage health, providing novel evidence surrounding the toxic mechanisms of environmental pollutants underlying the onset of articular diseases. Exposure to environmental pollutants can severely affect and compromise human health. Hydroquinone (HQ), a benzene metabolite, is a widespread pollutant agent used in several commercial and industrial processes, such as dye intermediate, and as stabilizer in paints and motor fuels [1] and represents 3% of the particle matter phase of cigarette smoke [2]. HQ has a proven pro-carcinogenic activity and can mediate immune- and mielo-toxicity by promoting apoptosis and oxidative stress [3,4,5,6]. The effects of pollution on musculoskeletal health remain mostly uncharacterised. While some studies showed the potential danger of pollutants on chondrogenesis and joint formation [7,8], our knowledge of their effect and mechanism of action in adult tissues is very limited. We have recently demonstrated that exposure to HQ can aggravate joint damage in an experimental animal model of inflammatory arthritis: HQ promoted inflammation, an increased influx of immune cells, synovial proliferation and oxidative stress in mice and rats in which inflammatory arthritis was induced by subcutaneous injection of collagen type II (Collagen-induced arthritis model, CIA), through the activation of the Aryl Hydrocarbon Receptor (AhR) pathway [9,10,11]. Importantly, these studies showed a synergistic effect of HQ in exacerbating the degenerative effects of pro-inflammatory cytokines such as IL-1β and TNFα in pathological conditions in the joint [11]. However, the effect of HQ on articular cartilage (AC) has not been investigated yet. The AC is the connective tissue covering the edges of the bones in the diarthrodial joints. Articular chondrocytes are the only type of cells populating the tissue [12]. They are responsible for the synthesis and turnover of collagens and highly sulphated proteoglycans, which are the main components of the thick extracellular matrix (ECM), conferring the biomechanical properties upon the tissue [13,14]. The AC is elastic and resistant to compression and allows the joints to move smoothly [14]. Degeneration of the AC is a common feature in several musculoskeletal conditions, such as osteoarthritis (OA) and rheumatoid arthritis (RA), which affect millions of people worldwide and represent, therefore, a very heavy socio-economic burden. The characterisation of factors influencing their onset can help in designing strategies to prevent or delay their onset and reduce their impact on patients’ quality of life. In this work, we investigated the effect of HQ on articular chondrocytes to elucidate how it can affect articular cartilage health. We showed that proteoglycan content was significantly reduced in the articular cartilage of rats exposed to HQ in comparison to vehicle-exposed animals. HQ decreased the viability of isolated chondrocytes, promoted oxidative stress and downregulated the expression of phenotypic marker genes alone and in synergy to Interleukin-1β (IL-1 β), one of the main pro-inflammatory cytokines promoting cartilage degeneration both in inflammatory and non-inflammatory arthritis. HQ mediated its catabolic activity in articular chondrocytes through the activation of the AhR pathway. Together, our findings suggest that the AhR activation in articular chondrocytes could be an important mechanism through which pollutants can compromise cartilage health. Six to eight-week-old male Wistar rats were supplied by the Animal Facility of the Faculty of Pharmaceutical Sciences and Chemistry Institute of the University of São Paulo. Rats were supplemented with food and water ad libitum. All procedures were performed according to the guidelines of the Brazilian Society of Science of Laboratory animals for the proper care and use of experimental animals. Experimental procedures were approved by the Ethics Committee on Animal Use (CEUA) of the University of São Paulo (protocol number 435). Before euthanasia, animals were anaesthetised with a solution containing Ketamine/Xylazine (Ceva Sante Animale, Paulinea, Brazil) 80:8 mg/kg through intraperitoneal injection. CIA induction was performed as described before [15]. Two mg/mL of bovine collagen type II (Chondrex, Woodinville, WA, USA) was dissolved in 0.1 M acetic acid (LabSynth, Diadema, Brazil), by gentle stirring and the solution was emulsified in equal volumes of Complete Freund Adjuvant (CFA, Sigma-Aldrich, St. Louis, MO, USA). At day 7, 200 µL of the emulsified solution was injected subcutaneously at the base of the tail, and 100 µL was administered on day 14 through the same route. For comparison, the non-CIA group was used as a healthy control group. CIA rats were divided into two groups: one group was exposed to an HQ solution (Hydroquinone 99%, Sigma-Aldrich, St. Louis, MO, USA) at 25 ppm (1.5 mg/60 mL) through an ultrasonic nebuliser (NS®, São Paulo, Brazil) for 1 h/daily for 35 consecutive days as previously described [9,10,11]; the control group was instead exposed to a vehicle solution (5% ethanol in saline) for the same amount of time. Bovine articular cartilage explants were isolated from the metatarsal and metacarpal joints of adult cows purchased within less than 24 h of death from a local abattoir. As tissues and cells were removed from cows euthanised in an approved abattoir and were not killed for the purpose of this study, no ethical approval was deemed to be needed by the Ethical Committee of the University of Surrey. The articular cartilage was dissected in sterile conditions and processed for chondrocyte isolation or explant cultures as previously described [14]. Bovine articular chondrocytes (BACs) were cultured with complete medium (CM-DMEM F12 (Thermo Fisher, Waltham, MA, USA), supplemented with 10 % Fetal Bovine Serum (FBS; Gibco Invitrogen, Carlsbad, CA, USA) and 1% of antibiotics and antimycotic solution (Sigma-Aldrich, St. Louis, MO, USA) at 37 °C in a 5% CO2 incubator (Binder, Tuttlingen, Germany). Cells at passages 1 to 3 were used for the experiments described in this manuscript. Cell viability was measured through the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT; Sigma-Aldrich, St. Louis, MO, USA) method. One hundred thousand BACs/well were seeded in 96-well plates and cultured in CM for 24 h. Thereafter, cells were washed with PBS and cultured with DMEM/F-12 supplemented with 0.1% of bovine serum albumin (BSA; Sigma-Aldrich, St. Louis, MO, USA) for 24 h. BACs were treated as described in the individual experiments. Then, 0.5 mg/mL of MTT solution was added to each well and incubated for 3 h in the dark at 37 °C. The medium was then removed, and the blue formazan crystals were dissolved in 200 µL of dimethyl sulfoxide (DMSO; Sigma-Aldrich, St. Louis, MO, USA). The optical density reading was recorded at 570 nm in a CLARIOstar plate reader (BMG LABTECH, Offenburg, Germany). Cell viability and death were determined by a flow cytometry-based method using Annexin V (BD Biosciences, San Jose, CA, USA) and 7-Aminoactinomycin D (7AAD; BD Biosciences, San Jose, CA, USA) double-staining. BACs (1 × 104 cells/well) were seeded in 24-well plates and cultured with CM overnight. Then, cells were washed with PBS and treated with HQ (10, 25 or 50 µM) for 24 h. After that, cells were washed with PBS, detached with trypsin (Gibco—Fisher Scientific, Pittsburgh, PA, USA), centrifuged and incubated with an APC-conjugated Annexin V (1:100) for 30 min at room temperature. The cells were then incubated with 7-AAD (1:200) and 10,000 events were recorded in a flow cytometer (Accuri C6, BD Biosciences, San Jose, CA, USA). Data are expressed as a percentage of viable cells (double negative population), apoptotic cells (APC positive and 7-AAD negative), necrotic cells (APC negative and 7-AAD positive) or late apoptotic cells (APC and 7-AAD positive). For gene expression analyses, 7.5 × 104 BACs were plated per well in 24-well plates and cultured with CM for 24 h. The cells were washed with PBS and treated as described in the Results section. Total RNA was extracted from BACs using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions. Three hundred and fifty nanograms of total RNA from each sample were reverse transcribed to cDNA using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Waltham, MA, USA). Quantitative PCR was performed with hot-start DNA polymerase (Qiagen, Hilden, Germany), as previously described [16]. Primer sequences are listed in Table 1 (All primers were purchased from Sigma-Aldrich, St. Louis, MO, USA). All data were normalised to internal control of β-actin values. All experiments were performed in a PCR system (CFX96TM Optics Module; BioRad, Hercules, CA, USA). BACs were plated at a density of 25 × 104 cells per well in a complete medium in 24-well plates. Micromass cultures were prepared as described before [17]. The micromasses were stimulated as described in the individual experiments with HQ ± IL-1β (20 ng/mL; RP0106B-025 Kingfisher Biotech, Saint Paul, MN, USA). After 48 h, the culture media were collected, and the micromasses were washed with PBS and fixed with cold methanol (Sigma-Aldrich, St. Louis, MO, USA). Micromass cultures were stained overnight with Alcian blue (Carl Roth, Karlsruhe, Germany) as previously described [17]. Stained cultures were washed extensively with distilled water and were extracted with 6M guanidine hydrochloride (Sigma-Aldrich, St. Louis, MO, USA). The optical density of the extracted cultures was read at 630 nm with a CLARIOstar spectrophotometer (BMG LABTECH, Offenburg, Germany). The absorbance values were normalised to protein content determined by the Bicinchoninic acid assay (BCA) assay, following manufacturer instructions (ThermoFisher Scientific; Waltham, MA, USA). Images of the micromasses were acquired at room temperature with a stereomicroscope (SZTL 350 Stereo Binocular Microscope, VWR®; Radnor, PA, USA). Three-micrometre thick sagittal sections of bovine cartilage explants and 5 μm sections of metatarsal regions from CIA-rats paws were stained with safranin-O (SO; Sigma-Aldrich, St. Louis, MO, USA) as described before [16]. Images were taken using the same settings for all the images on an optical microscope (Eclipse Ci, Nikon, Tokyo, Japan). Images were acquired through 10× magnification objective lenses, and the SO-staining was quantified by using ImageJ (NIH; Bethesda, MD, USA). Four fields per slide (totalling 16 images per experimental group) were acquired, and the mean of the staining intensity of each group was compared. For the paws, the histologic scoring system was evaluated and scored in a blinded manner by two investigators, following the Standardised Microscopic Arthritis Scoring of Histological section (SMASH) recommendations for scoring histological sections from inflammatory arthritis experimental animal models [18]. The release of glycosaminoglycans (GAGs) by micromass cultures of BACs and bovine cartilage explants in culture media was measured by using the Dimethyl methylene Blue (DMMB, Sigma-Aldrich, St. Louis, MO, USA) assay (adapted from Farndale et al., 1982 [19]. Briefly, 200 µL of the DMMB solution was added to 20 µL of culture media in a 96-well plate. Subsequently, the absorbance was measured at 525 nm with a CLARIOstar spectrophotometer (BMG LABTECH, Offenburg, Germany). Fifty microliters of Sulphanilamide solution (1% in 5% phosphoric acid; Promega; Madison, WI, USA) were added to 50 µL of the culture media of micromass cultures of BACs or bovine cartilage explants treated with HQ ± IL-1β (20 ng/mL). After a 10 min incubation at RT, 50 µL of N-1-napthylenediamine dihydrochloride solution (NED, 0.1% in water; Promega, Madison, WI, USA) was added to the mix. The absorbance was then measured at 550 nm with a CLARIOstar spectrophotometer (BMG LABTECH, Offenburg, Germany). The intracellular accumulation of reactive oxygen species (ROS) was quantified in BACs cultures using the fluorescent probe CM-H2DCFDA (Invitrogen, Carlsbad, CA, USA). Ten thousand chondrocytes/well were seeded in 24-well plates and stimulated with HQ for 24 h. The cells were then incubated with 10 μM CM-H2DCFDA for 30 min at 37 °C in the dark. The cells were resuspended in PBS and 10,000 events were acquired in a MACSQuant flow cytometer (Miltenyi Biotec, Bergisch Gladbach, Germany). Results are presented as arbitrary units of fluorescence. BACs were seeded at a density of 7 × 104 cells/well in CM in a 24-well plate. The cells were co-transfected with 450 ng/well of pGL4.43 [luc2P/XRE/Hygro] luciferase reporter vector (Promega, Madison, WI, USA) and with 50 ng/well of the control vector expressing Renilla reniformis luciferase by using Lipofectamine (ThermoFisher, Waltham, MA, USA) following manufacturer instructions. After transfection, the cells were stimulated with CM (vehicle) or with the AhR ligand 6-formylindolo(3,2b)carbazole (FICZ, 10 µM, Sigma-Aldrich, St. Louis, MO, USA) or with HQ (10 or 25 µM) for 24 h. Luciferase activity was determined using the Dual luciferase reporter assay system (Promega, Madison, WI, USA). Firefly luciferase activity was normalised by the Renilla luciferase activity. Data are expressed as a fold increase of relative luminescence units in comparison to the vehicle. One-way ANOVA with the Tukey-Post test was used to compare the statistical differences between multiple groups, and a two-tailed t-test was used for comparisons between two groups. Values of p < 0.05 were considered statistically significant. Data are expressed as the mean ± standard error of the mean (SEM). Statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, Boston, MA, USA). Rheumatoid arthritis (RA) is a chronic inflammatory musculoskeletal condition whose hallmarks are synovial inflammation, bone erosion and cartilage damage [20]. We previously showed that in CIA animals, exposure to HQ exacerbated synovial inflammation [9,10,11]. Here we show that while exposure to HQ did not significantly alter cartilage structure and composition in healthy rats, HQ increased proteoglycan loss in the articular cartilage of the paw joints of the treated CIA animals in comparison to control ones (Figure 1A–C). This suggests a more widespread toxic effect of HQ on joint tissues in pathological conditions. To investigate the effects of HQ exposure on chondrocyte homeostasis, we treated bovine articular chondrocytes with different concentrations of HQ (1 μM up to 100 μM) and monitored cell growth across time by using an MTT assay. Ten to one hundred μM HQ slowed cell growth over a 96h time course (Figure 2A). Apoptosis was promoted by the xenobiotic at concentrations higher than 25 μM, as shown by increased Annexin V expression measured by FACS analysis (Figure 2B,C). We then tested the effect of HQ on the expression of cartilage phenotypic markers and matrix remodelling enzymes, as their modulations are hallmarks of the initiation of pro-catabolic events leading to cartilage degradation in different forms of arthritis [21,22]. Incubation of BACs with 10 μM and 25 μM of HQ promoted downregulation of SRY-box transcription factor 9 (SOX-9), collagen type II (Col2a1) and collagen type X (Col-X) mRNAs (Figure 3A–C) whilst it upregulated the expression of the matrix remodelling enzymes metalloprotease 3 (MMP-3) and A disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5) mRNAs (Figure 3D,E). Proteoglycan content was overall reduced in chondrocyte micromasses exposed to HQ (Figure 3F,G). Our results, therefore, confirm a pro-catabolic activity of HQ in the articular cartilage. HQ can induce the generation of ROS in different biological systems [10,11,23]. Increased oxidative stress in chondrocytes has been strongly associated with cartilage degeneration in musculoskeletal conditions [24]. We then tested whether HQ exposure could influence the production of ROS in BACs cultures. Our data showed that incubation of chondrocytes with HQ for 24 h was sufficient to trigger a significant increase of ROS in treated cells in comparison to non-stimulated cells (Figure 3H). Nitric oxide (NO) metabolism can also suppress the synthesis of proteoglycans (PGs) and stimulate MMPs activity [25]. Stimulation of articular chondrocytes with HQ for 48 h significantly increased nitrite production, as evaluated by the Griess reaction (Figure 3I). Interleukin 1beta (IL-1β) is a pro-inflammatory cytokine overexpressed in the articular cartilage during the progression of both osteoarthritis and rheumatoid arthritis [26,27,28]. Thus, to investigate whether HQ could exacerbate the pro-inflammatory effect of this cytokine, BACs were pre-stimulated with a recombinant bovine IL-1β at 20 ng/mL for 4 h before being exposed to different concentrations of HQ for 24 or 48 h. HQ did not exacerbate the pro-catabolic effects of IL-1β in modulating the expression of phenotypic markers and matrix remodelling enzymes in monolayer cultures of BACs (Figure 4A,B). However, HQ stimulation further reduced the content and increased the release of GAGs in micromass cultures of BACs (Figure 4C–E). While this synergic effect was not reproduced in cultures of bovine cartilage explants (Figure 5A–C), HQ further increased nitrite production induced by IL-1β in both culture systems (Figure 4F and Figure 5D). We previously demonstrated that HQ mediates its cytotoxicity in the synovium via activation of the AhR pathway in murine experimental models of inflammatory arthritis [9,10,11]. To test whether this mechanism of action is preserved in the articular chondrocytes, we measured the expression levels of AhR, of the aryl hydrocarbon receptor nuclear translocator (ARNT) and of the Cytochrome P450 Family 1 Subfamily A Member 1 (Cyp1a1), an endpoint target gene in the AhR pathway in BACS stimulated with 10 μM and 25 μM HQ. All the genes were upregulated in stimulated chondrocytes (Figure 6A–C). This upregulation was rescued by pre-incubation of the cells with α-naphthoflavone (αNF, 10 µM; Sigma-Aldrich, St. Louis, MO, USA), an AhR antagonist (Figure 6A–C). In its activated form, the AhR translocates from the cytoplasm to the nucleus and forms a heterodimer with the ARNT, which binds to specific DNA sequences located in the xenobiotic responsive elements (XRE) present in the promoter regions of the target genes (50-TA/TGCGTG-30) genes, regulating their expression [29,30]. To demonstrate the activation of the AhR pathway, we, therefore, performed a luciferase-based XRE reporter assay in articular chondrocytes stimulated for 24 h with HQ. As shown in Figure 6D, treatment with HQ increased the reporter gene activity in stimulated cells in a concentration-dependent manner. Finally, we showed that both the phenotypic changes as well as the pro-catabolic activity induced by HQ were mediated via the activation of the AhR pathway, as the HQ-mediated downregulation of SOX-9 and upregulation of MMP-3 were rescued by the pre-incubation of the articular chondrocytes with the AhR antagonist αNF (Figure 6E,F). Inflammatory arthritis and osteoarthritis are diseases, respectively, affecting ~0.30% of the worldwide population [31] and 16% of the population over the age of 15 [32]. Both diseases are characterised by the degeneration of joint tissues. Several factors can contribute to the onset of these diseases and have been largely described. Nonetheless, the impact of environmental pollution on pre-existing musculoskeletal conditions is still vastly unknown. Here we focused on investigating whether HQ, a benzene metabolite contained in motor fuels and other environmental pollutants, can exacerbate the degenerative effect of inflammation on the articular cartilage. Exposure to HQ decreased chondrocyte viability and promoted apoptosis in a dose and time-dependent manner, confirming previous observations in different biological systems [5,6]. In musculoskeletal conditions such as osteoarthritis and rheumatoid arthritis, the articular chondrocytes undergo phenotypic changes: the expression of phenotypic markers is altered, and the activity of matrix remodelling enzymes increase [21,33,34]. These changes contribute to the progressive loss of the biomechanical properties of the tissue and ultimately lead to its degradation [35,36]. Our data suggest that HQ could heavily contribute to many of these phenomena. Here we showed that exposure to HQ increased cartilage damage in the paws of rats in which inflammatory arthritis was induced by injection of Collagen type II. To better understand the molecular mechanisms modulated by HQ in the tissue, we stimulated isolated cells to the xenobiotic: HQ promoted downregulation of Col2a1 and SOX-9 while also inducing upregulation of MMP-3, which can degrade several types of collagens, proteoglycans and matrix proteins in the articular cartilage [37]. The pro-catabolic activity induced by HQ was also shown by the reduction of GAG staining and increased GAG release upon exposure of chondrocytes micromass cultures to the xenobiotic. Oxidative stress can be induced through several mechanisms and can contribute to cellular metabolic decline as well as promote degenerative mechanisms [38]. The induction of oxidative stress in chondrocytes has been strongly associated with increased cartilage degradation in musculoskeletal conditions [23]. Moreover, nitrite production has also been shown to contribute to oxidative stress and degeneration of tissue integrity in the joints [39]. Previous data showed that exposure to environmental pollutants could promote ROS and NO generation in chondrocytes [40]. HQ exposure has been associated with oxidative damage and could affect the oxidative balance in other biological systems [9,10,11,41,42]. Indeed, here we confirmed the pro-oxidative effect of HQ on the articular chondrocytes, which could contribute to the overall phenotypic changes induced by the xenobiotic on these cells. Interleukin 1β (IL-1β) is a major pro-inflammatory trigger in both osteoarthritis and rheumatoid arthritis. IL-1 stimulates catabolic changes, suppresses anabolic pathways and decreases matrix synthesis [27]. We previously demonstrated the synergistic effect of HQ and inflammation in promoting synovial tissue degeneration [9,10,11]. Here we wanted to investigate whether we could see the same detrimental effect on the articular cartilage to determine whether environmental pollution could be considered a contributing factor in the degradation of this tissue in pathological conditions. Indeed IL-1β and HQ had a synergistic effect in reducing proteoglycan content and in promoting oxidative stress in articular chondrocytes, suggesting that exposure to environmental pollutants could potentiate inflammatory processes involved in the degradation of the articular cartilage. Interestingly, similar effects were observed in synoviocytes derived from patients affected by rheumatoid arthritis and co-stimulated with HQ and TNF-alpha [11]. AhR is a ligand-dependent transcription factor that translocates to the nucleus upon activation by xenobiotics and pollutants [43]. In this study, we showed that the HQ mediates its toxic effects through the activation of the AhR pathway. We demonstrated that HQ upregulated the expression of the AhR itself and of its downstream effectors, suggesting that xenobiotics and pollutants can directly influence the health of the articular cartilage. We confirmed that after HQ exposure, AhR translocates from cytoplasm to the nucleus, forms a heterodimer with the AhR nuclear translocator (ARNT) and promotes the transcription of target genes such as Cyp1a1. The activation of the AhR pathway has been shown to mediate several detrimental effects, such as aggravation of articular diseases, cancer development and endocrine disruption [44,45,46]. Indeed, we and others previously showed that this receptor plays a major role in exacerbating rheumatoid arthritis in smokers [9,10,11,46,47,48]. Interestingly our data showed an upregulation of Sox9 expression in response to stimulation with α-naphthoflavone, a competitive antagonist of the AhR. This might suggest that AhR is involved in the modulation of Sox9 activity, such as a constitutive inhibitory activity. The AhR has been shown to transactivate class I heterodimeric nuclear receptors while antagonising the activation of homodimeric ones in breast cancer cells [49]. The cAMP Response Element binding protein (CBP/p300) is an important coactivator of both the transcriptional activity of Sox9 and AhR (as reviewed in [50]). These data suggest a new uncharacterised interaction between the two transcription factors. Further experimental work will be necessary to validate this hypothesis. In summary, our data demonstrated a clear detrimental effect of HQ on articular cartilage homeostasis and shed novel insight into how environmental pollutants can exacerbate the degenerative effect of pro-inflammatory mechanisms underlying the onset of articular diseases.

PMC10001216

Kui Kang,Mengyi Zhang,Lei Yue,Weiwen Chen,Yangshuo Dai,Kai Lin,Kai Liu,Jun Lv,Zhanwen Guan,Shi Xiao,Wenqing Zhang

Oxalic Acid Inhibits Feeding Behavior of the Brown Planthopper via Binding to Gustatory Receptor Gr23a

28-02-2023

ligand identification,gustatory receptor,Nilaparvata lugens,oxalic acid,antifeedant

Plants produce diverse secondary compounds as natural protection against microbial and insect attack. Most of these compounds, including bitters and acids, are sensed by insect gustatory receptors (Grs). Although some organic acids are attractive at low or moderate levels, most acidic compounds are potentially toxic to insects and repress food consumption at high concentrations. At present, the majority of the reported sour receptors function in appetitive behaviors rather than aversive taste responses. Here, using two different heterologous expression systems, the insect Sf9 cell line and the mammalian HEK293T cell line, we started from crude extracts of rice (Oryza sativa) and successfully identified oxalic acid (OA) as a ligand of NlGr23a, a Gr in the brown planthopper Nilaparvata lugens that feeds solely on rice. The antifeedant effect of OA on the brown planthopper was dose dependent, and NlGr23a mediated the repulsive responses to OA in both rice plants and artificial diets. To our knowledge, OA is the first identified ligand of Grs starting from plant crude extracts. These findings on rice–planthopper interactions will be of broad interest for pest control in agriculture and also for better understanding of how insects select host plants.

Oxalic Acid Inhibits Feeding Behavior of the Brown Planthopper via Binding to Gustatory Receptor Gr23a Plants produce diverse secondary compounds as natural protection against microbial and insect attack. Most of these compounds, including bitters and acids, are sensed by insect gustatory receptors (Grs). Although some organic acids are attractive at low or moderate levels, most acidic compounds are potentially toxic to insects and repress food consumption at high concentrations. At present, the majority of the reported sour receptors function in appetitive behaviors rather than aversive taste responses. Here, using two different heterologous expression systems, the insect Sf9 cell line and the mammalian HEK293T cell line, we started from crude extracts of rice (Oryza sativa) and successfully identified oxalic acid (OA) as a ligand of NlGr23a, a Gr in the brown planthopper Nilaparvata lugens that feeds solely on rice. The antifeedant effect of OA on the brown planthopper was dose dependent, and NlGr23a mediated the repulsive responses to OA in both rice plants and artificial diets. To our knowledge, OA is the first identified ligand of Grs starting from plant crude extracts. These findings on rice–planthopper interactions will be of broad interest for pest control in agriculture and also for better understanding of how insects select host plants. Plant visual and chemical cues are important for host location and host acceptance of phytophagous insects. The cabbage root fly, Delia radicum, makes use of leaf colors to discriminate among host plants [1]. Host selection in oligophagous species is involved with the balance of phagostimulatory and deterrent inputs with the addition of a specific chemical sign stimulus [2]. Understanding the mechanisms underlying this selection strategy is of practical importance in agriculture for pest control. For instance, tandem deployment of the napier grass, which attracts greater oviposition by stemborer moths than maize but is not suitable for survival, and the molasses grass, which causes over 80% reduction in stemborer infestation of maize, can interfere with host selection by stemborers [3]. The combination of stimuli that have negative effects on food selection by pests and positive effects on diverting pests from the protected resource to a trap can reduce pest abundance on the protected resource; this is called the push–pull strategy [4]. Plant-derived antifeedants can be used as push components of the push–pull strategy [4]. Identification of receptors sensing these chemicals will definitely improve our understanding of how insects select host plants. To date, most identified antifeedants are secondary metabolites, which are toxic or deterrent to insects [2,5,6]. As an example, oxalic acid (OA), the simplest dicarboxylic acid common in plant tissues, provides protection for plants against insects and foraging animals, including the brown planthopper (BPH), Nilaparvata lugens, and aphids [6,7,8]. Some antifeedants (e.g., tricin) could be even used as indicators of crop resistance to target insect pests, and are therefore potentially valuable for breeding resistant crop varieties [9]. In insects, gustatory receptor neurons (GRNs), mainly found in gustatory sensilla, are able to convert the chemical signal into an electrical one and transmit it to higher-order brain structures for processing, which in turn dictates behavior [10]. Secondary compounds as deterrents stimulate a subset of bitter GRNs, which then inhibit feeding activity or induce repulsion [2]. With the decoding and publication of the genomes of Drosophila melanogaster and other insect species, fruitful molecular studies on crop pest gustation have been enabled. Gustatory receptors (Grs), expressed in GRNs, are required for responses to specific tastants. Grs mainly include sweet, bitter, and CO2 receptors [11]. Although diverse sugars and bitter compounds have been identified as ligands of insect Grs, many Grs have yet to be functionally annotated, particularly those sensing acids [12]. In D. melanogaster, sweet gustatory receptors were reported to non-specifically respond to low pH with mild attraction [13,14]. The fly also uses ionotropic receptors and mammalian orthologs to perceive acids [12,15,16]. However, there is currently no report of insect Grs sensing aversive acids derived from plants. The BPH, causing extensive damage by sap feeding and transmitting viruses, feeds solely on Oryza sativa and its allied wild forms, such as O. perennis and O. spontanea [17]. Its gustatory sensilla are mainly located in the small passageway leading from the food duct to the cibarium and the stylet groove on the labial tip, and probably regulate the initial stages of probing and the maintenance of sap ingestion [18,19]. In our previous study, 32 N. lugens Gr sequences (NlGrs) were obtained following analyses of the BPH genome and transcriptomes [20]. In previous studies, we explored the roles of NlGrs sharing orthologous relationships with D. melanogaster Grs [21,22,23]. Here, we would like to investigate NlGrs with unknown functions. In insects, most functionally characterized Grs have seven transmembrane (TM) domains. However, only a few Grs are 7-TM proteins in the BPH. NlGr23a, which was predicted to possess typical GR characteristics with 7-TM domains and was successfully cloned, was selected as the focus of the present study. Through a multi-stage bioassay-directed fractionation of rice crude extracts, we identified OA as a ligand of NlGr23a. We also found that NlGr23a mediated the repulsive responses to OA in both rice plants and artificial diets. A BPH laboratory strain (Ctl) was obtained from Guangdong Academy of Agricultural Sciences (GDAAS, Guangdong, China), and this strain was reared in a continuous laboratory culture on susceptible rice seedlings (variety Huang Hua Zhan). All BPHs were maintained at 26 ± 2 °C with 80 ± 10% humidity and a light–dark cycle (16 h light and 8 h dark). One-day-old brachypterous female adults and the susceptible rice variety Taichung Native 1 (TN1) aged 30–40 days were used for the following bioassays. Allocation of insects was randomly done to minimize the effects of subjective bias. OA, glycerine, adonitol, methoxylamine hydrochloride, N-methyl-N-(trimethylsilyl) trifluoroacetamide, tetrabutylammonium bisulphate (TBA), KH2PO4, and caffeine (purity, ≥99%) were purchased from Sigma-Aldrich Company (MO, USA). HPLC grade reagents including petroleum ether (PE), chloroform (CH3Cl), ethyl acetate (EAC), methyl alcohol (MeOH), acetonitrile (ACN), isopropyl alcohol, and pyridine, as well as analytical grade reagents including sucrose, trans-aconitic acid, salicylic acid, mandelic acid, and ethyl alcohol were obtained from Aladdin Reagent (Shanghai, China). Maleic acid was purchased from MedChemExpress (Shanghai, China). Hydrochloric acid (HCl) was provided by the Guangzhou Chemical Reagent Factory (Guangzhou, China). Total RNA from BPHs was prepared as previously described [20]. A PrimeScriptTM RT reagent kit with gDNA Eraser (Takara, Kyoto, Japan) was used with 1 µg RNA for first-strand complementary DNA (cDNA) synthesis. A fragment of full-length NlGr23a cDNA was amplified using 2 × SuperStar PCR Mix with Loading Dye (Genstar, Beijing, China) following the manufacturer’ss instructions. The cloning primers are listed in Table S1. The NlGr23a cDNA was inserted into the Hind III and EcoR I sites of pIZ/V5-His vectors (Invitrogen, Carlsbad, CA, USA) using T4 DNA ligase (NEB, Beijing, China). The NlGr23a-Sf9 stable cell lines were obtained according to a previously described method [21]. Briefly, Sf9 cells were cultured in Grace’s Insect Medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum at 27 °C. Cells were plated into 6-well plates and left to settle for 20 min before being transfected with either 2 µg of the recombinant plasmid pIZ-NlGr23a-V5-His or pIZ-V5-His vector (negative control), and 6 µL Fugene HD transfection reagent (Promega, Madison, WI, USA) in 100 µL per well of Grace’ss Insect Medium. Forty-eight hours after transfection, cells were cultivated under Zeocin selection. The final concentration of Zeocin to maintain cells was 100 µg/mL to obtain stable cell lines. To further confirm the sufficiency of NlGr23a for OA-mediated response, the HEK293T cells were transiently transfected with NlGr23a/pcDNA3.1-FLAG. The HEK293T cells were cultured in Dulbecco’ss modified Eagle medium (Gibco, CA, USA) supplemented with 10% fetal bovine serum in an atmosphere of 5% CO2 and 95% relative humidity at 37 °C. NlGr23a/pcDNA3.1-FLAG was constructed by inserting a C-terminally FLAG-tagged human codon-optimized NlGr23a ORF into the pcDNA3.1 vector using BamHI and EcoRI sites. The nucleotide sequences of the codon-optimized genes were listed in Supplementary Figure S1. The HEK293T cells were transfected with 0.5 µg plasmid DNA per well using 1.5 µL LipofectamineTM 3000 Reagent and 1 µL P3000™ Reagent (both from Invitrogen, Carlsbad, CA, USA), following the manufacturer’s protocol. A calcium imaging assay guided approach was adopted to isolate and identify active compounds interacting with NlGr23a from the crude extracts of rice plants. Firstly, we ground stems and leaves of TN1 (5 g) and performed serial solvent extractions on macerated subsamples with 25 mL of isopropanol (IPA) at 75 °C for 15 min, 25 mL CHCl3-IPA (1:2, v/v; 5 h; room temperature), and 25 mL MeOH-CHCl3 (2:1, v/v; overnight; room temperature). Each extract was centrifuged at 2000 rpm for 15 min to collect the supernatant. The pooled supernatants were then concentrated to dryness in vacuum at 25 °C and re-dissolved in 12 mL CHCl3 to obtain the crude rice extract. Secondly, 6 mL crude extract was subjected to a column chromatography with silica gel, eluting with solvents in the order of increasing polarity (PE-CHCl3-EAC-MeOH) to yield four fractions. We carried out the first round of ligand screening with four initial fractions. Thirdly, semi-preparative HPLC separation of the bioassay-active fraction (active fraction I) of the first round screening was performed on an Agilent 1260 HPLC system (Agilent Technologies, Santa Clara, CA, USA) equipped with an Agilent ZRBAX SB-C18 column (4.6 mm × 150 mm, 3.5 µm) by gradient elution with ACN (A) and water (B). The gradient program was as follows: 3 min, 5% B; 22 min, 5–20% B; 15 min, 20–40% B; 15 min, 40–50% B; 8 min, 50–95% B; 12 min, 95–5% B, all at a flow-rate of 1 mL/min. The eluate from the column was continuously monitored at 205 and 290 nm after sample loading (5 µL), and the fraction collector was programmed to collect fractions during periods in which most substances were detected. The second round of screening for the ligand was then carried out with these subfractions. Fourthly, the active semi-prep-HPLC fraction (active fraction II) was subjected to further fractionation with 100% ACN at a flow-rate of 1 mL/min for the third round of screening. Last, the bioassay-positive fraction (active fraction III) from the third round was derivatized and elucidated using GC-MS. The GC-MS detection for the derived samples was performed on an Agilent 7890A gas chromatograph equipped with an Agilent 5975C VL MSD detector (Agilent Technologies, Santa Clara, CA, USA) as previously described [24]. Calcium imaging using calcium indicator dye was performed as previously described, with some modifications [21,25]. Cells were seeded in a glass bottom cell culture dish (φ 20 mm, Nest, Jiangsu, China) at 70% confluency and grown overnight. After being washed in Hanks’ balanced salt solution (HBSS, Solarbio, Beijing, China) three times, cells were incubated for 30 min with 2 mL HBSS, which was added with 1 µM Fura2-AM or 5 µM Fluo4-AM (Invitrogen, Carlsbad, CA, USA) under shaking conditions (60 rpm) at room temperature in darkness. Subsequently, calcium indicator dye was removed, and cells were then washed twice with HBSS and were covered with 2 mL of fresh HBSS. Imaging experiments were conducted on a Leica DMI6000B confocal microscope (Leica Microsystems, Wetzlar, Germany) equipped with a CCD camera. Sf9 cells transfected with Fura2-AM were stimulated with 340 and 380 nm, and emission was set at 510 nm. HEK293T cells transfected with Fluo4-AM were stimulated with 488 nm, and emission was set at 518 nm. A total of 200 µL test solution was added into the dish using a pipette. Data acquisition and analysis were performed using Leica LAS-AF software (version 2.6.0). Cell assays were repeated for at least three times. Sample size of cell assays to achieve adequate power was chosen on the basis of a previous report [26]. Template DNA for dsRNA synthesis was amplified using gene-specific primers (see Supplementary Table S1). The resulting purified products were then used to synthesize dsRNA using a MEGAscript T7 High Yield Transcription Kit (Promega, Madison, WI, USA). The concentration of NlGr23a dsRNA (239 bp) was quantified with a NanoDrop 2000 instrument (Thermo Fisher Scientific, Waltham, MA, USA). Finally, the quality and size of the dsRNA were further verified via electrophoresis in a 1% agarose gel. GFP dsRNA was used as a control. BPHs were collected from the culture chamber and anesthetized with CO2 for 20 s. Approximately 250 ng dsRNA was injected into each individual. After injection, BPHs were reared on fresh rice plants. Individuals exhibiting motor dysfunction and/or paralysis in 12 h were excluded from next steps. Total RNA of the whole body was extracted from five randomly selected individuals to examine the gene silencing efficiency at different time-points (24 h, 48 h and 72 h post injection) by quantitative real-time PCR (qRT-PCR). Bioassays were conducted 48 h after injection. The qRT-PCR procedure was performed using a Light Cycler 480 (Roche Diagnostics, Basel, Switzerland) with a SYBR® FAST Universal qPCR Kit (KAPA, Woburn, MA, USA), following the manufacturer’s instructions. Each reaction mixture included 1 µL of cDNA template equivalent to 1 ng of total RNA, 0.3 µL of each primer (10 µM), and 5 µL SYBR mix in a total volume of 10 µL. The experiment was repeated for three biological replicates, and three reactions for each biological replicate were performed. Gene expression levels were normalized to the expression level of BPH β-actin [27]. The specific primers used for qRT-PCR are listed in Supplementary Table S1. The amplification conditions were as follows: 95 °C for 5 min, followed by 45 cycles of 95 °C for 10 s, 60 °C for 20 s, and 72 °C for 20 s. Rice with a higher content of OA (TN1+OA) was obtained by the immersion method. Briefly, TN1 rice at the tillering stage was immersed in OA solution (20 mM) for 5 min and air-dried for 1 h. The resulting OA level was determined as follows. Firstly, the rice was washed and drained, then ground to a powder using liquid nitrogen. Secondly, the rice powder was placed in a 50 mL centrifuge tube and mixed with 0.5 mol/L HCl (fresh weight of rice:HCl = 1:5; w:v). The homogenate was placed in a boiling water bath for 15–20 min and then centrifuged at maximum speed. The resulting supernatant was diluted with ddH2O. Finally, the sample was filtered with a microporous filter membrane (0.45 µm). The OA content was determined using an UltiMate 3000 HPLC (Dionex Corporation, Sunnyvale, CA, USA) equipped with an Agilent ZRBAX SB-C18 column (4.6 mm × 150 mm, 3.5 µm). The mobile phase was 5 mM TBA in 0.5% KH2PO4 (pH 2.0) with a flow rate of 1 mL/min. The eluate from the column was continuously monitored at 220 nm after sample loading (5 µL). To evaluate BPHs food preferences across rice diets with different OA levels, we performed two-choice assays. At the beginning of each experiment, ten BPHs were placed on the middle of a sponge, which sealed an upside-down plastic cup containing one TN1 stem and one TN1+OA stem. Insects were starved for 6 h before each experiment. We scored the distribution of insects on TN1 or TN1+OA at 30 min intervals for 4 h. Values were totaled, and a position index (PI) for the 4 h period was calculated. PI = (BPHs on TN1+OA − BPHs on TN1)/(BPHs on TN1+OA + BPHs on TN1). The PI values ranged from −1 to 1, with PI = 0 indicating no position preference for either TN1 or TN1+OA, PI > 0 indicating preference for TN1+OA, and PI < 0 indicating preference for TN1. The experiment was repeated with five biological replicates. Probing marks were detected based on a previously described method [28]. Briefly, BPHs (n = 4) reared on one rice stem were confined for 4 h using a 50 mL polypropylene tube. The exposed plant parts were stained with 1% eosin Y (Aladdin Reagent, Shanghai, China), then probing marks on the plant surface were counted. The experiment was repeated with five biological replicates. OsICL over-expression lines were developed by Towin Biotechnology Co., Ltd (Wuhan, China) in accordance with a previous report [29]. The rice (O. sativa ssp. japonica) wild-type (WT) and transgenic plants used in this study were in the cv Nipponbare background. T2 transgenic lines resistant to hygromycin were chosen for the analysis of the OA content. For honeydew excretion assay, female adults were enclosed in a pre-weighed parafilm sachet (each parafilm sachet contains three female adults) that was attached to the leaf sheath of the rice plant. The honeydew of each parafilm sachet was weighed after 48 h. The weight change of the sachet was recorded as the honeydew excretion. Twelve replications were carried out. For host choice assay, two OsICLox transgenic plants and two WT seedlings placed alternately were confined in ventilated plastic cylinders and infested with 30 females. The numbers of BPHs settling on each plant were counted at 48 h post release. Twelve replications were carried out. The no-choice test apparatus for evaluation of feeding decision behavior contained a glass tube, parafilm, and artificial diet D-97 [30]. Two layers of stretched parafilm encased the diet (40 µL), either with OA (100 μM, +OA) or without (−OA), and sealed the tube. BPHs (n = 6–10) were introduced into the tube placed onto the food-containing parafilm at the beginning of the experiment in each of three replicates. We counted the insects remaining on each parafilm (Non) at 30 min intervals for 4 h. The proportion of BPHs accepting food sources was calculated as food acceptance (food acceptance = Non/Ntotal), where Ntotal represents the total number of test insects. To evaluate the position preference of BPHs between two ends with artificial diets with or without OA, we performed dual choice assays. Parafilms containing 40 µL of either +OA or −OA artificial diet were located at the two ends of a glass tube separately. Ten BPHs per replicate were introduced into the middle of the cylinder at the beginning of the experiment; four replicates were performed. In the EPG recording assay, BPH feeding behavior was recorded on a Giga-8 DC EPG amplifier (GDAAS, Guangdong, China). All experiments were carried out at 26 °C ± 1 °C and 70% ± 10% relative humidity under continuous light conditions. The feeding behavior of individual BPH on liquid diet sacs (LDS) was monitored for 3 h. Each treatment was replicated 5 times. The signals recorded were analyzed using PROBE 3.4 software (Wageningen Agricultural University, Wageningen, The Netherlands). Each feeding behavior was expressed as the duration of each waveform as a proportion of total monitoring time (%). To directly assess the feeding behavior, we performed food choice assays as described previously, with modifications [31,32]. Briefly, 10 BPHs were starved for 6 h and introduced into a glass tube. Then, two layers of parafilm wrapping 2% or 10% sucrose solution sealed the tube. The indicated concentration of OA was diluted with 10% sucrose solution and colored with blue dye (Brilliant Blue FCF, 0.2 mg/mL) and 2% sucrose solution was mixed with red dye (sulforhodamine B, 0.1 mg/mL). BPHs were allowed to make a choice between these two sucrose solutions for 4 h. After feeding, BPHs were dissected to observe their mid-gut colors under the microscope for the presence of red (NR), blue (NB), or purple (NP) dye. The preference index was calculated using the following equation: preference index = (NR − NB)/(NR + NB + NP). A preference index of −1.0 or 1.0 indicated a complete preference for either 10% sucrose with OA or 2% sucrose alone, respectively. A preference index of 0 indicated no bias between the two food alternatives. Each treatment had at least eight biological repeats. To investigate whether olfaction was involved with OA sensing in BPHs, female insects were anesthetized with CO2. Then, the second and third antennal segments, which are the primary olfactory organs of the BPH [33], were removed using a spring scissor. Finally, food choice assays with antennectomized insects were performed as before. The tissue slices were processed for immunofluorescence microscopy as previously described [34]. The labium of a one-day-old brachypterous female adult was removed, washed with 70% ethanol, and fixed in 4% paraformaldehyde at room temperature for 2 h. After fixation, the labium was embedded in Tissue-Tek O.C.T. compound (Sakura Finetek, Tokyo, Japan) and frozen at −20 °C. The embedded specimens were mounted on an object holder and cryosectioned using a Leica CM1950 cryostat (Leica Biosystems, Wetzlar, Germany) at 6 µm thickness. The cryosections were placed on adhesion glass slides (CITOGLAS, Jiangsu, China) and air dried at room temperature for 4 h. HPLC purified rabbit antibody against NlGr23a peptide CTLESRKVLSIKSKN (8 µg/mL) was used as the primary antibody, and Alexa Fluor 555®-conjugated goat anti-rabbit antibody (1:400; Invitrogen, Carlsbad, CA, USA) was used as the secondary antibody. Prior to observation, the samples were stained with Hoechst 33342 (Invitrogen, Carlsbad, CA, USA) and washed two times with PBS. The tissue slices were visualized with a Zeiss LSM 880 laser scanning confocal microscope (Carl Zeiss, Oberkochen, Germany). All statistical analyses were performed using IBM SPSS Statistics 24 (IBM, Armonk, NY, USA). The non-parametric Mann–Whitney U test was used to test for significant differences between two groups. Comparisons within multiple groups were evaluated with one-way ANOVA followed by Duncan’s test. Data were checked for normal distribution using the Shapiro–Wilk test. To identify the specific ligand of NlGr23a, we cloned the full-length NlGr23a open reading frame (GenBank accession No. MT387198; date last accessed: Feb 2023), which encodes 451 amino acid residues with seven predicted transmembrane domains (Supplementary Figure S2). Then, the NlGr23a-expressing stable Spodoptera frugiperda (Sf9) cell line was established to carry out four rounds of ligand screening from crude extracts of rice stems and leaves. The initial calcium imaging results revealed that the ethyl acetate (EAC) and methyl alcohol (MeOH) fractions caused significantly higher calcium ion (Ca2+) concentration (i.e., cellular response) as indicated by the ratio of cytoplasmic fluorescence intensities (Figure 1A). The EAC fraction was then further fractionated into three portions for the next round of screening; Sf9 cells expressing NlGr23a showed a response only to fraction 2 (time of retention, tR = 20–35 min, Figure 1B; see also Supplementary Figure S3A for HPLC chromatogram of the EAC fraction). This fraction was subjected to further fractionation with acetonitrile (ACN), and we found that only the subfraction collected in 5–10 min specifically evoked Ca2+ release (Figure 1C; see also Supplementary Figure S3B for HPLC chromatogram of the subfraction). The bioassay-positive subfraction was isolated and analyzed using gas chromatography-mass spectrometry (GC-MS). Four compounds, oxalic acid (OA), glycerol, phthalic acid, and trisiloxane, were identified (Figure 1D; see also Supplementary Figure S3C for GC-MS chromatogram of the control solution). As the peak intensity of phthalic acid was quite weak, and trisiloxane was probably an artefact of culture vessel silanization (see Supplementary Table S2 for GC-MS analysis details), OA and glycerol were selected as potential ligands. Tests using commercial OA and glycerol showed that NlGr23a-Sf9 cells responded dramatically to OA stimulation (Figure 1E). The responses increased with higher concentrations of OA solution (Figure 1F). In addition, expressing NlGr23a in the human embryonic kidney 293T (HEK293T) cells also allowed these cells to respond to OA (Figure 1G). This suggested that NlGr23a showed full functionality in HEK293T cells without an insect-specific co-receptor. Given NlGr23a interacted with bioactive molecules in the MeOH fraction, NlGr23a may also respond to other ligands in rice (Figure 1A). To characterize the response profiles of NlGr23a, we tested responsiveness of the NlGr23a-expressing HEK293T cells to additional phytochemicals. Stimulation with only OA induced a Ca2+ increase in NlGr23a-HEK293T cells, whereas other organic acids showing inhibitory effects against BPHs elicited no response [6] (Supplementary Figure S4). No response was also observed in sucrose, caffeine, and HCl (Supplementary Figure S4). Hence, NlGr23a may be specifically required for OA sensing in a structure- and dose-dependent manner. We used the immersion method to increase the OA content of the rice variety TN1, resulting in 40% higher content in the OA-treated rice plants (TN1+OA) than in the untreated controls (Figure 2A). Insects fed TN1+OA showed a positional avoidance response (position index, PI = −0.24) and made twice as many probing marks (caused by insects testing food for palatability and withdrawing) as those fed TN1, indicating that BPHs spent more time in searching for food on TN1+OA (Figure 2B,D). Our analysis indicated that BPH feeding was inhibited as the content of OA in rice plants increased. As OA was a ligand of the NlGr23a receptor, we hypothesized that the antifeedant activity of OA is dependent on this receptor in insects. The NlGr23a gene was therefore silenced using RNA interference (RNAi) in vivo. NlGr23a expression in the whole insect was decreased significantly 24 to 72 h post-injection (Supplementary Figure S5). As expected, the positional avoidance response to OA decreased in dsNlGr23a-treated BPHs (PI = 0.02) but not in dsGFP-treated insects (PI = −0.23) (Figure 2C). In addition, dsGFP-treated BPHs produced 77% more probing marks on TN1+OA rice plants than on TN1, but this difference disappeared after injection of NlGr23a dsRNA (Figure 2E). In rice, isocitrate lyase (ICL) catalyzes the production of glyoxylate, an efficient precursor for OA biosynthesis, by splitting isocitrate [29]. We used OsICL-overexpressing transgenic plants (OsICLox) to stimulate OA accumulation. The OA level was significantly increased in OsICLox (See Figure S6A for OA contents in OsICLox and WT plants). Compared with dsGFP-treated BPHs, which fed on transgenic rice plants, dsNlGr23a-treated BPHs feeding on OsICLox plants showed a 26% increase in the weight of honeydew secretion (Figure 2F). Moreover, the interference of NlGr23a expression significantly increased the number of BPHs infesting transgenic plants (Figure 2G). These results suggested that NlGr23a mediated OA perception in rice. Then, we measured OA contents in three rice varieties and found there is a significant difference in OA content between the susceptible TN1 rice and the resistant rice varieties IR36 and IR56 (Supplementary Figure S6B). A population of sensory neurons tuned to OA is probably involved in detection of the dynamic changes of soluble OA in rice plants to provide clues for the suitable habitats or feeding times. BPH adults offered artificial diets either containing (+OA) or missing OA (−OA) with no alternative (“no-choice” tests) showed significantly lower acceptance of +OA compared to −OA after 1.5 h exposure (Figure 3A). When offered a choice (“dual-choice”), +OA food was avoided in favor of −OA food, based on insect positioning relative to the food sources (PI = −0.52, Figure 3B). There were three main types of EPG waveforms that occurred during the process of BPH feeding on LDS: NP (non-penetration), PW (pathway wave), and N4 (artificial diet ingestion) (Figure 3C, top). OA, even at a low concentration, was thus capable of interfering with food sucking behavior. At 100 µM OA in the diet (+OA), the pre-feeding phases were longer than those of the control (−OA), and the N4 phase was very significantly shorter (Figure 3C, bottom). The proportion of time spent feeding fell further as OA concentrations increased, down to only 0.56% at an OA concentration of 10 mM (Figure 3C, bottom). Further, we added two different food dyes into sucrose solutions with or without OA to measure direct feeding. BPHs were given a choice between 2% (w/v) sucrose and 10% sucrose plus different concentrations of OA. Sucrose is a potent sucking stimulant for BPHs [17]. In the absence of OA, the insects showed a preference for the five-fold higher concentration of sucrose (Figure 3D). However, when sucrose was contaminated with OA, BPHs avoided the OA-laced food in a dose-dependent manner (Figure 3D). A similar experiment was conducted using an equal concentration of sucrose (10%); BPHs showed similar preference for 10% sucrose solutions added with different food dyes without OA and exhibited repulsion to the food when sucrose solutions were mixed with OA (Supplementary Figure S7A). These results indicated that the antifeedant activity of OA was dose dependent. To exclude the possibility of olfaction-mediated OA sensing, we surgically removed the primary olfactory organs of the BPH, the second and third antennal segments [33]. Antennaectomized insects had normal OA avoidance (Supplementary Figure S7B). Thus, the olfactory system is not required for OA avoidance. The antifeedant activity of OA was decreased in NlGr23a-inactive insects compared to the control insects injected with GFP dsRNA in no-choice tests. In BPHs fed −OA artificial diets, the level of food acceptance was nearly the same for both treated (dsNlGr23a) and control (dsGFP) groups (Figure 3E, top). In insects fed +OA diets, those with silenced NlGr23a spent more time on the OA-containing food than controls, significantly so between 1.5 h and 2.5 h after exposure (Figure 3E, bottom). In the dual-choice assays, BPHs injected with dsNlGr23a were less sensitive to OA (PI = −0.28) than controls (PI = −0.48, Figure 3F). When BPHs were fed on diets without OA, the NP duration was higher, and the N4 duration was commensurately lower in NlGr23a-silenced insects (Figure 3G, left), which indicated that RNAi against NlGr23a influenced their feeding behavior. At 10 mM OA, NlGr23a-silenced BPHs exhibited significantly more N4 and PW phases and fewer NP phases than controls (Figure 3G, right). Similar results were observed at 2 mM or 5 mM OA (Supplementary Figure S8). Additionally, knockdown of NlGr23a dramatically reduced OA feeding avoidance (Figure 3H). Taken together, these results indicated that NlGr23a-silenced BPHs were less sensitive to OA in both rice plants and artificial foods. The BPH has a highly modified labium adapted to a piercing and sucking method of feeding. To validate the expression of NlGr23a in the labium to sense OA, we performed immunohistochemistry analysis with the anti-NlGr23a antiserum to determine the distribution of NlGr23a-expressing cells in the oral sensory organs of female adults. The NlGr23a antibody labeled NlGr23a in the labial tip (Figure 4A–D). Negative control experiments were conducted using pre-immune, and no immunoreactive cells were observed (Figure 4E). Uniporous chemosensilla and domed multiporous chemosensilla were both present on the flattened labial tip, which is the first component of the mouthparts to touch the feeding substrate [18]. BPH individuals test the plant surface prior to probing by dabbing it with the labial tip [18]. NlGr23a found in the labial tip probably provides BPHs with information, which influences their subsequent feeding behaviors. OA is commonly present in rice plants. The species of only 11 out of 93 orders of higher plants do not store OA [35]. The fact that a secondary compound is deterrent to an insect does not imply that the insect does not eat plants containing that compound. Whether or not it does so depends on its sensitivity to the compound, the background of other deterrent and phagostimulatory information in which it is perceived, and the degree of food deprivation incurred by the insect [2]. Rice plants contain many metabolites, including both deterrents and stimulants to feeding [6,36]. In the EPG experiments of this study, 10 mM OA in artificial diets was capable of completely inhibiting feeding phases (Figure 3C). However, the OA content of the background material TN1 was measured as above 5 mM (Figure 2A). The difference in active OA concentration may be due to the different background of other chemicals. Moreover, OA concentrations in rice plants are dynamic, maybe varied in tissues, and affected by rhythm, rice variety, and developmental stages, as well as abiotic factors [37]. OA isolated from leaf sheath extracts of rice seems to mediate the phloem-feeding habit as a general inhibitor, which commonly occurs in plant tissues outside the phloem [6]. Such factors may also explain why the PI values in dual choice tests in rice tended to be higher than those from tests using the artificial diet containing OA (Figure 2B and Figure 3B). There are certainly other possible explanations. Firstly, in the case of the artificial diets, the diets either included or completely excluded OA, while in the case of rice plants, the OA content in TN1+OA was only 39% higher than in TN1 (Figure 2A). Secondly, BPHs tend to move up and down along the same plant instead of quickly moving to a different plant. Although insect bitter Grs are assumed to detect plant secondary compounds or bitter tastants, it is still very difficult to identify the ligands of bitter Grs, especially new examples. The pioneering identifications typically used electrophysiological and behavioral genetic analyses in Gr mutants of the model insect D. melanogaster. Additionally, most ligands tested to date have been directly purchased compounds, such as caffeine, strychnine, umbelliferone, saponin, chloroquine, and l-canavanine [31,38,39,40,41]. In other species, especially in agricultural insect pests, Grs are often assumed to match those of the orthologous fruit fly receptors. As non-sugar Gr genes are subject to rapid adaptation driven by the vastly different ecological niches occupied by insect species, and shared sequence identity among species is low, it is difficult to identify matching receptors. Alternatively, candidate ligands have been chosen from chemical classes that are known to elicit neurophysiological or behavioral responses. For example, a collection of known oviposition stimulants of Papilionidae xuthus were selected to test for interaction with PxutGr1, and eventually synephrine was identified as the specific ligand [42]. In order to expand potential ligand sources for non-sugar Grs, plant extracts have been tested in few cases. Extracts of whole citrus caused an increase in Ca2+-dependent luminescence in Spodoptera frugiperda 9 cells after the introduction of PxutGr1 [42], and similar results were observed for crude extracts of cotton leaves and 3 Grs in Helicoverpa armigera (HarmGr35, HarmGr50, and HarmGr195) [43]. However, no specific ligand has so far been identified based on separation of these crude extracts. In this study, we started from rice crude extracts and eventually identified OA as a ligand of NlGr23a. Insects discriminate a wide range of tastants, including sugars, bitter compounds, NaCl, and sour substances. In contrast to sweet and bitter-tasting chemicals, acids elicit varied behavioral responses depending on their structure and concentration [44]. To date, multiple classes of receptors have been reported to be involved in acid taste in Drosophila. For appetitive responses, Ir25a and Ir76b function in the legs for sensing proton concentration and the structure of carboxylic acids to mediate oviposition preference for acidic food [45]. Ir25a and sweet Grs are required for attractive taste sensation of lactic acid [14]. Multiple receptors, including Ir25a, Ir76b, Ir56d, and glycerol receptor Gr64e, contribute to the fatty acid recognition [13,46]. For aversive responses, Ir7a expressed in bitter GRNs is necessary for rejecting foods laced with high concentrations of acetic acid [44]. Moreover, Otopetrin-like A (OtopLA) forms a proton-conducting ion channel to transduct sour-taste and then mediates both the strong repulsion to highly acidic food and mild attraction to low acidity [15,16] In this study, we found that NlGr23a is required for the repulsive response to OA in the BPH.

PMC10001230

Christian Lévêque,Yves Maulet,Qili Wang,Marion Rame,Léa Rodriguez,Sumiko Mochida,Marion Sangiardi,Fahamoe Youssouf,Cécile Iborra,Michael Seagar,Nicolas Vitale,Oussama El Far

A Role for the V0 Sector of the V-ATPase in Neuroexocytosis: Exogenous V0d Blocks Complexin and SNARE Interactions with V0c

26-02-2023

V-ATPase,V0d,V0c,SNARE,complexin,neurotransmission,SCG neurons,exocytosis,chromaffin cells,surface plasmon resonance (SPR),amperometry

V-ATPase is an important factor in synaptic vesicle acidification and is implicated in synaptic transmission. Rotation in the extra-membranous V1 sector drives proton transfer through the membrane-embedded multi-subunit V0 sector of the V-ATPase. Intra-vesicular protons are then used to drive neurotransmitter uptake by synaptic vesicles. V0a and V0c, two membrane subunits of the V0 sector, have been shown to interact with SNARE proteins, and their photo-inactivation rapidly impairs synaptic transmission. V0d, a soluble subunit of the V0 sector strongly interacts with its membrane-embedded subunits and is crucial for the canonic proton transfer activity of the V-ATPase. Our investigations show that the loop 1.2 of V0c interacts with complexin, a major partner of the SNARE machinery and that V0d1 binding to V0c inhibits this interaction, as well as V0c association with SNARE complex. The injection of recombinant V0d1 in rat superior cervical ganglion neurons rapidly reduced neurotransmission. In chromaffin cells, V0d1 overexpression and V0c silencing modified in a comparable manner several parameters of unitary exocytotic events. Our data suggest that V0c subunit promotes exocytosis via interactions with complexin and SNAREs and that this activity can be antagonized by exogenous V0d.

A Role for the V0 Sector of the V-ATPase in Neuroexocytosis: Exogenous V0d Blocks Complexin and SNARE Interactions with V0c V-ATPase is an important factor in synaptic vesicle acidification and is implicated in synaptic transmission. Rotation in the extra-membranous V1 sector drives proton transfer through the membrane-embedded multi-subunit V0 sector of the V-ATPase. Intra-vesicular protons are then used to drive neurotransmitter uptake by synaptic vesicles. V0a and V0c, two membrane subunits of the V0 sector, have been shown to interact with SNARE proteins, and their photo-inactivation rapidly impairs synaptic transmission. V0d, a soluble subunit of the V0 sector strongly interacts with its membrane-embedded subunits and is crucial for the canonic proton transfer activity of the V-ATPase. Our investigations show that the loop 1.2 of V0c interacts with complexin, a major partner of the SNARE machinery and that V0d1 binding to V0c inhibits this interaction, as well as V0c association with SNARE complex. The injection of recombinant V0d1 in rat superior cervical ganglion neurons rapidly reduced neurotransmission. In chromaffin cells, V0d1 overexpression and V0c silencing modified in a comparable manner several parameters of unitary exocytotic events. Our data suggest that V0c subunit promotes exocytosis via interactions with complexin and SNAREs and that this activity can be antagonized by exogenous V0d. V-ATPase is an important player in acidifying intracellular compartments in eukaryotes. The molecular integrity of this enzyme guarantees energy-dependent proton transfer into specific compartments. Many intracellular organelles such as endosomes, trans-Golgi, secretory granules, synaptic vesicles and lysosomes are all critically dependent, for their function, on an acidic pH generated by the V-ATPase [1]. This pH sensor [2] and mechano-chemical energy transducer [3] is composed of two reversibly attached intertwined V1 and V0 domains, each having a complex subunit composition [4,5]. The structural organization of this enzyme is intricate, and the reversible association of V0 and V1 regulates the coupling of ATPase activity to proton transport and consequent acidification of membrane compartments [6,7]. Synaptic vesicle acidification is functionally associated with vesicular neurotransmitter uptake and it has been reported that the extra-membranous V1 dissociates from fully loaded vesicles synaptic vesicles [7]. The V1 domain performs the ATPase activity while proton transport takes place through the membrane-embedded V0 domain. The latter is composed of a tight assembly of several subunits (V0a, V0d, V0e, Ac45 and ATP6AP2), all surrounding a very hydrophobic rotor composed of several copies of V0c and a single V0c” subunit. V0e, Ac45 and ATP6AP2 are single-pass transmembrane proteins, V0c and c” are tetraspans with two cytosolic loops, and V0a possesses 8 transmembrane domains (TMD) and a prominent cytosolic N-terminus. Although the boxing-glove-shaped V0d [8] has no TMD [5,9], it is a stable component of the V0 domain [10,11] and is required for assembly of V-ATPase complex [12]. Yeast V0d participates in the so called “stator” [13] and has been shown to interact with the large N-terminal domain of V0a [14,15,16] following V1 dissociation, which results in an auto-inhibited form of V0, that is no longer capable of proton translocation. In humans, there are two isoforms of V0d: a ubiquitous V0d1 and a second isoform, V0d2, selectively expressed in osteoclasts, lung, kidney and epididymis [17]. Both isoforms interact with the central stalk V1 subunits V1D and V1F as well as with the V0c rotor [5,9,18]. V0d1 knockout in the mouse is embryonically lethal, highlighting its important physiological role [19]. Consequently, it has been proposed that the V0d subunit could couple the cytosolic part of the enzyme to its membrane-embedded component and therefore potentially couple ATP hydrolysis to proton transport [20]. Independently of its critical role in proton translocation, the V0 domain is implicated in exocytosis [21], neurotransmitter release [22,23] and also in numerous membrane fusion events in intracellular compartments [24,25,26,27]. However, genetic inactivation studies cannot be used to examine the role of the V0 domain in membrane fusion events since proton translocation is also needed for vesicle loading with neurotransmitters and hormones. At the molecular level, neurotransmitter release requires the formation of the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) complex composed of two proteins of the presynaptic plasma membrane, syntaxin and SNAP-25, and a synaptic vesicle membrane protein, VAMP/synaptobrevin [28]. This minimal fusion machinery is regulated by other factors such as the small soluble presynaptic protein complexin, which that binds assembled SNARE complexes and may promote oligomerization of the SNARE complexes and fusion [29,30]. Several studies have shown that V0 interacts with and potentially regulates SNARE proteins [22,24,31,32,33]. Inhibition of the V0c loop 3.4 interaction with VAMP2 was shown to significantly decrease neurotransmission and thus highlighted the importance of V0c interaction in modulating SNARE-dependent neurotransmission [33]. Chromophore-assisted light inactivation (CALI) later demonstrated that the photo-inactivation of the V0a subunit rapidly impaired neuronal synaptic transmission and catecholamine release from chromaffin cells [7]. In contrast, photo-inactivation of the V1 catalytic subunit A, very much like pharmacological inhibition of proton transport, induced a delayed inhibition of neurosecretion, strongly arguing that V0 regulates exocytosis independently from proton transport [7] without being directly involved in forming a membrane fusion pore [7,34]. More recently, we showed, using CALI experiments, that photo-inactivation of V0c in CA3 pyramidal neurons, rapidly inhibits neurotransmitter release downstream of synaptic vesicle acidification, thus corroborating the importance of the V0 domain subunits in directly modulating neurotransmission [35]. However, the exact function of the V-ATPase in regulating membrane fusion events remains a matter of debate. In this study, we discovered that V0c interacts directly with complexin as well as with a mature assembly containing complexin and the trimeric SNARE complex. This interaction is mediated by the first cytosolic loop 1.2 of V0c, which also mediates the V0c interaction with V0d1. We show that V0c bound to V0d is no longer available to interact with complexin and the SNARE complex. Similarly to V0c silencing, V0d1 overexpression or its intraneuronal injection inhibited exocytosis. Altogether, these results bring new insight for the role of V-ATPase in exocytosis and reinforce the hypothesis that the V0 sector of the V-ATPase is an important modulator of SNARE-dependent neurosecretion. Reagents: Unless otherwise stated, chemicals were principally from Sigma-Aldrich® (L’lsle-d’Abeau Chesnes, Saint-Quentin-Fallavier, France). Oligonucleotides were from Eurofins genomics (Ebersberg, Germany), Glutathione Sepharose and CM5 sensor chips for Surface Plasmon Resonance (SPR) were from Cytiva (Saint-Germain-en-Laye, France). Protease inhibitors were either from GE Healthcare (cOmplete), (Chicago, IL, USA) or Thermo Scientific (HaltTM protease inhibitor cocktail), (Waltham, MA, USA). Polyclonal anti V0d1 was from Proteintech (Rosemont, IL, USA) and anti actin was from Sigma-Aldrich. Anti-HSV rabbit polyclonal antibody was from Abcam (ab19355), (Paris, France), anti-T7 from Novagen (Pretoria, South Africa) and anti-GST polyclonal antibody was from GE-Healthcare. Anti-syntaxin 1 (10H5) [36], anti-complexin 1 (SP33) and 2 (LP27) [37,38] and anti-SNAP-25 BR05 [39] antibodies were a generous gift from M. Takahashi. Monoclonal anti-rat/mouse VAMP2 (6F9) (aa 2-SATAATVPPAAPAGEGG-18) and anti-rat/mouse SNAP-25 (6C11) (aa 196-NQRATKMLGSG-206) were produced and protein-A purified by Genecust (Boynes, France). V0c L1.2 (aa 35-KSGTGIAAMSVMRPELIMKS-54) and L3.4 (aa 117-GVRGTAQQPRLF-155) peptides were synthesized by Genecust. Fos-Choline-12 (FC12) was purchased from Anatrace (Maumee, OH, USA) and CHAPS from Euromedex (Souffelweyersheim, France). Nunc Maxisorp ELISA plates were from Thermo Scientific). MEM, horse and fetal calf serum as well as Glutamax and penicillin streptomycin were from (Life Technologies™, Illkirch-Graffenstaden, France). Coverslips were from Amilabo (Lyon, France). Falcon® Petri dishes from Thermo Sciuentific, poly-D-lysine was from Sigma-Aldrich®. Neurosensor 510 (7-(Diethylamino)-4-(4-methoxyphenyl)-2-oxo-2H-1-benzopyran-3-carboxaldehyde) was purchased from (Tocris, Noyal-Châtillon-sur-Seiche, France). Expression plasmids and cloning procedures: pET21-Complexin1-His plasmid construct was generated by cloning the rat complexin1 coding sequence in pET21 between Nde1 and Hind III restriction sites. Complexin1-GST expressing plasmid was constructed as follows: The Nco1 restriction site in pET28a was replaced by the one of Nde1, and the GST coding sequence was introduced downstream of the Not1 restriction site of this plasmid. Rat complexin 1 coding sequence was then inserted (Nde1-EcoR1) upstream of GST in this modified vector. Bacterial expression plasmids pET16-VampΔTM-Myc-His, pRSF-StxΔTM and pQE30-His-SNAP25 producing soluble SNARE proteins His6-tagged Vamp (1-96), untagged syntaxin 1 (1-265) and His6-tagged SNAP25 were described previously [40,41]. His6-T7-HSV-tagged full-length Plasmid pRSF-Duet-SNAP25-StxΔTM co-expressing untagged SNAP25 and syntaxin 1 (1-265) was obtained by successively inserting syntaxin (1-265) sequence into NdeI/XhoI sites of pRSF-Duet-1 (Novagen) and SNAP25 sequence into the NcoI/EcoRI sites of the resulting plasmid. His6-T7-HSV tagged full-length V0c-subunit construct was previously described [33]. Full-length V0c-L1.2s mutant was constructed using two overlapping PCR fragments. Both fragments overlapped in the loop 1.2 (L1.2) region and encoded the following scrambled L1.2 sequence (L1.2s): SMGITLGEIPARAMMVS. To amplify the 5′ half of the sequence, T7 was used as a forward primer and 5′-cgagaccatcatggcccttgctgggatctcgcccagagtgatgcccatactCTTGGCTGTGCCATAGGC-3′ as reverse. The 3′ half was amplified using 5′-agtatgggcatcactctgggcgagatcccag caagggccatgatggtctcgAAGTCCATCATCCCAGTGG as forward and 5′-gcgtcgacCTACTTTGTGGAGAGGATTAG-3′ as reverse. These two fragments were then mixed in the absence of any primer, and the full-length V0c L-1.2s was PCR amplified and cloned in pET28 using EcoR1 and Sal1 sites. The same procedure was used to generate full-length V0c-L3.4s mutant. Both fragments overlapped in the loop 3.4 region and encoded the following scrambled L3.4 sequence (L3.4s): GQATVQPLGRRF. To amplify the 5′ half of the sequence, T7 was used as forward primer and 5′-gaatcgccggcccagaggctggacagtggcctgaccAGC ATCTCCGACAATGCC-3′ as reverse. The 3′ half was amplified using 5′-ggtcaggccactgtccagcctctgggccggcgattcGTGGGCATGATCCTGATCC-3′ as forward and 5′-gcgtcgacCTACTTTGTGGAGAGGATTAG-3′ as reverse. These two fragments were then mixed in the absence of any primer, and the full-length V0c-L-3.4s was PCR amplified and cloned in pET28 using EcoR1 and Sal1 sites. Rat V0d1 was amplified by PCR using a commercial Y2H adult rat brain plasmid cDNA library (Origine). EcoR1 and Sal1 sites were used for insertion in plasmid constructs (Forward: GCGAATTCtcgttcttcccggagcttT, reverse: GCGTCGACctagaagatggggatat agttg). Construction of GST-V0d1 expression plasmid was obtained by insertion of amplified V0d1 into EcoR1/Sal1 digested pGEX-5x-1. Construction of 6His-HA-V0d1 expression plasmid was performed as follows. HA tag sequence (YPYDVPDYA) was introduced by linker insertion at the 5′ side of pET28a (Novagen) MCS to generate pET28-HA-Nter. Forward (5′-p-GATCCTATCCTTATGATGTTCCTGATTATGCAG) and reverse primers (5′-p-AATTCTG CATAATCAGGAACATCATAAGGATAG) were annealed and ligated to BamH1/EcoR1 digested pET28a. EcoR1/Sal1 sites were used to insert amplified full-length V0d1. GST-HSV expression plasmid was constructed using a phosphorylated linker encoding the HSV tag (QPELAPEDPED) sequence and ligated to BamH1/EcoR1 digested pGEX-4T1 MCS. Forward (5′-p-GATCCCAGCCTGAACTCGCTCCAGAAGACCCGGAAGATG) and reverse (5′-p-AATTCATCTTCCGGGTCTTCTGGAGCGAGTTCAGGCTGG) primers. Bicistronic pIRES-2-EGFP plasmid co-expressing myc-V0d1 was constructed using Xho1-Sal1 restriction sites. In the myc-V0d1-expressing pIRES-2-turbo-RFP, the coding sequence of EGFP was replaced by the one of turboRFP amplified by PCR from pINDUCER11 [42]. Recombinant protein expression: Complexin1-His, complexin1-GST, His-Vamp (1-96) and t-SNAREs (syntaxin 1 (1-265) + His-SNAP25) were expressed in BL21 and purified as previously described [33,40]. Soluble trimeric SNARE complexes were produced by co-transfecting BL21 with pET16-VampΔTM-myc-His and pRSF-Duet-SNAP25-StxΔTM. Bacteria were grown in TB medium supplemented with both ampicillin and kanamycin. Expression was induced for 4 h at 37 °C by 0.5 mM isopropyl-thio-βD-galactoside. BL21 expressing V0d1 constructs were cultured in TB, and protein expression was induced with 0.3 mM IPTG for 17 h at 18 °C. pET-28 containing 6His-V0c construct was transfected in the OverExpressTM C43(DE3) bacterial strain (Avidis, France) and expression induced as for V0d1. All bacterial pellets were stored at −20 °C before protein purification. Protein purification: Protease inhibitors were present in all homogenisation steps. Recombinant soluble trimeric SNARE complexes were obtained from 2l of induced culture. Purification was performed at room temperature when not otherwise stated. Pelleted bacteria (7 g) were resuspended in 25 mL of buffer H (sodium phosphate 50 mM, 0.5 M NaCl, 20 mM imidazole, pH 8) and lysed in a French press. The homogenate was centrifuged (200,000× g, 30 min, 4 °C), and the supernatant incubated for 1 h at 4 °C with 1.5 mL packed Ni-NTA Agarose beads (Qiagen). Beads were washed with buffer H adjusted to 50 mM imidazole. Purified proteins were eluted in 0.5 mL fractions by increasing imidazole concentration to 250 mM. Peak fractions were dialyzed against 20 mM Tris-HCl, 1.0 mM EDTA pH7.4 and loaded on a 1 mL HiTrap-Q column equilibrated in the same buffer on an AKTA-purifier system. Proteins were eluted with a 10–500 mM NaCl gradient in the dialysis buffer. The main peak fractions eluting at 375 mM NaCl were pooled. Proteins were quantified by Bradford assay and analysed on SDS-PAGE gel and Western blot. Aliquots were stored at −20 °C. Bacterial pellets from 6His-V0c expression, were resuspended in wash buffer (50 mM Tris-HCl, pH 8.0, 1 mM EDTA) supplemented with 0.2 mg/mL lysozyme and subjected to French press. Homogenates were centrifuged 5 min at 2500× g and the membrane fraction was isolated from supernatant by centrifuging at 200,000× g for 37 min. Membrane were washed once by resuspension of the pellet in wash buffer and centrifugation. Membranes (5 mg/mL protein) were solubilized in 50 mM Tris-HCl, pH 8.0, 10 mM β-mercapto-ethanol, 2% FosCholine-12 at 4 °C for 1 h. The buffer was then adjusted to 0.5 M NaCl, 20 mM imidazole, and the recombinant V0c was purified over Ni-NTA beads (QIAGEN). Bacterial pellets expressing V0d1 constructs were resuspended in 50 mM Tris-HCl pH 8.0, 150 mM NaCl and subjected to French Press. Insoluble material was eliminated by centrifugation 37 min at 200,000× g. GST-V0d1 was purified by batch incubation of supernatant (1 h 4 °C) with 1 mL Glutathione-Sepharose. The suspension was loaded on a disposable 10 mL column. Washing (20 mM HEPES pH7.4, 150 mM NaCl, 0.1% Triton X-100 then 20 mM HEPES pH7.4, 150 mM NaCl) and elution (20 mM HEPES pH 7.4, 150 mM NaCl, 10 mM reduced glutathione) were performed manually. For purification of 6His-HA-V0d1, the high-speed supernatant was adjusted to 0.5 M NaCl and 20 mM imidazole and batch incubated with 2 mL Ni-NTA-Sepharose beads (1 hr, room temperature). The suspension was loaded on a disposable column, washed with 20 mM HEPES, 150 mM NaCl, in the presence of 20 mM and 40 mM imidazole successively and eluted by fractions in the presence of imidazole 0.5 M. For both GST- and 6His-V0d1 preparations, protein-containing fractions were pooled, desalted against 10 mM HEPES pH 7.4 on Zeba-spin columns (Thermo scientific), aliquoted, fast frozen in liquid N2 and stored in aliquots at −80 °C. Protein concentrations were determined by parallel Coomassie staining and A280 absorbance. The very high hydrophobicity of V0c renders less accurate its protein assay by classical methods. Therefore, all 6His-HSV-V0c protein preparations were submitted to comparative relative quantification on Western blot using anti HSV antibodies and GST-HSV fusion protein as a standard. Preparation of rat brain extracts: Rat brain fraction enriched in plasma membrane (LP1) was purified as described [43]. Briefly, proteins were solubilized at 1.5 mg/mL in 25 mM Tris pH 7.4, 150 mM NaCl containing 1.5% CHAPS, 5 mM EDTA and proteases inhibitors before centrifugation 1 h at 14,000× g and filtration through 0.22 µm filters. V-ATPase mediated proton uptake: Proton uptake experiments were performed as previously described [33]. Briefly, rat brains were homogenized in 0.32 M sucrose, 10 mM HEPES, pH 7.4, and 0.2 mM EGTA in the presence of protease inhibitor cocktail, and the post-nuclear supernatant was layered onto a 0.8 M sucrose cushion and centrifuged 20 min at 257,000× g. Synaptosomal pellet was resuspended in hypotonic buffer (10 mM Tris-HCl pH 8.5, 1 mM PMSF) and centrifuged 20 min at 40,000× g. The synaptic vesicle-enriched supernatant was adjusted to 10 mM Tris-HCl pH 8.5, 60 mM sucrose, 140 mM KCl, 2 mM MgCl2, and 50 μM EGTA (assay buffer). Synaptic vesicles were preincubated with 2 μM acridine orange (AO) before triggering proton uptake by the addition of 500 μM Mg-ATP. ATP-dependent proton transport was monitored, using a Biotek Sirius HT injector plate reader, by the quenching of AO fluorescence at 525 nm with data acquisition every 15 s. ELISA: HSV-V0c was probed with anti-HSV antibody (1/4000), GST-V0d1 and GST with goat anti-GST polyclonal antibody (1/4000). VAMP2, syntaxin and complexin were, respectively, probed with monoclonal antibodies 6F9 [33], 10H5 and SP33 at 1.0–1.2 µg/mL. Proteins were immobilized overnight at 4 °C on Maxisorb™ (Nunc) 96 wells microplates at 1 µg/well in 100 µL NaHCO3 0.1 M pH 8.9. After a 1 h blocking step at 37 °C in 200 µL binding buffer (BB: 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 3% BSA w/v), incubations with interacting proteins (100 µL) at the indicated concentrations were performed overnight at 4 °C in BB supplemented with 0.1% Triton X-100. For sequential binding, incubation with the first interactant was performed for 6 h at 4 °C, and then the solution was replaced by a fresh one containing both the first and the second interactant (V0c or V0d), and the plates were further incubated overnight at 4 °C. After interactions, plates were washed three times with 200 µL/well of washing buffer (WB: 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% BSA w/v, 0.1%Triton X-100) and incubated 1hr at 4 °C with 100 µL of specific primary antibodies in BB supplemented with 0.1% Triton X-100. Wells were washed (3 × 200 µL/well WB) and incubated for 1 hr at 4 °C with 100 µL horse radish peroxidase-coupled anti-IgG (either donkey anti-rabbit, goat anti-Mouse or rabbit anti-goat, depending on the primary antibody). After three washes, plates were revealed with TMB solution standard (Uptima, Interchim, Montluçon, France) according to supplier specifications. Colour development was stopped with 100 µL of 1.0 M H2SO4 and absorbance read at 450 nm. Measurements were systematically performed in triplicates. Unless otherwise stated, blanks were always subtracted in the represented histograms. GST was used as a control binding partner when GST-V0d1 binding was measured. Signal in GST wells was either subtracted from GST-V0d1 signal or shown in parallel. Surface plasmon resonance analysis: SPR experiments were performed at 25 °C using Biacore 3000 (GE healthcare) or Biacore T200 (Cytiva). The running buffer was HBS (10 mM HEPES pH 7.4, 150 mM NaCl) supplemented or not with CHAPS 0.2%. Non-specific binding on control flow cells was automatically subtracted from experimental measurements to yield the specific signal. Proteins were coupled on sensor chips CM5 (Cytiva) or CMDP from Xantec (2D carboxylmethyldextran). About 25 fmoles of GST, Complexin1-GST or GST-V0d were covalently immobilized using amine coupling chemistry at pH 5. Analytes were injected at 10 µL/min, and blank run injections of running buffer were performed in the same condition permitting to yield double-subtracted sensorgrams using Biaevaluation 4.2 software (GE Healthcare). SPR detection of native SNAREs/complexin on immobilized recombinant V0c: Solubilized proteins from LP1 were diluted 5-fold in running buffer (25 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.2% CHAPS) and injected over 2 flow cells (CMDP chip) in series including a control flow cell functionalized with irrelevant mouse antibodies and another one with anti HSV antibodies that previously captured HSV-V0c. Superior cervical ganglion neurons and synaptic transmission recordings: 6–8 week cultures, EPSP recording and injection of recombinant His-V0d (6.5 µM) or BSA (20 µM) were performed as described previously [44]. EPSPs were recorded at 0.1 Hz. The peak amplitudes were normalized to the values before injection. The averaged and smoothed values (Origin 7.5) obtained from an eight-point moving average algorithm were plotted against recording time with t = 0 corresponding to the start of 3 min presynaptic injection of His -tagged V0d. Data are mean ± SEM and statistical significance was evaluated using a two-tailed Mann–Whitney U test. Chromaffin cell culture and catecholamine release recordings: Freshly dissected primary bovine chromaffin cells were cultured in DMEM in the presence of 10% fetal calf serum, 10 µM cytosine arabinoside, 10 µM fluorodeoxyuridine and antibiotics as described previously [45]. Plasmids and siRNAs were introduced into chromaffin cells (5 × 106 cells) by Amaxa Nucleofactor systems (Lonza) according to manufacturer’s instructions and as described previously [46]. At 48 h/96 h after transfection, catecholamine secretion was evoked by applying K+ (100 mM) in Locke’s solution without ascorbic acid for 10 s to single cells by mean of a glass micropipette positioned at a distance of 30–50 μm from the cell. Electrochemical measurements of catecholamine secretion were performed using 5 µm diameter carbon-fibre electrodes (ALA Scientific, Farmingdale, NY, USA) held at a potential of +650 mV compared with the reference electrode (Ag/AgCl) and approached closely to the transfected cells essentially as described previously [47]. Amperometric recordings were performed with an AMU130 (Radiometer Analytical, Villeurbanne, France) amplifier, sampled at 5 kHz, and digitally low-pass filtered at 1 kHz. Analysis of amperometric recordings was performed as described previously [48], allowing automatic spike detection and extraction of spike parameters. The number of amperometric spikes was counted as the total number of spikes with an amplitude >5 pA. Data were analysed using SigmaPlot 13 software. In the figure legends, n represents the number of cells analysed. Statistical significance has been assessed using t-test as data fulfilled requirements for parametric tests. Catecholamine content measurement: Chromaffin cells were incubated for 30 min with 0.5 µM of Neurosensor 510 (NS510) at 37 °C and then washed to remove excess sensor before fixation. Labelled cells were visualized using a Leica SP5II confocal microscope and quantification of NS510 signal was performed in individual cells. We have previously shown that the cytosolic loop (L3,4) linking transmembrane regions 3 and 4 of V0c binds VAMP2 and that this interaction modulates neurotransmission [33]. We therefore screened for other potential V0c binding partners. Recombinant V0c was immobilized on the chip of an SPR apparatus and a detergent extract of a lysed rat brain synaptosomal fraction was injected into the flow cell (Figure S1A). Bound proteins were then detected by injecting monoclonal antibodies against proteins involved in exocytosis (Figure S1B). In addition to the previously reported SNARE proteins (VAMP2, syntaxin 1 and SNAP25 [33,49]), complexin1 and 2 were specifically identified in association with V0c (Figure S1B). As complexin could be indirectly associated with V0c due to its association with the SNARE complex [49], we used purified recombinant proteins to investigate whether a direct interaction occurred. Further investigations of complexin/V0c interactions were performed using complexin 1, hereafter referred to as complexin. When complexin was immobilized on ELISA plates, a strong binding of V0c (20 nM) was detected (Figure 1A), while up to 1 μM of V0c did not generate significant binding to BSA-blocked ELISA wells in the absence of complexin (Figure 1A and Figure S2). V0c binding to complexin was not inhibited in the presence of VAMP2 suggesting that VAMP2 and complexin interact with V0c through distinct domains (Figure 1A). In order to identify the molecular determinants on V0c that are important for complexin binding, we probed the implication of both cytosolic V0c loops that link, respectively, V0c TMDs 1 and 2, and 3 and 4. For this purpose, we perturbed the integrity of V0c loops 1.2 (L1.2s) or 3.4 (L3.4s) in the full-length protein by scrambling their linear sequences in the full-length protein, and we monitored interactions using an SPR-based method. V0c binding to immobilized complexin dissociates very slowly, indicating a strong interaction (Figure 1B). Binding of V0c containing a scrambled loop 3.4 to immobilized complexin was nearly identical to that of wild-type V0c (Figure 1B). However, V0c with scrambled loop 1.2 failed to bind complexin (Figure 1B). The implication of V0c loop1.2 in complexin binding was confirmed by the experiment showing that a peptide corresponding to loop 1.2 totally inhibited V0c binding to immobilized complexin, while loop 3.4 peptide had no effect (Figure 1C). In accordance with the potentially late implication of V0c in exocytosis [33,35], we investigated whether V0c interacts with the SNARE complex in the presence of complexin. The three SNARE proteins (devoid of any TM domains) were co-expressed in bacteria and SDS-resistant purified SNARE complexes were obtained (Figure S3). SNARE complexes were immobilized on ELISA plates, and binding of V0c was assayed with or without preincubation with complexin. As shown in Figure 1D, V0c interacts with the SNARE complex in the presence of complexin. In summary, V0c interacts with complexin through the cytosolic loop 1.2 and the presence of complexin does not inhibit V0c binding to VAMP2 or to the SNARE complex. Although V0d1 is a soluble protein, it remains stably associated with the V0 sector after dissociation of the V1 sector [14,15,50]. In order to assess the implication of V0d1 in the interaction of V0c with other molecular partners, we started by characterizing recombinant V0d1 binding to a series of immobilized proteins. As shown in Figure 2A, ELISA experiments revealed that GST−V0d1 bound to immobilized V0c, but did not interact with VAMP2, the dimeric syntaxin/SNAP−25 t−SNARE complex, the trimeric SNARE complex, complexin or complexin associated with the SNARE complex. We then used SPR to identify the domain of V0c that interacts with V0d. As shown in Figure 2B, purified recombinant V0c bound stably to immobilized V0d1 and showed no apparent dissociation, in accordance with previous data showing that V0d stably co-partitions with V0 in detergent-resistant membranes [11]. The specificity of this interaction was corroborated by the absence of V0c binding when it had been pre-incubated with an excess of V0d1 (Figure 2B). Binding of V0c that has a scrambled linear sequence of loop 3.4 to immobilized V0d1 was nearly undistinguishable from wild-type V0c; however, V0c with a scrambled linear sequence of loop 1.2 failed to bind V0d1 (Figure 2B). The involvement of V0c loop 1.2 in V0d binding was supported by the observation that the addition of free L1.2 peptide totally inhibited V0c binding to immobilized V0d1, while free L3.4 peptide addition had no effect (Figure S4). The involvement of V0c loop 1.2 in interaction with both V0d1 and complexin was confirmed by the observation that co-injecting a molar excess of complexin inhibited V0c binding to immobilized V0d1 (Figure 2C). We then assessed if V0d could modulate the V0c interaction with VAMP2 and the SNARE/complexin complex, using ELISA competition experiments. As shown in Figure 2D, the presence of GST-V0d1 prevents V0c from binding to VAMP2 (inhibited by 71.3% ± 3.2) and to in situ reconstituted SNARE complex (inhibited by 100%). The blocking effect of V0d is also observed in the presence of complexin for V0c binding to VAMP2 (inhibited by 90.7% ± 1) and to the SNARE complex (inhibited by 96.1% ± 6.2). This interference probably results from V0d interaction with V0c loop 1.2 inducing a steric hindrance of VAMP2 access to loop 3.4. The importance of V0 subunits in the proton pump activity of the V-ATPase, and therefore in cell viability, compromises interpretation of data from gene inactivation and mRNA silencing to address the direct implication of these subunits in exocytosis. A multimer of V0c subunits constitutes the V-ATPase rotor, associated with a single V0d subunit. Although it does not have a membrane anchor, V0d behaves like an extrinsic membrane protein, and V0d1 is not detected in cytosolic fractions (Figure S5). Based on our biochemical results indicating that V0d inhibits V0c interactions with VAMP2, SNARE complex or complexin, we reasoned that introduction of soluble V0d into the cell would stably occupy free binding sites on the multimeric V0c rotor, impeding association with exocytotic proteins. Superior cervical ganglion neurons (SCG) in culture have been widely used to explore the dynamics of presynaptic protein–protein interactions and understand their implication in neurotransmitter release [51]. As VAMP2 binding to V0c is involved in modulation of neurotransmission [22] and in order to test the hypothesis that V0d1 may regulate V0c interactions with SNARE proteins, we injected purified recombinant His-tagged V0d1 protein (6.5 µM) into presynaptic SCG neurons and monitored neurotransmitter release through postsynaptic recordings. As shown in Figure 3A (left), neurotransmission started to decrease only a few minutes after injection and reached a plateau shortly after 20 min, with a mean inhibition ratio of 28% ± 3.9 p < 0.01 at 9 min after injection and a maximal inhibition of 35% after 35 min. Of note, the control BSA injections did not lead to any significant decrease in neurotransmission (Figure 3A, right). As any perturbation of the V-ATPase function could drastically impact enzymatic activity and neurotransmitter uptake into synaptic vesicles, we verified whether the presence of excess V0d could impact proton pumping and, consequently, if neurotransmission decrease does not result from a defect in acidification of the vesicle lumen, which may lead to reduced neurotransmitter loading. We therefore monitored synaptic vesicle V-ATPase activity in purified synaptic vesicles in the presence or absence of an excess of V0d1, by monitoring the intra-vesicular accumulation of fluorescent protonated Acridine Orange. As shown in Figure 3b, the addition of up to 9 µM of V0d did not inhibit vesicular acidification, in contrast to application of the proton pump inhibitor bafilomycin A1, which resulted in total inhibition of proton pump activity, and completely prevented Acridine Orange (AO) accumulation in synaptic vesicles (Figure 3B). This clearly demonstrates that excess V0d does not perturb vesicular acidification. Hence, a presynaptic V0d overload, which prevents V0c from binding to complexin and the SNARE complex, rapidly diminished acetylcholine release by a mechanism independent of the proton pump activity of the V-ATPase. To investigate the effects of V0d1 on catecholamine exocytosis, we overexpressed myc-tagged V0d1 from a bicistronic mRNA co-expressing EGFP in chromaffin cells. Using carbon-fibre amperometry [52,53], we monitored catecholamine release triggered by KCl depolarization and analysed several exocytosis parameters (Figure 4). Catecholamine release from EGFP-expressing cells was not different in any of the measured parameters compared to un-transfected cells (data not shown). Among all the parameters analysed, four were significantly decreased upon V0d overexpression: spike number (57 ± 4% of control), the total amount of currents during the spikes Imax (# 74 ± 2% of control), the quantal size Q (70 ± 4% of control) as well as the pre-spike foot (PSF) charge Q (# 73 ± 6% of control). The kinetic parameters of individual exocytosis events T Half and the time to peak were not significantly affected. The percentage of spikes with a detectable foot (about 25% in the two conditions) was not modified, and no significant variation in the pre-spike or foot parameters were observed. Altogether, these results showed that V0d overexpression dramatically reduced the number of exocytotic events and reduced the amount of catecholamine released in the remaining events. Our biochemical analysis suggests that the effect of V0d1 is exerted through a reduction in the number of V0c interaction sites available for complexin/SNARE complexes. In order to corroborate this interpretation, we monitored catecholamine release from chromaffin cells and analysed the parameters of exocytosis after siRNA silencing of V0c (Figure 5A,B). V0c silencing leading to a reduction in V0c levels by 40–50% without affecting V0a1 and V1A expression levels (data not shown) resulted in an approximately 40% decrease in the quantal content (Figure 5), presumably due to a perturbation of catecholamine loading into secretory granules. Interestingly, very similar to the effects of V0d1 overexpression, V0c silencing, using two distinct siRNA, significantly decreased spike number/cell (# 50% of control) and Imax (# 70% of control) (Figure 5). These results suggest that V0c availability is implicated in determining the number of fusion events. In addition, and in contrast to V0d1 overexpression, V0c silencing led to a significant decrease in several additional PSF parameters: the number of PSF per cell (# 40% of control), the amount of current per PSF (# 80% of control) and the quantal size Q of the foot (# 60% of control). In order to ascertain the specificity of the observed V0c siRNAs effects, we performed rescue experiments and we reintroduced wild-type V0c insensitive to the siRNA. As shown in Figure 5, rescue experiments restore wild-type recording parameters demonstrating that the observed changes using the siRNA are due to a specific decrease in V0c. To assess more specifically the effect of V0d1 overexpression and V0c silencing on catecholamine content in secretory granules, we used the selective noradrenalin and dopamine fluorescent indicator NS510, which specifically accumulates in chromaffin secretory granules [54]. As illustrated in Figure S6, overexpression of V0d1 and silencing of V0c reduced by 16% and 30%, respectively, the mean intensity of the NS510 staining. These observations suggest that long term overexpression of V0d1, as well as a reduction in the level of V0c expression, moderately reduced catecholamine content in secretory granules that might result from a reduction in secretory granule acidification. It is, however, unlikely that this moderate effect on catecholamine loading could explain the potent reduction in the number of individual exocytotic events, arguing for the importance of the V0d/V0c interaction in the modulation of exocytosis. It has been reported that the V1 domain of the V-ATPase is absent from fully loaded as well as docked synaptic vesicles [6,7] and that conformational rearrangements in V0a and V0d subunits take place upon V1 dissociation leading to a so-called auto-inhibited V0 domain [5,14,15,16]. Several reports already showed that independently of its participation in the proton pumping activity of the V-ATPase, the V0 domain is implicated in exocytosis [21,22,23] as well as in membrane fusion of intracellular compartments [24,25,26,27]. In addition, the direct interactions of the V0 domain subunits with the minimal membrane fusion machinery, i.e., the SNARE complex or individual SNARE proteins, have been reported with functional consequences [22,31,33,55]. Complexin is a major regulatory partner of the SNARE complex [49]. In vertebrates, among the four isoforms, complexins 1 and 2 are mainly neuronal, and complexin 3 and 4 are retina specific [56]. Despite its small size, complexin has a very intricate function with distinct adjacent and functionally different domains. Each of these domains can separately exert opposing properties as both an inhibitor or a facilitator of synaptic vesicle fusion modulating evoked and spontaneous release [29,49,57,58,59]. It interacts through its central α-helix domain with the SNARE complex [29,60,61] and is implicated in organizing high-order fusogenic SNARE/synaptotagmin complexes [29,62]. Structural analysis has shown that complexin is associated with the SNARE complex in an anti-parallel orientation, leaving the C-terminal domain oriented towards the N-terminal domains of syntaxin and VAMP in the assembled SNARE complex [60,63]. Additionally, the interaction of the C-terminal domains of complexins 1 and 2 with lipids [64] and with the highly curved membranes of synaptic vesicles was suggested to be important for its inhibitory function [65,66,67]. In this study, we report a direct interaction of the V0c subunit of the V-ATPase with complexin as well as the tetrameric complexin/SNARE complex. We show that the interaction of complexin with V0c is mediated by the loop 1.2. As the loop 3.4 of V0c binds VAMP2 and V0c binding to complexin is not competed by VAMP2, one may assume that V0c can interact concomitantly with two essential components of the exocytotic machinery. Moreover, we demonstrate that the loop 1.2 of V0c mediates an extremely stable interaction with V0d1 in line with the existence of contact sites between V0d and V0c rotor in yeast [15] and mammalian [18] V-ATPase. Our biochemical experiments indicate that an excess of V0d1 hinders V0c interaction with VAMP2, complexin and the complexin/SNARE complex. Since V0c interacts with complexin and VAMP2 via two distinct loops (loop 1.2 and loop 3.4, respectively) and complexin does not inhibit V0c/VAMP2 interaction, the inhibitory effect of V0d on V0c binding to VAMP2 may result from a steric hindrance due to the higher molecular weight of V0d compared to complexin. In order to gain an insight into the physiological implication of V0c loop 1.2 in exocytosis, we reasoned that introducing soluble V0d would impede V0c loop 1.2 interaction with complexin and the SNARE complex. In a first approach, we monitored excitatory postsynaptic potentials (EPSPs) in connected superior cervical ganglion (SCG) neurons after presynaptic injection of bacterially expressed and purified V0d1. SCG neurons are a well-established culture system suited for the study of neurotransmitter release mechanisms. The very short axonal connections provide a setup where somatically injected effectors rapidly reach nerve terminals. Upon V0d1 microinjection in SCG neurons, we observed rapid inhibition of neurotransmission similar to that observed upon injection of a peptide corresponding to V0c L3.4 loop [33] and other agents that affect late steps in exocytosis [68]. As V0d is a functional component of the V-ATPase complex, we addressed the possibility that introducing free V0d could modify intramolecular interactions between V-ATPase subunits and consequently inhibit proton pumping, which would then compromise neurotransmitter loading into synaptic vesicles. In this case, vesicles devoid of neurotransmitter might still fuse without generating an EPSP. To explore this possibility, we studied the effect of an excess of V0d1 on synaptic vesicle acidification using an Acridine Orange-based assay. Although the V-ATPase inhibitor bafilomycin produced an immediate and complete block of proton accumulation in synaptic vesicles, V0d1 concentrations higher than those injected into SCG neurons failed to decrease acidification. These data strongly suggest that an excess of cytosolic V0d1 is unlikely to perturb synaptic vesicle loading with neurotransmitters. Similarly to inhibition of acetylcholine release in SCG neurons, catecholamine secretion was altered after V0d1 overexpression in chromaffin cells, a well-defined cellular model to study neuroendocrine secretion [69]. In addition to a significant decrease in the number of spikes (Figure 4A,C) and maximal current reduction (Figure 4D), the quantal content of granules was affected upon V0d1 overexpression (Figure 4D) without affecting time to peak kinetics. Apart from the pre-spike foot (PSF) charge which was decreased, V0d1 overexpression did not affect any other PSF parameter (Figure 4E). It is, however, of note that under these conditions of V0d1 overexpression, a reduction in catecholamine content of approximatively 16% was observed using the fluorescent indicator NS510 (Figure S6). The observed decrease in the quantal content by roughly 30% may thus partly be due to the long-term genetic nature of V0d1 overexpression. It is possible that co-translational abundance of V0d1 with endogenous native levels of other V-ATPase subunits may perturb V-ATPase assembly and therefore granule acidification and catecholamine loading. On the other hand, the modest effect observed on catecholamine loading under these conditions is unlikely to explain the robust differences in spike number, Imax and charge observed when V0d1 is overexpressed, arguing for a role of the V0d1–V0c interaction in the regulation of exocytosis. In an earlier report, we have shown that inhibiting V0c interaction with VAMP2 significantly inhibits neurotransmission [33]. Previous data showed that the native multimeric V0c rotor binds to a single V0d1 subunit [1,5,18]. Our electrophysiological recordings in SCG neurons as well as the biochemical results suggest that inhibition of exocytosis upon V0d1 injection is likely due to binding of the exogenous V0d1 to V0c subunits in the V0 rotor that are not in contact with the single intrinsic V0d1 subunit. Consequently, this could prevent V0c interactions with complexin and SNARE proteins. In order to corroborate this hypothesis in chromaffin cells and compare the effect of V0d1 overexpression and V0c availability on catecholamine secretion, we performed siRNA V0c downregulation (Figure 5A,B) and monitored catecholamine secretion parameters. As expected, the granule quantal content was significantly decreased, but catecholamine secretion, although diminished, still took place (Figure 5C). Both V0c siRNAs triggered a decrease in V0c expression and led to an inhibition of the number of individual release events as well as maximal current that reached very similar levels of inhibition that were observed upon V0d1 overexpression. In contrast to the effects of V0d1 overexpression, a reduced V0c expression level affected the number of PSF per cell, the spike shape (Imax and half width), the footImax highlighting the importance of V0c availability in modulating release. Previous structural studies of the yeast V-ATPase showed that, in the auto-inhibited V0 domain, V0d interacts with the N-terminal domain of V0a upon V1 dissociation and engages interactions with the V0c rotamer subunits [5,14,15,16]. In bovine V-ATPase, V0d shows stable interactions with 4 c-subunits and 1 c” in the assembled V-ATPase [18]. However, how the exogenous V0d incorporates into auto-inhibited V0 is still to be determined at the structural level. All these data reinforce our understanding of the implication of the V-ATPase V0 subunits in modulating SNARE-dependent neurotransmission and corroborate the importance of V0c in SNARE-dependent neurosecretion. In a fully assembled V-ATPase, a V0c multimer accommodates only one V0d subunit [1,5]. Moreover, we show that the V0d subunit is absent from cytosol. We speculate that, upon V1 dissociation, V0c subunits that are not linked to V0d1 would be free to bind VAMP2 as well as the cytosolic complexin [70]. The V0c subunits of the V0 sector associated with several VAMP2 molecules would interact with membrane t-SNAREs and engage the formation of a SNARE rosette around the V0c rotor [71]. Upon overexpression of V0d1, V0d1-free V0c subunits in a V0 rotor may become sequestered and hindered from binding to VAMP2 [33] and complexin, resulting in an inhibition of formation of the SNARE rosette and thereby neurotransmitter release. Using molecular dynamics [72], it has recently been suggested that confinement of a rosette of SNARE complexes [71,73] is essential for rapid fusion pore formation, and fusion pore expansion is accompanied by a release from this confinement. A potential consequence of the presence of an excess of V0d1 would be that V0c would fail to form the SNARE complex rosette (Figure 6), leading to a decrease in the frequency of membrane fusion events. Whether the single endogenous V0d1 subunit per V-ATPase is implicated in modulating exocytosis has not yet been addressed, and future investigations are needed to clarify this point.

PMC10001242

Shohei Udagawa,Akira Ooki,Eiji Shinozaki,Koshiro Fukuda,Kensei Yamaguchi,Hiroki Osumi

Circulating Tumor DNA: The Dawn of a New Era in the Optimization of Chemotherapeutic Strategies for Metastatic Colo-Rectal Cancer Focusing on RAS Mutation

25-02-2023

circulating tumor DNA,liquid biopsy,colorectal cancer,RAS mutation

Simple Summary Molecularly targeted therapies have greatly contributed to the development of colorectal cancer treatments. Genomic profiling to identify gene alterations is a rapidly developing field. Liquid biopsies have recently drawn considerable attention because they offer several advantages over tissue biopsies and can be used to detect several soluble factors, including circulating tumor DNA. In this review, we discuss the usefulness of analyzing circulating tumor DNA to design more personalized and effective cancer treatments and discuss several ongoing clinical trials that aim to evaluate its utility. Genomic profiling using circulating tumor DNA could be integrated into clinical strategies for cancer treatment in the near future. Abstract Genotyping of tumor tissues to assess RAS and BRAF V600E mutations enables us to select optimal molecularly targeted therapies when considering treatment strategies for patients with metastatic colorectal cancer. Tissue-based genetic testing is limited by the difficulty of performing repeated tests, due to the invasive nature of tissue biopsy, and by tumor heterogeneity, which can limit the usefulness of the information it yields. Liquid biopsy, represented by circulating tumor DNA (ctDNA), has attracted attention as a novel method for detecting genetic alterations. Liquid biopsies are more convenient and much less invasive than tissue biopsies and are useful for obtaining comprehensive genomic information on primary and metastatic tumors. Assessing ctDNA can help track genomic evolution and the status of alterations in genes such as RAS, which are sometimes altered following chemotherapy. In this review, we discuss the potential clinical applications of ctDNA, summarize clinical trials focusing on RAS, and present the future prospects of ctDNA analysis that could change daily clinical practice.

Circulating Tumor DNA: The Dawn of a New Era in the Optimization of Chemotherapeutic Strategies for Metastatic Colo-Rectal Cancer Focusing on RAS Mutation Molecularly targeted therapies have greatly contributed to the development of colorectal cancer treatments. Genomic profiling to identify gene alterations is a rapidly developing field. Liquid biopsies have recently drawn considerable attention because they offer several advantages over tissue biopsies and can be used to detect several soluble factors, including circulating tumor DNA. In this review, we discuss the usefulness of analyzing circulating tumor DNA to design more personalized and effective cancer treatments and discuss several ongoing clinical trials that aim to evaluate its utility. Genomic profiling using circulating tumor DNA could be integrated into clinical strategies for cancer treatment in the near future. Genotyping of tumor tissues to assess RAS and BRAF V600E mutations enables us to select optimal molecularly targeted therapies when considering treatment strategies for patients with metastatic colorectal cancer. Tissue-based genetic testing is limited by the difficulty of performing repeated tests, due to the invasive nature of tissue biopsy, and by tumor heterogeneity, which can limit the usefulness of the information it yields. Liquid biopsy, represented by circulating tumor DNA (ctDNA), has attracted attention as a novel method for detecting genetic alterations. Liquid biopsies are more convenient and much less invasive than tissue biopsies and are useful for obtaining comprehensive genomic information on primary and metastatic tumors. Assessing ctDNA can help track genomic evolution and the status of alterations in genes such as RAS, which are sometimes altered following chemotherapy. In this review, we discuss the potential clinical applications of ctDNA, summarize clinical trials focusing on RAS, and present the future prospects of ctDNA analysis that could change daily clinical practice. Therapeutic drugs for metastatic cancers are chosen based on the organ of origin. Cancer treatment has advanced remarkably over the last few decades, and the survival rate has improved significantly. Molecular targeted therapies have contributed greatly to the development of cancer treatments. Advances in genomic profiling have enabled the identification of genetic alterations that cause cancer and have supported the development of personalized cancer treatments for each gene alteration. Frequently mutated genes in non-hypermutated colorectal cancer (CRC) include APC, TP53, KRAS, PIK3CA, FBXW7, SMAD4, TCF7L2, and NRAS [1]. The RAS family is one of the most frequently mutated gene families and has been extensively studied in metastatic CRC (mCRC). RAS is one of the major proteins involved in the mitogen-activated protein kinase (MAPK) signaling cascade. The RAS oncogene family includes KRAS, NRAS, and HRAS. The most common RAS mutations occur in KRAS; approximately 40% of CRC cases have KRAS mutations, while HRAS mutations are rarely detected [1]. KRAS and NRAS mutations are negative predictive factors for the efficacy of anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibodies (mAbs), which act as both primary and secondary resistance markers [2,3]. RAS mutations are associated with poor prognosis in advanced stages [4]. Although it seems likely that constitutive activation of the RAS signaling pathway is involved in tumor progression, the reason for poor prognosis is still not well understood [5]. BRAF also acts as a downstream RAS effector in the MAPK signaling cascade. The BRAF V600E mutation, which is found in 8–10% of patients with mCRC, is also a negative predictive factor for the efficacy of anti-EGFR mAbs [6,7,8]. Therefore, analysis of genetic alterations is increasingly important for developing personalized cancer treatments. Tissue biopsy is extensively used to determine suitable therapeutic drugs based on the molecular profile of an individual. However, tissue biopsy has several limitations, including potential complications, difficulty in performing repeated biopsies due to the invasiveness of the procedure, difficulties in collecting tissue, and intra- and inter-tumor heterogeneity. Liquid biopsy can overcome these limitations because it is less invasive and involves the collection of bodily fluids such as blood and urine. Therefore, it allows for the repeated analysis of gene alterations over time. Liquid biopsies, including those performed for circulating tumor DNA (ctDNA), are clinically used to detect RAS and BRAF mutations and perform comprehensive genomic profiling in CRC [9,10]. This review summarizes liquid biopsy and ctDNA analyses and their applications to CRC in clinical trials. Tissue examination is necessary for cancer diagnosis and management. Histological analysis can reveal the genetic profile of the tumor and enable a more accurate prognosis and prediction of systemic chemotherapy efficacy. However, while tissue biopsy is necessary for the development of a therapeutic strategy, it is invasive with potential complications such as bleeding, pain, infections, and neuropathy, and it can be difficult to obtain tissue samples due to tumor volume or anatomical reasons [11]. The extraction of tumor tissues is sometimes required after the onset of resistance, and tissue biopsies may be difficult to repeat for several reasons. In addition to safety, the number of tumor cells obtained can vary. Fine needle aspiration or core needle biopsies often result in the extraction of less tumor tissue for molecular analysis [12]. Moreover, tissue biopsies are affected by tumor heterogeneity. Metastatic tumors could have different genetic profiles, even if they were derived from a primary tumor within the same patient [13]. When the treatment decision is based on a single biopsy, intra-tumor heterogeneity can lead to therapeutic failure [14]. As an alternative to traditional tissue biopsy, liquid biopsy is useful for cancer diagnosis. Liquid specimens are obtained using minimally invasive techniques and can be used to detect several soluble factors related to tumor genetics, such as cell-free DNA (cfDNA), ctDNA, circulating tumor cells, and exosomes. Cancer-associated genetic alterations, such as point mutations, copy number variations, amplification, rearrangements, aneuploidy, and fusion and methylation patterns, have been detected in ctDNA [12]. Cancer patients have higher levels of plasma cfDNA than tumor-free patients; however, high levels of cfDNA are not specific to cancer [15]. Confounding factors that can contribute to the release of cfDNA include smoking, pregnancy, exercise, and numerous non-malignant disorders, such as inflammatory conditions, anemia, heart disease, metabolic syndrome, and autoimmune disorders [16]. Compared to tissue biopsy, liquid biopsy is minimally invasive and allows repeated analyses over the course of treatment for the dynamic monitoring of molecular changes in the tumor. Furthermore, liquid biopsy can overcome difficulties related to both intra- and inter-tumor heterogeneity. First reported in 1948 by Mandel and Metais [17], cfDNA is used in prenatal assessments. The size distribution of cfDNA in the plasma of pregnant women ranges from 160 bp to 180 bp [18]. Increased cfDNA levels in the blood of patients with various types of cancer were first reported in 1977 [19]. Moreover, cfDNA may be released from healthy, inflamed, or diseased tissues where cells are undergoing apoptosis or necrosis and is detected in body fluids such as blood, urine, cerebrospinal fluid, pleural fluid, ascites, and saliva [20,21,22,23,24]. In patients without cancer, cfDNA concentrations range from 0–100 ng/mL (mean, 13 ± 3 ng/mL). In contrast, the mean cfDNA concentration in patients with cancer is 180 ± 38 ng/mL [19]. ctDNA is a DNA fragment derived specifically from a tumor, thus differentiating it from cfDNA. The difference in DNA concentration between patients with and without cancer is reflective of the ctDNA derived from cancer cells. The half-life of ctDNA is approximately 2 h [25], while that of protein biomarkers is several days [26]. Therefore, with ctDNA, real-time changes in the genetic status of the tumor can be evaluated, which is in contrast to commonly used protein biomarkers such as carcinoembryonic antigens. ctDNA can quickly reflect changes in tumor burden following surgery and chemotherapy and can be used to predict disease progression and recurrence. Protein markers are not involved in tumorigenesis, whereas genetic alterations detected in ctDNA are generally the cause of tumorigenesis. Therefore, ctDNA is a more sensitive and specific biomarker than protein biomarkers because it reflects genetic alterations derived from tumors in real-time [27]. Compared to tissue biopsy, ctDNA may have the advantage of a short turn-around time (TAT), which is defined as the number of days between the test order date and the report date [28]. ctDNA genotyping significantly shortened the screening duration in SCRUM-Japan GOZILA, an observational ctDNA-based screening study that evaluated the utility of ctDNA in patients with advanced gastrointestinal cancer [29]. In CRC, the median TAT when detecting ctDNA using comprehensive ctDNA testing with the Guardant360® assay was significantly lower than that for tissue testing when the complete process from sample acquisition to results was considered [30]. High analytical sensitivity and specialized equipment are required for the detection of ctDNA. Current techniques used for the quantification of tumor-associated genetic alterations can result in false-negative results; however, concordance between tissue and plasma tests for ctDNA is generally high in CRC [9,31]. Both tissue and plasma tests for ctDNA sometimes yield false-negative and false-positive results, but they can be more reliable when used in combination [32,33]. Utilizing ctDNA analysis alongside tissue tests increases the identification of biomarkers by 19.5% because it allows identification even without conclusive tissue results due to tissue insufficiency, test failure, or false negatives [30]. The amount of ctDNA in an individual is lower than that of cfDNA, and it is sometimes as low as 0.01–1.70% in curable CRC [34]. This makes it difficult to detect and quantify ctDNA with the sensitivity required for meaningful clinical use. The amount of ctDNA produced is influenced by several factors. ctDNA detection depends on the ctDNA shedding rate per cancer cell, but this can vary by multiple magnitudes between patients. Thus, the discordance between plasma- and tissue-based analyses could be due to low ctDNA shedding from tumors; the median variant allele frequency (VAF) between concordant and discordant cases was statistically different [9]. In addition, ctDNA levels reflect the total systemic tumor burden and size [25,35]. ctDNA levels decrease after complete surgery or in response to chemotherapy and generally increase with disease progression before radiological examination. Furthermore, the ctDNA detection rate varies for each organ. Because of the difficulty in detecting ctDNA, the detection rate of RAS mutations is low in patients with CRC with lung and peritoneal metastases [9,36]. This may be caused by differences in the distribution of DNAase depending on the metastatic site [37]. ctDNA is detected in >75% of patients with advanced pancreatic, ovarian, colorectal, bladder, gastroesophageal, breast, melanoma, hepatocellular, and head and neck cancers, but it is detected in less than 50% of patients with primary brain, renal, prostate, or thyroid cancers. In addition, ctDNA from neoplasms confined to the central nervous system and mucinous neoplasms is frequently undetectable [38]. Circulating tumor cells can also release ctDNA and therefore influence the detection of ctDNA [39]. Measuring ctDNA levels could be confounded by biological signaling arising from somatic mosaicism. Clonal hematopoiesis (CH) is a somatic mosaicism resulting from the accumulation of somatic mutations in hematopoietic stem cells. CH is influenced by age, prior radiation therapy, chemotherapy, and tobacco use and can be detected by ctDNA analysis. However, we must consider the possibility that CH can be interpreted, incorrectly, as a mutation [40,41]. In CRC, TP53, GNAS, PTEN, and KRAS mutations have been reported as CH; however, the complete distinction between tumor-derived mutations and CH is difficult to achieve [42,43]. Several techniques exist for evaluating ctDNA; however, these techniques require high sensitivity because of the low amount of ctDNA. Liquid biopsy analyses are available and include polymerase chain reaction (PCR)- and next-generation sequencing (NGS)-based platforms. PCR-based assays can only detect targets with known driver mutations, and they fail to detect complex genomic alterations. Highly sensitive PCR-based assays, such as droplet digital PCR (ddPCR) and beads, emulsion, amplification, and magnetics (BEAMing), have been developed [44]. ddPCR is a highly sensitive and accurate quantification method that detects low-frequency variants by amplifying single DNA molecules. Amplicon sequencing and hybridization capture reduces the background error rates of sequencing [45]. The limit of detection for ddPCR is 0.01–0.10% [46]. BEAMing is a PCR-based technique that uses flow cytometry to detect ctDNA [47]. The OncoBEAMTM RAS CRC Kit, which detects 34 mutations in KRAS/NRAS codons 12, 13, 59, 61, 117, and 146, is a platform for detecting RAS mutations in the plasma using BEAMing technology. The OncoBEAMTM RAS CRC Kit detects alterations at a 0.01% allele frequency [47]; it received market approval on 1 July 2019, from Japan’s Ministry of Health, Labour, and Welfare, and it has been covered under insurance since 1 August 2020. In a study comparing four commercial platforms that detect KRAS/NRAS ctDNA mutations, BEAMing exhibited higher sensitivity than the IdyllaTM KRAS Mutation Test, ddPCR, and NGS [48,49,50]. There is a high degree of concordance, of 86.4–93.3%, between ctDNA analysis with BEAMing and tissue analysis [9,51,52,53]. However, discordance was observed between plasma and tissue analyses employing BEAMing of RAS mutations associated with lung metastasis [9]. Other factors associated with discordance include peritoneal metastasis, mucinous carcinoma type, administration of treatment prior to liquid biopsy, longest diameter, and lesion number. Due to high concordance, we do not have to consider the cutoff for patients with only liver metastases; however, we need to consider the cutoff when patients have peritoneal metastases alone with a lesion diameter <20 mm, lung metastases alone with a lesion diameter <20 mm, or <10 lesions in total [9,36,53]. Therefore, caution should be exercised when assessing RAS mutations with BEAMing. NGS can be used to analyze a large number of genes (hundreds to thousands). NGS is designed to detect multiple classes of genetic alterations, including indels, rearrangements, and copy number alterations, in both known and unknown driver genes [54]. NGS is limited by its relatively low sensitivity and high cost; however, the last decade has witnessed improvements in NGS in terms of reliability and cost [55,56]. The Guardant360® assay (Guardant Health, Inc., Redwood City, CA, USA) and FoundationOne® Liquid (Foundation Medicine, Cambridge, MA, USA.) are among the most popular NGS-based ctDNA testing methods. FoundationOne® Liquid received market approval on 22 March 2021, from Japan’s Ministry of Health, Labour, and Welfare and has been covered under insurance since 1 August 2021. Caris AssureTM liquid biopsies, whole exome DNA sequencing, and whole transcriptome RNA sequencing are comprehensive tumor profiling technologies that include all 22,000 genes. This comprehensive approach identifies cancer biomarkers and assesses the molecular features of the patient using circulating nucleic acid sequencing, which is a novel molecular profiling approach that analyzes cfDNA, cell-free RNA, genomic DNA, and RNA from circulating white blood cells [57]. Methods for evaluating ctDNA are divided into tumor-informed and tumor-agnostic, with the previously mentioned techniques for evaluating ctDNA being tumor-agnostic (Table S1). The tumor-informed approach requires genomic profiling of the tumor tissue. This approach identifies the alterations derived from tumors. However, the tumor-agnostic approach does not require the mutational status of tumor tissue and is based on panel-based sequencing. Many studies based on a tumor-informed approach have been conducted. The advantages of the tumor-informed approach include personalized analysis and accuracy in tracking the molecular responses [58]. SignateraTM is a novel, highly sensitive, and specific approach for ctDNA detection. This is a personalized and tumor-informed approach for minimal residual disease (MRD) assessment. A primary tumor sample was used for whole-exome sequencing to assess the differences in over 20,000 genes between a patient’s tumor sample and a normal DNA sample. Sixteen highly ranked patient-specific mutations were selected for the panel. The samples are amplified using a patient-specific assay, barcoded, pooled, and sequenced using an NGS platform. Somatic alterations derived from tumors are then detected in the plasma [59]. SignateraTM has been useful in assessing MRD in recent clinical studies. Personalized approaches have advanced with the development of methods to detect cancer-specific genomic alterations. Cancer prognosis is assessed based on clinical observations, tumor type, staging, and histopathological and biomolecular characterization. The amount of ctDNA could be a prognostic factor (Table S2) [60,61,62,63,64,65,66]. In the CORRECT trial, a retrospective exploratory analysis evaluating the efficacy and safety of regorafenib in mCRC patients, high baseline KRAS mutant allele frequency (MAF) and circulating DNA concentrations were associated with a shorter median overall survival (OS) [60]. ctDNA measured using VAF at baseline was a prognostic factor potentially related to initial tumor volume in patients with RAS wild-type (WT) mCRC who were eligible for initial therapy with panitumumab plus FOLFOX (fluorouracil, leucovorin, and oxaliplatin) [61]. In addition, several reports have revealed an association between the amount of baseline ctDNA, VAF or MAF and prognosis in mCRC patients [62,63,64,65,66]. ctDNA methylation markers are gaining attention for the diagnosis and prognosis of CRC. A model using five selected cfDNA methylation markers was useful as an independent prognostic risk factor in multivariate analysis [67]. In addition, PIK3CA mutations at baseline are associated with poor outcomes in patients with RAS WT mCRC [68]. ctDNA analysis may allow us to obtain information on the factors influencing prognosis. The early detection of micrometastatic lesions that are undetectable by radiological examination is essential to reduce the risk of incurable metastasis. Recurrence was monitored following cancer treatment. ctDNA is sufficiently sensitive for the detection of MRD following surgical resection [35]. Positive ctDNA detection in resected early-stage colon cancer (CC) precedes the radiological detection of recurrence by more than a few months [69,70]. ctDNA analysis following surgery is a promising prognostic assessment and can aid in the identification of patients with a very high risk of recurrence [71,72,73], which could reduce unnecessary chemotherapy. A ctDNA-guided approach for treating pathological stage II CC reduces adjuvant chemotherapy (AC) without compromising recurrence-free survival [74]. Patients who did not previously require AC may benefit from AC if ctDNA predicts recurrence. A prospective, multicenter cohort study indicated an association between ctDNA and recurrence in patients with stage I–III CRC following curative surgery. Following curative surgery, 10.6% of patients tested positive for ctDNA on postoperative day 30. Notably, ctDNA-positive patients were seven times more likely to relapse than ctDNA-negative patients (hazard ratio (HR), 7.2; 95% confidence interval (CI), 2.7–19.0; p < 0.001). Moreover, seven patients who were positive for ctDNA following AC had recurrences. Among 75 patients with longitudinally collected plasma samples, 14 of 15 ctDNA-positive patients experienced recurrence compared to 2 of 60 ctDNA-negative patients. ctDNA indicative of recurrence was detected 8.7 months earlier than the diagnosis using standard-of-care radiologic imaging [69]. Similar results have been reported in breast, lung, and bladder cancer [75,76,77]. Thus, ctDNA could help clinicians decide to pursue more intense therapy in patients with a higher risk of recurrence. ctDNA can be used to stratify patients according to their risk of recurrence, enabling therapeutic intervention before the development of clinical metastasis. In CIRCURATE-Japan, three clinical trials using SignateraTM are ongoing to evaluate the clinical benefits of ctDNA and refine AC for CRC. The GALAXY study was designed to monitor ctDNA status in stage II–IV patients who were eligible for curative surgery [78]. ctDNA was analyzed before surgery and at 4, 12, 24, 36, 48, 72, and 96 weeks after surgery. The VEGA trial (jRCT1031200006) is a randomized phase III study to evaluate CAPOX therapy as an AC for high-risk stage II or low-risk stage III CC patients with ctDNA-negative status four weeks after curative surgery. Patients were randomized in a 1:1 ratio to receive either CAPOX therapy for three months or surgery alone. AC may not be required after curative surgery in patients with ctDNA-negative CC. The ALTAIR trial (NCT04457297) is a randomized, phase III study to evaluate preemptive trifluridine/tipiracil therapy in patients with CC who are positive for ctDNA after curative surgery for up to 2 years. The BESPOKE study (NCT04264702) examined the effect of SignateraTM use on AC decisions. The benefit of preemptive therapy in patients who are positive for ctDNA before radiologic imaging is unknown. Several clinical trials on AC using ctDNA analysis are currently ongoing. The DYNAMIC-II (ACTRN12615000381583), MEDOCC-CrEATE (NL6281/NTR6455), COBRA (NCT04068103), and IMPROVE-IT (NCT03748680) trials are investigating the administration of AC depending on ctDNA levels in patients with stage I or II CRC. In these trials, ctDNA-positive patients receive AC or follow-up if ctDNA is negative. The PEGASUS (NCT04259944) and DYNAMIC-III (ACTRN12617001566325) trials including resected stage III or T4N0 stage II CC are also ongoing. Both trials are investigating ctDNA-guided AC. ctDNA-negative patients will receive de-escalated AC and ctDNA-positive patients will receive escalated AC. OPIMIZE (NCT04680260) is an open-label, randomized phase II trial for patients receiving radical treatment for metastatic spread of CRC. Patients were randomized between the standard-of-care and ctDNA-guided treatments. ctDNA-positive patients receive FOLFOXIRI (fluorouracil, leucovorin, oxaliplatin, and irinotecan), and ctDNA-negative patients receive capecitabine or observation only. The results from these trials could shift AC from a conventional strategy to a ctDNA-guided strategy. Genetic alterations in tumor DNA are important markers for deciding the treatment regimen and predicting the response to treatment. High levels of KRAS mutant (MT) alleles in the plasma are a clear indicator of response to treatment in metastatic CC [79,80]. ctDNA allows the design of specific treatments according to genetic alterations. Combinations of BRAF inhibitors and anti-EGFR mAbs are effective in mCRC patients harboring a BRAF V600E mutation. The use of ctDNA analysis to detect BRAF V600E offers an opportunity to administer BRAF inhibitors. ctDNA can be used to detect BRAF V600E in the plasma of patients in whom it was not detected in tissue analysis due to spatial heterogeneity [81]. Microsatellite instability (MSI) is associated with a higher risk of cancer and has been assessed in solid tumors. MSI-high tumors are sensitive to immune checkpoint inhibitors (ICIs). MSI is typically assessed in tumor tissues using immunohistochemistry and PCR-based assays. Recently, a high concordance rate between MSI measured using conventional tissue and ctDNA-based approaches has been reported. In the near future, ctDNA-based approaches to detect MSI could be used to identify patients who could benefit from ICIs [82]. In melanoma, assessment of the ctDNA baseline could indicate clinical outcomes in patients receiving ICI treatment [83,84,85]. In patients with mCRC with HER2 amplification, a combination of pertuzumab and trastuzumab may be effective. The TRIUMPH trial is a prospective phase II study involving mCRC patients with HER2 amplification and investigating pertuzumab and trastuzumab as a second-line or later treatment. Twenty-five patients with HER2 amplification, confirmed using ctDNA, received pertuzumab plus trastuzumab and achieved an overall response rate (ORR) of 28%, compared to 30% in 27 tissue-positive patients [86]. In the HERACLES trial, which evaluated trastuzumab plus lapatinib or pertuzumab plus trastuzumab-emtansine in patients with HER2-positive mCRC, comprehensive ctDNA analysis identified that more than 85% of patients showed primary resistance when treated with lapatinib and trastuzumab [87]. Moreover, an adjusted ERBB2 plasma copy number has been correlated with progression-free survival (PFS) and best objective response [88]. Therefore, ctDNA analysis could be useful for identifying patients who would benefit from HER2 blockade. ctDNA analysis could provide relevant molecular profiling required for tumor-agnostic treatment. NTRK fusion is an oncogenic driver that is also present in CRC. The FDA granted tumor-agnostic approval to the TRK inhibitors larotrectinib and entrectinib. Tissue testing is routinely used to detect NTRK fusions; however, these fusions can also be detected using ctDNA with a high positive predictive value [89]. Monitoring tumor responses using ctDNA during the course of treatment could allow for changes in the drugs administered before the observation of disease progression on radiological examination. A small or absent early decrease in ctDNA levels during mCRC treatment was associated with short PFS and OS in a systematic review and meta-analysis [90]. Targeted therapies are effective for specific genetic mutations; however, most patients eventually develop secondary resistance. Designing the next treatment requires the identification of the mechanism of acquired resistance. The advantages of liquid biopsies include the simplicity of specimen collection and the ability to provide snapshots to detect the emergence of resistant clones. Almost all patients with CRC acquire resistance to KRAS mutation inhibition during anti-EGFR mAb therapy [91,92]. Multiple alterations conferring resistance to anti-EGFR mAbs, other than RAS mutations, were also observed. The cfDNA profiles of 42 patients with EGFR extracellular domain (ECDs) mutations, which are implicated in acquiring resistance to anti-EGFR mAbs, harbor MEK1 and BRAF mutations and KRAS, MET, ERBB2, and KIT amplifications [93,94]. FLT3 amplification and MAP2K1 are resistant to anti-EGFR mAbs [95]. Multiple alterations were observed in most cases. Predictive markers for sensitivity to anti-EGFR mAbs include RAS and several other alterations. In patients with HER2 blockade in CRC, the emergence of resistance alleles such as PIK3CA is observed, which indicates that they might be sub-clonal [88]. In addition, some patients showed clear progression in one lesion, whereas the response was stable in the other. This indicates heterogeneity within a single patient [88]. Acquired resistance has also been observed against BRAF-targeted therapy in the form of NRAS and MEK1/2 mutations, BRAF amplification, or CRAF overexpression [96]. ctDNA can be used to track the emergence of resistant clones throughout the course of treatment because of the accessibility of plasma from patients. Understanding the mechanisms underlying acquired resistance to treatment could lead to improved personalized anticancer therapy and the development of combinatorial treatment strategies. Changes in resistance to anticancer therapy are not fully understood. Comprehensive gene profiling rather than single molecular evolution should be used to understand drug resistance. The concept of anti-EGFR mAb rechallenge was first reported by Santini et al. [97]. They assessed the activity of cetuximab rechallenge in patients with mCRC. The results were promising, with a response rate (RR) of 53.8% and median PFS (mPFS) of 6.6 months. However, they did not distinguish between anti-EGFR mAbs rechallenge and reintroduction. Rechallenge is defined as anti-EGFR mAb re-administration after an anti-EGFR-mAb-free period in patients with prior resistance to anti-EGFR mAbs. Reintroduction, on the other hand, is defined as anti-EGFR mAb re-administration after prior anti-EGFR discontinuation in patients without resistance. Additionally, an intermittent anti-EGFR mAb strategy has been proposed. Recently, it was reported that intermittent panitumumab, instead of continuous panitumumab with chemotherapy, produced a long PFS with reduced skin toxicity [98]. Thus, some similar strategies have been proposed for anti-EGFR mAbs. The development of these treatment strategies requires the elucidation of resistance mechanisms. Acquired resistance to anti-EGFR mAbs is associated with the emergence of RAS mutations [91,92]. RAS mutations are likely to be present at undetectable levels before the administration of anti-EGFR mAbs; the number of RAS MT cells increases to a detectable level during the administration of anti-EGFR mAbs [99]. Second-line therapy without anti-EGFR mAbs, however, causes the reduction or disappearance of RAS MT subclones. One hypothesis to explain this observation is that targeted therapies apply selective pressure on heterogeneous tumors, including the undetectable RAS MT populations, and resistant cells survive; however, these resistant cells may be innately limited [100]. Therefore, RAS mutations may exist as sub-clonal mutations with low allele frequencies. The half-life of these mutations is approximately 3–4 months after withdrawal of anti-EGFR mAbs [100]. This could restore sensitivity to anti-EGFR mAbs [53]. The disappearance of RAS mutations could be attributed to a decline in the percentage of acquired mutated RAS alleles to below the limit of detection during treatment without anti-EGFR mAbs [95]. Tracking the dynamics of resistant sub-clonal populations allows the identification of patients who can be rechallenged with the same drugs. There is a rationale for anti-EGFR mAb rechallenge following failure of second-line treatment with an anti-EGFR-mAb-free therapeutic window (Figure 1). We analyzed current knowledge regarding anti-EGFR mAb rechallenge based on ctDNA. The prognosis of mCRC after progression to first and second therapies is poor. Third- or later-line therapies include trifluridine/tipiracil (+ bevacizumab) or regorafenib. Both therapies were clinically beneficial in a phase III clinical trial compared to the best supportive care. However, their efficacy is limited; the mPFS is approximately 2 months, and the ORR is approximately 1–4% [101,102,103,104]. In addition, toxicities, such as gastrointestinal toxicity and hematologic toxicity, should be assessed because of adverse events (AEs) frequently caused by trifluridine/tipiracil (+ bevacizumab). In addition, regorafenib causes hand–foot syndrome, fatigue, diarrhea, and hypertension. Therefore, anti-EGFR mAb rechallenge is expected to become a common new therapeutic strategy with a high response rate. Trials of anti-EGFR mAb rechallenge and the analysis results based on liquid biopsies have been reported (Table 1). The CRICKET trial is a multicenter phase II trial for assessing the activity of cetuximab rechallenge as a third-line treatment for patients with RAS and BRAF WT mCRC who benefitted from first-line cetuximab- and irinotecan-based treatment, with at least a partial response (PR) and a PFS of at least 6 months, and then became resistant. The time between the end of first-line therapy and the start of third-line therapy was ≥4 months. Liquid biopsies for ctDNA analysis were performed at the baseline. The ORR was 21%. Four patients who achieved confirmed PR had no RAS mutations. However, eight patients with RAS MT ctDNA had stable disease (SD) or progressive disease. Patients with RAS WT ctDNA had significantly longer PFS than those with RAS MT ctDNA in a retrospective analysis (mPFS, 4.0 vs. 1.9 months; HR, 0.44; 95% CI, 0.18–0.98; p = 0.03). A similar trend was observed for OS [105]. An E-challenge trial, a multicenter phase II study, evaluated whether there is a correlation between the anti-EGFR-mAb-free interval (aEFI) and efficacy. Patients with an aEFI ≥ 16 weeks between the last dose of cetuximab (during previous treatment) and the start of the cetuximab rechallenge were included. Other criteria included RAS WT and complete response (CR), PR, or SD that persisted for 6 months or more for anti-EGFR mAb. The primary endpoint was the ORR. The ORR was 15.2%, and PR was observed in all patients. There was no statistically significant difference in ORR, PFS, or OS stratified using the median aEFI (311 days). However, in the additional liquid biopsy for ctDNA, the RR for KRAS G12/G13/A59/Q61, BRAF V600E, and EGFR S492R mutants increased; the RR of patients with all WT was 25% compared to 12.5% in those with any of the mutations [106]. A post hoc biomarker study (JACCRO CC-08/09AR) was performed to evaluate the association between survival outcomes and RAS status in ctDNAs. The JACCRO CC-08 and 09 trials evaluated the efficacy and safety of anti-EGFR mAb rechallenge as a third-line therapy for patients with KRAS WT mCRC who achieved a clinical response to first-line therapy with anti-EGFR mAbs. The RAS status was evaluated using the OncoBEAM RAS CRC Kit. RAS status in ctDNA is associated with clinical outcomes in patients with mCRC receiving anti-EGFR mAb rechallenge. Patients with RAS mutation at baseline had significantly shorter PFS and OS than those without RAS mutation (mPFS, 2.3 vs. 4.7 months; HR, 6.2; p = 0.013 and mOS, 3.8 vs. 16.0 months; HR = 12.4; p = 0.0028). The disease control rate was 80% in patients with no RAS mutations and 33.3% in patients with RAS mutations [107]. CAVE is a phase II single-arm trial to assess the efficacy of cetuximab rechallenge plus avelumab in patients with NRAS and KRAS WT mCRC who achieved CR or PR to first-line therapy with anti-EGFR mAbs. Avelumab, an immune checkpoint inhibitor, exhibits antibody-dependent cytotoxicity that is enhanced by cetuximab. Therefore, the combination of cetuximab and avelumab could result in synergistic activity and could be a strategy for potentiating antitumor activity. The primary endpoint was OS. A post hoc analysis was performed to assess the efficacy of cetuximab plus avelumab according to ctDNA levels; mOS was 11.6 months (95% CI, 8.4–14.8 months); mPFS was 3.6 months (95% CI, 3.2–4.1 months); the ORR was 7.8%; and one patient had CR. Among the 67 patients who were assessed for ctDNA, patients with RAS/BRAF WT had longer mOS and mPFS when compared to patients with mutated ctDNA (mOS, 17.3 vs. 10.4 months; HR, 0.49; 95% CI, 0.27–0.90; p = 0.02 and mPFS, 4.1 vs. 3.0 months; HR, 0.42; 95% CI, 0.23–0.75; p = 0.004) [108]. In these trials, the ORR was 0–20%, and the mPFS was approximately 3 months. While these results were not satisfactory, a better trend was observed in patients with no ctDNA mutations than in patients with some mutations. Therefore, evaluation of ctDNA related to resistance could be useful for identifying patients eligible for anti-EGFR mAb rechallenge. Trials to prospectively evaluate the efficacy of anti-EGFR mAb rechallenge using ctDNA have been conducted. The CHRONOS study, an open-label, single-arm, phase II clinical trial, was the first to prospectively evaluate the efficacy of rechallenge with EGFR inhibitors based on the mutational status of ctDNA. The main inclusion criteria were RAS/BRAF WT in tissue, CR or PR to anti-EGFR mAbs, progression after the last treatment without anti-EGFR mAbs, and RAS and BRAF WT and EGFR ECDs in ctDNA. The primary endpoint was the ORR. The plasma RAS status was measured using ddPCR. Among the patients with no detectable alterations in RAS, BRAF, and EGFR ECDs in ctDNA, eight (30%) achieved PR. In this study, there was no correlation between the aEFI and the probability of response. However, the CHRONOS study showed that patients with a shorter aEFI (within 12 months) responded to anti-EGFR mAb rechallenge. Therefore, the optimal aEFI varied between patients and was not based on a certain period. The results of CHRONOS indicate that selecting patients based on ctDNA would enable the selection of more appropriate candidates for anti-EGFR mAb rechallenge because it would allow the exclusion of resistant cases [109]. PURSUIT is a multicenter, single-arm phase II trial that evaluated the efficacy of anti-EGFR mAb rechallenge in patients with mCRC and plasma RAS WT. In the monitoring phase, REMARRY prospectively monitored plasma RAS status in patients with RAS/BRAF WT mCRC following a refractory response to anti-EGFR mAbs. Plasma RAS status was measured at disease progression during subsequent therapies, and patients were enrolled in the PURSUIT trial if they tested negative for plasma RAS. Other key eligibility criteria included CR or PR to previous anti-EGFR mAb treatment and an interval ≥4 months since the last administration of anti-EGFR mAbs. Plasma RAS status was measured using the OncoBEAMTM RAS CRC Kit. The primary endpoint was not met, and the confirmed ORR was 14%. The subgroup analysis showed a significantly higher confirmed ORR in patients with a longer aEFI than in those with a shorter aEFI (>365 vs. <365 days, 44.4% vs. 7.3%, p = 0.0037). The aEFI is assumed to be a factor in predicting the effectiveness of the anti-EGFR mAb rechallenge in the PURSUIT trial, as opposed to that in the CHRONOS study. Notably, five patients with plasma RAS WT had a confirmed response (ORR, 16%), whereas no response was observed in seven patients with plasma RAS mutations (ORR, 0%) (p = 0.25) [110]. VELO is a randomized phase II trial to evaluate anti-EGFR mAb rechallenge as a third-line treatment in patients with mCRC. The patients were randomized 1:1 to receive panitumumab plus trifluridine/tipiracil or trifluridine/tipiracil alone. The main inclusion criteria were achieving CR or PR to anti-EGFR mAb as first-line therapy and an aEFI of at least 4 months. Plasma samples were prospectively collected from all patients. Patients who received panitumumab plus trifluridine/tipiracil had a PFS of 4 months, whereas those who received only trifluridine/tipiracil had a PFS of 5 months. In addition, at 6 months, the PFS rate was 38.5% in patients with RAS/BRAF WT and 13% in those with RAS/BRAF MT. Thirteen patients (20.9%) had RAS/BRAF WT at baseline ctDNA analysis [111]. Anti-EGFR mAb rechallenge was investigated in combination with a cyclin-dependent kinase 4/6 inhibitor in a phase II trial; however, the results were not favorable. The 4-month disease control rate was 20%, and the mPFS was 1.8 months [112]. Anti-EGFR mAb rechallenge is well tolerated and not substantially different from anti-EGFR mAb as a first-line therapy. Confirmation of ctDNA mutational status is essential when considering anti-EGFR mAb rechallenge. The optimal interval between the initial administration and re-initiation of anti-EGFR mAb therapy remains unclear. Several Clinical Trials of anti-EGFR mAb rechallenge are Ongoing (Table 2). PULSE (NCT03992456) is a randomized, phase II, open-label study designed to compare the OS of panitumumab rechallenge with that of standard-of-care treatment (trifluridine/tipiracil or regorafenib) for patients with mCRC with no resistance mutations, as determined by liquid biopsy. The inclusion criteria are progression after at least four months of treatment with an anti-EGFR mAb and >90 days between the recent administration of anti-EGFR mAb and liquid biopsy. Secondary objectives include comparisons of PFS, ORR, clinical benefit rate, and quality of life, as measured using a linear analog self-assessment questionnaire. A total of 120 patients will be randomized 1:1 to receive panitumumab rechallenge or standard-of-care treatment. This trial will optimize the third-line regimen after the progression of anti-EGFR mAb in patients with RAS/BRAF WT mCRC. PARERE (NCT04787341) is a prospective, open-label, multicenter, phase II study aimed at evaluating the anti-EGFR mAb rechallenge sequence of RAS/BRAF WT, chemo-refractory mCRC with previous benefit from first-line anti-EGFR-mAb-based treatment according to ctDNA analysis using liquid biopsy. RAS/BRAF WT ctDNA at the time of screening, at least a PR or SD ≥6 months since the first anti-EGFR-mAb-containing regimen, and at least 4 months between the end of first-line anti-EGFR mAb and liquid biopsy were required. A total of 214 patients were randomized in a 1:1 ratio to receive panitumumab followed by regorafenib after progression or the reverse sequence. The primary endpoint is OS. The secondary endpoints are 1st-PFS, 2nd-PFS, time to failure strategy, ORR, and safety. The results of this study will validate the appropriate placement of anti-EGFR mAb rechallenge in treatment strategies and provide useful knowledge regarding the aEFI. CAPRI II GOIM (NCT05312398) is an open-label, phase II study investigating the efficacy and safety of a biomarker-driven, cetuximab-based treatment regimen over three treatment lines in patients with RAS/BRAF WT mCRC at the start of first-line treatment. Patients will be treated with cetuximab in combination with chemotherapy as follows: FOLFIRI (fluorouracil, leucovorin, and irinotecan) plus cetuximab (first-line), FOLFOX plus cetuximab (second-line), and irinotecan plus cetuximab (third-line). If RAS and/or BRAF mutation status is detected in ctDNA during disease progression, patients will be treated with FOLFOX plus bevacizumab as a second-line therapy or with regorafenib or trifluridine/tipiracil (investigator’s choice) as a third-line therapy. In cases where RAS/BRAF WT is observed through liquid biopsy at each time point of progression, patients are treated with cetuximab rechallenge in combination with irinotecan. In total, 200 patients will be enrolled. The primary endpoint is the ORR. The secondary endpoints are PFS, OS, AEs, EORTC QLQ C30, and DERMATOLOGY LIFE QUALITY INDEX. This study will reveal the significance of continuous anti-EGFR mAb administration in patients with RAS/BRAF WT mCRC based on dynamic and longitudinal liquid biopsy assessments of RAS/BRAF status. CAVE II GOIM (NCT05291156) is a phase II, open-label, randomized clinical study to assess the efficacy of the combination of avelumab and cetuximab as a rechallenge strategy in patients with RAS/BRAF WT mCRC who achieved CR or PR after first-line therapy with cetuximab. A total of 173 patients were randomly assigned in a 2:1 ratio to receive either avelumab plus cetuximab or cetuximab alone. Patients with RAS/BRAF WT on liquid biopsy at screening were enrolled in the study. The primary endpoint is OS. The combination of cetuximab plus avelumab for patients with NRAS and KRAS WT mCRC was effective in the CAVE trial, a phase II single-arm trial. Cetuximab in combination with avelumab could potentiate antitumor activity as an anti-EGFR mAb rechallenge. Although plasma samples for liquid biopsy were not collected, a randomized phase III trial, the FIRE-4 study (NCT02934529), is being conducted to evaluate irinotecan plus cetuximab as a third-line therapy in patients with RAS WT mCRC. Achieving CR or PR with a PFS of ≥6 months, FOLFIRI plus cetuximab as a first-line treatment, FOLFOX plus bevacizumab as a second-line treatment, and RAS WT tumor status were the inclusion criteria. A total of 550 patients were randomized to receive cetuximab rechallenge in combination with irinotecan-based chemotherapy or anti-EGFR-mAb-free treatment. The primary endpoint is OS. Approximately 55% of mCRC patients have RAS mutations at diagnosis [113]. However, administration of anti-EGFR mAbs is not recommended for patients with RAS WT mCRC. Surprisingly, reversal from RAS MT to RAS WT has recently been reported [114,115,116]. This phenomenon is called NeoRAS WT. The mechanism underlying NeoRAS WT mCRC remains unclear. In the presence of CRC with a low allele frequency of RAS close to the cut-off level, the MAF of RAS generally lies below the detection threshold after treatment, which could result in NeoRAS WT mCRC (Figure 2). The evolutionary pressure imposed by the tumor microenvironment and treatments leads to pulsatile levels of RAS MT clones and negative selection against them [117]. The incidence of NeoRAS WT mCRC was estimated as 18.8–83.3% in a recent report [117,118,119,120,121,122]. The variation in the results could be attributed to factors such as the small sample size and lack of consensus on the definition of NeoRAS WT mCRC. Decisions pertaining to RAS WT based on ctDNA analysis are limited by false negatives from ctDNA analysis. In cases where only RAS mutations are analyzed, it is not clear to what extent the detection of RAS mutations depends on reversion to RAS WT or false negatives. NGS and methylation analyses are useful for confirming or excluding the presence of ctDNA in plasma samples [118]. The detection of at least one somatic mutation other than RAS is an indicator of sufficient ctDNA in the sample, and methylation can also be used as a cancer-related biomarker for the amount of ctDNA present in a plasma sample. Among 18 patients with no RAS/BRAF mutations in plasma ctDNA samples, true RAS conversion occurred in 15 patients, as determined by NGS and methylation analysis [123]. Anti-EGFR mAbs are effective in patients with RAS WT and could also be effective in patients with NeoRAS WT. A pilot study evaluated the efficacy and safety of anti-EGFR mAb plus chemotherapy in patients with NeoRAS WT mCRC. The ORR was 55.6% in patients with NeoRAS WT mCRC treated with a regimen of cetuximab plus FOLFIRI compared to 42.9% in patients where RAS MT ctDNA was detected. The PFS was 13.3 months in patients with NeoRAS WT mCRC compared to 3.5 months in patients with RAS MT mCRC ctDNA. Therefore, anti-EGFR mAbs may be effective in patients with NeoRAS WT mCRC [124]. Several clinical trials have evaluated the efficacy of anti-EGFR mAbs in treating patients with NeoRAS WT mCRC. Multiple trials for treatments for NeoRAS WT mCRC are ongoing (Table 3). The CETIDYL study (NCT04189055) is a single-arm, phase II study to evaluate the efficacy of cetuximab (Cohort 1) and cetuximab and irinotecan (Cohort 2) as salvage therapies in patients with NeoRAS WT mCRC who previously received standard therapies for liver metastasis. Seventy-two patients were enrolled in the study. Patients were initially included in Cohort 1. Inclusion in Cohort 2 will start when the results of Cohort 1 are available. The primary endpoint is the ORR. The KAIROS study (EudraCT number 2019-001328-36) is a single-arm, phase II study that aims to evaluate the safety and efficacy of cetuximab plus chemotherapy as a second-line treatment in 112 patients with NeoRAS WT mCRC. The primary endpoint is the ORR. The MoLiMoR study (NCT04554836) is a prospective, randomized, phase II study conducted to evaluate the efficacy and safety of FOLFIRI-based first-line therapy with or without intermittent cetuximab for NeoRAS WT mCRC. In the FOLFIRI + cetuximab group, treatment was shifted to FOLFIRI at the emergence of RAS mutation as well as to FOLFIRI + cetuximab in cases of repeated conversion to RAS WT. Key eligibility criteria were true RAS MT and left-sided mCRC. A total of 144 patients were randomized to receive FOLFIRI + intermittent cetuximab or FOLFIRI. The primary endpoint is the PFS. The C-PROWESS study (jRCTs031210565) is a multicenter, single-arm, phase II study investigating the safety and efficacy of panitumumab and irinotecan in 30 patients with NeoRAS WT mCRC. The key eligibility criteria are mCRC tissue with RAS MT, refractory or intolerant to fluoropyrimidine, oxaliplatin, or irinotecan, and RAS WT in ctDNA. The primary endpoint is ORR [125]. CONVERTIX (2017-003242-25) was a single-arm, phase II study to evaluate the efficacy of second-line treatment with panitumumab + FOLFIRI in RAS WT mCRC patients who underwent RAS MT at the initiation of first-line treatment with FOLFOX plus bevacizumab. However, this study was terminated early because of a lack of eligible patients; 23 patients were screened, but none met the selection criteria according to the abbreviated clinical study report. The information that can be extracted from ctDNA could be used to confirm real-time tumor genetic information and to optimize the strategy of chemotherapy regimens, such as anti-EGFR mAb rechallenge and anti-EGFR mAb for NeoRAS WT mCRC. The advantages and limitations of ctDNA information should be considered when interpreting these results. The development of technologies to assay ctDNA will provide a basis for personalized medicine and will likely change treatment strategies not only in CRC but also in various types of cancers.

PMC10001244

Kamalakshi Deka,Yinghui Li

Transcriptional Regulation during Aberrant Activation of NF-κB Signalling in Cancer

02-03-2023

NF-κB signalling,cancer,chromatin landscape,epigenetic

The NF-κB signalling pathway is a major signalling cascade involved in the regulation of inflammation and innate immunity. It is also increasingly recognised as a crucial player in many steps of cancer initiation and progression. The five members of the NF-κB family of transcription factors are activated through two major signalling pathways, the canonical and non-canonical pathways. The canonical NF-κB pathway is prevalently activated in various human malignancies as well as inflammation-related disease conditions. Meanwhile, the significance of non-canonical NF-κB pathway in disease pathogenesis is also increasingly recognized in recent studies. In this review, we discuss the double-edged role of the NF-κB pathway in inflammation and cancer, which depends on the severity and extent of the inflammatory response. We also discuss the intrinsic factors, including selected driver mutations, and extrinsic factors, such as tumour microenvironment and epigenetic modifiers, driving aberrant activation of NF-κB in multiple cancer types. We further provide insights into the importance of the interaction of NF-κB pathway components with various macromolecules to its role in transcriptional regulation in cancer. Finally, we provide a perspective on the potential role of aberrant NF-κB activation in altering the chromatin landscape to support oncogenic development.

Transcriptional Regulation during Aberrant Activation of NF-κB Signalling in Cancer The NF-κB signalling pathway is a major signalling cascade involved in the regulation of inflammation and innate immunity. It is also increasingly recognised as a crucial player in many steps of cancer initiation and progression. The five members of the NF-κB family of transcription factors are activated through two major signalling pathways, the canonical and non-canonical pathways. The canonical NF-κB pathway is prevalently activated in various human malignancies as well as inflammation-related disease conditions. Meanwhile, the significance of non-canonical NF-κB pathway in disease pathogenesis is also increasingly recognized in recent studies. In this review, we discuss the double-edged role of the NF-κB pathway in inflammation and cancer, which depends on the severity and extent of the inflammatory response. We also discuss the intrinsic factors, including selected driver mutations, and extrinsic factors, such as tumour microenvironment and epigenetic modifiers, driving aberrant activation of NF-κB in multiple cancer types. We further provide insights into the importance of the interaction of NF-κB pathway components with various macromolecules to its role in transcriptional regulation in cancer. Finally, we provide a perspective on the potential role of aberrant NF-κB activation in altering the chromatin landscape to support oncogenic development. The nuclear transcription factor NF-κB was discovered in 1986 as a Nuclear Factor that binds an immunoglobulin kappa light chain of activated B-cells [1]. NF-κB was subsequently reported to regulate the expression of various important target genes having diverse physiological functions in multiple cell types through its specific DNA binding activity [2,3]. The family of NF-κB transcription factors in mammals comprises five members—RelA (p65), RelB, c-Rel, NF-κB1 (p105/p50) and NF-κB2 (p100/p52) [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21]. Activation of NF-κB occurs via two major signalling pathways—the canonical and non-canonical pathways that involve distinct regulatory mechanisms and NF-κB members. One of the prominent features of NF-κB transcription factors is their association with the member protein of IκB inhibitor family in the cytoplasm making them unavailable for transcriptional activation in the nucleus. The IκB family typically consists of five members (IκBα, IκBβ, IκBε, IκBζ and BCL3), all sharing similar structures. However, unprocessed p100 and p105 proteins are also categorized as members of the IκB family of proteins due to the presence of typical ankyrin repeats (ANK) in their C-terminal region. An alternative transcript of p105 gene, only reported to be expressed in some murine lymphoid cells, has also been named as one of the members of the IκB family (IκBγ) [22,23,24]. Hence, activation of both the canonical and non-canonical NF-κB pathway involves phosphorylation-dependent degradation of IκB factors by stimulus-response-activated IκB kinases (IKKs). The canonical NF-κB pathway is mediated through the activation of NF-κB essential modifier (NEMO)-dependent IKK (IKKγ), whereas non-canonical NF-κB pathway activation requires a NEMO-independent kinase complex involving IκB kinase α (IKKα) and the NF-κB-inducing kinase (NIK) [25]. Upon activation of the canonical NF-κB pathway, IκB kinases (IKKα, IKKβ and NEMO) phosphorylates inhibitory IκBs and target the latter for proteasomal degradation, resulting in the subsequent nuclear accumulation of NF-κB dimers [26,27,28,29]. In the non-canonical NF-κB pathway, NIK phosphorylates IKKα on Ser 176 position, which in turn phosphorylates p100 subunit, leading to cleavage and ubiquitination mediated degradation of C-terminal half of p100 protein generating active p52 subunit [30]. However, reports also suggest the presence of atypical nuclear-localized IκB proteins, referred to as the BCL3 subfamily (Bcl3, IκBNS, IκBζ and IκBη). These IκBs are reported to show entirely different sub-cellular localization, activation kinetics and functional diversity. They are not only capable of interacting with NF-κB transcription factors inside the nucleus but are also found to get induced and not degraded after NF-κB activation, compared to typical IκB members. In addition, they do not exclusively act as inhibitors of the NF-κB pathway, instead they can regulate the transcriptional activity of NF-κB transcription factors both positively and negatively [25,31,32,33,34]. Nuclear-localized BCL3 act as transcriptional coactivators by removing suppressive p50/p50 homodimers from the promoter of its target genes, in turn allowing binding of activating p50/p65 heterodimer [35]. Bcl3 is also reported to suppress transcription via blocking of the ubiquitination of p50 to stabilize a suppressive NF-κB complex in the nucleus [36]. One interesting finding on the nuclear role of IκB family proteins is its direct binding to NF-κB target sites. Wang et al., showed that Bcl3 forms a complex with p52 homodimer to activate transcription when bound to G/C-rich κB sites in the DNA, whereas the same complex represses transcription when bound to A/T-centric sites in the DNA [37]. In a recent finding, it is reported that atypical IκB Bcl3 enhances the generation of p52 homodimer, subsequently upregulating the expression of target genes involved in proliferation, migration and inflammation [38]. Another BCL3 family member protein, IκBζ, is reported to inhibit transactivation of p65 and its DNA-binding activity in the nucleus [39]. In contrast, IκBη have been shown to be a positive regulator of NF-κB-mediated expression of pro-inflammatory cytokines [40]. Nuclear IκBNS is also shown to interact with several different NF-κB factors in the nucleus but its biological role towards the activity of NF-κB transcription factors is yet to be elucidated [41,42]. In the presence of activating stimuli, the NF-κB-signalling cascade can be induced via either of the pathways depending on the type of stimuli, dimers formed and kinases involved in the post transcriptional modification (PTM) of IκBs and processing of NF-κB factors. In addition to innate and adaptive immune response-dependent activation of the NF-κB pathway, the range of stimuli activating either the canonical or non-canonical pathway varies to a large extent. The canonical pathway of NF-κB is highly inducible and is activated by a diverse range of stimuli, such as radiation, DNA damage, cytokines (TNF-a, IL-1, IL-6), chemokines (MCP-1, IL-8), growth factors, adhesion molecules (ICAM-1, VCAM-1, ELAM), reactive oxygen species (ROS), pattern-recognition receptors (PRRs) and pro-inflammatory receptors such as TNF receptor superfamily (TNFRs) and Toll Like receptor superfamily (TLRs) [43,44,45,46,47,48,49,50,51]. In contrast, the non-canonical NF-κB pathway relies on specific sets of cytokine/receptor molecules for its activation, such as tumour necrosis factor (TNF) receptor superfamily proteins, including BAFF receptor (BAFFR), CD-40, lymphotoxin β receptor (LTβR), Fn14 and receptor activator of nuclear factor kappa-B (RANK) [52,53,54,55], all of which signal through a MAP3K member kinase (MAP3K14) called NF-κB-inducing kinase (NIK), making it a master regulator of the non-canonical NF-κB pathway [56,57,58,59]. Once activated, each subunit of NF-κB signalling cascade, p65 (RelA), RelB, c-Rel, p105/p50 (NF-κB1) and p100/52 (NF-κB2) associate with each other to form distinct transcriptionally active homo/heterodimers [60]. Though they all possess a conserved 300-amino-acid-long amino-terminal Rel homology domain (RHD) that is important for dimerization, DNA binding and interaction with IκBs, as well as nuclear translocation, the role of transactivation is characterised to specific members. RelA (p65), RelB and c-Rel contain the carboxy-terminal transactivation domains (TAD), which form transcriptionally active heterodimers only with p50 and p52 subunits, in turn assisting in DNA-binding activity and activated target gene expression. [61,62]. Reports also suggest formation of homodimers within Rel proteins as well as p50 and p52 subunits [63,64,65,66]. Gourisankar and his group have solved the crystal structures of homodimers of several NF-κB pathway factors such as p65 (RelA) homodimer in complex with a DNA target (2.4 Å resolution) and p50 homodimer bound to a palindromic κB site (2.3 Å resolution) [12,65]. Interestingly, p50/p50 homodimer has been described to exert inhibitory effects on NF-ĸB regulated gene expression [67,68]. c-Rel homodimer is also reported to have the ability to bind IκBα, which in turn inhibits its DNA binding but not cytoplasmic retention [69,70]. A recent report also showed atypical IĸB protein, Bcl3-mediated enhanced generation of p52 homodimer, in turn enhancing transcription of genes involved in cancer-associated biological processes [38]. There are also many other combinations of dimers reported but the most prominent dimers are the RelA-p50 and RelB-p52 dimers, which are activated by the respective canonical and non-canonical pathways [71,72]. The complexity of the NF-κB-signalling mechanisms is further illustrated by the specificity of NF-κB dimers in the transcriptional activation of different target genes. In addition, NF-κB member proteins also undergo various post-translational modifications (PTMs), like phosphorylation and acetylation, regulating their interaction and crosstalk with components of other signalling pathways. As mentioned earlier, the phosphorylation status of the IκBs determines the activation state of the NF-κB pathway. The availability and activity of NIK, one of the major activating components of the non-canonical pathway also depends on the PTM state of members of its degradation complex containing TRAF3/TRAF2/cIAP1/cIAP2 proteins, which keeps the level of NIK low under constitutive conditions. Upon activation, the degradation complex is recruited to the active receptor complex. This leads to the degradation of cIAP1-cIAP2, thus allowing NIK to dissociate from the complex and subsequently activate the non-canonical NF-κB pathway [73,74,75,76,77,78,79]. However, in an interesting finding in both normal B cells and B cell-derived tumors, it has been shown that CD40 or BAFF receptor activation results in the complete degradation of TRAF3 and partial degradation of TRAF2 but not cIAP1-cIAP2. These findings demonstrate a ubiquitination cascade in which TRAF2 ubiquitinates and activates cIAP1-cIAP2, which then ubiquitinates TRAF3, leading to its degradation and enhanced NIK stabilization as well as processing of NF-κB2/p100 [80]. Low levels of TRAF proteins lead to the higher accumulation of NIK, which then phosphorylates p100 and IKKα, thereby activating the kinase activity important for multiple site phosphorylation of p100 at its C-terminal. Phosphorylated p100 is ubiquitinated by β-TrCP, leading to cleavage-dependent ubiquitination-mediated degradation of C-terminal part of p100, generating active p52 subunit [56,81]. The processing of p100 is important in context to various steps of regulation in the activation of NF-κB pathway. Unprocessed p100 binds RelA, RelB or c-Rel subunit via its C-terminal ankyrin repeats, further inhibiting the activity of Rel subunits [82,83]. Hence, in the context of activation of the non-canonical NF-κB pathway, stabilization of NIK and processing of p100 acts as one of the major steps involving multiple PTMs of its regulator molecules [58]. Specific stimuli-dependent activation of the non-canonical NF-κB pathway is important in regulating various important biological functions such as lymphoid organogenesis, B-cell survival and maturation, dendritic cell activation and bone metabolism [84,85,86,87,88,89,90]. In spite of being tightly regulated by various activators and inhibitory factors, the aberrant activation of the NF-κB pathway has been observed in many lymphoid malignancies. Besides its role in immune regulation, NF-κB members have been documented to regulate transcriptional activities that promote the malignant transformation and survival of cancer cells. Several studies demonstrate the presence of promiscuous mutations responsible for inhibiting TRAF2, TRAF3 and cIAP1/2 complex or enhancing the expression/stability of NIK and other receptor molecules like CD-40 and LTβR. Such mutations are associated with the abnormal activation of the non-canonical NF-κB pathway [85,91,92,93,94,95]. Additionally, recent studies suggest the interdependency of NF-κB-driven expression of target genes with epigenetic changes in the genome. In this review, we will discuss the activation and regulation of NF-κB signalling in inflammation and cancer in context to its interaction with transcription factors (TFs), kinases, epigenetic modifiers and non-coding RNAs. We focus on discussing the interdependent role of NF-κB-signalling components with transcription factors and chromatin modifiers in the aberrant activation of the NF-κB pathway, as well as in the active transcriptional activation of its target genes (summarized in Figure 1). Even before Vichow’s hypothesis on the origin of cancer from the site of inflammation, several inflammation-associated viral and bacterial infections (Hepatitis B, Helicobacter pyroli) were found to be associated with increased risk of malignancies of the liver, colon and stomach [96,97,98,99]. Additionally, statistical reports estimate that inflammatory viral infections contribute to >15% of all cancers [100,101]. It has been postulated that cancer cells can hijack the normal inflammatory mechanism to boost their growth and survival. In general, the normal function of immune cells is to trigger the innate and adaptive immune response to differentiate between self/non-self and destroy/engulf the foreign invaders. However, in the tumour microenvironment, cancer cells can alter the protective functions of immune cells and convert them to act as tumour-promoting cells. They are reprogrammed to secrete pro-survival inflammatory cytokines, allowing better proliferation, survival, migration, invasion and inhibition of apoptosis in cancer cells. Hence, tumour-promoting inflammation acts as one of the major hallmarks of cancer. But what remains unanswered are the regulatory molecules that link inflammation to cancer progression. One of the major pathways reported to be involved in manipulating the immune response machinery in the tumour microenvironment is the NF-κB pathway. Several studies have converged on the role of the NF-κB pathway as one of the critical missing links between inflammation and cancer. The first evidence comes from various studies reporting extensive sequence similarity between c-Rel and viral oncoprotein v-Rel in their N-terminal domain, a region referred to as the Rel Homology Region (RHR), and identification of oncogene Bcl3 as a member of the IκB family [102,103]. In addition, many cancer cell types show elevated levels of NF-κB expression and activation. Endogenous activation of NF-κB is reported in Hs294T melanoma cells due to altered equilibrium between IκBα degradation and resynthesis, leading to overall decrease in the level of IκBα expression [104]. Moreover, human colorectal cancer (CRC) epithelial cells have been observed to express enhanced NF-κB and IκBα, which is accompanied by the increased expression of cox-2 gene [105]. Similar selective activation of the NF-κB pathway is also reported in breast cancer. The RelA subunit of NF-κB is reported to be activated in breast cancer cell lines, whereas breast tumours are shown to exhibit an absence or low level of nuclear RelA, in contrast to activated c-Rel, NF-κB1 and NF-κB2 along with bcl2 expression, as compared to nontumorigenic adjacent tissue [106]. Most interestingly, the NF-κB family of transcription factors have been shown to contribute to the function and maintenance of tumour-initiating cells (TICs) in breast cancer. Experimental data indicates the activation of both the canonical and non-canonical NF-κB pathway to be important in the function of TICs by stimulating epithelial-to-mesenchymal transition (EMT) and upregulating the expression of the inflammatory cytokines IL-1β and IL-6 [107]. In a recent finding, Monica et al. reported the involvement of the NF-κB pathway towards resistance to endocrine and chemotherapies in breast cancer [108]. Furthermore, BRCA1 signalling, which is one of the prominent pathways activated in various cancer types including breast cancer and ovarian cancer, additionally possesses the capability to induce the NF-κB pathway [109]. Additionally, in lung carcinoma, the enhanced expression of IKKβ and NF-κB is reported as an important factor for tumour initiation and progression [110]. Another NF-κB activity dependent type of cancer is melanoma. Studies using mice models have shown that initiation of such tumours is HRas-mediated and involves the regulation by IKKβ in the activation of NF-κB [111]. In the cell line model of Diffuse large B-cell lymphoma (DLBCL), constitutive activity of IKK and high NF-κB DNA-binding activity is reported in the ABC-DLBCL subtype but not in the GC-DLBCL subtype [112]. Recently, Eluard et al. reported the presence of a new subset of Diffuse large B-cell lymphoma (DLBCL) patients showing enhanced RelB activation with aberrant gene expression and mutation profiles [113]. Based on these studies, it can be postulated that abnormal activation of the NF-κB pathway in various cancer types is critical for the survival of transformed cells, particularly in the suppression of apoptosis and senescence. Apart from endogenous elevated levels of NF-κB expression and activation in cancers (summarized in Table 1), there are also reports on aberrant activation of NF-κB pathway in cancer cells. Hence, there appears to be various extrinsic and intrinsic factors regulating the malignancy-associated enhanced activation of the NF-κB pathway in cancers. In addition to the role of elevated NF-κB activity in the survival of transformed cells, NF-κB is found to be activated in cancer stem cells (CSCs). In CSCs, it promotes the release of pro-inflammatory cytokines that exert anti-apoptotic and pro-proliferative activities [129]. One of the prominent factors involved in the activation of the canonical NF-κB pathway in both solid and hematologic malignancies is the tumour microenvironment (TME). A large number of immune cells (macrophages, dendritic cells, neutrophils, mast cells, T cells and B cells) are recruited to the TME, leading to the enhanced production of cytokines, growth and angiogenic factors and proteases that degrade the extracellular matrix to support cancer development and progression [130]. In solid tumours, the sustained activation of the NF-κB pathway is predominantly achieved through the continuous release of cytokines by tumour-associated macrophages (TAM) in the TME. One of the predominant properties of TAM is the ability to switch from M1- to M2-phenotype with enhanced release of anti-inflammatory cytokines [131,132], suggesting a crosstalk between cancer cells and neighboring macrophages. Interestingly, IKKβ and NF-κB are also reported to assist in the polarization of macrophages towards the M2 type, which fosters and protects the tumour cells instead of attacking them [133,134]. Hence, this permits malignant cells to bypass tumour immunosurveillance activity via NF-κB-mediated polarization of macrophages from the M1 to M2 phenotype. Activation of either of the canonical or non-canonical pathways in both solid and hematologic malignancies also depends on different sets of inflammation-associated cytokines and receptors activated in tumour cells. Induction of the canonical NF-κB pathway is initiated by pattern recognition receptors and diverse tumour-promoting cytokines, such as TNF, IL-1, and IL-17 [135]. On the contrary, activation of the non-canonical NF-κB pathway is triggered by signalling via a specific subset of TNFR superfamily members such as B-cell-activating factor belonging to TNF family receptor (BAFFR) [52,55], CD-40 [53], lymphotoxin β-receptor (LTβR) [54], receptor activator for nuclear factor κB (RANKL) [136], TNFR2 [137,138], Fn14 [139], etc. In addition to the involvement of immune regulatory molecules in activation of both the canonical and non-canonical NF-κB pathways towards cancer-promoting mechanisms rather than their classical immunosurveillance roles, the important observation is their expression in non-immune cells. Enhanced expression of CD-40 is reported in many non-immune cells, such as the intestinal epithelial cells (IECs) of patients with colon cancer. This, in turn, leads to the aberrant activation of non–canonical NF-κB pathway, suggesting the important link between immunosurveillance and tumorigenicity [140,141,142]. LTβR is expressed in lymphoid stromal and epithelial cells. BAFFR is predominantly expressed in B cells, whereas RANK, which is best known for its role in osteoclastogenesis, is also reported to be highly expressed in various cancer types like breast and prostate cancer cells, mediating the migration and skeletal metastasis of cancer cells [143,144]. In addition to the receptor dependent aberrant activation of NF-κB pathways in cancer, activating mutations in other signalling components of the non-canonical NF-κB pathway have been documented particularly in lymphoid malignancies [91]. Such activation is driven by the presence of selected mutations inactivating the genes encoding negative regulators of the pathway (TRAF2, TRAF3, TNFAIP3, BIRC3, MAP3K14, CYLD, cIAP1/cIAP2) and activating the regulator molecules (NF-κB1, NF-κB2, CD40, LTβR, TACI, and NIK) in various cancer types like multiple myeloma (MM), splenic marginal zone lymphoma (SMZL), MALT lymphoma and B-cell lymphoma [15,93,145,146,147,148,149] (Table 2). Mutations leading to constitutive activation of the kinase NIK in multiple myeloma have been found in NIK itself, that disrupts its binding with TRAF3, in turn causing dissociation of NIK from the inhibitory complex having TRAF2 and the ubiquitin ligases cIAP1 and cIAP2. This, in turn, results in NIK stabilization, leading to aberrant activation of the non-canonical NF-κB pathway [93,119]. The genetic selection of these driver mutations by cancer cells highlights the critical importance of the NF-κB pathway towards cancer progression and enhanced malignancy. In the case of multiple myeloma, mutations are also reported in many other signalling subunits of non-canonical NF-κB pathway—NF-κB2, TRAF2, TRAF3, BTRC encoding β-TrCP, which alters the inhibitory degradative pathway of NIK kinase by TRAF2/TRAF3 complex, leading to malignancy-associated activation of non-canonical NF-κB signalling [150]. Overexpression of NIK due to t(17;22) chromosomal translocation is also associated with the occurrence of multiple myeloma [93]. Oncogenic mutations in the TP53 protein are reported to be associated with higher RelA expression, in turn activating the canonical NF-κB pathway in human B-cell lymphomas such as Hodgkin lymphoma and, to a lesser extent in T-cell lymphoma cell lines as well [151,152]. Chromosomal translocation t(10;14)(q24;q32) of the NF-κB2 gene is observed to be associated with a variety of hematological malignancies, such as MALT lymphomas [153]. The translocation moves the IgG promoter to a region upstream of the bcl-10 gene, resulting in expression of a truncated bcl-10 protein, leading to activation of NF-κB [153]. Another reported translocation is t(11;18), which results in the generation of a chimeric protein, AP12-MALT1, which leads to NF-κB activation in B-cell lymphomas [154]. The modified NF-κB2 gene codes for the protein that lacks the ankyrin regulatory domain but still binds the kappa B sequence in vitro. Such rearrangement of NF-κB2 has been reported in both B-cell and T-cell lymphoma patients suggesting that translocation dependent truncation of the ankyrin domain may be a common mechanism in the abnormal activation of NFKB2 gene and its relevant role in lymphomagenesis [15,148,153,155,156,157]. Another genomic rearrangement event reported in DLBCL is on chromosome 10q24, which results in increased NFKB2 mRNA expression, causing constitutive expression of NF-κB2 [15,158]. Chromosomal translocation of the c-Rel gene to chromosome 2p 13–15 causing its enhanced amplification has been reported in DLBCLs with a large cell component, constituting approximately 50% of B-cell non-Hodgkin’s lymphomas [149,159,160,161]. This chromosomal aberration of c-Rel has also been found in primary mediastinal (thymic) B-cell lymphomas and follicular large cell lymphomas, and is reported to be associated with extra nodal presentation [120]. Another member of the NF-κB family, RelA, is mapped to be translocated to 11q13, a site where a number of genes involved in neoplastic development have already been mapped, suggesting a link between chromosomal translocation and the tumour-inducing role of RelA [162]. Activating mutations (translocation t(14;19)(q32;q13.1)) in another member of the NF-κB pathway, Bcl3, which is a proto-oncogene, have also been observed in B-cell leukaemia. The chromosomal translocation of Bcl3 results in its enhanced expression in leukemic cells as compared to normal blood cells [163]. In addition to biallelic deletion and chromosomal translocation, several other mutations, including missense mutation, frameshift mutation and in frame deletion are also reported to inactivate the TRAF3 gene, in turn inducing the activation of NF-κB pathway [91]. Besides the reported genomic abnormalities, there are several other factors that can influence the transcriptional activity of NF-κB pathway. Reports suggest that crosstalk with certain activating and inhibitory kinases such as Glycogen Synthase Kinase (GSK-3β), p38 and PI3K can either modulate the transcriptional activity of NF-κB or its upstream signalling pathways [164,165,166]. Kinases are reported to modulate the activity of NF-κB in glioma cell lines and pancreatic cancer cells through post-translational modification (PTM) of the NF-κB subunits (p65/p50) [166,167,168,169]. Further studies indicate that GSK-3β has no role in the nuclear accumulation of NF-κB, but instead alters the DNA-binding activity of NF-κB subunits by inducing hypermethylation of the target DNA [167,168,169,170,171]. Other kinases documented to regulate the NF-κB pathway include the Jun- N-terminal kinase (JNK) and p38 [172]. Though both the kinases can be induced by the same stimuli (TNFα) that activate NF-κB pathway, they have been found to display differential functions on NF-κB activity. p38 acts as a co-factor to modulate the transactivation machinery of NF-κB to regulate TNF-induced IL-6 gene expression, whereas a counteracting relationship occurs between JNK and NF-κB. NF-κB complexes downregulate the c-Jun amino-terminal kinase (JNK) cascade via upregulation of gadd45β/myd118 gene expression. Gadd45β, in turn, targets MKK7/JNKK2, a specific and essential activator of JNK. Mechanistically, binding of gadd45β with MKK7 blocks the catalytic activity of the latter, causing inhibition of the JNK pathway [173,174,175,176]. Hence, the aberrant activation of NF-κB pathway depends on multiple factors including cell type, micro-environment, PTMs, enzymatic activity of regulatory molecules and chromosomal abnormalities. Reports suggest the dependency of NF-κB components on various epigenetic factors for its activation in cancer cells. Reduced expression of histone methyltransferase EZH2 stimulates the expression of TRAF2/5 via the de-repression of their expression due to H3K27 hypermethylation by EZH2. The elevated TRAF2/5 expression, in turn, enhances TNFα-induced activation of NF-κB signalling, leading to an uncontrolled inflammatory reaction which ultimately contributes to tumorigenesis [178]. The enhanced activation and expression of NF-κB-signalling component proteins in various cancer types also depends on the epigenetically modified state of its own component genes and its target genes. Triple negative breast cancer cells display a high level of NF-κB activation due to the enhanced expression of NIK that is caused by the epigenetic alteration (histone H3 acetylation) of the NIK gene [179]. Hence, these studies suggest the plausible de-regulation of the NF-κB pathway due to epigenetic alterations. As discussed, the aberrant activation of the NF-κB pathway in cancer is a multifactorial event. Depending on the prevalent tumour microenvironment, malignancy-promoting mutations in the components of the NF-κB-signalling cascade, and the inflammatory molecules released by the tumour immune cells, the biological importance of the NF-κB pathway is diverted from the immunosurveillance mechanism towards tumour-promoting functions. NF-κB signalling has been shown to activate the expression of various inflammatory mediators, such as IL1β, TNF and IL6, which promote cancer development [180,181]. However, the question remains as—what factor(s) drives the variation in inflammatory response by the NF-κB pathway from a protective role towards a tumour-promoting role. The answer to this oncogenic shift is related to the severity of inflammation response which mostly occurs during chronic inflammatory conditions. During acute inflammatory conditions, NF-κB activation acts as a tumour immunosurveillance mechanism to assist in the targeting and elimination of transformed cells. For example, protein kinase D1-mediated activation of NF-κB signalling can induce the expression of antioxidant proteins such as MnSOD and anti-apoptotic proteins including A20 and cIAPs to prevent the accumulation of pro-tumorigenic ROS that can cause oncogenic mutations [182,183,184,185,186]. NF-κB-mediated inhibition of ROS accumulation can also repress the activity of pro-tumorigenic transcription factors such as STAT3 and AP1 [185]. In contrast, under chronic inflammatory conditions, the continuous presence of NF-κB stimuli seem to outperform the inhibitory role of the negative NF-κB regulators, leading to constitutive activation of NF-κB signalling. Such constitutive activity of NF-κB can exert pro-tumorigenic effects ranging from cell proliferation and cell survival to malignant cell invasion and metastasis. Many cancers arise from sites of chronic infection or inflammation due to elevated ROS production by neutrophils in response to invading pathogens. This innate immune response in turn causes DNA damage and genetic mutations, thereby triggering tumour initiation [186,187]. Though both the canonical and non-canonical NF-κB pathways are reported to be activated in various invasive and malignant cancers, the functional mechanism for downstream substrates involved in activation of the non-canonical NF-κB pathway are well characterized compared to the canonical pathway. The invasive nature of Glioblastoma Multiforme (GBM) cells has been reported to be associated with high RelB expression [116,117]. Work on mouse tumour xenograft models also showed activation of the non-canonical NF-κB pathway leading to regulation of the expression of its own regulator protein NIK, which, in turn, is reported to induce dramatic cell shape changes, increase tumour cell invasion and promote aggressive orthotopic tumour growth [123]. Point mutations at the promoter region of the telomerase reverse transcriptase (TERT) gene is one of the most frequent non-coding mutations in cancer. TERT promoter mutations (TPMs) are cancer type-specific and among the first few mutations reported in melanomas, glioblastomas and hepatocellular carcinomas [188,189,190,191]. In an interesting finding, non-canonical NF-κB signalling is reported to drive the expression of the TERT gene carrying −146 C > T mutation in its promoter region, causing telomerase reactivation, which is otherwise not activated via binding of ETS transcription factor [192,193]. This data specifically highlights a novel role of the non-canonical NF-κB pathway in the reactivation of telomerase in cancers. Hence, the level of inflammatory response and genetic changes in the cancer cells can act as some of the major factor(s) deciding the difference between acute inflammatory response versus aberrant activator response of the NF-κB-signalling pathway in cancer. Apart from activating the expression of its immune response target genes, aberrantly activated NF-κB signalling in cancer cells contribute to cancer progression by acting as a transcriptional activator of various other pro-tumorigenic genes involved in cell proliferation, inhibition of apoptosis, invasion, metastasis and angiogenesis. In-depth studies also show that NF-κB controlled genes regulating oncogenic properties are significantly different. NF-κB-dependent cancer-relevant genes mostly encode for cytokines, cell cycle genes like cyclin D1, matrix metalloproteinases (MMPs) and anti-apoptotic proteins. Numerous NF-κB target genes such as cIAP1/2, TRAF1/2, Bcl-xL, XIAP, MnSOD and IEX-1L confer antiapoptotic properties [106,194,195,196]. Specifically, the NF-κB target gene cIAP1/2 functions as an inhibitory factor of cancer cell apoptosis through directly binding and suppressing the effector caspases [197,198]. NF-κB signalling controls the epithelial to mesenchymal transition and metastasis, often via upregulation of matrix metalloproteinases (MMPs) [199]. In breast cancer, NF-κB is also reported to induce the expression of EMT-related genes such as Twist, intercellular adhesion molecule-1 (ICAM-1), endothelial leukocyte adhesion molecule 1 (ELAM-1), vascular cell adhesion molecule 1 (VCAM-1), MMPs and serine protease urokinase-type plasminogen activator (uPA), along with the expression of one of the major tumour promoting genes Bcl2 [200,201]. Interestingly, one study revealed a role for NIK in the phosphorylation, enzymatic activity and pseudopodal localization of membrane type 1 MMP in highly invasive tumours like glioblastoma that is distinct from its established kinase function in the non-canonical NF-κB pathway [202]. NF-κB signalling also contributes to tumour progression and invasion by controlling pro-angiogenic genes such as vascular endothelial growth factor (VEGF) and its receptors, macrophage inflammatory protein-1 (MCP-1) and CXC-chemokine ligand 8, also known as IL-8 (CXCL8) [203,204,205,206,207]. Activated NF-κB signalling in cancer transactivates the expression of cyclin D1 and c-myc that promote cancer cell proliferation [208,209]. Angiogenesis, the phenomenon of new blood vessel formation is one of the hallmark phenotypes of cancer cells. Tumour angiogenesis is dependent on proinflammatory cytokines, chemokines and growth factors such as MCP-1, IL-8, TNF-α and VEGF, secreted by tumour-associated macrophages (TAMs) via the activated NF-κB pathway. Furthermore, the recruitment of bone marrow-derived cells (BMDCs) to tumours for vasculogenesis is essential for tumour angiogenesis, which is found to involve NF-κB-mediated enhanced expression of IL-8 and angiogenin [210,211]. Subsequently, the expression and activation level of different NF-κB subunits can induce varying severity in different cancer types. In the case of ER-positive breast carcinoma, higher expression of RelB is associated with decreased relapse-free survival (RFS) and overall survival (OS) rate, whereas in other tumours, such as lung carcinoma, enhanced expression of NIK and RelB is associated with enhanced metastasis and shorter OS. Poor RFS outcome is reported to be associated with higher expression of non-canonical NF-κB target gene myoglobin [212,213,214]. Elevated RelB activity reported in a new subset of DLBCL patients is found to confer resistance to DNA damage-induced apoptosis along with increased cIAP2 expression [113]. In a more recent finding, sustained activation of the non-canonical NF-κB signalling is also shown to drive doxorubicin resistance in DLBCL via enhanced glycolysis [215]. Hence, these studies indicate the existence of a high degree of NF-κB dysregulation in cancer. While we discussed the multifaceted roles of the NF-κB pathway linking inflammation and cancer, it is also important to understand the interacting map of the components of this pathway with other macromolecules, which, in turn, regulate the transcription of pro-oncogenic transcripts (Figure 2). While NF-κB regulates the expression and activity of various regulatory factors, its own activity can also be regulated via direct association with several other transcription factors. The most prominent ones are proto-oncogenic transcription factors such as STAT3, p53, AP1 and ETS-related genes ERG, implicating their plausible cooperative function with NF-κB factors in inflammation and cancer [216,217,218]. Hence, depending on the promoter sequence and structure of the target genes, the functional link between NF-κB and other transcription factors might vary. One of the well-characterized factors known to co-associate with NF-κB is the STAT family members. NF-κB, in association with STAT3, regulates the expression of various cell cycle genes, anti-apoptotic genes and genes encoding cytokines and chemokines [219]. Studies suggest that the direct interaction of RelA and NF-κB1 members with STAT3 facilitates both the recruitment of NF-κB and STAT3 onto each other’s promoter sites [220,221,222]. In another context of regulation, STAT3 modifies the RelA subunit by recruiting acetyltransferase p300, resulting in the acetylation-dependent retention of NF-κB in the nucleus [223]. Such regulation leads to the enhanced activity of NF-κB (a tumour-promoting phenomenon) and hence, chronic stimulation of cytokines in the tumour microenvironment. Cross talk of NF-κB with transcription factor p53 also occurs [221]. Enhanced secretion of the pro-inflammatory cytokine TNFα triggers the formation of an active complex containing nuclear RelA and p53 on κB binding motifs, suggesting the importance of p53 in NF-κB-mediated gene expression induced by canonical stimuli [224,225]. In addition, some reports suggest that the RelA subunit and transcription factor p53 can regulate their respective transcriptional activities. p53 has been shown to inhibit NF-κB transcriptional activity, while the RelA subunit can also inhibit p53-dependent transactivation of target genes [221]. This constitutive activation of NF-κB, evoked by a p53 hot-spot mutant protein frequently found in tumours, provides an explanation for the fact that p53 mutations arise more than p53 deletions in tumours of various origin [222,226]. More recently, another transcription factor, the ETS family member ERG, has been identified to cross talk with NF-κB. As reported by various groups, the functional role of ERG is validated in various leukemia, Ewing sarcoma and prostate cancer [227,228,229,230]. Interestingly, NF-κB activation is elevated in ERG fusion-positive prostate cancer patients and cancer cell lines [231]. ERG is also reported to regulate expression of the NF-κB target gene, ICAM-1 in endothelial cells [232,233]. Another interesting study also revealed the cooperative function of p52 with transcription factor ETS1 in the reactivation of telomerase in cancers via a hotspot −146 C > T TERT promoter mutation [192]. On a similar line, a recent finding has shown the involvement of the non-canonical NF-κB pathway in altering the genomic binding landscape of transcription factor ETS1 that supports glioma progression [234]. Hence, a cross talk is predicted between NF-κB and other TFs at the level of activation and transcriptional regulation of NF-κB target genes, which requires further studies for in depth understanding of the mechanisms involved. Considering the challenge with highly evolving cancer cells which are resistant to many available therapies either through selected genetic mutations or positive adaptation to the cancer microenvironment, it is critical to understand new alternative modes of regulations adopted by cancer cells. In recent times, one such regulatory molecule showing relevance in context to its crosstalk with the components of NF-κB pathway is non-coding RNAs (ncRNAs). Altered regulation at the level of epigenome mediated by non-coding RNAs (microRNAs—miRNAs and long noncoding RNAs—lncRNAs) has been found to be a prevailing factor impacting various types of malignancies. Several miRNAs are transcriptional targets of NF-κB, such as miR-9, miR-21, miR-143, miR-146 and miR-224, which, in turn, act as a feedback mechanism for modulating the activity of NF-κB [235,236,237,238,239,240,241]. Out of these, miR-21 and miR-143 are reported to be involved in regulating the malignant phenotypes like invasion and metastasis in cancer types including breast cancer and HCC [238,239]. On the other hand, NF-κB can also induce the expression of proteins important for the transcriptional regulation of miRNAs. One such example is the NF-κB driven expression of Lin28 protein, which inhibits the processing and maturation of let-7 miRNAs—a family of tumour suppressor miRNAs whose expression is downregulated in many cancer types. Let7 miRNA also targets IL6. Thus, Lin28-mediated downregulation of Let7 miRNA leads to the higher expression of IL6 and further enhances NF-κB signalling in a positive feedback loop mechanism [242]. Subsequently, NF-κB activity is also regulated by the presence of several miRNAs mostly via repressive mechanisms. One such miRNA is miR-502e, which is reported to act as a tumour suppressor factor by altering cell proliferation in hepatoma cell lines and hepatocellular carcinoma by targeting NIK, thereby modulating the activity of non-canonical NF-κB signalling [243]. Many highly expressed long non-coding RNAs (lncRNAs) are also reported to regulate the activity of NF-κB. The lncRNA NKILA, was reported to mask the phosphorylation motifs of IκB, further inhibiting the activation of NF-κB [244]. Along with the aberrant activation of the NF-κB-signalling pathway, the expression of long non-coding RNAs (ncRNAs) is also dysregulated in different types of cancer cells, further regulating the degree of malignancy. The upregulated expression of lncRNA H19 in melanoma cells and Helicobacter pylori-induced expression of H19 in gastric cancer cells have been reported to be associated with enhanced cancer cell invasion and migration via activation of the NF-κB- and PI3K/Akt-signalling pathways [245,246]. Another NF-κB-associated lncRNA reported to be upregulated in cancer cells is lncRNA NEAT1. Its overexpression promotes proliferation, migration and invasion, influences the expression of EMT markers, and activates the NF-κB pathway in HeLa and SiHa cells [247]. H19 and NEAT1 are also reported to be associated with the resistance of cancer cells to chemotherapeutic drugs including bortezomib and dexamethasone respectively [248,249]. Hence, it can be speculated that subunits of NF-κB function in association with ncRNAs to impart their pro-tumorigenic roles along with chemoresistance functions in tumour cells whose mechanism remains elusive and requires further clarification. Though the NF-κB family of proteins lack endogenous chromatin modifying enzymatic activity, they can exert changes in the chromatin landscape either by acting as a mediator to recruit and position chromatin modifiers onto target genes in a specific sequence dependent manner or by regulating the expression and activity of those modifiers [63,250]. One noteworthy feature of NF-κB family members is their ability to form multimeric complexes. Apart from forming multimeric complexes with its own family proteins, NF-κB subunits are reported to form complexes with other proteins, which includes chromatin modifiers as well. Upon lymphotoxin treatment, non-canonical NF-κB signalling is activated and RelB/p52 dimer gets associated with the SWI/SNF chromatin remodeling complex via an adapter protein, requiem, to induce the expression of the BLC gene (CXCL13). Such interaction suggests an indirect role of activated NF-κB signalling in the epigenetic regulation of oncogene expression [250]. Additionally, the NF-κB pathway also acts as a key regulator in the enhanced expression of chromatin modifiers and its subunits/interacting proteins, such as Enhancer of Zeste Homologue 2 (EZH2), a histone-lysine N-methyltransferase enzyme involved in the epigenetic modification of histone protein (H3K27), thus conferring the hypermethylation-mediated repressive gene expression of anti-oncogenic genes [251]. In colorectal cancers, NF-κB activation in response to TNFα has been reported to induce the expression of EZH2, leading to the inhibitory promoter hyper-methylation of pro-apoptotic protein kinase cδ binding protein (PRKCDBP) and resultant increased growth of cancer cells [252]. Subunits of the NF-κB pathway can also act in a de-repression mechanism to remove the repressive chromatin marks and complexes. Some inducible gene promoters harbor high levels of the H3K9 dimethyl modification, associated with transcriptional silencing. However, upon stimulation, these marks are removed by the Aof1 histone demethylase, whose recruitment requires initially bound c-Rel dimers within the promoter region [253,254]. The NF-κB pathway is also reported to regulate RNA Polymerase II elongation by changing the chromatin landscape via recruitment of General Control Non-Derepressible 5 (GCN5) acetyltransferase complexes that primarily modify H4K5/K8/K12 lysine residues. The accumulation of acetylated H4 histone proteins leads to the association with BRD4, which then positively regulates transcription by recruiting the elongation factor P-TEFb [255]. Hence, the ability of the components of the NF-κB pathway to alter the chromatin landscape is not only limited to its signature DNA binding property but also extended to the recruitment of various chromatin modifiers assisting in transcriptional regulation. Since the discovery of NF-κB nearly four decades ago, the multi-faceted roles of NF-κB members and their new transcription-binding partners in cancer have been gaining more clinical relevance in recent years. Although inflammation was previously implicated to promote the malignancy of human cancers, the causal mechanisms underscoring the link between inflammation and cancer have not been adequately characterized. Recent studies showing the aberrant activation of the NF-κB pathway in various cancer types and the regulation of NF-κB members in various tumorigenic events support the role of NF-κB as a hub linking inflammation and cancer. Although the occurrence of activating mutations in the NF-κB pathway is predominantly observed in hematological malignancies, the activation of NF-κB in solid tumours is also not negligible. The functional shift of the NF-κB pathway from inflammation to oncogenesis is mostly driven by the onset of chronic inflammatory conditions. NF-κB members can exert pro-oncogenic functions during cancer development through the activation of target gene transcription by their heterodimers. In addition, NF-κB components have also been demonstrated to interact with other factors, including transcription factors, kinases, epigenetic modifiers and other biological molecules like ROS and ncRNAs, to drive multiple oncogenic activities. Despite substantial progress in the understanding of various aspects of NF-κB signalling in cancer, the approaches for the targeted inhibition of specific components in the signalling pathway are limited due to various challenges. These challenges arise from the complex nature of its activity in different cancer types. Recent genomics studies have revealed the active selection of a wide range of driver mutations in cancer cells, some of which are important to facilitate the activation of the NF-κB pathway. In addition, epigenetic alterations have been documented to contribute to the aberrant activation of the NF-κB pathway. Conversely, the activated NF-κB pathway is also reported to confer changes in the chromatin landscape of cancer cells towards enhanced malignant phenotypes. Hence, these findings can potentially pave new ways for the development of precision medicine to improve the efficiency of existing cancer therapies and overcome the phenomenon of multidrug resistance in most of the cancer types.

PMC10001255

Agnieszka Sroka-Oleksiak,Wojciech Pabian,Joanna Sobońska,Kamil Drożdż,Tomasz Bogiel,Monika Brzychczy-Włoch

Do NAAT-Based Methods Increase the Diagnostic Sensitivity of Streptococcus agalactiae Carriage Detection in Pregnant Women?

23-02-2023

GBS,PCR,pregnant women,primers,real-time PCR,Streptococcus agalactiae

The aim of the study was to evaluate particular polymerase chain reaction primers targeting selected representative genes and the influence of a preincubation step in a selective broth on the sensitivity of group B Streptococcus (GBS) detection by nucleic acid amplification techniques (NAAT). Research samples were vaginal and rectal swabs collected in duplicate from 97 pregnant women. They were used for enrichment broth culture-based diagnostics, bacterial DNA isolation, and amplification, using primers based on species-specific 16S rRNA, atr and cfb genes. To assess the sensitivity of GBS detection, additional isolation of samples preincubated in Todd-Hewitt broth with colistin and nalidixic acid was performed and then subjected to amplification again. The introduction of the preincubation step increased the sensitivity of GBS detection by about 33–63%. Moreover, NAAT made it possible to identify GBS DNA in an additional six samples that were negative in culture. The highest number of true positive results compared to the culture was obtained with the atr gene primers, as compared to cfb and 16S rRNA primers. Isolation of bacterial DNA after preincubation in enrichment broth significantly increases the sensitivity of NAAT-based methods applied for the detection of GBS from vaginal and rectal swabs. In the case of the cfb gene, the use of an additional gene to ensure the appropriate results should be considered.

Do NAAT-Based Methods Increase the Diagnostic Sensitivity of Streptococcus agalactiae Carriage Detection in Pregnant Women? The aim of the study was to evaluate particular polymerase chain reaction primers targeting selected representative genes and the influence of a preincubation step in a selective broth on the sensitivity of group B Streptococcus (GBS) detection by nucleic acid amplification techniques (NAAT). Research samples were vaginal and rectal swabs collected in duplicate from 97 pregnant women. They were used for enrichment broth culture-based diagnostics, bacterial DNA isolation, and amplification, using primers based on species-specific 16S rRNA, atr and cfb genes. To assess the sensitivity of GBS detection, additional isolation of samples preincubated in Todd-Hewitt broth with colistin and nalidixic acid was performed and then subjected to amplification again. The introduction of the preincubation step increased the sensitivity of GBS detection by about 33–63%. Moreover, NAAT made it possible to identify GBS DNA in an additional six samples that were negative in culture. The highest number of true positive results compared to the culture was obtained with the atr gene primers, as compared to cfb and 16S rRNA primers. Isolation of bacterial DNA after preincubation in enrichment broth significantly increases the sensitivity of NAAT-based methods applied for the detection of GBS from vaginal and rectal swabs. In the case of the cfb gene, the use of an additional gene to ensure the appropriate results should be considered. The recto-vaginal colonization of Streptococcus agalactiae in pregnancy is estimated at approximately 10–40% and constitutes one of the greatest risk factors of premature birth or the development of sepsis, meningitis and pneumonia among newborns [1,2,3,4,5]. Since 1996, the American College of Obstetricians and Gynecologists (ACOG) followed by the Centers for Disease Control and Prevention (CDC) and the American Academy of Pediatrics (AAP) has recommended screening for group B Streptococcus (GBS) carriage in all pregnant women between 35 and 37 weeks of gestation and intrapartum antibiotic prophylaxis in case of GBS-positive results [1]. In microbiological diagnostics, the gold standard for GBS screening is incubating specimens in enrichment broth and then culturing them on solid media. Although these methods are simple and inexpensive, they are limited by their time-consuming nature and lower sensitivity compared to molecular methods. The American Society for Microbiology (ASM) recommended the additional application of nucleic acid amplification techniques (NAAT)-based methods, usually polymerase chain reaction (PCR) variants [1]. On the other hand, the CDC approved real-time PCR assays targeting the cfb gene (which encodes CAMP factor) directly from samples if the results of culture are negative or not available [6]. Moreover, the research by Tickler et al. describes cases of obtaining negative results for GBS colonization in PCR based on the detection of the cfb gene accompanying positive results in the culture. This is related to the presence of GBS strains with a chromosomal deletion in the region of the cfb gene [7]. There are also other genes are also used in GBS detection by molecular methods, e.g., atr, sip, sodA, scpB or 16S rRNA [8]. Each of them, depending on the tests performed, is characterized by a different degree of sensitivity and specificity. Based on the available literature data, it was found that of the genes listed above, except for cfb, the atr gene is the most commonly used [9,10,11,12,13,14,15]. False-negative results from culture or non-standardized NAAT methods can result in delayed treatment, increasing the risk of serious neonatal and maternal infections and/or mortality [1,10]. Hence, it is crucial to develop existing or novel diagnostic methods to prevent complications and improve outcomes. The aim of this study was to evaluate the sensitivity of GBS detection in a group of pregnant women by NAAT-based methods on cfb and atr genes and the 16S rRNA-conserved gene in comparison to the enrichment broth culture. The second goal was to assess the sensitivity of NAAT-based methods with and without preincubation in selective Todd-Hewitt (TH) broth supplemented with colistin and nalidixic acid. The study included 97 pregnant women aged 23–40 years old from whom vaginal and rectal swabs were collected in different trimesters of pregnancy. The inclusion criteria used for the recruitment of patients to the investigation were the following: age between 18 and 40 years old, pregnancy, lack of antibiotic or probiotic use for up to 30 days before getting pregnant and during pregnancy and written consent to participate in the research. The pregnant women who met at least one of the following conditions were excluded from the study: autoimmune diseases, immune disorders, a high-risk pregnancy, rupture of the membranes, premature birth or clinical symptoms of urinary tract infection. The study has been approved by the Bioethics Committee (No. KBET/1072.6120. 51.2017) and conducted in accordance with the provisions of the Declaration of Helsinki. The research material consisted of swabs from the lower vagina (vaginal introitus) (n = 124) and rectum (n = 124) collected in duplicate from all recruited pregnant participants (n = 97) during a routine prenatal medical visit. The first vaginal and rectal swabs were placed in a non-nutrient Amies transport medium (Eurotubo) and the second swabs in 2 mL of 0.9% NaCl. The research samples were delivered to the Department of Microbiology, Jagiellonian University Medical College. Each time, the first vaginal and rectal swabs were used for the culture diagnostics according to ASM recommendations [1], while the second swabs were used for bacterial DNA isolation (Figure 1). The swabs were preincubated in the TH liquid medium supplemented with colistin and nalidixic acid (Becton Dickinson, Microbiology Systems, Cockeysville, MD, USA) for 18–24 h at 37 °C in aerobic conditions and then transferred to a solid medium, including Columbia blood agar with 4% sheep blood (Becton Dickinson, Microbiology Systems, Cockeysville, MD, USA) and Granada medium (Becton Dickinson, Microbiology Systems, Cockeysville, MD, USA). The cultures were incubated for 18–24 h at 37 °C under aerobic conditions. The growing colonies were subjected to classical species identification towards GBS (assessment of Gram-stained preparations, presence of catalase with 3% hydrogen peroxide), Streptococcal Grouping Kit latex test (Becton Dickinson, Microbiology Systems, Cockeysville, MD, USA), and finally API Strep test (bioMérieux, Marcy-l’Étoile, France) and/or MALDI-TOF-based identification. After microbiological identification, the swabs in the TH medium (after preincubation) were frozen at −80 °C in order to preserve the materials for possible further tests. Bacterial DNA was extracted using the manufacturer’s protocol for the EURx Bacterial and Yeast Genomic DNA Purification Kit (EURx, Gdańsk, Poland). Additionally, during the enzymatic lysis step, 7 µL of lysozyme (50 mg/µL, Sigma-Aldrich, St. Louis, MO, USA) and 3.5 µL of mutanolysin (2 U/µL, Sigma-Aldrich, St. Louis, MO, USA) were added to the samples. To assess the sensitivity of GBS detection, an additional isolation of bacterial DNA was performed and then “de novo” amplified, after samples preincubation in TH broth supplemented with colistin and nalidixic acid (Figure 1). The isolates were used to perform a PCR amplification to verify the presence of S. agalactiae DNA in each sample. Based on the literature data, three pairs of primers were selected: the GBS species-specific 16S rRNA gene—F1-IMOD [16], atr—encoding the glutamine transport protein of S. agalactiae [17] and cfb—encoding the CAMP factor in S. agalactiae [18]. The sequences of all primers are shown in Table 1. The standardization of the reaction conditions was carried out with the use of a DNA sample extracted from the S. agalactiae ATCC 12386 reference strain. Conventional PCR was used for the detection of GBS species-specific 16S rRNA and atr genes. The total volume of the reaction mixture was 20 µL and consisted of 4 μL of Silver Taq Polymerase (Syngen, Wrocław, Poland), 0.6 μL of forward and reverse primers at the concentration of 10 µM each, 11.8 μL of nuclease-free water (A & A Biotechnology, Gdańsk, Poland) and 3.0 μL of DNA extracts. The amplification was performed in a T-100 thermocycler (BioRad, Hercules, CA, USA) using the following thermal profile (the same for both 16S rRNA and atr genes primers): 95 °C–15 min, 35 cycles (95 °C–20 s, 55 °C–60 s, 72 °C–60 s) and final elongation at 72 °C for 10 min. In all the amplification rounds, positive and negative controls were used (DNA of GBS reference strain and nuclease-free water, respectively). Next, 5 µL of each amplicon was subjected to electrophoretic separation in 1.5% agarose gel (Prona ABO, Gdańsk, Poland) in a 1× concentrated TBE running buffer (Sigma-Aldrich, St. Louis, MO, USA). The amplicons were visualized in the FastGene® FAS-Digi PRO (Nippon Genetics Europe, Düren, Germany) gel documentation system. The size of the product for the 16S rRNA was 405 bp, while for the atr gene it was780 bp; a marker of DNA size ranging from 100 to 1000 bp (A & A Biotechnology, Gdańsk, Poland) was used. A real-time PCR method was applied to detect the cfb gene. The total volume of the reaction mixture was 25 µL and consisted of 12.5 μL of RT HS-PCR Mix probe (A & A Biotechnology, Gdańsk, Poland), 0.5 μL of each primer (10 µM), 0.5 μL of fluorescent probe (10 µM), 8.5 μL of nuclease-free water (A & A Biotechnology, Gdańsk, Poland) and 2.5 μL of DNA extracts. The amplification was performed in CFX96 thermocycler (BioRad, Hercules, CA, USA) using the following thermal profile: 95 °C–10 min, 40 cycles (95 °C–15 s and 60 °C–60 s). The statistical analyses were carried out using STATISTICA version 13.3 with the Medical Kit, version 4.0.67. The McNemar test was used for the evaluation of statistically significant differences between each method, and the Cochran Q test with Dunn’s post-hoc test were used to compare the results obtained from the three methods. The level of significance was set at α = 0.05. The study involved 97 pregnant patients, from whom vaginal and rectal swabs were collected in different trimesters of pregnancy. The majority of the samples were collected in the third trimester of pregnancy (n = 84), then in the first trimester (n = 23) and the second trimester (n = 17). Some patients were studied in both the first and third trimesters or in the second and third trimesters, hence it was possible to collect material twice at different stages of pregnancy from the same patient. In standard microbiological diagnostics, 27 vaginal and 22 rectal samples were GBS-positive, while the remaining 97 vaginal and 102 rectal samples were negative. The amplification products for 16S rRNA gene and atr gene are presented in the Figure 2a,b. In each of the PCR methods used, a band was obtained at the appropriate height for the positive control—the reference strain S. agalactiae ATCC 12386—and no band for the negative control (DNase and RNase-free water), which proves that the appropriate conditions and course of the amplification reaction were used. The summary results for GBS detection by PCR (with the use of 16S rRNA, atr and cfb genes-targeting primers) performed directly from clinical materials without previous incubation in TH broth with antibiotics are presented in Figure 3. The positive amplification results for 16S rRNA conserved genes were observed in the case of 37.10% (n = 46) vaginal and 23.39% (n = 29) rectal samples (Figure 3). Meanwhile, twenty-seven samples (nineteen vaginal and eight rectal) were true positive (in accordance with the enrichment broth culture method which is a gold standard). False-negative results were related to nine vaginal and thirteen rectal samples. The positive results for atr gene presence were detected in the case of 16.13% (n = 20) vaginal and 7.26% (n = 9) rectal samples (Figure 3). Among them, respectively, eighteen (90.0%) vaginal and eight (88.89%) rectal samples were true positive. Moreover, twenty-three samples (nine vaginal and fourteen rectal) were positive in culture but negative in PCR (interpreted as false negative). Meanwhile, three additional samples (two vaginal and one rectal) were negative in culture but positive for atr gene in the PCR method (Figure 4a). The results with th use of cfb gene were positive for 14.52% (n = 18) vaginal and 7.26% (n = 9) rectal samples (Figure 3). Among them, respectively, 83.3% (n = 15) vaginal and 88.89% (n = 8) rectal samples were true positive. Meanwhile, 20.97% (n = 26) of samples were false negative (12 vaginal and 14 rectal). Moreover, four samples were detected as cfb positive (of which two were also positive for atr gene) but negative in the culture method (Figure 5a). In order to improve the sensitivity of GBS detection by PCR and real-time PCR with the use of atr and cfb primers, additional isolation of bacterial DNA from the TH medium after the preincubation step was performed. After this additional step, for atr primers, all the samples positive in culture were also detected by PCR. Moreover, one additional vaginal sample was positive in PCR, but negative in culture (Figure 4b). The application of preincubation in an enrichment broth medium also improved the detection of GBS with an application of cfb-targeting primers and increased the proportion of positive results by 9.68% (n = 12) in vaginal and by 9.68% (n = 12) in rectal samples, but still 1.61% (n = 2) samples were false negative for the cfb gene (Figure 5b). The summary results for the cfb and the atr genes detection after preincubation are shown in Figure 6. Due to the large discrepancies in the results with the use of species-specific 16S rRNA primers in relation to the culture method with preincubation in enrichment broth and primers tested in this study (for atr and cfb gene), we decided not to make this comparison. The discrepancies were most probably due to a close relatedness (16S rDNA sequences) between some other Gram-positive cocci and the inability of the applied primers to distinguish between them and amplify S. agalactiae DNA exclusively. The observed differences in the detection of GBS in samples without preincubation in TH broth with antibiotics for the atr and cfb genes were not statistically significant (p = 1.00) in contrast to the results obtained for 16S rRNA (p < 0.000001) also in relation to the culture method with the preincubation in enrichment broth (p < 0.000004). This observation had a direct impact on the sensitivity and specificity, which were presented in Table 2. The application of the preincubation step before bacterial DNA isolation for PCR purpose allowed for a sensitivity of 100% for the atr gene (both in vaginal and rectal samples), compared to 100% (vaginal samples) and 90.90% (rectal samples) for the cfb gene (Table 2). Summarizing, the use of 18–24 h preincubation in a TH broth with antibiotics increases the sensitivity of GBS detection in PCR by about 33–63% (33.30–63.60% for atr primers and 44.40–54.50% for cfb primers). Additionally, for epidemiological purpose, an analysis of GBS carriers’ status in the third trimester of pregnancy was performed, according to the ASM recommendations [1]. In our study among all 97 pregnant patients, 84 were in the third trimester of pregnancy, which were further analyzed. Based on the NAAT, after preincubation in TH broth, twenty-one (25%) patients were identified as GBS carriers–three more compared to NAAT-based methods directly from clinical materials and two more compared to the gold standard (Table 3). In our study, we assessed the sensitivity of selected NAAT-based methods (PCR and real-time PCR) with the use of three different target genes (16S rRNA, atr and cfb) to detect of GBS from vaginal and rectal swabs with preculture in selective broth and compared to direct testing of clinical samples. All the results were compared to the culture method with the preincubation in enrichment broth – the gold standard in microbiological diagnostics [1]. Our research clearly supports the necessity to use the preincubation step (despite the fact of extending the time required to obtain the final results) before performing molecular tests, regardless of the targeted gene (atr, cfb, etc.). This has a direct impact on the increase of sensitivity by about 33–63%. After the preincubation step (for 18–24 h in TH broth medium), we showed higher sensitivity of atr-targeting primers for GBS detection in comparison to the cfb and 16S rRNA-directed primers. The preculture did not have an effect on the specificity of detection by atr and cfb primers, which was similar to these noted before preincubation. Based on the literature data concerning the identification of GBS by molecular methods, cfb is the most frequently used gene [9,10,11,12,13,14,15]. Although the obtained sensitivity and specificity of GBS detection based on cfb gene investigation, as it has been shown previously, is relatively high, our research shows that these values may be lower when compared with the sensitivity of other genes, e.g., atr. Similar conclusions can be drawn about the basis of the results in the research by Carrillo-Avila et al. [19] and Mousavi et al. [20]. For example, in the studies carried out by the team of Carrillo-Avila the number of positive results using the cfb gene was 73, and for the sip gene it was 75, when performed on 78 samples which indicated positive by culture. This resulted in sensitivity and specificity at the levels of 93.58% and 94.62% for cfb and 96.15% and 95.45% for sip gene, respectively [19]. In the previous study, the sensitivity and specificity for the cfb gene-based investigation were estimated at the level of 73.33% and 87.23%, while for the atr gene—80% and 86.70%, respectively [20]. Although the obtained differences in the results between genes are not statistically significant, in the context of individual pregnant patients who receive false-negative results, this is of great importance. At this point, it is also worth emphasizing that some commercial tests or automatic systems use the cfb gene-based methodology for GBS detection. For example, in the research by Vieira et al., in addition to the real-time PCR and culture methods, Xpert® GBS rapid test (Cepheid, Sunnyvale, CA, USA) was used [21]. This device enables automatic isolation, purification and amplification of the target sequence for the cfb gene by qPCR. The short time from the isolation to the results, which is only 50 min, favors the use of this method. However, the sensitivity and specificity of Xpert GBS in comparison to qPCR were, respectively, 53.2% and 93%. By contrast, for the reference culture method the figures were 61.80% and 75.80%, respectively [21]. In the subsequent studies, the use of Xpert GBS allowed for much higher sensitivity (86.70%) and specificity (95.60%) of the results, but the reference culture method was applied only for this comparison and no additional molecular tests were performed. Nevertheless, it was also noted that Xpert GBS can generate errors caused by factors such as excess mucus or feces causing inhibition of the PCR reaction and influencing the microfluidic channel in the cassette [22]. Furthermore, in the work by Helmig et al. with the use of Xpert GBS, the sensitivity and specificity of detection were estimated at 100% (86.28–100%) and 97.50% (91.26–99.70%) respectively, and the positive predictive value was 92.60%. However, for one patient, no result was obtained despite retesting [23]. An alternative to commercial GBS identification tests may be IDI-Strep B kit (IDI, Sainte-Foy, QC, Canada), which also targets the cfb gene. The sensitivity and specificity of this test are comparable to Xpert GBS. However, due to the high cost of testing, these platforms can only be used for the detection of maternal intrapartum colonization [24]. Therefore, in the routine diagnostics of GBS carriage, in addition to culture, the use of PCR targeting cfb gene and additionally another gene should be considered. The atr gene tested in our research is a good candidate, which has also been previously confirmed by the research of other authors. In the work by de-Paris et al. [25], positive results with the use of the culture method were obtained in 42 (15.96%) samples, and the use of particular genes primer sequences enabled the detection of the atr gene in as many as 71 (26.99%) of GBS-positive samples. All the samples with positive results in culture were also positive in the PCR method, so the sensitivity of the PCR compared to the gold standard was 100%. Obtaining this high sensitivity was possible thanks to the application of the preincubation step in selective enriched broth before PCR was performed. Nevertheless, in other studies, the specificity for the detection of the atr gene by PCR was from 82.60% [26], through 85.60% [27] to 100% [28]. On the other hand, the specificity of the molecular methods compared to culture was estimated at 73.10–73.60% vs 95.60% [28] with a negative predictive value of 100%, which is important in the context of obtaining negative results that are true negatives. This is then equivalent to skipping antimicrobial prophylaxis during a labor [29]. Based on the literature data, no commercial PCR test based on the atr gene has been reported so far. For both the atr and cfb genes, the total number of GBS positive results in the vaginal samples was 30, while in the rectum samples it was 23 (for the atr gene) and 21 (for the cfb gene). There were three discrepancies within the samples group—one of them was positive for the atr gene and negative for cfb, while two other samples were vice versa. Although the differences between the results obtained for the atr and cfb genes detection were not statistically significant, in this situation it should be justified to consider application of methodology based on the use of a third gene to conclude the results. A negative result for cfb, while being positive for another GBS specific gene, could be the reason for the chromosomal deletion of this gene, which was described in the introduction [7]. Therefore, the obtained results would be false negative in relation to culture methods. This would explain our results for two rectal samples, which were positive in the culture and for the atr gene but simultaneously negative for the cfb, despite the use of preincubation and reamplification steps. The use of species-specific 16S rRNA-encoding gene primers in amplification without preincubation significantly increased the percentage of positive results in relation to those obtained with the use of atr and cfb genes and culture methods. Undoubtedly, the advantage of primers based on 16S rRNA is the high probability of GBS detection, but it can result in generating false-positive results, which is also confirmed by our research. The sequences of these primers were chosen from a veterinary publication on the detection of GBS in milk [16], but according to the authors, using a pair of F1-IMOD primers, a large collection of S. agalactiae isolates, including bovine and human isolates, as well as reference strains were identified in 100% [16]. The online sequence analysis showed that the primers also enable the detection of other species belonging to Streptococcus spp. which explains the significantly higher sensitivity and low specificity in relation to the other molecular methods and the culture method. An additional argument supporting the above explanation is the source of the collected samples—vagina and rectum, which are ecosystems abundant in various species of bacteria, including the Streptococcus spp. representatives. Nevertheless, this genus is one of the dominant groups of bacteria also in human milk [30]. Based on these examples, one of the limitations of current methods (in addition to those mentionedbefore) is the abundance of amplification methods, manifested in the use of different primer pairs—allowing for amplification of different genes or GBS genome regions, which also translates into different results. Comparing the PCR results to the gold standard (culture method according to ASM recommendation), in our study we obtained five positive results for the cfb and atr genes (two of them were positive for both genes, one for the atr gene only and two for the cfb gene only), which were negative in culture. This observation can be the result of a low number of bacterial cells (cfu/mL—colony forming unit) or vaginal colonization by GBS at the low level, damaged cells and presence of DNA fragments only. Other explanations could be the presence of other bacteria in the clinical material that constitute the vaginal microbiota, but inhibit the growth of GBS despite the use of selective broth in the culture [25]. For example, the growth of S. agalactiae in TH broth with gentamicin and nalidixic acid may be inhibited by the presence of bacteria of the Enterococcus genus [31], which may explain the negative results in culture and positive results for these samples in PCR/real-time PCR method. In order to avoid this situation, in our study we used a preincubation step in TH broth with colistin (instead gentamicin) and nalidixic acid. The discrepancies in our results of culture and molecular methods may result from the presence of small numbers of bacterial cells in the tested material or sample collection procedure itself. The culture method might then have been insufficient for GBS detection [22,27]. In addition, there are GBS strains that do not show hemolysis on the Columbia blood agar media, which makes their identification difficult and has a direct impact on discrepancies while NAAT-based methods are applied (compared to culture as a reference) [24]. However, in our study Granada medium was also used to exclude colonies with a different phenotype. Considering the dynamics of genetic variation in S. agalactiae strains, which may manifest in different phenotypes, the implementation of molecular methods should be considered as an auxiliary tool in the routine diagnostics of GBS carriage. Moreover, the future direction of the current method should be based on the development of a universal multiplex PCR to simultaneously detect GBS species and resistance genes. This is important in light of the increasing resistance to macrolides and clindamycin and the limitations of current molecular methods. NAAT-based assays could play an important role and their results could support rapid and effective therapy. This study demonstrated that in the S. agalactiae detection by PCR methods, a greater diagnostic value lies in primers enabling the amplification of genes specific for this species: atr and cfb in relation to primers based on 16S rRNA genes. Moreover, the use of 18–24 h preincubation in TH broth with appropriate antibiotics significantly increases the sensitivity in the detection of GBS carriers by NAAT-based methods compared to samples derived directly from clinical material.

PMC10001263

Jiro Miyamae,Masaharu Okano,Fumihiko Katakura,Jerzy K. Kulski,Tadaaki Moritomo,Takashi Shiina

Large-Scale Polymorphism Analysis of Dog Leukocyte Antigen Class I and Class II Genes (DLA-88, DLA-12/88L and DLA-DRB1) and Comparison of the Haplotype Diversity between Breeds in Japan

06-03-2023

dog,dog leukocyte antigen (DLA),polymorphism,haplotype diversity

Polymorphisms of canine leukocyte antigen (DLA) class I (DLA-88 and DLA-12/88L) and class II (DLA-DRB1) genes are important for disease susceptibility studies, but information on the genetic diversity among dog breeds is still lacking. To better elucidate the polymorphism and genetic diversity between breeds, we genotyped DLA-88, DLA-12/88L, and DLA-DRB1 loci using 829 dogs of 59 breeds in Japan. Genotyping by Sanger sequencing identified 89, 43, and 61 alleles in DLA-88, DLA-12/88L, and DLA-DRB1 loci, respectively, and a total of 131 DLA-88–DLA-12/88L–DLA-DRB1 haplotypes (88-12/88L-DRB1) were detected more than once. Of the 829 dogs, 198 were homozygotes for one of the 52 different 88-12/88L-DRB1 haplotypes (homozygosity rate: 23.8%). Statistical modeling suggests that 90% of the DLA homozygotes or heterozygotes with one or other of the 52 different 88-12/88L-DRB1 haplotypes within somatic stem cell lines would benefit graft outcome after 88-12/88L-DRB1-matched transplantation. As previously reported for DLA class II haplotypes, the diversity of 88-12/88L-DRB1 haplotypes varied remarkably between breeds but was relatively conserved within most breeds. Therefore, the genetic characteristics of high DLA homozygosity rate and poor DLA diversity within a breed are useful for transplantation therapy, but they may affect biological fitness as homozygosity progresses.

Large-Scale Polymorphism Analysis of Dog Leukocyte Antigen Class I and Class II Genes (DLA-88, DLA-12/88L and DLA-DRB1) and Comparison of the Haplotype Diversity between Breeds in Japan Polymorphisms of canine leukocyte antigen (DLA) class I (DLA-88 and DLA-12/88L) and class II (DLA-DRB1) genes are important for disease susceptibility studies, but information on the genetic diversity among dog breeds is still lacking. To better elucidate the polymorphism and genetic diversity between breeds, we genotyped DLA-88, DLA-12/88L, and DLA-DRB1 loci using 829 dogs of 59 breeds in Japan. Genotyping by Sanger sequencing identified 89, 43, and 61 alleles in DLA-88, DLA-12/88L, and DLA-DRB1 loci, respectively, and a total of 131 DLA-88–DLA-12/88L–DLA-DRB1 haplotypes (88-12/88L-DRB1) were detected more than once. Of the 829 dogs, 198 were homozygotes for one of the 52 different 88-12/88L-DRB1 haplotypes (homozygosity rate: 23.8%). Statistical modeling suggests that 90% of the DLA homozygotes or heterozygotes with one or other of the 52 different 88-12/88L-DRB1 haplotypes within somatic stem cell lines would benefit graft outcome after 88-12/88L-DRB1-matched transplantation. As previously reported for DLA class II haplotypes, the diversity of 88-12/88L-DRB1 haplotypes varied remarkably between breeds but was relatively conserved within most breeds. Therefore, the genetic characteristics of high DLA homozygosity rate and poor DLA diversity within a breed are useful for transplantation therapy, but they may affect biological fitness as homozygosity progresses. The major histocompatibility complex (MHC) molecules play important roles in inducing acquired immunity by presenting peptides derived from foreign antigens, such as germs and viruses that T cells recognize as non-self, resulting in the elimination of these antigens. The MHC molecules are classified into class I (MHC-I) and class II (MHC-II), and regulate self- and non-self discrimination in immunity by presenting antigen peptides to CD8+ and CD4+ T cells, respectively [1,2]. The MHC genes encoding MHC-I and MHC-II molecules are composed of multigene families; each of them is extremely polymorphic in many animals. For example, so far, more than 34,000 human leukocyte antigen (HLA) alleles have been identified and reported in the IPD-IMGT database (https://www.ebi.ac.uk/ipd/imgt/hla/ (accessed on 28 November 2022)). In addition, specific HLA alleles associated with susceptibility or resistance to various diseases [3,4,5] and matching of HLA polymorphisms between donor and recipient in transplantation are important factors for suppressing alloimmune responses [6,7,8,9]. The domesticated dog (Canis lupus familiaris) is one of the major companion animals of humans that also is used for biomedical research, such as on the pathobiology of cancers and autoimmune diseases, whose clinical phenotypes (presentations) are similar to those in humans, and in transplantation studies as a preclinical model [10,11]. A draft of the dog genome sequence was determined in the early 2000s, and the dog leukocyte antigen (DLA) loci were located on two chromosome (chr) segments, chr 12 and chr 18 [12,13]. Overall, three DLA class I (DLA-I) loci (DLA-88, DLA-12, and DLA-64) and four DLA class II (DLA-II) loci (DLA-DRA, DLA-DRB1, DLA-DQA1, and DLA-DQB1) were mapped on chr 12, and one divergent DLA-I locus DLA-79 was mapped on chr 18. To date, 173 DLA-I and 297 DLA-II alleles have been identified and released by the Canine MHC Nomenclature Committee into the IPD-MHC database (https://www.ebi.ac.uk/ipd/mhc/group/DLA/ (accessed on 28 November 2022)). Of the 173 DLA-I alleles identified in several polymorphism studies using more than 500 dogs in total, 139, 17, 9, and 8 alleles were identified in the DLA-88, DLA-12, DLA-64, and DLA-79 loci, respectively [14,15,16,17,18]. In contrast, the polymorphism analyses of the class II region, using more than 10,000 dogs of over 200 breeds, identified 1, 181, 30, and 86 alleles in DLA-DRA, DLA-DRB1, DLA-DQA1, and DLA-DQB1 loci, respectively [19,20,21,22]. Genetic diversity of the DLA-II haplotypes with linked DLA-DRB1, DLA-DQA1, and DLA-DQB1 alleles has been analyzed extensively in various dog breeds. Kennedy et al. reported that DLA polymorphisms are relatively limited within a dog breed, but there are significant differences in the types and frequencies of the DLA-II haplotypes between dog breeds [20,21,22]. In addition, there are reports on DLA-88–DLA-DRB1 haplotypes in the Beagle, DLA-88–DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes in the German shepherd dog and some other dog breeds [22,23,24]. Several PCR-based genotyping methods for the DLA-II genes were established, and specific DLA-II polymorphisms were associated to various diseases such as rheumatoid arthritis [25], diabetes mellitus [26,27], and chronic enteritis [28] by case-control studies in various dog breeds. In contrast, though the association between DLA-79 polymorphisms and multiple immune-mediated diseases was reported, there were no clear associations found between polymorphisms of the other DLA-I genes and disease except for an association with pancreatic acinar atrophy in German Shepherds [23,29]. A reason for this limited association of DLA-I polymorphisms with different dog diseases might be that a locus-specific DLA-I genotyping method for association studies had not been established properly because copy number variations of the DLA-I genes per haploid were unknown. In this regard, we recently found that a gene conversion event between DLA-88 and DLA-12 had generated a hybrid DLA-I locus DLA-88L, resulting in two distinct DLA-I haplotype structures, DLA-88–DLA-12–DLA-64 and DLA-88–DLA-88L–DLA-64 [18,30] (Figure 1A). We describe these two alternative haplotypes as the 88-12/88L-64 haplotypes. We also developed a polymorphism analysis method to separate the DLA-88 and DLA-12/88L alleles based on their genomic structures [30]. However, no simple technology was developed to efficiently separate the DLA-12 and DLA-88L alleles from each other (DLA-12/88L), until the present study (Figure 1B). Thus, there are few published studies on the genetic diversity of different combinations of DLA-I and DLA-II polymorphisms in various breeds. In this study, to characterize the intra- and inter-breed DLA diversity, including both DLA-I and DLA-II genes in various breeds, we developed a new genotyping method to separate DLA-12 from DLA-88L accurately and performed polymorphism analysis of the relatively polymorphic DLA-I genes, DLA-88 and DLA-12/88L, and the most polymorphic DLA-II gene, DLA-DRB1, using 829 dogs of 59 breeds that were collected in Japan. We also estimated three-locus DLA-88–DLA-12/88L–DLA-DRB1 haplotypes (88-12/88L-DRB1) from the detected allele information and evaluated the genetic diversity within and between breeds based on the three-locus haplotype frequency. Furthermore, since the DLA-88, DLA-12/88L, and DLA-DRB1 genes have the characteristics of classical MHC genes such as HLA-A, HLA-B, and HLA-DRB1, their DLA polymorphisms are thought to play important roles for the allo-recognition mechanism during transplantation. Hence, to evaluate the possibility of 88-12/88L-DRB1-matched transplantation using somatic stem cells in the field of veterinary medicine, we simulated statistically the proportion of recipient dogs that possibly could undergo 88-12/88L-DRB1-matched transplantation of somatic stem cells if these cells were established from the 88-12/88L-DRB1 homozygotes. Peripheral blood samples from 829 dogs of 59 breeds were collected from the Animal Medical Center (ANMEC) at Nihon University, Marble Veterinary Medical center, and the Nippon Veterinary and Life Science University in accordance with the guidelines for animal experiments specific to each location when the dog owner approved to use the blood for research. Of these, 403 were genotyped initially using RNA samples extracted in the previous study [18,30], and the remaining 426 were genotyped using newly extracted genomic DNA samples (Table 1). We initially genotyped RNA samples (converted to cDNA for amplification) because transcribed MHC genes and alleles are detected more easily without contamination of amplicons originating from pseudogenes or duplicated genes if primer locations crossover to at least two homologous locations. Limitations of the RNA-based genotyping method [15,18] were corrected by also genotyping genomic DNA samples. The genomic DNA was extracted from peripheral blood mononuclear cells by using TRIzol LS Reagent (Invitrogen/Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) or Kaneka Easy DNA Extraction Kit version 2 (Kaneka Corporation, Hyogo, Japan) according to the manufacturer’s protocols. Polymorphism analysis for DLA-88 and DLA-12/88L was performed using the genomic DNA from 426 dogs obtained for this study and 403 dogs that already were genotyped in our previous study [18]. The first PCR was performed independently using a specific primer set (88-seg-F and 88-seg-R2) to amplify the 4.0 kb genomic region, including DLA-88 and using a specific primer set (88L/12-seg-F and 88L/12-seg-R) to amplify the 5.6 kb genomic region including DLA-12/88L (Figure 1B and Supplementary Table S1A). The composition of the PCR solution was 20 ng of DNA, 0.4 unit of KOD FX DNA polymerase (TOYOBO, Osaka, Japan), 10 uL of 2× PCR buffer, 2 mM of dNTP and 0.4 uM of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 63 °C/30 s, and 68 °C/4 min for DLA-88 and 98 °C/10 s, 58 °C/30 s, and 68 °C/5 min for DLA-12/88L. After the PCR products were purified using ExoSAP-IT (GE Healthcare, Piscataway, NJ, USA) and diluted, the 2nd PCR to distinguish between DLA-12 and DLA-88L alleles was performed using a DLA-12 specific primer set (12-F and 88/12/88L-R) and a DLA-88 and DLA-88L specific primer set (88/88L-F and 88/12/88L-R) (Supplementary Table S1B) [15,18]. The composition of the PCR solution was 1 uL of the first PCR product diluted 1000-fold, 0.4 unit of KOD FX DNA polymerase, 10 μL of 2× PCR buffer, 2 mM of dNTP, and 0.4 μL of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 63 °C/30 s, and 68 °C/90 s. Polymorphism analysis for DLA-DRB1 was performed using RNA samples from the 403 dogs as templates by using DLA-DRB1 specific primer sets (DRB1-F and DRB1-R) (Supplementary Table S1D) [31]. The cDNA samples were synthesized with the oligo-dT primer using the RevaTra Ace reverse transcriptase reaction (TOYOBO, Osaka, Japan) after DNase I treatment using 1 μg of RNA (Invitrogen/Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) according to the manufacturer’s protocol. The polymorphism analysis was also performed using DNA samples from the 426 dogs as templates by using DLA-DRB1 specific primer sets (DRB1-g-F and DRB1-g-R) (Supplementary Table S1E) [32]. The composition of the PCR solution consisted of 20 ng of cDNA or genomic DNA, 0.4 units of KOD FX DNA polymerase, 10 uL of 2× PCR buffer, 2 mM of dNTP, and 0.4 uM of each primer in 20 uL. The cycling parameter was as follows: an initial denaturation with 94 °C/1 min followed by 33 cycles of 98 °C/10 s, 60 °C/30 s, and 68 °C/45 s. After purification of PCR products using ExoSAP-IT (GE Healthcare, Piscataway, NJ, USA), the purified PCR products were sequenced directly with Big Dye Terminator Kit Ver. 1.1 or Ver. 3.1 (Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA) and ABI3130 genetic analyzer (Life Technologies/Thermo Fisher Scientific, Carlsbad, CA, USA). The nucleotide sequences of the PCR products for DLA-88, DLA-12, and DLA-88L were determined using sequencing primers i1F-T and i3R-T (Supplementary Table S1C). When the DLA-88 allele sequences were difficult to determine due to sequence offsets by nucleotide insertions and deletions in intron 2 and/or exon 3 of the DLA-88 alleles [30,33], additional sequencing was performed using another primer i2F2 (Figure 1B). DLA allelic sequences were assigned using Sequencher Ver. 5.0.1 DNA sequence assembly software (Gene Code Co., Ann Arbor, MI, USA) by comparing them with known DLA-88, DLA-88L, DLA-12, and DLA-DRB1 allele sequences released in the GenBank (https://www.ncbi.nlm.nih.gov/genbank/ (accessed on 26 April 2022)) and the IPD-MHC Canines database (https://www.ebi.ac.uk/ipd/mhc/group/DLA/ (accessed on 26 April 2022)). Allele sequences from the Sanger sequencing data also were assigned using the MHC allele assignment software Assign ATF ver. 1.0.2.45 (Conexio, Western Australia, Australia). New alleles were confirmed by PCR and direct sequencing again. The PCR products were cloned into the pTA2 cloning vector with the TA cloning kit (TOYOBO, Osaka, Japan), and the nucleotide sequence in 4 to 8 clones per DNA sample was analyzed to avoid PCR and sequencing artifacts. We defined the alleles amplified by using the DLA-88 and DLA-88L specific primer set as the DLA-88L allele and the alleles amplified by using the DLA-12 specific primer set as DLA-12 allele in the 2nd PCR of DLA-12/88L. Since all the DLA-88L alleles reported so far in the IPD-MHC Canines database have been named “DLA-88*”, we also followed the rule for the identified DLA-88L novel alleles as well as all published DLA-88 alleles. The official name of the novel allele was assigned according to the DLA nomenclature in the IPD-MHC database. Novel DLA alleles that have not been given an official allele name were named “DLA-88*nov”, “DLA-12*nov”, or “DLA-DRB1*nov” as tentative allele names. The 88-12/88L-DRB1 haplotypes for each dog, which are 88-12-DRB1 or 88-88L-DRB1, were identified and estimated manually based on genotyping data of 829 dogs as previously described [18,20]. We initially identified the 88-12/88L-DRB1 haplotypes for homozygous dogs and estimated the haplotypes for heterozygous dogs on the basis of those within the homozygous dogs. To confirm our manual haplotype estimation, we also estimated the 88-12/88L-DRB1 haplotypes in each breed by using the maximum likelihood method of the PHASE program [34]. Calculation of expected heterozygosity (He), Hardy–Weinberg equilibrium (HWE) test, and principal component analysis (PCA) based on the 88-12/88L-DRB1 haplotype frequencies in each breed were performed by GenAlEx Ver. 6.5 [35]. In the PCA, to reduce the 88-12/88L-DRB1 haplotype numbers as explanatory variables, we used the haplotype frequencies composed of the field-1 level alleles. This level reflects differences in immuno-responsiveness due to changes in the amino acid sequences of the peptide-binding region and T-cell recognition region of each DLA allele [33]. The inbreeding coefficient (Fis) and haplotype richness (Hr) were calculated by FSTAT Ver. 2.9.4 (available from https://www2.unil.ch/popgen/softwares/fstat.htm (accessed on 21 June 2021). In this case, the significant deviation of FIS from zero was also tested by FSTAT Ver. 2.9.4. Pearson product-moment correlation coefficient was calculated with R ver. 3.6.3 (available from https://www.r-project.org/ (accessed on 17 March 2020)) to evaluate differences in DLA-DRB1 allele diversity in the same dog breeds from different countries, UK and Japan. A phylogenetic tree was constructed by the Neighbor Joining method and assessed using 10,000 bootstrap replicates after aligning the DLA sequences using the MEGA X software (available from https://www.megasoftware.net/ (accessed on 3 March 2020) [36]. A pairwise sequence similarity plot was displayed by a graphical user interface GenomeMatcher [37]. Table 2 and Supplementary Table S2 show detailed information on the types, numbers, and frequencies of DLA-88, DLA-88L, DLA-12, and DLA-DRB1 alleles identified in the 829 dogs. In total, 193 DLA alleles (89 in DLA-88, 18 in DLA-88L, 25 in DLA-12, and 61 in DLA-DRB1) were identified, and 17, 7, 5, and 6 were novel alleles of DLA-88, DLA-88L, DLA-12, and DLA-DRB1, respectively. Figure 2 shows frequencies of the 20 most frequent DLA alleles and the number of animals carrying the alleles in popular breeds in Japan. The highest frequent alleles in each DLA gene were DLA-88*006:01 (allele frequency: 8.9%), DLA-12*001:01:01 (45.1%), and DLA-DRB1*015:01 (14.0%). Although 43 DLA-12/88L alleles were identified, 65.7% (545 dogs) carried DLA-12*001:01:01. In the DLA-DRB1 gene, DLA-DRB1*015 group alleles (DLA-DRB1*015:01, DLA-DRB1*015:02, DLA-DRB1*015:03, and DLA-DRB1*015:04) were the most common, and 37.7% had the alleles. This result showed a similar ratio (23.6%) to the previously published report [20]. A phylogenetic tree of 109 different alleles was reconstructed using the DLA-88, DLA-12, and DLA-88L nucleotide sequences of exon 2–intron 2–exon 3 without indels (746 bp: alignment length). Ninety-nine of the 109 alleles were identified in the 426 dogs that we sequenced in this study. Another 10 DLA-88 nucleotide sequences of exon 2–intron 2–exon 3 were obtained from the IPD-MHC and NCBI databases, and seven (DLA-88*028:04, DLA-88*032:02, DLA-88*045:02, DLA-88*046:01, DLA-88*047:01, DLA-88*049:01 and DLA-88*050:01) were not detected in our study. The phylogenetic tree clearly divided the DLA-88 and DLA-12 alleles into two lineages, and 16 of the 17 DLA-88L alleles were included in the DLA-88 lineage (Figure 3A). The phylogenetic tree clearly divided the DLA-88 and DLA-12 alleles into two lineages, and 16 of the 17 DLA-88L alleles were included in the DLA-88 lineage (Figure 3A). Of the 17 DLA-88L alleles, 14 were composed of two separate lineages containing two common alleles, DLA-88*017:01 and DLA-88*029:01 (Supplementary Table S2). In contrast, DLA-88*nov65, which was assigned as a DLA-88L allele, aligned with the DLA-12 lineage, and its nucleotide sequence showed a high similarity of 99.87% to DLA-12*004:01 (Figure 3B). The intron 2 sequence of DLA-88*nov65 was highly different from DLA-88*501:01 and DLA-88*017:01, which grouped with the DLA-88 and DLA-88L alleles, respect ively (Figure 3B). The migration or transportation of dog breeds between different geographic locations has been shown to have a detectable effect on breed structures with the generation of genetically differentiated sub-populations [38,39,40]. Therefore, to evaluate the genetic bias of the DLA polymorphisms between the same dog breeds in Japan and another country, we compared our DLA-DRB1 genotyping data with the previously published DLA-DRB1 polymorphism data in the United Kingdom (UK) [21]. We compared the proportion of the dogs with each of the different DLA-DRB1 alleles in 10 breeds that had been analyzed in more than 12 dogs per breed in the present (Japan) and previous studies (UK). Of the 10 breeds in the UK and Japan, moderate to strong correlations with correlation coefficients ranging from 0.453 (Beagle) to 0.977 (Cavalier King Charles Spaniel), and a median value of 0.853 was confirmed in the nine breeds (Beagle, Golden Retriever, Labrador Retriever, Dachshund, Miniature Schnauzer, American Cocker Spaniel, Cavalier King Charles Spaniel, Shih Tzu, and Yorkshire Terrier) (Figure 4). The Beagles showed a moderate correlation coefficient, but a large difference was observed between the two countries in the proportion of individuals carrying DLA-DRB1*006:01 (74.6% in the UK vs. 10.8% in Japan). However, for Border Collies, whereas 7 out of 10 alleles were commonly observed in both countries, the DLA-DRB1 allele frequency differed markedly, and no positive correlation was observed between the proportions (correlation coefficient r: −0.159) of alleles in each country. To identify the two 88-12/88L-DRB1 haplotypes (88-12-DRB1 or 88-88L-DRB1) within the 829 dogs, we searched homozygous dogs with the three-loci, any two-loci (88-12/88L, 88-DRB1 and 12/88L-DRB1) and one-locus from our genotyping data. Firstly, 52 different sub-haplotypes of the 88-12/88L-DRB1 haplotypes were identified within 198 dogs (23.8%) that were homozygous at the three loci. Then, 54 sub-haplotypes of the 88-12/88L-DRB1 haplotypes were identified within 40 dogs and 163 dogs that were homozygous at two-loci (88-12/88L in 7 dogs, 88-DRB1 in 7 dogs, 12/88L-DRB1 in 16 dogs) and at one-locus (DLA-88 in 3 dogs, DLA-12/88L in 111 dogs, and DLA-DRB1 in 49 dogs), respectively. In total, 106 sub-haplotypes of the 88-12/88L-DRB1 haplotypes were identified. In addition, 84 sub-haplotypes of 88-12/88L-DRB1 were estimated from the haplotype estimation of the 428 remaining heterozygous dogs with reference to the 106 sub-haplotypes of the 88-12/88L-DRB1 haplotypes. Consequently, 190 88-12/88L-DRB1 sub-haplotypes in total were obtained from 803 dogs of 49 breeds (Supplementary Table S3). However, the remaining 26 dogs could not be assigned to the 88-12/88L-DRB1 haplotypes, because the combination of sub-haplotypes was not narrowed down to less than three loci. Of 190 88-12/88L-DRB1 sub-haplotypes, 131 were detected two or more times, while the other 59 sub-haplotypes were detected just once within heterozygous dogs (Supplementary Table S3B). The frequencies of 131 sub-haplotypes within the 88-12-DRB1 and the 88-88L-DRB1 haplotype structures were 79.4% and 20.6%, respectively (Table 3). Of the 131 different 88-12/88L-DRB1 haplotypes, 29 were high-frequency haplotypes with a frequency of 1.0% or more (i.e., the detected number of the haplotype was ≥16) (Table 4). Of these 29 highly frequent haplotypes, four haplotypes (DLA-88*006:01–DLA-12*001:01–DRB1*056:01 (Haplotype(Hp)-ID 116), DLA-88*502:01–DLA-12*001:01–DRB1*001:02 (Hp-ID 6), DLA-88*001:03–DLA-12*001:01–DRB1*046:01 (Hp-ID 91), and DLA-88*511:01–DLA-12*001:03–DRB1*092:01:1 (Hp-ID 117) were observed in only one breed. The other 25 haplotypes were observed in two or more breeds, and 11 sub-haplotypes (Hp-IDs 2, 20, 22, 23, 37, 46, 51, 52, 69, 73, and 99) showed high haplotype frequencies of 70% or more in specific dog breeds (Table 4). Therefore, more than 50% of the high-frequency 88-12/88L-DRB1 haplotypes (15 of 29 haplotypes) were found in breeds with a large number of dogs tested. DLA-88*004:02–DLA-12*001:01–DRB1*006:01 (Hp-ID 12) was detected as the most frequent haplotype in two breeds, Pomeranian (haplotype frequency: 46.6%) and Yorkshire Terrier (Hp frequency: 31.7%) (Supplementary Table S3A). In addition, the DLA-88*003:02–DLA-88*017:01–DRB1*009:01 (Hp-ID 31), and DLA-88*501:01–DLA-12*001:01:01–DRB1*001:01 (Hp-ID 8) were commonly observed in multiple breeds with relatively similar frequencies (Supplementary Table S3A). We investigated the genetic diversity of the 88-12/88L-DRB1 haplotypes using a total of 725 dogs within 24 different dog breeds (analyzed using ≥ 10 dogs/breed) and mongrels (mixed breeds). The number of different haplotypes in each breed ranged from three Shetland Sheepdogs to 27 Toy Poodles (Table 5), and up to 34 different haplotypes among the mongrels (Figure 2). Six dog breeds (Miniature Schnauzer, Shetland Sheepdog, Shiba, American Cocker Spaniel, Papillon, and Bernese Mountain Dog) had one particular 88-12/88L-DRB1 sub-haplotype at a frequency of greater than 50% (Figure 5). Only two or three haplotypes represented more than 80% of all the haplotypes in seven breeds (Miniature Schnauzer, Shetland Sheepdog, Shiba, American Cocker Spaniel, Golden Retriever, Miniature Pinscher, and Shih Tzu). In contrast, more than 20 different haplotypes were detected in Chihuahua and Toy Poodle, and each haplotype frequency was distributed similarly (Figure 5). We calculated the genetic diversity indices, such as observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (Fis), and haplotype richness (Hr) to evaluate the 88-12/88L-DRB1 diversity in each breed (Table 5). The mean Ho value in the 24 dog breeds was 0.736. The Ho values deviated significantly from HWE in 8 breeds (Cavalier King Charles Spaniel, Golden Retriever, Labrador Retriever, American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle), and the Ho values were significantly lower than He values in five breeds (American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle). The Hr values in the 24 breeds ranged from 2.13 in Shetland Sheepdog to 7.79 in Toy Poodle. To evaluate genetic relationship of the 88-12/88L-DRB1 haplotypes among different dog breeds, PCA was performed using the 24 breeds listed in Table 5. Of the 24 breeds plotted by PCA, 22 breeds were distributed closely around the centroid of the quadrants as if they were almost one population. The Shetland Sheepdog and Miniature Schnauzer breeds diverged markedly from the other 22 breeds. (Figure 6A and Supplementary Table S3A). The positions of the Shetland Sheepdog and Miniature Schnauzer breeds within the matrix appear to have reflected the presence of their dominant haplotypes, 88*003–88*017–DRB1*002 (Hp-ID 20) in Shetland Sheepdog (Hp frequency: 67.1%) and 88*013–12*003–DRB1*009 (Hp-ID 23) in Miniature Schnauzer (68.8%). Since the 88*003–88*017–DRB1*002 (Hp-ID 20) also were commonly observed among Welsh Corgi and Border Collie, these two breeds were located slightly outside the large group of the other breeds and closer to Shetland Sheepdog. Removing the Shetland Sheepdog and Miniature Schnauzer outliers from the PCA analysis changed the genetic relationship slightly between some of the 22 breeds on the basis of the 88-12/88L-DRB1 haplotype frequencies (Figure 6B). For example, the French Bulldog, Bulldog, Border Collie, and Yorkshire Terrier share the 88*028–88*029–DRB1*015 (Hp-IDs 24 and 25) at relatively high frequencies (13.4% to 43.4%), and these four breeds grouped more closely together and at some distance from the other breeds. Similarly, the Golden retriever and Labrador retriever, sharing the 88*508–12*001–DRB1*012 (Hp-ID 21), and American Cocker Spaniel and Cavalier King Charles Spaniel, sharing the 88*003–88*017–DRB1*009 (Hp-ID 31) separated further from each other and at a greater distance from the centroid (0, 0) of the PCA plot (Figure 6B). Assuming that somatic stem cells could be established from the 52 types of homozygotes of the 88-12/88L-DRB1 haplotypes and that these cells could be used as donors for 88-12/88L-DRB1-matched transplantations, we statistically modeled the number of dogs that might be recipients from the 829 individuals analyzed in this study (Figure 7). From our statistical simulation, if donor cells are established from 9, 28, and 52 types of high frequency 88-12/88L-DRB1 homozygotes, 411 (51.2%), 650 (80.7%), and 733 (90.9%) dogs might be considered eligible for 88-12/88L-DRB1-matched transplantation as recipients. Furthermore, in 17 of 24 dog breeds (70.8%) listed in Table 5, 50% or more of these dogs might benefit from the 88-12/88L-DRB1-matched transplantation by using donor dogs with the most frequent 88-12/88L-DRB1 haplotypes in each breed (Table 6). Additionally, homozygotes of all haplotypes listed in Table 6, except 88*006:01–DLA-12*001:01–DRB1*015:01 (Hp-ID 37), were detected in the present study (Supplementary Table S3A). In this study, we genotyped the DLA-88, DLA-12/88L, and DLA-DRB1 loci by Sanger sequencing using 829 dogs of 59 breeds and identified 89, 43, and 61 alleles, respectively. We also developed a two-stage PCR method for the polymorphism analysis of the DLA-88, DLA-88L, and DLA-12 genes by separating DLA-88 and DLA-12/88L with the 1st PCR and DLA-12 and DLA-88L with the 2nd PCR (Figure 1B). This polymorphism analysis by PCR and sequencing clearly distinguished the DLA-88L allele from the DLA-88 allele, which was difficult with conventional RNA-based methods [15,18]. In fact, the previously reported DLA-88*042:02 [17] belongs to DLA-88L rather than DLA-88, and this allele along with DLA-88*008:02 and DLA-DRB1*004:01 constituted the 88-88L-DRB1 haplotype in the Maltese breed (Supplementary Table S2A). Therefore, this simpler and more accurate two-stage PCR method is an important tool to use for a better understanding of the DLA loci and haplotype differences and for evaluating various immune responses in dogs. Overall, we identified 29 novel DLA-88, DLA-88L, and DLA-DRB1 alleles in this study. Of them, DLA-88*nov65 was newly detected as a DLA-88L allele that showed a different phylogenetic relationship from other DLA-88L alleles, and was highly similar to DLA-12*004:01 of the DLA-12 lineage (Figure 3). Interestingly, our previous study showed that DLA-12*004:01 was generated by a gene conversion event within the exon 2 region between the DLA-12 and DLA-88 alleles [30]. Therefore, the DLA-88*nov65 also might have been generated by gene conversion between the DLA-88 and DLA-12 alleles, similar to DLA-12*004:01. There may be many other unidentified DLA alleles generated by such gene conversions events. In contrast to the DLA-I genes, the polymorphisms and diversity analyses of the DLA-II genes (DLA-DRB–DLA-DQA–DLA-DQB) have been performed previously in many different dog breeds [21,22,41,42]. Although the Japanese native species of Shiba has not been well analyzed previously, our current analysis showed that DLA-DRB1*056:01 (allele frequency: 55.4%) was the most frequent allele, followed by DLA-DRB1*092:01:1 (18.9%), and DLA-DRB1*011:03 (16.2%) (Supplementary Table S3A). These DLA-DRB1 alleles were detected only in Shiba, and therefore their detection is extremely rare even in past polymorphism analyses of the DLA-II genes, including other dog breeds of Asian origin [21,42]. The DLA polymorphism information on Japanese native breeds is extremely limited [42,43]. In this study, although we analyzed Japanese native species Shiba, Akita, Japanese Spitz, Chin, and Shikoku, the number of animals analyzed was less than 10 animals except for the 37 in the Shiba breed. Therefore, more DLA allele information is necessary for Japanese native species as well as for dog breeds that have not yet been analyzed. Recent genomic analysis of the remains of extinct Japanese wolves (Canis lupus hodophilax) showed phylogenetically that after the Japanese wolf and modern dog ancestry had diverged from grey wolf lineages, gene flow occurred from the ancestor of Japanese wolves into the ancestor of Japanese dogs, including Shiba, and this flow likely continued and contributed to differentiate between the lineage of Japanese dogs and West Eurasian dogs [44]. Interestingly, DLA-DRB1*056:01 of Shiba was detected in Finnish and Russian wolves with frequencies of 4.0% and 2.9%, respectively [45], and Shiba DLA-DRB1*092:01:1 was detected in Canadian and Croatian wolves with frequencies of 6.0% and 11.0%, respectively [46,47]. These results indicated that Shiba might be a unique breed that shared some of its genomic sequences, including the DLA genomic region, with its ancestor in a different way than those of the European modern dog breeds. High homozygosity of the DLA haplotypes generally implies a loss of DLA genetic diversity. The Ho values showed significantly lower values than the He values in 5 dog breeds, American Cocker Spaniel, Shiba, Papillon, Shih Tzu, and Beagle (Table 5). This suggests a high level of inbreeding in these five breeds. The high Fis values also observed in Shih Tzu (0.209) and Papillon (0.171) strongly suggest that the DLA diversity in these breeds of our population sample is decreasing by inbreeding (Table 5). Moreover, Shetland Sheepdog showed an extremely low Ho value of 0.314 (Table 5). The loss of genetic diversity due to high homozygosity might increase the deleterious genetic variation in pure-breed dogs [48]. Also, high homozygosity of the DLA region due to both inbreeding and genetic bottlenecks by selective artificial breeding was associated with the development of autoimmune diseases in Italian Greyhounds [49]. In contrast, MHC heterogeneity of the Sea lion in wild populations appears advantageous to protect against infectious diseases [50], whereas the pregnancy rate in horses was reported to be decreased by sharing common MHC types between males and females [51]. Therefore, loss of the DLA diversity may affect biological fitness as homozygosity progresses. The homozygous rate of DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes was 35% in a previous study [22]. These three DLA-II genes are located together within 100 kb, while DLA-88 is located far from DLA-DRB1 by over 1.0 Mb [13], resulting in a much stronger linkage disequilibrium (LD) within the DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotype than the 88-12/88L-DRB1 haplotype. Therefore, the lower 88-12/88L-DRB1 homozygous rate (23.8%) in this study than that of DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotype in the previous study [22] might be associated with LD and rates of recombination between different DLA gene loci. The detection of 52 types of homozygotes for the 88-12/88L-DRB1 haplotypes suggests that 90.9% of the dogs analyzed in our study would have a successful 88-12/88L-DRB1-matched transplantation if their somatic stem cells were used in such a procedure. In comparison, if induced pluripotent stem cells (iPSCs) were established from homozygotes of 30 and 50 types of HLA haplotypes (HLA-A–HLA-B–HLA-DRB1) that are frequently observed in Japanese, 82.2% and 90.7% of Japanese would benefit from HLA-matched iPSC transplantation [52]. However, the HLA homozygosity rate for humans is relatively low at 0.5 to 1.5% for the HLA-A–HLA-B–HLA-DRB1 haplotype [53,54,55]. Therefore, the HLA of 15,000 and 24,000 individuals would need to be genotyped to identify these 30 and 50 HLA homozygotes, respectively [52]. In this regard, the MHC homozygotes, preferred donors for somatic stem cell sources, would be much easier to detect in dogs than in humans due to their higher rate of MHC homozygosity. Moreover, our new data on the frequency of DLA haplotypes in various dog breeds could help in the implementation of somatic stem cell transplantation along with a recent development of clinical-grade canine iPSCs derivation [56,57] and assist with the high expectations for regenerative medicine in the veterinary field [58]. In the PCA using the 88-12/88L-DRB1 haplotype frequencies, the values of the first principal component (PC1) and the second principal component (PC2) were extremely low at around 10% in both analyses, but with relatively strong diversity between the DLA haplotypes among the 24 dog breeds, which are popular in Japan (Figure 6). The Hr values of 88-12/88L-DRB1 were less than five in 11 breeds, suggesting that there were relatively few breeds with many different types of 88-12/88L-DRB1 haplotypes (Table 5). Moreover, haplotype frequency bias was confirmed for six breeds, with some haplotype frequencies at 50% or more within each breed (Figure 5). These results showed that the DLA diversity is highly conserved within most breeds, but divergent between almost all breeds, as similarly observed in a previous study on the DLA-II haplotype diversity [20,22]. Therefore, the DLA allele and DLA haplotype frequencies appear to change largely depending on the breeds. Of the 829 dogs of 59 breeds analyzed in this study, 68.5% (568 of 829 dogs) were from the top 20 most popular breeds registered in Japan (Table 1). In contrast, only a few dogs were analyzed from breeds that are listed in the top ten most popular breeds in the USA (American Kennel Club; https://www.akc.org/ (accessed on 28 November 2022)), such as German Shepherd and Rottweiler, which were less popular breeds in Japan. The genetic closeness of the DLA haplotypes among different breeds can be evaluated more accurately by enhancing the DLA genotyping data of breeds from which only a small number of dogs were analyzed in our present study. We showed that there are differences in the distribution of DLA-DRB1 alleles within the same breeds, such as Border Collie and Beagle, particularly if they are located in different countries, such as Japan and the UK (Figure 4). Since differences in the DLA diversity within a single breed between different countries have also been reported in some other breeds [49,59,60], geographical location can affect DLA diversity resulting in differences in the susceptibility for various diseases even in a single breed between different countries. From such a discussion, further DLA polymorphisms analysis for various breeds in different countries is warranted to better comprehend the intriguing features of DLA diversity. A limitation of our study concerning DLA haplotypes containing both DLA-I and DLA-II genes was not to include DLA-DQA1 and DLA-DQB1 polymorphisms that might be linked to the 88-12/88L-DRB1 haplotypes. This was beyond the scope of our present study. The three genes of DLA-DRB1, DLA-DQA1, and DLA-DQB1 often show strong linkage disequilibrium [22], but novel DLA-DRB1–DLA-DQA1–DLA-DQB1 haplotypes might be generated by the recombination between the DLA-DR and DLA-DQ genes. Although the mismatch of HLA-DQ polymorphisms in organ transplantation is associated with the production of de novo donor-specific antibodies (DSA) against the HLA-DQ molecules and contributes to poor graft outcomes [61,62], such studies are lacking in dogs. Therefore, in regard to transplantations in dogs, further studies are necessary to genotype DLA-DQA1 and DLA-DQB1 and consolidate the extended haplotypes that were identified in our study, including all DLA-I and DLA-II genes, to select the most suitable organ donors in future. Several specific DLA-II alleles and haplotypes have been reported so far to associate with various diseases in different breeds. For example, DLA-DRB1*010:01:1 or the DLA-DRB1*010:01:1–DLA-DQA1*002:01–DLA-DQB1*015:01 haplotype is associated significantly with the risk of necrotizing meningoencephalitis in Pug [63]. DLA-DRB1*094:01 is associated significantly with acquired retinal degradation syndrome in Dachshunds [64]. These susceptible DLA-DRB1 alleles were also detected in the Pugs and Dachshunds in our study (Supplementary Table S3A). However, the association between the DLA alleles and diseases is unknown because we did not survey the medical history of the individuals used in this study. Also, we have not evaluated the association between decreased heterozygosity of the DLA haplotype and decreased biological fitness. Considering these limitations of the present study, we would like to elucidate the association between DLA polymorphisms and diseases and fertility in the future by investigating the disease history in each of the major breeds in our study population. The genetic diversity of DLA haplotypes varied remarkably between breeds but was relatively conserved within the breed in our large-scale polymorphism analysis. The genetic characteristics of the high DLA homozygosity rate and poor DLA diversity within the same breed are useful for transplantation therapy, but they also may affect biological fitness negatively as homozygosity progresses. This DLA polymorphism information might be useful in future studies for the realization of canine transplantation medicine and elucidation of the pathology of various diseases and for the development of DLA-haplotype-based veterinary medicine.

PMC10001277

José Salomón Altamirano-Flores,Luis Ángel Alvarado-Hernández,Juan Carlos Cuevas-Tello,Peter Tino,Sandra E. Guerra-Palomares,Christian A. Garcia-Sepulveda

Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling

28-02-2023

HIV-Vif,undersampling,machine learning

Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests.

Identification of Clinically Relevant HIV Vif Protein Motif Mutations through Machine Learning and Undersampling Human Immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Although great advances have been made towards determining the way viral genetic diversity affects clinical outcome, genetic association studies have been hindered by the complexity of their interactions with the human host. This study provides an innovative approach for the identification and analysis of epidemiological associations between HIV Viral Infectivity Factor (Vif) protein mutations and four clinical endpoints (Viral load and CD4 T cell numbers at time of both clinical debut and on historical follow-up of patients. Furthermore, this study highlights an alternative approach to the analysis of imbalanced datasets, where patients without specific mutations outnumber those with mutations. Imbalanced datasets are still a challenge hindering the development of classification algorithms through machine learning. This research deals with Decision Trees, Naïve Bayes (NB), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs). This paper proposes a new methodology considering an undersampling approach to deal with imbalanced datasets and introduces two novel and differing approaches (MAREV-1 and MAREV-2). As theses approaches do not involve human pre-determined and hypothesis-driven combinations of motifs having functional or clinical relevance, they provide a unique opportunity to discover novel complex motif combinations of interest. Moreover, the motif combinations found can be analyzed through traditional statistical approaches avoiding statistical corrections for multiple tests. Human immunodeficiency virus (HIV) and its clinical entity, the Acquired Immunodeficiency Syndrome (AIDS) continue to represent an important health burden worldwide. Since the first reports of HIV more than 35 years ago, 78 million people have been infected with HIV and 35 million have died from AIDS-related illnesses. In 2021, approximately 1.5 million people contracted HIV and 650,000 people died from HIV-related diseases (UNAIDS, https://www.unaids.org/en, accessed on 28 October 2022. Although the overall number of new infections has declined since 2010, the resource limited countries of Latin America, Asia, and Africa have shown a steady increase in new infections and excess deaths due to HIV [1]. Different strategies have been employed in the fight against HIV and AIDS, mostly focused on either preventative measures or the development of novel anti-retroviral drugs targeting the main viral enzymes involved in HIV replication [2]. On the other hand, current HIV research efforts continue to focus on increasing our understanding of viral-host interactions at the molecular level, with the aim to discover those worth exploiting to interfere with viral tropism, fusion, replication, integration, and transmission. Our understanding of the function of some viral proteins such as the protease, reverse transcriptase, and integrase enzymes has allowed for the development of potent preventative and therapeutic strategies [3]. However, for some accessory and non-structural viral proteins, little is known with regards to the function and their potential as candidate targets for antiviral drug development. While the use of molecular biology techniques allows for an estimation of functional or clinical relevance of these proteins, complex genetic and clinical variable comparisons decrease the statistical power of such studies. The HIV genome has 9719 base pairs (HXB2 reference strain) and a total of 3 open reading frames encoded in a prototypical lentivirinae genome organization comprised of gag, pol, and env genes, long terminal repeat regions (LTRs) and accessory-protein-encoding regions (Vif, vpr, tat, rev, vpu, and nef). The gag gene encodes for the matrix, capsid, nucleocapsid, and p6 proteins, pol encodes for the enzymes protease, reverse-transcriptase, and integrase and env encodes for the glycoproteins GP41 and GP120. The different aforementioned accessory proteins facilitate or promote HIV replication and viral fitness. The best studied accessory proteins include tat (which acts as viral transcriptional transactivator), rev (which regulates RNA trafficking), and nVifef which promotes viral maturation and release from the host cell [4,5]. Vif is a 192-amino acid HIV accessory protein essential for replication. Vif protein counteracts human antiviral proteins of the APOlipoprotein Bmessenger RNA Editing enzyme, Catalytic polypeptide-like (APOBEC3) family. APOBEC3 proteins are zinc-dependent deaminases which mutate viral cytidine (dC) to uridine (dU) in both viral DNA and RNA molecules, thus interfering with the fidelity of the viral genome. APOBEC3 is a host innate mechanism that protects human cells from exogenous viruses and endogenous mobile retroelements. The Vif protein allows HIV to evade such innate mechanisms. This viral protein has recently become a candidate target for both therapeutic and preventive interventions in HIV/AIDS. Nevertheless, little is known about the clinical relevance of Vif accessory protein, particularly among HIV-infected patients of developing countries and Latin America [6]. Members of the human APOBEC family of proteins include APOBEC1, APOBEC2, APOBEC3, and the poorly expressed APOBEC4. The APOBEC3 subfamily has seven known members, including APOBEC3A, APOBEC3B, APOBEC3C, APOBEC3DE, APOBEC3F, APOBEC3G, and APOBEC3H. Among all APOBEC3 subfamily members, APOBEC3G is notable for exerting the strongest antiviral effect [7]. APOBEC3G is incorporated into the HIV-1 virions as they emerge from an infected cell when HIV-1 lacks the capacity to encode for Vif protein. During the second round of viral replication, after infecting a second cell, APOBEC3G would normally cause extensive dC to dU mutations of the single-stranded viral DNA during reverse transcription [8]. HIV’s Vif protein inhibits and interferes with APOBEC3G activity and thus renders the virus immune to this important innate immunity. However, HIV-1 evolution and quasi-species diversification within a single human being might lead to the accumulation of mutations in the Vif region, which might affect protein function and have clinical significance by either decreasing viral replication or affecting integration and transmission. The use of machine learning approaches has been extensively applied to the search of statistical associations between genetic and clinical variables during the last years given their known capacity at tackling high dimensional data [9,10]. Previously, some research groups have applied combined algorithm based approaches, such as ANN coupled to genetic algorithms, grammatical evolution, and genetic programming, to the discovery of genetic associations and classification [11,12,13,14,15]. Other combined-algorithm approaches have been SVMs with genetic algorithms [16] and ANNs coupled to Rule Association Mining (Apriori algorithm) [17]. Although combining different machine learning approaches does not guarantee better performance, there is ample evidence supporting the statistical benefits and capabilities at discovering novel genetic associations in the context of infectious diseases [18,19]. One important factor in assessing the importance of different genetic variables mentioned in previously published studies is their combined effect on classification performance. We previously applied this approach to the study of HIV’s Vif gene mutations by using four different machine learning approaches for the discovery of clinical endpoint associations [20]. A mayor caveat to our previous effort was the availability of an imbalanced dataset arising from the difficulty in collecting large cohort samples and extensive genetic data. Data imbalance is a fundamental and challenging problem in machine learning that limits the power of small clinical datasets. This limitation has also been shown to be present in other non-medical applications such as fraud detection, finance, ecology, and biology [21,22]. As such, in this study we set forth to evaluating the performance of state-of-the-art machine learning approaches (Decision Trees, NB, SVMs, and ANNs) enhanced with an undersampling process for dealing with the data imbalance in the dataset. Furthermore, we present a probabilistic method capable of suggesting the most clinically relevant variable combinations associated to clinical outcomes. The paper is organized as follows: Section 2 describes the dataset and the undersampling approach. The methods are presented in Section 3, followed by the results and conclusions sections. For the purpose of this study we relied on a previously consolidated dataset including Vif protein amino acid physicochemical changes and clinical outcome variables (CD4 T cell numbers and HIV viral load at both initial diagnosis and on follow-up) [23]. From the original 192 amino-acid sites conforming the Vif protein, those pertaining to 17 protein motifs were encoded into binary data as either conserved or mutated, as described previously [20]. Eight of the 17 variables representing Vif protein domains are known to interact with APOBEC3 proteins (herein designated as APOBEC-1 to APOBEC-8). Other motifs considered in this study include the Nuclear Localisation Inhibitory Signal (NLIS), two (CBF-1 and -2) interaction sites as well as three Cullin-5 binding sites (Cul5-1, Cul5-2, and Cul5-3). When the different Vif motif sequences implied a non-conservative change in physicochemical properties, the genetic variable for that motif was encoded as a “1”, and when the site was conserved it was encoded as “0”. The values for the clinical endpoints (outcome class) were encoded based on thresholds recommended by the World Health Organization and the U.S. Centers for Disease Control and Prevention. The CD4Ini and CD4Hist clinical endpoints reflect the levels of CD4+ T cells number (cells/per micro liter) at the first time of diagnosis (CD4Ini) and as the median number of CD4+ cells from quarterly assessments during two years of patient follow-up (CD4Hist). For both CD4Ini and CD4Hist, ≥500 CD4+ T cells/μL corresponds to a value of “0”, as CD4+ T cell numbers above this threshold are not indicative of poor clinical prognosis. Contrarily, the clinical endpoint is encoded as “1”, when ≤500 CD4+ T cells/μL when the cell numbers are below normal and reflecting immunodeficiency. Similarly, VLIni and VLHist outputs reflect another clinical aspect used to assess HIV-prognosis, where high viral loads are associated with worsening clinical progression. As mentioned above, VLIni and VLHist reflect HIV viral titres at the time of initial diagnosis and the median of quarterly follow-up assessments of viral load (copies/milliliter). For both VLIni and VLHits ≥ 10,000 copies/mL/μL corresponds to a value of “1”, as viral loads above 10,000 cp/mL are suggestive of intense viral replication and worsening clinical prognosis. Contrarily, this value is encoded as “0”, when ≤500 copies/mL/μL when the viral load is below 10,000 cp/mL and stable [24]. In the case of binary classification, the class-imbalance is defined as the over representation of one class (the majority class) over another class (the minority class). Over representation affects the learning process of the algorithms as most of them are designed to construct the most general and simplest hypothesis from the data [25]. Undersampling can lead to a bias towards the over-represented class during the learning process. Different approaches have been used to resolve the problem of undersampling, which range from applying data balancing strategies (either undersampling or oversampling), modifying the machine learning process to address data imbalance or through data penalization to enhance minority class attribute detection [26]. Undersampling balancing strategies are the most popular approach as they are based on the original dataset, whereas oversampling requires the generation of artificial data, derived from the original dataset but not necessarily true in content [27]. As the use of oversampling involves the generation of artificial data, in this work we decided to use an undersampling approach to better preserve the biological distribution of genetic variables and clinical endpoints of our dataset. Figure 1 describes the undersampling process. The original dataset contains examples where n is the minority class and m is the majority class. The algorithm identifies the least represented class (i.e., n) and then creates a new balanced dataset by subtracting m class elements until it is similar in size to n class subset. These undersampled balanced sets are generated 100 times (), and each one is used for machine learning and training. This paper compared the classification performance of the well-known machine learning methods: Decision Trees, NB, SVMs, and Multi-Layer Perceptron (MLP). Decision trees represent the simplest and most widely used non-parametric supervised learning method. There are many algorithmic implementations to generate decision trees from data including Iterative Dichotomiser 3 (ID3) [28], its successor—C4.5, Classification And Regression Tree (CART), Chi-square Automatic Interaction Detection (CHAID), and Multivariate Adaptive Regression Splines (MARS). This paper focus only on the CART implementation [29] available in Scikit-learn [30,31]. For CART, the use of the Gini index and a max depth of five were used as predefined parameters, as they provided a similar performance to the C4.5 algorithm. Contrary to C4.5, CART helped identify the most significant variables and to eliminate non-significant ones [32]. NB classifiers include several highly-scalable and simple probabilistic classifiers that rely on Bayes theorem with strict independence assumptions between features. When coupled with kernel density estimation they can achieve elevated classification accuracy levels [26]. The NB classifier is defined as: where , because this classifier assumes that the variables, are conditionally independent, given the class, and are the classes or labels [33]. NB usage relied on calculations of the prior probabilities and estimation on the prior probabilities. MLP is based on classical ANN models, in particular the Perceptron introduced by F. Rosenblatt in 1957 [34]. MLP architecture is a more complex ANN where at least one or more hidden layers are included before the clinical endpoint variable layer [35]. MLP is also known as backpropagation [36,37,38,39], a generalization of the delta rule learning algorithm proposed by B. Widrow in 1962 [40]. MLPs are also referred to as feedforward neural networks. Figure 2 illustrates a general MLP architecture with input variables (green), a hidden layer (blue) and a single clinical endpoint (red). There is a single MLP for each of the clinical endpoint variable classes: CD4Ini, CD4Hist, VLIni, and VLHist. For MLP training, we use the logistic activation function, a hidden layer with 8 neurons, 2 outputs, and 10,000 epochs with the Limited-memory BFGS algorithm (the Broyden–Fletcher–Goldfarb–Shanno algorithm), which is a method for numerical optimization [41]. SVMs are state-of-the-art algorithms initially introduced by Cortes and Vapnik as support-vector networks [42,43]. SVM were developed in an effort to develop artificial intelligence strategies for complex problems. SVM have mostly been applied to classification or regression problems. For classification purposes, SVMs aim to produce a mathematical n-dimensional space function capable of non-linearly distinguishing between different classes from complex and multivariate (training and test) datasets Given a dataset where (inputs), (clinical endpoint), and l is the size of the dataset. The SVM classifier is defined as which is a linear combination of kernels, , where the sign function () gives the class [42]: with constrains, , and . The parameter C is known as the margin and the Support Vectors (SV) will have non-zero Lagrange multipliers, ; is the kernel function performing the non-linear mapping into feature space , known as the “kernel trick” [26,42,43]. There are many kernel functions available for use with SVMs including linear, Gaussian Radial Basis Function (RBF), sigmoid, and polynomial. Our approach made use of the RBF kernel, where the width of a kernel is given by the parameter. Across this research, SVMs used RBF as kernel with the following values: and . In order to assess the relevance that the different Vif variables (input) have on each of the included clinical endpoint variables (output), a series of steps were used, including: Generating p balanced datasets through undersampling (see Section 2); Constructing input variable combinations of less than 10 in size (k); Identifying the variable combinations of each balanced datasets providing the best classification performance; Calculating the relevance of each variable through a probabilistic approach, and; Optimizing the selection of the most relevant variables by using a threshold value. For the first step, balanced datasets are generated through undersampling by creating p partitions, which include all elements of the minority class (n) and an equal number of randomly selected elements of the majority class (i.e., n examples out of m), as shown in Figure 1. After producing balanced datasets, a second step addresses the construction of k size variable combinations by using each of them as input in different classification algorithms. For this, a five-fold cross-validation training process using weighted accuracy was used. The construction of the variable combinations relied on using greedy step-wise variable selection, as shown in Figure 3, in such a way as to identify the best variable capable of discriminating between the clinical endpoint classes. This process was repeated for a second variable in combination with the first identified and the process was repeated k-times so as to identify the k best variable combinations available. A third step involved discovering the best k combinations for each p balanced dataset. As the discovery of a global optimum is not guaranteed, a reasonably good local optimum (based on classification performance) was used, as shown in Figure 4. Global optimums are not realistically feasible as the search space exponentially explodes with k. In a fourth step, variable relevance assessment is achieved using the p best combinations through a probabilistic approach. For this, the probability of each input Vif variable appearing at position on the variable combination matrix produced in the previous step is calculated using Equation (3). where indicates the probability that the variable was selected at the position of the generated combinations. The frequencies for the variable and that of the different variables at the position are expressed as . This equation is applied for each one of the k positions (). These probabilities define the relevance score (r) for each variable by using Equation (4): where indicates the relevance score for the variable , considering its probability of appearing on each of the k positions in the combination matrix. This process assigns greater weight to the variables that are found closest to the root (lower entropy) of the combination matrix and less weight to those that appear farther from the root (higher entropy). In a fifth step, the relevance scores obtained in the previous step are then used for sorting the variables considering their relevance scores and by establishing a threshold value (which involves calculating the upper limit of a 99% confidence interval of their relevance scores) to determine the most relevant variables (those surpassing the threshold limit). The first Method for Assessing the Relevance of Each Variable (hereafter called MAREV-1) considers the classification results produced by each algorithm (CART, Multinomial NB, SVMs, and MLP) on balanced datasets. This yielded a total of 400 variable combinations having the highest classification performances, all of which were then tested further, including traditional statistical analysis, as mentioned below, see Section 3.5.3. The second method, MAREV-2, selects only the best variable combinations assessed as classification performance for each algorithm (the third step described above), see Section 3.5. This yielded four input variable combinations, one per algorithm. Again, as mentioned above for the score assessment on each variable, all were then tested through the following traditional statistical analysis. Once the most relevant variables had been identified in the previous steps, subsequent analysis involved establishing the clinical importance of the different machine learning algorithm-suggested variable combinations and their status (Mut or Cons) through traditional statistical association methods. For this, the Vif protein conserved sites, synonymous amino acid substitutions, or those being non-synonymous but conserved in physicochemical properties were encoded as “0” (Cons in the following discussion, figures, and tables). Contrarily, mutations leading to non-synonymous amino acid substitutions resulting in non-conserved physicochemical properties of the Vif protein (polar to non-polar changes, acidic to basic changes, gross molecular structure size changes, as well as changes in susceptibility to post-translational modifications such as phosphorilation, ubiquitination, SUMOylation, methylation, and glycosylation) were encoded as “1” (Mut). The definition of explicit variable-value combinations used the ID3 algorithm as implemented in the Waikato Environment for Knowledge Analysis (WEKA) workbench v3.6 [44]. ID3 was used for generating a decision tree for each clinical endpoint relying on tree branches to incorporate variable status (Mut or Cons) combinations. The calculation of the statistical significance of variable frequency differences between clinical endpoint groups relied on two-sided Fisher’s exact test using IBM SPSS Statistics (version 21, IBM Corporation, Armonk, NY, USA). The position of the Vif encoding region within the HIV-1 reference sequence HXB2, and the position and nomenclature of the Vif protein motifs and their putative ligands, is provided in Figure 1. The APOBEC-1 variable, corresponding to the N-terminal APOBEC3 binding site (DRMR), was excluded from the original dataset as it remained conserved. The assessment of the relevance of each variable, as explained in Section 3.5, was based on the classification performance from four different classifiers (CART, MLP, SVMs, and Multinomial-NB) as implemented in the Scikit-learn package [30]. We have identified the top 100 variable-combinations associated to each clinical endpoint class by applying the proposed method to assess variable relevance. We obtained 1600 top-performing genetic variable-combinations associated to each clinical endpoint (CD4Ini, CD4Hist, VLIni, and VLHist) using the four classification algorithms. The balanced-accuracy was calculated with a 5-Cross-Validation approach during each training process. Algorithm accuracy was defined as the correct identification of both true positive and true negative registry examples (patients) and encompasses true-positive and true-negative predictive rates. Out of the four machine learning algorithms tested, MLP superseded the three other machine learning algorithms during the analysis of each of the four clinical endpoints, accurately classifying, 79.6%, 76%, 68.5%, and 66.3% of CD4Ini, CD4HIts, VLIni, and VLHist patient registries, respectively. The classification performance of each machine learning algorithm for each clinical endpoint is summarized in Table 1. Although the best classification results achieved higher values than those previously reported elsewhere [20], this can easily be explained by the use of balanced datasets and 5-Cross-Validation settings in this report. The genetic variable combinations providing the best classification performance are summarized in Table 2. Considering the top scores per clinical endpoint shown in Table 2, the best discrimination was achieved for the CD4 T cells counts (CD4Ini and CD4Hist clinical endpoints). On the other hand, low performance was observed on the VLIni clinical endpoint [71.5–80.2], and even lower for the VLHist [68.5–73.8]. Some variables were shown to be present in all “top combinations” identified for each different clinical endpoints. These were: [BCbox-3, BCbox-2, and APOBEC-2] for CD4Ini, [APOBEC-2, APOBEC-4, and BCbox-3] for CD4Hist, [APOBEC-2 and APOBEC-4] for VLIni, and [NLIS, APOBEC-2, and BCbox-1] for VLHist. Only the variable APOBEC-2 was present in 15 of the 16 best-combinations, except for in the combination with the highest classification when using MLP with the CD4Ini clinical endpoint. On the other hand, BCbox-3 was present in all the best combinations related to the CD4 T cell count. After defining the 100 best-combinations per clinical endpoint by each algorithm, an assessment on the relevance of each variable was then undertaken. This involved calculating the probabilities for each variable of being selected as the most informative (i.e., root variable) in each of the best combinations. The relevance scores (r) per algorithm and positions are shown in Appendix A, see Table A1, Table A2, Table A3 and Table A4. After evaluating all the variables for each clinical endpoint, a threshold was calculated per clinical endpoint and used for selecting the most relevant variables as mentioned previously; see Section 3.5. The calculated threshold values for the most relevant variables are summarized in Appendix A, see Table A6a. The variables indicated as most relevant for CD4Ini (ordered by their relevance scores) were: [BCbox-3, APOBEC-3, APOBEC-5, APOBEC-2]; for CD4Hist: [APOBEC-2, APOBEC-3, APOBEC-5]; for VLIni they were [APOBEC-2, BCbox-1, APOBEC-3] and, finally; for VLHist they were [NLIS, APOBEC-3, APOBEC-5]. Considering these most relevant variables, APOBEC-3 proved to be associated with all the clinical endpoints, while APOBEC-2 and APOBEC-5 were present in only three clinical endpoints. BCbox-1 was seen to be the most relevant for only VLIni. BCbox-3 was only relevant for CD4Ini, and NLIS was suggested as being the most relevant in only VLHist. The most relevant variables identified were in agreement with the best variables identified in previous efforts using alternative approaches [20], as shown in Table A7b; see Appendix A. This was also the case for the second variables in the clinical endpoints CD4Hist and VLIni. Another difference was that the quantity of variables defined as the most relevant when using the MAREV-1 approach was much higher for the clinical endpoints CD4Ini and CD4Hist than reported previously. In this approach, the variable assessment process was done considering only the combinations of variables having the best classification performance, see Table 2. As happens with MAREV-1, MAREV-2 also calculated the probability for each variable to appear at every available position. This was later used to determine the score per variable and clinical endpoint as shown in Table A6b); see Appendix A. The variables discovered to be more relevant for CD4Ini (ordered by their scores) were: [BCbox-3, BCbox-2]; [APOBEC-2, APOBEC-4, BCbox-3] for CD4Hist; [APOBEC-2, APOBEC-4, BCbox-1] for VLIni; and [APOBEC-2, NLIS, BCbox-1] for VLHist. None of the variables were shown to be present in all clinical endpoints unlike MAREV-1. However, APOBEC-2 was present in CD4Hist, VLIni and VLHist. On the other hand, APOBEC-2 and APOBEC-4 are related to CD4Hist and VLIni; BCbox-1 is relevant for VLIni and VLHist. Finally, BCbox-3 is relevant for CD4Ini and CD4Hist. BCbox-2 is only relevant for CD4Ini, while NLIS is relevant for VLHist. These variables are compared with the previous findings and those suggested by the 100-model analysis (see Table A7c in Appendix A). The comparison among the variables identified as the most relevant by the previous approach, MAREV-1 and MAREV-1, show a coincidence in some of the variables detected as most relevant. This is the case of BCBox-3 in CD4Ini and APOBEC-2 in both CD4Hist and VLIni. Although MAREV-1 and the previous approach agreed on assigning NLIS as the most relevant variable for VLHist, this motif was only suggested as the second most relevant for this clinical endpoint by MAREV-2. The decision trees defined with the variables determined by the MAREV-1 are shown in Figure 5, while those using the MAREV-2 are shown in Figure 6. ID3 branch frequency was used to identify specific combinations of input variable status ( or ) as related to the clinical endpoints in Fisher’s exact test. Only branches having more than 1 variable were considered, yielding a total of 20 variable combinations for the MAREV-1 approach (6 for CD4Ini, 5 for CD4Hist, 6 for VLIni, and 3 for VLHist) whereas the MAREV-2 approach identified 22 different relevant variable combinations (4 for CD4Ini, 6 for CD4Hist, 6 for VLIni, and 6 for VLHist. The results of the statistical assessment for the MAREV-1 and MAREV-2 approaches are shown in Table 3. Four of the 20 ID3-combinations defined from the MAREV-1 approach were detected as associated with clinical endpoints after further statistical testing. One was present for CD4Ini (p-value ), two for CD4Hist (p-value , p-value ), and one for VLIni (p-value ). None of the associated combinations were present in VLHist. The combination for CD4Ini [BCboc-3, APOBEC-3] suggests protection from having lower numbers of CD4 T lymphocytes at the time of initial medical assessment as it was present in only 6 patient samples having ≤500 CD4 T cells, compared to 53 patient samples not having said combination. In the case of CD4Hist, only one combination [APOBEC-2, APOBEC-3] suggested protection from having less than 500 T Lymphocytes on medical follow-up, as was also found in our previously published work. A second combination [APOBEC-2, APOBEC-3, APOBEC-5] was found to be associated with the risk of progression to less than 500 CD4 T lymphocytes on medical follow-up. The absence of said combination was detected in 14 out of 15 sequences with ≥500 CD4 T cells. Finally, in the case of VLIni, the [APOBEC-2, BCbox-1, APOBEC-3] combination suggested a risk of having higher HIV viral loads on the first medical examination as it was absent in 22 out of the 26 cases with less than 10,000 virus copies. On the other hand, the 22 ID3-combinations generated using the variables defined by the MAREV-2 yielded 5 clinical associations. Both of the associations found in CD4Ini involved variables BCBox-2 and BCBox-3 where the conservation of both protein regions was associated with a higher risk of having lower initial CD4 T lymphocytes on the first medical examination (p-value ). This variable combination was present in 26 of the patient cases with CD4 cells/L, compared with a single occurrence in a patient having . A second variable combination, [BCBox-2 and BCBox-3], was associated with protection from low CD4 T lymphocytes counts as it was observed to be more frequent in patients having CD4 cell count/L (p-value ). Regarding historic CD4 T cell counts, one variable combination [APOBEC-2, BCbox-3] was associated with the risk of having low CD4 T cell counts on medical follow-up as it was present in 20 cases with a CD4 cell count below 500 and not in patients having CD4 T cells/L. Regarding initial viral load assessments, [APOBEC-2, APOBEC-4, BCbox-1] was associated with the risk of having high viral titres (≥10,000 viral copies) at the time of initial medical examination and was present in 11 patients having ≥10,000 viral copies, yet in only a single patient having lower viral loads. Finally, [NLIS, APOBEC-2, BCbox-1] was observed to be associated with a higher risk of low historical viral loads on patient follow-up as it was seen only once in a patient having <10,000 copies but it was present in 6 patients having more than 10,000 copies of the virus. As mentioned before, eight novel HIV associations were identified through this approach: three by MAREV-1, and five with MAREV-2. Distinct Vif protein regions were identified through this approach as being highly relevant by MAREV-1, mainly involved in APOBEC3 interactions and Elongin B/C binding. Relevant APOBEC3 interaction motifs included APOBEC-3, which was found to be conserved in all cases as well as APOBEC-2, which only failed to be relevant with regard to CD4Ini. Similarly, APOBEC-5 was found to be absent in CD4Hist while BCbox-1 was related to VLIni. Similarly, MAREV-2 also identified APOBEC-3, APOBEC-2, and APOBEC-4, and the Elongin B/C-box binding motifs, BCbox-1, BCbox-2, and BCbox-3 as most relevant. The results from the MAREV-2 for VLHist agree with our previously published findings by suggesting a higher relevance of the NLIS segment. These results help supporting the variables detected as more informative in our previous findings [20], being: (i) [BCbox-3] for CD4Ini, (ii) [APOBEC-2] for CD4Hist, VLIni and VLHist, (iii) [BCbox-1] for VLIni and VLHist, and iv) [NLIS] for VLHist. Additionally, the MAREV-1 approach places relevance for the variables [APOBEC-3 and APOBEC-5] while MAREV-2 places relevance for [APOBEC-4, BCbox-2, and BCbox-3]. On the other hand, the four associations determined with MAREV-1 and the five determined by MAREV-2 were less than the seven suggested with the previously methodology. Only one of said associations was present when using both approaches. Fewer associations were found when considering the viral load clinical status, both the initial and historical. This was the case for VLHist, where no association was found when using the MAREV-1 approach. However, determining which set of associations have more biological significance requires further research. Table 3 concentrates the most relevant associations of genetic variable combinations with each of the four clinical endpoint variables out of the 20 and 22 hypotheses tested by the MAREV-1 and MAREV-2 algorithms, respectively. On initial examination, the reiterative appearance of APOBEC and Elongin B/C Box motifs stands out in the results generated by both algorithms, irrespective of site status (mutated or conserved). This is a reflection of the importance of Vif protein, a function which involves both binding of Elongin B/C and recognition of APOBEC molecules to provide HIV with the capacity to escape from APOBEC-mediated innate immunity. From within the eight different APOBEC binding sites included in the analysis, APOBEC-2 and APOBEC-3 stand out for the number of times they appear in the associations shown in Table A5. Interestingly, the APOBEC-2 and -3 sites bind APOBEC3G and APOBEC3F, the two most relevant members of the APOBEC3 family of antiviral proteins. Nevertheless, our results are indicative that the APOBEC3G and APOBEC3F protein binding site (APOBEC-2) is perhaps the least important of all the genetic Vif variables assessed. This is based on the fact that both MAREV-1 and MAREV-2 results show higher viral titres and lower CD4 T cell numbers (suggesting ongoing viral robustness) even in the presence of APOBEC-2 mutations, as long as the other APOBEC-binding regions or Elongin B/C binding regions remain conserved. This was observed in historic CD4 T cell numbers, the initial viral loads, and regarding the historic viral loads. Similarly, the recursive appearance of Elongin B/C box-1 and box-3 binding sites also highlights the relevance that the Elongin interactions have for the Vif protein mediated ubiquitination of APOBEC3 anti-viral proteins. Overall, our results emphasize the clinical relevance of both APOBEC3G and Elongin B/C binding sites from among the remaining Vif protein domains assessed. Figure 7 illustrates the position of the Vif encoding region within a reference (HXB2) HIV-1 genome, the Vif protein domains and regions, as well as some of the putative or known ligands. Even greater detail is provided by our results regarding the weight of each of these genetic variables when individual clinical outcomes are considered. At least one previous report has identified that amino acid substitutions in Elongin B/C sites lead to a loss-of-infectivity in HIV [45]. The results of both MAREV-1 and -2 suggest that initial CD4 T cell numbers seem to depend more on Elongin B/C site status than any other Vif protein attribute. When Elongin B/C box mutations are present, such as in [BCbox-3, APOBEC-3] (MAREV-1) and [BCbox-3, BCbox-2] (MAREV-2), a greater number of patients are seen to be present in the ≥500 cells/L class than in the ≤500 cells/L class. This supports the notion that Elongin B/C binding box mutations are detrimental to viral fitness and thus prevent HIV from escaping APOBEC3 inhibition or interference. An additional interesting finding relates to historic CD4 T cell numbers and viral loads. HIV patients are normally enrolled into anti-retroviral therapy protocols after being diagnosed, irrespective of CD4 T cell counts and viral load numbers. The clinical impact that viral mutations have at this stage, after initiating treatment, has largely been linked to protease, reverse-transcriptase, and integrase sites, those most subjected to selective pressures by anti-retroviral drugs. Our results indicate that the conservation of APOBEC binding motifs are essential to viral fitness (and worsening clinical progression), at least in the MAREV-1 results. As such, [APOBEC-2, APOBEC-3, APOBEC-5] and [APOBEC-2, APOBEC-3] were more common among patients having lower CD4 T Cell numbers on follow-up. This was also true for BCbox-3 in MAREV-2 results, where [APOBEC-2, BCbox-3] was also more common among patients having ≤500 cells/L. Previous reports have highlighted how the conservation of APOBEC binding sites is crucial for vif-mediated viral fitness. Our results suggest that the mutation of certain APOBEC3 binding site motifs (i.e., APOBEC-2) is tolerated without a significant effect on viral fitness as long as other, perhaps more important, remaining motifs are conserved (i.e., APOBEC-3 and or -5) [46]. This paper proposes a new methodology based on machine learning algorithms (CART, NB, SVMs, and MLP) combined with an undersampling approach to deal with an imbalanced HIV dataset. Additionally, we present evidence of the classification performance of two different approaches (MAREV-1 and MAREV-2) for the identification of associations of Vif protein motifs with clinical endpoints in HIV. These variables subsequently proved to play a crucial role when different combinations of them were linked to HIV outcome, a difficult task that is not possible to achieve in human terms without relying on statistical corrections that decrease the statistical power of the study. These findings are in agreement with the known properties and with the functional and clinical relevance of the different Vif protein motifs found to be relevant. Needless to say, further research employing cell biology and molecular epidemiology tools is warranted so as to provide further support for these claims. Efforts are currently underway in our group to test the clinical utility of the identified variable combinations in a novel, larger HIV cohort. When comparing the different strategies described in this manuscript, MAREV-2 was able to identify many more clinical associations, at least one per clinical outcome. This might be interpreted to suggest that this approach might prove more useful in future analysis and in clinical settings. Many techniques are currently available to deal with imbalanced datasets. Although we studied the capacity of an undersampling approach to resolve this limitation, future work will explore the performance of oversampling techniques. These results provide further evidence on the usefulness and potential that machine learning methods have at analyzing complex datasets. Given the exponential growth of applications of artificial intelligence and classification strategies, this field is likely to benefit from the results presented herein. Elongin B/C binding site mutations might prove to be the single most important Vif genetic feature determining CD4 T cell numbers at the time of clinical debut and at a time when viral replication has not been subjected to the influence of anti-retroviral drugs (as patients are treatment-naïve at this time). This opens the possibility that molecular approaches targeting HIV-1 Elongin B/C binding motifs or those inhibiting the interactions of Elongin B/C and Vif might provide innovative preventative strategies in the fight against HIV. Overall, our results provide insight into the utility that both MAREV-1 and -2 algorithms have at discriminating complex genetic variable combinations linked to clinical endpoints in HIV, the practical utility of screening for accessory protein encoding region mutations in HIV prognosis, as well as at guiding the development of novel therapeutic interventions in HIV.

PMC10001284

Hae Do Jung,Seok Cho,Joo Yong Lee

Update on the Effect of the Urinary Microbiome on Urolithiasis

02-03-2023

microbiota,urolithiasis,urinary tract

Microbiota are ecological communities of commensal, symbiotic, and pathogenic microorganisms. The microbiome could be involved in kidney stone formation through hyperoxaluria and calcium oxalate supersaturation, biofilm formation and aggregation, and urothelial injury. Bacteria bind to calcium oxalate crystals, which causes pyelonephritis and leads to changes in nephrons to form Randall’s plaque. The urinary tract microbiome, but not the gut microbiome, can be distinguished between cohorts with urinary stone disease (USD) and those without a history of the disease. In the urine microbiome, the role is known of urease-producing bacteria (Proteus mirabilis, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Providencia stuartii, Serratia marcescens, and Morganella morganii) in stone formation. Calcium oxalate crystals were generated in the presence of two uropathogenic bacteria (Escherichia coli and K. pneumoniae). Non-uropathogenic bacteria (S. aureus and Streptococcus pneumoniae) exhibit calcium oxalate lithogenic effects. The taxa Lactobacilli and Enterobacteriaceae best distinguished the healthy cohort from the USD cohort, respectively. Standardization is needed in urine microbiome research for urolithiasis. Inadequate standardization and design of urinary microbiome research on urolithiasis have hampered the generalizability of results and diminished their impact on clinical practice.

Update on the Effect of the Urinary Microbiome on Urolithiasis Microbiota are ecological communities of commensal, symbiotic, and pathogenic microorganisms. The microbiome could be involved in kidney stone formation through hyperoxaluria and calcium oxalate supersaturation, biofilm formation and aggregation, and urothelial injury. Bacteria bind to calcium oxalate crystals, which causes pyelonephritis and leads to changes in nephrons to form Randall’s plaque. The urinary tract microbiome, but not the gut microbiome, can be distinguished between cohorts with urinary stone disease (USD) and those without a history of the disease. In the urine microbiome, the role is known of urease-producing bacteria (Proteus mirabilis, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Providencia stuartii, Serratia marcescens, and Morganella morganii) in stone formation. Calcium oxalate crystals were generated in the presence of two uropathogenic bacteria (Escherichia coli and K. pneumoniae). Non-uropathogenic bacteria (S. aureus and Streptococcus pneumoniae) exhibit calcium oxalate lithogenic effects. The taxa Lactobacilli and Enterobacteriaceae best distinguished the healthy cohort from the USD cohort, respectively. Standardization is needed in urine microbiome research for urolithiasis. Inadequate standardization and design of urinary microbiome research on urolithiasis have hampered the generalizability of results and diminished their impact on clinical practice. Microbiomes are defined as microbiota, their genomes, and the surrounding environmental conditions. Microbiota are ecological communities of commensal, symbiotic, and pathogenic microorganisms [1,2]. Sometimes, the term “microbiome” is used interchangeably with “microbiota.” Microorganisms are conventionally classified into pathogens, normal flora, and probiotics; however, normal flora have been recently referred to as “indigenous microbiota”. Research on the human urinary microbiome has the potential to increase understanding of a variety of urologic disorders, including lower urinary tract symptoms (LUTS) and urologic cancer [3,4,5]. Many recent studies have reported the role of the microbiome in urinary stone formation (urolithiasis) [6,7,8,9,10,11,12,13,14,15]. Urolithiasis can be classified as being caused by infection (magnesium ammonium phosphate, carbonate apatite, and ammonium urate), non-infectious causes (calcium oxalate, calcium phosphate, and uric acid), or hereditary abnormalities (cystine, xanthine, and 2,8-Dihydroxyadenine) [16]. Urolithiasis is a disease with a high recurrence rate, and its occurrence rate is rising worldwide [17]. As a direct consequence of urolithiasis, patients with stone recurrence have a deterioration in their quality of daily life, and the financial burden associated with managing urolithiasis is increasing [18]. Thus, the various etiologies, including the microbiome of urolithiasis, which can be a potential cause for stone recurrence, must be understood. Here, we provide a reviewing update focusing on the role of the urinary microbiome in urolithiasis. Figure 1 shows the process of calcium oxalate stone formation. An increase in calcium and oxalate and a decrease in urine volume initiate urinary stone formation and urine saturation. Among these processes, the formation of Randall’s plaque is related to crystal nucleation and growth. The formation of free particles is related to crystal aggregation. The formation of fixed particles is related to urothelial damage, and through this process, stone retention is repeated and develops into urinary stone disease (USD). The urinary microbiome could be involved in stone formation through hyperoxaluria and calcium oxalate supersaturation, biofilm formation and aggregation, and urothelial injury. The Human Microbiome Project (HMP) aimed to explore microbial communities and their connections to their human hosts [19,20]. The HMP was launched in 2008. Initially, however, the HMP did not include the bladder [9]. Samples were obtained from the lungs, skin, oral cavity, gastrointestinal tract, and vagina. Urine was once believed to be sterile in healthy persons, despite including several microorganisms. Modern clinicians have linked bacteria in the urine to infection or, less frequently, an undefined syndrome called “asymptomatic bacteriuria” [21]. The “sterile urine” paradigm has been the foundation for this and other existing ideas for a long time [21]. However, microbial communities (microbiota) have recently been found in the female urinary bladder [22,23,24,25,26,27,28,29,30]. As a result, the concept of “sterile urine” is obsolete. In response, new methods to study urine have been developed. Urine initially found to have “no growth” by the usual methodology has been shown to include bacteria that could be cultivated using 16S rRNA sequence analysis [22]. As a result, Hilt et al. [28] have developed an enhanced quantitative urine culture (EQUC) methodology that employs 100 times as much urine as a traditional culture and a wide range of media and environmental conditions to isolate and identify many organisms missed by conventional culturing. When comparing EQUC to the conventional clinical technique, a 90% false negative rate was found for the conventional clinical approach [28]. The previous widespread testing methods (such as the standard urine culture and dipstick) are inadequate for research purposes and may also be inappropriate for clinical purposes. However, the clinical feasibility and future roles of 16S rRNA sequencing and EQUC have yet to be determined [21]. In addition, knowledge of the “normal” urinary microbiome has been hindered because there was no formal definition of bladder health [31]. The Prevention of Lower Urinary Tract Symptoms Consortium has recently defined bladder health as “A complete state of physical, mental, and social well-being related to bladder function and not merely the absence of LUTS” [32]. However, although it is difficult to identify a single group of common bacteria found in a healthy bladder, comparative studies have provided insights into the overlap and various compositions of urine-based microbial communities [33]. One study found that the urinary microbiome is composed primarily of species from a few genera, most frequently Lactobacilli (the generic term ‘Lactobacilli’ is useful to designate all organisms that were classified as Lactobacillaceae [34]), Gardnerella, and Streptococcus, and has a lower biomass than the vaginal microbiome [31]. Due to the significant anatomical and physiological differences between men’s and women’s lower urinary tracts, it is not surprising that the urine microbiome of the bladder also exhibits unequal stratification [33]. One study discovered that Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria were the phyla that men and women shared most frequently. However, in general, in healthy females, Streptococcus, Lactobacilli, and Prevotella are abundant, whereas, in males, Lactobacilli, Corynebacterium, and Gardnerella are more prevalent [35]. Another study found that Lactobacilli predominates in the bladder in healthy women, while the bladders of healthy men contain high levels of Enterococcus, Proteus, and Klebsiella in their bladder [29]. Urine samples obtained by catheterization and 16S rRNA sequencing were used in a multicenter cross-sectional study by the NIH-NICHD-funded Pelvic Floor Disorders Network [36]. They described characteristics of the urine microbiomes of well-characterized asymptomatic women (n = 84) compared to those of women with mixed urinary incontinence (MUI) (n = 123). These researchers confirmed earlier reports that some members of the genus Lactobacilli may be linked to urinary symptoms, including urgency urinary incontinence, even though the proportion of women with predominant Lactobacilli did not differ between women with MUI and asymptomatic women with matching ages [36]. Bajic et al. [37] classified 28 men with surgical histories of benign prostate enlargement (BPE)–LUTS and 21 men with histories of non-BPE–LUTS surgery using the International Prostate Symptom Score (IPSS). The authors obtained and analyzed paired catheterized and voided urine samples using EQUC and 16S rRNA sequencing. Overall, 39% of the urine specimens obtained by catheterization and 98% of specimens obtained by self-voiding included microbiome, and there was a significant difference between the microbiome of these types of urine specimens. The findings of the study suggest that catheterized urine may be a better method for collecting the urinary microbiome of male patients compared to voided urine. They examined catheterized urine samples from a group of men, some with and some without BPE, and discovered that the severity of LUTS was linked to the presence of bacteria in bladder urine. Specifically, the study showed that men with mild, moderate, and severe LUTS had detectable bacteria in their bladder urine at rates of 22.2%, 30.0%, and 57.1%, respectively (p = 0.024). The authors determined that severe LUTS in men may be related to detectable bladder bacteria compared to men with milder or no symptoms. Few studies have examined the relevance of the urinary microbiome in urological cancers. A study compared the urine of six healthy people with that of eight bladder cancer patients and measured an increased amount of the genus Streptococcus in the bladder cancer group using 16S rRNA sequencing [38]. A more recent study compared urine samples using 16S sequencing from twelve patients diagnosed with bladder cancer and eleven healthy participants selected from an age-matched cohort [39]. Although there were no significant differences between the two groups in the diversity or overall composition of the microbiome, certain taxa were considerably over-represented in either the bladder cancer or healthy subgroups. The most prevalent bacteria in the healthy subgroup were Veillonella, Streptococcus, and Corynebacterium. In contrast, numerous taxa were highly represented in the bladder cancer group, including members of the colorectal cancer-associated genus Fusobacterium. In the urine microbiome, the role of urease-producing bacteria in stone formation is already understood. Urease-producing bacteria are Proteus mirabilis, Klebsiella pneumoniae, Staphylococcus aureus, Pseudomonas aeruginosa, Providencia stuartii, and Serratia marcescens and Morganella morganii. Those bacteria break down urea and induce ammonia and carbon dioxide production, leading to renal tubular injury, urine alkalinization, and subsequent formation of phosphate salts that urease-producing bacteria form struvite stones [8]. Recent studies have shown that enterobacteria in the urinary microbiome, including Escherichia coli, may be associated with urolithiasis. In a study to determine if the presence of uropathogenic E. coli affected calcium deposition in the urinary tracts of mice, calcium deposition was 2.7-fold higher after inoculation with E. coli [40]. According to Hirano et al. [41], the aggregation of crystalline and organic matter in the urine of stone formers may be due to in part to the adhesive properties of certain bacteria. These bacteria have the ability to actively participate in the formation of stones by causing the aggregation of these materials. E. coli and P. mirabilis worsened calcium oxalate encrustation on the surface of polyurethane film, the substance used in urinary stents, as demonstrated by Venkatesan et al. [42]. The authors further proposed that the biofilm produced by these bacteria might cause calcium oxalate encrustation. Bacteria bind to calcium oxalate crystals, causing pyelonephritis, which leads to changes in nephrons that form Randall’s plaque. In addition, E. coli decreases citrate levels by secreting citrate lyase, which causes calcium oxalate supersaturation [40]. Ultimately, bacteria attach to the urothelium and repeat this process. Chutipongtanate et al. [43] examined the lithogenic potential of Gram-negative and Gram-positive bacteria on calcium oxalate. The researchers employed both morphological evaluation and a new screening method, as well as gold-standard assays, to determine that bacteria have the capacity to directly enhance the growth and aggregation of calcium oxalate crystals. This study was the first to utilize this novel screening method. In this study, calcium oxalate crystals were generated in the presence of two uropathogenic bacteria, i.e., E. coli and K. pneumoniae. In addition, the non-uropathogenic bacteria, i.e., S. aureus and S. pneumoniae, exhibited calcium oxalate lithogenic effects. Because not all urinary tract infection patients develop calcium oxalate stones, it can be understood as a hypothesis, but the microbes in the urine are not entirely essential for forming calcium oxalate stones. Even then, we must consider that the role of the urinary microbiome for stone formation in chronic kidney disease and kidney transplant patients could be more critical due to the perturbation (dysbiosis) of the urinary microbiome induced by immunosuppressive medications and repeated antibiotic treatments [44,45]. Recently, Kachroo et al. [12] have performed the first comparative shotgun metagenomic analysis of the urinary microbiome from patients either with pure calcium oxalate stones with an active episode of USD or without a history of USD. The goal of this study was to compare the shotgun metagenomics of voided midstream urine samples from a small number of patients (n = 5 calcium oxalate stone formers, n = 5 healthy controls) to identify the associated microbial functions across prokaryotic, viral, fungal, and protozoan domains. According to their findings, the genes involved in oxalate metabolism, transmembrane transport, proteolysis, and oxidation-reduction pathways were expressed at lower levels in calcium oxalate stone formers. Genes enriched in the control group mapped overwhelmingly to Lactobacillus crispatus from 17 draft genomes taken from the data and more than 42,000 full-length reference genomes, while genes related to calcium oxalate mapped to P. aeruginosa and Burkholderia sp. A recent comparative multi-omics clinical study by Zampini et al. revealed that the urinary microbiome, but not the gut microbiome, could be distinguished between cohorts with USD (excluding cases of infectious stones) and those without a history of the disease [46]. In the urine microbiome, 8.8% of the operational taxonomic units (OTUs) were differently abundant, with 1.6 times more OTUs in the healthy group than in the USD group. Lachnospiraceae in the stool of the USD cohort, Lactobacilli in the urine of the healthy cohort, and Enterobacteriaceae in the urine of the USD cohort were the taxa that best distinguished the healthy cohort from the USD cohort. When assessing the differential concentrations of specific metabolites by USD status, 53 were enriched in the healthy group and 16 were enriched in the USD group, a 3.3-fold greater number of enriched metabolites in the healthy cohort compared to the USD cohort. This is the first study to use metagenomics to compare the urinary microbiome in USD and healthy persons. Multiple lines of evidence in this study indicate that the urinary microbiome contributes more to the development of USD than the gut microbiome. The microorganisms in urinary microbiome associated with urolithiasis is summarized in Table 1. Since the discovery of the human urinary microbiome, various technological and participant-related issues and irregular sampling settings have impacted urinary microbiome research [31]. In other words, the inadequate standardization and design of urinary microbiome research on urolithiasis have hampered the generalizability of results and diminished their impact on clinical practice [11]. Thus, in the United States, standardization of microbiome studies for urolithiasis has begun with the MICRObiome contributions on the Complexity Of the Stone Matrix (MICROCOSM)—the first international consortium focused on microbiome-urolithiasis research [13]. MICROCOSM was created to reduce the inconsistencies in microbiome research findings brought on by experimental factors, including sample collection, storage, DNA extraction, sequencing, and data analysis. In addition, taxonomic assignment using OTUs or amplicon sequence variants (ASVs) for the sequencing data could address the variation in results due to differences in the experimental design or population characteristics [14]. With OTUs’ classification, in the urinary tract, the family Enterobacteriaceae and the genus Veillonella were most related with USD patients, whereas the genus Lactobacilli was most connected with healthy people. However, with ASVs assignment, Veillonellaceae were more frequently found in the urine microbiome of healthy participants, whereas Actinomycetaceae and Enterobacteriaceae were most frequently found in USD patients [14]. Brubaker et al. [5] have proposed guidelines developed at the UROBIOME 2020 conference for documenting and reporting data originating from urinary microbiome studies. Key recommendations for urinary microbiome research are: first, using appropriate nomenclature to describe the urine specimen: “Urogenital” sample for voided urine samples versus “Urinary Bladder” for catheterized (transurethral or suprapubic aspirates). Second, aligning the urine sampling technique with the purpose of the research. For studies of bladder urinary microbiome in adult women, the authors recommend the collection of catheterized urine samples when possible. Third, using nucleic acid preservatives and maintaining cold temperatures while expediting sample transfer to the laboratory. Fourth, aligning the sample processing plan with the research question (culture±culture-independent techniques). Fifth, minimizing the batch effects as much as possible (differences in kits and sequencing runs; record information to account for differences in downstream analyses if necessary). Finally, adopting a standard metadata checklist for urinary microbiome studies. The gut and kidney appear to have a bidirectional relationship, as supported by growing evidence [47]. Research suggests that the gut microbiome plays a significant role in the gut–kidney axis [48], and disruption of the gut microbial community, or dysbiosis, has been linked to the development of several renal disorders. This may indirectly contribute to chronic kidney disease and hypertension [49], as well as urinary stone disease [50]. Before current sequencing techniques were developed, it had already been demonstrated that the gut microbiome forms stones. That is, the absence of Oxalobacter formigenes leads to the formation of stones [51]. Bacteria that use oxalate as an energy source are called oxalotrophs. Lactobacilli and Bifidobacterium are examples of generalist oxalotrophs, which are able to degrade substances beyond oxalate for carbon energy. On the other hand, Oxalobacter formigenes is an example of a specialist oxalotrophs. This bacterium can degrade oxalate in the intestinal tract via the expression of two enzymes, Formyl-CoA transferase and Oxalyl-CoA decarboxylase [8]. Siener et al. [52] presented clinical results that the absence of Oxalobacter formigenes induces hyperoxaluria, which may lead to the formation of calcium oxalate stones. Among 37 patients with idiopathic calcium oxalate stone, Oxalobacter formigenes in the intestinal microbiome was positive in 11 patients and negative in 26 patients. Oxalobacter formigenes was not detected in 70% of patients. In the case of multiple stone formers, it was found that 60–80% of patients were negative for Oxalobacter formigenes. However, there was no difference in the concentration of urine oxalate between positive and negative Oxalobacter formigenes in 24-h urinalysis. In the case of enteric oxaluria, 0.5 mmol/d or 40 mg/d or more should be detected, but hyperoxaluria was not seen at 0.3 and 0.4 mmol/d in both groups. Therefore, a contradiction occurred in the mechanism that the absence of Oxalobacter formigenes, which we already knew, induces hyperoxaluria. Ticinesi et al. [53] reported an urolithiasis and intestinal microbiome study that examined the cause of the contradiction between Oxalobacter formigenes, hyperoxaluria, and calcium oxalate stones. They analyzed the metagenomics of 52 stone formers and 48 healthy controls. However, very few Oxalobacter formigenes were found among 100 subjects, less than 0.001% of the total sample. Relative abundance also showed no difference. As a result of analyzing the concentration of oxalate in the 24-h urine test in both groups, the strains presented showed differences. Assimos commented regarding the results of this study [54], the abundance of Faecalibacterium, Enterobacter and Dorea reduced stone formation, and Faecalibacterium is recognized as generating short chain fatty acids. It is thought to attenuate the inflammation and oxidative stress associated with stone formation. The negative correlation between Oxalobacter formigenes and hyperoxaluria is because Oxalobacter formigenes was rarely found in the 100 subjects, and the results were different from those known so far. This is why, even in the absence of Oxalobacter formigenes, it does not cause enteric hyperoxaluria, showing the possibility that it is not a valid theory or that there is no clinical value in the theory. Bostanghadiri et al. [55] described that the contradiction of Oxalobacter formigenes could be due to a variety of factors, including the study’s population, lifestyle, and eating habits, all of which could affect the gut microbiome, particularly Oxalobacter formigenes. Stern et al. [56] examined in a pilot study the significant variations in the gut microbiome of urolithiasis patients in comparison to patients without kidney stone formation. Their findings showed that the genus Prevotella was 2.8-fold more prevalent in the control group without kidney stones, but the genus Bacteroides was 3.4-fold more abundant in the kidney stone group. According to a 24-h urine examination, the genus Eubacterium was negatively correlated with oxalate levels and the genus Escherichia was negatively correlated with citrate levels. Recently, Kim et al. [57] conducted a prospective cohort study aimed at examining the association between the prevalence and incidence of renal stones and the gut microbiome in a relatively large-scale study of 915 participants. Patients were divided into the following groups according to the presence or absence of renal stones at the initial and subsequent visits: G0, no renal stones (control), those without renal stones at the initial and subsequent visits; G1, incidental renal stones, those without renal stones initially but with renal stones at the follow-up visits; and G2, prevalent renal stones, those with renal stones at the beginning of the experiment. The median follow-up period was 4.0 years (interquartile range, 2.0–5.0 years; maximum 5.5 years). The abundances of other taxa, as opposed to Oxalobacter formigenes, varied significantly between the control group and the renal stone group, as seen in prior reports, and their findings were consistent with the results of those studies. In contrast to the accidental stone group, they discovered that Bifidobacterium was more prevalent in the no stone group. Dorea, Incertae sedis, and Faecalibacterium abundances were discovered to be lower in the incidental stone group than in the no stone group. The incidental stone group also showed higher abundances of Fusobacteria, Phascolarctobacterium, and Erysipelatoclostridium and reduced abundances of Eubacterium eligens group and Dialister as compared to the no stone group. Finally, they observed Faecalibacterium was found in lower abundance in the incidental stone group than in the no stone group, but there was no difference in abundance between the prevalent stone and no stone groups. Deng et al. [58] reported that 16S ribosomal RNA (rRNA) gene sequencing reveals an altered composition of the gut microbiome in postoperative individuals with renal stones. They used 16S ribosomal RNA (rRNA) gene sequencing to examine the relationships between the gut microbiome and renal stone formation. In summary, 20 patients were chosen, and data on health and eating patterns from the previous 1–3 months were gathered at the time of admission. A total of 40 samples were examined, yielding 493 operational taxonomic units (OTUs), with an average of 67,888 ± 827 reads per sample. OTU-based partial least squares discriminant analysis (PLS-DA) analysis with OTU-based results revealed differences between the RS1 (fecal specimen taken before surgery) and RS2 (fecal specimen taken one month after surgery) groups, with a significantly greater level of OTU7 in the RS2 group. Taxonomy-based comparisons of the gut microbiome revealed changes in the flora composition, with greater prevalences of Enterobacteriaceae, Gammaproteobacteria, Escherichia, and Enterobacteriales in the RS2 group and Pseudomonadaceae, Pseudomonadales, and Pseudomonas in the RS1 group. According to correlation analysis, a lower level of urea was correlated with a higher prevalence of Enterobacteriaceae, Gammaproteobacteria, and Escherichia, while a lower level of creatinine was correlated with a higher frequency of Escherichia. These findings may offer new perspectives for the prevention, diagnosis, and treatment of renal stones as they strongly imply that the gut microbiome plays a significant role in kidney stone formation. Yuan et al. [59] reported an association of dietary patterns with gut microbiome in kidney stone and non-kidney stone individuals. In their study, both the calcium oxalate kidney stone group and the non-kidney stone group had a gut microbiome with significantly different abundances in the high urolithiasis risk dietary patterns compared to the low urolithiasis risk counterparts, while Pseudomonas, Sphingomonas, Slackiain, Corynebacterium, Arcobacter, Stenotrophomonas, Hydrogenoanaerobacterium, and Faecalitalea were found to be more abundant in the high urolithiasis risk kidney stone group, which is significant in the formation of kidney stones, so were metabolic pathways linked to inflammation, lipid, and mineral metabolism. Their results suggested that dietary habits may influence the prevention and treatment of calcium oxalate stones by controlling the gut microbiome’s homeostasis. In a recent study by Xiang et al. [60], a machine learning model was developed to forecast the likelihood of calcium oxalate kidney stones based on a combination of clinical and gut microbiome features. The study included data from a total of 180 subjects, with 120 subjects allocated to the training set and the remaining 60 subjects used for validation purposes. The authors evaluated the performance of eight distinct machine-learning techniques using clinical and gut microbiome data from 66 non-kidney stone individuals and 54 kidney stone patients. They identified three stone-related bacteria (Flavobacterium, Rhodobacter, and Gordonia). Clinical data were comprised of five characteristics: oxalate concentration, acetic acid concentration, citrate concentration, phosphorus concentration, and urinary PH. Area under the curve (AUC) of predictive models using only three genus was 0.763, the AUC of using five pieces of clinical information was 0.902, and the AUC of using three genus plus five pieces of clinical information was 0.936. In the end, the gut microbiome plays a role in forming calcium oxalate stones, but it needs to be further elucidated in the future. The microbiome of urinary stones refers to the microbiome discovered using EQUC and 16S rRNA gene sequencing in stones themselves. Bacteria can be identified in between 15 and 70 percent of stones [61,62,63]. In 13% to 44% of samples, calcium oxalate stones had positive cultures. The most frequent bacteria found in stone cultures were E. coli (15–35%), Pseudomonas spp., and Proteus (urease-producing bacteria), which are frequently linked to the development of struvite stones [62,63]. An analysis of the microbiome of calcium stones has recently been reported [15]. In 52 urolithiasis patients, patients who had used antibiotics within 4 weeks, pregnant or hospitalized patients, and patients with a history of struvite stones were excluded. After removing the stones with a ureteroscope and laser, an EQUC and 16S rRNA sequencing were performed. In 16s rRNA sequencing, 55.8% of cases were positive. After comparing with a negative control and removing difficult-to-identify stones, 12 stones were confirmed as sequencing positions, and 40 were considered negative. When the stones sequencing positive and negative were compared, calcium oxalate was confirmed in 90% of the negative group, and brushite was found in the positive group. Uric acid and cystine stones were found only in the negative group. The struvite stones were the same in both groups. When looking at the results of expanded culture and sequencing, various bacteria were distributed in the 12 stones, and very diverse bacteria were found in the sequencing metagenomics. The most prevalent bacterial taxon was Enterobacteriaceae (including the genera Escherichia and Klebsiella), Staphylococcus, Veillonella, Streptococcus, Corynebacterium, Haemophilus, Proteus, Lactobacilli, and Bifidobacterium. Multiple enriched bacterial species were discovered using EQUC and 16S rRNA sequencing Bacterial species isolated from the urinary stone that were concordant with 16S rRNA gene sequencing identification include S. epidermidis, E. cloacae, E. coli, and L. gasseri. The authors describe the value of their research as follows: a method for comparing the microorganisms of urine and stone is presented, and it shows similar microbiomes of selective urine in the upper urinary tract and the bladder. A calcium oxalate stone indicates that the presence of bacteria is low. This result is inconsistent with the experiment mentioned above that showed E. coli in mice could promote calcium oxalate stone formation. In a meta-analysis, Staphylococcus and Aerococcus genera dominated the microbiome of stone samples across two studies, with Enterobacteriaceae present in high abundance based on OTUs, and Enterococcus predominant based on ASVs [14]. When there was a urinary tract infection, urolithiasis growth was observed in 61.5% of cases, and stones grew in only 12.5% of cases with no urinary tract infection [64]. This study demonstrates the need to treat urinary tract infection to prevent the progression of infectious stones. However, more research is needed to determine whether treating the microbiome in calcium oxalate stones as a prevention strategy is worthwhile. Oxalobacter formigenes may protect against calcium stones via two distinct mechanisms: oxalate degradation in the gut lumen, which reduces mucosal absorption, and promotion of endogenous oxalate secretion by the gut mucosa [65]. From Eubacterium lentum, the oxalate-degrading proteins oxalyl-CoA decarboxylase and formyl-CoA transferase was isolated [66,67]. Turroni et al. [68] discovered that Bifidobacterium subsp. lactis DSM 10140, Bifidobacterium adolescentis MB 238, and Bifidobacterium longum MB 282 had the highest levels of oxalate degradation. In addition, according to Turroni et al. [68], a variety of Lactobacilli. can degrade oxalate. The presence of oxc and frc genes was discovered in isolates of Lactobacillus acidophilus and Lactobacillusgasseri that degraded more than 50% oxalate. Wigner et al. [6] have reviewed the use of probiotics to prevent calcium oxalate stones. Prior research has mostly concentrated on administering Oxalobacter formigenes to patients with urolithiasis. Due to its antibiotic sensitivity and low pH, this bacterium is not a good probiotic. Thus, later research focused on well-known probiotics, including Lactobacilli and Bifidobacterium strains, Eubacterium lentum, Enterococcus faecalis, and E. coli, to identify bacteria that are capable of degrading oxalate. However, Oxalobacter formigenes has the best potential for oxalate degradation of all the bacteria examined. Subsequent research has revealed the existence of Oxalobacter formigenes strains that are resistant to low pH and contain oxygen, confirming the possibility of using Oxalobacter formigenes in clinical settings [69,70,71]. No reduction in the viable cell count was seen when the Oxalobacter formigenes strain was cultured in anaerobic media at pH 6.8 and pH 3.0 for up to 2 h [71]. Mass spectrometry (MS)-based shotgun proteomics study of anaerobic Oxalobacter formigenes cultures found superoxide dismutase (substantial protection against oxygen toxicity) to be expressed [70]. Additionally, it was demonstrated that several variables, including the acid produced by yogurt bacteria and oxygen permeability through the packaging, affect the survivability of probiotic bacteria in yogurt [72,73,74]. Hoppe et al. [69] conducted clinical study (16 patients with urolithiasis). There were two groups of patients. The first group consisted of nine patients who received Oxalobacter formigenes as a frozen cell paste containing 1 g of live cells equivalent to >1010 CFU (IxOC-2). The second group was made up of seven patients who received two enteric-coated capsules of Oxalobacter formigenes (137 mg of lyophilized bulk powder of freeze-dried live cells, equivalent to ~107 CFU) per dose (IxOC-3). In order to evaluate the degree of oxalate extraction, urine and plasma samples were obtained. In their study, Oxalobacter formigenes intake can reduce the urinary oxalate levels in patients with urolithiasis (IxOC-2: 22–48%, IxOC-3: 38.5–92%). Combining probiotics is one possible solution to address the issue of antibiotic sensitivity. Such an approach may help mitigate the problem of intestinal colonization caused by antibiotic use. According to reports, combining different probiotics may also be a good solution because the entire intestinal microflora participates in the breakdown of oxalate and reduces its excretion in urine [75,76]. Campieri et al. [76] discovered that a daily dose of a mixture of freeze-dried Lactobacilli strains (L. acidophilus, L. plantarum, and L. brevis), Bifidobacterium infantis, and Streptococcus thermophilus (administered as a daily dose at 8 × 1011 CFU, 4 weeks probiotic therapy) caused a significant reduction in urinary oxalate excretion, i.e., by about 40%, in six patients with idiopathic calcium oxalate urolithiasis and mild hyperoxaluria. Wigner et al. [6] concluded that it is necessary to perform additional research with a bigger population under highly controlled circumstances. No studies have attempted to comprehensively evaluate changes in the gut microflora, and most studies rely on small study groups, which frequently follow an unplanned diet. Additionally, there are discrepancies between the ability of these bacteria to metabolize oxalate in vitro and in vivo, according to studies on Lactobacilli and Bifidobacterium strains, E. lentum, E. faecalis, and E. coli that have primarily been conducted in animals and in vitro [7,66,67,68,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92]. Stepanova et al. [10] have performed experiments (i) to evaluate whether ceftriaxone treatment could affect the number of intestinal oxalate-degrading bacteria, their overall oxalate degradation activity, and their influence on oxalate homeostasis in rats, (ii) to assess the impact of commercially available probiotics and synbiotics (food ingredients or dietary supplements combining probiotics) on total fecal oxalate-degrading activity, and (iii) to measure how well synbiotics can reverse ceftriaxone-induced disturbance of the oxalate homeostasis and fecal oxalate-degrading activities in rats. They randomly divided 28 female Wistar rats (weighing 200–300 g) into four groups (n = 7). Group 1 (control) was treated with sterile water (0.1 mL, i.m., 14 days); Group 2 was treated with synbiotics (30 mg/kg, per os, 14 days); Group 3 was treated with ceftriaxone (300 mg/kg, i.m., 7 days); and Group 4 was treated with ceftriaxone and synbiotic. On days 1 and 57 following the termination of treatment, the number and total activity of oxalate-degrading bacteria, as well as urine and plasma oxalate concentrations, were assessed. Oxalate homeostasis was altered by ceftriaxone treatment, which also significantly increased urine oxalate levels. The number of oxalate-degrading bacteria in the fecal microbiome did not differ significantly between the groups on day 57 following treatment discontinuation. However, total oxalate-degrading activity was significantly higher in both synbiotic treatment groups than in the control and ceftriaxone alone-treated groups. Urinary oxalate excretion was markedly decreased in the rats given synbiotics. The overall amount of oxalate-degrading activity in the fecal microbiome was not correlated with the number of oxalate-degrading bacteria, and this activity was inversely correlated with plasma and urine oxalate concentrations. These findings imply that supplementation with synbiotics enhances the total oxalate-degrading capacity of the gut microbiome, leading to a substantial reduction in urine oxalate excretion [9]. Ticinesi et al. [78] reviewed the effects of the administration of oxalate-degrading bacteria on lithogenic risk. Overall, the findings were inconsistent, with some studies reporting significant decreases in urinary oxalate excretion following probiotic treatment [71,76,80,84,86,91], while others showed no changes from baseline [81,89]. Regarding the inconsistency of findings, the authors noted that oxalate excretion represents a proxy endpoint for stone recurrence and is merely one of several factors that contribute to the definition of lithogenic risk [78]. The urinary microbiome could affect calcium oxalate supersaturation, biofilm formation and aggregation, and urothelial injury. Standardization (study design, type of sample, sample collection, sample storage, DNA extraction methods, and data analysis) is needed in urine microbiome research for urolithiasis.

PMC10001285

Theoharis C. Theoharides,Duraisamy Kempuraj

Role of SARS-CoV-2 Spike-Protein-Induced Activation of Microglia and Mast Cells in the Pathogenesis of Neuro-COVID

22-02-2023

ACE2,brain,coronavirus,cytokines,inflammation,microglia,spike protein,toll-like receptors

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). About 45% of COVID-19 patients experience several symptoms a few months after the initial infection and develop post-acute sequelae of SARS-CoV-2 (PASC), referred to as “Long-COVID,” characterized by persistent physical and mental fatigue. However, the exact pathogenetic mechanisms affecting the brain are still not well-understood. There is increasing evidence of neurovascular inflammation in the brain. However, the precise role of the neuroinflammatory response that contributes to the disease severity of COVID-19 and long COVID pathogenesis is not clearly understood. Here, we review the reports that the SARS-CoV-2 spike protein can cause blood–brain barrier (BBB) dysfunction and damage neurons either directly, or via activation of brain mast cells and microglia and the release of various neuroinflammatory molecules. Moreover, we provide recent evidence that the novel flavanol eriodictyol is particularly suited for development as an effective treatment alone or together with oleuropein and sulforaphane (ViralProtek®), all of which have potent anti-viral and anti-inflammatory actions.

Role of SARS-CoV-2 Spike-Protein-Induced Activation of Microglia and Mast Cells in the Pathogenesis of Neuro-COVID Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). About 45% of COVID-19 patients experience several symptoms a few months after the initial infection and develop post-acute sequelae of SARS-CoV-2 (PASC), referred to as “Long-COVID,” characterized by persistent physical and mental fatigue. However, the exact pathogenetic mechanisms affecting the brain are still not well-understood. There is increasing evidence of neurovascular inflammation in the brain. However, the precise role of the neuroinflammatory response that contributes to the disease severity of COVID-19 and long COVID pathogenesis is not clearly understood. Here, we review the reports that the SARS-CoV-2 spike protein can cause blood–brain barrier (BBB) dysfunction and damage neurons either directly, or via activation of brain mast cells and microglia and the release of various neuroinflammatory molecules. Moreover, we provide recent evidence that the novel flavanol eriodictyol is particularly suited for development as an effective treatment alone or together with oleuropein and sulforaphane (ViralProtek®), all of which have potent anti-viral and anti-inflammatory actions. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), leading to complex immune responses [1,2] that involve the release of several inflammatory cytokines/chemokines [1,3,4,5,6,7,8], especially interleukin-1beta (IL-1β) [9] and IL-6, often referred to as “cytokine exacerbated production” [3,10,11]. Almost 50 percent of patients infected with SARS-CoV-2 experience post-acute sequelae of SARS-CoV-2 (PASC) [12,13,14] shortly after the initial infection [15], known as “Long-COVID syndrome” [16,17,18]. A recent report indicated that about 45% of COVID-19 survivors showed persistent symptoms at about 4 months in post COVID-19 population, with fatigue being most frequently experienced in hospitalized and non-hospitalized cohorts [14]. Long COVID is characterized by persistent fatigue that is not dependent on the initial severity of the disease [19] and presents with persistent symptomatology many months post-acute infection [20]. At least 20–45 percent of COVID-19 survivors experience various neuropsychiatric [14,21,22,23,24,25,26,27,28,29,30,31,32,33], neurological [34,35,36,37,38,39,40,41,42] and neurodegenerative [37,43] issues, sleep disturbances [44], and cognitive deficits [28,29,30,31,32,33,45,46,47,48], especially brain fog [16,17,49,50,51,52,53,54,55]. The length of long COVID may depend on the persistence of some viral antigens [56] and the magnitude of continued inflammatory reactions to SARS-CoV-2 [57]. Long COVID has been considered as the “Next national health disaster” for the US [58] that could have a “$3.7 trillion economic impact rivaling the Great Depression” [59]. A systematic review of the literature using the MEDLINE data base (1 January 1990–1 January 2023) was conducted to identify peer-reviewed publications relevant to the diagnosis, pathogenesis and treatment of neuro-COVID using the search terms angiotensin-converting enzyme 2 (ACE2), blood-brain barrier (BBB), brain, chemokines, corona virus, COVID-19, cytokines, endothelial cells, fatigue, fog, inflammation, Long-COVID, mast cell, microglia, neuroinflammation, toll-like receptors, and vasculature. Emphasis was placed on publications reporting original data, especially in humans, even though reviews and papers using animal models were also included. We advance the premise that brain perivascular inflammation is a critical pathogenetic factor in long COVID mostly due to SARS-CoV-2 activating brain mast cells and microglia resulting in the release of inflammatory, neurotoxic, and vasoactive mediators [60,61]. The precise mechanism of long COVID pathogenesis has yet to be fully elucidated [62]. It is well-understood that SARS-CoV-2 enters cells through the coronavirus spike (S) protein binding to its cell surface receptor, ACE2 [63]. How SARS-CoV-2 enters the brain is not yet clearly known, but increased levels of cytokines were reported in the cerebrospinal fluid (CSF) of patients with COVID-19 [64,65]. The virus may enter the brain from the nose through the nasal neural mucosa [66] following the olfactory nerve tract [67] or via the gustatory–olfactory trigeminal pathway and cause BBB dysfunction. Autopsy from a deceased infant with COVID-19 showed severe neuronal loss in the capillaries of the choroid plexus [68]. Another autopsy study detected choroid plexus morphological changes in the microglia [69,70], as well as neuronal necrosis and glial cell hyperplasia in the brain of a deceased patient with COVID-19 [71]. While the exact pathogenetic mechanisms [50] remain unclear, evidence points to the involvement of neuroinflammation [72,73,74], and neurovascular inflammation that can damage brain blood vessels [75,76] and brain cells [72,77,78]. The neurological issues of long COVID [79] may be attributed to the entry of SARS-CoV-2 into the brain [80], but the routes of viral entry are not yet clear [81]. The S protein is involved in the fusion of the viral membrane with the surface membrane of the host. The S trimer structure has three receptor-binding domains (RBD), while the post-fusion structure expresses N-linked glycans that may protect the immune system [82]. Recent evidence and our studies indicate that the spike protein can also directly activate microglia [83,84,85], leading to proinflammatory effects [86] and microglia–synapse elimination [87]. SARS-CoV-2 can also lead to brain vascular damage and endothelial dysfunction [88,89,90,91], BBB disruption [92,93,94,95,96,97,98] and reduced blood flow to the brain [99]. A recent study reported that neuroinflammation could disrupt the “blood-central nervous system (CNS) barrier” in a mouse model of multiple sclerosis (MS) that involved the interaction of inflammatory, endothelial, and mesenchymal pathways [100]. Perivascular inflammation with lymphocytic and microglial infiltration was noted in the brains of 52 deceased patients with COVID-19 [101]. A cross-sectional study identified BBB disruption along with increased microglial activation markers and increased B-cell responses against self and non-self-antigens [102]. Another study with 24 neuro-COVID patients also reported increased intrathecal immunoglobulin, neopterin, and neurofilament light chain (NfL) levels [103]. Apolipoprotein E4 (ApoE4) has been associated with COVID-19 [104,105,106] and with severe COVID-19 [100,101]. In fact, ApoE4 could possibly predict COVID-19 pathogenesis [107]. In particular, ApoErs429358 polymorphism was associated with an increased risk of COVID-19 infection [108]. Elevated ApoE4 levels were reported to also reflect BBB disruption and predict cognitive decline [109]. SARS-CoV-2 has been reported to activate toll-like receptors (TLRs) [110,111] leading to the release of immune molecules that could contribute to neurologic symptoms [112]. TLRs are important in recognizing viral particles and orchestrate innate immune responses. Viral activation of TLRs causes the release of inflammatory cytokines from immune cells [113]. Microglia have many receptors, including TLRs [114], and they are activated by damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) [114]. TLRs were recently shown to mediate COVID-19 pathogenesis [115]. One paper reported that SARS-CoV-2 envelope protein could produce inflammatory cytokines from mouse bone-marrow-derived macrophages via TLR2 activation, independent of viral entry [116]. Another study demonstrated that SARS-CoV-2 S protein could stimulate BV-2 microglia leading to the release of interleukin-1 beta (IL-1β), IL-6, and tumor necrosis factor-alpha (TNF-α) with increased expression of TLR4 [84]. Another paper reported that infection of HMC3 microglia also led to the release of IL-1β, IL-6, and TNF-α [83]. Moreover, activation of TLR4 increased the expression of ACE2 [117], further enhancing viral infectivity in an autocrine loop fashion. In fact, TLR4 has been considered as a therapeutic target for neurological complications associated with SARS-CoV-2 infection [118,119]. Increased levels of pro-inflammatory cytokines, especially IL-6, have been detected in the CSF of COVID-19 patients [65] and have been implicated in neurologic diseases associated with COVID-19 [64]. Our recent findings show that SARS-CoV-2 can stimulate human microglia to secrete distinct pro-inflammatory mediators via activation of different receptors: recombinant whole-length S protein results in secretion of IL-1β and chemokine (C-X-C motif) ligand 8 (CXCL8) not via activation of ACE2, but rather activation of TLR-4, while the recombinant receptor binding domain (RBD) of the S protein stimulates the release of IL-18, TNF-α, and S100B via ACE2 [120]. Microglia are specialized macrophage-like immune cells of the CNS and constitute about 7 percent of non-neuronal cells in the brain [121]. It has been reported that one microglial cell serves 1 to 100 neuronal cells in various brain areas with different neuronal densities [121]. Microglia are important for CNS homeostasis both in health and disease states [122], especially neurodegenerative [123,124,125,126,127,128,129] and neuroinflammatory [122,128,130,131] diseases, including COVID-19 [83,132]. During neuroinflammatory response and brain homeostasis maintenance, microglia can change their numbers, morphological characteristics, molecular pattern, and functions [132]. Activated microglia release pro-inflammatory cytokines, free radicals, and fatty acid metabolites. Cytokines and chemokines released from activated microglia induce activation of astrocytes with additional release of proinflammatory mediators that further exacerbates neuroinflammatory response. Dysregulated microglia and T-cell interactions and microglial nodules in the perivascular compartment of the brain were associated with systemic inflammatory conditions in COVID-19 [133]. Microglial activation is significantly higher in the brain stem than in non-COVID cases. Further, COVID-19 cases without dementia show more microglial activation in the brain stem [134,135]. The neuroinflammatory response is indicated by the presence of microglial reactivity indicators such as CD68-positive ameboid microglia, ionized calcium binding adaptor molecule 1 (IBA1), and human leukocyte antigen-DR (HLA-DR) in COVID-19 [132,134]. COVID-19 shows more T lymphocytes and microthromboses in the lung associated with more microglial activation in the brain stem [135]. In other words, the long-term consequences of COVID-19 could be due to persistent inflammation rather than persistent viral replication [135]. SARS-CoV-2 induces neuropsychiatric and neurological disorders such as cognitive decline, depression, dizziness, delirium, and sleep disorders that lead to neuronal damage, neurodegenerative disorders, and dementia [136]. Thus, SARS-CoV-2 can cause BBB disruption and worsen neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease, especially in aged people [136,137,138]. SARS-CoV-2 infection can cause dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis [139], which may be the cause of the emotional changes observed during and after viral infection [140]. Several reports have shown the impact of the pandemic on acute and chronic mental health. Further, these studies also focused on the psycho-social factors and stress resilience of mental health and disease pathologies [141,142]. TLR4 contributes to the immune response and pathogenesis of COVID-19, and thus, TLR4 could be a therapeutic target in COVID-19 [113,143,144]. SARS CoV-2 activates TLR4 and 8 and induces cytokine release from microglia and monocytes [145]. Microglia express receptors for neurotensin (NT) [146] and corticotropin-releasing hormone (CRH), secreted under stress [147], which are especially associated with COVID-19 [148]. Microglia are typically characterized as resting (M0), pro-inflammatory (M1), and anti-inflammatory and neuroprotective (M2) phenotypes with different cytokine expressions associated with neuroinflammatory response. We reported that cultured human microglia can be activated by neuropeptides such as NT to release IL-1β and CXCL8 [149] that induces proinflammatory response. Microglial-derived proinflammatory cytokines and chemokines induce astrogliosis, amyloid deposition, and subsequently, further worsening neuroinflammation [122]. Psychological stress can increase microglial reactivity to other challenges [150] and lead to cognitive decline [151] and neuroinflammatory response. Microglia are increasingly involved in the pathogenesis of psychiatric disorders [132,152,153]. In fact, microglia-induced neuroinflammation was considered a risk factor for the pathogenesis of major depressive disorder [154,155]. Moreover, SARS-CoV-2 neurotropism may increase the severity of neuropsychiatric issues [156]. A recent report indicated that the SARS-CoV-2 protein elicited a robust nuclear factor kappa B (NF-κB)/nucleotide-binding domain (NOD)-like receptor protein 3 (NLRP3) inflammasome-mediated pro-inflammatory response and increased Iba1 expression in a BV-2 mouse microglial cell line [84]. In addition, post-mortem reports of COVID-19 patients showed significant microglial activation and neuroinflammation associated with brain pathology [157,158,159,160]. Increasing reports indicate that elevated inflammatory cytokines and neuroinflammatory responses [72,128,161] can damage brain blood vessels [75,162] and other brain cells [72,77,78], possibly through abnormally excessive activation of microglia [60,61]. As such, long COVID could be referred to as “brain autoimmunity” [163]. Mast cells communicate with microglia [32,164], leading to their activation [33,164,165,166] and contributing to neuroinflammation [32,33] and neurodegenerative diseases [32,167]. This effect is not seen in mast-cell-deficient mice [168,169]. In fact, mast cell proteases can trigger astrocytes and glia/neurons and release IL-33 [170]. Stabilization of mast cells was shown to inhibit lipopolysaccharide (LPS)-induced neuroinflammation by suppressing the activation of microglia [171]. Activation of mast cells and microglia in the hypothalamus and brain [172] could lead to cognitive dysfunction [173] and neuronal apoptosis (Figure 1) [173]. In addition, mast cells can activate the hypothalamic–pituitary–adrenal (HPA) axis [174,175,176,177] through the release of histamine [178], IL-6 [179], and CRH [180]. It is interesting that stress has been linked to the possible priming of immune cells thus contributing to neuroinflammation in AD [181,181]. Furthermore, NT [182,183] and substance P (SP) [2] induce CRH receptor-1 (CRHR1) expression in mast cells. Mast-cell-derived histamine [184] and tryptase [185] can trigger microglia and induce neuroinflammation [33]. Mast cells have been shown to be an early activator of LPS-induced neuroinflammation and BBB damage in the hippocampus [172]. In addition, food allergy that depends on mast cell activation has been shown to increase activated microglia and TNF in the hippocampus, associated with behavioral and learning impairments [186]. Another paper reported that early stress in mice and humans disrupted interactions between mast cells and glia via the involvement of histamine [187]. As such, mast cells can participate in neuroinflammation [188,189] by releasing histamine and several inflammatory cytokines and chemokines [190]. Mast cells are ubiquitous in the body [191]. They are mostly known for mediating allergic and anaphylactic reactions [192], and several other diseases such as mastocytosis [193]. The functions of mast cells in health and several pathologic conditions were reviewed recently [194,195,196,197]. Mast cells respond to allergic but also to various other non-allergic stimuli [193]. Activated mast cells can secrete as many as 100 biologically powerful mediators, including pro-inflammatory molecules [190] such as bradykinin, chymase, histamine, tryptase, chemokine (C-C motif) ligand 2 (CCL2), CXCL8 [198], IL-6 [199], IL-1β, and TNF-α [200]. A particular potent stimulus of the mast cells is the peptide SP, especially when primed by the “alarmin” cytokine IL-33 [201,202,203,204]. In addition, we showed that SP can induce expression of the IL-33 receptor (ST2) [200], thus further increasing mast cell stimulation. Mast cells can also be stimulated to secrete mitochondrial DNA (mtDNA) [205], which serves as an additional “alarmin” and can trigger an auto-inflammatory reaction [206,207]. Mast cells are also found in the CNS perivascularly [29,208], especially in the meninges [28,209] and the median eminence of the hypothalamus [122,209,210], where they could have numerous functions (Table 1). We have called brain mast cells the “immune gate to the brain” [29]. Functional interactions have been reported between mast cells and neurons [209,211] that are often positive for CRH [183,209]. Mast cells are the richest source of histamine in the CNS, particularly in the amygdala, hippocampus, hypothalamus, and thalamus [212,213]. Stimulated brain mast cells contribute to postoperative cognitive dysfunction (POCD) through the release of inflammatory and neurotoxic mediators from activated microglia [86,173]. Activation of mast cells [183] and microglia in the hypothalamus [49] could cause cognitive dysfunction [173] that is also seen in patients with mastocytosis [47,214,215] and may be related to brain abnormalities [216]. Allergic stimulation of nasal mast cells resulted in stimulation of the HPA axis [174,175,176,177], possibly via mast cell release of histamine [178], IL-6 [178,217], and CRH [180]. The influence of stress on mast cells has also been reviewed [140,218]. Restraint stress in rodents increased BBB permeability [210,219,220] via CRH [219,221,222]. Mast-cell-released cytokines [223,224] increased BBB permeability [210,219] and permitted mammary adenocarcinoma brain metastases in mice [221]. This process could worsen with stress, acting via CRH stimulation of mast cells [219,221] and an increase in dura vascular permeability. Meningeal mast cells affected the integrity of the BBB and promoted T-cell brain infiltration [225]. Inflammation mediated by mast cells and microglia disrupted the BBB [226]. Mast cell responsiveness may be regulated not only by the neuroimmune stimuli but also by the effects of the different receptors involved. For instance, mast cells express high-affinity neurokinin-1 (NK-1) receptors for SP [2]. Moreover, SP and NT [182] induced the expression of CRHR-1 in human mast cells. Secretion of mediators can occur by utilizing different signaling [227,228,229,230] and secretory [228,230] pathways. The regulation of mast cells by neurotransmitters and neuropeptides has been reviewed [231,232,233], with emphasis on CRH [177], hemokinin-1 (HK-1) [234], nerve growth factor (NGF [235], NT [236], SP [237], and somatostatin [238,239] acting via activation of high-affinity receptors. Activated mast cells could release a number of pro-inflammatory and vasoactive mediators that could contribute to long COVID syndrome symptoms [177,240]. Some mediators are pre-stored in secretory granules (e.g., histamine, tryptase, and TNF-α) [241,242] and are released immediately following stimulation, while others are newly synthesized and then released, such as chemokines (e.g., CCL2, CCXL8) [198], and cytokines (IL-6 [199], IL-1β [243], TNF-α [200]). Apart from allergic triggers acting via IgE, mast cells are stimulated by non-allergic agents [192,203,244], especially neuropeptides [231], such as SP [237,243] and the SP-related HK-1 [234], which have pro-inflammatory properties. Under such conditions, especially when primed by IL-33 [203,204], mast cells can release various inflammatory mediators without the release of histamine or tryptase [245], thus contributing to inflammatory disorders [189,192]. Moreover, mouse mast-cell proteases 6 (MMCP 6) and MMCP 7 stimulated the release of IL-33 from mouse fetal-brain-derived cultured primary astrocytes in vitro [170]. A case in point is the selective release of IL-6 [199,246], which is elevated in systemic mastocytosis and correlated with disease severity [247,248,249] and can increase mast cell numbers [250]. Mast cells are activated by viruses [251,252] such as SARS-CoV-2 [17,18,20,53,55,57,253,254,255,256,257,258,259,260,261]. Recent studies have also reported mast cell activation in the lungs [254] and perivascular inflammation in the brains [75] of COVID-19 patients. We hypothesized that the spike protein can get into the brain either directly or through the activation of mast cells, which then disrupts the integrity of the BBB (Figure 1) [79]. Two studies reported elevated serum levels of chymase in patients with COVID-19 [253,260]. Moreover, a recent study demonstrated that mast cells enhance cellular entry of SARS-CoV-2 through the generation of chymase-spike complexes [52]. Chymase converts angiotensin I to angiotensin II and may act in an autocrine fashion to increase the expression of ACE2, which then facilitate viral entry. Another paper reported that mast-cell-derived histamine can increase SARS-CoV-2 entry into endothelial cells [90]. Mast cells also release extracellular mtDNA [205], which was shown to be significantly elevated in COVID-19 patients [262]. Extracellular mtDNA can then stimulate the secretion of pro-inflammatory mediators from other immunocytes [206,207]. While a number of molecules are elevated in the blood of patients with COVID-19 [34,35,36,263], the results have been inconsistent and have focused primarily on pro-inflammatory mediators. A few studies have investigated blood biomarkers that may reflect brain injury in COVID-19 patients [264,265]. Anti-receptor antibodies and autoimmune gene expression [266] have also been reported. IL-15 is implicated in viral clearance with anti-viral properties, including in COVID-19 [267,268]. We showed elevated IL-18 in the serum of patients with COVID-19 [269]. IL-18 remains elevated longer than other cytokines in inflammatory and autoimmune disorders [270,271], including COVID-19 [269]. Calprotectin (S100A8/A9) was associated with microglia activation [272] and was elevated in the serum of patients with COVID-19 [269]. Calprotectin was also in the CSF of patients with Multiple Sclerosis (MS) [273] and demyelinating polyneuropathy [274]. Neuroligins (NLGs) and neurexins are implicated in synaptic function and cognitive disease [275]. NLG1 levels were reduced in the cortex and the CSF of AD patients [276] or those with mild cognitive impairment (MCI) [277]. NLG4 was associated with cognitive decline [278], while neuropilin-1 (NRP-1) was shown to facilitate SARS-CoV-2 entry by binding to the spike protein [279]. Moreover, S100β was shown to be associated with COVID-19 severity [280] and promote microglia activation [281,282,283] and has been linked to neuroinflammation and cognitive decline [284]. Neurofilament light chain (NfL), microtubule-associated protein-2 (MAP-2), and glial fibrillary acidic protein (GFAP) indicate axonal/neuronal damage and brain injury [264,285,286,287,288]. Elevated levels of osteopontin have been associated with reduced cognition [289,290]. A recent study indicated that COVID-19 was associated with brain pathology in the UK Biobank [291] and was associated with neuroinflammation involving primarily the chemokine CCL11 in a mouse model [292]. CCL11 has been implicated in neuroinflammatory disorders [293], while osteopontin was reported to disrupt the BBB [294]. Chemokine CCL19 and its receptor C-C chemokine receptor type 7 (CCR7) axis are involved in the immune response to viral infections [268,295]. Increased levels of CCL19 were associated with disease severity in COVID-19 patients [296]. To date, there are no effective drugs to either treat long COVID or mitigate the release of inflammatory mediators from microglia. Understanding how neuro-immune and toxic triggers contribute to long COVID and how to regulate this response is of clinical importance (Figure 2). One of the major impediments has been the lack of appropriate disease surrogates either in vivo or in vitro [297], as well as the lack of effective inhibitors of neuroinflammation. Apparently, there have been therapeutic considerations of “stabilizing” the BBB [226,298]. For inflammation, non-steroidal anti-inflammatory drugs (NSAIDs) did not improve COVID-19 [297]. Biologics have also been tried in COVID-19. Even though IL-6 has been reported to be elevated and possibly an independent risk factor, clinical trials using IL-6 inhibitors did not show any consistent benefit in COVID-19 [299]. One study reported that a clinically available IL-1β antagonist significantly improved COVID-19 with secondary hemophagocytic lymphohistocytosis (sHLH) that was characterized by pancytopenia, hyper-coagulation, and acute kidney injury [300]. Glucocorticoids have been used extensively in severe, hospitalized patients with COVID-19 [301], but the results are confusing. One paper reported a reduction in mechanical ventilation and a 20 percent reduction in the mortality rate of COVID-19 patients but also longer hospital stays and longer viral clearance time [302]. A more recent systematic review and meta-analysis showed a trend toward a higher discharge rate, but the effect was minimal and not significant [301]. Another analysis of 16 randomized control trials reported that systemic corticosteroids slightly reduced 30-day mortality in severe patients, but there was no benefit up to 120 days [303]. A multicenter observational cohort study conducted in 55 Spanish intensive care units reported that early administration of high doses of dexamethasone since symptom onset could actually prove harmful for 90-day mortality [304]. In fact, it has been argued that even though glucocorticosteroids may improve outcomes in severe, intubated patients with COVID-19, they could also reduce the production of antiviral IgG antibodies [305], thus hampering protection from other infections and worsening long-term outcomes [306]. Inhibition of brain inflammation could instead be accomplished with the use of some natural flavonoids [79,307,308,309,310,311,312]. In particular, the flavone luteolin inhibits both microglia [149,313,314] and mast cells [315,316], as well as related inflammatory processes [147,311]. A novel luteolin analogue, tetramethoxyluteolin [147], can inhibit secretion of the cytokines IL-1β and TNF-α [149], as well as the chemokines CCL2 and CCL5 [198], from human microglia [149,314] and mast cells [220,299]. Flavonoids have been reported to prevent neuroinflammation [311,312,317,318], provide neuroprotection [311,318,319,320,321], and reduce cognitive dysfunction [322,323,324,325,326], especially brain fog [48,327,328]. However, flavonoids are difficult to dissolve in aqueous solutions and also have poor oral absorption and bioavailability. Two formulations containing liposomal luteolin (BrainGain® and FibroProtek® were successfully used to treat a severe COVID-19 patient with brain fog [329]. We have identified a novel flavonoid that is structurally similar to luteolin, the flavanone eriodictyol [330,331,332], which is also partially water-soluble and may be particularly suited for development as an effective treatment (Figure 2) because of its multiple beneficial actions (Table 2) [333,334,335]. A new and novel dietary supplement (ViralProtek®) combines eriodictyol [334,335,336,337] with oleuropein from olive leaves [338,339,340] and sulforaphane from broccoli [341] and was recently shown to have strong coronavirus inhibitory properties. Neuro-COVID is a common presentation of long COVID patients and could be at least partly caused by the activation of brain mast cells and microglia, leading to perivascular inflammation and disruption of neuronal connectivity and neuronal signal transmission. In the absence of any approved drugs, a combination of certain natural compounds could help minimize these processes and associated symptoms.

PMC10001302

Min Gyu Song,So Hee Kim,Eun Bi Jeon,Kwang Soo Ha,Sung Rae Cho,Yeoun Joong Jung,Eun Ha Choi,Jun Sup Lim,Jinsung Choi,Shin Young Park

Inactivation of Human Norovirus GII.4 and Vibrio parahaemolyticus in the Sea Squirt (Halocynthia roretzi) by Floating Electrode-Dielectric Barrier Discharge Plasma

28-02-2023

sea squirt,FE-DBD plasma,human norovirus GII.4,Vibrio parahaemolyticus,disinfection,food pathogen,quality

Human norovirus (HNoV) GII.4 and Vibrio parahaemolyticus may be found in sea squirts. Antimicrobial effects of floating electrode-dielectric barrier discharge (FE-DBD) plasma (5–75 min, N2 1.5 m/s, 1.1 kV, 43 kHz) treatment were examined. HNoV GII.4 decreased by 0.11–1.29 log copy/μL with increasing duration of treatment time, and further by 0.34 log copy/μL when propidium monoazide (PMA) treatment was added to distinguish infectious viruses. The decimal reduction time (D1) of non-PMA and PMA-treated HNoV GII.4 by first-order kinetics were 61.7 (R2 = 0.97) and 58.8 (R2 = 0.92) min, respectively. V. parahaemolyticus decreased by 0.16–1.5 log CFU/g as treatment duration increased. The D1 for V. parahaemolyticus by first-order kinetics was 65.36 (R2 = 0.90) min. Volatile basic nitrogen showed no significant difference from the control until 15 min of FE-DBD plasma treatment, increasing after 30 min. The pH did not differ significantly from the control by 45–60 min, and Hunter color in “L” (lightness), “a” (redness), and “b” (yellowness) values reduced significantly as treatment duration increased. Textures appeared to be individual differences but were not changed by treatment. Therefore, this study suggests that FE-DBD plasma has the potential to serve as a new antimicrobial to foster safer consumption of raw sea squirts.

Inactivation of Human Norovirus GII.4 and Vibrio parahaemolyticus in the Sea Squirt (Halocynthia roretzi) by Floating Electrode-Dielectric Barrier Discharge Plasma Human norovirus (HNoV) GII.4 and Vibrio parahaemolyticus may be found in sea squirts. Antimicrobial effects of floating electrode-dielectric barrier discharge (FE-DBD) plasma (5–75 min, N2 1.5 m/s, 1.1 kV, 43 kHz) treatment were examined. HNoV GII.4 decreased by 0.11–1.29 log copy/μL with increasing duration of treatment time, and further by 0.34 log copy/μL when propidium monoazide (PMA) treatment was added to distinguish infectious viruses. The decimal reduction time (D1) of non-PMA and PMA-treated HNoV GII.4 by first-order kinetics were 61.7 (R2 = 0.97) and 58.8 (R2 = 0.92) min, respectively. V. parahaemolyticus decreased by 0.16–1.5 log CFU/g as treatment duration increased. The D1 for V. parahaemolyticus by first-order kinetics was 65.36 (R2 = 0.90) min. Volatile basic nitrogen showed no significant difference from the control until 15 min of FE-DBD plasma treatment, increasing after 30 min. The pH did not differ significantly from the control by 45–60 min, and Hunter color in “L” (lightness), “a” (redness), and “b” (yellowness) values reduced significantly as treatment duration increased. Textures appeared to be individual differences but were not changed by treatment. Therefore, this study suggests that FE-DBD plasma has the potential to serve as a new antimicrobial to foster safer consumption of raw sea squirts. The sea squirt (Halocynthia roretzi) is an attached marine animal that belongs to the phylum Chordata. Since ancient times, it has been enjoyed as food because of its unique smell and taste [1]. Sea squirts consume plankton as food. They attach to rocks in the sea at a water temperature of 5 to 24 °C and depth of 6 to 20 m. In Korea, sea squirts are produced mainly on the south and east coasts [2]. The sea squirt production period is limited from late spring to summer; hence, it is often made into traditional Korean salted and fermented food, jeotgal, for long-term storage [3]. However, due to economic growth and industrialization, pollution along the coast close to the land is accelerating [4]. In addition, most sea squirts are consumed, raw without heat treatment, therefore, contamination with harmful microorganisms can cause food poisoning. Human norovirus (HNoV) is a typical virus that causes food poisoning. Norovirus is a circular RNA virus belonging to the Caliciviridae family and has an incubation period of 24–48 h. Symptoms of infections begin with typical gastroenteritis symptoms, such as abdominal pain, diarrhea, fever, and nausea. Genogroups of norovirus are largely classified into five categories, of which GI, II and IV are known to infect humans [5]. HNoV food poisoning is transmitted via a fecal-oral route, in which food is contaminated by the feces of infected patients, and when those contaminated foods are consumed, illness occurs [6]. HNoV occurs especially in shellfish such as oysters and sea squirts that are produced on contaminated coasts [7,8,9], and these can be consumed raw, so attention should be paid to safety. Vibrio parahaemolyticus is a gram-negative halophilic bacterium that primarily lives in seawater. Infection occurs by ingesting raw or insufficiently cooked fish and shellfish contaminated with these bacteria. These are food poisoning bacteria that cause acute gastritis symptoms, mainly accompanied by abdominal pain, diarrhea, vomiting, chills, and mild fever [10]. Symptoms usually occur after an incubation period of 4–96 h. Incidents of food poisoning caused by Vibrio parahaemolyticus mainly occur in the summer between June and October [11,12,13]. In Korea, 30.5% of raw fish products, including sea squirts, are reported to be infected with V. parahaemolyticus [11,12,13,14]. Plasma is the fourth state of matter. It can be used for non-thermal disinfection which can be applied to various biomedical applications [15]. In the generated plasma, ions and electrons are separated, and reactive species with high chemical reactivity and ozone are formed [16]. Active oxygen species (ROS) and active nitrogen species (RNS) produced by plasma-generated gas ionization exhibit antibacterial effects through direct and specific attacks on microbial cell envelopes and intracellular components [17]. Dielectric barrier discharge (DBD) plasma technology is a solution to problems related to thermal disinfection using plasma. The antibacterial effect of DBD plasma has the advantages of low-temperature treatment, minimal nutrient destruction, and texture maintenance [18]. In DBD plasma, the current is limited by using a dielectric covering on the two electrodes and discharging the current between the two electrodes at atmospheric pressure to generate plasma, which exhibits a disinfection effect [19]. Recently, floating electrode-dielectric barrier discharge (FE-DBD) plasma, which further enhances DBD plasma, has been developed. The FE-DBD plasma grounds one of the two electrodes, which is directly discharged through a sample (food) to generate ROS and RNS on the surface of the sample. It has stronger disinfection power [20]. Studies have investigated the effect of reducing harmful microorganisms using FE-DBD plasma [21,22,23]; however, the effect on reducing HNoV and V. parahaemolyticus among sea squirts is still insufficiently understood. Therefore, in this study, the effect of reducing HNoV GII.4 and V. parahaemolyticus in sea squirt by FE-DBD plasma treatment and the resulting quality change were investigated. HNoV GII.4 used in this study was isolated from patients with gastroenteritis symptoms caused by norovirus at the Gyeonggi Institute of Health and Environment (GIHE; Gyeonggido, Republic of Korea) in 2019. After confirming the genotype of HNoV, it was stored in the Waterborne Virus Bank (WAVA; Seoul, Republic of Korea). The HNoV GII.4 used in this experiment was purchased from WAVA and delivered frozen. After purchase, it was manufactured from stock containing 500 μL phosphate buffer solution (PBS; pH 7.2), stored in a −80 °C freezer for further experimental use. V. parahaemolyticus (ATCC 27969) was used in the experiments. Stock cultures were stored at −80 °C in tryptic soy broth (TSB; Difco Laboratories, Detroit, MI, USA) containing 30% glycerol. Vibrio parahaemolyticus was cultivated in TSB containing 2.5% NaCl. This process was performed twice for the bacterial activity. The strain (10 μm) was incubated in 5 mL TSB for 24 h at 37 °C and centrifuged at 4695× g for 10 min at 4 °C (SUPRA22K, Daejeon Hanil Science Industry). After centrifugation, the TSB was removed and the pellets were mixed in 9 mL of sterile 0.85% NaCl. Frozen sea squirt used as a sample for the research was purchased at a local market in Tongyeong, Korea. Immediately after purchase, it was stored in a freezer (−18 °C) and the experiment was conducted within 48 h. The frozen sea squirt was completely defrosted and the intestines were collected with sterile tweezers and scissors and then homogenized with a homogenizer (stirrer, Daihan Scientific Co., Wonju, Republic of Korea). Homogenized intestines were divided into 3 g portions and placed in Petri dishes. The Petri dishes containing samples were inoculated with 10 μL (2.55 log copy/μL) HNoV GII.4 and 100 μL (4.16 log CFU/g) V. parahaemolyticus, respectively, and were placed into a clean bench (CHC LabCo. Ltd., Daejeon, Republic of Korea) for 1 h. The FE-DBD plasma device was screen-printed with a high-voltage electrode on glass with a thickness of 10 μm. The thickness was 7 mm, and the genetic material composed of SiO2 was also screen-printed to a thickness of 100 μm. The operating voltage was supported by inverters that generated 47 kHz sine waves with an amplitude of 2.8 kV. Plasma was generated between the surface of the sample, which acted as a virtual ground using nitrogen, and the glass under the electric electrode. During the procedure, a 1.5 L flow rate per min and the distance between the plasma release electrode and the sample was maintained at 3 mm and treated for 5, 15, 30, 45, 60 and 75 min. The electrical voltage and current characteristics of the FE-DBD were measured using a high-voltage probe (P6015A, Tektronix, Beaverton, OR, USA) and a pickup probe (P6021A, Tektronix, Beaverton, OR, USA), respectively. The plasma discharge mainly occurred at 1 kV, and the peak discharge was measured at 16 mA. The electric scattering power was measured at 0.55 W. The root mean square value (RMS) voltage and current were measured at 2.0 kV and 13.5 mA, respectively. During the experiment, we used samples as negative control without any treatment of FE-DBD plasma for each contact time. For PMA treatment, samples inoculated with HNoV GII.4 were immediately mixed with 200 μM PMA (Biotium, Hayward, CA, USA) and placed at room temperature for 5 min to ensure sufficient dye penetration. Afterwards, to photoactivate the dye, the sample was exposed to 40 W LED light (Dinebio, Seongnam, Republic of Korea) with a wavelength of 460 nm at room temperature for 20 min. A control treated with PMA and not exposed to halogen light was included to determine whether the dye treatment interfered with virus detection. Finally, virus samples were differentially detected as potentially infectious or non-infectious virus particles of HNoV, according to optimized PMA pretreatment prior to RT-qPCR analysis. An RNeasy mini kit (Qiagen, Hilden, Germany) was used for RNA extraction of HNoV GII.4 and the experiment was conducted according to the manufacturer’s instructions. Proteinase K extraction activity was processed according to the ISO 15216-1:2017 method. Proteinase K (Sigma, St. Louis, MO, USA) was added to the FE-DBD plasma-treated sea squirt, shuffled in an incubator (37 °C) for 1 h, and then deactivated in a thermostat (60 °C) for 15 min. In addition, centrifugation (5400 rpm, 4 °C) was performed for 10 min using a centrifuge (SUPRA22K, Hanil Science Industrial Co., Gimpo, Republic of Korea), and a clear solution (approximately 3.0 mL) in the upper layer was collected in a sterile conical tube. This solution was stored in a freezer at −80 °C and used for the detection and quantification analysis of HNoV GII.4. Reverse transcription of cDNA was performed as described by Kageyama et al. [24]. In order to amplify the gene of HNoV GII.4, RNase free water, enzyme mix (5 units/μL), 5X RT-PCR buffer, 10 mM dNTP, and 10 μM primer (forward and reverse extraction) were added to amplify the gene of HNV GII.4, and then 10 μM primer (forward and reverse NA) RT-qPCR A TP800-thermal cycler dice real-time system (TaKaRa) device was used for quantitative analysis of real-time reverse polymerase chain reaction using real time reverse transcription-quantitative polymerase chain reaction (RT-PCR). Primers and probes were designed to fit the ORF-1 and ORF-2 overlapping regions of HNoV GII.4, increasing sensitivity and specificity; the base sequences of the primer and probe are shown in Table 1. In addition, RNA from HNoV GII.4 was used as a positive control and RNase-free water was used as a negative control. The FE-DBD plasma-treated V. parahaemolyticus samples were homogenized in stomacher (Easy Mix, AES Chemunex, Bruz, BRE, France) by adding 0.85% sterile NaCl solution to a sterile bag. The homogenized solution was diluted with 9 mL of 0.85% sterile NaCl solution. The diluted sample (1 mL) was placed in a Petri dish, mixed with 2.5% NaCl containing tryptic soy agar (Difco, Ditroit, MI, USA), and cultured at 37 °C for 24 h, and a cluster of 15–300 was calculated per 1 mL of the sample solution. VBN was measured by a microdiffusion analysis method using Conway units. Distilled water (25 mL) and 5 mL of 20% trichloroacetic acid (TCA) were added to 5 g of the sample, mixed well, leached, and filtered for 30 min, and then, a 2% TCA solution was added to the filtrate to use a Conway unit container as a test solution. The test solution was then added to the outer chamber of the Conway unit. Next, 1 mL of 0.01 N boric acid and 100 μL of a Conway reagent (0.066% (w/v) methyl red and 0.066% (w/v) bromocresol in ethanol) were added to the inner chamber of the Conway unit, and 1 mL of 50% (w/v) carbonate was added to the other chamber of the unit. The unit was then sealed and slowly stirred in a horizontal direction to mix reagents in the outer chamber and incubated at 37 °C for 120 min. After incubation, the inner chamber of the Conway unit was titrated with 0.02 N sulfuric acid. The color of the sea squirt was measured using a color meter (UltraScan PRO, Hunterlab, Reston, VA, USA) after FE-DBD plasma treatment and calibrated to the original value of the standard plate (‘L’ = 98.48, ‘a’ = 0.14 and ‘b’ = 0.41). The color was measured through the 6 mm aperture of a color meter using a D65 illuminant. Values were represented by three coordinate values: ‘L’ (brightness +, darkness −), ‘a’ (red +, green −), and ‘b’ (yellowness +, blue −) depending on the Hunter color. For pH measurement, 3 g of FE-DBD plasma-treated (0, 5, 15, 30, 45, 60 and 75 min) sea squirt and 27 mL of sterile distilled water were mixed. Thereafter, the pH was homogenized with a stomacher (Easy Mix, AES Chemunex, French Ren) for 3 min and the pH value was measured three times using a pH meter (Orion Star A211, Thermo Scientific, Troy, MI, USA). The texture profile analysis (TPA) of sea squirts was modified and set using published parameters [25,26]. The mechanical properties of sea squirts were obtained from FE-DBD plasma samples treated for each time and evaluated using a CT3 texture analyzer (Middleboro, MA, USA). The TPA characteristics evaluated were hardness (g/cm2) and chewiness. A spherical stainless-steel probe TA18 (diameter 12.7 mm) was used to perform two consecutive compression cycles separated after 5 s with the compression system set to reach 50% deformation. The probe was set to a trigger force of 5.0 g at a constant speed of 0.5 mm/s. To eliminate the influence of the weight or size of the sea squirts, the texture profile of animals of the same size was recorded within an error range of ±1 g. All measurements were recorded in three independent experiments, and each test was performed using three samples. One-way analysis of variance (ANOVA), and Duncan’s multi-range tests were performed using the statistical packages for social science (SPSS) version 25.0 (SPSS Inc., Chicago, IL, USA). Statistical analysis was performed to determine significant differences between the mean values of viruses and bacteria. Paired t-tests were performed to evaluate the statistical significance of the differences in PMA treatment reduction using SPSS software (log CFU/g). Statistical significance was determined at the 5% probability level (p < 0.05). Table 2 showed the trend of HNoV GII.4 in sea squirts inoculated with HNoV GII.4 according to FE-DBD plasma treatment (0, 5, 15, 30, 45, 60 and 75 min). The initial HNoV GII.4 titer of the sample without FE-DBD plasma treatment was 2.55 log copy/μL. The results were analyzed by comparing the non-PMA and PMA-treated viruses. First, samples that were not PMA-treated showed significant differences (p < 0.05) in the reduction effect as the treatment time increased, except for 5–15 min of treatment time. Compared to the control, the log reduction effect of treatment time was as follows: 5 min treatment (0.11 log reduction), 15 min treatment (0.26 log reduction), 30 min treatment (0.35 log reduction), 45 min treatment (0.57 log reduction), 60 min treatment (0.95 log reduction), and 75 min treatment (1.29 log reduction). Second, the PMA-treated samples showed a significant difference (p < 0.05) in the remaining treatment time except for 15–30 min. The differences between the PMA and non-PMA-treated samples were compared using t-tests and the results were follows: 5 min treatment (2.44 − 2.07 = 0.37), 15 min treatment (2.29 − 1.92 = 0.37), 30 min treatment (2.20 − 1.79 = 0.41), 45 min treatment (1.98 − 1.65 = 0.33), 60 min treatment (1.60 − 1.39 = 0.21), and 75 min treatment (1.26 − 0.96 = 0.30). The D-values of the non-PMA and PMA-treated samples calculated by the first kinematic model were 61.7 and 58.4 min, respectively, and R2 were 0.97 and 0.92, respectively (Figure 1). Table 3 shows the reduction of V. parahaemolyticus in sea squirts using FE-DBD plasma. In the untreated controls, the microbial titer was 4.16 log CFU/g, which tended to decrease with increasing treatment time (5–75 min) (p < 0.05). FE-DBD plasma for 5, 15, 30, 45, 60, and 75 min resulted in 4.00 (0.16 log reduction), 3.98 (0.18 log reduction), 3,76 (0.4 log reduction), 3.54 (0.62 log reduction), 3.41 (0.75 log reduction), and 2.66 (1.5 log reduction) log CFU/g, respectively. Excluding the 5–15 min treatment, significant differences (p < 0.05) were observed in the reduction effect as the treatment time increased. The D-value of V. parahaemolyticus, calculated using the first-order kinetics model, was 58.9 min and R2 was 0.90 (Figure 2). D1 values were obtained using a first-order kinematic model based on the survival curves of HNoV GII.4, HNoV GII.4 with PMA and V. parahaemolyticus generated for samples treated with various DBD plasma treatment times. In the case of the dynamics for the inactivation of microorganisms, the decimal reduction time of the log linear kinetic model is widely accepted. The D1 values of HNoV GII.4 and HNoV GII.4 with PMA were 61.96 and 58.68 min, respectively, and the R2 values were 0.97, 0.92. The D1 value of Vibrio was 58.70 min and the R2 value was 0.90. This indicated that the log linear kinetic models for HNoV GII.4, HNoV GII.4 with PMA, and V. parahaemolyticus were suitable for determining the D1 value (Table 4). The VBN and pH values are listed in (Table 5). VBN compared samples treated for 5–75 min with the control. The value was 8.3–12.4, and it increased significantly when the cell were treated for more than 30 min. The pH was also compared with the control for 5–75 min treated samples, and the values were at least 5.78, up to 5.94, with significant differences between the individuals. Hunter color compared to the control not treated with FE-DBD plasma and the samples treated with FE-DBD plasma for 30 and 60 min are shown in Table 6. Compared with the control, “L” (Lightness), “a” (redness), and “b” (yellowness) showed significant differences as the FE-DBD plasma treatment time increased, and the value gradually decreased. Texture was analyzed to determine the effect of DBD plasma treatment on the sea squirt surface texture (Table 7). The hardness range obtained as a result of FE-DBD plasma treatment was 214–265 (g/cm2) and the chewing range was 11–19 (g/cm2); the difference between individuals was not associated with the treatment time. Increasing consumption of marine products is a global trend [27]. However, since marine products are exposed to seawater environments where various microorganisms exist and there is a possibility that they may be exposed to humid environments for a long time during production and distribution, the growth and metabolism of microorganisms may proceed actively [11]. In addition, hygiene management is very important because it is easy to change, and microbial contamination can easily occur in various channels such as distribution, processing, and consumption, such as post-change and rapid degradation of quality after fishing [28]. In Korea, sea squirt production was 22,833–38,248 metric tons per year as of 2015–2019, which make it one of the most industrially important aquaculture species in Korea [29]. However, most of them are caught by natural marine fishing; therefore, fish farms are distributed along the coast, and coastal waters are likely to be affected by various pollutants. In fact, Shin et al. [30] reported that the bacterial content was high on the coast of large rivers or densely populated areas and that fecal pollutants derived from land flow into the coast due to the occurrence of rainfall. According to a study by Vincent-Hubert et al. [31], many harmful microorganisms, such as HNoV and Vibrio spp., were detected off the coast, and Elbashir et al. [32] also reported that norovirus and Vibrio spp., which live on the coast, were sufficiently infectious to aquaculture. HNoV GII.4 and V. parahaemolyticus can occur frequently in marine products consumed without insufficient heating, and in Korea, where a lot of raw marine products are consumed, these cause infections in summer as well as during the four seasons. Globally, the problem of norovirus has been reported to result in social costs of approximately USD 60 billion annually [33]. The number of food poisoning cases caused by V. parahaemolyticus was approximately 855 in Korea between 2016 and 2020 [34], and the risk of food poisoning seems to be frequent even now when industrial development and food hygiene are taken more seriously. In preparation for such microbial contamination, food is subjected to appropriate disinfection treatment according to the characteristics of the food. The most important aspect of food disinfection is that there should be no change in quality. Excessive heat treatment may negatively affect the taste or texture of food, destroy nutrients, and cause discoloration [35]. Therefore, in this study, the effect of sterilizing HNoV GII.4 and V. parahaemolyticus in sea squirts was investigated using FE-DBD plasma, a non-thermal atmospheric plasma technology that did not significantly affect the quality of food, and quality analysis (VBN, pH, Hunter color, and texture) was also performed. Unlike DBD plasma using two existing floating electrodes, FE-DBD plasma directly causes plasma on the sample surface using one floating electrode, showing strong disinfection ability. In the case of FE-DBD plasma treatment by HNoV GII.4 among sea squirts, it was found that there was a significant difference according to the processing time in this study, and the HNoV GII.4 decreased as the duration time increased, and up to 1.26 log copy/μL decreased. Similar to this study, the FE-DBD plasma treatment for HNoV GII.4 in salted clams reported by Jeon et al. [21] also showed reduction of viable HNoV GII.4 by FE-DBD treatment, and the FE-DBD plasma treatment was effective in deactivating HNoV GII.4. In addition, Csadek et al. [36] showed a reduction in RNA viruses of up to 3.4 and 1.4 log copy/μL in 15 min when treated with high-power and low-power cold plasma viruses, respectively. Filipic et al. [37] demonstrated the effectiveness of cold plasma treatment on the inactivation of viruses such as norovirus and hepatitis virus A. Most virus inactivation methods are based on attacking the capsid, a structure that encloses the virus [38]. Active oxygen and nitrogen species produced during plasma exposure cause cell wall erosion, cell membrane destruction, functional changes, capsid destruction or structural changes, and loss of infectivity [39]. RT-qPCR has significantly improved virus detection capabilities, is also widely used in norovirus detection, and has proven to be effective [40,41,42]. Although RT-qPCR has excellent functionality in virus detection and quantification, it is limited in that it cannot distinguish between living and dead viruses owing to the persistence of DNA and RNA [43]. Recently, to overcome these shortcomings, a dye called propidium monoazide (PMA) has been added to distinguish between infectious and non-infectious viruses. PMA infiltrates the damaged viral capsid when treated with fluorescent dyes and then photosynthesizes using UV light to covalently bind to damaged RNA, preventing further amplification of RNA reverse transcription and RT-qPCR, thereby enabling the distinction between living viruses and dead viruses. However, PMA treatment is not effective for all virus inactivation methods because it depends on method of inactivation of the target virus [44]. In this study, when comparing non-PMA-treated samples with PMA-treated samples, the average of 0.34 log copy/μL was further reduced, and PMA treatment was found to be effective in detecting living viruses. Kim et al. [45] recently reported that PMA treatment helped detect live viruses in HNoV in oysters irradiated with electron beams, and Choi et al. [46] also found that up to 0.92 log copy/μL was reduced in DBD plasma treatment in HNoV in oysters, and the plasma treatment caused sufficient damage to the capsules of the virus. Active species from plasma can be used to disinfect most microorganisms, including viruses, bacteria [47], and fungi [48], resulting in cell wall erosion, cell membrane destruction, functional changes, and DNA damage. In this study, when the reduction of V. parahaemolyticus in sea squirt through FE-DBD plasma treatment was observed, there was a significant viable reduction as the treatment time increased, and a maximum reduction of 1.5 log CFU/g was observed at 75 min. Kim et al. [49] recently confirmed the disinfection effect of DBD plasma treatment on Escherichia coli and V. parahaemolyticus which reduced V. parahaemolyticus by up to 1.3 log CFU/g after 60 min of treatment, similar to the result of this study. In this study, VBN, pH, Hunter color, and texture were measured using quality parameters according to the FE-DBD duration. VBN is used to determine the degree of decomposition of protein-rich foods, such as meat and seafood [50]. If VBN is 5–10 mg/100 g, it is very fresh, and if VBN is 15–25 mg/100 g, the level above the normal line is usually determined to be corrupt [51]. In this study, there was no significant difference compared to the control until the 15 min treatment, but there was a significant difference from 30 min onwards, and the VBN content increased with time. In a study by Shin et al. [52], the VBN level of processed sea squirt products increased over time, and in a study by Oh [53], the VBN content of marine products increased over time. In this study, as the FE-DBD plasma treatment period increased, the VBN content increased accordingly, so it was judged that the treatment should be conducted while maintaining a low temperature. However, although the VBN content increased, it is likely that there will be no problems in intake and processing because the values remain located between very fresh and normal levels. No significant differences between the pH of the control and FE-DBD plasma-treated samples were observed at 45 min and 60 min. Considering that there were no significant differences between the samples with the longest treatment time and the control, observed differences are likely to be differences between individuals rather than a change associated with duration of FE-DBD plasma treatment. Choi et al. [39] reported a DBD plasma study on oysters where there was no pH change due to an increase in duration of plasma treatment. As the duration of FE-DBD plasma treatment increased, there was a significant difference in Hunter color. Abdi et al. [22] reported that when red pepper was treated with low-temperature plasma for 20 min, there was a significant decrease in the control. In addition, a study by Choi et al. [46] showed that the “L”, “a”, and “b” values were significantly reduced in frozen pork treated with corona discharge plasma jet. Owing to the characteristics of FE-DBD plasma, which is composed of one electrode, it is likely that the color change was caused by direct reaction of the plasma on the sample surface. Texture seems to be an individual characteristic, not a difference according to processing duration, and it is likely that the FE-DBD plasma does not cause a difference in texture. Choi et al. [54] reported that there was no significant difference in the change in the texture of oysters corresponding to the duration of DBD plasma treatment. Because of the nature of the sea squirt used in this study, it was likely that the observed results were due to severe differences between individuals in factors such as shape and thickness, which can affect texture. However, as shown in previous studies, plasma treatment is not expected to have a significant effect on texture. The current study demonstrated that 1.29 and 1.50 log reductions of HuNoV and V. parahaemolyticus in the sea squirts were achieved following FE-DBD plasma treatment for 60 min. These results suggest that PMA/RT-qPCR may be useful in detecting HuNoV infectivity following FE-DBD plasma treatment for an extended exposure time. Based on first-order kinetics (R2 = 0.92 and 0.90, respectively, for HuNoV and V. parahaemolyticus) following the FE-DBD plasma treatment of sea squirt, there was no significant difference in pH between the control group and the 45–60 min treatment time. Hunter color values of “L”, “a”, and “b” decreased as the FE-DBD plasma treatment period increased. Texture was not significantly different under FE-DBD plasma treatment. The results also suggest that FE-DBD plasma could minimize changes in quality, which may be a potential new physical method for improving the safety of sea-squirt consumption by reducing pathogenic microorganisms in sea squirts.

PMC10001303

Yue He,Kristina B. V. Døssing,Ane Beth Sloth,Xuening He,Maria Rossing,Andreas Kjaer

Quantitative Evaluation of Stem-like Markers of Human Glioblastoma Using Single-Cell RNA Sequencing Datasets

02-03-2023

glioblastoma stem cells,GBM stem-like markers,quantitative evaluation,single-cell RNA sequencing,CD133,SOX2,CD24,CD15

Simple Summary A common issue in glioblastoma stem cells (GSCs) studies is the need to efficiently and precisely target GSCs using reliable biomedical markers. Using single-cell RNA sequencing datasets, we quantitatively evaluated an extensive number of GSCs markers with multiple parameters that dictate the feasibility of various laboratory and therapeutic applications. We present promising marker candidates with their scores on the corresponding parameters and apply sequential selection based on these parameters. Both previously approved and novel markers are proposed according to the evaluation. We demonstrate the possibility of choosing a biomedical marker in a nonarbitrary way and provide quantitative references for potential GSCs markers. Abstract Targeting glioblastoma (GBM) stem-like cells (GSCs) is a common interest in both the laboratory investigation and clinical treatment of GBM. Most of the currently applied GBM stem-like markers lack validation and comparison with common standards regarding their efficiency and feasibility in various targeting methods. Using single-cell RNA sequencing datasets from 37 GBM patients, we obtained a large pool of 2173 GBM stem-like marker candidates. To evaluate and select these candidates quantitatively, we characterized the efficiency of the candidate markers in targeting the GBM stem-like cells by their frequencies and significance of being the stem-like cluster markers. This was followed by further selection based on either their differential expression in GBM stem-like cells compared with normal brain cells or their relative expression level compared with other expressed genes. The cellular location of the translated protein was also considered. Different combinations of selection criteria highlight different markers for different application scenarios. By comparing the commonly used GSCs marker CD133 (PROM1) with markers selected by our method regarding their universality, significance, and abundance, we revealed the limitations of CD133 as a GBM stem-like marker. Overall, we propose BCAN, PTPRZ1, SOX4, etc. for laboratory-based assays with samples free of normal cells. For in vivo targeting applications that require high efficiency in targeting the stem-like subtype, the ability to distinguish GSCs from normal brain cells, and a high expression level, we recommend the intracellular marker TUBB3 and the surface markers PTPRS and GPR56.

Quantitative Evaluation of Stem-like Markers of Human Glioblastoma Using Single-Cell RNA Sequencing Datasets A common issue in glioblastoma stem cells (GSCs) studies is the need to efficiently and precisely target GSCs using reliable biomedical markers. Using single-cell RNA sequencing datasets, we quantitatively evaluated an extensive number of GSCs markers with multiple parameters that dictate the feasibility of various laboratory and therapeutic applications. We present promising marker candidates with their scores on the corresponding parameters and apply sequential selection based on these parameters. Both previously approved and novel markers are proposed according to the evaluation. We demonstrate the possibility of choosing a biomedical marker in a nonarbitrary way and provide quantitative references for potential GSCs markers. Targeting glioblastoma (GBM) stem-like cells (GSCs) is a common interest in both the laboratory investigation and clinical treatment of GBM. Most of the currently applied GBM stem-like markers lack validation and comparison with common standards regarding their efficiency and feasibility in various targeting methods. Using single-cell RNA sequencing datasets from 37 GBM patients, we obtained a large pool of 2173 GBM stem-like marker candidates. To evaluate and select these candidates quantitatively, we characterized the efficiency of the candidate markers in targeting the GBM stem-like cells by their frequencies and significance of being the stem-like cluster markers. This was followed by further selection based on either their differential expression in GBM stem-like cells compared with normal brain cells or their relative expression level compared with other expressed genes. The cellular location of the translated protein was also considered. Different combinations of selection criteria highlight different markers for different application scenarios. By comparing the commonly used GSCs marker CD133 (PROM1) with markers selected by our method regarding their universality, significance, and abundance, we revealed the limitations of CD133 as a GBM stem-like marker. Overall, we propose BCAN, PTPRZ1, SOX4, etc. for laboratory-based assays with samples free of normal cells. For in vivo targeting applications that require high efficiency in targeting the stem-like subtype, the ability to distinguish GSCs from normal brain cells, and a high expression level, we recommend the intracellular marker TUBB3 and the surface markers PTPRS and GPR56. Glioblastoma multiforme (GBM) is the most common aggressive brain cancer, with a poor prognosis, a median survival of 14 months, and only one in 20 patients being alive after five years [1,2]. Since the current Standard of Care (SoC) was introduced in 2005 [3] with macroradical surgery, external radiation therapy, and temozolomide therapy, there have been no major changes in the therapy or in the poor prognosis [4,5]. One of the major challenges to avoiding tumor recurrence is stem-like cells. These are cells in the GBM bulk tumor that possess the capacity for self-renewal and tumorigenesis [6,7]. After the surgical removal of the bulk tumor and treatment with chemo- or radiotherapy, any potentially stem-like cell residues left are likely to develop into a recurrent tumor [8,9,10]. Recent studies based on single-cell RNA sequencing (scRNA-seq) technology uncovered the GBM “stem-like” cells through gene set enrichment analysis featuring enrichment terms including “nervous system development” and “gliogenesis” [11], similar to the biological features of neural-progenitor cells. This resemblance is due to the fact that the GBM stem-like cells share many marker genes with somatic neural progenitor cells. Before the era of scRNA-seq, much effort was devoted to discovering GBM stem-like cells and their biomedical markers in order to therapeutically target these cells. Several markers have become recognized over the years, such as CD133 (PROM1) [12,13], SOX2 [14,15], CD24 [16], and CD15 [17]. Among these markers, CD133 has obtained the most attention so far. CD133 was initially identified as a protein bound to CD34 hematopoietic stem and progenitor cells [18]. The tumorigenic capability of CD133 positive cells was confirmed by both in vitro sphere formation [12,19,20] and in vivo xenograft assays [20,21]. However, some other studies claimed that there was a lack of robustness when using CD133 as a cancer stem cell marker [22,23]. Generally, there is a lack of direct comparison between the proposed stem-like markers for GBM, as most studies are independent and investigate only one or a few markers using different methods. scRNA-seq allows the identification of all the possible markers for the GBM stem-like subtype [11,24,25], avoiding the process of “trial and error”. However, the markers discovered by this approach are too many and indistinguishable to be applied in targeted assays that only allow a limited number of markers. In addition, in clinical settings, only a few markers can be applied at a time. Therefore, it is crucial to develop a pipeline to find the best markers among the many marker genes identified by scRNA-seq data. The aim of this study is to quantitatively evaluate GBM stem-like markers identified by publicly available scRNA-seq data through multiple reality-relevant parameters: the universality and significance of GBM stem-like markers, the ability to distinguish GBM stem-like cells from normal brain cells, the expression level, and the cellular location of the translated protein. Different combinations of parameters are applied to reduce the number of candidates for different application requirements. With stringent standards, we propose the intracellular marker TUBB3 and the cell-surface markers PTPRS and GPR56 due to their excellent score for all parameters. This study involves 37 GBM samples from three SMART-seq2 [26] based studies. All the data were obtained from the Gene Expression Omnibus (GEO) database. In total, there are 1091 cells from Darmanis et al. (GSE84465) [25], 7930 cells from Neftel et al. (GSE131928) [11], and 875 cells from Patel et al. (GSE57872) [24]. In addition, there are 982 normal brain cells including oligodendrocyte progenitor cells (OPCs), oligodendrocytes, vascular cells, neurons, astrocytes, and microglia from two studies (GSE67835, GSE84465) [25,27]. The GBM cells from Darmanis et al. [25] and Patel et al. [24], and normal brain cells from Darmanis et al. [25,27], were processed from FASTQ files, going through FastQC quality control [28], Trimmomatic processing [29], and cell filtration with adaptive criteria that filter out cells with a low sum of reads and detected features (genes) and cells with excessive mitochondrial gene reads [30]. The cell filtration was conducted using the “isOutlier” function from Scater [30]. After filtration, 913, 557 GBM cells and 863 normal brain cells were left. These datasets were normalized using “library-size-normalization’ [30]. The data obtained from Neftel et al. [11] are in the form of a normalized count matrix in Transcript Per Million (TPM); all the steps before the cell filtration were already conducted by the author, therefore, only the cell filtration was applied to these data, with 7781 GBM cells passing the criteria. For all the datasets, genes that have total reads, summed up from all the cells, of less than 100 and mitochondria genes starting with “MT-” in their names were discarded. The preprocessed data were integrated with the corresponding cell metadata in the SingleCellExperiment object for further analysis [31]. Clustering was conducted for each sample using the Leiden method from igraph [32], and the markers of each cluster were identified using Scanpy [33]. The marker genes for each cluster were first selected according to the criteria: logFC (log fold change) > 2 and p-value < 0.05, followed by extraction of the top 200 genes ranked by their p-value. The top marker genes were used for enrichment analysis using g:Profiler [34]. A cluster was considered “stem-like” if it possessed similar enrichment results to those of the “neural-progenitor-cell-like 1 (NPC-like 1)” or “neural-progenitor-cell-like 2 (NPC-like 2)” from the study by Neftel et al. [11]. The abundance of a certain gene was defined as the percentage of cells expressing this gene in the same sample, indicating the abundance of cells that express the gene across the sample (Equation (1)). In order to correctly integrate data from different studies, we adopted a rank-based method to overcome batch effect. The percentage-rank of a gene was defined to indicate its expression level relative to other genes within a cell, calculated by the following steps: all the genes with non-zero reads in a cell were ranked in ascending order; the rank of the investigated gene was normalized by the number of genes with non-zero reads in the same cell and multiplied by 100% (Equation (2)). The exclusion of all the zero reads from the ranking was performed to avoid a biased ranking caused by cell number differences between samples, as samples with more cells would also cause the inclusion of more genes. The inclusion of more genes would result in a higher proportion of zero reads in each cell, in turn increasing the rank value for all non-zero reads. All the scatter plots, box plots, and bar plots were generated using ggplot2 [35]. The volcano plots were made using EnhancedVolcano [36]. The TSNE plots were generated using plotReducedDim from Scater [30]. The brain illustration was created using BioRender.com. Through clustering and enrichment analysis, we identified 28 stem-like clusters out of the total 92 clusters across the 37 GBM samples. The typical enrichment results of NPC-like 1 and NPC-like 2 are presented in Tables S2 and S3. The attributes of the NPC-like 1 and 2 subtypes indicated by the enrichment results are typical for neural stem cells. All of the enrichment analyses were based on the top 200 marker genes ranked by p-value after the preselection with the criteria: log fold change (logFC) > 2 and p-value < 0.05. In the following sections, we will be using the gene name PROM1 for CD133. PROM1 serves as a marker gene for eight clusters out of the 28 total stem-like clusters, and the eight clusters belong to eight different samples (Figure 1). In summary, PROM1 was found to be significantly overexpressed in 28.6% of the stem-like clusters. According to the volcano plot (Figure 1), PROM1 ranks in the top 9.4%, 20.6%, 25.7%, 35.3%, 51.8%, 58.2%, 82.2%, and 93.2% (by p-value) among all the overexpressed markers (the selected zone on the right of the volcano plot Figure 1 by the criteria: logFC > 2 and p-value < 0.05) for the eight clusters, respectively. Overall, the significance of PROM1 as a marker gene is not the most outstanding among all the markers of the eight clusters. Through previous studies and our own investigations, it is believed that PROM1 features the stem-like subtype in GBM [37,38]. However, the fact that only eight out of the total 28 stem-like clusters are marked by PROM1 and its relatively low significance among all the markers led us to search in a wider range for other options that might target the GBM stem-like subtype better. Our study featured 2173 unique stem-like marker candidates combined from the top 200 marker genes of the 28 stem-like clusters (Figure 2a). The principal filtration step is to select markers with high specificity to the stem-like subtype, indicated by the frequency of a gene being a marker gene (for the 28 stem-like clusters) and its significance (represented by median ranked p-value) (Figure 2b). The remaining markers could be further narrowed down with two optional approaches. One is to select markers that are significantly overexpressed in GBM stem-like cells compared with normal brain cells. The other is to select markers that exhibit higher expression levels relative to other genes expressed by the same cell (quantified by percentage-rank), and meanwhile are overexpressed compared with non-stem-like GBM clusters (quantified by logFC). The second approach finds markers suitable for assays that require a high expression level for their efficiency. Finally, the location of the expressed markers was also considered as it relates to the feasibility of certain applications (examples provided in the discussion section). The following section presents the markers selected by the principal selection step in combination with different optional criteria. The frequency for each of the 2173 candidates to be a marker gene for a stem-like cluster was normalized by the total number of stem-like clusters and multiplied by 100%. The p-values of the marker genes were ranked ascendingly within each stem-like cluster, normalized by the number of marker genes for the cluster, and multiplied by 100%. The smaller the median p-value rank, the more significant the marker (indicated by the x-axis in Figure 2a. The median was taken across the 28 stem-like clusters). A higher value on the y-axis indicates a higher frequency of the gene being the marker gene for a stem-like cluster (Figure 2b). Based on these two parameters, the markers in the upper left corner of (Figure 2b) are optimal for specifying the stem-like subtype from other GBM cells. We applied the criteria: frequency > 14% and median p-value rank < 50% to obtain markers in this zone. This filtration step was passed by 251 marker genes. These 251 marker genes were used for further selection with the two optional approaches, as described previously. In search for the most significant and frequently shared markers by the GBM stem-like clusters, BCAN, SOX4, PTPRZ1, GPM6A SOX11, MAP2, TUBB2B, NREP, PTPRS, TUBB3, TUBA1A, DBN1, OLIG1, FXYD6, PMP2, SEMA5A,MLLT11, ASCL1, S100B, and MAGED1 are among the best candidates (Figure 2b). They are each found to be the marker for between 46% and 64% of the 28 stem-like clusters, and appear in a high significance range. Considering both parameters, they are superior to 99% of the total 2173 preselected stem-like markers. The markers in the lower right corner of Figure 2b are less representative of the GBM stem-like clusters. The most prominent markers selected by this method, such as BCAN, are expressed universally by the GBM stem-like cells. In comparison, PROM1 is expressed sparsely by the same group of cells (Figure 2f). The expression levels of the 251 selected marker genes were compared between GBM stem-like cells and normal brain cells. The median percentage-rank of each marker for all the GBM stem-like cells and for all the normal brain cells was used to calculate p-value and logFC, as shown in Figure 2c. The p-values were obtained by applying the Wilcoxon rank-sum test to the GBM stem-like cells and the normal cells group. Among the 251 candidates, C8orf46, FAM115A, GPR56, HMP19, LPPR1, MAGED4B, MLLT4, NGFRAP1, PTCHD2, and SEPT7 (red crosses in Figure 2d) were not expressed by the normal cells, indicating specificity to GBM stem-like cells compared with normal brain cells. Among these candidates, GPR56 outperforms the others in the frequency–significance selection (Figure 2d). Because zero reads were excluded from rankings and did not enter the ranking-based comparison, they are not shown in Figure 2c. Most of the remaining 241 markers present considerably low p-values in the comparison. The p-value criteria shown in (Figure 2c) is at 0.01, and all the highlighted markers still havep values far below it. PTPRS and TUBB3 were found to remarkably distinguish GBM stem-like cells from normal brain cells while remaining favorable in the frequency–significance selection (Figure 2d; they are both orange genes and also appear in the upper left corner of the frequency–significance selection). The cancer-specificity of TUBB3 is explicitly shown in Figure 3d, in comparison with the general progenitor cell marker BCAN. In fact, all the markers colored orange or brown in Figure 2c can be considered to distinguish GBM stem-like cells from normal brain cells. The advantageous markers from the cancer–normal comparison are also marked on the frequency–significance figure, to show the options that excel in both selections (Figure 2d). The expression level of a marker can be of crucial importance for some targeting assays [39]. Therefore, we present the relative expression level represented by percentage-rank and logFC (from the differential expression analysis among GBM clusters), for the 251 genes that passed the frequency–significance selection (Figure 4). The median percentage-rank across all GBM stem-like cells and the median logFC over all the GBM stem-like clusters were plotted together. Markers that are expressed at a high level compared with other genes in the same cells and meanwhile overexpressed by the stem-like clusters within GBM are shown (Figure 4). The markers that can distinguish GBM stem-like cells from normal cells are also marked in the same figure (Figure 4) to show the combined results. Among the 20 markers selected by the frequency–significance plot, BCAN, PTPRZ1, PMP2, GPM6B, TUBB3, and S100B outperform the others in the expression-level selection (Figure S1). TUBB3 excels in all three selections (frequency–significance selection (Figure 2b), the distinction to normal cells (Figure 2c), and expression level selection (Figure 4). This means that TUBB3 is highly specific to the stem-like subtype within GBM and commonly found for the stem-like subtype, distinguishes cells from healthy brain cells, and is expressed at a sufficiently high level for ligand binding. Because all the markers used in this selection have a logFC greater than 2, which is considered significant in biological comparisons, the requirement shown by the y-axis of Figure 4 could have been lowered if more choices were needed. As well as the criteria involved in the selection steps described above, the cellular location of the protein translated from the corresponding marker gene is also crucial for the feasibility of using the marker in various applications [40]. Generally, it is easier to target markers located in the cell membrane than intracellular markers. Among the 251 selected marker candidates from the frequency–significance selection, about half of them are expressed on the cell membrane (marked as blue dots in Figure 2b, marker names given in Figure S2). Of all the markers that distinguish the GBM stem-like subtype from normal cells (orange, brown dots, and red crosses in Figure 2d), PTPRS, ATP1A3, MAGED4, NNAT, ASIC4, ITGA7, GPR56, HMP19, LPPR1, MAGED4B, and PTCHD2 are cell membrane markers. If we apply more stringent criteria with all the previously mentioned parameters, then PTPRS and GPR56 stand out as highly representative stem-like, cancer-specific, highly expressed surface markers. They only have a relatively lower logFC compared with TUBB3 (Figure 4). Information regarding the location of the proteins encoded by the markers was identified in the Human Protein Atlas database [41] (refer to proteinatlas.org). For some of the promising markers selected above, we examined their “abundance” (the proportion of cells that express non-zero values of the gene within a sample) and their percentage-rank across samples. The median abundance of PROM1 across samples is 18%. In comparison, BCAN, PTPRZ1, SOX4, and GPM6A, as representative markers from the frequency–significance selection, exhibit median abundances of 75%, 97%, 84%, and 88%, respectively. As a marker of prominent cancer-specificity, LDHB has a median abundance of 97%. The three recommended markers, TUBB3, PTPRS, and GPR56, by all standards exhibit a median abundance of 68%, 94%, and 86% (Figure 5). The percentage-rank of each selected marker was also shown across the samples. Among all the cells that express the marker genes: BCAN, PTPRZ1, SOX4, GPM6A, LDHB, TUBB3, PTPRS, and GPR56, the percentage-rank is over 50% for 92%, 96%, 74%, 96%, 86%, 90%, 75%, and 87% of the cells, respectively, all higher than the 60% for PROM1 (Figure 5). The median percentage-rank within each sample is higher than 50% for 100%, 97%, 73%, 100%, 81%, 88%, 77%, and 96% for the markers mentioned above. For PROM1, this value is 59% (Figure 5). The core strategy of this study is the use of nonparametric values such as frequency, percentage-rank, and p-value-rank in order to be able to integrate data from different studies. It allows us to quantitatively evaluate different aspects of a marker based on various patients from different studies [42,43,44]. Gene-expression data from different studies cannot be directly combined to draw comparative or statistical conclusions without eliminating the batch effects between them. However, most batch-correction tools either presume the data distribution or subtyping and inevitably introduce biases to the original data [45]. Therefore, we adopted the straightforward and robust rank-based method to integrate datasets [46,47]. Furthermore, the percentage-rank defined in this study has an advantage from its definition compared with gene counts. In a more explicit manner, it represents the expression level of a gene in comparison with all the other genes of the cell. Conversely, a normalized gene count does not provide much information in itself without conducting differential expression analysis. We believe that the rank-based data are more reliable and more relevant for a biomarker evaluation study. Furthermore, we believe that the most optimal way to validate their robustness is by presenting their universality among patients from different scRNA-seq-based datasets, together with their statistical significance. This is because bulk RNA sequencing data (such as TCGA sequencing data), which presents an average expression of each gene from all the cells in the sample, cannot be used for GSC marker identification validations. Our identification of the NPC-like 1 and 2 subtypes can be verified by comparing our enrichment result (Tables S2 and S3) with the enrichment provided by Neftel et al. [11]. Both NPC-like 1 and 2 subtypes exhibit typical neural progenitor cell features. As a validation for our results, many other studies identified the same marker genes for GSC as we highlighted in Figure 2b; see Table 1 for a list of methods and references. It was revealed that some cells identified as stem-like by their transcriptome profiles do not express PROM1 (Figure 5). This provides an explanation for the tumorigenesis ability of CD133 negative cells reported by multiple studies [22,63]. Bhaduri et al. also discovered the “sparse” expression of PROM1 [54]. We concluded that being CD133 positive is a sufficient but not necessary condition for being a GBM stem-like cell. In favor of our findings of the normal brain-cell-distinctive GBM stem-like markers, seven out of eight total orange markers were reported to be overexpressed in different cancers (Figure 2c). RCN1 was shown to be overexpressed in GBM stem-like cells compared with normal tissue [64]. METTL7B and MAGED4 were discovered to be overexpressed prognostic markers in various gliomas [65,66,67,68,69]. LDHB, PTPRS, UHRF1, and TUBB3 were proposed as universal prognostic and malignancy markers across many cancer types [70,71,72,73,74,75,76,77,78,79]. Studies regarding the differential expression of ATP1A3 in cancer tissue have not been found. The overexpression of multiple “red cross” markers (Figure 2d) was also reported in various cancer types by previous studies [61,80,81]. It should be clarified that most currently applied GBM stem-like markers are not normal-cell-distinctive, as they are also expressed by neural progenitor cells. For instance, previous studies suggested that CD133 can be used to separate stem cells from not only cancerous but also normal tissue [22,23,82], including brain [83]. Likewise, SOX2, CD15, and CD24 have also been identified as neural stem-cell markers [84,85]. Indeed, the denotation “stemness” refers to a particular biological feature of preserved multi-potency and self-renewal ability [86], which is not relevant to malignancy. To address this concern, we recommend focusing on the orange-, brown-, and red-cross-marked GBM stem-like markers in Figure 3d, to distinguish normal brain cells from GBM stem-like cells in clinical targeting. We quantified the “stemness” of the total 2173 preselected GBM stem-like markers based on their universality (frequency) and significance as a basic step, to maximize the efficiency of the markers in targeting GBM stem-like subtypes within tumors and among patients. This was followed by two alternative selections to further obtain the options that can either (1) exclude normal cells during targeting, or (2) markers that are expressed at a high level by the targeted subtype, or both. Furthermore, the location of proteins encoded by the selected markers was also considered. Some targeting technologies prefer markers found in the cell membrane to intracellular markers, due to the difficulty of crossing membranes of live cells with targeting agents, such as targeted protein drugs [40]. In the end, we confirmed that the representative markers selected by our methods exhibit relatively higher abundance and expression levels across samples compared with PROM1. TUBB3 outperforms the other candidates with the combination of the first three selections but is expressed intracellularly. PTPRS and GPR56 are cell-surface markers that stand out in the first two selections with slightly lower, but sufficient, expression levels. The stemness of these three markers is supported by the Neftel et al. study that identified them as marker genes for NPC1 or NPC2 subtypes [11]. Their cancer-specificity is supported by studies that reported their overexpression in various cancer types [61,71,72,73,77,78,79]. Although we started with a high number of candidates, only a few markers excelled at all the selections. Therefore, it is recommended to use only relevant parameters after the basic frequency–significance selection. For instance, efficient in vivo radionuclide-conjugated antibody targeting requires high expression of the biomarker in the targeted subtype [39], and the capability to exclude normal cells if the targeting agent is distributed ubiquitously in the brain. Surface markers are not compulsory in this case [39]. The optimal marker for this application is TUBB3, which is also found to be a marker for high-grade gliomas [87]. For in vitro isolation of glioblastoma stem cells from a bulk tumor, representative (determined by frequency–significance selection), and highly expressed surface markers are preferred, in which case cancer-specificity can be compromised. PTPRZ1 is the most suitable marker in this scenario. Immunohistochemistry (IHC) assays require sufficiently expressed, highly representative stem-like markers, while the other two criteria are not as crucial. Preferable markers for IHC assays include BCAN, PTPRZ1, PMP2, GPM6B, TUBB3, and S100B. The choice of markers should be customized with relevant parameters according to the specific needs of the study in question. More application scenarios with suggested GSC markers are summarized in Table S4. Targeting glioblastoma multiforme (GBM) stem-like cells (GSCs) is the major motive of this study. Using parameters that quantify the universality, significance, expression level, and cancer-specificity of the candidate markers, we successfully compared 2,173 candidate GBM stem-like markers using single-cell RNA sequencing data. Our analyses suggest 20 markers, BCAN, SOX4, PTPRZ1, GPM6A, SOX11, MAP2, TUBB2B, NREP, PTPRS, TUBB3, TUBA1A, DBN1, OLIG1, FXYD6, PMP2, SEMA5A, MLLT11, ASCL1, S100B, and MAGED1, as the most universal and significant GSC markers across patients. Among these 20 stem-like markers, BCAN, PTPRZ1, PMP2, GPM6B, TUBB3, and S100B are expressed at high levels. Comparing GSCs with normal brain cells, we found the markers LDHB, RCN1, PTPRS, METTL7B, UHRF1, MAGED4, ATP1A3, and TUBB3 to have outstanding cancer-specificity. Thus, they are recommended for developing GSCs targeting agents for patient applications. In conclusion, taking all the parameters and markers into account, TUBB3, PTPRS, and GPR56 outperform the other candidates. We propose them as markers for the future targeting of GSCs in a wide variety of clinical applications.

PMC10001305

Sayed Obaidullah Aseem,Phillip B. Hylemon,Huiping Zhou

Bile Acids and Biliary Fibrosis

02-03-2023

biliary fibrosis,bile acids,bile acid receptors,cholangiocytes,cholangiopathies

Biliary fibrosis is the driving pathological process in cholangiopathies such as primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Cholangiopathies are also associated with cholestasis, which is the retention of biliary components, including bile acids, in the liver and blood. Cholestasis may worsen with biliary fibrosis. Furthermore, bile acid levels, composition and homeostasis are dysregulated in PBC and PSC. In fact, mounting data from animal models and human cholangiopathies suggest that bile acids play a crucial role in the pathogenesis and progression of biliary fibrosis. The identification of bile acid receptors has advanced our understanding of various signaling pathways involved in regulating cholangiocyte functions and the potential impact on biliary fibrosis. We will also briefly review recent findings linking these receptors with epigenetic regulatory mechanisms. Further detailed understanding of bile acid signaling in the pathogenesis of biliary fibrosis will uncover additional therapeutic avenues for cholangiopathies.

Bile Acids and Biliary Fibrosis Biliary fibrosis is the driving pathological process in cholangiopathies such as primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Cholangiopathies are also associated with cholestasis, which is the retention of biliary components, including bile acids, in the liver and blood. Cholestasis may worsen with biliary fibrosis. Furthermore, bile acid levels, composition and homeostasis are dysregulated in PBC and PSC. In fact, mounting data from animal models and human cholangiopathies suggest that bile acids play a crucial role in the pathogenesis and progression of biliary fibrosis. The identification of bile acid receptors has advanced our understanding of various signaling pathways involved in regulating cholangiocyte functions and the potential impact on biliary fibrosis. We will also briefly review recent findings linking these receptors with epigenetic regulatory mechanisms. Further detailed understanding of bile acid signaling in the pathogenesis of biliary fibrosis will uncover additional therapeutic avenues for cholangiopathies. Biliary fibrosis is the defining and predominant pathological feature of many diseases of the biliary system. Diseases affecting the biliary system and cholangiocytes, the epithelial cells that line the biliary tree, are collectively called cholangiopathies [1]. These are a diverse group of diseases that together amount to considerable clinical significance. Fibrogenic cholangiopathies, which are highlighted by biliary fibrosis, include primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Both PBC and PSC are well-known clinical entities and subjects of intense clinical, translational and basic science research. PSC has a variable but progressive course of biliary fibrosis, eventually culminating in cirrhosis and its associated complications [2]. There are no currently approved pharmaceutical treatments for PSC, which may be ultimately treated with liver transplantation. PBC is treated with the secondary bile acid ursodeoxycholic acid (UDCA), which has been shown to improve the disease’s clinical course, including transplant-free survival [3]. However, in up to 40% of patients, PBC can be progressive despite UDCA treatment. Obeticholic acid is a synthetic bile acid approved for the treatment of PBC patients with inadequate response or intolerant to UDCA. Fibrogenic cholangiopathies can present clinically with significant cholestasis, particularly with progressive biliary fibrosis [3]. Cholestasis is the retention of bile acids and other bile components within the liver and serum of afflicted patients either due to an inability to secrete or an obstruction [4]. Cholestasis is a significant cause of morbidity in these conditions. Pruritus, one of the chief and debilitating symptoms, is thought to be directly related to the retention of biliary components [5]. However, bile acid retention and dysregulation may not simply be a cause for symptoms. More recent studies suggest a role for bile acids in both the progression of biliary fibrosis and the treatment of these conditions. In this review, we will highlight the role of bile acids in biliary fibrosis with a focus on the effects of bile acid on cholangiocytes. Cholangiopathies have diverse etiologies, which include autoimmunity (PBC), genetic (Alagille syndrome), infectious (AIDS cholangiopathy, parasitic infections), malignancy (cholangiocarcinoma), ischemia (ischemic cholangiopathy in post-liver transplant) and idiopathic (PSC, biliary atresia) [1,4]. Regardless of the type of insult to the biliary tree, chronic injury to cholangiocytes and bile ducts can lead to progressive biliary fibrosis (Figure 1). This is initiated with chronic insult causing the cholangiocytes to transform into a proliferative and secretory state. It is accompanied by the recruitment of inflammatory and immune cells [4]. Bile duct proliferation, along with an inflammatory response, is termed ductular reaction (Figure 1). Paracrine and autocrine signals allow interactions between cholangiocytes, immune cells and myofibroblasts. Cholangiocytes release paracrine signals that activate hepatic stellate cells (HSCs) and portal fibroblasts into myofibroblast-type cells [4]. The current paradigm suggests that it is these activated HSCs and portal fibroblasts that lay down the extracellular matrix (ECM) of biliary fibrosis. Probably later in the course of disease, cholangiocytes undergo senescence, a state of cell cycle arrest but highly secretory [6]. The mismatch of bile duct injury and cholangiocyte senescence results in ductopenia, a phenomenon observed later in the disease [4]. With the persistent injury, biliary fibrosis progresses to cirrhosis, ultimately to decompensation with portal hypertension and its associated manifestations, and increased risk of hepatobiliary malignancy. Bile acids play important roles in regulating the lipid, cholesterol, fat, and fat-soluble vitamin trafficking and absorption through bile acid micelle formation [7]. The de novo biosynthesis of bile acids also functions as a cholesterol catabolic pathway [8]. Hepatocytes express all the necessary enzymes for bile acid synthesis, which is carried out by the classical (neutral) and alternative (acidic) pathways. The classical pathway, which is responsible for the majority of bile acid synthesis, converts cholesterol into cholic acid (CA) and chenodeoxycholic acid (CDCA). Cholesterol-7 alpha-hydroxylase (CYP7A1) is the rate-limiting enzyme for this biochemical pathway. The alternative pathway produces mostly CDCA. These primary bile acids are then conjugated mostly with glycine in humans, but also with taurine. Conjugated bile acids are secreted into bile ducts where they can be picked up by cholangiocytes through the action of apical sodium-dependent bile acid transporter (ASBT), also known as ileal bile acid transporter (iBAT), and released into the peribiliary plexus for delivery back to the hepatocytes, creating the cholehepatic shunt [8,9,10]. Bile acids not taken up by cholangiocytes are stored in the gallbladder and then released into the intestine after food intake, where they have essential functions in nutrient absorption. Bile acids are efficiently reabsorbed in the small intestine by passive mechanisms and active mechanisms in the terminal ileum by ASBT, such that up to 95% of secreted bile acids are reabsorbed [11]. A small fraction of bile acids undergo further modification by the gut microbiome: first deconjugation, followed by various other transformation reactions, including the oxidation of α- hydroxyl groups at C-3, C-7 and C12 to form oxo groups and the reduction of these to form β-hydroxyl groups, yielding 3-iso, 7-epi and 12-epi bile acids. In addition, selective species of the genus Clostridium carry out 7α-dehydroxylation of the primary bile acids, CA and CDCA, forming the secondary bile acids, deoxycholic acid (DCA) and lithocholic acid (LCA), respectively [11]. There are over 50 bile acid metabolites identified in humans [12]. Secondary bile acids can also be reabsorbed by the gut and transferred to the serum, followed by reuptake in the liver. Bile acid secretion into the intestine and reabsorption back to the liver via the portal vein is known as enterohepatic circulation [11]. Cholangiocytes are polarized epithelial cells that line the biliary tree. The apical membrane has microvilli and a primary cilium that responds to mechanical, chemical and osmolar stimuli to maintain cholangiocyte homeostasis [13]. Smaller intrahepatic bile ducts are lined by small, cuboidal cholangiocytes, whereas larger ducts are lined by large, columnar cholangiocytes. There is likely a significant variety and a spectrum in the functions, gene expression, and role in the pathology of cholangiocytes rather than the simple categorization of small versus large [14]. Regardless, both small and large cholangiocytes express various bile acid transporters. ASBT can facilitate the transport of bile acids from the bile into cholangiocytes. At the basolateral membrane, organic solute transporter (OST) alpha and beta and multidrug resistance-associated protein 3 (MRP3) can transport bile acids into the systemic circulation [15]. These, along with several other electrolyte transporters with both absorptive and secretory functions and aquaporins, modulate the biliary composition of bile acids, electrolytes and water, thereby carrying out an important function in the ultimate content of the bile secreted into the duodenum [15]. Importantly, cholangiocytes secrete bicarbonate at the apical membrane through the anion exchanger 2 (AE2) to form the so-called “bicarbonate umbrella” [16]. This is thought to protect cholangiocytes from toxic substances in the bile, including certain bile acids that are prevented from diffusing into cholangiocytes by maintaining them in their anion form by the alkaline lumen. Disruption of the bicarbonate umbrella is associated with certain pathological conditions. Recent studies have examined the bile acid composition in cholangiopathies, mostly PBC and PSC. Most studies have focused on the serum bile acid composition as a surrogate of the bile acid pool in the enterohepatic circulation. There is also a more limited number analyzing the bile acid composition in bile obtained by endoscopic means. PBC patients were shown to have increased total serum bile acid levels with predominantly increased primary bile acids in a study by Chen et al. Serum secondary bile acids were reduced and there were no identifiable differences in the fecal bile acid levels [17]. Mousa et al. showed that a cohort of PSC patients similarly had increased total and conjugated bile acids. An increased ratio of primary to secondary bile acids was also noted in this PSC cohort. Hepatic decompensation was associated with the increased concentration and conjugated fraction of many bile acids, but the glycine to taurine conjugation ratio appeared protective [18]. It is worth noting that most of the PSC patients in this study had decompensated disease (80–100%). Another study of urinary bile acid levels, revealed elevated levels in most liver disease patients, with PBC and PSC showing the highest total levels and PSC showing the highest CA and CDCA levels [19]. In contrast, the total fecal bile acids were shown to be reduced in PSC with IBD compared to those without PSC [20]. An earlier study of both PBC and PSC cohorts analyzing 17 bile acids in the serum showed that the concentrations of total bile acids and taurine and glycine conjugates of primary bile acids were increased in both cohorts [21]. Secondary bile acids were reduced in the PSC cohort. The PBC cohort, however, did not show a reduction in secondary bile acids, contradicting the study by Chen et al. [17,21]. These inconsistencies are possibly due to cohort differences, particularly the stage of disease, such as non-cirrhotic, cirrhotic and decompensated cirrhotic. A more recent study of largely non-cirrhotic patients found that the bile fluid concentrations of most bile acids were reduced in PSC except for taurolithocholic acid, a noxious secondary bile acid [22], consistent with a previous study [23]. One obvious justification for the lower bile acids in the bile fluid in contrast to the elevated bile acids in the serum shown by other studies may be that, due to the PSC-related dysfunction, the liver has a reduced capacity for secreting bile acids into the bile fluid, resulting in cholestasis. However, this assumption cannot be maintained without a concomitant analysis of serum bile acids and can be confirmed in future studies simultaneously showing lower bile acids in bile fluid and higher serum bile acids. Other possibilities include leakage from the biliary tree or dilution, but the biliary bile acid concentration appears to be intact [23,24]. In cirrhosis, regardless of etiology, the bile acid pool may be depleted due to decreased synthesis and disproportionate partitioning into the liver and serum due to poor hepatic secretion [25,26]. Therefore, further analyses are required to determine how much of the serum and bile fluid bile acid levels are related to advanced liver disease versus etiologically to specific cholangiopathies. Bile acids have been directly and indirectly implicated in biliary fibrosis and various animal models of biliary fibrosis have been used to identify potential mechanisms. Fibrosis is a response to injury and inflammation. Bile acids, owing to their detergent properties, were previously considered cell-injurious by solubilizing the plasma membrane. However, the effects of bile acids on cholangiocytes and other hepatic cells may be much more complex [15]. Certain bile acids, such as glycochenodeoxycholic acid (GCDCA) and lithocholic acid (LCA), may cause necrotic cell death and lead to an inflammatory response [27,28]. However, the pathophysiological relevance of these observations is unclear since most LCA is conjugated or bound to serum albumin/lipoproteins [29,30]. Generally, hydrophobic bile acids are more likely to be harmful (such as with the LCA feeding model) and hydrophilic ones (UDCA/norUDCA) are more likely to be protective [28]. Administering hydrophilic bile acids to mouse models of biliary injury and fibrosis was protective [31]. However, bile duct injury, inflammation and associated fibrosis may not be solely attributable to hydrophobic bile acids, as taurocholic acid (TCA) has also been implicated in cholestatic injury and fibrosis [32]. Despite these lines of evidence, controversy remains as to whether bile acids have direct injurious effects on cholangiocytes. At pathophysiological concentrations, bile acids resulted in a cytokine-induced inflammatory response in hepatocytes only [33]. Furthermore, limited studies in human cholangiopathies thus far have not revealed significant changes in the composition of biliary bile acids to suggest toxicity to cholangiocytes [22,34]. Larger, high-quality studies of biliary or duodenal bile acid composition in cholangiopathies are needed to provide further insight. Bile acids may become noxious in the absence of protective mechanisms such as impaired phospholipid secretion. This is supported by the seminal study of multidrug resistance protein 2 (Mdr2) gene knockout in mice resulting in impaired biliary phospholipid secretion with consequent bile-acid-induced biliary duct injury [35]. The biliary injury leads to an inflammatory response and the activation of hepatic myofibroblasts, which subsequently mediate biliary fibrosis [36]. Further support comes from the study of a genetic disorder, progressive familial intrahepatic cholestasis type 3 (PFIC3), where mutations in MDR3 markedly reduce biliary phospholipid levels. This condition is associated with cholangiocyte injury, ductular reaction, inflammation and fibrosis [37]. Analyses of biliary phospholipids in PSC thus far have not revealed any changes compared to controls to suggest such a mechanism at play [23,24]. Another potential path to the noxious effects of bile acids may be through the disruption of the protective bicarbonate umbrella and transporters such as AE2 involved in its maintenance. Indeed, bile acid toxicity is increased in a pH-dependent manner, especially with the disruption of a cholangiocyte apical glycocalyx [38]. Congruently, reduced AE2 expression has been noted in PBC patients [39]. In contrast, canalicular and basolateral efflux pumps may be upregulated, while basolateral uptake systems are downregulated in adaptive changes to curb bile acid accumulation within cholangiocytes [40]. Biliary fibrosis may involve mechanisms without direct injury to cholangiocytes. Increased serum bile acids may directly activate hepatic myofibroblasts to lay down the ECM of biliary fibrosis. For example, CDCA treatment of a hepatic stellate cell (HSC) cell line LX2 resulted in proliferation and collagen deposition, suggesting activation [41]. Conjugated secondary 12a-hydroxylated bile acids, taurodeoxycholate (TDCA) and glycodeoxycholate (GDCA), found to be significantly increased in non-alcoholic steatohepatitis (NASH) patients and mouse models, activated LX2 cells much more than other bile acids tested [42]. However, elevated serum bile acids of normal composition are unlikely to directly activate HSCs to produce fibrosis. This notion is supported by the fact that pediatric patients with mutations and deficiency in the Na+–taurocholate cotransporting polypeptide (NTCP, SLC10A1), the major transporter of conjugated bile salts from the plasma compartment into the hepatocyte, have markedly elevated conjugated serum bile acids but with normal liver function and without signs of injury or fibrosis [43]. Most other inborn bile acid transporter defects (such as PFIC2/3, Alagille syndrome and cystic fibrosis) present with injury to hepatocytes, cholangiocytes or both and associated inflammation that leads to hepatobiliary fibrosis [15]. Furthermore, if serum bile acids directly activated HSCs, a perisinusoidal fibrosis pattern would be the more typical pattern expected. In contrast, in fibrogenic cholangiopathies, such as PSC and PBC, fibrosis starts in a periductal manner that eventually propagates to cirrhosis. Therefore, while the direct activation of HSCs by serum bile acids is plausible, it is unlikely to be the primary mechanism of biliary fibrosis in fibrogenic cholangiopathies, especially in the earlier stages. Bile acids may play a role in other aspects of biliary fibrosis in cholangiopathies, namely cholangiocyte proliferation, ductular reaction and cholangiocyte senescence. These processes will be reviewed along with the function of bile acid receptors in the following sections. GPBAR1 (TGR5) is expressed in all hepatic cells except hepatocytes [44]. It is mainly activated by secondary and unconjugated bile acids (LCA>DCA>CDCA>CA) (Table 1). GPBAR1 has been shown to localize in multiple cholangiocyte compartments, but ciliary-membrane-bound GPBAR1 appears to have pronounced effects [45]. In vitro experiments to determine GPBAR1 signaling from different compartments utilized the phenomenon of early confluent cholangiocytes that do not form a cilium until an additional 7 days of confluence [45]. Ciliated cholangiocytes had reduced cAMP but elevated extracellular regulated protein kinase (ERK) activation and suppressed proliferation (Figure 2A). Non-ciliated cholangiocytes had the opposite cAMP and ERK phenotype and activated proliferation with GPBAR1 activation (Figure 2A) [45]. Structurally or functionally defective cilia result in several cholangiopathies (e.g., polycystic liver disease and biliary atresia) characterized by cholangiocyte hyperproliferation [46,47], possibly through apical GPBAR1 activation with increased intracellular cAMP [45,48]. Mouse models of biliary fibrosis and cholestasis showed an increased cholangiocyte proliferation via the Gpbar1-mediated activation of the epidermal growth factor receptor and ERK pathway [49]. Gpbar1-knockout mice had reduced cholangiocyte proliferation with bile duct ligation (BDL), which causes intrahepatic biliary fibrosis presumably through effects on apical Gpbar1 activation (Figure 2A). These findings point to a deleterious role for GPBAR1 in cholestasis, where bile acid activation of GPBAR1 may stimulate small-cholangiocyte proliferation, a necessary component of the ductular reaction, which ultimately progresses to biliary fibrosis. This notion is supported by other lines of evidence in different mouse models. Gpbar1 deletion in NASH- and CCl4-induced injury mouse models led to reduced hepatic fibrosis. The fibrogenic role of Gpbar1 was proposed to be through bile acid-induced HSC activation via the ERK/p38 MAPK signaling pathways [42]. Currently, there are no GPBAR1 antagonists being studied in biliary fibrosis. However, a first-in-class GPBAR1 antagonist, designated SBI-319, was recently demonstrated to inhibit cholangiocyte proliferation and cystogenesis in models of polycystic liver disease (T. Masyuk et al. unpublished data presented at the Liver meeting parallel 36 section). In contrast, other lines of evidence do not support such a deleterious role for GPBAR1. Firstly, GPBAR1 levels are reduced in PBC and PSC patients [50]. GPBAR1 agonists promote bile flow [51] and may have antiapoptotic effects in cholangiocytes [52,53], which would be protective in cholangiopathies. In a mouse model of cholangiopathy, dual FXR and GPBAR1 agonists improved biliary fibrosis, but not selective GPBAR1 or FXR agonists [54]. Furthermore, GPBAR1 appears to attenuate liver fibrosis in a diabetic mouse model [55]. Some of these contradictory observations may be explained by cell- and injury-specific effects of GPBAR1. Consistently, bile-acid-mediated GPBAR1 activation in Kupffer cells induced pro-inflammatory cytokines via the c-Jun N-terminal kinase (JNK) pathway (Figure 2A) [56]. The bile acid induction of cytokine production in Kupffer cells may suppress hepatic Cyp7a1 in a negative feedback loop [57]. Conversely, GPBAR1 activation in monocytes and macrophages inhibited nuclear factor kappa B (NF-κB) and subsequently pro-inflammatory cytokine production in a model of atherosclerosis [58]. Therefore, GPBAR1 appears to have opposing roles in different cell populations. The pro- and anti-inflammatory cytokine production through different immune cell populations may have a role in propagating or ameliorating biliary fibrosis [59,60]. The effects of these cell-specific observations have not yet been studied in detail in biliary fibrosis models. The FXR nuclear receptor is widely expressed, including in the liver, intestine and immune cells. The activation of FXR by bile acids varies according to species (CDCA>>>DCA>CA>LCA) (Table 1) [61]. FXR, through its tissue-specific effects, is the master regulator of bile acid homeostasis. FXR activation in the liver and intestine leads to gene expression changes to downregulate bile acid de novo synthesis. This is accomplished by the FXR-dependent transactivation of small heterodimer partner (SHP) by phosphorylation and inhibiting proteasomal degradation in hepatocytes [62]. SHP subsequently represses CYP7A1, the rate-limiting enzyme in bile acid synthesis. Intestinal FXR signaling leads to the release of fibroblast growth factor (FGF)15/19 secreted from the ileum, which activates fibroblast growth factor receptor 4 (FGFR4) and ERK signaling to also suppress CYP7A1. The FGFR4 and ERK signaling additionally inhibit SHP proteasomal degradation, which may also suppress CYP7A1 [62]. FXR has immune-modulating effects through interactions with NF-κB in hepatocytes and various immune cells, leading to the suppression of NF-κB-induced inflammatory cytokines [7,63,64]. Conversely, pro-inflammatory cytokines may repress FXR activation by various mechanisms, including NF-κB activation [65]. Under cholestatic conditions, FXR orchestrates the above and other adaptive transporter changes to counteract the damaging effects of cholestasis. These adaptive mechanisms are not restricted to the liver hepatocytes but also occur in the kidney, intestine and cholangiocytes [66]. They include the increased expression of ASBT and OSTα/β in cholangiocytes due to ductular proliferation, which allows bile acid reabsorption from obstructed bile ducts (Figure 2B) [66]. The overall effect is expected to promote bile flow in cholestasis, thereby preventing the hepatic accumulation of toxic bile acids. Animal models, however, have produced conflicting data regarding the role of FXR in cholestasis. Whereas FXR deletion had protective effects, FXR agonists reduced hepatic injury and inflammation in rodent models of cholestasis [67,68]. In humans, FXR expression is increased in both PBC and PSC [69,70]. Yet, the expected FXR-mediated downregulation of CYP7A1 was only observed in PBC patients, while PSC patients had unaltered CYP7A1 expression. Given the role of FXR in regulating cholestasis and the associated inflammatory response, its activation is expected to reduce biliary fibrosis. Indeed, activation of the FXR-SHP axis in rodent models of cholestasis prevented fibrosis [71]. A direct effect on the FXR expressed in HSCs has been proposed as a potential mechanism for this anti-fibrotic effect. However, HSCs express low levels of FXR [72]. Therefore, cholangiocyte signaling and crosstalk with HSCs may be another target of the effects of FXR signaling [73]. In a rat liver transplant model, biliary transit time and bile duct injury were prolonged with pre-transplant ischemia of the donor liver, which correlated with reduced Fxr expression [74]. Cholangiocytes under hypoxic conditions had reduced expression of Fxr and increased expression of the fibrogenic factor Tgfβ [74]. Loss-of-function mutations in the FXR gene result in PFIC5 consisting of severe cholestasis and rapid progression of fibrosis to cirrhosis [75]. These studies point to the important role of FXR in regulating biliary fibrosis. Further support comes from clinical trials that have provided evidence for the use of FXR agonists in cholestatic liver diseases. Prominently, obeticholic acid is an approved FXR agonist for the treatment of PBC. Long-term treatment has shown stabilization of biliary fibrosis [76]. There are multiple other FXR agonists in the clinical trial stages to treat cholestatic liver diseases [77]. Interested readers are referred to recent review articles on the subject [78,79]. S1PR2 is a member of five S1PRs, originally discovered as endothelial differentiation gene 5 [80]. S1PR2 is expressed in most tissues and associated with different G-proteins specific to cells and stimuli [81]. It is highly expressed in the liver and is the predominant S1PR in hepatocytes and cholangiocytes [32,82]. Conjugated primary bile acids activate S1PR2, with TCA being the most potent activator (Table 1) [82,83]. In hepatocytes, conjugated bile acids activate the extracellular signal-regulated kinase 1 and 2 (ERK1/2) and protein kinase B (AKT) signaling pathways through S1PR2, which upregulates sphingosine kinase 2 (SphK2). S1PR2 and SphK2 appear to have important roles in hepatic lipid and glucose metabolism. Mice deficient in either S1pr2 or Sphk2 develop rapid steatosis on a high-fat diet [83]. In cholangiocarcinoma cell lines, conjugated bile acids activated S1PR2 and upregulated cyclooxygenase 2, which led to invasive growth and bile duct proliferation [84,85]. The TCA-mediated activation of ERK1/2 and AKT in cholangiocytes was inhibited by S1PR2 antagonists or siRNA knockdown, which also inhibited cholangiocyte proliferation and migration (Figure 2C) [32]. In cholestatic mice, S1PR2 expression was increased, and S1PR2 deficiency attenuated cholestasis-mediated cholangiocyte proliferation, cholestatic injury, inflammation and fibrosis (Figure 2C) [32]. Similarly, macrophage-specific knockdown of S1PR2 reduced cholestasis-associated inflammation and fibrosis by attenuating the chronic-liver-injury-associated NOD-like receptor family pyrin domain containing 3 inflammasome [86]. Similar results were observed in the liver fluke (Clonorchis sinensis)-infected mouse model of hepatobiliary injury and fibrosis where inflammation, bile duct hyperplasia and periductal fibrosis could be attenuated by an S1PR2 inhibitor [87]. The significance of these findings in human cholangiopathies is yet to be clarified. Cholangiocytes predominantly express M3R, which is responsible for mediating the effects of parasympathetic innervation but is also activated by TLCA [88]. Parasympathetic effects through the vagus nerve increase bile flow and HCO3− secretion, which are inhibited with vagotomy [89]. Similarly, M3R-deficient mice showed reduced bile flow but did not spontaneously develop cholestasis, injury or fibrosis [90]. They were, however, more susceptible to DDC-induced liver injury. Likewise, M3R agonist treatment of Mdr2−/− mice also reduced liver injury, but in neither model was there a substantial effect on biliary fibrosis [90]. These findings suggest that M3R may have a role under cholestatic conditions when the increased bile acid exposure would further activate M3R to increase bile flow and HCO3− secretion in order to limit the cholestatic damage. Consistently, denervated transplanted livers have an increased risk of ischemic cholangiopathy, especially with prolonged ischemic time [91]. However, further supportive data from other cholangiopathies are lacking. While hepatocytes may express low levels of VDR, cholangiocytes and non-parenchymal hepatic cells express VDR abundantly [92]. Vdr-deficient mice develop spontaneous liver fibrosis, which is proposed to be through ungated effects of the Tgfβ1/Smad activation in HSCs [93]. Consistently, VDR agonists protected against CCL4-induced liver fibrosis [93]. VDR also functions as a receptor for LCA [94]. Vdr deficiency in the BDL cholestasis model resulted in increased liver damage [95]. The damage was partly due to limited adaptive changes in bile acid transporters and partly due to increased bile duct rupture due to EGFR-mediated altered E-cadherin expression [95]. These findings suggest that cholestasis may activate VDR to produce adaptive changes in bile acid transporters as a protective mechanism against the toxic effects of cholestasis. Similar findings were reported in a Vdr/Mdr2−/− double knockout model that had worsened inflammation and fibrosis [96]. Taken together, these lines of observations support bile-acid-related and -unrelated protective roles of VDR in cholangiopathies. PSC and PBC patients frequently have vitamin D deficiency which appears to correlate with disease severity [97,98,99]. VDR polymorphisms and lower expression have been reported in PBC, which may have a role in the progression of the disease [100,101]. Further detailed insight into the expression and phenotype of VDR in PSC and other cholangiopathies is needed for a better understanding of the role of this receptor in fibrosing cholangiopathies [102]. PXR and CAR are highly expressed in the liver, followed by the intestine [103]. They act as sensors for both exogenous and endogenous toxic products to signal detoxification and metabolism [104]. These receptors may also have a role in liver disease. Both receptors form heterodimers with retinoid X receptor for downstream signaling [103]. PXR is activated by LCA and its derivatives [105]. Its activation appears to protect against LCA- or BDL-induced liver toxicity by regulating bile acid synthesis and transporters [105,106,107]. It may also have a role in regulating inflammation arising from hepatocytes via NF-κB signaling [107]. CAR may have complementary roles to PXR and FXR. In a PXR and FXR double knockout model, CAR expression was increased, and its activation was protective by the modulation of genes involved in bile acid and bilirubin metabolism [108]. Yet, genetic deletion of PXR, CAR or both demonstrated that CAR deficiency led to more severe liver toxicity and that the protective mechanisms of PXR and CAR were through the modulation of different enzymes and transporters [109]. PXR may be increased in PSC, but its protective effects may be tampered by the attenuation of its target genes through other mechanisms [110]. Despite these lines of evidence, the role of PXR or CAR in biliary fibrosis remains to be fully evaluated. Furthermore, it is not clear if diseased cholangiocytes express PXR or CAR in cholangiopathies. RORγt is a nuclear receptor expressed in immune cells where it has an important role in cell maturation and differentiation, particularly in regulating the pro- and anti-inflammatory homeostasis of T helper (Th) 17 and other subsets of CD4+ Th cells [111]. RORγt has been implicated in inflammatory and autoimmune diseases, including inflammatory bowel disease (IBD) [112]. RORγt is directly bound by the secondary bile acid 3-oxo-LCA, which can modulate RORγt transcriptional activity [113]. Several studies have implicated microbiome-generated secondary bile acids in RORγt-regulation of Th17 and other CD4+ cells in IBD, which may be either through directly regulating RORγt transcriptional activity or indirectly via other bile acid receptors [111,113,114]. RORγt inverse agonists potentially have anti-inflammatory properties by modulating IL-17a/f levels and may be an attractive target for autoimmune disease [111]. IL-17a may activate HSCs [115]. BDL mouse models have demonstrated an increased expression of IL-17, TGF-β and RORγt [116]. Consistently, RORγt knockdown in a hepatocyte-injury mouse model reduced the hepatocyte epithelial–mesenchymal transition and ameliorated liver fibrosis [117]. These studies suggest that RORγt may have a fibrogenic role in the liver with its effects on hepatocytes. Whether RORγt is also expressed in cholangiocytes or has a role in biliary fibrosis has not been studied in detail. Gut microbiota deconjugate a small portion of bile acids through their bile salt hydrolases (BSH). Further transformation reactions include hydroxyl group oxidation, epimerization and 7α/7β-dehydroxylation forming secondary bile acids. Secondary bile acids can be reabsorbed by the gut by passive transport and transferred to the serum followed by reuptake in the liver. The gut microbiota is known to be altered in cholangiopathies [118]. For example, in PSC, microbial diversity is reduced while certain groups such as Enterobacteriaceae, Enterococcus and Veillonella are over-represented [118]. Members of the family Enterobacteriaceae contain very potent lipopolysaccharides (LPS) that may contribute to the enhanced inflammatory response in the liver. Gut dysbiosis affects not only bile acid metabolism but also intestinal permeability, short-chain fatty acid availability and the metabolism of macromolecules. These processes can combine to affect dietary energy utilization, inflammation, and liver injury [118]. Germ-free mice demonstrate an increased bile acid uptake from the gut [119]. Germ-free conditions were also shown to result in worsened biliary injury and fibrosis in the Mdr2−/− model of biliary fibrosis [120,121]. There are several possible explanations for this observation. Germ-free conditions were shown to increase the expression of the rate-limiting enzyme in the synthesis of bile acids, Cyp7a1, while also increasing and expanding the small intestinal expression of Asbt [119]. These effects combined to increase the serum and hepatic bile acid content [119], which is one explanation for the toxicity of germ-free conditions. Similarly, antibiotic treatment to obtain germ-free Mdr2−/− mice demonstrated an increased hepatic bile acid concentration and disruption of the bile duct barrier function with resultant bile duct injury [122]. Both processes were shown to be dependent on the reduced bile acid activation of FXR under germ-free conditions [122]. In contrast, the opposite effects were observed in NOD.c3c4 mice, which are a model of immune-mediated biliary injury. Germ-free or antibiotic-treated NOD.c3c4 showed reduced biliary injury, although fibrosis was not affected [123]. These seemingly conflicting observations are likely due to the mouse models used, with NOD.c3c4 being susceptible to microbiota, likely because of its autoimmune predilection. In contrast, the Mdr2−/− studies point to the important role of gut microbiota in bile acid homeostasis. However, neither model addresses the dysbiosis observed in cholangiopathies. A more elegantly designed study evaluated the effect of inoculating gnotobiotic mice with PSC-derived microbiota. These mice showed a Th17-cell response in the liver and an increased susceptibility to DDC-induced hepatobiliary injury and fibrosis [124]. The role of bile acids in this model was not studied in detail. Other studies have pointed out the deleterious effects of bacterial strains in these models. E. faecalis and E. coli strains accelerated inflammation and mortality, while Lachnospiraceae colonization of Mdr2−/− mice reduced fibrosis, inflammation and translocation of pathobionts by producing short-chain fatty acids [121]. K. pneumoniae enrichment in PSC microbiota may disrupt the epithelial barrier to initiate bacterial translocation and liver inflammatory responses [124]. Similarly, bile duct colonization with Enterococci conferred a risk of disease progression in PSC [125]. It is important to point out that bile acids and the microbiome have an inter-dependent relationship with both exerting effects on the other. Changes in the composition of bile acids due to cholestatic disease may also modulate the microbiome into a deleterious phenotype [126,127]. Consistently, fecal transfer from cholestatic Mdr2−/− mice into wild-type mice induced significant liver injury along with NLRP3 inflammasome activation and cholestasis [126]. Further careful studies in these models are required to determine the effects of dysbiosis on bile acid species, homeostasis and bile acid effects on biliary fibrosis. Epigenetics are reversible, heritable processes that regulate gene expression and determine the cell phenotype without altering the genomic DNA sequence. These processes may be classified into DNA modifications such as methylation, non-coding RNA-mediated gene modulation, histone modifications, and chromatin organization/remodeling [128,129]. These mechanisms have overlapping responses to diverse stimuli and combine to affect gene expression. Various histone-modifying enzymes with their respective modifications have been reported to regulate bile acid metabolism [130,131,132]. Of note, FXR-mediated regulation of bile acid transporters, BSEP, multidrug resistance-associated protein 2 (MRP2) and NTCP recruits the coactivators mixed lineage leukemia 3 (MLL3) or MLL4 to the promoters for histone H3 lysine 4 methylation, which allows for gene expression of these transporters [133,134]. More recently, microRNA-210 was reported to downregulate MLL4 and consequently the FXR target genes, BSEP and SHP. Silencing of microRNA-210 in mice attenuated bile-acid-induced liver injury through MLL4. Cholestatic mice and PBC subjects were shown to have increased microRNA-210 and reduced MLL4 expression [135]. Multiple other non-coding RNAs and epigenetic modifications have been implicated in the pathogenesis of PBC [136,137,138]. Similarly, epigenetic and epigenomic changes have been implicated in the pathogenesis of PSC [128,139,140] including those mediating the interactions between cholangiocytes and hepatic myofibroblasts [141,142,143,144]. The role of bile acids in the epigenetic regulation of biliary fibrosis remains an understudied area. Long noncoding RNA H19 (lnRNA-H19) has been reported to be markedly elevated in an Mdr2−/− mouse model and PSC [145]. LnRNA-H19 may be released from cholangiocytes in exosomes. An lnRNA-H19 deficiency significantly protected mice from liver fibrosis in BDL and Mdr2−/− mice along with reduced HSC activation by cholangiocyte-derived, lnRNA-H19-deficient exosomes [146]. It was further demonstrated that lnRNA-H19 deficiency protects mice from BDL-induced cholangiocyte proliferation and biliary fibrosis [147]. The lnRNA-H19-mediated cholangiocyte proliferation was shown to be through the bile-acid-induced expression and activation of S1PR2 and SphK2 [147]. An additional mechanism includes lncRNA-H19-mediated macrophage activation and associated inflammation under cholestatic conditions [148]. These observations indicate the direct and indirect roles of bile-acid-induced lnRNA-H19 in regulating biliary fibrosis. Further studies are required to examine the direct role of bile acid species in regulating other epigenetic mechanisms of biliary fibrosis in detail. Biliary fibrosis is the predominant pathological process in cholangiopathies, responsible for the progression to advanced liver disease and associated complications, including the risk of hepatobiliary malignancy. Cholangiopathies are also defined by cholestasis, in which bile acids accumulate in the liver and serum compartments. Cholestasis may worsen with progressive biliary fibrosis. Animal models and human genetic diseases have demonstrated a clear role for bile acids in the pathogenesis and progression of biliary fibrosis. While previously possible direct cytotoxic properties of some bile acids were implicated in cholestatic disease, it is now evident that bile acids primarily acting through receptors and signaling pathways impose their damaging and protective effects. Cholangiocytes, the targets of disease in cholangiopathies, have a role in normal bile acid homeostasis but are also affected by cholestasis. Bile acids, through the activation of GPBAR1 (TGR5) or S1PR2, result in cholangiocyte proliferation, driving in part the ductular reaction, and may be associated with biliary fibrosis. This process presumably amplifies the cholangiocyte production and secretion of fibrogenic signals to activate hepatic myofibroblasts, which produce the ECM of biliary fibrosis. The bile-acid-induced activation of FXR, VDR, M3R, PXR or CAR may lead to protective effects. FXR activation in cholangiocytes leads to modifications in bile acid transporters as adaptive changes to protect against cholestasis. FXR may also attenuate inflammation, indirectly protecting cholangiocytes from inflammatory damage. Combined, these effects of FXR activation may attenuate biliary fibrosis. Similarly, VDR activation under cholestatic conditions may also be protective against cholangiocyte injury and fibrosis. M3R, PXR and CAR are protective against cholangiocyte injury, but either do not attenuate fibrosis or have not been evaluated in detail for biliary fibrosis. RORγt, another bile acid receptor, may have anti-fibrotic effects through its regulation of immune cells and transition to a mesenchymal phenotype, but this receptor has not been studied in cholangiocytes in detail. Similarly, further details of the signaling pathways of bile acid receptors, including epigenetic activators and repressors, have not been explored in detail. Despite the advancements in understanding bile acid signaling through several receptors and potential roles in cholestasis, controversies and conflicting observations remain in understanding the full picture of bile acid signaling in cholestatic disease models. GPBAR1 (TGR5) appears to have cell-specific effects that can either promote or antagonize components of cholestatic liver injury. Similarly, while there is mounting clinical evidence supporting the beneficial role of FXR agonism in cholestatic liver diseases, surprisingly, FXR deletion had protective effects in rodent models of cholestasis. Further light can be shed on these controversies by lineage- and cell-specific deletion of these receptors in cholestatic animal models. Cholangiocyte-specific deletion of bile acid receptors may reveal their roles in cholestasis and biliary fibrosis in greater detail. Similarly, single-cell and spatial transcriptomic technologies may reveal greater detail in the bile-acid- and bile-acid receptor-specific signaling under cholestatic conditions. In clinical research, preclinical observations are increasingly applied to further therapeutic options for the two most common cholangiopathies, PSC and PBC. USDA, its derivatives and FXR agonists are studied in completed or ongoing clinical trials. Bile acid levels are altered in these diseases in the serum and stool. Yet, bile acid exposure to cholangiocytes, i.e., bile acid in the bile ducts, has not been examined in great detail. Further studies in larger cohorts would identify changes in bile acid levels as well as the changes in specific bile acid species in these conditions that would be stimulating cholangiocytes. Similarly, the interactions of the microbiome and bile acids and vice versa have to be studied in greater detail. Given the differences between murine and human bile acids, murine models with a humanized bile acid pool may serve an important role in addressing these questions.

PMC10001313

Hong Yang,Yuting Cui,Yanrong Feng,Yong Hu,Li Liu,Liu Duan

Long Non-Coding RNAs of Plants in Response to Abiotic Stresses and Their Regulating Roles in Promoting Environmental Adaption

24-02-2023

lncRNA,evolution,abiotic stress,drought,heat,cold,salt,heavy metal,stress memory

Abiotic stresses triggered by climate change and human activity cause substantial agricultural and environmental problems which hamper plant growth. Plants have evolved sophisticated mechanisms in response to abiotic stresses, such as stress perception, epigenetic modification, and regulation of transcription and translation. Over the past decade, a large body of literature has revealed the various regulatory roles of long non-coding RNAs (lncRNAs) in the plant response to abiotic stresses and their irreplaceable functions in environmental adaptation. LncRNAs are recognized as a class of ncRNAs that are longer than 200 nucleotides, influencing a variety of biological processes. In this review, we mainly focused on the recent progress of plant lncRNAs, outlining their features, evolution, and functions of plant lncRNAs in response to drought, low or high temperature, salt, and heavy metal stress. The approaches to characterize the function of lncRNAs and the mechanisms of how they regulate plant responses to abiotic stresses were further reviewed. Moreover, we discuss the accumulating discoveries regarding the biological functions of lncRNAs on plant stress memory as well. The present review provides updated information and directions for us to characterize the potential functions of lncRNAs in abiotic stresses in the future.

Long Non-Coding RNAs of Plants in Response to Abiotic Stresses and Their Regulating Roles in Promoting Environmental Adaption Abiotic stresses triggered by climate change and human activity cause substantial agricultural and environmental problems which hamper plant growth. Plants have evolved sophisticated mechanisms in response to abiotic stresses, such as stress perception, epigenetic modification, and regulation of transcription and translation. Over the past decade, a large body of literature has revealed the various regulatory roles of long non-coding RNAs (lncRNAs) in the plant response to abiotic stresses and their irreplaceable functions in environmental adaptation. LncRNAs are recognized as a class of ncRNAs that are longer than 200 nucleotides, influencing a variety of biological processes. In this review, we mainly focused on the recent progress of plant lncRNAs, outlining their features, evolution, and functions of plant lncRNAs in response to drought, low or high temperature, salt, and heavy metal stress. The approaches to characterize the function of lncRNAs and the mechanisms of how they regulate plant responses to abiotic stresses were further reviewed. Moreover, we discuss the accumulating discoveries regarding the biological functions of lncRNAs on plant stress memory as well. The present review provides updated information and directions for us to characterize the potential functions of lncRNAs in abiotic stresses in the future. Genomic DNA serves as the template for the transcription of RNAs. However, it does not always serve as the template for protein synthesis. Although up to 90% of the genome can be transcribed into RNAs, only a small fraction of them turned into protein-coding messenger RNAs (mRNAs). Thus, non-coding RNAs were initially considered as “junk DNA” or “dark matter” [1]. With the advent of rapid development of genome-wide RNA detection techniques, such as microarray and high-throughput transcriptome sequencing, numerous evidence showed that non-coding RNAs have diverse roles in biological functions in organisms including prokaryotes, eukaryotes, and viruses [2,3]. Non-coding RNAs include housekeeping RNAs (tRNA and rRNA) and regulatory RNAs, which consist of small non-coding RNAs (such as miRNA and siRNA) and long non-coding RNAs (also termed lncRNAs). LncRNAs are widely accepted as transcripts that are longer than 200 nucleotides that do not code for proteins. In the early stages, the classifications, nomenclature, and terminology of lncRNAs were confusing, due to their low conservation and differences in functionalities compared with the coding genes [4]. Based on empirical attributes, lncRNAs could be classified by associations with annotated protein-coding genes or DNA elements of known function, by their sequence or structure conservation, by biological or biochemical pathways, and by their functions, etc. [4]. However, these descriptive and distinctive properties could only capture a very small fraction of lncRNAs, and are not mutually exclusive with unavoidable shortcomings in comprehensive assessment. Although lncRNAs were primarily recognized as non-coding transcripts, some studies in the past years have found that lncRNAs could encode small polypeptides (small open reading frames containing fewer than 100 codons, smORFs) in various species [5]. Furthermore, some lncRNAs might have dual functions as both micropeptides and regulatory RNAs, such as ENOD40 [6], which expanded the complexity and definition of lncRNAs. With new findings in the future revealing the real dimensions and complexity of plant non-coding transcriptome, the definition and classification of lncRNAs might continue to evolve [7,8]. In recent years, lncRNAs have been evidenced to play diverse roles in plant growth and development, interacting with other biomolecules, especially pathogens, and modulating environmental biotic and abiotic stress responsiveness [9,10,11]. However, compared with mammals, the annotated functional lncRNAs in plants are still limited due to the ambiguity and versatility of lncRNAs. Therefore, our knowledge of plant lncRNAs is just the beginning and remains under-explored. The goal of this review is to explore the roles of lncRNA in plant adaptation to abiotic stresses encountered in the environment, from a new perspective. At the beginning of this review, we will emphasize the criteria and features of lncRNA, discuss the evolution of lncRNA in land plants, and summarize the growing literature on the roles of lncRNA that take in plant stress responses including heat, cold, drought, salt, and heavy metal stresses. Next, the roles of lncRNAs that might contribute to plant stress memory will be reviewed as well. Finally, we will outline the unsolved problems in the field and propose a roadmap for future directions and opportunities. Like mRNAs, lncRNAs are transcribed by nuclear RNA polymerases in plants and undergo similar post-transcriptional modifications. All five RNA polymerases in plants, including Pol I, II, III, and two Pol II-related, plant-specific RNA polymerases (Pol IV and Pol V) have been observed to transcribe their diverse lncRNA products, involving RNA-directed DNA methylation and regulating transposable elements in plants [8,12]. After their transcription, lncRNAs are subject to RNA capping, splicing, polyadenylation, and nuclear export to assure their proper structure, localization, and function [13]. Therefore, lncRNAs are composed of 5’ and 3’ untranslated regions (UTR), introns, and exons, such as protein-coding genes. However, the abundance and efficiency of polyadenylation of lncRNAs are lower than mRNAs in general [14]. Nonetheless, lncRNAs could generate multiple splice variants from one gene and display a remarkable degree of alternative splicing (AS). AS of lncRNAs have been explored and analyzed by ultra-deep RNA-seq analysis of a diel time-series in response to cold treatment, which showed dynamic expression and AS of lncRNAs [15]. Although lncRNAs contain fewer numbers of exons [16] and are expressed at lower levels than mRNAs [17], they were also found to be expressed in tissue- and cell-type-specific ways in many plant species, such as rice [18] and grapevine [19]. In grapevine, differentially expressed lncRNAs in leaf, inflorescence, and berry were discovered, revealing spatiotemporal and developmental stage-specific regulation of lncRNAs [19]. Spatial-temporal expression of lncRNAs was also found in maize, and over 90% of them were expressed specifically in a certain tissue or at a certain development stage [20]. The cell-specific expression profiling results from Arabidopsis root showed that only 25% of intergenic lncRNAs (lincRNAs) were expressed in more than half of the cell types [21]. Localization of lncRNAs in the cells could provide important clues to their functions. Most lncRNAs were found to be preferentially localized in the nucleus, linking to their functions of chromatin organization and regulating gene transcription [22,23]. RNA fluorescence in situ hybridization (FISH) and lncRNA promoter-driving reporter techniques could be employed to determine the subcellular localization of newly identified lncRNAs. Until now, some lncRNAs have also been found cytoplasmically located in Drosophila and mammals such as humans and mice [22,23]. However, studies with solid evidence in plants are still limited. The distribution of lncRNAs in the genomes of different plant species showed diverse patterns. Uneven chromosomal distribution of lncRNAs was found in grapevine [19], while, on the other hand, they are evenly distributed in the maize genome with only a slightly lower density in chromosome 1 [20]. Based on their orientation (sense or antisense) and positions related to other genes (intergenic, intronic, and coding areas), the major classes of lncRNAs could be classified as (1) lincRNAs (found in intergenic regions); (2) intronic ncRNAs (incRNAs); (3) natural antisense transcripts (NATs, occur in most protein coding genes); (4) sense lncRNAs (Figure 1). LncRNAs could also be classified according to their biochemical pathways, sequence or structural conservation, functions, and genomic locations [4]. Evolutionary conservation has been used as an indicator of genes, and homology search using nucleotides or amino acid sequences is an applicable method for protein-coding genes. However, for lncRNAs, the criteria are considered to be too narrow for their less conserved sequence level compared to coding genes, but more conserved than random intergenic regions or introns. Four dimensions of lncRNA conservation including sequence, structure, function, and syntenic transcription at a given genomic locus were proposed based on the findings in human lncRNAs in 2014 [24]. Strategies such as whole-genome alignment of the lncRNA sequence, direct comparison with lncRNA sequences in other species, structure or profile comparison, and genome position of orthologues of the neighborhood could be used to identify the homologs of a lncRNA of interest in different plant species [25,26,27]. Although the understanding of plant lncRNAs remains limited, one good example is lncRNA COOLAIR, which provides evidence that the structure and function of lncRNAs remain conserved in plants [28,29,30]. Genome-wide annotation and analysis showed that 37% of high-confidence lncRNAs are conserved between maize and teosinte [30]. Unlike animals, terrestrial plants have evolved a more complicated mechanism of transcription by expanding the RNA polymerase family with two Pol II-related enzymes [31], thus establishing novel regulation mechanisms of lncRNAs in plants adapting to their land life. In addition, not only does the nucleus encode lncRNAs, but also mitochondria-encoded lncRNA (mt-lncRNA) have been identified and functionally characterized in humans in 2007 [32,33]. Both mitochondria and chloroplasts are semi-autonomous organelles, which evolved from free-living prokaryotic organisms through endosymbiosis to organelles in eukaryotes. Although the information about mitochondria-encoded and chloroplasts-encoded lncRNAs in plants is very limited, reports show that ncRNAs are conserved across cyanobacteria [34]. In addition, hundreds of ncRNAs were found encoded by chloroplasts of Arabidopsis [35], meaning they should not be overlooked for their potential as players in gene regulation in land plant adaption to environmental stresses [36,37]. Large-scale comparative analysis of lncRNAs in different species has become a powerful tool for studying the functions of lncRNAs [25,38,39,40,41,42]. It requires two main ingredients for the comparative analysis of lncRNAs: (1) genomes and datasets of lncRNAs that can be compared, and (2) algorithms to identify and evaluate lncRNAs. The studies of mammalian lncRNAs showed that compared to the neutral expectation, lncRNAs had a reduced incidence of mutations in their promoters and exons, and increased conservation of splice sites under negative evolutionary constraint [38,43]. Splicing patterns of lncRNAs in mammals were also found to evolve rapidly [25,39,40]. On the other hand, studies in plants have shown that lncRNA exons evolve faster than protein-coding genes, by comparing the turnover of the DNA sequences and assessing the degree of the contribution of the primary sequences [25]. Higher conservation in lncRNA exons was observed compared with introns or random intergenic sequences within the lncRNA loci in rice [18]. The splice rates of lncRNAs were much lower in rice [18] and Arabidopsis [44] than in humans [45]. Recent studies in plants have begun promisingly to take the first steps for comparing and understanding plant lncRNAs from the perspective of evolution [26,27,46]; although, the species whose lncRNAs have been well annotated are still limited. The rapid development of high-throughput RNA sequencing technologies provides a broader range of lncRNA transcriptomes across plant species. In the meantime, filters and tools were developed and used to detect and identify candidate lncRNAs individually or in combination. The databases of plant lncRNAs and resources for discerning their functional properties have been well documented in recent reviews [47,48,49,50]. In this review, we summarized the most recent information in public databases including NONCODE v6.0, PNRD, AlnC, PLncDB v2.0, LncPheDB, CANTATAdb v2.0, and GreeNC v2.0 (Table 1) [42,51,52,53,54,55,56]. Among these databases, lncRNAs were predicted, annotated, or validated in over eight hundred plant species, including algae, bryophytes, pteridophytes, gymnosperms, and angiosperms. Detailed plant species that have been included in each database are listed in Supplemental Table S1. As mentioned previously, lncRNAs could generate multiple splice variants, thus some of the databases provided gene numbers of lncRNAs in addition to the lncRNA transcript numbers. In higher eukaryotes, increasing genome size usually correlates with lncRNA numbers in general [57,58]. To verify this theory in plants, the gene numbers that transcribe lncRNAs from the plant species in NONCODE and GreeNC were extracted and compared with their plant genome sizes (Figure 2). Although the numbers of lncRNAs are influenced by the depth of RNA-seq, the annotation of the plant species, and the filters they are using, the current finding gives us some interesting notions about their relations to genome size (Figure 2). It is worth noting that the correlation seems not to be high in plants; this may be due to the whole genome duplication and triplication events, and the polyploidization that happened during plant evolution. An example is that the number of lncRNAs in plant species in Poaceae, such as Zea mays and Hordeum vulgare, is not outnumbered by Triticum aestivum, despite the big difference in their genome sizes (17,000 Mbp in Triticum aestivum). Primary sequence conservation analysis across 10 plant species revealed that lncRNAs are more conserved in the intra-species and sub-species than in the inter-species [59], which suggests that most lncRNAs evolved relatively recently [57]. Homologous lncRNA is merely detectable beyond 50 million years of species divergence in mammals [25]. However, in plants, 90 of a total of 5497 lncRNA families that have been identified were found conserved, and originated more than 180 million years ago. They have a fast evolutionary rate, and they tend to be more conserved between closely related species [46]. LncRNAs in thirty-five plant species were analyzed and compared to Chinese cabbage, including 18 eudicots, 14 monocots, 1 angiosperm, 1 fern, 1 moss, and 1 green alga. Their results showed that relatively high sequence similarity was detected in four Brassicaceae, but no homologous lncRNAs were detected in the moss Physcomitrium patens (P. patens) [17]. On the contrary, although the evolutionary conservation of lncRNAs is significantly lower than mRNAs at the nucleotide level, numerous lncRNA-smORFs were found to be conserved across 479 different plant lineages at the amino acid sequence level [16]. About 83% of highly conserved lncRNAs-smORFs were distributed only in moss species and the number of conserved smORFs rapidly dropped at the transition from mosses to higher plant lineages. The conclusion is that most smORFs located on lncRNAs are evolutionarily young [16]. Another large-scale evolutionary analysis of plant lncRNAs was conducted using the datasets of 25 species of flowering plants, including monocotyledons and dicotyledons [46]. LncRNAs in Arabidopsis were grouped into five categories based on the degree of their conservation, which were (1) conserved in Arabidopsis, (2) in Brassicaceae, (3) in dicotyledons, (4) in angiosperms, and (5) with no conservation. Among them, 84.4% of total lncRNAs were grouped into the no conservation group, while only 2.1% of the total account for dicotyledon and angiosperm conserved groups. Their results showed that the conserved lncRNAs in Arabidopsis have lower gene numbers, longer sequence length, more exons in the intron/exon structure, higher expression levels, and a lower proportion of tissue-specific expression compared to the non-conserved group. In contrast, the genomic location of lncRNAs in each category was similar [46]. Based on the results of GO analysis, which showed that conserved lncRNAs were enriched in leaf response to stimulus, stress, cell death, and signal transduction [46], conserved lncRNAs may be subjected to greater selection pressure during evolution while adapting to the abiotic stresses. On the other hand, two Eutrema salsugineum ecotypes were compared, one from the semi-arid subarctic Yukon of Canada and one from the semi-tropical monsoonal of China, and only a negligible overlap was found between the two ecotypes [60]. Therefore, different environments could lead to the local adaptation of plants and result in differences in lncRNA expression under the same stress condition, contributing to the fast evolution of lncRNAs. The algal lineage began terraforming the terrestrial habitat more than half a billion years ago, enabling green life to live in very diverse habitats by overcoming abiotic challenges, such as drought stress, temperature fluctuations, salinity stress, and heavy metal stress [61]. Hundreds of studies have shown that plant lncRNAs could be induced by various abiotic stresses. Water plays a crucial role in plant life. Drought (including dehydration and desiccation in this review) is a major stressor during the plant landing process. Therefore, drought stress can affect plant development, yield, and survival, and results in ecological, morphological, physiological, and biochemical changes in plants. Recent findings identified several lncRNAs that respond to drought stress, including in rice [62], maize [63], wheat [64], tomato [65], cassava [66], switchgrass [67], Eutrema salsugineum [60], rapeseed [68], Oryza rufipogon [69], Populus trichocarpa [70], Brassica juncea [71], Cleistogenes songorica [72], etc. Examples include that different DEGs of lncRNAs that respond to drought stress were found in varieties with contrasting drought tolerance in peanut [73]; GhDNA1 was found to be associated with drought tolerance in cotton, which targets AAAG DNA double strands to regulate drought-responsive genes in trans (Figure 3A) [74]. These results support the conclusion that lncRNAs could be induced or suppressed in response to drought stress. Furthermore, these drought-responsive lncRNAs also have been reported to be associated with phytohormone signal transduction, biosynthesis of secondary metabolites, and sucrose metabolism pathways, which are related to the plant drought response [66,75,76,77]. Temperature fluctuations are another key factor limiting plant distribution, global crop yield, and production. Extreme climate changes have become more common as a result of global warming, causing significant environmental problems. LncRNAs have been found in the plant response to cold or heat stresses in Arabidopsis [78,79,80,81], Chinese cabbage [17], grapevine [82], banana [83], Brassica juncea [71], wheat [84], maize [85], cassava [86], alfalfa [87], etc. Natural long non-coding antisense heat-inducible asHSFB2a could regulate the expression of HSFB2a, one of the central regulators of the heat stress response in Arabidopsis [81]; in cotton, cold-responsive lncRNA XH123 is actively involved in the tolerance of cold stress at the molecular level during the seedling stage [88]; Cold induced lncRNA 1 (CIL1) in Arabidopsis was identified and experimentally proved to be a positive regulator in response to cold, by affecting hormone signal transduction, ROS homeostasis, and glucose metabolism [89]. Cold stress promotes the expression of COOLAIR, a set of alternatively processed antisense noncoding transcripts that reduce the expression of FLC, by recruiting protein complexes that impact chromatin states, or changing the abundance and shape of structural conformations [13,90]. A gene with homology to FLC was recently identified in kiwifruit, which also responds strongly to cold. The antisense lncRNA had an opposite expression pattern compared to AcFLCL, implying a model similar to Arabidopsis COOLAIR outside the Brassicaceae (Figure 3B) [91]. These results showed that lncRNAs are involved in plant responses to temperature stress. Salt stress negatively affects plant growth and triggers adaptive selections of the natural habitats of the plants, causing a serious threat to the environment and affecting human health. Salt stress responsive lncRNAs were explored in Arabidopsis [76], sweet sorghum [92], barley [93], cotton [94], alfalfa [95], soybean [96], duckweed [97], chickpea [98], Arachis hypogaea L. [99], etc. It was found that the 154 and 137 lncRNAs were differentially expressed in the less salt-tolerant cultivated M82 tomato genotype and the highly salt-tolerant S. pennellii under salt stress, with 73% of DE-lncRNAs in S.pennellii being down-regulated and 86% of DE-lncRNAs in M82 being up-regulated [65]. Knockdown of salt-inducible lncRNA973 in cotton showed reduced salt tolerance, and lncRNA973 overexpression lines had increased salt tolerance [94]. On the contrary, the expression of lncRNA354 was found to be decreased under salt stress, which weakened the binding to miR160b, a suppressor of GhARF17/18, thus up-regulating the amount of mir160b and enhancing root development, thereby synergistically regulating cotton salt stress tolerance (Figure 3C) [100]. The evidence suggests that lncRNAs are involved in the response to salt stress. Heavy metal pollution is becoming a serious environmental problem around the world. High levels of heavy metals in soil can harm plant development and survival, severely limiting plant growth, agriculture, and forestry. Moreover, heavy metals may eventually enter the food chain, with serious consequences for human health. In recent years, plant lncRNAs have been found to respond to heavy metals such as lead (Pb) [2], iron (Fe) [101], copper (Cu) [102], manganese (Mn) [103], and aluminum (Al) [104]; however, the mechanisms were less studied. In Poplar, the antisense lncRNA PMAT (Pb2+-induced multidrug and toxic compound extrusion, MATE) was reported to epistatically interact with PtoMYB46, and PtoMYB46 depresses the expression of PtoMATE directly or indirectly through PMAT, thereby reducing the secretion of citric acid (CA) and ultimately promoting Pb2+ uptake. Therefore, a PtoMYB46–PMAT–MATE pathway has been proposed to positively modulate Pb2+ uptake [2] (Figure 3D). These results suggest that lncRNAs are good indicators of heavy metal stresses and could be important regulators of plant response to heavy metal stresses. With the advantage of high-throughput RNA sequencing technologies, plant lncRNAs can be identified and annotated on a genome-wide scale. However, detection of lncRNAs usually requires a specific enrichment strategy at the library preparation stage due to their low expression levels. In general, paired-end sequencing is preferred over single-end. To identify lncRNAs, brief but practical guidelines are provided. Firstly, RNA-seq reads were mapped using either a reference-guided or de novo approach to reconstruct transcript models. Secondly, non-coding transcripts were set apart from the coding transcripts with information on the exon-intron structure. Lastly, assembled transcripts are filtered by transcript length, protein-coding potential, and characteristics similar to mRNA to identify lncRNA-producing loci. Transcripts could be extracted with the filter of >200 nt based on their size. Alignment-based methods using BLASTx with models from protein databases (such as NCBI refseq and Ensemble), or programs that discover possible protein-coding transcripts (such as PhyloCSF and RNAcode [105,106]) could be used to remove overlapping protein-coding genes. In addition, several alignment-free computational tools have been developed to assess the coding potential, such as CPC [107], LncFinder [108], lncScore [109], COME [110], and PLIT [111], to eliminate protein-coding RNAs. Detailed bioinformatic tools for lncRNA prediction and analysis have been well reviewed and documented previously [48,50,112], with more and more plant lncRNA identification and prediction tools, packages, algorithms, and pipelines been explored recently, relying on motifs, structures, homologs, feature relationships, and interactions of plant lncRNAs. Examples are sORFPred [113], DeepPlnc [114], LGC [115], PRPI-SC [116], PINC [117], LncMachine [118], etc. Although lncRNAs in different plant species have been explored and shared in the databases mentioned previously, the numbers and coverage of lncRNA loci in each study were largely associated with transcriptional complexity and the criteria that were used. Therefore, after the successful mining of plant lncRNAs, the candidates need to be experimentally validated, such as by mass spectrometry and ribosome profiling. Experimental validation could further improve the detection power of prediction tools. Despite a broad range of estimates for the numbers of lncRNAs in plants, the functional activity of most lncRNAs is still uncovered. In addition, not all lncRNAs that are expressed differently in response to various stress conditions have been functionally or experimentally validated. Only 506 lncRNAs in 57 plant species were listed with confirmed functions in EVLncRNAs v2.0, a database that provides information from low-throughput experiments (such as qRT-PCR, knockdown, Northern blot, and luciferase reporter assays). In recent years, accumulating evidence has supported the cellular functions of lncRNAs that were involved in plant response to abiotic stresses, with most of them confirmed by qRT-PCR. Representative lncRNAs were listed in Table 2. EVlncRNA-Dpred, which was developed using deep learning algorithms to separate low-throughput experiments from high-throughput sequencing RNAs and mRNAs [119], would be a useful tool for screening lncRNA transcripts for experimental validation. More computational strategies, methods, and toolboxes need to be developed and applied to discover plant lncRNAs based on their unique and similar characteristics in the future. Most lncRNAs reported act as promoters and enhancers, which correspond to the fact that most of them are localized to the nucleus [153]. This indicated that the roles of lncRNAs are more likely to be regulators and to function in cis or trans than the lncRNA itself. However, it is important to know if the lncRNA has the ability to create small peptides by ribosome footprints [154]. Exon skipping strategies could be checked for the function of the micropeptides to remove the ORF from the main lncRNA. To identify the expression patterns of lncRNAs under abiotic stresses and characterize their exact biological roles, the following methods could be used to verify their functions. Once a researcher discovers a lncRNA locus, the rapid amplification of cDNA ends (RACE) and reverse-transcriptase (RT)-PCR could be used to define the 5’- and 3’-ends of the transcript, as well as the multiple variants that may be caused by alternative splicing. The expression pattern of the lncRNA under different abiotic stress conditions and the tissue-specific expression pattern throughout the lifetime of plants could be confirmed by real-time qRT-PCR. Subcellular localization, GUS staining, and RNA FISH reveal the localization and quantitative information of lncRNAs [155]. High-throughput techniques such as cap analysis of gene expression (CAGE), polyA site sequencing, and RNA-seq could characterize the structure of lncRNAs as well. Expression profiling and co-expression network analysis of lncRNAs in multiple sample types and under various abiotic conditions are the most routine and reliable methods to reveal the possible biological processes in which they might function. Enrichment of specific biological functions or pathways of the co-expressed transcripts could be informative to annotate the lncRNAs. The regulatory network upstream of lncRNAs could be confirmed by ChIP-seq to identify all abiotic stress-related transcription factors that bind to the sites upstream or within the lncRNA. Loss- and gain-of-function experiments are important steps to understand the function of lncRNA and determine whether it acts locally in cis or whether it leaves the site of transcription and acts in trans. Mutant lines of the lncRNA synthesis locus, either mutated in the lncRNA promoter or the entire lncRNA transcript body, could be searched and purchased from the public library if available. Their functions could also be analyzed using CRISPR, RNAi, and overexpression lines of lncRNAs, which are generated by genetic transformation depending on the plant species. Transcriptional abundance changes of lncRNA could be confirmed by comparing it to its wild-type using qRT-PCR. The genotype, phenotype, and physiological changes of lncRNA modification lines during development and under various abiotic stresses need to be checked no matter whether lncRNA acts in cis or trans mode. Cis-acting lncRNAs directly act on one or several linked genes on the same chromosome, which are restricted to the site of the lncRNA’s synthesis. Altered expression of nearby genes suggests the cis-acting function of lncRNA. The cis-action function could be further confirmed by the complementary experiment using knock-in or heterologous expression of the lncRNA. To distinguish the function of the lncRNA from the function of the DNA regulatory elements embedded in the lncRNA locus, a poly A signal and its mutated control could be inserted near the 5’-end of the lncRNA to terminate transcription while not affecting the function of the DNA elements. Trans-acting RNAs diffuse from the site of synthesis and act at great distances on many genes, even those located on other chromosomes. If the expression level of nearby genes does not change, the lncRNA is more likely to act in a trans function, which could also be confirmed by a complementary experiment using heterologous expression of the lncRNA in mutant lines. To identify the potential interacting partners of lncRNAs, co-expression and network analysis provide the first clues to reveal the same biological function. Nucleic acids and proteins that interacted with lncRNAs could be identified using RNA immunoprecipitation assays (RIP), biotinylated RNA pull-down assays, or through combination with high-throughput strategies, such as RIP-seq. In-depth interactions of lncRNAs with DNA/chromatin, RNA, or proteins could be identified using all-to-all or one-to-all strategies [14]. Evolutionary sequence analyses of conserved primary sequences, 2D, and 3D structures could be used to identify potential functional binding elements within lncRNAs. Measurements of phytohormones, proline content, ROS levels, and other stress-related biological and physical indices are useful in identifying the downstream signaling pathways affected by the lncRNA and in clarifying the roles of the lncRNA in response to abiotic stresses. Detailed protocols for the approaches mentioned above have been well reviewed and documented in previous reviews [153,156,157,158]. To date, plant lncRNAs have been proven to be involved with abiotic stresses experimentally, acting as positive or negative regulators [17,159,160,161]. Plant lncRNAs have also been found to affect hormone signal transduction, ROS homeostasis, and carbohydrate metabolism [17,65,89,162]. A major mechanism of lncRNA function is transcriptional level regulation, via interaction with DNA or regulation of transcription factors. Elements of genes could be affected by lncRNAs, including promoters, exons, introns, untranslated regions, and terminators. LncRNA DglncTCP1, which is transcribed from the antisense strand of the transcription factor TCP1 in chrysanthemum, activates the expression of TCP1 and plays a cis-regulatory role to regulate cold tolerance responses [163]. In addition, it could bind to the same target of transcription factors and co-regulate or deregulate the target gene promoter, such as APOLO/WRKY42. Thus, it could regulate downstream genes for enhancement of root hair growth under low temperature and cold stresses in Arabidopsis [79,80]. Nuclear lncRNAs have also been reported to directly bind to and activate or repress transcription factors under abiotic stress as signal molecules [82,161]. Other than transcription factors, lncRNAs were found to interact with splicing factors to condition their stability and subcellular localization [158,164]. In Arabidopsis, the lncRNA ALTERNATIVE SPLICING COMPETITOR (ASCO) could hijack nuclear AS regulators to modulate alternative splicing patterns in response to auxin [165]. Further research also indicated that ASCO could integrate a dynamic network, including spliceosome proteins, to modulate transcriptome reprogramming [166]. Furthermore, lncRNAs could serve as structural or regulatory molecules to affect gene expression through the interaction of RNA under abiotic stresses. LncRNAs were found to function as competing endogenous RNAs (ceRNAs), stabilizing the mRNA and regulating rice responses to drought stress [62]. LncRNAs could also act as ceRNAs or endogenous target mimic (eTM) to competitively regulate the target genes of the miRNA. Twenty lncRNAs were reported as target mimics of the known miRNAs in Populus in response to drought stress [70]. Two pairs of miRNAs and lncRNA interacting partners were discovered in soybean in response to salinity stress [144]. Some lncRNAs could also act as precursors of miRNAs [82]. Large-scale analysis revealed that the miRNA precursors derived from lncRNAs were species-specific. The average percentage of lncRNAs acting as miRNA precursors was 0.50% among 37 plant species, with the highest in P. patens (3.27%) [17]. On the other hand, lncRNAs could also act as the target of miRNAs to produce phased small interfering RNAs (phasiRNAs) for the regulation of plant abiotic responses [159,167]. Recently, numerous smORFs have been discovered embedded in lncRNAs in different organisms, including plants, mammals, fungi, and bacteria [8,168]. LncRNA-smORFs in moss plant P. patens were analyzed comprehensively and systematically across 479 plant species, with numerous smORFs validated and functionally characterized experimentally [16]. Some of the lncRNAs-smORFs contain signal peptides and transmembrane domains with lower GC content and are located in AU-rich regions, which suggests their roles in cell-to-cell communication. Furthermore, they found that ~10% of the confirmed translation smORFs were hydroxyproline or proline-rich peptides. These smORFs could regulate photosynthesis, the generation of precursor metabolites and energy, and oxidoreductase activity, suggesting the roles they may play in plant development and abiotic stress tolerance [16]. Epigenetic modifications of genomic DNA and histones could influence gene expression. Genome regulation via DNA methylation and post-translational histone modifications, called RNA-mediated transcriptional gene silencing (TGS), is a common function of plant lncRNAs in response to abiotic stresses. In plants, lncRNAs can be produced by two specialized RNA polymerases, Pol IV and Pol V, controlling DNA methylation, which are essential for RNA-directed DNA methylation (RdDM). Plant RdDM relies on Dicer-like 3 and AGO4, which produce small interfering RNAs (siRNAs) from long double-stranded RNA and bind siRNAs to function, respectively. The 24 nt siRNAs are cleaved from lncRNAs by DCL3, and are methylated and bind to AGO to form the AGO–siRNA complex. LncRNAs act as scaffold RNAs that are recognized by the siRNA–AGO complex through sequence complementarity to form AGO4–siRNA–lncRNA, and then target the chromatin [96,169,170,171]. DNA methylation is catalyzed by DNA methyltransferases and plays a key role in plant abiotic stress responsiveness [172]. LncRNAs have been shown to regulate target genes under abiotic stress conditions by recruiting DNA methyltransferases or demethylases, regulating their DNA methylation [96,173,174,175]. Nuclear DNA is wrapped around histone octamers in nucleosomes, and gene expression is closely associated with chromatin topology. Transcriptional regulation was also affected by histone modifications such as methylation, acetylation, phosphorylation, and ubiquitination under various abiotic stresses. LncRNAs could guide RNA–protein complexes to bind to specific locations and interact with chromatin-modifying enzymes to target genes [140,176,177]. In addition, lncRNAs were found to regulate gene expression by mediating changes in chromatin structure, such as nucleosome positioning, chromatin remodeling, and chromosome looping. For example, lncRNAs transcribed by Pol V could interact with and serve as a binding scaffold for Involved in de novo 2 (IDN2), an RNA-binding protein that is required for RdDM and physically interacts with a subunit of the SWI/SNF complex (ATP-dependent chromatin remodeler), stabilizing the specific nucleosomes [178]. In Arabidopsis, the AUXIN-REGULATED PROMOTER LOOP (APOLO) locus, which is transcribed by Pol V and recognizes its targets by short sequence complementarity and the formation of R-loops, is involved in modulating [179,180]. Therefore, lncRNAs can function as a guide, a nucleator, a scaffold for numerous complexes, a template, a decoy, or a signal. The mechanisms by which lncRNAs epigenetically regulate gene expression have been well reviewed [181], but how they are involved in important cellular processes in plants under abiotic stresses needs to be further investigated in the future. Plants have evolved sophisticated regulatory systems to adapt to diverse abiotic stress challenges, especially those that constantly happen, such as drought and extreme temperatures. Some plants could develop stress responses after the first stress stimuli, which leads to enhanced tolerance or resistance the next time the stress is encountered, which is called stress memory [182]. So far, Arabidopsis [183,184], maize [185,186], rice [123], Boea hygrometrica [187], and switchgrass [67] have been proven to establish drought stress memory after repeated exposure to drought or dehydration. A total of 238 lncRNAs involved in drought memory responses were found in rice and showed markedly different expression levels in subsequent drought treatments than in the first drought stress [123]. Association analysis of lncRNAs and mRNAs also showed that some memory-related mRNA transcripts, including serine/threonine-protein kinase, and phenylalanine ammonia-lyase, were associated with lncRNAs [123]. Furthermore, 12 drought memory miRNAs were generated from lncRNAs in rice [123]. The molecular responses to multiple dehydration stresses of lncRNAs in switchgrass, which is an excellent biofuel feedstock and soil-conserving plant, were researched systematically. A total of 441 differentially expressed lncRNAs were found during multiple dehydration stresses, and among them, 39 lncRNAs were annotated and suggested to play important roles in dehydration stress memory [67]. GO and KEGG analysis of the antisense genes, upstream, and downstream genes of the lncRNAs showed enrichment in the GO term of “response to stress”, and pathways of “biosynthesis of amino acids”, “plant hormone signal transduction”, “aminoacyl-tRNA biosynthesis”, “ribosome”, “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “glycolysis” [67]. On the other hand, recent reports have shown that the establishment and maintenance of plant short-term and long-term stress memories are associated with and governed by epigenetic processes and chromatin dynamics, including DNA methylation, histone modifications, and RNA-directing modifications [172,187,188,189,190,191]. LncRNAs are widely involved in regulating plant epigenetic modification, plant hormone (such as ABA and ethylene) biosynthesis and signal transduction, and alternative splicing [67,123,192]. These imply that lncRNAs may play roles at sophisticated levels in plant stress memory, and functional studies of lncRNAs in plant stress memory are still very limited and need to be investigated further. As a very challenging group of transcripts to study, the diversity of lncRNA molecules, sequences, and structures is growing rapidly, and their functional mechanisms are broad topics. We have tended to focus on how lncRNAs are conservatively evolving in plants to adapt to the land environment, as well as their roles in plant response to abiotic stresses and the establishment of stress memories. Increasing pieces of evidence provided in different plant species prove that lncRNAs have critical roles in abiotic stress responses. However, most functional plant lncRNAs were found in angiosperms, while fewer lncRNAs were tested in bryophytes and ferns. Studies of lncRNAs in a wider range of plant species will help understand the evolution and diversity of their functions in adapting to different environments. On the other hand, most studies on stress-responsive lncRNAs have been focused on a single stress type or single stress, with only a few studies considering the roles of lncRNAs under multiple stresses in combination or under repeated stress exposures. Therefore, more effort is needed to discover and reveal the molecular pathways and mechanisms of lncRNAs in plants. Systematic screening and integrative crosscheck of lncRNAs would provide new insights to identify and functionally characterize potential key lncRNAs that are essential in plant response to and memory of various and repeated abiotic stresses. In addition, the conservation of their biological functions and metabolisms among different plant groups (such as Eudicotyledoneae and Monocotyledoneae, etc.) remains largely unclear. Therefore, large-scale assessments of plant lncRNA functionality and comparative analyses of lncRNA conservation across plant species will be powerful tools for identifying lncRNAs and studying their functions. GWAS-derived genomic analysis for functional candidate lncRNAs associated with abiotic stress in large populations could be employed to predict single-nucleotide polymorphisms and identify functional polymorphisms of lncRNAs. Furthermore, transposable elements, which play significant roles in evolution and are often found to be highly enriched in the upstream regions of lncRNAs and also regulate lncRNAs, might also be likely to regulate the adaption of plants to abiotic stresses, facilitating the evolution of land plants. Meanwhile, the conservation of lncRNAs might be underestimated, and new approaches capable of mapping lncRNA structure and interactions, as well as models capable of accurately capturing evolutionary constraints on lncRNA loci, need to be developed in order to discover the new biology of lncRNAs. Growing evidence shows that in humans, the expression of lncRNAs is correlated with tumorigenesis, metastasis, and poor prognosis in many types of cancer, both in animal tests and clinical experiments. These findings indicate that many lncRNAs could be used as prognostic biomarkers and potential therapeutic targets [193,194]. Comparatively, the application of lncRNAs in plant breeding is still in its initial stages. Although lncRNAs mediate the regulation of plants in response to abiotic stresses in many species, their potential to be valuable genomic resources in plant molecular breeding or as indicators is yet to be confirmed. In addition, breeding strategies based on lncRNAs to optimize the balance between plant growth and abiotic stress conditions are required to be further developed [195]. To sum up, the potential applications of lncRNAs in plants are currently lacking. Much work remains to be completed in this area, due to the rapid evolution and multifaceted molecular functions of lncRNAs. Taken together, the rapid and remarkable progress of lncRNAs in different plant species has significantly expanded our knowledge, but there are still many areas and papers that we have not included in this review. Despite the relevant results reported recently, the evolution, functions, mechanisms, and applications of plant lncRNAs under abiotic stresses remain to be understood. It will be a long journey, and more efforts are needed to fully understand their roles.

PMC10001320

Sophie Hillinger,Julia Saeckler,Konrad J. Domig,Stefanie Dobrovolny,Rupert Hochegger

Development of a DNA Metabarcoding Method for the Identification of Insects in Food

03-03-2023

insects,DNA metabarcoding,food authenticity,species identification,NGS

Insects have the potential to become an efficient and reliable food source for humans in the future and could contribute to solving problems with the current food chain. Analytical methods to verify the authenticity of foods are essential for consumer acceptance. We present a DNA metabarcoding method that enables the identification and differentiation of insects in food. The method, developed on Illumina platforms, is targeting a 200 bp mitochondrial 16S rDNA fragment, which we found to be suitable for distinguishing more than 1000 insect species. We designed a novel universal primer pair for a singleplex PCR assay. Individual DNA extracts from reference samples, DNA extracts from model foods and food products commercially available were investigated. In all of the samples investigated, the insect species were correctly identified. The developed DNA metabarcoding method has a high potential to identify and differentiate insect DNA in the context of food authentication in routine analysis.

Development of a DNA Metabarcoding Method for the Identification of Insects in Food Insects have the potential to become an efficient and reliable food source for humans in the future and could contribute to solving problems with the current food chain. Analytical methods to verify the authenticity of foods are essential for consumer acceptance. We present a DNA metabarcoding method that enables the identification and differentiation of insects in food. The method, developed on Illumina platforms, is targeting a 200 bp mitochondrial 16S rDNA fragment, which we found to be suitable for distinguishing more than 1000 insect species. We designed a novel universal primer pair for a singleplex PCR assay. Individual DNA extracts from reference samples, DNA extracts from model foods and food products commercially available were investigated. In all of the samples investigated, the insect species were correctly identified. The developed DNA metabarcoding method has a high potential to identify and differentiate insect DNA in the context of food authentication in routine analysis. Limited land available for livestock production, increasing greenhouse gas emissions and a rising world population are becoming a challenge for the growing meat and protein demand worldwide [1]. Insects have the potential to become a sustainable, efficient, and reliable source of food for humans and overcome some of the burdens of the meat industry [2]. Depending on their species and state of metamorphosis, insects can contain remarkable amounts of proteins, calories, fat, vitamins, and minerals, and therefore complement or even replace meat in the human diet [3]. In large parts of the world such as Africa, Asia and South America, the so-called entomophagy (the consumption of insects as a food source for humans) is common in traditional cuisine [1]. Insects are widespread in many regions worldwide and comparatively easy to propagate. Since many years, they have been among the most important sources of nutrients, especially for developing countries that are regularly affected by starvation [3,4]. In Europe, entomophagy is not yet widespread, but since the 21st century, public and economic interest has been growing due to EU subsidies. The production and placing on the market of insects and parts thereof are regulated in Europe by the legislation on Novel Foods [5]. The yellow mealworm (Tenebrio molitor), rather their larvae, was the first insect to be approved in the EU, followed by the migratory locust (Locusta migratoria), the house cricket (Acheta domesticus), and the buffalo worm larvae (Alphitobius diaperinus) [6,7,8,9]. Further insect species can be expected to be approved by the EU. Insects can not only be an intentional component in food products but can also occur unintentionally as storage pests [3]. Due to possible production of allergenic substances and symbiosis with mycotoxins, the presence of pests can be of great importance to human health [10]. Food authenticity is important in terms of food fraud, the quality and safety of ingredients and cross-contamination. Food can be considered authentic if it is in its original state and complies with its declaration. Premium or high-priced products are especially prone to be adulterated by cheap or low-quality ingredients and thus need to be verified by analytical methods to support control [11]. At least the insects recently approved for food use should be detectable and discriminable from other insect species. DNA-based analytical methods are gaining more and more importance because they enable specific and fast analysis and have a broad range of applications. DNA is not only present in almost all foods but is also quite heat tolerant and can therefore be used as a parameter in processed foods [12]. Polymerase chain reaction (PCR) assays for insect identification have been developed in singleplex and multiplex PCR format [13,14]. However, the number of detectable insects is low due to the currently still small number of detection methods for insects in food. Multiplex methods are also limited in the number of optical channels in the detection unit of the real-time PCR device. In addition, a method should allow the analysis of highly processed foods, but published assays may fail to amplify degraded DNA because the designed primer system forms PCR products that are too long. These limitations can be overcome by using methods of barcode sequencing with universal primer systems. DNA barcodes are usually composed of conserved regions at both ends and a variable part between the primer binding sites to discriminate between the species of interest [15,16]. In traditional DNA barcoding, PCR products gained through amplification of the designated DNA barcode region, e.g., cytochrome oxidase I gene [17] are then subjected to Sanger sequencing [18,19,20]. To increase the efficiency of this method, a combination of DNA barcoding with next generation sequencing (NGS) is favorable [21,22]. So-called DNA metabarcoding enables the detection of a larger number of species simultaneously and identifying them through reference sequences [23,24]. For this purpose (correct) database entries are required [25]. DNA metabarcoding methods to identify and differentiate species have already been developed and published, e.g., the detection of mammals and birds in foods, and bivalve species in seafood [26,27,28,29]. In this study, we aimed to develop a DNA metabarcoding method that uses relatively short PCR products of approximately 200 bp in length, allowing identification and differentiation of insect species in processed food products. The method was developed using the Illumina MiSeq® and iSeq® platforms. Insect samples (pure material of individual species) from the Institute for Sustainable Plant Production, Vienna, Austrian Agency for Health and Food Safety (AGES) and insect-containing food obtained from supermarkets and online shops were used for the experiments. Experts at the Institute for Sustainable Plant Production confirmed identity of the insect species used as reference samples. Preferably, reference samples were ordered alive or alternatively already dried or frozen. Furthermore, self-made insect cookies and burgers (which have served as model foods in previous studies) were obtained from the Food Control Authority of the Canton of Zurich, Zurich, Switzerland. The cookies contained three insect species in equal proportions, while the burgers had an asynchronous composition from 0.1 to 10% [14]. All samples were kept at a temperature of −20 °C until DNA extraction was performed. The samples used for the development of the method, including the four insect species commonly consumed in Europe, are listed in Table 1. The selection criterion was the affiliation of these insects to the main representatives of edible insects [1]. At the beginning, all samples were either cut into smaller pieces or homogenized in a mortar or lab mill. After that step, samples were lysed in the presence of a hexadecyltrimethylammonium bromide/polyvinylpyrrolidone extraction solution (CTAB/PVP-buffer) and proteinase K at elevated temperature under constant shaking. Then, DNA extraction was performed using a commercially available kit. The Maxwell RSC PureFood GMO and Authentication Kit from Promega (Madison, WI, USA) and the Maxwell® 16 instrument (Promega, Madison, WI, USA) were used for DNA isolation following the manufacturer’s instructions. The DNA extraction procedure was verified by including negative and positive extraction controls. The yield of the DNA extracts was measured fluorometrically with the fluorometer using Qubit® 2.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). The Qubit® dsDNA broad range assay kit (2–1000 ng) and, for low DNA concentrations, the Qubit® dsDNA high sensitivity assay kit (0.2–100 ng) were used according to the manufacturer’s protocol. The purity of the DNA was also checked using the ratio of the absorption at 260 and 280 nm (QIAxpert spectrophotometer, Qiagen, Hilden, Germany). The DNA extracts were frozen at −20 °C until further use. We used the “Worldwide list of recorded edible insects, Jongema, 2017” as the basis for our search for insect DNA sequences. Reference sequences in FASTA format for individual species were downloaded from the National Center for Biotechnology Information (NCBI, Bethesda, MD, USA) and imported into the CLC Genomics Workbench software (version 11, Qiagen, Hilden, Germany). Preferably, entire mitochondrial DNA sequences were derived from the NCBI RefSeq database due to their expert-proven reliability. The sequences of the mitochondrial 16S rDNA were extracted from the complete genomes and multiply aligned by using the default settings of the CLC Genomics Workbench software (version 11, Qiagen, Hilden, Germany). The primers used were manually designed for this multiple sequence alignment. Four forward and three revers primers have been designed and tested in 12 combinations to amplify a ~200 bp barcode region of mitochondrial 16S ribosomal DNA from different insect species. The sequences of the primers tested are shown in Table 2. The formation of primer dimers was checked by using the OligoAnalyzer Tool provided by Integrated DNA Technologies (IDT, Coralville, IA, USA). Calculations of the annealing temperature of the primers were performed using specialized computer programs as displayed in the TIB Molbiol product description (Berlin, Germany). The target-specific primers, including the overhang adapter sequences were purchased from TIB Molbiol (Berlin, Germany). To verify the successful amplification of the designed primers, real-time PCR of DNA from positive controls was performed (PCR results of the reference samples are shown in Figure S1 (Supplementary Materials)). The individual DNA extracts of the insect species were used as reference samples or positive controls (see Table 1). During PCR-optimization, the DNA input amount of 12.5 ng and the amount of ‘ready-to-use’ HotStarTaq Master Mix Kit from Qiagen (Hilden, Germany) were kept constant and applied as previously published [26]. The annealing temperature (58–62 °C), primer concentrations (final concentrations 0.1–0.8 µM), the addition of magnesium chloride solution (1.5 mM or 3 mM MgCl2) and PCR cycle numbers (30, 35 and 40) were varied. Real-time PCR reactions were carried out using a fluorescent intercalating dye (EvaGreen® (20× in water)) in 96-well plates on the LightCycler® 480 System (Roche, Penzberg, Germany). The correct length of the PCR products was checked by agarose gel electrophoresis, and melting curve analysis was used to detect any non-specific artifacts. The volume of the PCR reactions was 25 µL, made up of 22.5 µL reaction mix and 2.5 µL of diluted DNA sample (5 ng/µL) as template. For the no-template control (NTC), water was used instead of DNA. Possible contamination is checked by including negative extraction controls. The reaction mixture comprises Master Mix with fluorescent dye, primers, nuclease-free water and no or additional magnesium chloride solution. DNA sequencing of the samples was performed using the MiSeq® and iSeq® 100 platform from Illumina (San Diego, CA, USA). The DNA extracts were typically diluted to a concentration of 5 ng/μL, those with a lower concentration were used undiluted. DNA libraries were prepared as described previously [26] with minor modifications (magnetic beads volume: 36 µL; average library size: 226 bp; the iSeq® 100 instrument denatured the diluted libraries automatically during the sequencing process). The DNA library was diluted with 10 mM Tris-HCL at pH 8.6 to the concentration of 4 nM (MiSeq®) or 1 nM (iSeq® 100), respectively. The concentration of the pooled DNA libraries (5 µL for MiSeq® and 7 µL for iSeq® 100) was measured using the Qubit® 4.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). All paired-end sequencing runs were carried out using either the iSeq® 100 i1 Reagent v2 (300 cycles) or MiSeq® Reagent Kit v2 (300 cycles) at a final loading concentration of 8 pM. A 5% PhiX spike-in was used as sequencing control. Reference samples and the DNA extracts from model foods were sequenced on both sequencing platforms (two sequencing runs, one replicate per run). The commercial food products were sequenced with the MiSeq® or the iSeq® 100 platform (Illumina, San Diego, CA, USA). The obtained DNA sequences of the reference samples were compared after sequencing on the MiSeq® and the iSeq® 100 platform for each individual reference sample. The data of the sequence comparison are presented in Supplementary Figure S2. The raw sequence image data (bcl-files) was processed with the help of the instrument’s conversion software bcl2fastq2 (version 2.19.0.316, Illumina, San Diego, CA, USA) and DNA FastQ files were generated. The resulting FastQ files were used as input for bioinformatics tools performing downstream analysis. For downstream analysis, the published and adapted analysis pipeline in Galaxy (version 19.01) was used [29]. Taxonomic assignments were made by aligning dereplicated sequences against a customized database of pre-assigned reference sequences (DNA barcodes) provided by NCBI using BLASTn [30]. The entries of the current AGES customized database are listed in Table S1. The AGES customized database contained entries from insects assigned to the nine orders Blattodea (cockroaches), Isoptera (termites), Coleoptera (beetles), Diptera (flies), Hemiptera (cicadas, bugs; suborders: Coleorrhyncha, Heteroptera, Sternorrhyncha), Hymenoptera (wasps, bees, and ants), Lepidoptera (butterflies and moths), Odonata (dragonflies and damselflies), and Orthoptera (grasshoppers, locusts, and crickets). Furthermore, included: Archaeognatha, Zygentoma, Ephemeroptera, Plecoptera, Embioptera, Notoptera, Dermaptera, Mantodea, Phasmatodea, Mantophasmatodea, Zoraptera, Psocoptera, Phthiraptera, Thysanoptera, Raphidioptera, Megaloptera, Neuroptera, Strepsiptera, Trichoptera, Mecoptera, Siphonaptera, Cicadomorpha, Fulgoromorpha, and Arachnida. In addition, a simultaneous comparison of all DNA sequences was performed with CLC Genomics Workbench (version 11, Qiagen, Hilden, Germany). The purpose of the present study was to develop a DNA metabarcoding method, which can be used for the authentication of various insect species and products thereof. The samples tested consisted of 18 references samples, DNA extracts from insect cookies and burgers, as well as from commercial food products. Therefore, we focused our search on DNA barcodes no longer than 200 bp to enable detection of species in raw and processed insect-containing food products. Mitochondrial DNA, in particular the mitochondrial 16S ribosomal DNA gene, was chosen as a source of markers since we have already used this gene for our mammalian and poultry assay. Furthermore, the DNA libraries should be sequenced with 300-cycle Illumina reagent kits to allow for the simultaneous analysis of insect samples along with those of mammalian and poultry species using the recently published DNA metabarcoding method [26]. We designed primers targeting a region of the mitochondrial 16S ribosomal RNA gene (Table 2). All primers were tested for their applicability on the different DNA extracts from reference samples (Table 1). With the primer pair Fwd-I-3 and Rev-I-1 a high amount of PCR products with the expected length was obtained and thus, this primer set was considered applicable for use in practice. The optimal PCR-conditions were determined as follows: HotStar-Taq Master Mix Kit (Qiagen, Hilden, Germany) was used in the reaction mix at a final concentration of 1x and the final concentration of primers was 0.4 µM in the presence of additional magnesium chloride (final concentration of magnesium 4.5 mM). The PCR protocol involved a 15 min initial denaturation at 95 °C, followed by 35 cycles of 30 s each at 95 °C, 58 °C, and 72 °C, and a 10 min final elongation at 72 °C. An alignment of selected DNA barcode sequences for relevant insect species is displayed in Figure 1. The binding positions of forward and reverse primer are marked in blue and green, respectively. The pairwise comparison tool of the CLC Genomics Workbench software was used to compare the selected DNA barcode region of 1100 insect species to identify similarities and differences. A typical graphical representation of a sequence comparison of DNA sequences from the 18 reference samples is shown in Figure 2. A color scheme is used to highlight the relationship between the DNA barcode regions, with blue representing differences and dark red representing high similarity in the variable region of the DNA sequences. Analysis of the data showed that 92% of all insects (DNA barcodes) under investigation can be discriminated from each other. The sequence alignment data revealed that the selected DNA barcode region cannot discriminated between all species of the following genera: Drosophila spp., Chrysomya spp., Bactrocera spp., Cheumatopsyche spp., Sinopodisma spp., Fruhstorferiola spp., Chorthippus spp., Stenocatantops spp., Gomphocerus spp., Traulia spp., Filchnerella spp., Bryodema spp., Oedaleus spp., Tetrix spp., Cryptolestes spp., Anax spp., Euphaea spp., Actias spp., Dendrolimus spp., Ostrinia spp., Magicicada spp., Bombus spp., Bryodemella spp., Culex spp., Pomacea spp., Pontia spp., Rapisma spp., Traulia spp. and Vespa spp. Furthermore, the following pairings cannot be distinguished with the developed marker system, because the base sequence in the variable region of the amplified barcode is identical: Musca domestica:Dasyhippus barbipes, Pliacanthopus bimaculatus:Miromantis yunnanensis, Omocestus viridulus:Dnopherula yuanmowensis, Pseudoeoscyllina brevipennisoides:Euchorthippus unicolor, Pseudotmethis rubimarginis:Filchnerella rubrimargina:Filchnerella helanshanensis, Filchnerella qilianshanensis:Sinotmethis brachypterus, Bryodema kozlovi:Bryodema nigroptera:Bryodemacris uvarovi:Bryodemella tuberculata diluta, Bryodemella holdereri holdereri:Bryodema dolichoptera:Angaracris rhodopa, Oedaleus abruptus:Parapleurus alliaceus. The optimized DNA metabarcoding method was applied to identify insect species in individual DNA extracts from reference samples. The results obtained by DNA metabarcoding are shown in Table 3. The table displays average values for the total raw reads, the total reads that passed the analysis pipeline in Galaxy, and the total reads correctly assigned to the eighteen species (based on two replicates, with one exception for “Pachnoda marginata”). The number of correctly assigned reads ranged from approximately 17,000 and 148,000 reads for the selected number of samples for the sequencing experiment, resulting in a clear identification of the insect species. The four EU-approved edible insect species and all other insect species tested were identified at a high rate (>97% identity with reference sequences) using this workflow. The comparison of the DNA sequences after sequencing of the reference samples on the MiSeq® and the iSeq® 100 platform showed no deviation (Figure S2). Although all of the eighteen reference samples were correctly assigned on both sequencing platforms, in case of Galleria mellonella (Greater wax moth), Gryllodes sigillatus (Tropical house cricket), Plodia interpunctella (Indian meal moth), and Lethocerus indicus (Water bug) the obtained sequences by next generation sequencing were not identical to the reference sequences. There were up to four mismatches between the reads of the individual representative sequences and the corresponding reference sequences in the user-defined database imported from NCBI, indicating gaps or errors in the database (Figure S2). We investigated the suitability of the DNA metabarcoding method for processed and heat-treated food samples with known insect species composition. Therefore, we analyzed model food products (five insect cookies and four insect burgers) from a Swiss laboratory from a previous research project. A detailed product information is given in Köppel et al., 2019 [14]. In general, the cookies and burgers contained three insect species (Tenebrio molitor, Acheta domesticus, Locusta migratoria) in a ratio from 0.1 to 10.0% (w/w) in the presence of wheat flour or ground meat, respectively. The results obtained for the nine model foods are summarized in Table 4. The DNA metabarcoding method allowed the correct and sensitive identification of all insect species present down to a spiking level of 0.1% in model food samples. It was also shown that the barcode developed, with a length of 200 base pairs, allows discrimination of the three insect species in the heat-treated model samples, even if the spiking material was prepared asynchronously. These results indicate that the DNA metabarcoding method based on the primer set Fwd-3 and Rev-I-1 is applicable for the detection of insect species in processed food products. To assess the suitability of our DNA metabarcoding method to commercially available foods, 38 food products declared to contain insects were analyzed. According to the declaration, 23 samples (1–23) should contain buffalo worm species, four samples (24–27) should contain mealworm species, and 11 samples (28–38) should contain cricket species. In order to represent a wide spectrum of available products, both pure insect samples in dried, milled or roasted form and mixed products with a very low insect content of only 0.1% were selected. The results showed that insect DNA could be detected in all samples, and the number of correctly assigned sequences reached at least about 73,000 reads. Table 5 summarizes the results obtained for the 38 commercial food products from supermarkets and online stores. Our results confirmed the presence of the three species according to their declaration and the suitability of the method for the identification of insect components down to a presence of only 0.1%. DNA metabarcoding is considered an advanced tool for monitoring food authenticity, a reference method for the detection of animal species and birds has already been developed [29]. In this study we developed a DNA metabarcoding method that has great potential for identifying insect species in food and can serve as an effective screening method for species authentication in food products that may contain insects. A singleplex PCR assay was developed for the amplification of the short DNA target region of the mitochondrial 16S rDNA gene that serves as a DNA barcode. The applicability of the novel DNA metabarcoding method was investigated by analyzing individual DNA extracts from reference samples, nine heat-treated model foods, as well as DNA extracts from 38 commercially available food products. Analysis of the tested samples demonstrated that the method is suitable for insect identification, even in processed or complex foods down to an insect content of only 0.1%. This sensitivity was also achieved in model foods with asynchronous composition of insect-containing ingredients. There were 38 commercial foods with declared insect ingredients, including compound products and pure products in dried, roasted and powdered form, that were checked for correct labeling. Noticeably, the declared insect ingredient was confirmed in all 38 commercial products tested. For many insects, PCR methods are lacking for reliable detection, so a major advantage of DNA metabarcoding is the simultaneous detection of a large number of insect species in one testing approach. To determine further performance parameters of the DNA metabarcoding method presented and to assess whether the method also allows semi-quantitative statements, an interlaboratory validation of the method should be carried out. Although the insect species currently relevant in food production were clearly detected, successful differentiation on species level was not possible for all samples examined in silico. A limiting factor for the application of metabarcoding methods is still the lack of sequencing equipment in laboratories and gaps in sequence database content, especially for insect species. The transferability of the method to different platforms, it runs successfully on both Illumina MiSeq® and iSeq® 100 instruments, and by combining different applications (joint sequencing of plant and animal species, bacteria, etc.), the costs can be kept sufficiently low, should laboratories consider purchasing such equipment.

PMC10001329

Piero Giuseppe Meliante,Federica Zoccali,Marco de Vincentiis,Massimo Ralli,Carla Petrella,Marco Fiore,Antonio Minni,Christian Barbato

Diagnostic Predictors of Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

23-02-2023

head and neck squamous cell carcinoma,immunotherapy,PD-1/PD-L1,immunotherapy molecular marker,immunotherapy resistance,pembrolizumab,nivolumab,chemotherapy

Programmed cell death ligand-1 (PD-L1) binds PD-1 on CD8+ lymphocytes, inhibiting their cytotoxic action. Its aberrant expression by head and neck squamous cell carcinoma (HNSCC) cells leads to immune escape. Pembrolizumab and nivolumab, two humanized monoclonal antibodies against PD-1, have been approved in HNSCC treatment, but ~60% of patients with recurrent or metastatic HNSCC fail to respond to immunotherapy and only 20 to 30% of treated patients have long-term benefits. The purpose of this review is to analyze all the fragmentary evidence present in the literature to identify what future diagnostic markers could be useful for predicting, together with PD-L1 CPS, the response to immunotherapy and its durability. We searched PubMed, Embase, and the Cochrane Register of Controlled Trials and we summarize the evidence collected in this review. We confirmed that PD-L1 CPS is a predictor of response to immunotherapy, but it should be measured across multiple biopsies and repeatedly over time. PD-L2, IFN-γ, EGFR, VEGF, TGF–β, TMB, blood TMB, CD73, TILs, alternative splicing, tumor microenvironment, and some macroscopic and radiological features are promising predictors worthy of further studies. Studies comparing predictors appear to give greater potency to TMB and CXCR9.

Diagnostic Predictors of Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Programmed cell death ligand-1 (PD-L1) binds PD-1 on CD8+ lymphocytes, inhibiting their cytotoxic action. Its aberrant expression by head and neck squamous cell carcinoma (HNSCC) cells leads to immune escape. Pembrolizumab and nivolumab, two humanized monoclonal antibodies against PD-1, have been approved in HNSCC treatment, but ~60% of patients with recurrent or metastatic HNSCC fail to respond to immunotherapy and only 20 to 30% of treated patients have long-term benefits. The purpose of this review is to analyze all the fragmentary evidence present in the literature to identify what future diagnostic markers could be useful for predicting, together with PD-L1 CPS, the response to immunotherapy and its durability. We searched PubMed, Embase, and the Cochrane Register of Controlled Trials and we summarize the evidence collected in this review. We confirmed that PD-L1 CPS is a predictor of response to immunotherapy, but it should be measured across multiple biopsies and repeatedly over time. PD-L2, IFN-γ, EGFR, VEGF, TGF–β, TMB, blood TMB, CD73, TILs, alternative splicing, tumor microenvironment, and some macroscopic and radiological features are promising predictors worthy of further studies. Studies comparing predictors appear to give greater potency to TMB and CXCR9. Programmed cell death ligand-1 (PD-L1) is a physiologically expressed transmembrane molecule crucial in immune tolerance. The programmed cell death-1 (PD-1) molecule binds on the surface of CD8+ T lymphocytes, blocking their cytotoxic action [1,2,3]. The aberrant expression of PD-L1 by head and neck squamous cell carcinoma (HNSCC) cells inhibits the cytotoxic activity of T cells, leading to immune escape [4,5]. After this discovery, several antibodies active against the PD-1/PD-L1 axis were tested. Large phase III trials (KEYNOTE-040, KEYNOTE-048, CheckMate-141) demonstrated that immune checkpoint inhibitors (ICIs) against PD-1 and PD-L1 outperformed the past gold standard therapy in terms of oncologic outcomes in the treatment of recurrent or metastatic (R/M) HNSCC [4,6,7]. Pembrolizumab (KeytrudaTM, Merck & Co., Inc., Rahway, NJ, USA) and nivolumab (OPDIVOTM, Bristol-Myers Squibb Company, New York, NY, USA), two humanized monoclonal antibodies against PD-1, have been approved in HNSCC treatment by the U.S. Food and Drug Administration (FDA) and the European Medical Agency (EMA) [8]. However, ~60% of patients with recurrent or metastatic HNSCC fail to respond to immunotherapy and only 20 to 30% of treated patients have long-term benefits from ICIs [4,8,9]. Despite the enormous development that immunotherapy is having, the tools for selecting candidates for it have not evolved hand in hand [10,11]. The marker currently used to predict response to therapy is the combined positive score (CPS) for PD-L1 expression by the neoplastic cell (calculated as 100 times the number of PD-L1-positive cancer cells, lymphocytes, and macrophages, divided by the number of viable tumor cells). It is well known from the literature that CPS > 1% is related to immunotherapy response [4,6,7]. Whereby, only patients with CPS > 1 are treated with anti-PD-1/L1 drugs [9]. This predictor does not always work, and above all, it often does not indicate a long-term response. Furthermore, there is still a proportion of patients, as evidenced in large clinical studies, who, despite being below this threshold, would still respond to immunotherapy [4,6,7,8,9,12]. We aim to analyze all the fragmentary evidence presented in the literature to identify what future diagnostic markers could be useful for predicting, together with PD-L1 CPS, the response to immunotherapy and its durability, as well as identifying the most suitable therapy for the individual patient. We searched the PubMed, Embase, and Cochrane Register of Controlled Trials databases for markers that could predict response to immunotherapy treatment as well as treatment selection. The keywords used by the authors were: head and neck squamocellular carcinoma or head and neck squamous cell carcinoma, immunotherapy, pembrolizumab, nivolumab, markers of response to immunotherapy, predictors of response to immunotherapy, immune checkpoint inhibitors. We have only considered articles in English and without time limits or restrictions on the type of publication. We first screened for titles and abstracts and then read the body of the selected articles. Within the latter, we also manually searched the bibliography for relevant manuscripts. We have decided not to include findings from other types of cancer and treatments not tested on HNSCC. Similarly, we decided not to include discoveries made only in animal models as they are still too far from clinical applicability. In the end, after several meetings between all the authors in which we discussed the results of our research, we summarized the evidence collected within the present manuscript. PD-L1 expression is predictive of response to anti-PD-1/L1 immunotherapy. In a few cases, those who have shown susceptibility to immune checkpoint inhibitors do not express this marker [4,6,7]. As consequence, the selection of patients treated with immune checkpoint inhibitors is challenging in HNSCC treatment [13]. We divided the markers into four categories: checkpoint target, tumor neoantigens, tumor immune microenvironment, and radiological features. Yearley et al. studied the expression of the other ligand of PD-1, namely PD-L2. The interaction between PD-L2 and PD-1 occurs with a higher molecular affinity than that with PD-L1. Despite this, the latter is considered the main molecule responsible for PD-1-mediated immunotolerance [1,14]. PD-L2 expression usually correlates with that of PD-L1. This correlation can be traced back to one of the mechanisms of overexpression, the one that is based on genetic modifications. PD-L1 and PD-L2 are located at chromosomal locus 9p24.1, only 42 kilobases apart. It has been observed that among the mechanisms through which gene overexpression occurs, both translocation and amplification are to be counted [15]. Furthermore, the 9p24.1 locus amplification can lead to a PD-L2 overexpression through the Janus kinase 2 (JAK2)/signal transducer and activator of transcription 1 (STAT1) signaling pathway [15]. It has been observed that only PD-L2 expression is an independent predictor of response to immunotherapy in HNSCC, whereas the positivity to both PD-L1 and PD-L2 confers a greater response than PD-L1 positivity alone. PD-L2 alone was observed in 61.4–62.7% of PD-L1-negative patients and it correlates with progression-free survival (PFS) independent of PD-L1 status (Table 1). According to Wang et al., this could be one of the explanations for why some patients with PD-L1-negative malignancies respond to pembrolizumab or nivolumab therapy [16]. There is disagreement about the predictor role of PD-L2, and in some studies its values correlated with increased or decreased OS [16,17,18]. PD-L2 levels are independent predictors of progression-free survival and clinical response to pembrolizumab therapy in HNSCC patients [16,17] and when the patient is not undergoing immunotherapy, its expression is indicative of poor relapse-free survival, overall survival, and progression-free survival [17,18]. IFN-γ is considered one of the main inducers of PD-L1 expression in cancer cells. Its mechanism of action exploits the activation of STAT1, causing the expression of interferon responsive factors (IRFs) through Janus kinases (JAK-STAT pathway, especially STAT1) [38,39]. It has been observed that the inhibition of IFN-γ significantly reduces the expression of PD-L1 [40] (Table 1, Figure 1). Indeed, both PD-L1 and JAK2 reside on chromosome 9p and gene overexpression mechanisms of PD-L1 could also involve JAK2, which is, in turn, an activator of IFN-γ [1,15]. IFNs I and II activate the AKT-mTOR cascade, located downstream of the phosphatidylinositol 3 kinase (PI3K) signaling, increasing PD-L1 expression [41]. On the other hand, AKT-mTOR pathway suppression reduces IFN-γ-induced PD-L1 expression. Furthermore, the increased expression of PI3K-AKT signaling through the inhibition of its suppressor phosphatase and tensin homolog gene (PTEN) increases PD-L1 expression [41,42,43,44]. The action of IFN-γ is not limited to neoplastic cells, but also other components of the tumor mass. It has been observed that endothelial cells, under the stimulus of IFN-γ, produce PD-L1, a molecule that is otherwise not constitutively expressed [45,46]. MicroRNA post-transcriptional regulation is a crucial level of gene expression [47,48] and affects the expression of IFN-γ and PD-L1. MiR-513 binds the 3′UTR of the PD-L1 gene and inhibits its expression induced by IFN-γ, which, in turn, is an inducer of miR-513 and miR-155 [1,49,50,51]. Considering all these mechanisms, the interpretation of the level of active IFN-γ signaling as it is associated with response to PD-L1 immunotherapy is complex. A possibility is that the IFN-γ-related mRNA expression profile is a predictor of the clinical response to anti-PD-1 therapy in HNSCC [12] and it was proposed to measure the levels of IFN-γ together with those of PD-L1 to identify the subjects most likely to respond to immunotherapy [13]. Studying databases at Memorial Sloan Kettering Cancer Center and the Cancer Genome Atlas, Zhang et al. observed that predictors of response to ICIs are age and mutations of ARID1A, PIK3-CA, TP53 mutation is a negative predictor (Table 1) [34]. Hypoxia is an immune escape mechanism adopted by neoplastic cells by which an “immune desert” is generated in the tumor microenvironment. This means that the migration of T cells and their function are inhibited with their consequent lack of action. Consequently, when administering a drug inhibitor of the PD-1/L1 axis, such as pembrolizumab or nivolumab, the cells are more vulnerable to the action of T cells, but the latter are not present in the tumor microenvironment, therefore they cannot kill them. Potential markers of this phenomenon are hypoxia-inducible factor-1α (HIF-1α) and relative molecular signaling (Table 1) [21,22]. Further studies are needed to understand the correlation with response to immunotherapy in HNSCC. Like IFN-γ, EGFR is also an inducer of PD-L1, with both using JAK2 for signaling, and the overexpression of EGFR correlates with JAK2 and PD-L1 in neoplastic cells [25]. Overexpression of VEGF and TGF-β is also linked to immune tolerance. Their inhibition is associated with the recovery of the activity of the immune system against neoplastic cells observed histologically with an increase in effector T lymphocytes and a reduction in regulatory ones and myeloid-derived suppressor cells. Considering oncological therapy, the relevant data concern the newfound susceptibility to anti-PD-L1 and CTLA-4 immunotherapy by tumors in which the activity of these two molecules was suppressed [23]. The production of TGF-β has a role in both intracellular and extracellular environments. Its secretion by cancer-associated fibroblasts inhibits CD8+ T cells and decreases the dendritic cells in draining lymph nodes [52,53,54,55]. Overexpression of CD-73 by HNSCC cells is associated with immunosuppression of the tumor microenvironment and favors epithelial-to-mesenchymal transition and metastasis. By comparing tumors that overexpress it with those that have a lower representation, it has been seen that the presence of the molecule in high quantities is associated with reduced responsiveness to immunotherapy. Shen et al., in addition to having observed its association with the reduced response to immune checkpoint inhibitors, have also hypothesized its future role as a therapeutic target [24]. Alternative splicing analysis highlighted some expression profiles correlated with improved survival in HNSCC patients. Selected regulating splicing factors (DDX39B, PRPF39, and ARGLU1) have also been identified. Comparing the tumors that had an expression profile associated with the best prognosis with those of a control group, a different representation of the inflammatory infiltrate was observed. Therefore, the possibility of not only using this expression profile as a prognostic predictor but also as a predictor of the response to immunotherapy was hypothesized [26]. The tumor microenvironment also houses CD8+ T cells that are supposed to kill cancer cells. However, Chen and Mellman observed that there are tumor areas without these cells, called “immune deserts”, in which immunotherapy cannot work and, in tumors in which the inflammatory infiltrate is present, this could be non-functional [56,57]. Like PD-L1 expression, the tumor microenvironment is also subject to changes induced by therapies, and its composition should be retested to evaluate sensitivity to immunotherapy [10]. The high presence of CD8+ cells in the tumor microenvironment is independently associated with a lower incidence of local recurrences, and with a higher PFS and OS (Table 1) [58]. Tumor-infiltrating lymphocytes (TILs) are prognostic factors in HNSCC, their presence correlates with response to immunotherapy, and there is no agreement regarding the correlation between the expression of TILs and that of PD-L1 in HNSCC. Therefore, some authors state that TILs should be evaluated independently of PD-L1 as a prognostic factor in treatment with ICIs [59,60,61]. Tertiary lymphoid structures (TLSs) are ectopic aggregates that reproduce the structure and organization of lymphatic organs. They are also present in solid tumors with an asymmetric distribution greater in the periphery and less in the center of the mass. Some authors have hypothesized their use as predictors of response to immunotherapy [32,33]. Usually, the immunogenicity of a neoplastic cell is also linked to the number of mutations. This, in turn, correlates with the susceptibility to the action of T lymphocytes. PD-1/L1 immunotherapy drugs simply suppress the immune escape mechanism adopted by cancer cells. In this way, their antigenicity becomes a target of lymphocytes. Indeed, it has been observed that a high tumor mutation burden (TMB-high) is a predictor of the response to anti-PD-1/L1 therapy in HNSCC [62]. There is no correlation between TMB and PD-L1, as TMB is significantly associated with PFS and treatment response in patients with immunotherapy [27]. Furthermore, the measurement of peripheral blood tumor cell mutation burden (bTMB), together with TMB and inflammatory biomarkers, is an independent predictor of pembrolizumab efficacy (Table 1) [28,29]. Progression-free survival (PFS) is significantly greater in individuals with a high number of mutations detected in circulating tumor DNA compared to those with a low number of mutations when treated with immune checkpoint inhibitors (Table 1) [30]. In a meta-analysis with over 1000 patients and 7 different cancer types, including HNSCC, Litchfield et al. analyzed predictors of response to ICIs using whole-exome and transcriptomic data. They observed that clonal TMB and TMB are the strongest predictors of response to ICIs. In their article, they considered TMB as the number of non-synonymous mutations per tumor cell and observed an odds ratio between responders to therapy, i.e., complete responders or partial responders, versus non-responders, i.e., stable disease and progressive disease, of 1.74 (95% confidence interval [1.41–2.15], p = 2.93). The odds ratio for total TMB was similar, with a value of 1.70 ([1.33–2.17], p = 1.93). Conversely, subclonal TMB was not significantly associated with TPI response [31]. The tumor microenvironment is crucial in immune tolerance [34]. The predictors of response to therapy are not only molecular but the cell ratio or the macroscopic characteristic of the mass also gives us information about the response to immunotherapy. Neutrophil-to-lymphocyte ratio (NLR) > 4 and a sum of the target lesion greater than 4 cm are significantly associated with poor response to immunotherapy and poor survival [35]. Some radiological factors may also be useful in evaluating the immunological status of squamous cell cancer of the head and neck [10,26,32,33,56,57,58]. Starting from oral squamous cell carcinoma samples, Togo et al. observed that poor fluor-D-glucose (FDG) uptake in a PET scan is a marker of poor PD-L1 expression and CD8+ infiltrate in the stroma and of poor prognosis (Table 1) [36]. In particular, the most accurate metabolic parameter is the derived neutrophil-to-lymphocyte ratio [63]. Moreover, as PET visualization, magnetic resonance imaging has also been studied to identify radiological predictors of PD-L1 expression. The benchmark parameter was dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) which was correlated with PD-L1 expression. Tekiki et al. observed that the value of DCE-MRI is significantly correlated with that of PD-L1 in oral squamous cell carcinoma (Table 1) [37]. The reviewed literature shows interesting new perspectives have emerged, which are worthy of further clinical investigation. The search for PD-L2 could be an experimental theme [17,18]. The currently approved medications for the treatment of HNSCC are molecules active against PD-1 (pembrolizumab and nivolumab) [8]. Thus, they do not account for tumor overexpression of PD-L1 or PD-L2. Although a correlation between PD-L1 and -2 was observed, this is not the rule, being homogeneous throughout the tumor tissue [17,18]. Since the prognostic value in terms of survival of PD-L2 has shown heterogeneous results, we suggest further investigation is needed [17,18]. As also noted by Wang et al., the status of PD-L2 expression by HNSCC should be considered to predict the efficacy of anti-PD-1 therapy. Furthermore, the role of PD-L2 should be investigated not only for its ability to interact with PD-1 but also because it mediates the invasion and chemoresistance of neoplastic cells [16]. The prognostic value of PD-L2 regardless of immunotherapy might be found in the actions of the molecules that are not only involved in immune escape. Among its functions is promoting the epithelial-mesenchymal transition, one of the fundamental steps for the metastasis of solid tumors [64]. One of the most promising immune checkpoint inhibitor response markers in HNSCC is IFN-γ. In fact, it has already been proposed together with PD-L1 to identify eligible patients [19,20]. IFN-γ has a high correlation with PD-L1 expression and there is widespread consensus among researchers regarding its central role in modulating PD-L1 expression. Starting from the research of Noguchi et al., it could be observed that even if the inhibition through IFN-γ antibodies considerably reduces the expression of PD-L1, this is not totally reset. Thus, we conclude that IFN-γ is critical in regulating PD-L1 expression by neoplastic cells and tumor microenvironments, but it is not the only regulatory mechanism [40]. There is a strong consensus regarding the study of gene mutations and response to immunotherapy. TMB and PD-L1, although unrelated, are both predictors of response to treatment with immune checkpoint inhibitors [27,30]. As noted by Litchfield et al., TMB appears to be the most impactful element influencing the response to ICI. This finding is not specific to HNSCC but pertains to a heterogeneous patient population affected by seven different cancer types, including head and neck cancer [31]. Therefore, there is no direct comparison between the various predictors of response to ICI, and, as stated by Burcher et al., we can observe that TMB helps us predict response to immunotherapy in HNSCC [27,28,30]. The possibility to measure TMB in its circulating form, i.e., as bTMB, highlights how liquid biopsy can be useful to investigate the characteristics of neoplasms. Circulating tumor cells have been detected in 65% of patients with HNSCC and their potential use in the diagnosis and prognosis has yet to be fully explored [65]. In the same way, infection markers such as HPV have also been identified in the plasma of patients affected by HNSCC, demonstrating how the circulating component of these neoplasms must be taken into consideration [66,67]. Circulating tumor cells in HNSCC could be used both as a prognostic factor and as a basis for analyzing the expression of molecules such as PD-L1 and other markers of response to immunotherapy [68]. Promising future results could also be achieved using non-invasive biopsy, such as salivary biomarkers [69,70]. Among other molecules identified, such as HIF-1α and JAK2, a clinical investigation is required [21,22,25]. The study of TLSs as a predictor of response to immunotherapy seems promising, but there is still no clarity about their applicability as predictors of immunotherapy. Therefore, at present, further studies are needed before incorporating this parameter into the assessment of prognosis and response to anti-PD-1/PD-L1 therapy [32,33]. TGF-β and VEGF act cooperatively in modulating the action of the immune system in the tumor microenvironment. Their ability to regulate response to immunotherapy appears to be relevant, but there are no experimental data on human patients. The studies, albeit promising, concern mouse models, so we are far from understanding their clinical applicability as predictors of the response to pembrolizumab, nivolumab, or other forms of immunotherapy currently in use. However, there are still some molecules whose value in this sense needs to be investigated with further studies [23]. CD-73 is a molecule overexpressed within tumors compared to healthy tissue. Usually, high levels correlate with a worse prognosis for patients affected by HNSCC, in terms of overall survival. This observation is partly attributed to the ability of CD-73 to promote epithelial-to-mesenchymal transition and metastasis, as well as the reduction of CD8+ T cell infiltration in the tumor microenvironment. Furthermore, its elevated expression is associated with a higher incidence of TP53, HRAS, and CDKN2A mutation, and negatively correlates with TMB, which is a factor of immunogenicity and a potential predictor of good response to immunotherapy [24] The analysis of alternative splicing as a prognostic factor of survival and response to immunotherapy, although interesting and compared with a control, still needs studies in larger cohorts and validation with further research [26]. The currently approved system for assessing the eligibility of HNSCC patients for the ICIs pembrolizumab and nivolumab is CPS for PD-L1 greater than or equal to 1%. This inference derives from the significant major response that the patients with this trait showed in the large trials that led to the approval of immunotherapy drugs in the treatment of HNSCC [4,6,7]. Two problems are yet to be fully understood: (i) the possibility of identifying that portion of the population below this threshold that would still respond to the therapy and (ii) how to predict which patients, while satisfying this criterion, do not respond effectively and/or durably to immunotherapy. Some authors have observed that PD-L2 can also become a target of immunotherapy. After observing that IL-6 increases PD-L2 expression in HNSCC cell lines, they proposed to study IL-6 inhibitors in the treatment of HNSCC with high expression of this interleukin, but to date, there are no clinical trials on this subject [18]. Indoleamine 2, 3-dioxygenase (IDO) is a gene induced by IFN-γ. Some authors have hypothesized the utility of its measurement as its function in tryptophan catabolism could be one of the mechanisms by which neoplastic cells suppress T cells. Moreover, IDO increases the number of T regulatory cells (T-regs) and myeloid-derived suppressor cells (MDSCs) which further inhibit CD8+ T cells. There are still no conclusive studies on its correlation with any drug resistance to immune checkpoint inhibitors [10]. Some authors have already tried to create genetic scores that can serve as a guide to therapy based on prognostic-related differentially expressed ferroptosis-related genes (PR-DE-FRGs), but these still need to be validated through clinical trials [71]. The research on intratumoral PD-L1 expression by CPS has some challenges. One of these has recently been highlighted by Rasmussen et al., who observed that there is significant heterogeneity of expression within the tumor with zones of higher expression and zones of lower expression. The observed agreement within tumors for multiple biopsies using a 1% cut-off was 52% for the PD-L1 CPS [72]. In a first evaluation, some parts of the neoplastic tissue do not significantly express PD-L1, and they are therefore not susceptible to the action of immunotherapy. On the other hand, it means that a PD-L1 CPS < 1% in a single biopsy is not representative of the mass. The application of anti-PD-1 drugs to those patients, hypothetically, could cause an initial reduction of the mass which, however, would not lead to curing of the patient, but only to selecting the population of resistant neoplastic cells. This may be one explanation for the non-long-term response seen in some patients. How PD-L1 is measured may also play a role in predicting response to therapy. Some authors have observed intra- and inter-operator variability using commercial kits (SP263 and 142) and a platform-independent test (E1L3N). The SP263 kit and the E1L3N platform were observed to have nearly perfect intra- and interobserver agreements. SP142 has a moderate interobserver agreement and reduced intra-observer agreement [73]. Furthermore, PD-L1 expression within the tumor is not constant. We know that the expression of this molecule varies and can be modified by pharmacological therapy. Radiotherapy, chemotherapy, and anti-angiogenesis agents induce the production of IFN which increases the production of PD-L1. Therefore, a tumor which at a given moment is apparently not sensitive to immunotherapy does not necessarily become so after some treatments. It, therefore, becomes useful to repeat the biopsies after having given chemotherapy and to evaluate whether the patient could be a candidate for the use of immune checkpoint inhibitors [10]. Relevant data concern the concordance between the biopsies and the surgical specimen taken from the HNSCC, in terms of the CPS for PD-L1. Up to 39% of samples analyzed by De Keukeleire et al. had discordance in this value and 34% of the samples were discordant in the measurement of TILs, further confirming that it is necessary to perform multiple and serial biopsies over time to monitor the neoplasm in the most effective way [60]. Other authors have proposed the use of the tumor proportion score (TPS) for PD-L1, as an immunotherapy response marker, but, given its correlation with the CPS, the use of which is now validated, it is necessary to verify whether its use is necessary [74]. In terms of predictors of response to therapy, we should not ignore the radiotherapy that is often used in HNSCC [75]. PD-L1 and p16 expression correlate with increased tumor radiosensitivity, while survivin and c-Met expression indicate radioresistance. These markers could be useful, together with those listed above, in evaluating the best therapy for the patient [76]. Using a machine learning mechanism that allows for a combined score across 7 tumor histotypes, a score with 11 variables has been created, highlighting that TMB and CXCL9 are the main predictors for all cancers considered [31]. Meta-analysis by Litchfield et al. analyzed 723 articles with a total pool of over 1000 patients and described several potential non-histospecific predictors of response to ICIs. In addition to TMB, they indicated several other predictors related to immunotherapy response such as frameshift insertion/deletion burden (OR = 1.38 [1.15–1.66], p = 1.63), nonsense-mediated decay (NMD) escaping (NMD-escape) fs-indel burden (OR = 1.38 [1.15–1.66], p = 5.63), proportion of mutations fitting tobacco (OR = 1.39 [1.02–1.88], p = 3.53), UV (OR = 1.34 [1.12–1.60], p = 1.23), and APOBEC (OR = 1.39 [1.09–1.76], p = 8.13) mutation signatures, as well as SERPINB3 mutations (OR = 1.33 [1.12–1.59], p = 1.23), and Fs-indel mutations escaping-NMD. Among markers of immune infiltration into the tumor microenvironment, CXCL9 expression also appears to be a predictor of response to ICIs (OR = 1.67 [1.38–2.03], p = 1.33). It is a chemokine that interacts with T cells by binding CXCR3, inducing the recruitment of CD8+ lymphocytes to the tumor microenvironment and inducing the differentiation of inflammatory T helper type 1 (Th1) and Th17 CD4 cells [31,77,78]. Additionally, CD8A (OR = 1.45 [1.20–1.74], p = 1.03), the T cell inflamed gene expression signature (OR = 1.43 [1.05–1.96], p = 2.53), and CD274 (PD-L1) expression level (OR = 1.32 [1.10–1.58], p = 3.03) were shown to influence response to immunotherapy [31]. We did not include these findings within the results of our study but mention them since these findings were from a heterogeneous cancer population. Although Litchfield et al. stated that most of the markers had pan-cancer significance, many molecules were not extendable to all neoplastic histotypes [31]. Furthermore, for appropriate validation, each of these predictors needs to be clinically validated in randomized trials. Moreover, the presence of histospecific biochemical markers would make the creation of a unique score sometwhat underpowered compared to the actual possibilities that molecular medicine could offer. In fact, this would cause us to ignore or marginalize some histospecific markers which could be very useful in the therapeutic choice, such as CD38 (OR = 1.29 [1.03–1.61], p = 2.63), CXCL13 (OR = 1.38 [1.11–1.73], p = 3.83), IM-PRES (OR = 1.31 [1.05–1.65], p = 1.83), T effector signature from the POPLAR trial (OR = 1.38 [1.13–1.70], p = 1.93), and cytolytic score (OR = 1.22 [1.00–1.51], p = 4.93), which are not extendable to all populations of the meta-analysis. The same issue is seen for the loss of TRAF2, which increases the efficacy of ICIs by reducing the cytotoxic activity of TNF and increases T cell-induced apoptosis [31,79]. However, the loss of TRAF2 does not significantly impair the efficacy of immunotherapy in HNSCC, but it does in urothelial cancer and melanoma. Similarly, in the cohort of all the neoplasms considered it is significant, but then, specifically analyzing HNSCC, it is not [31]. The decision to adopt an immune checkpoint inhibitor therapy in R/M HNSCC may also be based on predictors other than the molecular expression of the neoplasm. Cachectic patients and those who have significant weight loss during therapy have been observed to have worse survival regardless of PD-L1 expression. Independent predictors of 6-month progression-free survival are low performance status, low subcutaneous adipose tissue level, and weight loss [80]. Analyzing the predictors, TMB and CXCL9 appear to have the greatest accuracy in predicting the response to immunotherapy in a heterogeneous population composed of patients with neoplasms of different origins [31]. Much remains to be studied on the predictors of response to immunotherapy, and it is estimated that about 40% of the factors that determine the outcome of immunotherapy have yet to be discovered [31]. Interesting new perspectives have emerged, regarding the already-known use of CPS for PD-L1. We suggest that, whenever possible, it is useful to look for it in multiple biopsies from the same cancer and not on a single specimen and that these should be repeated over time during treatment. Large-scale trials are necessary to validate anti-PD-1/L1 immunotherapy markers such as PD-L2, IFN-γ, EGFR, TMB and bTMB, age, mutations of ARID1A, PIK3-CA and TP53, NLR, VEGF, TGF–β, TILs, alternative splicing, tumor size, and tumor microenvironment, including TLS, PET, and MRI contrast enhancement profiles. CD-73 seems to offer interesting perspectives both as a marker and as a potential therapeutic target, and further studies are needed. Biological predictors of radiosensitivity should also be validated to better identify patients who will respond to radiotherapy [69,70]. Studies comparing predictors appear to give greater potency to TMB and CXCR9.

PMC10001332

Giuseppe Broggi,Lucia Salvatorelli

Editorial: Bio-Pathological Markers in the Diagnosis and Therapy of Cancer

26-02-2023

cancer,diagnosis,prognosis,biomarkers,therapy

Editorial: Bio-Pathological Markers in the Diagnosis and Therapy of Cancer Identifying novel biomarkers with diagnostic, prognostic and predictive value in terms of therapeutic response is a current topic in the clinical practice of oncologists, pathologists and medical researchers in general [1]. The introduction of molecular techniques capable of investigating the genetic landscape of human neoplasms provides further impetus to this need [1]. The present collection included thirteen original research (OR) articles, eight review papers and one study protocol, in which various aspects of the topic of the Special Issue have been investigated. Youn et al. investigated the differential transcriptomic landscapes of pediatric and adult chronic myeloid leukemia (CML) cells [2]. Through the use of RNA sequencing, they found that several genes were differentially up- and/or down-regulated in pediatric CML CD34-positive cells to those observed in both pediatric unaffected CD34-positive cells and adult CML CD34-positives cells [2]. Many of these genes were involved in the Rho pathway, whose altered regulation could explain some clinical differences between the adult and the pediatric form of CML [2]. The radiological study by Hsu et al. aimed to identify some thin-slide Computed Tomography (CT) features that could reliably predict lung invasive adenocarcinoma (IA) among its radiological mimickers presenting as pure ground-glass nodules (pGGNs), including atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) [3]. As pGGNs smaller than 2 cm are relatively difficult to biopsy adequately and lung IA exhibits a poorer prognosis than its mimickers, in this clinical context, it is crucial to identify reliable radiological features that can direct clinicians to a decision on whether to perform a biopsy or not [3]. On a series of 181 pGGNs smaller than 2 cm, the authors found that the following factors were associated with an increased risk of IA: (i) larger size, (ii) lobulation and (iii) air cavity; in addition, the multivariate analysis showed that the latter was a statistically significant predictor of IA [3]. Gassenmaier et al. performed a clinico-pathologic and immunohistochemical study to investigate the differential expression of PReferentially expressed Antigen in Melanoma (PRAME) between thin melanomas (Breslow thickness: ≤1.0 mm) and dysplastic compound nevi (SDN) and its prognostic significance in the former [4]. In more detail, diffuse PRAME staining was shown to strongly favor the diagnosis of melanoma over SDN, while no significant differences in PRAME expression were observed between metastasizing and non-metastasizing thin melanomas [4]. Berrino et al. reported the prevalence rates of gene fusions in human cancer types [5]; 125 specimens from patients affected by different malignancies, including colorectal and lung cancer and melanoma, were analyzed using RNA-based targeted next-generation sequencing [5]. They found higher fusion rates in their cohort than those reported in Memorial Sloane Kettering Cancer Centre (MSKCC) cohorts, emphasizing the need for more frequent application of these techniques in clinical practice [5]. Hoyer et al. aimed to compare the sensitivity and sensibility of individualized viral-cellular-junction test (vcj-PCR) + cytology with those of standard methods (high-risk HPV-DNA test + cytology) in the post-treatment follow-up of women affected by high-risk squamous intraepithelial lesion/cervical intraepithelial neoplasm grade 3 (HSIL/CIN3) [6]. The former technique, in spite of its high specificity, was found to be less sensitive indetecting recurrent CIN2/3 than the latter [6]. In the research by Schwertner et al.,Nectin-1 expression was demonstrated to be a significant predictor of susceptibility of malignant melanoma cells to Oncolytic Herpes Simplex Virus both in vitro and in vivo [7]. Caja et al. found on Dextran sulphate sodium (DSS)-induced colitis and azoxymethane (AOM)-induced colorectal carcinogenesis rat models a significant downregulation of TGF-β1 and inflammatory cytokines, along with a collagen scaffold remodeling, in both diseases, indicating that the latter could be considered as a potential preneoplastic feature of colonic mucosa [8]. Serine and arginine-rich splicing factor 1 (SRSF1) is a splicing factor protein whose expression and function have been recently found to be altered in several human malignancies [9]; our research group investigated the potential diagnostic role of this protein in neuropathologist’s practice, demonstrating its frequent immunohistochemical expression among adult diffuse astrocytomas and oligodendrogliomas, along with its negativity among ependymomas and pilocytic astrocytomas [10]. Similarly, the immunohistochemical study by Piombino et al. showed that Wilms’ tumor 1 (WT1) immunoreactivity could represent a strong diagnostic tool to distinguish dermatofibrosarcoma protuberans from other dermal/subcutaneous bland-looking mesenchymal spindle cell lesions, such as dermatofibromas, deep fibrous histiocytomas, neurofibromas, spindle cell lipomas, dermal scars, nodular fasciitis, skin leiomyomas and solitary fibrous tumors [11]. Engels et al. investigated the sensibility and specificity rates of the detection of lymph node metastases in prostate cancer by one-step nucleic acid amplification (OSNA) [12]; when compared with “conventional” histopathologic examination of lymph nodes, high levels of concordance with this method were seen [12]. According to Chen et al., the loss of Tid1/DNAJA3 Co-Chaperone was found to stimulate tumor growth and recurrence risk in surgically resected hepatocellular carcinoma [13]. Mayer and colleagues demonstrated a strong correlation between restricted water diffusion in diffusion-weighted magnetic resonance imaging (DW-MRI) and tumor hypoxia, with the overexpression of B-lymphocyte induced maturation protein (Blimp-1) and vascular endothelial growth factor (VEGF), as surrogate markers for hypoxia, on tissue specimens of the matched patients [14]. The last OR article by Puglisi et al. focused on the grade of sensitivity to radiotherapy of cancer stem cells (CSCs)isolated from locally advanced rectal cancer biopsies [15]; based on these findings, the authors hypothesized that an in vitro prediction model of the potential response to radiotherapy could be done to personalize the treatment of these patients and to avoid radiation toxicity [15]. Among the review paper, the first one by Paydary et al. studied the literature evidence related to the use of immune-checkpoint inhibitors (ICIs) in gastro-esophageal cancer therapy, emphasizing that first-line ICI may mostly be used in patients exhibiting high PD-L1 levels, irrespectively of histopathology or anatomic location [16]. The potential role of cardiac biomarkers such as troponin and N-terminal prohormone of brain natriuretic peptide as less expensive and useful tools to predict, early diagnose and monitor different cancer-related cardiac conditions, was reviewed by Semeraro et al., who recognized the limitations reported in the literature, that often did not allow for a secure and standardized use of these markers in clinical practice [17]. Similarly, Honrubia-Peris and colleagues provided readers with an update on the available biomarkers capable of predicting therapeutic response to ICIs in advanced non-small cell lung cancer (NSCLC) [18]; some future perspectives, including the potential use of non-invasive markers (liquid biopsies or plasma determinations) were also discussed [18]. Despite being the most common intraocular malignancy in adults, uveal melanoma (UM) is a relatively unusual tumor, and its rarity is reflected in the paucity of currently known valid prognostic and predictive factors [19,20]. Gajdzis et al. filled the need to provide the scientific community with “the status of the art” concerning prognostic and immunohistochemical markers of this lesion, emphasizing that some of these could be useful in predicting the metastatic risk of UM patients [21]. The role of oncometabolites and their relationship with cancer initiation and progression were narrated and discussed by Beyoğlu et al. [22], while González-Gascón-y-Marín and colleagues performed a critical review of the predictive biomarkers of chronic lymphocytic leukemia (CLL), including 11q deletion, TP53 alterations and IGHV and NOTCH1 mutations [23]. The research group by Torrisi et al. summarized the literature data related to the biological impact of hypoxia on glioblastoma (GBM) invasiveness and acquisition of radio-resistant phenotype with activation of SRC proto-oncogene non-receptor tyrosine kinase, providing some suggestions to potentially overcome the current limitations in GBM treatment [24]. Russo et al. compared the previously reported rates of sensitivity and specificity and the oncologic outcomes of different techniques such as photodynamic diagnosis (PDD) fluorescence, narrow-band imaging (NBI) and white light cystoscopy (WLC) in visualizing and sampling non-muscle invasive bladder cancer NMIBC [25]; from their data meta-analysis, tumor resection with PDD and NBI was shown to exhibit lower recurrence rates and higher diagnostic sensitivity than WLC alone, and NBI resulted in better disease sensitivity and specificity than conventional WLC [25]. Finally, Diaz-del Castillo and colleagues presented a study protocol aimed at investigating the type, location and intensity of pain, its consequences on the quality of life of multiple myeloma (MM) patients, as well as the potential damage suffered by bone nerves in this condition [26]. We thank all the authors of the above-mentioned papers and all the reviewers who have dedicated their time and efforts to the success of this Special Issue.

PMC10001335

Drew M. Nassal,Rebecca Shaheen,Nehal J. Patel,Jane Yu,Nick Leahy,Dimitra Bibidakis,Narasimham L. Parinandi,Thomas J. Hund

Spectrin-Based Regulation of Cardiac Fibroblast Cell-Cell Communication

26-02-2023

cell-cell communication,cardiac fibroblast,spectrin,STAT3,exosomes

Cardiac fibroblasts (CFs) maintain the fibrous extracellular matrix (ECM) that supports proper cardiac function. Cardiac injury induces a transition in the activity of CFs to promote cardiac fibrosis. CFs play a critical role in sensing local injury signals and coordinating the organ level response through paracrine communication to distal cells. However, the mechanisms by which CFs engage cell-cell communication networks in response to stress remain unknown. We tested a role for the action-associated cytoskeletal protein βIV-spectrin in regulating CF paracrine signaling. Conditioned culture media (CCM) was collected from WT and βIV-spectrin deficient (qv4J) CFs. WT CFs treated with qv4J CCM showed increased proliferation and collagen gel compaction compared to control. Consistent with the functional measurements, qv4J CCM contained higher levels of pro-inflammatory and pro-fibrotic cytokines and increased concentration of small extracellular vesicles (30–150 nm diameter, exosomes). Treatment of WT CFs with exosomes isolated from qv4J CCM induced a similar phenotypic change as that observed with complete CCM. Treatment of qv4J CFs with an inhibitor of the βIV-spectrin-associated transcription factor, STAT3, decreased the levels of both cytokines and exosomes in conditioned media. This study expands the role of the βIV-spectrin/STAT3 complex in stress-induced regulation of CF paracrine signaling.

Spectrin-Based Regulation of Cardiac Fibroblast Cell-Cell Communication Cardiac fibroblasts (CFs) maintain the fibrous extracellular matrix (ECM) that supports proper cardiac function. Cardiac injury induces a transition in the activity of CFs to promote cardiac fibrosis. CFs play a critical role in sensing local injury signals and coordinating the organ level response through paracrine communication to distal cells. However, the mechanisms by which CFs engage cell-cell communication networks in response to stress remain unknown. We tested a role for the action-associated cytoskeletal protein βIV-spectrin in regulating CF paracrine signaling. Conditioned culture media (CCM) was collected from WT and βIV-spectrin deficient (qv4J) CFs. WT CFs treated with qv4J CCM showed increased proliferation and collagen gel compaction compared to control. Consistent with the functional measurements, qv4J CCM contained higher levels of pro-inflammatory and pro-fibrotic cytokines and increased concentration of small extracellular vesicles (30–150 nm diameter, exosomes). Treatment of WT CFs with exosomes isolated from qv4J CCM induced a similar phenotypic change as that observed with complete CCM. Treatment of qv4J CFs with an inhibitor of the βIV-spectrin-associated transcription factor, STAT3, decreased the levels of both cytokines and exosomes in conditioned media. This study expands the role of the βIV-spectrin/STAT3 complex in stress-induced regulation of CF paracrine signaling. Cardiac mechanical function depends on the coordinated activity of cardiac myocytes organized in interconnected muscle fibers and supported by a fibrous extracellular matrix (ECM) maintained by resident cardiac fibroblasts (CFs) [1,2,3]. Under physiological conditions, CFs typically reside in a quiescent (non-activated) state; however, cardiac stress or injury induces a dramatic transition in CF phenotype, characterized by increased proliferation, contractility and excessive ECM production leading to cardiac fibrosis [4,5,6,7]. While cardiac fibrosis is critical for repairing damaged myocardial tissue, dysregulation of the process in disease contributes to cardiac mechanical and electrical dysfunction [3,8,9]. CFs have the remarkable ability to sense local injury signals and communicate distress in a paracrine manner to distal cells, including other CFs, myocytes, immune cells, and endothelial cells [10,11]. Mounting data indicate that CFs engage in a cell-cell communication network following cardiac injury that depends, at least in part, on small (30–150 nm) extracellular vesicles (exosomes) capable of delivering proteins, lipids, mRNA and other bioactive cargo within the heart or even to other organs [12,13,14,15,16,17]. However, the mechanisms responsible for tuning CF exosome-dependent communication networks in response to chronic stress remain unclear. Mechanistic insight into the biogenesis of exosomes and their evolution with disease presents great promise for not only improving existing treatments but expanding therapeutic approaches. Spectrin family members are actin-associated cytoskeletal proteins that support cellular architecture and membrane stability in metazoan cells [18,19,20]. Beyond mechanical support for the membrane, spectrins facilitate intracellular signaling through the formation of macromolecular complexes involving ion channels, regulatory, adapter molecules, and transcription factors. The βIV-spectrin isoform, in particular, has been shown to act as a dynamic scaffold that organizes local signaling domains for regulation of signal transduction events in a variety of cell types, including CFs. Recently, we discovered a novel role for βIV-spectrin in regulating the subcellular localization and activity of the pleiotropic transcription factor signal transducer and activator of transcription 3 (STAT3) [21]. Further, we found that disruption of βIV-spectrin (in response to chronic stress) promotes STAT3 subcellular redistribution and aberrant activity to alter CF gene expression, proliferation, and contractility [22,23]. At the organ level, βIV-spectrin deficiency results in enhance maladaptive remodeling, fibrosis, and cardiac dysfunction, consistent with the CF phenotype [21,22]. Here, we demonstrate that beyond controlling the local activity of individual CFs, βIV-spectrin supports a long-range communication network between CFs and other cardiac cells. Using a genetic mouse model of βIV-spectrin deficiency (qv4J mouse), we demonstrate that loss of βIV-spectrin triggers the release of paracrine stress signals from CFs in a STAT3-dependent manner with the capacity to alter the behavior of recipient quiescent CFs. Interestingly, we show that βIV-spectrin-deficient CFs secrete higher concentrations of both inflammatory paracrine protein factors as well as bioactive exosomes, when compared to quiescent WT CFs. Our work identifies a novel role of the spectrin-based pathway in facilitating long-range communication responsible for impacting healthy cardiac function. Adult (2–4 mos, 18–22 g) male and female C57BL/6J wildtype (WT, control) and βIV-spectrin truncated (qv4J) littermate mice were used (see Table 1 for complete list of abbreviations). qv4J animals genetically express a Spnb4 allele with a spontaneous insertion point mutation at C4234T (Q1358 > Stop) resulting in a premature stop codon in βIV-spectrin repeat 10 leading to the lack of repeats 11 through the C-terminus including the putative STAT3 binding region [22,24,25]. qv4J animals were acquired from Jackson Laboratory. All procedures were conducted in accordance with the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health following protocols approved by the IACUC at The Ohio State University. Animals were euthanized using isoflurane and cervical dislocation followed by collection of tissue or cell isolation. Primary mouse CFs were isolated from left and right ventricles under sterile conditions, as described [22,26]. Briefly, ventricular tissue was minced in 2 mg/mL collagenase (Worthington Biochemical, Lakewood, NJ, USA) dissolved in 1× Ham’s F-10 buffer (Corning) at 37 °C. After digestion, the cell extract was filtered and centrifuged. After discarding the supernatant, cells were resuspended in normal feeding media containing 1× DMEM supplemented with 10% FBS, 1% l-glutamine, and 1% Pen/Strep. Cells were plated onto tissue culture treated plates for 4–5 h to allow for adhering. Culture media containing nonadherent cells (e.g., myocytes, endothelial cells) was then removed from culture and discarded. Fresh feeding media was replenished for adhered to CFs. Cells were grown in culture to the desired confluency. All experiments were performed at passage one (P1) conditions [22]. After CFs reached ~50–60% confluency, cells were washed with PBS and cultured with exosome depleted FBS (ThermoFisher Scientific, Waltham, MA, USA, Catalog #: A2720803) in DMEM medium with 1% l-glutamine and 1% Pen/Strep for 24 h. CF conditioned culture media (CCM) was collected and centrifuged to remove dead cells/debris. For experiments testing the differences in molecular weight fractions, conditioned media underwent a differential ultracentrifugation process to separate large (includes exosomes) and small (includes proteins and peptides) MW fractions, as described with slight modifications [27,28]. Briefly, CCM was filtered through a 100,000 MW Amicon ultra centrifugal filter unit (Millipore Sigma, Burlington, MA, USA) and centrifuged at 10,000× g for 30 min at 4 °C. The flow through was collected as the small MW fraction, while the captured material was resuspended in equal volumes of fresh CCM as the large MW fraction. Separate isolations of WT CFs were seeded into 12-well culture-treated plates, as described [23]. Briefly, cells were adhered for 24 h with serum starvation. The next day medium was replaced with either the complete CCM, small MW media, or large MW media. Cells were trypsinized at 24, 48, and 72 h postplating. Cell pellets were resuspended in a fixed volume and manually counted using a hemacytometer to calculate total cell numbers. Manual counting was performed blindly by the same person throughout the study to maintain accuracy and reproducibility. Type I rat- collagen gels (2 mg/mL) were prepared by mixing 10× PBS, sterile H2O, acidic rat tail collagen, and 1 M NaOH. Cells were added (200,000 cells/mL) and mixed before gelation. Cell-collagen mixtures were cast into 24 well culture plates and incubated at 37 °C in 5% CO2 for 1 h. After casting, gels were covered with 1 mL of culture feeding media and released from bottom of wells. Photographs of gels following 24 h of incubation were analyzed using NIH ImageJ software (v. 1.53), as described [22]. Experiments were conducted in technical triplicates. CCM supernatant from WT and qv4J CFs was used to examine levels of cytokines/chemokines using Proteome Profiler Mouse XL array kit (R&D Systems, Minneapolis, MN, USA, Catalog #: ARY028) according to manufacturer’s protocol. Briefly, CFs were cultured in normal feeding media until 70–80% confluency was reached. Cells were then serum starved for 24 h. CCM was collected and centrifuged at 2000 rpm for 5 min to remove dead cells/debris. Supernatant was stored at −80 °C until samples were processed and analyzed. Manual cell counts were performed on a subset of experiments at the time of CCM collection. Cytokine/chemokine levels were normalized to a control CC motif chemokine ligand 3 (CCL3). Isolated CFs from WT and qv4J mice were grown in culture to ~50–60% confluency (4–5 d after plating). For experiments testing the effects of STAT3 inhibition, a subset of qv4J CFs were treated for 48 h with S3I-201 (100 µM) [21,22]. Cells were subsequently washed with PBS and cultured with exosome depleted FBS in DMEM medium for 24 h. CCM was collected and centrifuged at 2000× g for 30 min to remove any cells or debris. Manual cell counts were performed on a subset of experiments at the time of CCM collection. The supernatant was treated with Total Exosome Isolation reagent, according to the manufacturer’s protocol (0.5× volume of the media, Fisher: 4478359) and incubated overnight at 4 °C. After incubation, the media was centrifuged at 10,000× g for 1 h at 4 °C. The supernatant was discarded and the pellet containing the EVs was resuspended in PBS and stored at −80 °C until analysis. EV suspensions were then analyzed for size and count using an NS300 nanoparticle tracking analysis system. Statistical analyses were performed with SigmaPlot 14.5. Data distribution for all comparisons was first tested for normality and equal variance using the Shapiro-Wilk test and Brown-Forsythe test, respectively. For single comparisons, an unpaired two-tailed t-test (data presented as mean ± SEM) or Mann-Whitney U rank-sum test (data presented as the median with 25th and 75th percentiles [box] and 10th and 90th percentiles [whiskers]) was performed to determine p values. For multiple comparisons, a two-way ANOVA with Holm-Sidak post hoc test was used. p < 0.05 was determined significant. To test the hypothesis that βIV-spectrin regulates a cell-cell communication network, CFs isolated from adult WT hearts were treated with conditioned culture media (CCM) from CFs isolated from WT (control) or spectrin-deficient hearts (qv4J mice expressing truncated βIV-spectrin). A significant increase in proliferation was observed in WT CFs treated with qv4J CCM at 48 and 72 h compared to those treated with WT CCM (Figure 1), indicating that spectrin-deficient cells generate paracrine signals capable of altering the phenotype of distal cells. As a first step in identifying βIV-spectrin-dependent paracrine signals, WT and qv4J CF CCM was separated into small and large molecular weight (MW) fractions, with the large MW fraction consisting of exosomes and other extracellular vesicles and the small MW component containing soluble proteins and signaling molecules. Interestingly, both fractions from qv4J CCM significantly increased proliferation of WT CFs at 48 and 72 h of treatment relative to WT controls, suggesting that βIV-spectrin modulates multiple targets relevant for cell-cell communication (Figure 2). To identify specific paracrine factors secreted by βIV-spectrin-deficient CFs to alter the behavior of distal cells, a proteome profiler array assay was used to screen 111 cytokines/chemokines in CCM from WT and qv4J CFs. Increased expression of a host of pro-fibrotic and pro-inflammatory factors was observed in qv4J CCM compared to WT, including MMP3, periostin, CCL17, osteoprotegerin, and CX3CL1 (Figure 3), consistent with previous RNA-sequencing analysis showing significant upregulation of these factors at the gene level within qv4J derived CFs [22]. Although qv4J CFs show enhanced proliferation, which on its own could increase generation of paracrine factors due to larger cell numbers, the levels of cytokines/chemokines and exosomes were assessed in CCM collected only 24 h after the cells reached target confluency, which is not enough time for differences in proliferation rate to confound the results (compare cell numbers in WT and qv4J at 24 h timepoint in Figure 1). These data indicate that loss of βIV-spectrin triggers the release of a host of cytokines and chemokines with the capacity to modulate phenotype of neighboring cells. To further explore the role of βIV-spectrin in exosome biogenesis/secretion, exosomes from WT and qv4J CFs were isolated and characterized. The size distribution and number of isolated exosomes were quantified using a NanoSight Particle Tracking system (Nanosight300). This approach confirmed that WT and qv4J CCM was enriched in extracellular vesicles within the size range that is characteristic for exosomes (Figure 4, 30–150 nm). Consistent with the increased cell-cell communication from spectrin deficient CFs, the concentration of exosomes was significantly greater in qv4J CCM compared to WT CCM (Figure 4), despite similar CF numbers at time of collection (compare cell numbers in WT and qv4J at 24 h timepoint in Figure 1). βIV-spectrin alters CF gene transcription (including for several targets identified here, Figure 2) in a STAT3-dependent manner [22]. To determine whether altered STAT3 activity contributes to release of proinflammatory and profibrotic cytokines/chemokines from βIV-spectrin-deficient CFs, qv4J CFs were pre-treated with the STAT3 inhibitor S3I-201 (100 µM) or vehicle (3% DMSO in PBS) for 72 h before collection and analysis of CCM. Interestingly, STAT3 inhibition largely normalized the profile of secreted chemokines/cytokines in qv4J to that observed in WT CCM (Figure 5). STAT3 inhibition also reduced the concentration of exosomes secreted by qv4J CFs (Figure 6). These data suggest that βIV-spectrin regulates CF paracrine signaling in a STAT3-dependent manner. Here, we describe a novel role for βIV-spectrin in tuning a cell-cell communication network in heart. Specifically, we report that βIV-spectrin-deficient (qv4J) CFs secrete a host of pro-fibrotic and pro-inflammatory paracrine signals into CCM with the capacity to alter the proliferative activity of WT CFs. Furthermore, we demonstrate an increase in release of bioactive exosomes from qv4J CFs. Finally, we report that STAT3 inhibition normalized the secretory profile of qv4J CFs. Based on our findings, we propose that the βIV-spectrin/STAT3 axis serves as a new avenue for modulating cell-cell communication and cardiac function in the setting of chronic disease. Degradation of βIV-spectrin induces changes in STAT3 signaling and gene expression in both cardiomyocytes and CFs that drive altered cardiac function and fibrosis. At the cellular level, βIV-spectrin/STAT3 dysfunction promotes highly eccentric cardiomyocyte growth and increased collagen deposition, proliferation, and contractility in CFs. A similar phenotype is observed in qv4J mice. Further, the distinctive remodeling profile of hypertrophy and fibrosis was observed together even in cardiomyocyte- or fibroblast-specific βIV-spectrin knock out models, despite confirmation of βIV-spectrin expression in Cre negative cells [21,22]. Interestingly, global CF activation has been observed following ischemic injury, even in remote areas from the infarct region, although the mechanism for propagating the pro-fibrotic stimuli is unknown [29,30]. Our new findings reveal a potential mechanism for how those pathological changes are communicated throughout the myocardium. Together, these studies implicate a role for the βIV-spectrin/STAT3 complex in cell-cell communication. It will be important for future studies to test the role for this complex in cell-cell communication in vivo. In this context, we have the ability to home in on specific cell populations (e.g., CFs, myocytes, immune cells) using our cell-specific βIV-spectrin knockout model [21,22]. Over the last decade, there has been growing interest in the characteristics and functional effects of cardiac cell-derived exosomes in the setting of pathologic stress. Many studies have described specific miRNA expression changes in secreted exosomes following pathological stress. For example, increased circulating exosomes enriched with miR-22 have been reported following ischemic injury and proposed to aide in repair and remodeling following myocardial infarction [31]. However, the mechanisms underlying these pathologic changes in exosome secretion are not well defined. Here, we report that βIV-spectrin-deficiency increases the secretion of exosomes from CFs. Exosomes have an endosomal origin and are thought to be trafficked using the same pathways as exocytosis and endocytosis. The secretion of extracellular vesicles relies on the highly dynamic membrane-cytoskeletal interface [32,33]. In response to internal and external stimuli, cytoskeletal proteins undergo localized remodeling that results in detachment from the membrane, exposing locations for fusion and vesicle secretion [34,35,36]. Interestingly, it is thought that the actin-spectrin network can regulate not only the location of vesicle secretion, but the dynamics as well [34,37]. For example, in neurons, F-actin regulates the pore opening and speed of vesicle secretion [38,39]. Further, other studies have speculated that spectrin proteins can modulate actin assembly dynamics [20,40]. While it is known that βIV-spectrin associates with F-actin at the cell membrane, it is still unclear whether the dissociation of the βIV-spectrin-complex from F-actin directly alters the secretion of exosomes. Although we did not directly test this relationship, it will be interesting in the future to determine whether spectrin-dependent regulation of exosome secretion depends on βIV-spectrin/F-actin interaction. STAT3 has a multifaceted role in regulating cell-cell communication, cardiac inflammation, and fibrosis. In CFs, STAT3 and miR-21 form a positive feedback loop to increase proliferation and expression of fibrotic genes, while STAT3 inhibition leads to the downregulation of miR-21 and abrogated myofibroblast activation fibrosis [41]. Interestingly, miR-21 was also found to be abundant in cardiac fibroblast-derived exosomes [14]. This may explain the observed decrease in exosome production following STAT3 inhibition. Our findings add to the growing literature that STAT3 is a critical regulator of cell communication during disease. Importantly, our results demonstrate that the βIV-spectrin/STAT3 complex plays a role in exosome biogenesis. Cardiac wound healing (e.g., following myocardial infarction) relies on paracrine signaling between different cell types to carefully orchestrate the transition from inflammation to repair. Mounting data support the idea that CFs have a role in initiating the inflammasome. Here, we found that loss of βIV-spectrin lead to increased expression of pro-reparative and pro-inflammatory stimuli. For example, we observed increased expression of CCL17 and CXC3CL1, chemokine ligands that play a vital role in immune cell infiltration [42,43]. Further, we found increased expression of ECM-degrading matrix metalloproteinases (MMP2 and MMP3) and OPG, which are secreted from CFs to aide in repair of the myocardium [44,45]. The generation of these paracrine signals was abrogated with STAT3 inhibition and reintroduction of the full βIV-spectrin construct. These findings support our previous work that loss of βIV-spectrin and STAT3 dysregulation is a critical step for CF activation and communication. While our findings demonstrate regulatory capacity for the βIV-spectrin/STAT3 complex in exosome production, our study focuses on the communication between CFs. Going forward, it will be interesting to determine whether a similar pathway supports communication between CFs and other cardiac cells, like cardiomyocytes and immune cells and to assess its role in vivo using cell-specific βIV-spectrin knockout models. It will also be interesting to determine how βIV-spectrin links specific stress stimuli to changes in exosome production and/or cargo. We propose that stress-induced loss of βIV-spectrin not only triggers activation of CFs, but also initiates pathological signal generation that is required for remodeling. Overall, this study expands the role of the βIV-spectrin/STAT3 complex in mediating cell-cell communication. We found that βIV-spectrin deficiency in CFs resulted in increased secretion of cytokines and exosomes that induced phenotypic changes (increased proliferation and contractility) in quiescent CFs. The secretory profile of βIV-spectrin deficient CFs could be attenuated by inhibiting STAT3. Our findings implicate potential mechanisms as to how CFs modulate exosome secretion following chronic stress. Future work will dissect the interplay of these interactions in exosome biogenesis and secretion. It will be exciting to validate the contribution of the βIV-spectrin/STAT3 complex in cell communication in future in vivo studies.

PMC10001341

Francesco Cavallieri,Rubens G. Cury,Thiago Guimarães,Valentina Fioravanti,Sara Grisanti,Jessica Rossi,Edoardo Monfrini,Marialuisa Zedde,Alessio Di Fonzo,Franco Valzania,Elena Moro

Recent Advances in the Treatment of Genetic Forms of Parkinson’s Disease: Hype or Hope?

27-02-2023

DJ1,genetic,GBA,LRRK2,Parkinson’s disease,PINK1,PRKN,SNCA,treatment

Parkinson’s disease (PD) is a multifarious neurodegenerative disease. Its pathology is characterized by a prominent early death of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies with aggregated α-synuclein. Although the α-synuclein pathological aggregation and propagation, induced by several factors, is considered one of the most relevant hypotheses, PD pathogenesis is still a matter of debate. Indeed, environmental factors and genetic predisposition play an important role in PD. Mutations associated with a high risk for PD, usually called monogenic PD, underlie 5% to 10% of all PD cases. However, this percentage tends to increase over time because of the continuous identification of new genes associated with PD. The identification of genetic variants that can cause or increase the risk of PD has also given researchers the possibility to explore new personalized therapies. In this narrative review, we discuss the recent advances in the treatment of genetic forms of PD, focusing on different pathophysiologic aspects and ongoing clinical trials.

Recent Advances in the Treatment of Genetic Forms of Parkinson’s Disease: Hype or Hope? Parkinson’s disease (PD) is a multifarious neurodegenerative disease. Its pathology is characterized by a prominent early death of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies with aggregated α-synuclein. Although the α-synuclein pathological aggregation and propagation, induced by several factors, is considered one of the most relevant hypotheses, PD pathogenesis is still a matter of debate. Indeed, environmental factors and genetic predisposition play an important role in PD. Mutations associated with a high risk for PD, usually called monogenic PD, underlie 5% to 10% of all PD cases. However, this percentage tends to increase over time because of the continuous identification of new genes associated with PD. The identification of genetic variants that can cause or increase the risk of PD has also given researchers the possibility to explore new personalized therapies. In this narrative review, we discuss the recent advances in the treatment of genetic forms of PD, focusing on different pathophysiologic aspects and ongoing clinical trials. Parkinson’s disease (PD) is a complex and manifold neurodegenerative disease. Its pathology is characterized by a prominent early death of dopaminergic neurons in the pars compacta of the substantia nigra (SNpc) and the presence of Lewy bodies containing aggregated α-synuclein encoded by the SNCA gene [1,2]. Currently, the diagnosis of PD is clinical and based on the presence of bradykinesia, eventually being associated with rigidity and resting tremor [1]. It is well known that the neurodegenerative process of PD starts several years before the onset of motor symptoms [3]. This prodromal phase is heterogeneous and depends on the phenotype of PD (according to the “brain first, body first” onset of pathology) [4]. The PD clinical course and the response to treatment vary just like its etiology, which is multifactorial and complex [5]. It is currently hypothesized that pathogenic mechanisms linked with sex, genomic, epigenetic, and environmental factors lead to several alterations at the cellular level [6,7,8,9]. These may include: conformational changes with malfunction and accumulation of key proteins due to abnormalities in their clearance systems (ubiquitin–proteasome system; lysosome- and chaperone-mediated autophagy); dysregulation of mitochondrial function and oxidative stress; loss of trophic factors; alterations of intracellular Ca2+ homeostasis; and finally neuroinflammation [2,9,10]. Nevertheless, the pathological aggregation and spread of α-synuclein is considered the key event in PD pathogenesis [11,12]. This protein, mainly expressed in the brain, is fundamental for neurotransmitter release and synaptic vesicle function [13]. It has been hypothesized that environmental factors (viruses, bacteria, toxins, etc.) might start α-synuclein’s pathological accumulation, likely favored by a genetic predisposition [14,15,16]. The treatment of PD is currently only symptomatic, mainly focused on an improvement in motor and non-motor signs and symptoms [17]. However, PD management requires a multidisciplinary and holistic approach that should integrate pharmacological and non-pharmacological treatment. Among the latter, rehabilitative therapy and exercise should be implemented during all phases of PD [17]. Indeed, recent studies have shown the presence of a dose–response association between physical activity and all-cause mortality in PD patients. This underlies the need to increase and maintain physical activity in PD [18], and this can play a preventative and maintenance role regarding physical fitness and mental health [19]. Mutations associated with a high risk for PD (monogenic PD) underlie 5% to 10% of all PD cases [20,21,22]. However, this percentage tends to increase over time as a result of the continuous identification of new genes associated with PD [5,23,24]. Besides PD-related genes, the SNCA gene was the first gene associated with inherited PD [12]. In more recent years, mutations in the leucine-rich repeat kinase 2 (LRRK2) and parkin (PRKN) genes were found to be the most common causes of dominantly and recessively inherited forms of PD, respectively [20,21,22]. Heterozygous mutations in the β-glucocerebrosidase gene (GBA) currently represent the greatest genetic risk factor for developing PD [25]. An updated list of the genes associated with PD is provided in Table 1 [20]. The importance of the genetic contribution to PD pathogenesis is twofold, as illustrated in Figure 1. On the one hand, the identification of genetic variants linked to PD may elucidate the different pathophysiological mechanisms involved in the disease. On the other hand, this knowledge can help to investigate potential new experimental and personalized therapies tailored to the genetic profile of an individual patient [10]. In this narrative review, we discuss the recent advances in the treatment of the most relevant genetic forms of PD, focusing on different pathophysiologic aspects that have driven ongoing clinical trials. The GBA gene is located on chromosome 1 (1q21) and encodes the lysosomal enzyme glucocerebrosidase (GCase), which is involved in the metabolism of glucosylceramide (GL-1), a basic glycolipid component of the cell membrane [25]. Biallelic mutations in the GBA gene have been classically associated with Gaucher’s disease (GD), a systemic disorder with a varying degree of central nervous system (CNS) involvement [25]. After observing an increased risk of PD in patients with GD [26,27], several large-scale genetic studies have also demonstrated that heterozygous variants in the GBA gene are the most important genetic risk factor for developing PD. Indeed, heterozygous GBA variants account for 5–30% of PD cases depending on the population and age [27,28,29,30]. To date, more than 300 GBA variants have been associated with PD, with an overall odds ratio (OR) for developing the disease of approximately 3.5–6 [27]. This OR seems to be directly linked to the severity of GBA mutations; indeed, severe GBA mutations (i.e., L444P, IVS2+1G>A, c.84dupG, V394L, D409H, RecTL, RecNCil) are associated with a higher risk of PD compared to mild ones (i.e., N370S) [25]. In addition, the severity of GBA mutations may also influence the clinical phenotype and the severity of the disease [27,28,29,31]. PD patients carrying severe mutations have an earlier age of onset and greater risk of dementia, impulsive–compulsive behavior (ICB), and delusions when compared to PD patients carrying mild mutations in the GBA gene [27,28,29,31]. The molecular mechanisms underlying the pathogenesis of GBA-PD are complex and not yet fully understood. The direct correlation between the severity of both the clinical course and the GBA variants (on the basis of the deleterious effect on GCase enzymatic activity) support the hypothesis of a key pathogenic role played by the loss of GCase function. However, the scenario is far from being so defined. PD patients harboring GBA variants such as E326K, which has a less-pronounced effect on GCase enzyme activity, do not appear to have a more benign clinical course [29,31]. Several reports describe an association between this variant and a higher risk of cognitive problems [29,31]. In addition, if the risk of PD depended on the extent of the residual GCase activity, most patients with GD would be expected to develop PD. However, that does not seem to happen [29,31]. The link between GBA mutations and PD still requires more effort in order to tackle the right molecular mechanism that is dysfunctional in the genesis of PD. The mutated GCase is not able to fold properly in the endoplasmic reticulum (ER), causing the protein to accumulate in this cellular compartment [25]. This leads to two main relevant consequences. Firstly, the accumulation of misfolded GCase protein in the ER may directly lead to ER oxidative stress with subsequent neuronal loss in dopaminergic neurons [32]. Secondly, the reduction in endolysosomal GCase activity may cause α-synuclein accumulation [25,32]. In addition, the accumulation of GL-1, due to low endolysosomal GCase activity, can also play a role, affecting the membrane fluidity of lysosomes and accelerating the formation of toxic α-synuclein oligomers, which in turn may block the ER–Golgi trafficking of GCase and lead to further GL-1 accumulation [33]. The failure of the endolysosomal and autophagic pathways is considered one the most important alterations at a cellular level in PD [2,10,25]. This is not surprising, since these scavenger systems are crucial for the degradation of α-synuclein, whose accumulation in the dopaminergic neurons is one of the hallmarks of PD [25]. Novel therapeutic approaches in GBA-PD are based on both the attempt to increase GCase activity through gene therapy or GCase enhancers and to reduce substrate accumulation [34]. Table 2 summarizes the ongoing clinical trial in GBA-PD patients. The first novel therapeutic approach developed in GBA-PD patients was based on molecular chaperones, a class of protein that may facilitate the refolding of their substrates [35]. Chaperones may help to refold mutant GCase inside the ER, facilitating trafficking and increasing GCase levels in lysosomes [27,36]. Ambroxol is an inhibitory chaperone that mobilizes the sequestered mutant GCase from the ER, inducing a conformational change that facilitates transport to lysosomes and the recovery of GCase lysosomal function. In vitro and in vivo studies have confirmed that ambroxol can increase GCase activity and reduce α-synuclein levels [37]. In a phase 2, open-label study (NCT02941822) involving 17 PD patients with and without GBA mutations, ambroxol (at an escalating oral dose to 1.26 g per day) was able to both cross the blood–brain barrier (BBB) and to increase the GCase and α-synuclein concentrations in the cerebrospinal fluid (CSF) [38]. Based on these promising results, two phase 2 clinical trials are ongoing (NCT05287503, NCT02914366). In particular, NCT02914366 is a 52-week, randomized, placebo-controlled, quadruple-masking trial that is testing the hypothesis that oral ambroxol (1050 mg/daily) may improve cognitive and motor symptoms in patients with PD dementia (PDD). In addition, the multicenter, randomized, double-blind, placebo-controlled NCT05287503 trial (Ambitious study) is investigating whether the prolonged administration of high-dose oral ambroxol is able to change GCase activity and α-synuclein CNS levels and to reduce the progression of cognitive decline and motor disability in a cohort of 60 PD-GBA patients. Aside from ambroxol, other small-molecule chaperones (GC00188758, NCGC607, quetiapine, PGRN, HSP70, arimoclomol, LTI-291) have been investigated, but are so far supported by limited preclinical evidence [27]. Gene therapy in GBA-PD patients relies on the delivery of a normal GBA gene using an adenoassociated virus (AAV) vector [39]. The efficacy of this approach has been confirmed in different animal models of PD-GBA [39,40,41,42], showing a significant reduction in both α-synuclein accumulation and CNS inflammation [39]. Based on these premises, the first experimental gene therapy in PD-GBA patients has been recently developed [39]. This investigational drug (PR001) is composed of a viral vector (adenoassociated virus serotype 9) containing a codon-optimized plasmid encoding a wild-type human GBA gene, proved to be able to increase GCase activity, decrease glycolipid substrate accumulation, and improve motor abnormalities in GBA-PD models in vivo [27,39]. This has led to the development of the J3Z-MC-OJAA study, a Phase 1/2a, multicenter, open-label, ascending-dose, first in-human study that will evaluate the safety of intratecal LY3884961 administration in patients with moderate to severe PD with at least one pathogenic GBA mutation (NCT04127578). Two escalating dose cohorts (low-dose and high-dose) will be studied, and patients will be evaluated for the effect of LY3884961 on safety, tolerability, immunogenicity, biomarkers, and clinical efficacy measures with a 5-year follow-up. Another therapeutic approach in GBA-PD is focused on the reduction of GL-1 accumulation [25]. Venglustat is a potent, CNS-penetrant inhibitor of glucosylceramide synthase that can reduce the formation of GL-1 [33]. The efficacy of venglustat in GBA-PD patients was explored in the MOVES-PD trial, a randomized, double-blinded, placebo-controlled, dose-escalation study (NCT02906020). The first part of the study included 29 GBA-PD patients, and showed that venglustat had a favorable safety and tolerability profile [33]. However, the second part of the MOVES-PD trial (NCT02906020), which included 273 early-PD patients, did not meet the study’s primary objective, i.e., the efficacy on motor symptoms (primary endpoints: MDS-UPDRS part II–III). In particular, a progressive deterioration in clinical outcomes was noticed over time in the treatment arm compared to the placebo, leading to a premature interruption of the study [27]. Pathogenic mutations in the LRRK2 gene are common genetic risk factors for both familial and sporadic adult-onset PD [43]. Up until now, over 50 different LRRK2 variants have been identified [44]. The most common mutation, LRRK2-G2019S, accounts for up to 6–40% of familial PD cases depending on the ethnic group, and up to 2% of all sporadic cases [43]. PD patients with the G2019S mutation usually present with a substantial clinical overlap with idiopathic PD and a similar rate of progression [5]. However, they may more commonly have a postural-instability/gait-difficulties phenotype, levodopa-induced dyskinesias, and fewer nonmotor manifestations [5]. LRRK2 is located at chromosome 12q12 and encodes a multidomain protein of 2527 amino acids harboring different catalytic domains, including the MAPKKK kinase domain, the Ras of complex (ROC) GTPase domain, and the C-terminal of ROC (COR) domain [44]. Seven missense LRRK2 mutations have been identified as pathogenic, including R1441G, R1441C, R1441H, Y1699C G2019S, R1628P, G2385R, and I2020T, which are mainly located in the catalytic domains of the LRRK2 gene [45]. LRRK2 is widely expressed in several tissues including the brain, lungs, heart, and kidney [45]. At the brain level, LRRK2 mRNA and proteins are highly expressed in dopamine-innervated areas including the cerebral cortex, striatum, cerebellum, and hippocampus, while at low levels in dopaminergic neurons of the substantia nigra and ventral tegmental area [45]. At the cellular level, the LRRK2 protein is mainly found throughout the cytoplasm associated with various intracellular membranes and vesicular structures (i.e., early endosomes, lysosomes, plasma membrane and synaptic vesicles, ER, Golgi complex, and outer mitochondrial membrane) [45]. Through the phosphorylation of several substrates, LRRK2 is involved in several cellular functions including late-stage endocytosis, lysosomal trafficking, cytoskeletal remodeling, and synaptic-vesicle endocytosis [43,46]. The current understanding of LRRK2 functions and pathogenicity in PD is still incomplete [5]. It is believed that increased LRRK2 activity may raise the risk of PD because the increased kinase activity has been associated with nigrostriatal degeneration and Lewy body (LB) formation [5,43,44,45]. In addition, the G2019S mutation, located in the kinase domain of the gene, has been associated with increased phosphorylation activity in vivo [5]. In primary neuronal cultures, overexpression of LRRK2 G2019S, I2020T, R1441C, or Y1699C consistently induces neuronal toxicity, as evidenced by neurite shortening, cell death, and impaired functions of intracellular organelles [43]. Furthermore, many of these phenotypes may be alleviated by introducing kinase-inactive or guanosine triphosphate (GTP)-binding-deficient mutations and/or treatment with chemical inhibitors of LRRK2 [43]. LRRK2 may also cause α-synuclein neurotoxicity by increasing its propagation and aggregation in a kinase-dependent manner with the contemporary reduction in its clearance [47]. LRRK2 may also play a role in cell-to-cell transmission and long distance spreading of α-synuclein, presumably through regulation of the release, uptake, and lysosomal/proteasomal degradation of the protein [43]. LRRK2 can also interact with several other proteins throughout the endolysosomal pathway, and its excessive expression levels or kinase activity may disrupt vesicle trafficking and protein degradation [48]. The objective of the novel therapeutic approaches in LRRK2-PD is to reduce the pathological excessive kinase activity of the mutated gene. Table 3 summarizes the ongoing clinical trial in LRRK2-PD. The CNS-penetrant, selective, small-molecule LRRK2 kinase inhibitor DNL201 was able to inhibit LRRK2 kinase activity and improve lysosomal function in preclinical models [49]. In one phase 1 and one phase 1b clinical trial, which included 122 healthy volunteers and 28 PD patients, DNL201 (at single and multiple doses) inhibited LRRK2, was well tolerated, and showed robust CSF penetrance [49]. The safety, tolerability, pharmacokinetics, and pharmacodynamics of multiple oral doses of LRRK2 kinase inhibitor BIIB122/DNL151 were assessed in PD patients in a phase 1 study (NCT04056689) whose results are still pending. This molecule is now under investigation in two other ongoing trials. The objective of the phase 2b, multicenter, randomized, double-blind, placebo-controlled study LUMA (NCT05348785) is to assess the safety of BIIB122 oral tablets (225 mg once daily) and the possible impact of the drug on disease progression. A total of 640 early-stage PD patients without mutations in the LRRK2 gene will be enrolled. In addition, the phase 3, multicenter, randomized, double-blind, placebo-controlled study LIGHTHOUSE (NCT05418673) will aim to determine the safety profile and the efficacy of BIIB122/DNL151 to slow the progression of disease in 400 LRRK2-PD patients. The use of antisense oligonucleotides (ASOs) for LRRK2 inhibition represents another therapeutic approach under investigation in LRRK2-PD patients. This approach is supported by preclinical studies that showed that the administration of LRRK2 ASOs to the brain was able to reduce LRRK2 protein levels and fibril-induced α-synuclein inclusions [50]. Furthermore, mice exposed to α-synuclein fibrils treated with LRRK2 ASOs showed more tyrosine hydroxylase (TH)-positive neurons compared to control mice, suggesting that LRRK2 ASOs treatment could be a potential therapeutic strategy for preventing PD-associated pathology [50]. Based on these premises, the ongoing phase 1 single- and multiple-ascending-dose study REASON (NCT03976349) will assess the safety, tolerability, and pharmacokinetic profile of intrathecal injections of the LRRK2 ASO inhibitor BIIB094 in PD patients with and without LRRK2 mutations. Generally, patients carrying SNCA mutations present with early-onset parkinsonism with severe and early non-motor symptoms, including cognitive decline. However, many different PD phenotypes have been related to SNCA mutations [51,52]. Indeed, while in whole-gene multiplications, the number of SNCA copies clearly correlates with the disease severity, supporting the notion of a “dosage effect”, missense mutations cause more complex phenotypes with mutation-specific trends in clinical presentations [52]. α-synuclein, encoded by the SNCA gene, is a 14-kDa protein involved in synaptic vesicle release [11], mitochondrial function, and intracellular trafficking and is a potential chaperone [53]. The deposition of α-synuclein oligomers and fibrils disrupts synaptic-vesicle trafficking at the presynaptic terminal leading to an impairment of dopamine release [54] and dopamine transporter (DAT) function [54]. The deposition of α-synuclein oligomers may also impair mitochondrial function, leading to increased mitophagy and mitochondrial DNA damage and decreasing the mitochondrial biogenesis factor peroxisome proliferator-activated receptor γ co-activator 1α (PGC-1α) [55]. This is supported by animal models carrying A53T and A30P α-synuclein mutations [56,57]. A-synuclein deposition can also disrupt ER and Golgi trafficking, with a subsequent reduction in lysosomal enzyme levels, which in turn impairs the autophagic degradation of damaged organelles and protein aggregates [11]. Finally, α-synuclein fibrils activates microglia via TLR2 (Toll-Like Receptor 2), resulting in the activation of NF-kB and MAPK, and the production and release of pro-inflammatory mediators. Table 4 summarizes the ongoing clinical trials focused on α-synuclein. The treatment strategies may be subdivided into different approaches. One strategy relies on the reduction of α-synuclein synthesis using small interfering RNAs (siRNA) that target α-synuclein mRNA or antisense oligonucleotides (ASO); the latter has shown promising results in animal models [58,59]. However, no related clinical trial is currently ongoing. Another approach is based on the increase in the degradation of α-synuclein aggregates through autophagy and lysosomal function. As an example, the overexpression of lysosomal transcription factor EB (TFEB) in rats expressing α-synuclein decreases its oligomer’s levels, preventing organelle dysfunction and neurodegeneration [60]. This approach includes other possible targets including the mammalian target of rapamycin (mTOR) signalling and the cellular homolog of ABL1 (c-Abl) [10]. Indeed, α-synuclein overexpression inhibits autophagy by increasing mTOR activity, which can be modulated or inhibited by different substances or drugs, including rapamycin, curcumin, piperine, lithium (ongoing phase I clinical trial (NCT04273932), trehalose, corynoxine B, sodium valproate, and carbamazepine [10]. Studies in PD animal models and brain specimens from PD patients have revealed increased levels and activity of c-Abl in dopaminergic neurons with phosphorylation of protein substrates, such as α-synuclein and the E3 ubiquitin ligase [61]. The inhibition of c-Abl kinase activity by drugs used in the treatment of human leukaemia has shown promising neuroprotective effects in cell and animal models of PD [61]. This has led to the development of several ongoing phase 1 and phase 2 clinical trials testing the safety and efficacy of c-Abl inhibitors (i.e., imatinib, nilotinib, bafetinib, IkT-148009) in PD patients. In particular, the results from two recent trials have been recently published. The single-center, phase 2, double-blind trial NCT02954978 included 75 patients randomized vs. placebo and oral nilotinib 150-mg or nilotinib 300-mg for 12 months followed by a 3-month washout period [62]. This trial met its primary outcome (safety, tolerability, and detection in CSF), and is expected to guide a future phase 3 study to evaluate oral nilotinib as a disease-modifying medication for PD [62]. Nilotinib appeared to be reasonably safe and detectable in the cerebrospinal fluid, and exploratory biomarkers were altered in response to it [62]. Different results came from a 6-month, multicenter, double-blind trial (NCT03205488) that included 76 PD patients randomized to placebo vs. 150-mg nilotinib or 300-mg nilotinib once daily orally for 6 months, followed by a 2-month off-drug evaluation [63]. Unfortunately, nilotinib at 150 mg and 300 mg worsened on-medication PD motor scores (MDS-UPDRS-3) compared with placebo, with no differences in the change in off-medication MDS-UPDRS-3 scores [63]. Furthermore, there was no evidence of a treatment-related alteration of dopamine metabolites in the CSF. The authors concluded that, due to the low CSF exposure, lack of biomarker effect, and lack of efficacy, nilotinib should not be further tested in PD [63]. Furthermore, GCase may also decrease soluble α-synuclein in mice expressing mutant human A53T α-synuclein [64]. Two non-inhibitory GCase modulators (NCGC00188758 and NCGC607) have been found to increase GCase activity and decrease α-synuclein accumulation and toxicity in human neurons derived from induced pluripotent stem cells (iPSCs) [27]. Another different approach relies on the blockage of α-synuclein propagation between neurons. This approach is supported by preclinical studies, which showed that memantine, an antagonist of the NMDA (N-Methyl-D-Aspartate) subtype of glutamate receptor, may exert neuroprotective properties via the inhibition of cell-to-cell transmission of extracellular α-synuclein. A phase 3 trial (NCT03858270) is ongoing in order to evaluate the drug’s clinical impact on PD patients. A further promising approach to target α-synuclein aggregates is based on passive immunization using antibodies against α-synuclein, which may promote its lysosomal clearance. Antibodies against α-synuclein have already been tested in two recent RCTs, PRX002 (Roche) and BIIB054 (Biogen). Unfortunately, a randomized, double-blind, placebo-controlled study (SPARK) that evaluated the efficacy and safety of BIIB054 in PD patients did not meet both primary and secondary outcome measures for year one and also failed to meet secondary outcome measures [65]. This has led to the discontinuation of the development of BIIB054 for PD, and the SPARK study has been closed. Even the phase 2 PASADENA trial (NCT03100149), evaluating the safety and efficacy of intravenous prasinezumab (PRX002) in early-stage PD, did not show promising results [66]. This trial included 316 participants; 105 were assigned to receive placebo, 105 to receive 1500 mg of prasinezumab, and 106 to receive 4500 mg of prasinezumab [66]. Treatment with prasinezumab had no meaningful effect on global or imaging measures of PD progression compared to the placebo and was associated with infusion reactions [66]. However, another phase 2b, randomized, double-blind, placebo-controlled study with prasinezumab (NCT04777331), the PADOVA trial, is ongoing. As opposed to passive immunization, which involves administering anti-α-syn antibodies to the patient conferring temporary protection against the disease, active immunization involves stimulation of the immune system to produce antibodies against toxic α-syn conformations [67]. In this contest, different phase 1 clinical trials (PD03A [SYMPATH grant agreement 602999], PD01A [NCT01568099], UB-312 [NCT04075318]) targeted against oligomeric α-synuclein have been completed and have shown promising results with a positive antibody response [68,69]. Obviously, phase 2 trials are needed to test these promising results in a large cohort. Globally, due to the negative results of different anti-α-synuclein trials, a debate is ongoing as to whether toxic α-synuclein aggregation is the real culprit or rather if it is a loss of function in PD (gain-of-function vs. loss-of-function theories). The latter has been supported by the fact that the overexpression of native α-synuclein can rescue animal models of PD [70,71,72]. The loss-of-function α-synuclein theory is also based on the fact that α-synuclein is a critical protein in neuron (i.e., dopamine neurons) survival and that maintaining a certain level of biologically functional protein is an important consideration in targeting α-synuclein for therapies [70,71,72]. In this setting it has been assumed that a reduction in biologically functional α-synuclein, whether through aggregation or reduced expression, may at least in part be involved in the neurodegeneration of PD [70,71,72]. Up to 15% of monogenic PD cases are due to mutations in the PRKN gene [21,73]. From a clinical point of view, PD patients carrying biallelic mutations in the PARK gene show a typical early or juvenile age at onset (mean, 31 years), while tremor, bradykinesia and foot dystonia are the most commonly presenting signs [74]. Early dyskinesias, good response to dopaminergic treatment, and prominent motor fluctuations are other common findings in PARK gene mutation carriers [74]. On the contrary, cognitive alterations and dementia are very rare [74]. In addition, there is a debate on the role of monoallelic mutations in the PRKN gene; indeed, while some authors have suggested an association with an increased risk of PD [75,76]; other recent cohort studies have pointed out that heterozygous pathogenic PRKN mutations are common in the population but do not increase the risk of Parkinson’s disease [77,78]. PD due to PINK 1 biallelic mutations is characterized by a slow progression, good and persistent levodopa response, and minimal cognitive involvement [5]. In addition, some other features have been described, including dystonia, sleep benefit, and hyperreflexia [5]. The PRKN gene encodes for the E3 ubiquitin ligase parkin [79], which acts together with the PINK1 protein in a pathway of the mitochondrial quality control (MQC) system, which has neuroprotective effects [79]. The MQC has several functions, including the regulation of interconnected and dynamic networks of mitochondria through fusion and fission; government of mitochondrial morphology, regulation of ATP levels, and control of the constant and timely turnover of mitochondria [80]. The parkin protein, which is localized in the cytosol, is recruited to move to the outer mitochondrial membrane (OMM) of depolarized mitochondria [81] and promotes the ubiquitination of OMM proteins involved in upregulating mitochondrial fusion [82]. The ubiquitination is the step needed to remove these proteins and shift the balance between fission and fusion towards increased fission, promoting mitochondrial fragmentation and triggering the cellular autophagic machinery to mitophagy [83]. Parkin and PINK-1 may play similar roles in the cell, acting in a common pathway, where parkin acts downstream of PINK1. Indeed, missense mutants of parkin and PINK1 exhibited a loss of mitochondrial integrity because of reduced mitochondrial fission [82]. Parkin and PINK1 are involved in a direct interaction, whereby PINK1 phosphorylates parkin and activates its E3 ligase activity at the OMM [84]. If either parkin or PINK1 are mutated or down-regulated, the dysfunctional mitochondria will remain in the cytoplasm, creating an environment of oxidative stress ultimately resulting in cell death. Parkin has limited activity in the absence of PINK1, and this activity is potentiated through PINK1 expression [85]. In vivo PD PINK1 models using Drosophila display higher levels of misfolded mitochondrial respiratory complex components, ultimately leading to mitochondrial dysfunction and fragmentation [86]. PINK1 knockout (KO) mouse models showed a lower basal mitochondrial respiration in the dorsal striatum when compared to wild-type control mice [87,88]. The circuit also exhibited less dopamine release, suggesting that the neurotransmitter’s deficient release might be mostly caused by mitochondrial dysfunction and lower ATP levels [87]. Both PINK1 and parkin patient-derived midbrain dopaminergic neurons were found to have higher levels of α-synuclein, altered mitochondrial morphology, and increased vulnerability to mitochondrial aggressors [87]. Iron accumulation secondary to mitophagy dysfunction, both in PINK1- and PRKN-mutated PD patients, has raised the possibility to employ iron chelator drugs in this subgroup of PD patients [87,89]. At the moment, no ongoing preclinical or clinical studies targeting PRKN or PINK1 mutations or the related biochemical pathways have been reported. However, there are some interesting pre-clinical studies that might lead to future clinical applications. It has already been pointed out that mitophagy and mitochondrial dysfunction are major aspects of PRKN and PINK1-related PD. Thus, restoring normal mitophagy through targeting the PINK1/Parkin pathway seems to be the most promising therapeutic approach [90,91]. The mitochondrial receptor Nip3-like protein X (Nix) overexpression was found to rescue the organelle function in fibroblast lines from homozygous mutated PINK1 patients [87]. Similarly, the matrix protein nipsnap homolog 1 (NIPSNAP1) might rescue PINK1-Parkin-dependent mitophagy in PINK1 phenotypes, since the molecule has been shown to contribute to this mitophagy pathway in cellular models [92]. Zebrafish lacking NIPSNAP1 were shown to have reduced brain mitophagy and increased reactive oxygen species (ROS) [92]. However, the removal of too many mitochondria may have a paradoxical negative effect if normal homeostasis is affected by the intervention [87]. In this setting, the identification of biomarkers for mitochondrial dysfunctions to serve as a therapeutic response monitor is of paramount importance [87]. Clinically, PD patients with biallelic DJ-1 mutations exhibit early-onset dyskinesia, rigidity, and tremor, followed by later manifestation of psychiatric symptoms, such as psychotic disturbance, anxiety, and cognitive decline, and generally respond well to L-DOPA treatment [93]. DJ-1 was originally identified as an oncogene and later associated with PD and diabetes mellitus [94]. The DJ-1 protein is expressed in reactive astrocytes and, to a lower extent, in neurons [95,96]. It is involved in mitochondrial function, apoptosis regulation, pro-survival signaling, autophagy, inflammatory responses, protection against oxidative stress, and chaperone activity [97]. PD-associated DJ-1 variants result in a loss of protein function. Experiments using PD-patient-derived DJ-1-deficient cells showed predominantly mitochondrial dysfunction and a reduced dopaminergic differentiation potential [97]. One study found elevated levels of α-synuclein in iPSC-derived human neurons from biallelic DJ-1 mutation carriers [98]. DJ-1 loss of function is also associated with increased inflammatory responses. siRNA DJ-1-knockdown mouse astrocytes are less able to protect against neurotoxins such as rotenone when compared to wild-type controls [96]. Furthermore, there is a reduced expression of prostaglandin D2 synthase, which regulates anti-inflammatory responses. Similarly, DJ-1-deficient microglia have an increased sensitivity to pro-inflammatory signals such as lipopolysaccharide (LPS), as well as an impaired uptake and degradation of α-synuclein and autophagy [99]. It has been demonstrated that rasagiline, a monoamine oxidase inhibitor, reduces the pro-inflammatory phenotype in microglia in a DJ-1 knockout model [100]. Therefore, this drug may be particularly useful in the treatment of DJ-1 PD patients [101]. There are currently no human trials targeting DJ-1 for disease-modifying molecules. However, there are some interesting pre-clinical studies that might lead to future clinical applications. The most frequent approach in different pathological models is to increase DJ-1 levels in order to achieve neuroprotection in the face of oxidative stress [93]. Several studies using rat PD models have demonstrated the efficacy of recombinant wild-type DJ-1 for the protection of dopaminergic neurons [102,103,104]. However, all studies used intranigral injection for drug delivery, which is not clinically feasible [102,103,104]. A more promising way of DJ-1 delivery is based on the transactivator of transcription (TAT) cell-permeable peptide, used by HIV to cross plasma membranes [105]. Reduced dopaminergic dysfunction and improved behavior were achieved in a hemiparkinsonian mouse model, as well as reduced MPTP toxicity [106]. A second major strategy is the identification of drugs that inhibit the excessive oxidation of an important cysteine (Cys) residue at position 106 of the DJ-1 protein (Cys106). The reduced form of the protein (DJ-1 Cys106-SH) can be oxidized to a sulfinic acid form (DJ-1 Cys106-SO2H) and a sulfonic acid form (DJ-1 Cys106-SO3) in the presence of moderate or high oxidative stress (overoxidation) [93]. The reduced and sulfinic DJ-1 forms are stable; the sulfonic, on the other hand, is unstable and prone to aggregation (DJ-1 inactivation) [93]. The Cys106 sulfinate form (Cys-SO2−) stabilizes both human and Drosophila DJ-1 [107]. In this line of thought, studies using DJ-1 stabilizers in rat models were able to prevent dopaminergic neuronal death and even restore normal locomotor function. The most promising substance, compound-23, inhibited MPTP-induced locomotor deficits and cell death in the substantia nigra and striatum [93]. In this narrative review, we have illustrated the recent main advances in the treatment of genetic forms of PD. Each form has different pathophysiological characteristics that have not yet been fully elucidated. A precise identification of the pathophysiological mechanisms underlying specific genetic forms of PD represents a necessary effort in order to be able to develop customized gene-based treatments aimed at repairing different monogenic forms. While for the more frequent forms (i.e., GBA, LRRK2) experimental pharmacological trials are in progress or are about to begin, for the rarer forms (such as DJ1, PRKN, and PINK1), unfortunately, concrete therapeutic advances are still lacking.

PMC10001343

Floriane Racine,Christophe Louandre,Corinne Godin,Baptiste Chatelain,Stefan Prekovic,Wilbert Zwart,Antoine Galmiche,Zuzana Saidak

The Tumor Coagulome as a Transcriptional Target and a Potential Effector of Glucocorticoids in Human Cancers

28-02-2023

glucocorticoids,tumor coagulome,tumor microenvironment

Simple Summary Human tumors often establish a local hypercoagulant state that promotes vascular complications, such as venous thromboembolism. The concept of the tumor «coagulome» refers to the repertoire of tumor-expressed genes that locally regulate coagulation and fibrinolysis. Recent systems studies have helped to define the landscape of the coagulome across the spectrum of human tumors, unveiling its link with the tumor microenvironment. Understanding the key elements that regulate the expression of the coagulome is therefore essential. In this study, we explored the dynamic regulation of the tumor coagulome by glucocorticoids. We found that glucocorticoids regulate the coagulome through a combination of direct transcriptional and indirect effects. We show that this transcriptional regulation applies to human tumors, and we suggest that the direct transcriptional regulation of PAI-1 expression by the glucocorticoid receptor may regulate the tumor microenvironment. The transcriptional regulation of the coagulome by glucocorticoids that we report here may have vascular consequences and may account for some of the effects of glucocorticoids on the tumor microenvironment. Abstract Background: The coagulome, defined as the repertoire of genes that locally regulate coagulation and fibrinolysis, is a key determinant of vascular thromboembolic complications of cancer. In addition to vascular complications, the coagulome may also regulate the tumor microenvironment (TME). Glucocorticoids are key hormones that mediate cellular responses to various stresses and exert anti-inflammatory effects. We addressed the effects of glucocorticoids on the coagulome of human tumors by investigating interactions with Oral Squamous Cell Carcinoma, Lung Adenocarcinoma, and Pancreatic Adenocarcinoma tumor types. Methods: We analyzed the regulation of three essential coagulome components, i.e., the tissue factor (TF), urokinase-type plasminogen activator (uPA), and plasminogen activator inhibitor-1 (PAI-1) in cancer cell lines exposed to specific agonists of the glucocorticoid receptor (GR) (dexamethasone and hydrocortisone). We used QPCR, immunoblots, small-interfering RNA, Chromatin immunoprecipitation sequencing (ChIPseq) and genomic data from whole tumor and single-cell analyses. Results: Glucocorticoids modulate the coagulome of cancer cells through a combination of indirect and direct transcriptional effects. Dexamethasone directly increased PAI-1 expression in a GR-dependent manner. We confirmed the relevance of these findings in human tumors, where high GR activity/high SERPINE1 expression corresponded to a TME enriched in active fibroblasts and with a high TGF-β response. Conclusion: The transcriptional regulation of the coagulome by glucocorticoids that we report may have vascular consequences and account for some of the effects of glucocorticoids on the TME.

The Tumor Coagulome as a Transcriptional Target and a Potential Effector of Glucocorticoids in Human Cancers Human tumors often establish a local hypercoagulant state that promotes vascular complications, such as venous thromboembolism. The concept of the tumor «coagulome» refers to the repertoire of tumor-expressed genes that locally regulate coagulation and fibrinolysis. Recent systems studies have helped to define the landscape of the coagulome across the spectrum of human tumors, unveiling its link with the tumor microenvironment. Understanding the key elements that regulate the expression of the coagulome is therefore essential. In this study, we explored the dynamic regulation of the tumor coagulome by glucocorticoids. We found that glucocorticoids regulate the coagulome through a combination of direct transcriptional and indirect effects. We show that this transcriptional regulation applies to human tumors, and we suggest that the direct transcriptional regulation of PAI-1 expression by the glucocorticoid receptor may regulate the tumor microenvironment. The transcriptional regulation of the coagulome by glucocorticoids that we report here may have vascular consequences and may account for some of the effects of glucocorticoids on the tumor microenvironment. Background: The coagulome, defined as the repertoire of genes that locally regulate coagulation and fibrinolysis, is a key determinant of vascular thromboembolic complications of cancer. In addition to vascular complications, the coagulome may also regulate the tumor microenvironment (TME). Glucocorticoids are key hormones that mediate cellular responses to various stresses and exert anti-inflammatory effects. We addressed the effects of glucocorticoids on the coagulome of human tumors by investigating interactions with Oral Squamous Cell Carcinoma, Lung Adenocarcinoma, and Pancreatic Adenocarcinoma tumor types. Methods: We analyzed the regulation of three essential coagulome components, i.e., the tissue factor (TF), urokinase-type plasminogen activator (uPA), and plasminogen activator inhibitor-1 (PAI-1) in cancer cell lines exposed to specific agonists of the glucocorticoid receptor (GR) (dexamethasone and hydrocortisone). We used QPCR, immunoblots, small-interfering RNA, Chromatin immunoprecipitation sequencing (ChIPseq) and genomic data from whole tumor and single-cell analyses. Results: Glucocorticoids modulate the coagulome of cancer cells through a combination of indirect and direct transcriptional effects. Dexamethasone directly increased PAI-1 expression in a GR-dependent manner. We confirmed the relevance of these findings in human tumors, where high GR activity/high SERPINE1 expression corresponded to a TME enriched in active fibroblasts and with a high TGF-β response. Conclusion: The transcriptional regulation of the coagulome by glucocorticoids that we report may have vascular consequences and account for some of the effects of glucocorticoids on the TME. Human tumors often establish a local hypercoagulant state that promotes vascular thromboembolic complications. Venous thromboembolism (VTE) in particular, is a major source of mortality and morbidity in cancer patients [1,2,3]. The concept of the tumor « coagulome » refers to the repertoire of tumor-expressed genes that locally regulate coagulation and fibrinolysis [4]. Systems biology approaches, especially based on genomics, have been used to study the coagulome [5,6,7]. We have recently used systems biology to characterize the landscape of the human tumor coagulome and its association with the vascular risk, known to strongly depend on the primary tumor type [7]. Indeed, great differences exist across different tumor types in the expression of F3 mRNA, which encodes the main activator of coagulation, the tissue factor (TF). F3 is highly expressed in cancers known to be at a high risk of VTE, such as glioblastoma, primary pancreatic, or lung tumors, confirming the local activation of the coagulation cascade by TF as an essential determinant of the risk of hemostatic complications in cancer [7]. Recently, the systems biology approach to study the tumor coagulome has also unveiled its close link with the tumor microenvironment (TME), defined as the heterogeneous collection of cells, extracellular matrix, and secreted factors that are present in tumors [8]. The existence of an active interplay between the coagulome and the TME was especially highlighted in the context of fibrinolysis, the essential plasmin-dependent step that leads to the dismantling of polymerized fibrin [5,7]. Human tumors with high mRNA levels of PLAU, which encodes the urokinase-type plasminogen activator (uPA), a key activator of plasminogen, have a dense tumor infiltrate of monocytes–macrophages and express high levels of important regulatory immune molecules, such as the immune checkpoint CD276/B7-H3 [7]. In addition to their well-known contribution to hemostasis, it became clear that certain specific products of the coagulation cascade can interact with cognate receptors present on the surface of specific cell types in the TME [5]. The possibility that the coagulome might regulate different cell types within the TME offers promising opportunities, considering the role that the TME plays during carcinogenesis and in the modulation of tumor response to therapy [5,9]. Of all primary tumor types, Oral Squamous Cell Carcinoma (OSCC) and Pancreatic Adenocarcinoma (PAAD) express the highest levels of F3, PLAU, and SERPINE1 mRNA (encoding the uPA inhibitor PAI-1, Plasminogen Activator Inhibitor-1) [6,7]. OSCCs, which represent the most frequent type of head and neck cancers [10], have an interesting characteristic of expressing the regulators of coagulation and fibrinolysis at high levels. This characteristic may potentially explain why OSCCs are not at a high risk of vascular complications outside of the therapeutic context [11]. It also makes these tumors an interesting model for examining the regulation of the tumor coagulome and its impact aside from vascular complications. In this respect, a few recent studies have suggested that biomarkers of coagulation might predict tumor recurrence in patients with OSCC after surgical resection, i.e., in a situation of bleeding-related activation of the coagulation cascade [12,13]. Whether the tumor coagulome is a determinant of the oncological outcome in this context remains unknown, but understanding the regulation of the tumor coagulome seems to be of paramount importance, not only because of vascular complications, but also as a determinant of the TME. Glucocorticoids are key hormonal regulators involved in stress response that are produced by the adrenal gland. Their interaction with the Glucocorticoid Receptor (GR), a member of the nuclear receptor superfamily encoded by the Nuclear Receptor subfamily 3 group C member 1 (NR3C1) gene, is thought to mediate most of their direct transcriptional effects [14]. Glucocorticoids are also known to exert complex anti-inflammatory and immunosuppressive effects [15]. Compared to cortisol, which is the main endogenous ligand of the GR, synthetic agonists of GR, such as dexamethasone, exhibit a higher affinity and potency at the GR, and they are commonly used clinically for their anti-inflammatory properties [15,16]. In cancer patients, corticoids exert pleiotropic effects, ranging from the regulation of immune and inflammatory cells to modulation of the intermediary metabolism [17,18,19]. In recent studies, the GR was also identified as a direct modulator of oncogenic signaling and tumor progression, with different and sometimes contrasting effects on tumor growth depending on the tumor type and/or stage [20,21,22,23,24,25]. The recent study by Obradovic et al. showed that GR activation was able to directly promote metastasis in a mouse xenograft model of breast cancer [23]. More recently, the activation of the GR was also found to be a potential determinant of the response of cancer cells to chemotherapeutic agents [26,27]. Overall, glucocorticoids are emerging as powerful molecules with complex influences on several important facets of human tumor physiology. In this study, we investigated the effects of glucocorticoids on the cancer coagulome and the potential link with the TME. Details regarding the provenance of all cell lines used here, the cell culture protocols, and a list of all reagents used in this study are described in the Supplementary Materials and Methods Section or published previously [28]. Uncropped immunoblots are available as a Supporting Information file (immunoblot summary). RNA was extracted using RNeasy minikit (Thermofisher, Waltham, MA, USA) and reverse transcribed using High-Capacity cDNA Reverse Transcription kit (Thermofisher). cDNAs were amplified using the TaqMan Universal PCR Master Mix (4304437, Thermofisher) on an ABI 7900HT Sequence Detection System (Applied Biosystems) with gene-specific probes from Thermofisher: F3 (Hs01076029_m1), PLAU (Hs01547054_m1), and SERPINE1 (Hs00167155_m1). GAPDH was used as reference. Silencer select validated siRNA directed against NR3C1 (s6186) and control siRNA (ref. 4390844) were purchased from Thermofisher. Cells were transfected with siRNAs using Lipofectamine (Thermofisher, Courtaboeuf, France) and Optimem medium (Gibco by Life Technologies, Thermofisher, Courtaboeuf, France). Whole-tumor RNA-seq data on 321 OSCC and 186 pancreatic adenocarcinoma (PAAD) tumors from TCGA were retrieved from cBioportal at: http://cbioportal.org (accessed on the 1 July 2022) [29,30]. The GR activity score was calculated for each tumor based on 232 genes either positively or negatively associated with GR activity, as previously reported [25]. The gene expression data and GR Chromatin Immunoprecipitation sequencing (ChIP-seq) results from five human lung cancer cell lines (A549, H2122, H460, H1975, and H1944) treated +/− hydrocortisone (2.75 µM, 8 h) were retrieved from the Gene Expression Omnibus (GSE159546) [27]. The genomic loci for F3, PLAU, and SERPINE1 were analyzed using the Integrative Genomics Viewer (Broad Institute) to identify direct GR/gene interactions. Single-cell RNA-seq data for 5902 cells (18 HPV head and neck carcinoma tumors) were obtained from the Gene Expression Omnibus (GSE103322) [31]. This study includes data on 2215 malignant cells, as well as other cells of the TME. For 10 of the 18 tumors in this study, data were available for a sufficiently high number of cells for further analysis. The GR activity score was calculated based on West et al. [25]. The NCI-60 database, which contains data on 60 cancer cell lines from 9 types of tumors, was accessed using the CellMiner interface (https://discover.nci.nih.gov/cellminer/) (accessed on the 5 February 2023). Basal mRNA expression levels (Agilent mRNA/log2) were retrieved for the 60 cell lines [32]. Gene expression data from 54 of the cell lines originating from solid tumors were kept for further analysis; the six leukemia cell lines were excluded from analysis. Gene Set Enrichment Analysis (GSEA) was performed on OSCC and PAAD from TCGA using the Java GSEA desktop application (https://www.gsea-msigdb.org/gsea/index.jsp) (accessed on the 10 September 2022). We used C2 curated gene sets to compute the overrepresentation of specific gene sets in tumors with high expression of F3, PLAU, or SERPINE1 (high vs. low expression defined by the median). The analyses were performed using 1000 permutations [33]. Gene Ontology (GO) analyses were performed using PANTHER (Protein ANalysis THrough Evolutionary Relationships, http://www.pantherdb.org/ (accessed on the 4 October 2022)) [34]. Statistical overrepresentation test/GO Biological Process complete analysis was performed using Fisher’s exact test with FDR correction. The comparisons were performed against Homo sapiens all genes database. The Microenvironment Cell Population counter (MCP counter) analysis was used to quantify the relative abundance of ten types of immune and stromal cell populations based on the RNA-seq data in OSCC and PAAD TCGA tumors [35]. Immune-related scores (TGF-β response) were retrieved from Thorsson et al. [36]. Student’s t test or Wilcoxon–Mann–Whitney test was used to compare continuous variables, and Chi2 test was used to compare categorical data, as indicated. p < 0.05 was considered a threshold for significance. A False Discovery Rate (FDR) correction was applied when indicated. All statistical analyses were performed with R version 4.1.0 (https://www.r-project.org (accessed on the 4 October 2022)). To evaluate the effect of glucocorticoids on cancer cells, we treated OSCC cell lines PE/CA-PJ34 and PE/CA-PJ41 with dexamethasone in vitro. Long-term treatment with dexamethasone (6 days) had a small effect on cell viability (a 12% decrease for PE/CA-PJ41 and a 37% decrease for PE/CA-PJ34 at the maximal concentration of 10 µM) (Figure S1). We observed a major effect on PAI-1 expression with a significant 5-fold increase after 6 days of treatment of PE/CA-PJ41 cells with dexamethasone (10 µM) (Figure 1A,C; Supporting Information file, immunoblot summary). In both cell lines, PAI-1-specific immunofluorescence labeling was homogeneous across individual cells, suggesting that the regulation was not limited to a subpopulation of cancer cells (Figure S2). Dexamethasone also significantly decreased TF and uPA expression in PE/CA-PJ41 (30.7% for TF, p = 1.8 × 10−4 and 34.2% for uPA, p = 2.4 × 10−3) after 6 days of treatment. Note that PAI-1 was not detectable in the cell extracts of the PE/CA-PJ34 cell line (Figure 1A). However, PAI-1 was detectable in the supernatants, and we confirmed the increase in PAI-1 expression after dexamethasone treatment (Figure 1B). We examined the possibility of a transcriptional regulation by measuring gene expression by QPCR. We observed that dexamethasone induced a significant increase in the expression of SERPINE1 mRNA, the gene that encodes PAI-1, in PE/CA-PJ41 (>4-fold increase vs. control after 6-day exposure, p < 0.05) (Figure 1D). Dexamethasone treatment also significantly decreased the expression of F3 (encoding TF) and PLAU (encoding uPA) (p < 0.05), reaching the lowest levels after 48 h (Figure 1D). The effect of dexamethasone on the expression of F3 and PLAU was apparent from submicromolar concentrations (500 nM), pointing to the specificity of these effects (Figure S3). To address the mechanism of action of glucocorticoids on cancer cells in an inflammatory context, we examined the effect of dexamethasone in the presence or absence of the cytokine Tumor Necrosis Factor-α (TNF-α). TNF-α applied on PE/CA-PJ34 and PE/CA-PJ41 cells sharply increased F3 and PLAU mRNA and slightly increased SERPINE1 mRNA expression levels in both cell lines. Dexamethasone prevented the increase in F3 and PLAU in both cell lines (Figure 2). Conversely, dexamethasone directly increased SERPINE1 mRNA expression independently of TNF-α exposure (Figure 2). These findings were consistent with a dual effect of glucocorticoids on OSCC, with a predominant effect on anti-inflammatory signaling accounting for the negative regulation of TF and uPA, as opposed to a positive regulation of PAI-1. To address the role of the GR in the effects produced by dexamethasone, we used RNA interference directed against the gene NR3C1 that encodes GR. With this approach, we successfully decreased GR expression by >70% in both PE/CA-PJ34 and PE/CA-PJ41 cell lines (Figure 3A; Supporting Information file, immunoblot summary). Our results show that a decreased expression of GR (using siNR3C1) prevented the dexamethasone-induced increase in PAI-1 (Figure 3B; Supporting Information file, immunoblot summary). These results were confirmed at the mRNA level, with no increase in SERPINE1 mRNA with dexamethasone treatment in cells treated with siNR3C1 (Figure 3C). The decreased expression of GR (siNR3C1) did not prevent the decrease in TF and uPA expression with dexamethasone treatment, further confirming our observation regarding the dual regulation of the coagulome (Figure 3B). These findings were supported with a distinct pharmacological approach using a GR antagonist, mifepristone, which also prevented the increase in PAI-1 expression (Figure S4), again strongly suggesting the existence of direct GR-dependent regulation of PAI-1 expression in cancer cells. To address the relevance of these findings in a broader setting, we used data from a previous study (GSE159546) reporting the transcriptome of five lung cancer cell lines treated with hydrocortisone (2.75 µM, 8 h) [27]. Consistent with our in vitro results with OSCC, an induction of SERPINE1 was apparent in three of the cell lines (A549, H2122, and H1975). A decrease in PLAU expression was observed in four out of the five lung cancer cell lines. There was no consistent change in F3 (Figure 4A). In order to directly test the interaction between the GR and these genes, we used ChIP-seq data from GSE159546. In three out of the five human lung cancer cell lines (A549, H2122, and H1975), hydrocortisone stimulated the interaction between GR and exon 9 of the SERPINE1 gene. For three of the five cell lines, a direct interaction was observed either with the promoter region of SERPINE1 (A549 and H1944) or exon 1 of the gene (H1975) (Figure 4B). Multiple GR interaction sites were also observed in the 30 kb upstream region of the SERPINE1 coding sequence (Figure 4B). These data further established the existence of direct genomic effects of GR on the SERPINE1 locus in cancer cells. Next, we used a “GR activity score” described previously that is based on a GR activity profile in breast cancer cells [25]. We calculated the “GR activity score” for the 54 cell lines originating from solid tumors from the NCI-60 database. We observed that the GR activity score was positively correlated with SERPINE1 expression (r = 0.34, p = 0.013, Spearman) (Figure 5). Neither F3 nor PLAU expression was significantly correlated with the GR activity score. To examine the relevance of glucocorticoid regulation of the coagulome in human tumors, we used whole exome RNA-seq data from 321 OSCC and 186 PAAD tumors retrieved from TCGA (Figure 6). We performed GSEA to compare tumors stratified according to their expression of F3, PLAU, and SERPINE1 (by the median). Tumors with high levels of these coagulome genes were positively associated with the gene set “WP Glucocorticoid Receptor Pathway”, with the highest enrichment score for tumors expressing high levels of SERPINE1 (OSCC: NES = 2, FDR q = 0.009; PAAD: NES = 2.27, FDR q = 0.002) (Figure 6). These results further suggest the existence of a link between glucocorticoid signaling and the expression of F3, PLAU, and SERPINE1 in human tumors. We wanted to address whether tumors with high GR activity, as determined by West et al. [25] (upper half by median), combined with high SERPINE1 expression (upper half by median), here called “High++” (Figure 7A, Table S1), have a specific composition of their tumor microenvironment. A comparison of the clinical characteristics of the “High++” OSCC vs. all other OSCC showed that these tumors are of a higher grade (p = 0.0023, Chi2). The rates of the angiolymphatic invasion (ALI), extracapsular spread (ECS), and TNM stage were not significantly different between the two groups (Figure S5). We then used the MCP-counter to compare the relative infiltrate of different cell types in “High++” vs. all other OSCC tumors [35]. We found that for most cell types studied, including T cells, B cells, neutrophils, CD8 T cells, cytotoxic lymphocytes, NK cells, and dendritic cells, there was no difference between the “High++” group and the rest of the OSCC tumors. Significantly higher levels of fibroblasts (p = 0.0031, FDR), endothelial cells (p = 5.29 × 10−4, FDR), and cells of the monocytic lineage (p = 0.0024, FDR) were present in the “High++” group compared to other OSCCs (Figure 7B). These observations were confirmed in PAAD tumors, with significantly higher infiltration levels of fibroblasts (p = 0.02017 FDR), cells of the monocytic lineage (p = 0.0162 FDR), and endothelial cells (p = 7.33 ×10−4 FDR) in tumors with high GR activity score/SERPINE1 (Figure S6). In PAAD, tumor CD8 T cells (p = 0.02113 FDR) and cytotoxic lymphocytes (p = 0.0055 FDR) were also enriched in High++ tumors (Figure S6). After performing a differential gene expression (DEG) analysis, we used the top 200 genes that were the most significantly enriched in the “High++” group in a Gene Ontology study. The GO terms that were enriched to the greatest extent in the “High++” tumors are shown in Figure 7C, and are listed in Table S2 and include, for example, the GO term “wound healing” (Figure 7C). We also examined the immune-related scores defined by Thorsson et al. [36] in relation to the “High++” status of OSCC tumors. The most significant difference was found in the “TGF-β response” (1.75-fold higher TGF-β response in “High++” vs. “other”, p = 1.12 × 10−9 FDR). The expression of TGFB1, the gene encoding TGF-β, was significantly higher in “High++” tumors compared to the rest (p = 0.0013) (Figure 7D). To address the regulation of SERPINE1 by GR at a higher resolution in human tumors, we used RNA-seq data from a single-cell analysis of OSCC [31]. We calculated the “GR activity score” in the cancer cell population using 163 available genes from the 232 genes that constitute the signature. We analyzed the expression of SERPINE1 in all cancer cells (n = 2215) with either a high or low GR activity score (divided by the median). We found a significantly higher expression of SERPINE1 in cancer cells with a high GR activity score compared to those with low GR activity (p = 1.92 × 10−8) (Figure 8A). We identified 10 tumors from GSE103322 that had data on enough cells, i.e., >100 cells, for further analysis. For each of these tumors, we examined the extent to which the GR activity score and SERPINE1 expression overlapped (Figure 8B). The percentage of cancer cells with high GR activity and high SERPINE1 expression, as indicated in orange in Figure 8B, was calculated in individual tumors. For the tumor with the highest GR activity score (#17), the overlap with SERPINE1 expression was at 65.45%, whereas for the tumor with the lowest average GR activity score (#22), only 10% of the cancer cells had both high GR activity and a high expression of SERPINE1 (Figure 8B). We found a positive correlation (Spearman coefficient r = 0.7, p = 0.02) between the GR activity score for each tumor and the percent of cancer cells with SERPINE1 coexpression. These findings again validate our conclusion regarding the regulation of SERPINE1 by GR, suggesting that this regulation applies at the single-cell level. Glucocorticoids are powerful transcriptional regulators with broad effects on tumor physiology [37]. We investigated their effect on the coagulome of different primary cancer types: OSCC, a tumor type characterized by a high expression of TF and uPA [6] and lung and pancreatic cancers, two tumor types known to predispose cancer patients to a high risk of VTE. While previous studies have addressed the role and regulation of uPA and PAI-1, i.e., the core components of the coagulome, their regulation by glucocorticoids had to our knowledge not been shown in these tumors [38]. We observed a dual regulation of the coagulome by glucocorticoids, consisting of an indirect effect, possibly related to the inhibition of inflammatory signaling in cancer cells, and a direct transcriptional effect that applies to PAI-1 (SERPINE1). These findings were confirmed in a broad analysis, using a large panel of human cancer cells from the NCI-60 database that include 54 cell lines originating from eight types of primary solid tumors. We addressed the relevance of our findings in human tumors by showing that the expression of the coagulome genes, especially SERPINE1 (PAI-1), is linked to the “glucocorticoid receptor pathway” in OSCC and PAAD. We examined the possible consequences of this GR-dependent regulation by analyzing the cellular composition of the TME of these tumors. In tumors with high GR activity and high SERPINE1 expression, the TME was enriched in stromal cells (endothelial cells and fibroblasts) and monocytic cells. Finally, we suggest that the contribution of the GR–SERPINE1 axis also applies at the single-cell level. Based on our study, we therefore propose that the tumor coagulome is a target of glucocorticoids. Recent genomic studies have provided a detailed analysis of the landscape of the tumor coagulome across different tumor types, but we still know little about its dynamic regulation [5,7]. Our study further illustrates the complex effects of glucocorticoids on human tumors [37] and highlights a novel facet of their action. Our in vitro and in silico analyses suggest that components of the fibrinolysis cascade (uPA and PAI-1) are potentially more directly and more potently regulated than the coagulation cascade (TF) in human tumors. Importantly, the positive regulation of PAI-1 expression that we observed is consistent with the previously established positive transcriptional regulation of PAI-1 reported in various primary tissues [39,40]. It is also consistent with previous reports of a functional Glucocorticoid Response Element (GRE) in the SERPINE1 promoter [41]. High fibrinolytic activity, catalyzed by a high expression of uPA, the main upstream regulator of fibrinolysis, has been previously suggested to be a negative determinant of thrombosis risk in human tumors, by promoting the rapid turnover of fibrin clots [11]. Our findings open the interesting possibility that glucocorticoids might change the tumor fibrinolytic activity and therefore possibly increase the risk of VTE. Importantly, predicting the vascular risk of individual cancer patients remains notoriously difficult [3]. Whether the exploration of the tumor glucocorticoid signaling status might provide useful information in this context is a speculative yet promising possibility that remains to be tested. In addition to its impact on the vascular risk in cancer patients, the coagulation and fibrinolysis cascades potentially regulate tumor progression. Several experimental studies indicate, for example, that the fibrinolysis cascade may regulate the turnover of proteins of the extracellular tumor matrix, with possible consequences on tumor cell invasive growth [38]. While the regulation of the tumor matrix has been the focus of many biochemical studies, the link between the fibrinolysis cascade and the regulation of tumor stromal cells has been less studied, despite the key role played by these cellular components of the TME in tumor growth and response to treatments. With this in mind, a focus of our study was to use genomics to analyze the tumor ecosystem of OSCC and PAAD with high GR activity/high SERPINE1 expression. We identified fibroblasts, monocytic, and endothelial cells as the cell types with an increased density in the TME of these tumors. Interestingly, these findings are consistent with previous in vitro studies that have reported direct positive effects of PAI-1 on tumor endothelial cells [42], monocytic cells [43,44], and cancer-associated fibroblasts [45,46]. These studies suggest that PAI-1, and perhaps other components of the tumor coagulome, might not only be a target, but also an effector of glucocorticoids in tumor tissues. In this respect, studies performed on PAI-1 KO animals indicate that PAI-1 mediates the effects of glucocorticoids on bone and muscle tissues [47,48,49]. Based on our results and the conclusions of these studies, we speculate that a GR–SERPINE1 axis might be functional in human tumors. Importantly, a major limitation of our study, as is often the case with genomic studies addressing the regulation of the TME, is the fact that we examined the consequences of the GR–SERPINE1 axis with correlative analyses. Further studies are therefore warranted to experimentally confirm the consequences of the activation of the GR–SERPINE1 axis on the TME, including the regulation of the tumor matrix and the regulation of noncancer cells of the tumor stroma. A deeper knowledge of the consequences of GR–SERPINE1 signaling offers the exciting perspectives of a better understanding of the effects of glucocorticoids on tumor physiology. Glucocorticoids modulate the coagulome of cancer cells through a combination of direct transcriptional and indirect regulatory effects. We found that dexamethasone directly increased PAI-1 expression in a GR-dependent manner, and we confirmed the relevance of these observations in different types of tumors. The transcriptional regulation of the coagulome by glucocorticoids that we report here may not only have vascular consequences, but it may also account for some of the effects of glucocorticoids on the TME.

PMC10001351

Trang Uyen Nguyen,Harrison Hector,Eric Nels Pederson,Jianan Lin,Zhengqing Ouyang,Hans-Guido Wendel,Kamini Singh

Rapamycin-Induced Feedback Activation of eIF4E-EIF4A Dependent mRNA Translation in Pancreatic Cancer

24-02-2023

mTOR,eIF4E,eIF4A,p70-RSK1,ribosome footprinting,CR-1-31B

Simple Summary Pancreatic cancer is aggressive cancer with a low survival rate due to the lack of detection, effective treatment, and development of therapeutic resistance. New treatments and mechanistic details of therapeutic resistance are urgently needed. In this study, we explored the effect of inhibiting protein synthesis and its role in inducing feedback mechanisms that may impact the therapeutic response. We show that Rapamycin (sirolimus) treatment inhibited the synthesis of proteins required for cancer cell growth. Interestingly, rapamycin treatment induced the synthesis of proteins that lead to reactivation of the key kinases, and this limited the anti-tumor effect of rapamycin. We further show that the combination of rapamycin with the small molecule inhibitor CR-1-31-B increases the efficacy of rapamycin. Our study establishes the feedback mechanism induced by rapamycin and new therapeutic combinations that can be further developed as therapeutics for pancreatic cancer. Abstract Pancreatic cancer cells adapt molecular mechanisms to activate the protein synthesis to support tumor growth. This study reports the mTOR inhibitor rapamycin’s specific and genome-wide effect on mRNA translation. Using ribosome footprinting in pancreatic cancer cells that lack the expression of 4EBP1, we establish the effect of mTOR-S6-dependent mRNAs translation. Rapamycin inhibits the translation of a subset of mRNAs including p70-S6K and proteins involved in the cell cycle and cancer cell growth. In addition, we identify translation programs that are activated following mTOR inhibition. Interestingly, rapamycin treatment results in the translational activation of kinases that are involved in mTOR signaling such as p90-RSK1. We further show that phospho-AKT1 and phospho-eIF4E are upregulated following mTOR inhibition suggesting a feedback activation of translation by rapamycin. Next, targeting eIF4E and eIF4A-dependent translation by using specific eIF4A inhibitors in combination with rapamycin shows significant growth inhibition in pancreatic cancer cells. In short, we establish the specific effect of mTOR-S6 on translation in cells lacking 4EBP1 and show that mTOR inhibition leads to feedback activation of translation via AKT-RSK1-eIF4E signals. Therefore, targeting translation downstream of mTOR presents a more efficient therapeutic strategy in pancreatic cancer.

Rapamycin-Induced Feedback Activation of eIF4E-EIF4A Dependent mRNA Translation in Pancreatic Cancer Pancreatic cancer is aggressive cancer with a low survival rate due to the lack of detection, effective treatment, and development of therapeutic resistance. New treatments and mechanistic details of therapeutic resistance are urgently needed. In this study, we explored the effect of inhibiting protein synthesis and its role in inducing feedback mechanisms that may impact the therapeutic response. We show that Rapamycin (sirolimus) treatment inhibited the synthesis of proteins required for cancer cell growth. Interestingly, rapamycin treatment induced the synthesis of proteins that lead to reactivation of the key kinases, and this limited the anti-tumor effect of rapamycin. We further show that the combination of rapamycin with the small molecule inhibitor CR-1-31-B increases the efficacy of rapamycin. Our study establishes the feedback mechanism induced by rapamycin and new therapeutic combinations that can be further developed as therapeutics for pancreatic cancer. Pancreatic cancer cells adapt molecular mechanisms to activate the protein synthesis to support tumor growth. This study reports the mTOR inhibitor rapamycin’s specific and genome-wide effect on mRNA translation. Using ribosome footprinting in pancreatic cancer cells that lack the expression of 4EBP1, we establish the effect of mTOR-S6-dependent mRNAs translation. Rapamycin inhibits the translation of a subset of mRNAs including p70-S6K and proteins involved in the cell cycle and cancer cell growth. In addition, we identify translation programs that are activated following mTOR inhibition. Interestingly, rapamycin treatment results in the translational activation of kinases that are involved in mTOR signaling such as p90-RSK1. We further show that phospho-AKT1 and phospho-eIF4E are upregulated following mTOR inhibition suggesting a feedback activation of translation by rapamycin. Next, targeting eIF4E and eIF4A-dependent translation by using specific eIF4A inhibitors in combination with rapamycin shows significant growth inhibition in pancreatic cancer cells. In short, we establish the specific effect of mTOR-S6 on translation in cells lacking 4EBP1 and show that mTOR inhibition leads to feedback activation of translation via AKT-RSK1-eIF4E signals. Therefore, targeting translation downstream of mTOR presents a more efficient therapeutic strategy in pancreatic cancer. Pancreatic cancer (PDAC) is driven by mutant KRAS that feeds to PI3K-AKT-mTOR signaling to support anabolic pathways and cancer cell growth [1,2]. The mammalian rapamycin complex 1 (mTORC1) target is a central eukaryotic signaling complex that coordinates metabolism and cell growth [3,4]. Due to the role of mTORC1 in the initiation of protein translation and ribosome biogenesis, altered regulation of the activity of this complex has been implicated in numerous cancers, including PDAC [4]. mTORC1-dependent activation of S6K results in phosphorylation of S6 [5]. S6K also phosphorylates several other targets which are associated with enhanced translation such as the eukaryotic translation initiation factors 4B and 4F (eIF4B and eIF4F, respectively) and the eukaryotic elongation factor 2 kinase (eEF2K) [6,7]. Despite many targets of S6K being identified, inactivation of S6K has not been shown to affect global translation rates [8], though the prevention of phosphorylation of another mTOR target, 4E-BP1, does indeed substantially decrease the translation, specifically of mRNA containing 5′ terminal oligopyrimidine (5′-TOP) motifs [9,10]. Phosphorylation of 4E-BP1 results in dissociation from eIF4E, which may increase the affinity of the eIF4F complex to these 5′-TOP and TOP-like mRNA motifs [10,11]. Furthermore, the knockdown of 4EBP1/2 has been shown to greatly reduce the effect of mTOR inhibitor Torin1 on the TOP and TOP-like motif-dependent translation [10]. Due to the pathway convergence, the activity of mTORC1 can be affected by changes in the activity of many upstream proteins, which is often implicated in cancer, leading to increased activity of both S6K and 4E-BP1 [12,13]. The mTORC1 complex plays a central role in cancer development meaning its inhibition has been the target of numerous therapeutic studies [14,15]. Rapamycin was the first mTOR inhibitor to be identified, initially being proposed as an antifungal antibiotic, but has since been studied in the context of cancer treatment [16]. Despite this, targeting mTOR has not been effective in PDAC for many reasons including the fact that 4EBP1 is often lost in PDAC [17]. One explanation for this may be that mTOR specifically regulates the translation of ribosomal proteins and mRNAs containing TOP motifs in cells that have intact S6 and 4EBP1 signaling [10]. Another contributing factor could be that the phosphorylation of S6 alters the translation of shorter coding sequences (CDS) to a greater extent than longer CDS [18]. While many mTOR inhibitors have been developed and extensively studied in the context of 4EBP1 and cap-dependent translation, the contribution of S6 on translation is still poorly understood in pancreatic cancer [19,20,21]. We, therefore, set out to determine the translational changes induced by rapamycin in pancreatic cancer cells. Human pancreatic cancer cells PANC-1, MiaPaca2, and PANC10.05 show growth inhibition following rapamycin treatment for three days, MiaPaca2 showing higher sensitivity compared to PANC-1 and PANC10.05 (Figure 1A and Supplementary Material Figure S1A). Next, we measured the effect of rapamycin on global mRNA translation using AHA labeling. We observed up to 10% inhibition at 1 h and ~50% inhibition in global mRNA translation at 4 h following rapamycin treatment (Figure 1B). In correspondence with these findings, in a long-term colony formation assay, we observe growth inhibition following rapamycin treatment (Figure 1C,D and Supplementary Material Figure S1B,C). To characterize the effect of rapamycin on genome-wide translation, we utilized the technology of ribosome footprinting. Briefly, we treated PANC-1 cells with DMSO (n = 3) or rapamycin (50 nM; for 1 h; n = 3) and then deep sequenced the total RNA and ribosome-protected fragment (RF) RNA (Figure 1E). Quality control analysis of RNA and RF replicates showed significant correlations among the replicates with a Pearson coefficient >0.99 and >0.97, respectively (Supplementary Material Figure S1D,E). Read mapping to ribosomal RNAs, non-coding RNAs, library linkers, and incomplete alignments were removed from the analysis. Most of the remaining reads range from 25 to 35 nucleotides in length and map to protein-coding genes (Supplementary Material Figure S1F–K). The total number of RF reads mapped to exons was 2.7 million in DMSO and 2.3 million in rapamycin-treated samples. This corresponds to 20,213 protein-coding genes. We used the RiboDiff statistical framework to isolate the effect on mRNA translation [2]. With a very stringent statistical cut-off at q < 0.01 (FDR < 1%) and q < 0.05 (FDR < 5%), we identified 473 and 861 mRNAs whose translation was significantly repressed (TE down) (Figure 1F). We also detect a set of mRNAs showing a relative increase in ribosome occupancy (TE up n = 588; q < 0.01 and n = 954; q < 0.05) (Figure 1F). A full list of genes differentially affected by rapamycin is provided in Supplementary Table S1A,B. Hence, we identified the subset of mRNA whose translation is differentially regulated upon mTOR inhibition. We noticed that the TE down mRNAs showed a significant reduction in ribosome coverage through the 5′UTR and CDS of the transcripts suggesting that mTOR inhibition results in an overall reduction in the ribosomal occupancy on the affected transcripts (Figure 2A TE down q < 0.01 and Supplementary Material Figure S2A TE down q < 0.05). Next, we performed GSEA of the TE down genes (n = 861; q < 0.05) and observed KEGG pathways enrichment for cysteine and methionine metabolism, proteasome, peroxisome, cell cycle, and pathways in cancer (Supplementary Material Figure S2B). We also observed Hallmark pathway enrichment for peroxisome, androgen response, UV response, mTORC1 signaling, MYC, and E2F targets (Supplementary Material Figure S2C). mRNA transcripts whose translation is downregulated by rapamycin are ranked by q value (q < 0.01 and q < 0.05 cut-off) showing many genes involved in the cell cycle and cancer cell growth (Figure 2B). Notably, rapamycin treatment reduced the translation of genes involved in the cell cycle, Fc Gamma receptor-mediated phagocytosis, cysteine, methionine metabolism, and pathways in cancer (Figure 2C,D). This includes proteins such as MDM2, RPS6KB1 (p70 S6K), RPS6KB2 (p70 S6K), GOT2, SMAD4, and CDK2 (Figure 2B–D). Ribosome coverage showed a significant reduction in examples from the TE down groups, such as EPCAM, SMAD4, and MDM2 while the total RNA of these genes remained unaffected following rapamycin treatment (Figure 2E–G, Supplementary Material Figure S2D). Next, we validated the effect of translational change on the protein expression of RPS6KB1 and RPS6KB2. The total protein decreased following rapamycin treatment on PANC-1 cells while the total RNA remained unchanged (Figure 2H and Supplementary Material Figure S2D). Full immunoblots are shown in Supplementary Material Figure S5. These data suggest that rapamycin inhibits the translation of genes involved in cancer cell growth including p70 S6K, a downstream effector of mTOR signaling. Rapamycin is reported to affect the translation of TOP, TOP-like, and PRTE motifs [10,22]. In line with these findings, we observed the enrichment of TOP and PRTE motifs in the 5′UTR of TE down mRNAs (Supplementary Material Figure S2E). Additionally, we found that Rapamycin inhibited the TOP-dependent translation activity in PANC1 cells (Supplementary Material Figure S2F). Interestingly, we observed that a greater number of mRNAs are translationally upregulated (n = 588) compared to TE downregulated (n = 473) suggesting that mTOR inhibition in PANC-1 cells has a profound effect on the upregulation of translation (Figure 1E). Next, we analyzed the ribosomal coverage on the subset of TE-up mRNAs and observed increased ribosomal occupancy throughout the length of the transcripts (Figure 3A and Supplementary Material Figure S3A). GSEA shows enrichment of pathways related to PI3K-AKT-mTOR, IL6-STAT-JAK, androgen, TNF-NFkB, UV response, and IL2-STAT5 signaling (Supplementary Material Figure S3B). Transcripts whose translation is upregulated by rapamycin are shown as ranked by q value (q < 0.01 and q < 0.05 cut-off) (Figure 3B). Genes involved in PI3K-AKT-mTOR, TNFa signaling, IL2-STAT5, and mTORC1 signaling are translationally upregulated following rapamycin treatment in PANC-1 cells (Figure 3C). Ribosomal coverage on candidate genes, e.g., RPS6KA1 (p90 RSK1), PDK1, and MKNK2shows upregulation of ribosome throughout the transcript length except for 3′UTR (Figure 3D–F). After rapamycin treatment, we validated the upregulation of total protein for RPS6KA1 (p90 RSK1) and STAT5A (Figure 3G,H). Full immunoblots are shown in Supplementary Material Figure S5. Consistently, the total RNA for these candidate genes remained unaffected suggesting that the protein is upregulated by increased translation (Supplementary Material Figure S3C). Rapamycin treatment has previously been shown to activate phospho-AKT1 and eIF4E [23,24,25]. In PANC-1 cells, we observe that phospho-AKT1 is activated within 5 min and stays activated at 60 min following rapamycin treatment while phospho-S6 is inhibited as early as 10 min and remains inhibited until 60 min (Figure 4A).Additionally, we found that phospho-eIF4E is activated as early as 5 min and remains activated at 60 min following rapamycin treatment in PANC-1 cells (Figure 4B). Phospho-p90-RSK1 is activated following 10 min of Rapamycin treatment and remained upregulated at 60 min (Figure 4B). Total p90-RSK1 remained unaffected at early time points (Figure 4B). PANC-1 cells show a loss of 4EBP1 protein compared to the MiaPaca2 cells (Supplementary Material Figure S4A). Next, we compared the effect of Rapamycin on signaling in MiaPaca-2 cells. MiaPaca-2 cells showed a similar extent of inhibition of phospho-S6 and feedback activation of phospho-AKT1 within 60 min (Figure 4C). However, unlike PANC-1, MiaPaca-2 did not show significant changes in phospho-eIF4E and phospho-p90-RSK1 (Figure 4D). Phospho-S6 remains inhibited at longer time points in PANC-1 while phospho-AKT1 remained upregulated suggesting that S6-dependent translation remains inhibited in the presence of feedback activation of phospho-AKT1 (Supplementary Material Figure S4B). In addition, Rapamycin induced the translation of p90-RSK1 (RPS6KA1) in later time points (Figure 3G). p90-RSK1 (RPS6KA1) can further feed to activation of S6, eIF4B, and cap-dependent translation [26]. In summary, these data suggest that Rapamycin affects the translation of mRNAs in a specific manner in pancreatic cancer cells that lack 4EBP1 expression likely through phospho-AKT and/or phospho-p90-RSK1 activation. Rapamycin-mediated reduction of p70-S6K1 total protein and phospho-S6 results in inhibition of translation of cell cycle driving and cancer growth proteins. However, feedback activation of AKT1, eIF4E, and p90-RSK1 (RPS6KA1) contribute to the translational upregulation of proteins involved in PI3K-AKT-mTOR, TNFa, and IL2-STAT signaling (Figure 4E). Based on these, we tested the effect of the p70-S6K1 inhibitor in combination with rapamycin. p70-S6K1 inhibitor did not show any additional effect in combination with Rapamycin (Supplementary Material Figure S4C). Others have reported the translational effect of Rapamycin and other mTOR inhibitors in cells with functional/wildtype 4EBP1. In Thoreen et al., 2012 the authors performed ribosome footprinting with Torin 1 in wildtype (WT) and 4EBP1/2 double KO (KO) MEFs [10]. In 4EBP1 wild-type cells, they identified 1872 genes in the TE down and 2968 genes in the TE up category. Similarly, in the 4EBP1/2 double KO, they found 1833 genes in TE down and 3007 genes in TE up groups (Supplementary Material Figure S4D). In both WT and KO cells, they observe a higher number of genes in TE up compared to the TE down subset suggesting that feedback activation of the translation may not depend on the 4EBP1 status (Supplementary Material Figure S4D). Concurrently, we observe that the phospho-AKT1 activation is present in both PANC-1 and MiaPaca2 cells and may be independent of the 4EBP1 status (Figure 4A,C). In another study reported by Hseih et al., 2012 the authors performed ribosome footprinting following Rapamycin treatment in PC3 prostate cancer cells [22]. There was no significant difference in the number of TE up and TE down genes (Supplementary Material Figure S4D). Hseih et al., 2012 also used the allosteric inhibitor of mTOR, PP242 which reduced the p-AKT, p-S6, and p-4EBP1. Surprisingly with the PP242, they did not observe any genes showing significant TE upregulation (Supplementary Material Figure S4D). Hseih et al., 2012 report that in PC3 prostate cancer cells rapamycin only reduced the p-S6 and not the phospho-AKT and phospho-4EBP1. Together, the presented data indicates that phospho-AKT may be required for the feedback translational upregulation. Next, we utilized the luciferase translation reporter assay driven by RNA-G quadruplex reported in our previous study [27] to identify the effect of Rapamycin on eIF4A-dependent translation. We observed that Rapamycin treatment slightly enhanced the RNA G-quadruplex mediated translation in PANC1 cells (Figure 4F). Accordingly, eIF4A inhibitor CR-1-31B enhanced the anti-proliferative effect of rapamycin in PANC-1 and MiaPaca2 cells (Figure 4G and Supplementary Material Figure S4E). We calculated the synergy score using Synergy Finder. In Panc1, the p-values for synergy scores were not significant (p > 0.05), but with ZIP and Bliss scoring system the drugs were found to likely be synergistic, while with Loewe and HSA they were found to likely be additive. In Miapaca2 cells, the synergy scores show the drugs to likely be additive, with three scoring systems (ZIP, HSA, Bliss), and showed significant p-values. The combined sensitivity score of both drugs was not higher than just CR-1-31B alone in both cell lines suggesting that inhibiting eIF4A alone may elicit a greater response than inhibiting Rapamycin alone. Synergy scores and drug interactions are shown in Figure 4H. Both PANC-1 and MiaPaca2 cells showed comparable responses to CR-1-31B alone suggesting that the status of 4EBP1 did not affect the sensitivity to the eIF4A inhibitor (Supplementary Material Figure S4F). In summary, we show that mTOR inhibition results in the feedback activation of translation programs in 4EBP1 lacking pancreatic cancer cells, and targeting translation downstream of mTOR may be a better therapeutic strategy in pancreatic cancer. mTOR controls protein synthesis and supports cancer growth by activating S6 and 4EBP1/eIF4E/eIF4A-dependent translation programs [3,4,21,27,28,29,30]. Oncogenic signaling pathways such as PI3K, KRAS, and MYC converge at mTOR to activate the oncogenic translation program; hence, mTOR inhibition has been the target of many therapeutic studies [14,31,32]. However, pancreatic cancer cells bypass mTOR inhibition through loss of 4EBP1 expression, suggesting that S6 may have a differential role in mRNA translation [17,33]. Rapamycin, a well-known mTOR inhibitor, affects the translation of mRNAs in a cell type-specific manner through its alternate effects on phospho-S6 and 4EBP1 signaling [34]. In this study, we established that rapamycin treatment directly affects translation in pancreatic cancer cells PANC-1, lacking 4EBP1 expression. Rapamycin inhibited the translation of cell cycle and cancer growth-promoting genes such as p70-S6K, explaining the anti-proliferative effect of rapamycin on pancreatic cancer cells. However, rapamycin also induced the translation of a larger subset of mRNAs which includes p90-RSK1 (RPS6KA1) and MKNK2 (MNK2), indicating that rapamycin may activate the eIF4E-eIF4A-dependent translation. Rapamycin also activated the phospho-AKT1 while the phospho-S6 remains inhibited suggesting that the feedback activation of AKT1 may feed to eIF4E and activate the translation. In our previously published study, we have characterized the translational changes dependent on the 4EBP1 by using a doxycycline-inducible 4EBP14A mutant form in 293 cells. We observed that 4EBP1 majorly regulates the translation of proteins involved in insulin signaling and glucose metabolism [30]. However, in this study, we observe a distinct subset of mRNAs being affected likely by phospho-S6 inhibition in pancreatic cancer cells suggesting a distinct role of S6-dependent translation. Previously we have shown that eIF4A regulated the translation of RNA G-quadruplex containing mRNAs, and this includes key oncogenes such as MYC and KRAS [27,35]. We established that the eIF4A inhibitor CR-1-31B shows therapeutic activity in MYC-driven leukemia and KRAS-driven pancreatic cancer [35]. Rapamycin inhibited the p70-S6K while activating the p90-RSK that can signal to eIF4E, and accordingly, we observed increased phosphorylation of eIF4E. Based on this we hypothesized that targeting eIF4A with CR-1-31B may enhance the anti-proliferative activity of rapamycin. While the p70-S6K inhibitor did not show a significant increase in cell growth inhibition, a combination of CR-1-31-B significantly enhanced the anti-proliferative effect of rapamycin in pancreatic cancer cells. However, the combination effect did not exceed the effect of CR-1-31B alone suggesting that targeting eIF4A downstream of mTOR may show a better therapeutic effect regardless of the 4EBP1 and phospho-AKT status. In summary, we characterize the translational targets of rapamycin in 4EBP1 deficient pancreatic cancer cells and show that mTOR translationally controls the p70-S6K protein expression. In addition, we establish the feedback activation of translation of key cancer growth-promoting proteins such as p90-RSK1 and MKNK2 that signals to eIF4E-eIF4A dependent translation following mTOR inhibition. Accordingly, targeting eIF4E-eIF4A-dependent translation downstream of mTOR enhances the activity of rapamycin and this may be an effective therapeutic strategy to target oncogenic translation programs in cancer. Cancer cell lines were obtained from American Type Culture Collection and cultured as per instructions. PANC-1 cells were cultured in DMEM (Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA) supplemented with 10% fetal bovine serum (FBS; MilliporeSigma; St. Louis, MO, USA) and 100 U/mL penicillin, 100 µg/mL streptomycin, 0.292 mg/mL glutamine (Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA). PANC10.05 cells were cultured in RPMI-1640 (Gibco; Thermo Fisher Scientific, Inc.; Waltham, MA, USA) supplemented with 10% fetal bovine serum and penicillin-streptomycin-glutamine. MiaPaca-2 cells were cultured in RPMI-1640 supplemented with 15% fetal bovine serum and penicillin-streptomycin-glutamine. Cells were treated with indicated drugs for indicated time points in complete media. Rapamycin, S6Ki, and [±]CR-1-31B were purchased from SelleckChem and MedChem. PANC-1 cells were treated with DMSO or Rapamycin (50 nM; 1 h). Total RNA and ribosome-protected fragments were isolated as per the published protocol [36]. Deep sequencing libraries were generated from these fragments and sequenced on the HiSeq2000 platform. Genome annotation was conducted with the human genome sequence GRCh37 downloaded from the Ensembl public database http://www.ensembl.org (accessed on 6 April 2016). Sequence alignment was carried out as described in our previous study [35]. Ribosome footprint (RF) reads were filtered such that only reads with a minimum quality score of 25 were kept for at least 75% of nucleotides. The linker sequence was trimmed from the 3′ ends of the reads. Reads shorter than 15 nt were filtered out. These pre-processing steps were conducted with FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/index.html, accessed on 4 June 2016). Removal of ribosomal RNA was conducted by aligning RF reads to the ribosomal RNA sequences of GRCh37 downloaded from UCSC Table Browser (https://genome.ucsc.edu/cgi-bin/hgTables, accessed on 4 June 2016). The reads were then mapped to GRCh37 using HISAT2. (http://daehwankimlab.github.io/hisat2/, accessed on 4 June 2016) with default parameters. Only uniquely aligned reads were used for further analysis. Similar to RF reads, total mRNA sequencing reads were similarly aligned to GRCh37 using HISAT2, and splice alignment for paired-end mRNA-seq datasets was performed with default parameters. Only uniquely aligned reads were used for further analysis. Alignment quantification of both RF and mRNA sequencing was conducted with featureCounts [37], using the annotations of GRCh37 protein-coding genes as input. For further analysis, we only used reads aligned to exonic regions of the protein-coding genes. Ribo-Diff [38] was used to analyze translation efficiency based on ribosome footprinting and mRNA sequencing data. Genes with significantly changed translation efficiency were defined by a q-value cut-off of 0.05. The metagene2 package in R was used to construct metagene plots of RF coverage. To normalize coverage by the length of each subregion, binned RF coverage of Rapamycin and DMSO samples were computed for the 5′ UTR (10 bins), CDS (20 bins), and 3′ UTR (10 bins) subregions. RF coverage subregions were concatenated, averaged across replicate samples, and normalized to the maximum value in each plot. The Cufflinks package was used to select the predominant isoform for each gene [39,40]. PANC-1 cells were labeled for protein synthesis using Click-iT® AHA metabolic labeling reagent as per the manufacturer’s instructions (Invitrogen; Thermo Fisher Scientific, Inc.; Waltham, MA, USA). Briefly, cells were treated with either rapamycin (for 30 min, 1 h, 2 h, or 4 h) or DMSO. Cells were incubated in a methionine-free medium for 30 min before AHA labeling for 1 h. Then, they were fixed with 4% paraformaldehyde in PBS for 15 min, permeabilized with 0.25% Triton X-100 in PBS for 15 min, and then washed with 3% BSA. Cells were stained using Alexa Fluor 488 Alkyne with the Click-iT Cell Reaction Buffer Kit (both Invitrogen; Thermo Fisher Scientific, Inc.; Waltham, MA, USA). Flow cytometry was used to detect changes in mean fluorescence intensity. To generate IC50 curves, cells were treated with rapamycin from varying concentrations of 0.5 nM to 50 µM or a combination of rapamycin and either S6Ki (100 nM) or [±]CR-1–31B (10 nM) for 72 h. Cell viability was measured using ATP quantification with the CellTiter-Glo Luminescent Cell Viability Assay (Promega; Madison, WI, USA). The drug combination effect was calculated by using the Synergy Finder R package [41]. Briefly, the overview of synergy scores quantifies the effects of combining rapamycin with CR-1-31B in PANC-1 and MiaPaca2 cells. A score lower than −10 can be interpreted as antagonistic, between −10 and 10 as an additive, and higher than 10 as synergistic. We used four different models to calculate an expected response. The ZIP model references an additive effect as though the drugs do not interact; the Bliss model references the probability of additive effect between the drugs as though they are independent events; the Loewe model references an expected response as though the two drugs were identical; and the HSA model references the highest single drug response [41]. Cells were seeded in 6-well plates (25 × 103 cells/well for PANC-1 and 50 × 103 cells/well for PANC10.05) and allowed to adhere overnight in regular media. DMSO or Rapamycin (0.1 µM, 0.5 µM, or 1.0 µM) was added and refreshed every 3 days until the end of the experiment at day 10. Treated cells were fixed in 10% formalin solution and stained with 0.1% crystal violet before images were taken. For quantification, we used the ImageJ software 1.53v. We set the color threshold to include visible colonies within each well. The Analyze Particles feature was used to measure the percentage of surface area covered by cells. Lysates were made using lysis buffer consisting of 50 mM Tris HCl at 7.5 pH, 250 mM NaCl, 0.5% (v/v) NP-40, and 5 mM EDTA, with proteases and phosphatases. A total of 50 µg of protein was loaded into SDS-PAGSE gels and transferred onto iBlot 2 nitrocellulose membranes (Invitrogen). The following antibodies were purchased from Cell Signaling Technology: RPSKB1, RPSKB2, RPSKA1, STAT5A, AKT1, p-AKT S473, p-S6, S6, p-eIF4E S209, and eIF4E. β-actin (A5316) was purchased from Sigma. We used the RNA G-quadruplex-driven luciferase reporter assay as described in our previous study [27]. The TOP motif luciferase reporter construct was obtained from Addgene (plasmid cat. number # 26611). PANC-1 cells were transfected with the luciferase plasmid and treated with Rapamycin (50 nM) for 24 h. Luciferase assays were performed using the Dual-Luciferase Reporter Assay System (Promega E1960, Madison, WI, USA) following the manufacturer’s instructions. A hypergeometric test was performed to test for the significance of the enrichment of the gene overlap in the KEGG pathway. All data were analyzed with two-tailed t-tests unless specified. Supplementary display items are available in the online version of the paper. Raw and processed data for the ribosome footprinting and total mRNA sequencing are deposited in the NCBI Gene Expression Omnibus database (awaiting GSE accession number). Our study establishes the effect of mTOR inhibitor Rapamycin on translation programs in pancreatic cancer lacking 4EBP1 expression. We show that rapamycin induces feedback activation of eIF4A-eIF4E-dependent translation in cells lacking 4EBP1 protein to facilitate cancer cell growth and this can be further inhibited by using eIF4A specific inhibitor.

PMC10001352

Emil Carlsson,Umar Sharif,Wasu Supharattanasitthi,Luminita Paraoan

Analysis of Wild Type and Variant B Cystatin C Interactome in Retinal Pigment Epithelium Cells Reveals Variant B Interacting Mitochondrial Proteins

23-02-2023

cystatin C,variant B,mitochondria,aging,mistrafficking,translocator protein,halo-tag,age-related macular degeneration,Alzheimer’s disease

Cystatin C, a secreted cysteine protease inhibitor, is abundantly expressed in retinal pigment epithelium (RPE) cells. A mutation in the protein’s leader sequence, corresponding to formation of an alternate variant B protein, has been linked with an increased risk for both age-related macular degeneration (AMD) and Alzheimer’s disease (AD). Variant B cystatin C displays intracellular mistrafficking with partial mitochondrial association. We hypothesized that variant B cystatin C interacts with mitochondrial proteins and impacts mitochondrial function. We sought to determine how the interactome of the disease-related variant B cystatin C differs from that of the wild-type (WT) form. For this purpose, we expressed cystatin C Halo-tag fusion constructs in RPE cells to pull down proteins interacting with either the WT or variant B form, followed by identification and quantification by mass spectrometry. We identified a total of 28 interacting proteins, of which 8 were exclusively pulled down by variant B cystatin C. These included 18 kDa translocator protein (TSPO) and cytochrome B5 type B, both of which are localized to the mitochondrial outer membrane. Variant B cystatin C expression also affected RPE mitochondrial function with increased membrane potential and susceptibility to damage-induced ROS production. The findings help us to understand how variant B cystatin C differs functionally from the WT form and provide leads to RPE processes adversely affected by the variant B genotype.

Analysis of Wild Type and Variant B Cystatin C Interactome in Retinal Pigment Epithelium Cells Reveals Variant B Interacting Mitochondrial Proteins Cystatin C, a secreted cysteine protease inhibitor, is abundantly expressed in retinal pigment epithelium (RPE) cells. A mutation in the protein’s leader sequence, corresponding to formation of an alternate variant B protein, has been linked with an increased risk for both age-related macular degeneration (AMD) and Alzheimer’s disease (AD). Variant B cystatin C displays intracellular mistrafficking with partial mitochondrial association. We hypothesized that variant B cystatin C interacts with mitochondrial proteins and impacts mitochondrial function. We sought to determine how the interactome of the disease-related variant B cystatin C differs from that of the wild-type (WT) form. For this purpose, we expressed cystatin C Halo-tag fusion constructs in RPE cells to pull down proteins interacting with either the WT or variant B form, followed by identification and quantification by mass spectrometry. We identified a total of 28 interacting proteins, of which 8 were exclusively pulled down by variant B cystatin C. These included 18 kDa translocator protein (TSPO) and cytochrome B5 type B, both of which are localized to the mitochondrial outer membrane. Variant B cystatin C expression also affected RPE mitochondrial function with increased membrane potential and susceptibility to damage-induced ROS production. The findings help us to understand how variant B cystatin C differs functionally from the WT form and provide leads to RPE processes adversely affected by the variant B genotype. Age-related macular degeneration (AMD) is the leading cause of blindness in the developed world [1], and is associated with the deterioration of the retinal pigment epithelium (RPE) monolayer of cells and loss of photoreceptors in the posterior of the eye [2,3]. In addition to age, environmental risk factors are commonly cited as drivers of disease onset and progression, alongside a significant genetic component of overall AMD risk [4]. Mutations across 34 locus regions are now attributed to approximately half of the overall genetic risk [5], with more rare mutations in other genes described as minor risk factors. One of these genes is CST3, encoding the protein cystatin C. The 14 kDa cysteine protease inhibitor cystatin C is an abundantly expressed and directionally secreted protein from the RPE [6,7,8,9,10], where it is likely to regulate extracellular functions such as ECM remodeling through interactions with its molecular targets, the cathepsins in the eye. A single nucleotide polymorphism in the leader sequence of precursor cystatin C, encoding an A25T amino acid change resulting in a variant B protein, has been identified as a risk factor not only for AMD [11,12,13], but also Alzheimer’s disease (AD) [14,15,16]. The immediate functional effect of this mutation is not fully understood, but reduced secretion has been described from RPE cells as well as fibroblasts cells homozygous for variant B cystatin C [17,18,19,20]. Moreover, recently we applied an innovative CRISPR/Cas9 gene editing technique to create bi-allelic mutations in induced pluripotent stem cells (iPSCs) in order to express the AMD-linked variant B form of cystatin C under its endogenous promoter [21]. The resulting in vitro iPSC-differentiated RPE cells were shown to exhibit reduced cystatin C secretion, which led to an increase in RPE ability to degrade ECM material and migration capabilities. Furthermore, conditioned media from edited cells was able to stimulate the formation of significantly longer microvascular tubes compared to wild type conditioned media, which supports a role for angiogenesis relevant to AMD changes [22]. In addition, previous studies using live cell microscopy techniques have indicated that a fraction of the intracellular variant B cystatin C protein pool associates with mitochondria in cultured RPE cells [18,23]. This was highly unexpected, not predicted, and likely a consequence of its disturbed intracellular trafficking. The present study aimed to provide increased functional understanding of variant B cystatin C association with mitochondria. An increasing number of studies have highlighted the maintenance of mitochondrial homeostasis as an essential process in promoting RPE cellular health with age. In general, cells undergo a decline in mitochondrial activity with aging [24]. However, dysfunctional mitochondria [25], mitochondrial DNA damage [26], up- and down-regulation of mitochondrial proteins [27,28,29,30], and mitochondrial disintegration [31] have also been observed in the RPE with AMD. However, the mechanisms underlying this behavior are poorly understood. Although mitochondrial modulation to promote proteostatic control in the RPE and retina in general has been suggested as a possible therapeutic avenue for managing AMD and other degenerative diseases [26], specific pathways are yet to be identified. Given its unusual intracellular behavior, marked by an apparent mitochondrial association identified using visual analysis techniques, we hypothesized that variant B cystatin C may interact with one or more mitochondrial proteins that could have an impact on downstream mitochondrial function. Here we performed affinity purification mass spectrometry to elucidate and compare interactomes of wild type and risk-associated variant B cystatin C in RPE cells. Using Halo-tag technology, cystatin C fusion proteins were used as baits to capture interacting proteins on Halo-link resin, with eluates subjected to mass spectrometry analysis. We identified several proteins that exclusively bound to variant B cystatin C, including outer mitochondrial membrane (OMM) proteins, translocator protein (TSPO), and cytochrome B5 type B (CYB5B). In addition, analyses of mitochondrial functions revealed that the expression of variant B cystatin C in RPE cells resulted in alterations in susceptibility to mitochondrial ROS production and mitochondrial membrane potential (Δψm). This data provides for the first time information regarding the protein-protein interactions formed by the disease-linked variant B of cystatin C, and further demonstrates its impact on mitochondrial function. ARPE-19 cells (CRL-2302; ATCC) were cultured in Dulbecco’s Modified Eagle’s Medium/Nutrient Mixture F-12 Ham (DMEM:F12, Sigma, St. Louis, MO, USA) supplemented with 10% fetal bovine serum (FBS, Sigma, St. Louis, MO, USA) at 37 °C, 5% CO2. Upon reaching 80% confluency, cells were routinely passaged at a 1:3 ratio by dissociation with trypsin-EDTA solution (Sigma, St. Louis, MO, USA). cDNA encoding WT or variant B cystatin C (encoding 1–146 aa full length protein) were subcloned into the pHTC HaloTag® CMV-neo plasmid (Promega, Madison, WI, USA) between the EcoRI and XhoI restriction sites, using standard molecular biology techniques. Sequences were confirmed by Sanger sequencing (Source BioScience, Nottingham, UK). Preparation of plasmids encoding EGFP-tagged WT or variant B cystatin C has previously been described [8]. For mammalian cell transfections, all plasmids were propagated in DH5α and purified using an EndoFree Plasmid Kit (Qiagen, Hilden, Germany). Halo-tagged cystatin C was transfected in ARPE-19 cells by electroporation using the Neon system (Invitrogen, Carlsbad, CA, USA). For preparation of cell lysates for pull down assay, 1 × 107 ARPE-19 cells were washed in DPBS and resuspended in 500 µL Neon resuspension buffer R. A total of 5 µg of plasmid was added to the mixture and electroporation was performed using 100 µL tips with the settings 1350 V, 20 ms, 2 pulses, after which the cells were immediately added to the culture medium. Cells were incubated in a T75 flask for 24 h, followed by washing once in ice-cold DPBS and harvesting by scraping. Fluorescent visualization of Halo-tagged cystatin C was performed with HaloTag TMRDirect ligand (Promega, Promega, Madison, WI, USA) added to the culture medium of transfected cells following the manufacturer’s instructions. ARPE-19 cells expressing Halo-tagged proteins were resuspended in 300 µL mammalian lysis buffer supplemented with protease inhibitor (Promega, Promega, Madison, WI, USA). Following 5 min of incubation on ice, the lysis mixture was homogenized by passing through a 25G needle 10 times. The sample was clarified by centrifugation at 14,000× g for 5 min, and the supernatant was diluted to 1 mL using TBS and added to 50 µL Halo-link resin (Promega, Promega, Madison, WI, USA) prewashed three times with wash buffer (TBS + 0.05% IGEPAL-630). Protein complexes were allowed to bind to the resin overnight at 4 °C using end-over-end mixing. The resin was washed three times in wash buffer and once in TBS, after which bound proteins were eluted in 50 µL 50 mM ammonium bicarbonate supplemented with 0.1% Rapigest SF (Waters, Milford, MA, USA) at 80 °C for 10 min. Eluted samples were stored at −80 °C until analyzed. 10 µL of eluted samples were mixed 1:1 in 2× SDS-PAGE sample buffer, boiled at 95 °C for 5 min, and resolved by SDS-PAGE on a 12% gel, followed by visualization by silver staining using a Pierce silver stain kit (Thermo Scientific, Waltham, MA, USA). Gels were imaged using a ChemiDoc XRS+ imaging system (Bio-Rad, Hercules, CA, USA). Mass spectrometry experiments and analysis were performed by the Warwick proteomics research technology platform using similar methods as described previously [32,33,34]. Following trypsin digestion, peptides were separated using reversed phase chromatography on an Ultimate 3000 RSLCnano system (Dionex, Sunnyvale, CA, USA). Mobile phase buffers A and B were 0.1% formic acid in water and 0.1% formic acid in acetonitrile, respectively. Samples were loaded onto an Acclaim PepMap µ-precolumn cartridge 300 µm i.d. × 5 mm 5 μm 100 Å (Thermo Scientific, Waltham, MA, USA) equilibrated in 2% aqueous acetonitrile containing 0.1% trifluoroacetic acid for 8 min at 10 µL min−1, after which peptides were eluted onto an Acclaim PepMap RSLC 75 µm × 25 cm 2 µm 100 Å (Thermo Scientific, Waltham, MA, USA) at 300 nL min−1 by increasing the mobile phase B concentration from 4–35% over 72 min, then to 80% over 3 min, followed by a 15 min re-equilibration at 4%. Eluting peptides were converted to gas-phase ions by means of electrospray ionization and analyzed on a Thermo Orbitrap Fusion (Q-OT-qIT, Thermo Scientific, Waltham, MA, USA). Survey scans of peptide precursors from 375 to 1575 m/z were performed at 120K resolution (at 200 m/z) with a 5 × 105 ion count target. Tandem MS was performed by isolation at 1.2 Th using the quadrupole, HCD fragmentation with a normalized collision energy of 33, and rapid scan MS analysis in the ion trap. The MS2 ion count target was set to 1 × 104 and the max injection time was 200 ms. Precursors with a charge state of 2–6 were selected and sampled for MS2. The dynamic exclusion duration was set to 40 s with a 10 ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on. The instrument was run in top speed mode with 2 s cycles. The raw data was processed using MaxQuant engine (https://www.biochem.mpg.de/6304115/maxquant (accessed on 1 November 2022)) against Homo sapiens database (http://www.uniprot.org/ (accessed on 1 November 2022)) and the common contaminant database from MaxQuant. Peptides were generated from a tryptic digestion with up to two missed cleavages, carbamidomethylation of cysteines as fixed modifications, and the oxidation of methionines as variable modifications. Precursor mass tolerance was 10 ppm, and product ions were searched at 0.8 Da tolerances. Scaffold (TM, version 4.8.2, Proteome Software Inc., Portland, OR, USA) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at >95.0% probability by the Scaffold Local FDR algorithm. Protein identifications were accepted if they could be established at >95.0% probability and contained ≥2 identified peptides. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters. Protein groups with significant total spectra intensity were determined according to the T-Test, using Benjamini-Hochberg multiple test correction and p < 0.1. Crude mitochondrial fractions from ARPE-19 cells expressing Halo-tagged WT or variant B cystatin C were isolated by differential centrifugation using a mitochondrial isolation kit (Thermo Scientific, Waltham, MA, USA; Cat. No. 89874), following the manufacturer’s instructions. The mitochondrial pellet was solubilized in 2 × SDS-PAGE sample buffer, and the mitochondrial and cytosolic fractions were analyzed by immunoblotting. Following SDS-PAGE using 12% gels, proteins were transferred to a nitrocellulose membrane using standard molecular biology procedures. Primary antibodies used were against cystatin C (Abcam, Cambridge, UK; ab109508), GAPDH (Abcam, Cambridge, UK; ab8245), CYB5B (Thermo Scientific, Waltham, MA, USA; PA5-52482), TOM20 (Cell Signaling Technology, Danvers, MA, USA; 42406), TSPO (Abcam, Cambridge, UK; ab109497), IRAP (also known as leucyl-cystinyl aminopeptidase; Cell Signaling Technology, Danvers, MA, USA; 6918), and α-tubulin (Abcam, Cambridge, UK; ab4074). After blocking in 5% milk in TBS supplemented with 0.1% Tween-20 (TBST) and probing with primary antibody, membranes were washed three times with TBST and incubated with secondary antibody conjugated to HRP (Sigma, St. Louis, MO, USA; A0545 or A9044), and then washed for an additional three times with TBST and developed using either Radiance Plus chemiluminescent substrate (Azure Biosystems, Dublin, CA, USA) or SuperSignal™ West Pico PLUS Chemiluminescent Substrate (Thermo scientific, Waltham, MA, USA). Blots were imaged using a ChemiDoc XRS+ imaging system (Bio-Rad, Hercules, CA, USA). Band intensity was assessed via densitometry and normalized against indicated housekeeping protein, and was used as a semi-quantitative estimate of protein concentrations. ARPE-19 cells seeded into 6-well plate (3 × 105 per well) were transfected with 0.5 µg of either EGFP only, WT, or variant B cystatin C endotoxin free plasmid DNA using the Neon electroporation system (same parameters used as for Halo-tag plasmids). Mitochondrial ROS detection experiments were performed using a commercially available kit (Cayman Chemical, Ann Arbor MI, USA) following the manufacturer’s protocol. Briefly, 24 h post-transfection, ARPE-19 cells were detached from wells using trypsin, resuspended in media, and centrifuged at 400× g for 5 min. Cell pellets were then resuspended in 100 µL of cell-based assay buffer (Cayman Chemical, Ann Arbor MI, USA) and centrifuged again at 400× g for 2 min. Cell pellets were resuspended in 500 µL of 20 µM Mitochondrial ROS detection reagent (Cayman Chemical, Ann Arbor MI, USA) and incubated for 30 min at 37 °C. Cell suspensions were centrifuged again and pellets were washed with Hank’s Balanced Salt Solution (HBBS) and centrifuged at 400× g for 5 min (step repeated twice). The final cell pellet was resuspended in 500 µL of warm HBBS +/− 10 µM antimycin A reagent (Cayman Chemical, Ann Arbor MI, USA) and incubated for a further 1 h at 37 °C. Cell suspensions were processed for flow cytometry analysis for mitochondrial ROS detection using the BD Accuri C6TM instrument (BD Biosciences, San Jose, CA, USA). A minimum of 4000 events were collected in the P1 gate for each condition. Samples were analyzed using the BD Accuri C6 program and were utilized to detect fluorescent intensity that correlated to ROS levels (FL2 channel) from transfected cells only (FL1 channel). Flow cytometry analysis, using the BD Accuri C6 SoftwareTM (BD Biosciences, San Jose, CA, USA), provided transfection efficiency measurements, but more importantly allowed for the selection of only transfected green cells (FL1 channel) from mixed non-transfected and transfected populations. Mitotracker Red FM staining was measured on the FL2 channel with fluorescent intensity numbers used for quantification. 1.5 × 106 ARPE-19 cells were transfected with 2.5 µg of either EGFP only, WT, or variant B cystatin C endotoxin free plasmid DNA using the Neon electroporation system (as above). 24 h post-transfection, ARPE-19 cells were stained with 100 nM Mitotracker Red FM (Thermo scientific, Waltham, MA, USA), a dye whose uptake is dependent on mitochondrial membrane potential (Δψm) for 30 min after which flow cytometry analysis was performed. A minimum of 10,000 events were collected in the P1 gate for each condition. Samples were analyzed on the BD Accuri C6 Flow Cytometry System (BD Biosciences, San Jose, CA, USA) in the same manner as for mitochondrial ROS detection in order to select transfected green cells (FL1 channel) from the mixed population. Mitotracker Red FM staining was measured on the FL3 channel with fluorescent intensity numbers used for quantification. To evaluate the different interaction partners of WT and variant B cystatin C, the two proteins were first expressed as Halo-tagged fusion proteins in ARPE-19 cells. The Halo-tag is a versatile fusion protein that allows irreversible attachment to a wide range of ligands while being biologically inert. In addition, successful expression can be confirmed directly by linking the tag to a fluorescent ligand. Expression constructs were generated by subcloning the full-length cystatin C protein coding sequence between the EcoRI and XhoI sites in the pHTC plasmid, resulting in a sequence-encoding cystatin C with a C-terminal Halo-tag attached. Positioning of the tag at the C-terminus allowed normal processing of precursor cystatin C, which involves cleavage of the N terminal signal sequence to form the mature protein. A high degree of expression of the respective proteins was achieved in transfected ARPE-19 cells (Figure 1a). Western blot analysis of cell lysates using an anti-cystatin C antibody showed bands consistent with the expected molecular weight of approximately 51 kDa for the Halo-cystatin C fusion proteins, in addition to the ~13 kDa endogenously expressed mature form of cystatin C (Figure 1b). Proteins from ARPE-19 whole cell lysates interacting with either WT or variant B cystatin C fused to a C-terminal Halo-tag were isolated by immobilizing the bait proteins via their Halo-tag on Halo-link resin, followed by extensive washing to remove contaminants/non-interacting proteins and elution of interacting proteins in Rapigest buffer (Figure 1c). As Rapigest is fully compatible with downstream trypsin cleavage and mass spectrometry analysis, this method was preferred to the standard SDS elution buffer, which would require an intermediate buffer exchange step before analysis. Silver staining of eluates indicated that several proteins had been pulled down (Figure 2a), and mass spectrometry analysis was performed to identify proteins interacting with cystatin C, as well as discern any differences between WT and variant B interaction partners. A total of 29 individual proteins (including the bait cystatin C) could be detected in the samples. Summaries of the identified proteins are listed in Table 1 and Table 2 and shown diagrammatically in Figure 2b. Volcano plots were generated for eluted fractions from both bait proteins compared with the eluted fractions of a control sample from pull down analysis with cells transfected with empty pHTC plasmid (Figure 2c,d). As expected, the majority of interacting proteins, including most of the high abundance proteins, were similar across WT and variant B pull downs. These included cathepsins C, L, K, H, B, O, F, and V, which are known targets of cystatin C, providing confidence that the assay and analysis had worked as expected and that the Halo-fusion forms of cystatin C were active. However, a number of different interacting proteins were also identified. Of particular interest, among the eight proteins that were exclusively pulled down by variant B cystatin C, three (TSPO, CYB5B, and DnaJ homolog subfamily C member 5) are known to be abundantly distributed in mitochondria. Previous studies have indicated that a fraction of variant B cystatin C molecules mislocalize to the mitochondria [18], which may at least in part explain why reduced secretion of this protein occurs in RPE [18,22] and fibroblast cells [17]. Interaction between variant B cystatin C and TSPO was also confirmed via immunoblotting in separate, independent pull down experiments, while a much fainter band indicating a possible interaction with CYB5B was also seen (Figure 3). Notably, a minimal band for CYB5B could also be seen in the WT system, however, this was consistently of significantly lower intensity in relation to input, indicating a lower amount of protein pulled down by the WT protein and weaker interaction. Interestingly, clear interactions between IRAP and both WT and variant B cystatin C were also confirmed (Figure 3), consistent with a previous study showing cystatin C association with 3T3-L1 adipocyte GLUT4 storage vesicles [35], of which IRAP is a well-known component [36,37]. To test whether the Halo-tagged WT and variant B cystatin C differ in their subcellular localization when expressed in ARPE-19 cells, crude mitochondrial fractions were isolated from cytoplasm using differential centrifugation. Western blot analysis of the fractions confirmed the findings of previous studies showing association of a portion of variant B cystatin C pool and mitochondria by showing that the protein was enriched in the mitochondrial fraction in comparison to the WT protein (Figure 4a). Furthermore, expression of EGFP-tagged WT and variant B cystatin C in ARPE-19 was analyzed by fluorescence microscopy following staining with Mitotracker dye (Figure 4b), successfully replicating previously described results [18] with selected cells evidencing partial co-localization of variant B cystatin C and mitochondria, while WT cystatin C displayed a perinuclear enrichment typical of proteins processed through the classical secretory pathway. After observing that variant B cystatin is significantly enriched in mitochondrial fractions compared to WT cystatin C, we next sought to see if the presence of variant B had an effect on mitochondrial function in relation to mitochondrial ROS production and Δψm. In relation to ROS production, a significant decrease in ROS production was observed in variant B-transfected RPE cells compared to WT cells (Figure 4c). Interestingly, upon the addition of antimycin A, an inhibitor of oxidative respiration that leads to a decrease in ATP production, similar levels of ROS were observed between WT and variant B cells, which resulted in a significant increase in fold change of mitochondrial ROS production (antimycin A/basal levels) for variant B-transfected RPE cells. These findings suggest that the presence of variant B at the mitochondria leads to increased susceptibility towards agents or stresses that compromise ATP production. Next, in order to quantitatively evaluate the effects of variant B cystatin C on Δψm in RPE cells, the optimization of Mitotracker Red FM concentration was performed in order to use the dye in its sensitive range of concentration where differences can be detected. This showed that a concentration of 100 nM was sufficient to stain most cells whilst also allowing changes to be detected at a fluorescent and cell number level when CCCP, an uncoupling agent that disrupts oxidative phosphorylation, was added (Figure 5a,b). The presence of variant B cystatin C in RPE cells increased Δψm as indicated by increased fluorescence intensity readings in variant B-expressing RPE cells compared to WT transfected cells (Figure 5c). This data showed that mitochondria in RPE cells that contained variant B cystatin C existed in a functionally altered hyperpolarized state. In the present study, we have identified, for the first time to our knowledge, the interactome of WT cystatin C and AMD/AD-associated variant B cystatin C. The findings highlighted specific variant B cystatin C interacting mitochondrial proteins while also confirming the enrichment of variant B in mitochondrial fractions isolated from RPE cells expressing this form of the protein. Furthermore, we showed that the presence of variant B cystatin C in RPE cells leads to alterations in mitochondrial ROS production and Δψm. Thus, this study delineates possible routes on which variant B cystatin C interacts with mitochondrial processes and proteins, impacting mitochondrial function and contributing to mitochondrial dysfunction, which is a characteristic feature described for both AMD and AD. Our mass spectrometry and pull down data showed that variant B cystatin C interacts with CYB5B and TSPO. This is of interest due to the fact that both CYB5B and TSPO are localized to the outer mitochondrial membrane, thus comprising plausible anchor points for mistrafficked cystatin C. TSPO has been previously highlighted for its potential role in neurodegeneration seen in AD [38]. While it is nearly absent in the healthy adult brain, it has been shown to accumulate at sites of senile plaques in an AD model [39], possibly as a form of defense mechanism. Additionally, administration of the small molecular TSPO ligand PK11195 has been reported to reduce soluble and deposited ß-amyloid [40]. Variant B cystatin C is inefficiently cleaved and/or processed, which has been proposed to result in an incompletely processed precursor protein [18] that has increased propensity to aggregate and form amyloid fibrils [41]. It is possible that aggregated variant B cystatin C attaches itself onto the mitochondria due to TSPO’s ability to associate with and modulate amyloid fibrils. Furthermore, TSPO is involved in other biological roles that may be compromised through its interaction with variant B cystatin C. Recent studies have demonstrated other roles in a wide range of processes for TSPO, such as oxidative stress, calcium transport, mitochondrial function, apoptosis, inflammation, and perhaps most intriguing, autophagy, all of which have been linked to RPE dysfunction and AMD development [31,42,43,44,45,46,47,48,49]. Selective autophagy of the mitochondria, also known as mitophagy, is a key process in the elimination of damaged mitochondria [50]. Failure to remove these fragments leads to an unwanted accumulation inside the cell and subsequent cellular dysfunction, while uncontrolled activation of mitophagy pathways can elicit cell death. Impairment of mitophagy is theorized to play a role in AMD pathogenesis, and in this light, whether variant B cystatin C contributes to this effect via interaction with TSPO warrants further investigation. The functional effects of variant B cystatin C enrichment at the mitochondria were investigated through measurement of mitochondrial ROS and Δψm levels. Expression of variant B cystatin C caused a decrease in basal mitochondrial ROS levels in RPE cells, but an overall increase in fold change of mitochondrial ROS levels upon the addition of antimycin A compared to cells expressing WT cystatin C. ROS are generally produced from the mitochondria and play an important role, either physiological or pathophysiological, directly dependent of their level, in a wide range of cellular processes such as cell survival/death and inflammation [51,52,53]. Excess ROS formation generally leads to a state of oxidative stress, causing the accumulation of ROS-associated damage in DNA, lipids, and proteins; such processes are believed to contribute to RPE dysfunction and development of AMD [54]. Increased levels of mitochondrial ROS promote production of pro-inflammatory cytokines in mouse embryonic fibroblasts and human immune cells harboring a missense mutation in type 1 TNF receptor (TNFR1), a change which causes an autoinflammatory disorder called tumor necrosis factor receptor-associated periodic syndrome (TRAPs) [55]. Furthermore, mitochondrial ROS have been linked to inflammasome activation [52]. It is possible that the initial decrease in mitochondrial ROS levels may be an adaptive protective response employed to protect variant B-expressing RPE cells against stresses encountered with age [56] that increase ROS levels and elicit inflammation. The concept of protection is supported by findings in hypoxic cells, specifically that an adaptive response mediated by hypoxia-inducible factor 1 (HIF-1) reduces mitochondrial ROS production levels through multiple mechanisms, including increasing efficiency of electron transport chain (ETC) components [57]. It has been demonstrated that TSPO overexpression in Jurkat cells resulted in increased gene expression of mitochondrial ETC molecules and ATP production [58], thus evidencing TSPO involvement in mitochondrial energy metabolism. Although in need of further investigation, variant B cystatin C interaction with TSPO may compromise TSPO mitochondrial energy metabolism function and make these mitochondria less efficient at consuming oxygen. If this is the case, then it is plausible that undamaged mitochondria that do not contain variant B may increase ETC efficiency, which would result in reduced overall ROS levels [57]. Here we also identified an interaction between variant B cystatin C and OMM CYB5B, comprising an additional plausible site for ETC modulation. An overall increase in fold change mitochondrial ROS levels (upon the addition of antimycin A) in variant B-expressing cells compared to WT-expressing RPE cells suggests that the presence of variant B renders RPE cells more susceptible and responsive to ETC inhibitors such as antimycin A. In relation to RPE cells, one such stress known to accumulate with age in cells and in the Bruch’s membrane (BrM) is represented by advanced glycation end products (AGEs), a group of heterogeneous molecules that impact RPE function. AGEs have been shown to increase mitochondrial ROS [59], impair respiration, and target complex III of ETC, similar to antimycin A [60]. Therefore, with exposure to age-related stresses such as AGEs, it is possible that variant B cystatin C-expressing RPE cells would exhibit increased response in mitochondrial ROS production and subsequent processes such as inflammation. Indeed, RPE cells grown on AGE-containing matrixes have previously been shown to employ protective mechanisms that enable them to survive against the stress of AGE, but renders them more responsive to pro-inflammatory stimuli [61]. The implications of increased fold change mitochondrial ROS, exposure to age-related stresses such as AGE, and links to cell survival/inflammation definitely warrants further investigation in order to get a better understanding of cystatin C-mediated mechanisms contributing to RPE dysfunction and AMD progression. In addition to altered ROS levels, an increase in ΔΨm was determined, indicating that variant B cystatin C-expressing RPE cells contain functionally altered hyperpolarized mitochondria. Hyperpolarized mitochondria have been observed in iPSC-derived neurons isolated from patients carrying a mutation in the Tau gene that causes the neurodegenerative disease frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) [62]. This hyperpolarized mitochondrial state of neurons was shown to lead to overproduction of mitochondrial ROS, which in turn caused oxidative stress and cell death, events also linked with RPE dysfunction and AMD progression. Generally, a positive correlation exists between ΔΨm and mitochondrial ROS production [63]. However, in the present study, an initial decrease in mitochondrial ROS levels was observed in variant B-expressing RPE cells, along with an increase in ΔΨm. Similar to the present study, in vitro human fibroblasts treated with nicotinamide (NAM), a form of vitamin B3 with antioxidant effects, displayed lowered mitochondrial ROS and increased ΔΨm as well as, intriguingly, extended cell life span [64]. This extension of lifespan, which was suggested to be due to decreased mitochondrial activity, is notable, as it supports the idea of variant B cystatin C-expressing RPE cells reducing ROS and increasing ΔΨm as a potential protective mechanism. Furthermore, NAM-treated cells displayed lower mitochondrial content through increased mitophagy [65]. It was initially hypothesized that mitophagic removal of high ROS producing, low depolarized mitochondria is the cause of overall presence of mitochondria displaying decreased ROS levels and increased ΔΨm in NAM-treated cells. However, a more recent study demonstrated that the NAM-induced reduction in mitochondrial ROS and increase in ΔΨm was due to decreasing electron flow through complex I of ETC, which led to decreased oxygen consumption and mitochondrial ATP production, as well as blockage of the mitochondrial permeability transition pore (mPTP), respectively [66]. Opening and closing of mPTP formed on the inner mitochondrial membrane is involved in regulating calcium levels that are driven by ΔΨm [67]. Opening this channel is linked with a large efflux of calcium and is often linked to cell death in diseases such as AD [68]. Taken together, it could be that decreased ROS levels and increase in ΔΨm caused by the presence of variant B cystatin C may be indicative of RPE cells protecting themselves. This protection may involve increased mitophagy, decreased mitochondrial activity, which is linked to extended lifespan, and closure of the mPTP channel to reduce cell death. This, in the short term, may be beneficial to help compensate for variant B-associated mitochondrial dysfunction, but in the long term, constant decreased mitochondrial activity would be detrimental to the functional output of these highly metabolic post-mitotic cells. The effects of variant B cystatin C on the ETC, mitochondrial activity, and processes such as mitophagy in in vitro models of aging are currently being addressed in our laboratory and will provide a better understanding of how this mutant contributes to RPE dysfunction and AMD development. Limitations of this study include the use of the ARPE19 cell line as opposed to using primary or iPSC-derived RPE tissue, as well as allowing cells to go into suspension for electroporation instead of allowing them to form confluent and polarized monolayers. Although such alternative systems may have provided a higher level of physiological relevance, the choice of ARPE19 cells was founded on the critical need to achieve high levels of transient expression of fusion protein in a well-characterized model system. It is therefore important that future studies focus on validating results in primary tissue to understand the mechanistic relationship between variant B cystatin C and AMD development. Furthermore, although our experiments indicate a functional effect of variant B cystatin C on mitochondria, the exact mechanism and conditions involved need to be assessed in future studies to fully understand their impact on mitochondria and link with age-related diseases. In conclusion, we identified for the first time mitochondrial proteins that interact with variant B cystatin C. In addition, our data shows that the presence of variant B cystatin C impacts mitochondrial function, as observed through decreased mitochondrial ROS production and increased ΔΨm. Notably, other models that show similar results suggest these mechanisms are indicative of protection mechanisms for the cells. However, fold change mitochondrial ROS data (upon the addition of antimycin A) suggests that variant B cystatin C-expressing cells are more susceptible to detrimental ETC modulating stresses such as AGE, which accumulate with age.

PMC10001353

Rehab F. Abdelhamid,Seiichi Nagano

Crosstalk between Oxidative Stress and Aging in Neurodegeneration Disorders

27-02-2023

oxidative stress,free radical,aging,neurodegenerative diseases

The world population is aging rapidly, and increasing lifespan exacerbates the burden of age-related health issues. On the other hand, premature aging has begun to be a problem, with increasing numbers of younger people suffering aging-related symptoms. Advanced aging is caused by a combination of factors: lifestyle, diet, external and internal factors, as well as oxidative stress (OS). Although OS is the most researched aging factor, it is also the least understood. OS is important not only in relation to aging but also due to its strong impact on neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). In this review, we will discuss the aging process in relation to OS, the function of OS in neurodegenerative disorders, and prospective therapeutics capable of relieving neurodegenerative symptoms associated with the pro-oxidative condition.

Crosstalk between Oxidative Stress and Aging in Neurodegeneration Disorders The world population is aging rapidly, and increasing lifespan exacerbates the burden of age-related health issues. On the other hand, premature aging has begun to be a problem, with increasing numbers of younger people suffering aging-related symptoms. Advanced aging is caused by a combination of factors: lifestyle, diet, external and internal factors, as well as oxidative stress (OS). Although OS is the most researched aging factor, it is also the least understood. OS is important not only in relation to aging but also due to its strong impact on neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). In this review, we will discuss the aging process in relation to OS, the function of OS in neurodegenerative disorders, and prospective therapeutics capable of relieving neurodegenerative symptoms associated with the pro-oxidative condition. Healthy aging is characterized by a gradual breakdown of physiological systems leading to a reduction in cognitive functions and brain health, but the timing and extent of this decline vary among older people. Oxidative stress (OS) is a crucial factor in the aging process that can cause direct damage to the brain’s cellular architecture, causing neurodegenerative disease. Aging is the main lead factor for many diseases, including cancer, metabolic, cardiac and neurodegenerative diseases. Aging is linked to a loss of homeostasis, involving degradation of structural components, reduced cellular maintenance and a decrease in overall physiological function/metabolism. There are two major aging theories: the free radical theory, which postulates chronological accumulation of defects in gene expression and environmental damage. According to the free radical theory of aging by Denham Harman, 1956 [1], alterations in normal metabolic and mitochondrial function are induced by production of free radicals, which cause damage, aging, and associated aging illnesses. The second and more recent theory is the mitochondrial theory of aging by J. Miquel and colleagues in 1980 [2]. This theory posits that the loss of balance between free radical production and repair mechanisms is responsible for aging. The redox network is also essential in antioxidant defense. Mitochondria serve a crucial function by turning stored energy into adenosine triphosphate (ATP) through oxidative phosphorylation and phospholipid synthesis, buffering calcium, and coordinating programmed cell death [3]. Free radicals such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and reactive sulfur species (RSS), which are present in all cells, but which are restricted by the antioxidant systems that neutralize them, are primarily produced by oxidative phosphorylation [4]. Free radicals are produced naturally and modulate cellular processes such as inflammation, cell survival, and stress responses, as well as numerous illnesses such as cardiovascular problems, muscular dysfunction, allergies, and malignancies [5]. Free radicals are most likely formed due to processes involving molecular oxygen that are catalyzed in the cell by oxidative enzymes and in connective tissues by trace metals such as iron, cobalt, and manganese. The primary distinction between healthy aging and accelerated aging is the balance between free radical elevation and the body’s ability to guard and fight against RNS and ROS (Figure 1). In the event of loss of homeostasis between free radical production and detoxification, ROS production may overpower antioxidant defenses, resulting in a noxious state known as OS and general degradation of normal cellular functions. This has been documented in numerous clinical studies involving mitochondrial malfunction and aging [6]. The brain is especially prone to oxidative damage due to its high oxygen consumption, limited antioxidant defenses, and high concentration of polyunsaturated fatty acids that are easily oxidized [7]. Lipid peroxides (LPO) are highly reactive molecules that include malondialdehyde (MDA), 4-hydroxy-2-nonenal (HNE), acrolein, isoprostanes (IsoPs), and neuroprostanes (NeuroPs). They can disrupt proteins and DNA structure and functions [8,9,10]. Increased MDA, IsoPs, and HNE have been observed in brain tissues of Tg2576 Alzheimer’s disease (AD) model mice and post-mortem AD brains [11,12]. Oxidative damage to DNA results in formation of oxidized base adducts, including 8-hydroxyguanosine (8-OHG) and 5,6-diamino-5-formamidopyridine in brains of mild cognitive impairment (MCI) patients. Despite the brain’s great potential for ROS formation, its defense system against OS remains restricted and diminishes with age, owing to low amounts of endogenous antioxidants such as catalase (CAT), glutathione (GSH), glutathione peroxidase (GPx), and vitamin E, compared to other tissues, such as liver [13,14]. The limited regenerative ability of postmitotic neurons renders OS more damaging to the brain, where the damage accumulates over time, compared to other organs [15]. Even though mitochondrial dysfunction is the primary source of free radicals, there are several other sources, such as genetic (endogenous), environmental, and lifestyle causes (exogenous) [16]. Environmental factors, including smoking, UV radiation, heavy metal ions, ozone, allergens, drugs or toxins, pollutants, and pesticides, may all contribute to elevated ROS production in cells [17,18]. Ionizing radiation transforms hydroxyl radicals, superoxides, and organic radicals into organic hydroperoxides and hydrogen peroxide. Subsequently, peroxides react with metal ions, particularly Fe and Cu through redox reactions, with further oxidative activity at the cellular level. Several studies have demonstrated that exposure of fibroblasts to alpha particles leads to an intracellular increase in oxygen and an acceleration of peroxide formation [19,20]. Ultraviolet radiation (UVA) induces oxidative processes by stimulating riboflavin, porphyrins, and NADPH-oxidase, resulting in synthesis of 8-oxo-guanine and a drop in intracellular GSH levels, with a return to normal after exposure cessation [21]. Heavy metals are crucial to the formation of free radicals [22]. Nickel, arsenic, iron, copper, cadmium and lead can produce free radicals by Fenton or Haber-Weiss reactions [23,24], as well as through direct reactions between metal ions and cellular constituents with similar effects, such as generation of thiol-type radicals. In brain tissue, lead causes lipid peroxidation and raises GPx concentrations. By attaching to sulfhydryl groups, arsenic generates peroxides, superoxides, and nitric oxide, and inhibits antioxidant enzymes such as glutathione-S-transferase (GST), GPx, and glutathione reductase (GR) [25]. Free radicals produced by these reactions can cause substitutions of DNA bases, including guanine for cytosine, guanine for thymine, and cytosine for thymine [26]. Even in healthy individuals, ozone exposure can impair lung function by increasing inflammatory infiltration in the respiratory epithelium [27]. Lifestyle factors such as smoking, drinking alcohol, diet, exercise, and frequency of exercise all contribute to OS [28]. Some research has shown that ROS are present at the skeletal muscle level and help to control how muscle works. Muscle fibers always produce small quantities of reactive oxygen radicals, which are elevated by muscle contraction [29]. These oxygen radicals have many direct and indirect effects on muscle activity (contractility, excitability, metabolism, and calcium homeostasis) and are involved in skeletal muscle fatigue during hard exercise [30]. Long, exhausting exercise and overcoming limits as a phase of overtraining cause a massive response to OS. Endogenous antioxidant status is improved by moderate exercise, low-intensity training, and training for a long time. ROS are an essential part of how cells talk to each other and how antioxidant genes are turned on and off. Nuclear factor kappa B and mitogen-activated protein kinase are up-regulated by physical activity [31,32]. These activate gene expression of various enzymes and proteins that maintain oxidative/antioxidant intracellular homeostasis [33]. Physical exercise is the main non-drug therapy for treating chronic diseases, especially heart diseases, and lifestyle changes [34]. Relevant studies [35] have shown that autophagy, a process that breaks down and recycles cellular organs and nutrients, is important for cardiovascular benefits of training. Cigarette smoke has oxidants, free radicals, and organic components such as nitric oxide and nitric superoxide. These cause neutrophils and macrophages to gather in lung tissues, increasing the production of oxidants in the area [36,37]. Enzymes that are responsible for fighting damage caused by OS and these enzymes and pathways are prone to free radical damage [38,39,40,41,42]. Oligodendrocytes are susceptible to oxidative damage due to their function in maintenance and creation of myelin and their limited repair mechanisms, suggesting that white matter may be more susceptible to oxidative activity than gray matter. Antioxidant defense enzymes in the brain, such as superoxide dismutase (SOD), CAT, GPx, and GST are essential for neutralizing toxic byproducts of oxidative phosphorylation. Allelic variants of polymorphisms encoding these antioxidants are associated with anomalies in SOD, CAT, GPx, and GST activity in the central nervous system [38,43,44]. Neurodegenerative disease is an irreversible condition in which neuronal function declines over time, leading to neuronal death. The incidence of neurodegenerative disease is increasing every year, especially in countries with aging populations. Common neurodegenerative diseases include Alzheimer’s (AD) and Parkinson’s (PD) diseases. Mitochondria are the primary source of ROS and are the main cause of neurodegenerative diseases (Figure 2). Mitochondrial synthesis of ATP through oxidative phosphorylation, has the substantial disadvantage of producing unpaired electrons [45,46]. The electron transport chain comprises five multiprotein complexes that mediate interaction of these electrons with oxygen, resulting in ROS hydrogen peroxide (H2O2), superoxide anions (O2−), and hydroxyl radicals (•OH) [14,47]. Electron transfer from NADH to ubiquinone is catalyzed by mitochondrial complex I (reduced nicotinamide adenine dinucleotide [NADH] coenzyme Q reductase) (coenzyme Q). Complex II also provides electrons to ubiquinone (succinate dehydrogenase). Electrons from reduced ubiquinone are donated to complex III (cytochrome bc1), and subsequently, to cytochrome c (CytC). Complex IV (CytC oxidase) is involved in interactions between molecular oxygen and electrons extracted from CytC, resulting in water production [48]. Complexes I, II, and III are most often linked to premature electron leakage to oxygen and are the primary source of ROS generation [49]. Additionally, elevated ROS levels trigger formation of additional reactive species, such as RNS, when O2 reacts with other molecules, such as nitric oxide, to generate peroxynitrite (ONOO−). Furthermore, in addition to ROS and RNS, mitochondria generate RSSs, which are very reactive. Free oxygen radicals gradually damage proteins, lipids, and nucleic acids, resulting in inefficient or aberrant cellular functions, inflammation, and cell death [50]. Mitochondrial internal components and mitochondrial DNA (mtDNA) in particular, are very vulnerable to OS-induced damage, resulting in impaired mitochondrial bioenergetics, increased ROS generation, and OS [51]. Enzymatic and non-enzymatic mechanisms safeguard antioxidant systems. SOD, CAT, GPx, GR, and thioredoxin (TRX) are the major enzymes involved in catalytic ROS elimination. Non-enzymatic complexes include vitamins A, C, and E, GSH, and proteins such as albumin and ceruloplasmin [52,53]. Despite substantial investigation, the genesis of amyotrophic lateral sclerosis (ALS) is still not fully clear. Ninety percent of ALS cases are sporadic and appear to lack a genetic foundation, whereas 10% of patients have familial ALS, mostly an autosomal dominant [54]. ALS has been linked to various occupational and environmental variables, such as exposure to various chemicals, metals, and pesticides, electromagnetic fields (EMFs), and lifestyle choices, including smoking and excessive exercise [55,56,57,58]. A recognized but poorly understood pathogenic ALS feature is abnormally high free radical levels and inadequate antioxidation. Undoubtedly, OS is essential for motor neuron death, but we do not know exactly when oxidative damage occurs [59]. OS biomarkers have been identified in brain tissue of ALS patients, cerebrospinal fluid (CSF), blood, and urine [60]. It is challenging to track OS biomarkers over a long period due to the short lifespans of ALS patients. Further, because of OS’s random start and the present lack of tools to forecast its progression, it is difficult to be certain whether OS is a cause of ALS-associated neurodegeneration or a result of other underlying etiologic variables [57]. Investigations using a murine ALS model have revealed altered mitochondrial structures and nuclear factor erythroid 2-related factor 2 (Nrf2) pathway activation during early ALS stages, indicating OS involvement in the disease’s early stages. OS damage is typically caused by stimulating formation of intracellular antioxidant molecules [61,62]. However, these studies used the murine mutant SOD1 ALS model, and SOD1 mutations only account for 20% of human familial ALS cases. Twenty individuals with sporadic ALS had significantly higher levels of lipid peroxidation and lower levels of the antioxidant enzymes CAT, GR, GSH, and glucose-6-phosphate dehydrogenase (G6PD) in their erythrocytes [63]. Progression of alterations parallel the pathophysiology of ALS, supporting OS participation in ALS development. Additionally, the aforementioned environmental and endoplasmic reticulum stress factors work together to promote pro-oxidative states that may ultimately harm motor neurons [64]. The intermembrane space (IMS) of mitochondria contains a protein known as Coiled-Coil-Helix-Coiled-Coil-Helix Domain-Containing Protein 10 (CHCHD10), which has no known function [65]. However, it is believed to be involved in maintaining mitochondrial crista morphology and proper oxidative phosphorylation. In particular, in multiprotein complexes I, II, III, and IV, overexpression of mutant CHCHD10 harboring an allele linked to ALS alters mitochondrial structure and impairs electron transport chain function [66,67]. In addition, fibroblasts from an ALS patient with a CHCHD10 mutation displayed mitochondrial ultrastructural damage and mitochondrial network fragmentation [66,67]. The most common neurodegenerative condition, AD, is characterized by a steady decline in behavior, cognition, and functioning that profoundly affect day-to-day activities. AD brains have higher protein, DNA, and lipid oxidation rates, as well as redox-active metals [51]. Pathological indicators of AD include extracellular senile plaques (SP) and intracellular neurofibrillary tangles (NFT). Protein aggregation is sporadic, and its molecular mechanism is poorly understood. Several investigations have revealed that AD brains display elevated OS, which is crucial to disease development [68]. Amyloid fibril precursors are neurotoxic owing to OS generated by toxic conformer of amyloid oligomers and additional neurotoxic effects such as neuronal membrane rupture, microglia and astrocyte activation, and Ca2+ dyshomeostasis [69,70,71]. Tau protein aggregation is another hallmark of AD although in most cases, it is of late onset. Several reports have indicated that neurotoxicity caused by beta amyloid alteration is the main trigger of tau alteration to form tangles. NFTs in the early stages of AD are intracellular deposits; however, these progress to extracellular deposits in later stages. NFTs are composed of paired helical filaments (PHFs) in which the major component is tau protein [72,73,74,75,76]. When tau proteins are hyperphosphorylated, they self-assemble into NFTs and may be detected in neurons [77,78]. According to recent data, amyloid deposition occurs 15–20 years before dementia manifests itself, and tau pathology appears thereafter [79,80,81,82,83]. Using multiphoton imaging, researchers identified a clear link between free radical generation and amyloid plaques in AD mouse models and human AD brain tissues, where fluorogenic free radical markers decreased following administration of a synthetic antioxidant [84]. Unlike AD, PD is clinically recognized by four cardinal motor symptoms: bradykinesia, stiffness, resting tremors, and trouble with posture and walking [83,84,85]. In PD brains, the substantia nigra pars compacta and to a lesser extent, the globus pallidus, putamen, and caudate nucleus, exhibit selective dopaminergic neuronal loss. The nigrostriatal pathway’s degenerating neurons release less of the neurotransmitter dopamine [86]. Lewy bodies are clusters of aberrant proteins seen in neurons of individuals with Parkinson’s disease. They are components of α-synuclein (α-syn) protein, broadly disseminated in the neurological system, but activities of which are not well understood [87]. α-syn fibrillation creates clumps that take up significant space within cells and ultimately result in neuronal death [88]. As in AD, pathophysiological pathways underlying PD are strongly related to OS [89]. Evidence of OS involvement may be seen in the substantia nigra of PD patients, where oxidized lipids, proteins, and DNA can be found [90,91]. Additionally, the monoamine oxidase (MAO) that breaks down dopamine creates hydrogen peroxide, whereas dopamine on its own produces superoxide anion and reactive quinones. These reactive substances cause other nearby neurons, as well as dopaminergic neurons, to become cytotoxic [92,93]. Multiple sclerosis (MS) is a multifactorial autoimmune disease of the central nervous system (CNS), characterized by chronic inflammation, demyelination, and axon and neuron loss. Depending on the location of the demyelinating lesions, MS patients can develop almost any neurological sign or symptom, including motor, sensory, and cognitive impairment [94,95]. OS is heavily involved in several MS pathological hallmarks such as myelin destruction, axonal degeneration, and inflammation [96]. In MS as in other neurodegenerative diseases OS triggers activation of autophagy and microglia as well as of the neuroimmune system. Neurons, astrocytes, and oligodendrocytes produce chemicals that bind to microglial receptors in healthy settings, suppressing their activated states [97,98,99,100]. When certain molecules (such as myelin CD47) are expressed less, microglial activation is increased, which may cause myelin debris to be phagocytosed and to provide neurotrophic factors [101,102]. Microglial physiological functions are transformed into harmful inflammatory insults by long-term damage, systemic inflammation, proinflammatory cytokine release, and ROS signaling [103]. Together, our results imply that during the onset and development of experimental autoimmune encephalomyelitis (EAE) and MS lesions, activated microglia and macrophages are directing tissue damage through their oxidative burst. Simultaneous activation of a sophisticated antioxidant response is insufficient to stop the apoptotic and degenerative processes, however. Due to a combination of circumstances, including high levels of iron and polyunsaturated fatty acids, high iron requirements and mitochondrial activity, and restricted cell regeneration, the CNS is particularly susceptible to OS. Oxidative damage also affects the immune response that is developing in the periphery and controls MS illness outside of the CNS. First, by lowering its electrical resistance and thus changing its permeability, elevated ROS levels harm the brain endothelium [94,104,105,106]. OS readily damages RNA due to its single-stranded structure and high concentration close to mitochondria, where most intracellular ROS are generated [107,108]. An overabundance of ROS can cause chemical modification or even separation of RNA bases and breakage of RNA strands [91,109]. Oxidatively damaged RNA accumulates in cells, leading to decreased protein synthesis, erroneous protein generation, and ultimately cell death [110,111]. Non-coding RNAs (ncRNAs), which do not encode proteins, make up the majority of RNAs in human cells [112,113]. As a category of ncRNAs, regulatory ncRNAs, including long ncRNAs (lncRNAs) and small ncRNAs, which includes microRNAs (miRNAs), circular RNAs (circRNAs), and PIWI-interacting RNAs (piRNAs), are involved in regulating gene expression [114]. Compared to mRNAs, these regulatory ncRNAs persist relatively longer. Hence, oxidative damage can cause prolonged effects. ncRNAs of several types have been linked to various neurogenerative diseases [115,116,117,118]. However, the direct relationship between ncRNA oxidation and neuronal diseases is still unclear, except in PD, in which the interaction between OS and regulatory ncRNAs has been well studied [119]. In contrast, regulatory small ncRNAs such as miRNAs and lncRNAs help to regulate ROS production [118]. Their interactions are involved in the pathophysiology of PD [120]. RNA oxidation is not only a hallmark of PD but also a crucial first step in the development of the disorder [112,121]. 8-oxo-7, 8-dihydroguanosine (8-oxoG) is one of many oxidative marks on RNA, and it is possibly the most common and well studied [122]. Guanine’s vulnerability to free radicals means that it can form this base adduct, leading to mismatched bases [109]. Neuronal apoptosis and autophagy are influenced by lncRNAs; thus, α-syn accumulation and degradation that restricts their function would have deleterious effects on cellular homeostasis [123]. For instance, free radical damage to miRNAs can cause them to misidentify their target mRNAs, which can increase expression of specific proteins [124]. A reduction in α-syn expression was mediated by two miRNAs in an experiment performed by Je and Kim [123]. High levels of α-syn and subsequent development of PD resulted from stress-induced oxidative loss of translational inhibition by these two miRNAs. Furthermore, Chen et al. found that OS triggered modification of circRNA N6-methyladenosine (m6A). m6A-modified circRNAs can modulate expression of stress response genes (UBC and PPP2CA), which may constitute the mechanistic basis for OS-induced neurodegenerative disorders [125]. The regulatory role of miRNAs in OS is closely linked to α-syn, which is responsible for inducing OS. Both microRNA-141-3p and microRNA-9-5p target the 3′ UTR of SIRT1 mRNA. Since SIRT1 inhibits formation of α-syn aggregates, knockdown of microRNA-141-3p and microRNA-9-5p may alleviate OS and boost the viability of in vitro PD model [126,127]. SOD, CAT, and GPx are responsible for detoxifying oxidants and repairing oxidative damage. MicroRNA-137 and microRNA-494-3p aggravate OS by reducing SOD and GPx activity in PD rats treated with these miRNAs. MicroRNA-335 suppresses expression of FTH1, thereby promoting release of Fe2+ ions and generation of free radicals [128]. Downregulation of microRNA-410 expression in PD is associated with elevated ROS production [129]. Regulatory lncRNAs and OS are hallmarks of PD. In PD, mitochondrial dysfunction is linked to ROS overproduction. α-syn aggregate formation can exacerbate OS by decreasing complex I activity or by activating microglia. Upregulation of beta-amyloid-cleaving enzyme-antisense (BACE1-AS), a lncRNA, was also associated with increasing levels of α-syn in PD [130]. In addition, the lncRNA microRNA-17-92a-1 cluster host gene (MIR17HG) promotes α-syn expression. MicroRNA-153-5p cannot inhibit α-syn expression because MIR17HG acts as a sponge for microRNA-153-5p [131]. Inhibition of autophagy by GSK3 promotes α-syn buildup and therefore aggregation. MicroRNA-15b-5p suppressed GSK3 expression, but binding of SNHG1 to microRNA-15b-5p rescued GSK3 expression [132]. Cellular effects of oxidatively damaged RNA and the mechanism by which regulatory ncRNAs affect OS are just beginning to be explored. Although there are peripheral indicators for OS, it is difficult to detect in the human brain in vivo. The following measures have been used to quantify brain OS (Table 1). Several surrogate OS or antioxidant activity indicators, including circulating lipid peroxides, GSH, and vitamins C and E, have been examined in peripheral blood [89,133,134,135]. AD patients display reduced peripheral vitamin A, C, and E levels [136] and SOD and GPx activity [137]. Lower plasma GSH levels are associated with more severe cognitive impairment in AD patients [138]. PD patients have inconsistently altered erythrocyte SOD activity [139,140]. Increased SOD activity may protect against OS damage. GSH is the single antioxidant assessed by 1H (MRS) [149]. GSH content in the human brain is lower than N-acetyl aspartate, creatine, and choline, making evaluation difficult. With MRS, it is challenging to discern between GSH and glutamate resonance peaks [141,150]. GSH-CH2 cysteine’s protons resonate at 2.93 and 2.97 ppm, overlapping with those of creatine (3.03 ppm) and aspartate (2.82 ppm) [151]. To detect GSH levels in the brain, spectral editing methods such as MEscher-GArwood-PRESS (MEGA-PRESS) [142] are needed to boost GSH signals so as to gather reliable nuclear signals. MEGA-PRESS combined with a 180° editing pulse in the original PRESS pulse sequence [142] may distinguish GSH-cysteine signals from other signals, notably creatine signals in the brain [89]. Recent research found that GSH levels are lower in AD patients’ hippocampus and frontal cortices [143,144]. The concentration of vitamin C (ascorbic acid) in the human brain is around 1.0 mM, which may be detected by MRS [152]. However, measuring vitamin C using 1H MRS is problematic owing to the similarities between the resonances of vitamin C (3.73, 4.01, and 4.50 ppm) and glutamate (3.75 ppm) [151]. MEGA-PRESS editing might potentially aid in measuring vitamin C levels in the human brain. Several earlier research [153] used 1H-MRS with the MEGA-PRESS to evaluate vitamin C levels in the human brain. Electromagnetic radiation absorption often occurs in the microwave region of the electromagnetic spectrum [154]. As a result, it is influenced by paramagnetic species that are present in a magnetic field. However, owing to short radical half-lives, compared to the EPR time scale, EPR spectroscopy cannot detect them directly [155]. To compensate for this, a stable chemical is frequently used to capture radicals to make them observable [156]. EPR has been implement in detecting neurodegenerative diseases as in AD [145] and as real time OS marker for post stroke patients [146]. Despite years of research in EPR spectroscopy, one of the main reasons why it has not been widely employed is presumably poor sensitivity, particularly at the levels of free radicals usually encountered in biological systems. More research is required before using EPR in human clinical trials. The intracellular over-reactive state can be measured using a radiotracer for positron emission tomography (PET) [147]. [62Cu] diacetyl-bis (N4-methylthiosemicarbazone) ([62Cu] ATSM) is a radiotracer widely used in PET. Evaluation of striatal OS in patients with PD using [62Cu]ATSM PET was previously confirmed [147]. In a recent study, this tracer was utilized to visualize localized OS in PD patients that was mostly caused by mitochondrial malfunction. In this investigation, deposition of [62Cu] ATSM was detected in the striatum of PD and ALS patients [147,148], indicating a localized over-reductive conditions caused by mitochondrial malfunction. The human body has several lines of defense against OS. Multiple antioxidant defense pathways are involved in the brain’s ROS detoxification. The metalloproteins SOD, CAT, and GPx constitute the first line of antioxidant defense against ROS. Age-related accumulation of highly reactive polyunsaturated fatty acids, iron, and ROS is exacerbated by suboptimal antioxidant levels. Apart from endogenous antioxidant defense mechanisms in aging brain and given the importance of OS in neurodegenerative diseases, manipulating ROS levels may be a viable therapeutic approach to slow neurodegeneration and reduce related symptoms. Avoiding OS-related causes is at the top of preventative approaches. Diet, exercise, lack of sleep, sedentary behavior and circadian rhythm abnormalities are all crucial components in regulating healthy aging. The gut flora is hypersensitive to extrinsic variables linked to an unhealthy lifestyle [157,158]. Preclinical studies showed that a high-fat diet (HFD) in mice changes the gut microbiome [159]. Pro-inflammatory bacteria such as Clostridium, Eubacterium, and Roseburia are positively correlated with hyperglycemic fluctuations in the brain [160]. The need for gut microbiome therapies is growing, and this area of research is continually developing. With impressive results, dietary and probiotic supplementation has been investigated as a possible therapeutic strategy for age-related disorders via changes in gut microbiota. Recent research has shown that exercise may alter gut microbiota, adding to the potential advantages of this strategy for treating disorders related to aging [161]. Nutraceuticals or natural compounds exist in food have been extensively studied worldwide due to their neuroprotective effects in vivo and in vitro, attributed to the antioxidative properties [162]. Nutraceuticals that show metal chelation ability and anti-inflammatory properties such as curcumin [163,164], green tea polyphenols [164], resveratrol [165] and vitamin E [166,167] are examples of natural antioxidants from foods and other sources that show promise as therapeutic agents for OS-related neurodegenerative diseases. Aging is not a disease that needs to be treated but a natural process; however, premature or unhealthy aging still needs more study to understand mechanisms and leading causes. There is a controversial view that OS leads to a short life span and is linked to age-related disease and quality of life. Numerous studies have shown a significant elevation in OS and free radical concentration as a common cause of aging and accompanying neurodegenerative diseases. However, in nature, comparative studies indicate long lives of some animals despite high levels of free radicals in their tissues. This means that there are many unknown mechanisms to cope with OS. Therefore, a healthy lifestyle to avoid factors that elevate OS, as well as increased intake of natural antioxidants, can protect against OS and prevent neurodegenerative diseases before their onset.

PMC10001356

Mengjie Xia,Shuting Mu,Yaowei Fang,Xiaomeng Zhang,Guang Yang,Xiaoyue Hou,Fuxiang He,Yaling Zhao,Yichen Huang,Wei Zhang,Juan Shen,Shu Liu

Genetic and Probiotic Characteristics of Urolithin A Producing Enterococcus faecium FUA027

28-02-2023

Enterococcus faecium,urolithin A,safety assessment,genome,phenotypic assays

Enterococcus faecium FUA027 transforms ellagic acid (EA) to urolithin A (UA), which makes it a potential application in the preparation of UA by industrial fermentation. Here, the genetic and probiotic characteristics of E. faecium FUA027 were evaluated through whole-genome sequence analysis and phenotypic assays. The chromosome size of this strain was 2,718,096 bp, with a GC content of 38.27%. The whole-genome analysis revealed that the genome contained 18 antibiotic resistance genes and seven putative virulence factor genes. E. faecium FUA027 does not contain plasmids and mobile genetic elements (MGEs), and so the transmissibility of antibiotic resistance genes or putative virulence factors should not occur. Phenotypic testing further indicated that E. faecium FUA027 is sensitive to clinically relevant antibiotics. In addition, this bacterium exhibited no hemolytic activity, no biogenic amine production, and could significantly inhibit the growth of the quality control strain. In vitro viability was >60% in all simulated gastrointestinal environments, with good antioxidant activity. The study results suggest that E. faecium FUA027 has the potential to be used in industrial fermentation for the production of urolithin A.

Genetic and Probiotic Characteristics of Urolithin A Producing Enterococcus faecium FUA027 Enterococcus faecium FUA027 transforms ellagic acid (EA) to urolithin A (UA), which makes it a potential application in the preparation of UA by industrial fermentation. Here, the genetic and probiotic characteristics of E. faecium FUA027 were evaluated through whole-genome sequence analysis and phenotypic assays. The chromosome size of this strain was 2,718,096 bp, with a GC content of 38.27%. The whole-genome analysis revealed that the genome contained 18 antibiotic resistance genes and seven putative virulence factor genes. E. faecium FUA027 does not contain plasmids and mobile genetic elements (MGEs), and so the transmissibility of antibiotic resistance genes or putative virulence factors should not occur. Phenotypic testing further indicated that E. faecium FUA027 is sensitive to clinically relevant antibiotics. In addition, this bacterium exhibited no hemolytic activity, no biogenic amine production, and could significantly inhibit the growth of the quality control strain. In vitro viability was >60% in all simulated gastrointestinal environments, with good antioxidant activity. The study results suggest that E. faecium FUA027 has the potential to be used in industrial fermentation for the production of urolithin A. Ellagitannins (ETs), the metabolic precursor of urolithins, can be hydrolyzed to ellagic acid (EA), which is subsequently metabolized by gut microorganisms to urolithins [1]. Among all types of those urolithins, urolithin A (UA) exhibited several potentially positive bioactivities, such as restoring muscle function [2], and antiobesity [3], antioxidant [4], anti-inflammation, and anticancer activities [5]. An increasing amount of the literature has recently focused on the impact of the natural compound UA on health, disease, and aging [6]. Numerous studies have shown that different urolithin metabotypes (UMs) produce significantly different amounts and types of urolithins [7]. The gut microflora in more than 40% of middle-aged and elderly people cannot metabolize EA to UA [8]. Cortés et al. found that the percentage of the UM-A population declines when the intestinal flora changes with age [9]. Given the influence of intestinal flora on UA formation [10], screening strains responsible for metabolizing EA to produce UA is of interest. Currently, little is known about the species of gut bacteria involved in EA conversion to UA. Strains found to metabolize EA to produce UA include Bifidobacterium pseudocatenulatum INIA P815 [11], Streptococcus thermophilus FUA329 [12], Lactococcus garvieae FUA009 [13], and Enterococcus faecium FUA027 [14]. S. thermophilus FUA329 was isolated from human milk. L. garvieae FUA009 and E. faecium FUA027 were screened from fecal samples. These bacteria have the potential to be developed as probiotics for the in vitro biotransformation of EA to produce UA, or for industrial fermentation to produce UA [15]. Our previous studies have proven that E. faecium FUA027, which was isolated from human fecal samples, metabolizes EA to UA by detecting UA from the fermentation broth of the strain through high-performance liquid chromatography (HPLC) and liquid chromatography tandem mass spectrometry (LC-MS/MS). The highest yield of UA produced by E. faecium FUA027 was 10.80 μM, thereby making this strain a promising candidate for development as a probiotic [14]. The safety and probiotic properties of the strain to be used as probiotics must be evaluated [16]. In this study, whole-genome sequence information analysis and phenotypic assays were used in combination to assess antibiotic resistance, metabolite toxicity, and survival under simulated gastrointestinal conditions. The safety of E. faecium FUA027 and its potential for use in the preparation of UA by industrial fermentation were confirmed. E. faecium FUA027 was preserved in the China General Microbiological Culture Collection Center (CGMCC) under the accession number CGMCC No. 24964. All FUA027 strains, unless otherwise noted, were cultivated in Anaerobe Basal Broth (ABB) medium and incubated under anaerobic conditions consisting of N2/H2/CO2 (80:10:10, v:v:v) at 37 °C for 24 h. Staphylococcus aureus ATCC 12600, Escherichia coli ATCC 25922, Yeast ATCC 24060, Aspergillus niger ATCC 6273, and Lactobacillus plantarum ATCC 4008 strains were used partly for inhibition experiments and partly as control strains in the experiments. S. aureus and E. coli were cultured at 37 °C in Luria–Bertani broth for 24 h. Yeast and A. niger were cultured on potato dextrose agar medium at 37 °C for 48 h. L. plantarum and S. thermophilus were cultivated in Man Rogosa Sharpe broth at 37 °C for 48 h. The genomic DNA was extracted from the E. faecium FUA027 culture grown in ABB by using a bacterial DNA extraction kit from Sangon, Shanghai, Co. Ltd. (Shanghai, China). For the DNA sample preparations, 1 µg DNA per sample was used as the input material. Sequencing libraries were created using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. In brief, the DNA sample was sonicated to obtain 350-bp fragments. The DNA fragments were end-polished, A-tailed, and ligated with the full-length adaptor for Illumina sequencing with further PCR amplification. Finally, the AMPure XP system purified the PCR products, and the size distribution of the libraries was analyzed using the Agilent 2100 Bioanalyzer and quantified using real-time PCR. The whole genome of FUA027 was sequenced using the Nanopore PromethION platform and Illumina NovaSeq PE150 at the Beijing Novogene Bioinformatics Technology Co., Ltd. (Beijing, China). The trimmed data for the FUA027 genome were combined with PE150 and Nanopore data and assembled using SMRT Link v5.0.1 software (https://www.pacb.com/support/software-downloads/, accessed on 15 October 2022). The quality of the genome assembly was validated using QUAST ver. 5.0.2. The final assembly was annotated using the NCBI Prokaryotic Genome Annotation Pipeline (http://www.ncbi.nlm.nih.gov/genome/annotation_prok/, accessed on 15 October 2022) [17]. We used Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), Clusters of Orthologous Groups (COG), the Non-Redundant Protein Database, the Transporter Classification Database, and Swiss-Prot to predict gene function. Bacterial virulence factors were identified by referring to the virulence factor database updated in 2019 (VFDB, http://www.mgc.ac.cn/VFs/, accessed on 11 October 2022) [18]. Protein sequences with >50% similarity in the extraction comparison results were identified as virulence genes. Antimicrobial resistance determinant identification was performed using the ABRicate program (https://github.com/tseemann/abricate, accessed on 11 October 2022) based on the ResFinder database (http://genomicepidemiology.org/, accessed on 11 October 2022) [19]. Antibiotic resistance genes of E. faecium FUA027 were identified using the comprehensive antibiotic resistance database (CARD, https://card.mcmaster.ca, accessed on 11 October 2022) [20]. Susceptibility testing was performed through disk diffusion according to EUCAST recommendations [21]. The strain FUA027 was purified, inoculated into 20 mL of ABB liquid medium, and incubated anaerobically at 37 °C for 24 h. Bacterial colonies were counted, and the concentration of the bacterial solution was adjusted to 1.0 × 108 CFU/mL. The bacterial solution was then added dropwise to a 20 mm agar plate. The FUA329 bacterial solution was evenly coated on the plate. Under aseptic conditions, antibiotic susceptibility papers were gently pressed onto the agar plates using forceps. While doing so, the spacing of each drug-sensitive tablet could not be <20 mm and the distance from the edge of the plate could not be <17 mm. The plates were sealed and continuously incubated at 37 °C for 14 h. The size of the inhibition circle was noted to determine the sensitivity of antibiotics. The hemolytic activity was studied using the method described by Buxton. In short, E. faecium FUA027 was inoculated onto Columbia Blood Agar and incubated at 37 °C for 24 h [22]. S. aureus ATCC 12600 was used as a control strain. The nitrate broth assay kit and amino acid decarboxylase assay kit obtained from Beijing Land Bridge Technology Co., Ltd. (Beijing, China). were used in the metabolic toxicity test. The test was performed following the manufacturer’s instructions. Detection of nitrate reductase activity: Under aseptic conditions, single colonies of the test strain and the quality control strain E. coli ATCC 25922 isolated from the plate were inoculated in a nitrate broth assay ampoule by using an inoculating needle. The plate was incubated at 37 °C for 24 h. After incubation, nitrate reduction reagents A and B were added dropwise at 5:2 (v:v), and the results were observed immediately. Three parallel experiments were conducted for each sample [23]. Detection of amino acid decarboxylase activity: Under aseptic conditions, a single colony of the test strain was picked from the plate by using an inoculating needle and inoculated into the amino acid decarboxylase series ampoule as well as the amino acid decarboxylase control tube. Sterile liquid paraffin was added to cover the surface of the medium, and lysine, ornithine, and arginine ampoules were incubated at 37 °C for 24 h. After the phenylalanine ampoule was incubated for 24 h, 4–5 drops of 10% FeCl3 aqueous solution were added to the ampoules, and the results were observed within 2 min. Following the incubation of the tryptophan ampoules for 24 h, 2–3 drops of the Kovacs reagent were added to the ampoules and the results were observed immediately. Three parallel experiments were conducted for each sample. The Hidden Markov model (HMM) was used to find probiotic-associated genes in the genome as well as environmental tolerance-related genes [24]. Additionally, we searched for genes related to adhesion factors in the annotation results. Putative genes involved in antimicrobial compound synthesis and secondary metabolism gene clusters in the E. faecium FUA027 genome were identified using AntiSMASH 6.0 (https://antismash.secondarymetabolites.org, accessed on 11 December 2022) [25] and BAGEL 4.0 (http://bagel4.molgenrug.nl/index.php, accessed on 11 December 2022) [26]. Referring to Pieniz et al.’s study, the survival of strains in a simulated gastrointestinal environment was measured using the viable plate count method [27]. The strain FUA027 was grown in ABB liquid medium at 37 °C for 24 h. Then, the culture was adjusted to an optical density (OD600) of 1.0 ± 0.05. Separate preparation of ABB liquid medium of different pH values and containing different bile salt concentrations: test tubes containing 9 mL of ABB liquid medium were adjusted with HCl to attain different pH values (i.e., 2.0, 2.5, 3.0, 3.5, and 4.0). The ABB liquid medium was supplemented with bovine bile salt, thereby achieving final concentrations of 0.1%, 0.2%, 0.3%, 0.4%, and 0.5% (w/v), respectively. Then, 1 mL of inoculum was added to each tube, and the normal ABB liquid medium was used as a control. Sampling was performed at 0, 1, 2, and 3 h. The samples were diluted with ABB medium and then coated and incubated on the plates for 24 h, and viable colonies on a plate were counted. The survival rate was calculated using the following formula: where (log CFU/mL) represents the number of viable bacteria after t hours of treatment, and (log CFU/mL) refers to the number of viable bacteria of E. faecium FUA027 before treatment. The FUA027 strain was cultured in ABB liquid medium at 37 °C for 18 h. The E. faecium FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min), then discard supernatant intact cells of the strain were harvested. The cell pellet was washed twice with and suspended in 1 mL sterile distilled water [28]. The concentration of this suspension was adjusted to approximately 1.0 × 108 CFU/mL. This was considered as a sample in the antioxidant test. Using antioxidant kits from Jiancheng Bioengineering Institute (Nanjing, China), in vitro antioxidant activities were measured including the measurement of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, hydroxyl radical, and superoxide anion scavenging activities [29]. Hydrophobicity: The E. faecium FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min), and the pellet was washed with and suspended in distilled water. The culture suspension was adjusted to an OD600 value of 0.5 ± 0.02 (). Then, an equal volume of xylene solution was added to the bacterial suspension and vortexed for 20 s at 37 °C for 1 h. The absorbance of the supernatant at 600 nm () was determined. Three parallel tests were conducted [30]. The hydrophobic rate was calculated using the following formula: Auto-aggregation: The FUA027 bacterial liquid was centrifuged (20 °C, 3000 rpm, 10 min) and washed with distilled water. Its OD600 value was adjusted to 0.5 ± 0.02 (). The bacterial suspension was allowed to stand at 37 °C for 4 h, and the absorbance of the supernatant at 600 nm () was determined. Three parallel tests were conducted. The auto-aggregation rate was calculated using the following formula: A single colony of E. faecium FUA027 was picked, inoculated into ABB liquid medium, and cultured anaerobically at 37 °C for 24 h. Then, 10 mL of bacterial solution was mixed thoroughly with an equal volume of ethyl acetate extract, vortex shaken for 30 s, and transferred to a separatory funnel. This mixture was allowed to stand at room temperature for 5 min. After the solution was stratified, the upper organic phase was collected and evaporated in a rotary evaporation flask. The rotary evaporator was used to rotary evaporate the organic phase at 60 °C for 10–15 min to ensure the absence of a smell of ethyl acetate. Then, 2 mL ethyl acetate was added to dissolve the residue in the rotary steaming bottle, fully mixed, and filtered through a nylon syringe filter (pore size: 20 μm). The liquid was collected as the antibacterial solution [31]. The experimental group was the upper organic phase of E. faecium FUA027 after extraction with ethyl acetate (concentrated five times) and the lower aqueous phase of E. faecium FUA027 after extraction with ethyl acetate. ABB medium extracts and ethyl acetate were used as blank controls. The Kirby–Bauer test for antibacterial effects: 100 μL of bacterial solution of Staphylococcus aureus ATCC 12600, Escherichia coli ATCC 25922, Yeast ATCC 24060, and Aspergillus niger ATCC 6273 were evenly applied to the plate, respectively. Then, four sterile filter papers of diameter 5 ± 0.5 mm were placed in each plate. A total of 10 μL of sample was added dropwise to each filter paper sheet and incubated for 12 h at 37 °C. Then, a vernier caliper was used to measure and record the diameter of the suppression ring. The inhibitory effect was evaluated on the basis of the inhibition circle diameter. Three independent tests were repeated [32]. The whole genome sequence of E. faecium FUA027 contained a single, circular 2,718,096-bp-long chromosome with an average GC content of 38.27% (Figure 1). The Glimmer program identified 2700 genes with an estimated coding ratio of 87.1%. Of them, 2617 were protein-coding genes and 83 were RNA genes. Among the 83 RNA genes, 17 genes coded for 5S, 16S, and 23S rRNAs; two genes coded for sRNAs; and 64 genes coded for tRNAs. (Table 1). The Plasmid Finder 2.0 tool did not find any plasmid sequences. The FUA027 genome sequence was submitted to NCBI under the accession number OM670243. In the clinical setting, probiotic strains resistant to a particular antibiotic are typically associated with infection. Antibiotic resistance genes in the probiotic genome are not in themselves a safety issue, if the genes are not likely transferred to other strains. Instead, probiotics containing these genes could theoretically act as a source of antibiotic resistance genes for potentially pathogenic bacteria. Probiotics must also be tested for the presence of antibiotic resistance genes because studies have confirmed that these genes may be transferred in food and in the intestinal environment. Enterococcus exhibits stronger natural resistance than other Gram-positive bacteria and acquires resistance genes through various mechanisms to produce multiple high-level drug-resistant strains [33]. Amino acid sequences of E. faecium FUA027 were compared with the drug resistance gene database CARD (https://card.mcmaster.ca/, accessed on 11 December 2022), and protein sequences with >50% similarity in the comparison results were extracted as antibiotic resistance genes. Eighteen antibiotic resistance genes were identified. A predictive analysis of drug resistance genes identified 10 types of aminoglycoside antibiotics, fluoroquinolones, lincosamides, and vancomycin. Probiotic E. faecium strain T-110 and non-pathogenic strain E. faecium NRRL B-2354 both contain a plasmid, according to Natarajan et al. [34]. Importantly, we used the MobileElementFinder tool to search for MGEs. As expected, the absence of MGEs was confirmed. Consequently, because E. faecium FUA027 has no plasmid and none of the antibiotic resistance genes associated with it are located on MGEs, these drug resistance properties cannot be transferred to other pathogenic bacteria through mobile elements, implying no occurrence of drug resistance transmission. Thus, this study from the genetic level confirms that E. faecium FUA027 is safe for the horizontal transfer of drug resistance. To corroborate the results of antibiotic resistance gene analyses, the antibiotic sensitivity test was conducted. Nevertheless, the presence of resistance genes did not exactly match the experimental results observed. According to the results, E. faecium FUA027 was resistant to nine antibiotic types (Table 2). In total, 27 antibiotics were detected. As shown in Table A1, E. faecium FUA027 was resistant to nine types of antibiotics. Combined with the results of the antibiotic susceptibility test in vitro, the antibiotic resistance genes in the genome were analyzed. E. faecium FUA027 was safe in terms of antibiotic resistance. According to the gene function classification, virulence genes carried by enterococci mainly encode for proteins related to adherence, exotoxin, exoenzyme, immunomodulation, and biofilm [35]. The VFDB was used to identify virulence factor genes in E. faecium FUA027; however, most putative virulence factor genes had <60% similarity with VFDB [36]. In total, seven potential virulence factor genes were identified (Table 3). These genes may encode for proteins involved in adhesion, immunomodulation, exoenzyme, and biofilm. Genes encoding enterococcal hemolysin A (hlyA), cytolysin (cyl), aggregation substance (as), enterococcal surface protein, sex pheromones (cob and ccf), and serum resistance-associated gene (sra), which are well-known potential virulence factors, were missing in E. faecium FUA027. According to Deng’s study, among 110 probiotic Enterococcus spp. 35 (31.8%) enterococcal strains exhibited β-hemolytic characteristics. However, in our study, FUA027 exhibited γ-hemolysis on blood plates and no genes encoding Hbl, Nhe, or cytotoxin K, which are associated with hemolysis and toxin production, were found in the genome (Figure 2). These results thus confirmed that E. faecium FUA027 would be used in the preparation of UA by industrial fermentation. The results of nitrate reductase activity revealed that E. faecium FUA027 did not contain nitrate reductase. No color change was observed in the tubes containing the test strains, and the color was red after the addition of trace zinc powder, indicating that the test group was negative. The tube containing the quality control strain E. coli ATCC 25922 was red and positive. The amino acid decarboxylase activity of E. faecium FUA027 was preliminarily detected on the basis of the color change in the amino acid decarboxylase medium. With E. faecium FUA027, the color of the amino acid decarboxylase medium remained unchanged and yellow, indicating that no biogenic amines (BA) were produced in the medium by the strain. The experimental results revealed that FUA027 did not possess lysine, ornithine, arginine, tryptophan, or phenylalanine decarboxylase activities. The main source of BA in food is the microbial decarboxylation of amino acids. For example, the decarboxylation of tyrosine, ornithine, and lysine produces tyramine, putrescine, and cadaverine, respectively. BA accumulation in food has serious implications for food safety and human health [37]. Of the 129 enterococci strains of three different origins (food, veterinary, and human) screened by Sarantinopoulos et al., none produced histamine, cadaverine, or putrescine [38]. However, >90% of E. faecium strains isolated from cheese have been identified as tyramine producers. Some E. faecium strains from humans also produced putrescine [39]. E. faecium FUA027 was found to not produce BA, and thus we believe that this strain may be used safely in industrial fermentation. Normal human gastric juice pH is approximately 1–3, and normal human intestinal pH is approximately 6.8–7.0. The pH in the stomach can rise to 4–5 after food is consumed. Probiotics can only exert their probiotic role if they resist the inhibitory effects of gastric acid and pepsin on the intestine [40]. A gene encoding conjugated bile acid hydrolase (cbh) and three genes encoding bile acid sodium symporter family proteins were discovered in E. faecium FUA027; these genes may have contributed to bile salt resistance. F0F1-ATPase is considered the main pH regulator inside cells. Eight genes coding for the F0F1-ATP synthase subunit were identified in the FUA027 genome. Furthermore, a cation transporter gene, two (Na+/H+) antiporter genes, and a sodium ion transporter gene linked to pH regulation and ion homeostasis were discovered (Table A2). The survival rates of E. faecium FUA027 in the in vitro acid tolerance test at different pH values are shown in Figure 3A. The survival rate declined steadily as the pH value decreased. Studies have shown that strains with a survival rate of >60% are acid-resistant strains. The survival rate of E. faecium FUA027 in the in vitro acid tolerance test at pH 3.0 was >60% and that at pH 2.0 was >50%. Compared to acid-tolerant strains, E. faecium FUA027 was less acid-tolerant. Another crucial sign for assessing the qualities of possible probiotics is the tolerance of strains to high bile salt concentrations in the human gastrointestine. Studies have shown that the small intestine contains approximately 0.3% of bile salts. In our study, the survival rate of the strain was higher than 67% at bile salt concentrations of 0.1%–0.3%. The strain survival rate was still >60.00% at bile salt concentrations of 0.4% and 0.5% (Figure 3B), which indicates that the strain has excellent bile salt resistance. We identified a gene coding for conjugated bile acid hydrolase (cbh), two conjugated bile acid hydrolase genes (namely nhaC and napA), and ABC transporter genes potentially contributing to bile salt resistance in E. faecium FUA027. Eight genes coding for the F0F1-ATP synthase subunit (namely atpB, atpE, atpF, atpH, atpA, atpG, atpD, and atpC) were identified in the FUA027 genome. Therefore, we suggest that the in vitro results of acid and bile salt tolerance in E. faecium FUA027 are explained by these related genes in its genome. Some probiotic metabolites can lessen the oxidative damage that causes aging and chronic diseases [41]. The results of the in vitro antioxidant ability of E. faecium FUA027 are presented in Table 4. The DPPH scavenging activity of the fermentation supernatant was as high as 57.62%, the superoxide anion scavenging capacity was 36.23%, and the clearance rate of hydroxyl radical was 30.12%. Polysaccharides, phosphonic acid, and peptidase, which are fundamental cell wall building blocks, are crucial for antioxidation. The extracellular metabolite structure is closely related to the antioxidant activity of the fermentation supernatant. In addition, the antioxidant activities of L. plantarum and E. faecalis were studied. The DPPH scavenging activity of L. plantarum was 62.78%, which was close to that of E. faecium FUA027. By contrast, the activity of E. faecalis was lower than that of E. faecium FUA027. Ten genes associated with the oxidative stress response were found in the FUA027 genome; these genes could help the strain avoid damage by O2− and H2O2−, such as peroxide-responsive repressor (perR), NADH peroxidase (npr), alkyl hydroperoxide reductase (ahpC/F), glutathione peroxidase (gpx), superoxide dismutase (sodA), thioredoxin reductase (trxB), and glutathione reductase (gor). Among them, perR regulates H2O2− induced oxidative stress. In the presence of H2O2− or with iron and manganese ion deficiencies, perR upregulates antioxidant enzymes such as catA and ahpC/F to scavenge H2O2− and alkyl hydroperoxides (Table A2). The presence of these antioxidant genes indicated that E. faecium FUA027 has high antioxidant activity. Based on the results of genomic and phenotypic experimental analyses, we speculate that this may be due to the expression of antioxidant genes in the E. faecium FUA027 genome, such as catalase, glutathione peroxidase, and superoxide dismutation, which make FUA027 possess a good antioxidant capacity. Probiotics play a beneficial role by adhering to intestinal mucosa and epithelial cells. We searched for gene annotation data related to adhesion, colonization, mucin binding, flagella hook, and fibrinogen/fibronectin binding. Adhesion lipoprotein, s-ribosylhomocysteine lyasef (luxS), segregation and condensation protein B (scpB) were found in the E. faecium FUA027 genome (Table 5) [42]. Biofilms of lactic acid bacteria can colonize the intestine, thereby protecting strains in gastrointestinal transit, producing certain antimicrobial compounds, and stimulating the immune response. Auto-aggregation is a crucial property of biofilm formation, and hydrophobicity may assist in adhesion. Auto-aggregation and hydrophobicity are vital indicators of the ability of microbes to respond to bacterial gut colonization. FUA027 exhibited higher hydrophobicity and auto-aggregation than the commercial probiotic strain Bifidobacterium longum BB536). This demonstrates that E. faecium FUA027 can better colonize the intestinal tract, and thus exert its probiotic properties. In the in vitro experiment, the inhibitory ability of E. faecium FUA027 against four test strains was investigated. As shown in Figure 4, FUA027 exhibited significant inhibitory effects on E. coli ATCC 25922 and S. aureus ATCC 12600, with inhibition circle sizes of 26.24 ± 0.34 mm and 22.12 ± 0.26 mm, respectively. The inhibition circle sizes were 9.2 ± 0.52 mm and 8.74 ± 0.38 mm for Yeast ATCC 24060 and A. niger ATCC 6273, respectively. E. faecium FUA027 had a significantly better inhibitory effect on bacteria than on fungi. Antimicrobial activity is a crucial property of probiotics against gastrointestinal infections. E. faecium mainly exerts its bacteriostatic effect by secreting organic acids. Furthermore, bacteriocins, bacteriocin-like, and hydrogen peroxide secreted by E. faecium can inhibit intestinal pathogenic microorganisms to some extent. Many bacteriocin-producing E. faecalis strains have been reported. Rahmeh et al. explored how E. faecium S6 exerts its antimicrobial effect by producing enterotoxins and organic acids [43]. Valenzuela et al. isolated an E. faecium PE 2-2 strain from seafood that inhibited S. aureus and demonstrated that this strain carried the enterocin A structural gene [44]. Basanta et al. reported that E. faecium L50 isolated from a Spanish dry fermented sausage produces enterocin L50 (EntL50, EntL50A, and EntL50B), enterocin P, and enterocin Q and exhibits a broad antimicrobial spectrum [45]. Enterococins are often used as a preservative for meat and dairy products. The most widely used enterococins are enterocin A and enterocin B, belonging to class II bacteriocin. In our study, four biosynthetic gene clusters associated with T3PKS, a cyclic lactone autoinducer, were identified using AntiSMASH 5.0, and BAGEL 4.0 predicted a bacteriocin from the class sactipeptide in the E. faecium FUA027 genome. Sactipeptides (sulfur-to-alpha carbon thioether cross-linked peptides) are ribosomally synthesized and post-translationally modified peptides that exhibit antibacterial activity [46]. In conclusion, in vitro experiments supported the presence and activity of extracellularly secreted bacteriocins, as they significantly inhibit the growth of E. coli ATCC 25922 and S. aureus ATCC 12600. In summary, we here described the whole-genome sequence of E. faecium FUA027. FUA027 has a 2,718,096-bp-long chromosome with an average GC content of 38.27%. Genomic screening revealed that FUA027 lacked key virulence factor genes and toxin-coding genes. Although 18 antibiotic resistance genes were screened from the strain, the strain has no plasmids or mobile elements and is therefore unlikely to undergo the acquisition and transfer of resistance genes. The safety of this strain was further confirmed through hemolysis tests, metabolic toxicity tests, and antibiotic resistance tests. The detection of antimicrobial gene clusters and adhesion- and stress-associated genes in the genome, along with the results of tolerance tests such as tolerance to acid and bile salt and in vitro antioxidant activity-related genes, revealed the probiotic properties of the strain. Genomic analysis combined with phenotypic studies confirmed the safety and probiotic properties of this strain as a potential probiotic candidate.

PMC10001372

Sophie Broadway-Stringer,He Jiang,Kirsty Wadmore,Charlotte Hooper,Gillian Douglas,Violetta Steeples,Amar J. Azad,Evie Singer,Jasmeet S. Reyat,Frantisek Galatik,Elisabeth Ehler,Pauline Bennett,Jacinta I. Kalisch-Smith,Duncan B. Sparrow,Benjamin Davies,Kristina Djinovic-Carugo,Mathias Gautel,Hugh Watkins,Katja Gehmlich

Insights into the Role of a Cardiomyopathy-Causing Genetic Variant in ACTN2

24-02-2023

alpha-actinin,embryonic heart,sarcomere,cardiomyopathy,proteomics,mitochondria

Pathogenic variants in ACTN2, coding for alpha-actinin 2, are known to be rare causes of Hypertrophic Cardiomyopathy. However, little is known about the underlying disease mechanisms. Adult heterozygous mice carrying the Actn2 p.Met228Thr variant were phenotyped by echocardiography. For homozygous mice, viable E15.5 embryonic hearts were analysed by High Resolution Episcopic Microscopy and wholemount staining, complemented by unbiased proteomics, qPCR and Western blotting. Heterozygous Actn2 p.Met228Thr mice have no overt phenotype. Only mature males show molecular parameters indicative of cardiomyopathy. By contrast, the variant is embryonically lethal in the homozygous setting and E15.5 hearts show multiple morphological abnormalities. Molecular analyses, including unbiased proteomics, identified quantitative abnormalities in sarcomeric parameters, cell-cycle defects and mitochondrial dysfunction. The mutant alpha-actinin protein is found to be destabilised, associated with increased activity of the ubiquitin-proteasomal system. This missense variant in alpha-actinin renders the protein less stable. In response, the ubiquitin-proteasomal system is activated; a mechanism that has been implicated in cardiomyopathies previously. In parallel, a lack of functional alpha-actinin is thought to cause energetic defects through mitochondrial dysfunction. This seems, together with cell-cycle defects, the likely cause of the death of the embryos. The defects also have wide-ranging morphological consequences.

Insights into the Role of a Cardiomyopathy-Causing Genetic Variant in ACTN2 Pathogenic variants in ACTN2, coding for alpha-actinin 2, are known to be rare causes of Hypertrophic Cardiomyopathy. However, little is known about the underlying disease mechanisms. Adult heterozygous mice carrying the Actn2 p.Met228Thr variant were phenotyped by echocardiography. For homozygous mice, viable E15.5 embryonic hearts were analysed by High Resolution Episcopic Microscopy and wholemount staining, complemented by unbiased proteomics, qPCR and Western blotting. Heterozygous Actn2 p.Met228Thr mice have no overt phenotype. Only mature males show molecular parameters indicative of cardiomyopathy. By contrast, the variant is embryonically lethal in the homozygous setting and E15.5 hearts show multiple morphological abnormalities. Molecular analyses, including unbiased proteomics, identified quantitative abnormalities in sarcomeric parameters, cell-cycle defects and mitochondrial dysfunction. The mutant alpha-actinin protein is found to be destabilised, associated with increased activity of the ubiquitin-proteasomal system. This missense variant in alpha-actinin renders the protein less stable. In response, the ubiquitin-proteasomal system is activated; a mechanism that has been implicated in cardiomyopathies previously. In parallel, a lack of functional alpha-actinin is thought to cause energetic defects through mitochondrial dysfunction. This seems, together with cell-cycle defects, the likely cause of the death of the embryos. The defects also have wide-ranging morphological consequences. Contractility in heart and skeletal muscle is achieved through highly organised structures, the sarcomeres, in which actin-rich thin filaments and myosin-rich thick filaments slide against each other. Both filaments have specially organised anchoring sites: the Z-disk for thin filaments, and the M-band for thick filaments [1,2]. In addition, single titin molecules span from Z-disks to the M-band as a third filament [3]. The Z-disk is a highly organised and complex structure. In it, alpha-actinin cross-links antiparallel actin filaments from adjacent sarcomeres and organises them, together with other actin cross-linking proteins, e.g., filamin C, into a highly ordered ultrastructural paracrystalline structure. Beyond its mechanical role, the Z-disk is recognised as a signaling hub, which integrates mechano-signaling and stress responses. Numerous proteins with signaling roles are known to be residents of the Z-disk, sometimes in a transient fashion [4]. The highly conserved alpha-actinin is a key protein of the Z-disk. It features an amino-terminal actin-binding domain consisting of two calponin homology domains (CH1, CH2) and a carboxy-terminal calmodulin-like domain. The middle rod domain, comprised of four spectrin-like repeats, allows it to form anti-parallel dimers. Titin binding is regulated via phospho-lipids (PIP2) to the actin-binding domain, triggering a conformational change of the calmodulin-like domain, leading to an open conformation capable of binding to titin Z-repeats [5]. The interaction of alpha-actinin 2 with actin filaments was shown to require the opening of the actin binding domain, where only the CH1 domain binds to the filament, while the second is dissociated from the interacting domain and acts as a negative regulator of the interaction [6,7]. This structural mechanism is the basis of the explanation of many mutations that map to the interface between CH1 and CH2 domains. There are four genes coding for alpha-actinin (ACTN1–ACTN4). ACTN2 is highly expressed in heart and skeletal muscle, and ACTN3 in some skeletal muscle subtypes. ACTN1 and ACTN4 are found predominantly in non-muscle cells, where they perform similar actin cross-linking functions. In line with their expression pattern, ACTN2 genetic variants have been described to cause autosomal dominant skeletal muscle and cardiac diseases [2]. Four missense variants in ACTN2 (p.Gly111Val, p.Ala119Thr, p.Met228Thr and p.Thr247Met) have been identified in the actin-binding domain in patients with Hypertrophic Cardiomyopathy (HCM) [8,9,10,11], a heart muscle disease that results in stiffer and hypertrophied hearts and can be associated with life-threating arrhythmias. Of note, both ACTN2 p.Met228Thr and p.Thr247Met map to the interface between CH1 and CH2 of the actin binding domain in the closed conformation. For ACTN2 p.Ala119Thr, p.Met228Thr and p.Thr247Met, there is genetic evidence of pathogenicity through co-segregation in a multi-generation family [8,9,10], while the pathogenicity of ACTN2 p.Gly111Val is only supported by functional biochemical studies [12]. Moreover, cellular studies using patient-derived induced pluripotent stem cell-derived cardiomyocytes support the role for the pathogenicity of ACTN2 p.Thr247Met [10,13]. No animal models have been generated to study these HCM-causing ACTN2 variants in vivo. Here, we present the generation of an Actn2 mouse model harboring the p.Met228Thr variant. In the heterozygous setting, mature male mice show molecular features in keeping with HCM. To our surprise, the homozygous Actn2 p.Met228Thr mice were found to have an embryonic lethal phenotype. A detailed analysis of the embryonic hearts at E15.5 suggests that alpha-actinin 2 not only controls heart morphology during development but also affects cell cycling and mitochondrial function. Moreover, we could identify mutant ACTN2 protein instability as a driving factor of the phenotype. The animal studies have been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Experimental procedures were performed in accordance with the Directive 2010/63/EU and UK Home office guidelines (project licences P572C7345 and PDCE16CB0) and approved by the respective institutional ethical review boards. Animals were housed in specific pathogen-free conditions, with the only reported positives on health screening over the entire time course of these studies being for Tritrichomonas sp. and Entamoeba spp. All animals were housed in social groups of mixed genotypes, provided with food and water ad libitum, and maintained on a 12 h light:12 h dark cycle (150–200 lux cool white LED light, measured at the cage floor). Phenotyping experiments and offline analysis were performed blinded. All in vivo phenotyping studies of adult mice were carried out using littermates and both sexes. Animals were sacrificed by cervical dislocation and death was confirmed by the cessation of circulation. The p.Met228Thr variant was introduced into the orthologous position in the mouse Actn2 gene using CRISPR-Cas9-mediated homology-directed repair in mouse embryonic stem cells. A detailed methodology is provided in the Supplementary Materials. Heterozygous Actn2 p.Met228Thr mice were viable and fertile. The expression of the mutated allele p.Met228Thr was confirmed at the protein level by mass spectrometry (Figure S1). Animals were backcrossed onto C57BL/6J (Envigo, London, UK) for at least six generations before generating wild-type and heterozygous littermates, or embryos; aged animals were backcrossed for at least two generations. Animals were genotyped for Actn2 p.Met228Thr mutation and a spontaneous genetic variant in the Nnt gene, occurring in C57BL/6J sub-strains [14] using Transnetyx services (Cordova, TN, USA). All animals of the colony were homozygous for the genetic variant in Nnt. Ultrasound echocardiography was carried out as previously described [15]. A detailed methodology is provided in the Supplementary Materials. Mouse embryos were collected at E15.5 following timed mating. Once embryos were drained of blood, hearts were either dissected and flash frozen in liquid nitrogen or used for High Resolution Episcopic Microscopy (HREM) and wholemount staining. For Theiler staging, PFA-fixed front limbs were examined under an inverted stereoscope (Tl3000 Ergo stereoscope, Leica, Mannheim, Germany) and classified according to [16]. A detailed methodology is provided in the Supplementary Materials. Chymotrypsin-like, Caspase-like and Trypsin-like activity assays were performed as previously described using commercially available indirect enzyme-based luminescent assay kits (Promega, Madison, WI, USA) [17]. A detailed methodology is provided in the Supplementary Materials. Tissue and data processing for HREM was performed as previously described [18]. A detailed methodology is provided in the Supplementary Materials. The staining of the cryosections of skeletal muscle was performed as described [5]. Immunolabelling with phospho-histone H3 (Thermo Fisher Scientific, Waltham, MA, USA), alpha-actinin (Sigma-Aldrich, St. Louis, MO, USA) and DAPI was carried out on cryosections of E15.5 hearts. A detailed methodology is provided in the Supplementary Materials. Tibial length measurements, mRNA isolation from ventricular tissue, reverse transcriptase and quantitative PCR (qPCR) were performed as described [19], using the Taqman probes (Applied Biosystems, Waltham, MA, USA) listed in Table S9. Western blotting was performed as described [19] with the antibodies listed in Table S9. Histology on paraffin-embedded samples (7 µm sections) was performed with hematoxylin and eosin, using standard protocols. An excised gel sample was digested with trypsin (Promega, Madison, WI, USA) and analysed by nano-UPLC–MS/MS using a Dionex Ultimate 3000 coupled online to an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), as described [20]. A detailed methodology is provided in the Supplementary Materials. Wildtype and homozygous (n = 6 per group) ventricular samples, previously collected from embryos at E15.5, were prepared and labelled using solutions provided in EasyPepTM mini MS sample prep kit (Thermo Fisher Scientific, Waltham, MA, USA) following manufacturer’s instructions. For data analysis, the MS and MS/MS scans were searched against the Uniprot database using Proteome Discoverer 2.2 (Thermo Fisher Scientific, Waltham, MA, USA) with 5% false discovery rate (FDR) criteria. Multiple test correction was performed using the Benjamini and Hochberg test (Perseus software [21]). Data were further analysed through the use of Ingenuity pathway analysis (IPA, QIAGEN Inc, Germantown, MD, USA), https://digitalinsights.qiagen.com/IPA, (accessed on 22 February 2023) [22]). Detailed information is provided in the Supplementary Materials. The proteomics data underlying this article are available in PRIDE [https://www.ebi.ac.uk/pride/archive/, (accessed on 3 January 2023)], and can be accessed with identifier PXD039226. The hearts were dissected from PFA-fixed embryos in PBS and treated with hyaluronidase (1 mg/mL in PBS, Sigma-Aldrich, St. Louis, MO, USA) for 45 min at RT. After three washes in PBS, permeabilization with 0.2% Triton X-100 was performed for 45 min. Following another three washes in PBS and blocking for 30 min with 5% pre-immune goat serum (Sigma-Aldrich, St. Louis, MO, USA) in antibody dilution buffer (10 mM Tris-HCL, pH 7.2, 155 mM NaCl, 2mM EGTA, 2 mM MgCl2, 1% BSA), the hearts were incubated with the primary antibody mixture (see Table S9) overnight at 4 °C. After 5 × 20 min of washing in PBT (PBS with 0.002% Triton X-100), the secondary antibodies (Cy3-goat anti mouse Igs, multilabelling quality; Cy2-goat anti rabbit Igs, multi-labelling quality, both Jackson Immunochemicals, West Grove, PA, USA via Stratech Scientific, Ely, UK, DAPI, Sigma-Aldrich, St. Louis, MO, USA and Alexa647-phalloidin, Thermo Fisher Scientific, Waltham, MA, USA) were applied overnight at 4 °C. The hearts were washed in PBT for 5 × 20 min and mounted in 0.1 M Tris–HCl (pH 9.5) and glycerol (3:7), with 50 mg/mL of N-propyl-gallate as an anti-fading agent. Microscopy was carried out using an SP5 confocal (Leica, Mannheim, Germany), equipped with 405 blue diode, argon and helium-neon lasers, using a 63/1.4NA oil immersion lens. The hearts were dissected from PFA-fixed embryos in PBS and treated with hyaluronidase (1 mg/mL in PBS, Sigma-Aldrich, St. Louis, MO, USA) for 45 min at RT. After three washes in PBS, permeabilization with 0.2% Triton X-100 was performed for 45 min. Following another three washes in PBS and blocking for 30 min with 5% pre-immune goat serum (Sigma-Aldrich, St. Louis, MO, USA) in antibody dilution buffer (10 mM Tris-HCL, pH 7.2, 155 mM NaCl, 2mM EGTA, 2 mM MgCl2, 1% BSA), the hearts were incubated with the primary antibody mixture (see Table S9) overnight at 4 °C. After 5 × 20 min of washing in PBT (PBS with 0.002% Triton X-100), the secondary antibodies (Cy3-goat anti mouse Igs, multilabelling quality; Cy2-goat anti rabbit Igs, multi-labelling quality, both Jackson Immunochemicals, West Grove, PA, USA via Stratech Scientific, Ely, UK, DAPI, Sigma-Aldrich, St. Louis, MO, USA and Alexa647-phalloidin, Thermo Fisher Scientific, Waltham, MA, USA) were applied overnight at 4 °C. The hearts were washed in PBT for 5 × 20 min and mounted in 0.1 M Tris–HCl (pH 9.5) and glycerol (3:7), with 50 mg/mL of N-propyl-gallate as an anti-fading agent. Microscopy was carried out using an SP5 confocal (Leica, Mannheim, Germany), equipped with 405 blue diode, argon and helium-neon lasers, using a 63/1.4NA oil immersion lens. Embryos were generated as described above. The dissection of whole hearts was carried out in fresh ice-cold PBS with 5mM EDTA. Once cleaned of surrounding tissues, hearts were briefly flushed with ice-cold PBS via the aorta. Hearts were fixed in 4% PFA for 15 min before being transferred to 2.5% glutaraldehyde/2% PFA for 2 h at room temperature, then 3 h at 4 °C. Subsequently, hearts were stored in 0.05% glutaraldehyde at 4 °C. Fixed hearts were briefly washed with fresh PBS and further dissected to remove the atria and blood vessels leaving the ventricles. This portion was cut crossways to produce a small tip fragment and a figure of eight fraction with most of the ventricular walls. These were further fixed in 1% osmium, dehydrated in ethanol, and embedded in Araldite. Before embedding, the larger fragment was divided into three parts: the left and right ventricular walls and the septum. Then, 70 nm sections were stained with Uranyless heavy metal stain followed by Pb Citrate (both Labtech International Ltd., Heathfield, UK). The sections were viewed in a JEOL 1400 electron microscope in the Centre for Ultrastructural Imaging, KCL. ImageJ version 1.53a was used for the colocalisation analysis and densitometry of Western blots. The ‘JACoP’ plugin (https://imagej.net/plugins/jacop, accessed on 22 February 2023) was used to calculate a correlation coefficient. The Image J plugin ‘Colocalisation finder’ (http://punias.free.fr/ImageJ/colocalization-finder.html, accessed on 22 February 2023) was used to generate cytofluorograms to visualise colocalisation. Cell Profiler 4.2.1. was used for nuclear assessment [23]. Sarcomere length was analysed as described [24], using MatLab (version 2021a) and the script ‘ZLineDetection’ (https://github.com/Cardiovascular-Modeling-Laboratory/zlineDetection, accessed on 22 February 2023). All values are given as mean ± standard error of mean (SEM). To compare two unpaired sample groups, data were tested for normality using the Kolmogorov–Smirnov test. Normally distributed data were analysed by Student’s t-test, and data that were not were analysed by Mann–Whitney U-test. Deviation from the expected Mendelian ratios and Theiler stages were assessed with the Chi-square test. For the comparison of the three groups with normally distributed data, a one-way ANOVA followed by Tukey’s post-hoc test was used. For the image analysis of wholemount staining, nested ANOVA was employed, allowing us to consider the number of measurements from each heart. Fisher’s exact test was used to test for the occurrence of VSD. All statistical analyses were performed with GraphPad Prism 9.3.1. Annotations used: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 versus WT, otherwise considered not significant (p > 0.05); n indicates number of animals in each group. In order to investigate the disease mechanisms causing HCM, the p.Met228Thr substitution was introduced into the Actn2 gene in mice by CRISPR/Cas9 genome-editing; successful engineering was confirmed by Sanger sequencing, and backcrossing eliminated CRISPR/Cas9 off-target effects. Heterozygous mice were viable and fertile. The alpha-actinin 2 protein carrying the missense change was detected in the heart by mass spectrometry (Figure S1), indicating the successful generation of the model. Young adult mice (3 months) underwent cardiac phenotyping by echocardiography. Mice carrying the heterozygous Actn2 p.Met228Thr variant had normal cardiac function and dimensions when compared to their wildtype (WT) littermates, irrespective of their sex (Table S1). In agreement, these mice had normal heart weights when normalised to tibial length (Table S1). At the molecular level, they showed no induction of the fetal gene programme typically seen in cardiomyopathic hearts (Figure S2A). The histology on these mice was unremarkable (Figure S3). In more mature mice (>34 weeks), cardiac function and dimensions were again normal in both sexes when compared to their WT littermates (Table S2). In support, mice had normal heart weights when normalised to tibial length (Table S2). However, male mice showed a significant induction of Nppb and Acta1, two transcripts of the fetal gene programme (Figure 1, determined at 38 weeks), while female mice did not (Figure S2B). Moreover, transcripts associated with hypertrophic signaling were induced in male hearts (Figure 1, significant for Fhl1, Ankrd1 and Ankrd2), but not in female hearts (Figure S2C, apart from a less than two-fold increase in Ankrd2). At the protein level, mature male mice displayed an increased expression of small heat shock protein HspB7 (Figure S4A,B), but not of Hsp27, and a trend of increased beta-myosin heavy chain expression. The skeletal muscle of the mice did not show any signs of pathology, regardless of genotype (Figure S5). In conclusion, the Actn2 p.Met228Thr variant does not produce an overt cardiomyopathy phenotype in mice, however, mature male mice display molecular features consistent with HCM. We next attempted to generate mice homozygous (Hom) for the Actn2 p.Met228Thr change by crossing heterozygous mice. Nine breeding pairs produced 18 litters with 83 weaned pups, comprising of 24 WT and 59 heterozygous animals, however, no Hom Actn2 p.Met228Thr offspring were found (Figure S6, Table S3). This was a clear deviation from the expected Mendelian ratios (Chi-square test, p < 0.0001). Four further litters from timed matings were collected at the time of birth and again failed to identify Hom offspring (Figure S6, Table S3), suggesting that Hom Actn2 p.Met228Thr are embryonic lethal. As the next step, we performed timed matings to harvest embryos at defined time points; E15.5 was the latest time point that viable Hom embryos were consistently present. At this timepoint, 30 litters produced 242 embryos, among them 56 Hom (Figure S6, Table S4). This was within the expected Mendelian ratios (Chi-square test, p > 0.05). The gross morphology and size of E15.5 embryos appeared normal regardless of the genotype (Figure S7). HREM was performed on E15.5 wildtype and Hom hearts. Strikingly, Hom hearts had significantly increased right ventricle luminal volume (p = 0.0006) and smaller left atrium volume (p = 0.0030) when compared to control hearts (Figure 2B and Figure S8). In addition, Hom hearts had significantly reduced left ventricular free wall thickness (p = 0.0021, Figure S9), with a visual trend of reduced wall thickness in two other areas of LV and RV (Figure S9), although this did not reach statistical significance. Three out of eight Hom hearts had peri-membranous ventricular septal defects (Figure 2A). Despite the occurrence only in Hom hearts, this was not statistically significant (p = 0.10, Fisher’s exact test). No atrial septal defects were observed. Although the aortic arch and pulmonary trunk of Hom embryos had normal gross morphology (Figure S10A), the pulmonary trunk volume was significantly reduced (p = 0.0499, Figure S10C), despite normal length (p = 0.52, Figure S10B). Furthermore, significant aortic stenosis was evident in both the ascending (p = 0.0025, 19%) and descending thoracic aorta (p = 0.0497, 32%, Figure S10B), but not in the aortic root (p = 0.0609, Figure S10B). Finally, dysplastic pulmonary valves with thickened leaflets were observed, particularly in the right and left leaflets (Figure S11A–C). Hom embryos were found to have malpositioned hearts within the body cavity, with a superior tilt of the heart (p = 0.0104, Figure S12A–C). Affected hearts also tended to have a leftward rotation (when measured from the base of the aorta to the ventricular sulcus), but this was not statistically significant. In summary, we identified significant morphological changes in the ventricular chambers, aorta, pulmonary valve and pulmonary trunk in the hearts of Hom Actn2 p.Met228Thr embryos, with a subset of embryos also having a peri-membranous ventricular septal defect. To exclude developmental delay as an explanation for the observed abnormalities in the Hom embryos, forelimb morphology was used for Theiler staging, and no developmental delay was observed (Figure S13, Chi-square test, p > 0.05). Wholemount immunofluorescence was performed to interrogate the expression and localisation of alpha-actinin 2 in E15.5 hearts. Of note, residual congealed blood was observed macroscopically in the ventricles of Hom hearts, much more than in the WT hearts (Figure S14), suggesting the less efficient contractility of Hom hearts during the fixation procedure. Sarcomeric structures were visualised with titin Z-disk epitope (T12) and were clearly present and well organised, with alpha-actinin detectable at the Z-disks of sarcomeres in both genotypes (Figure 3A). However, quantitative colocalisation analysis revealed reduced colocalisation of titin Z-disk epitope with alpha-actinin (Figure 3B,C). Moreover, sarcomere length was found to be substantially increased in the Hom hearts (2.2 versus 1.8 µm; Figure 4A). In addition, there was a reduction in the number of nuclei in the Hom hearts, and they were rounder in shape (Figure 4B, Table S5). Electron microscopy demonstrated regular Z-disks in both WT and Hom samples (Figure S15A), however, Z-disks appeared less uniform in the Hom hearts (Figure S15B). While Z-disk measurements revealed no difference in Z-disk width between both genotypes (p = 0.12), the width distribution was much wider in the Hom hearts (Figure S15C). The reduced number of nuclei (Figure 4B) prompted us to investigate cell-cycle markers. In a targeted transcript analysis for cell-cycle markers, Anln, Cdkn1a, Cdkn1b, Cdkn2b, Tp53 and Wee1 were all found to be dysregulated in the ventricles of Hom hearts (Figure 4C), with the most striking upregulations being observed for Anln, Cdkn1a and Wee1, which were all predicted to block the progression of the cell cycle. In support of Tp53 transcript upregulation, a non-significant trend (p = 0.13) towards the upregulation of the p53 protein level was observed on Western blotting (Figure S16). To further probe for potential defects in cell division in the Hom hearts, we performed staining for phosphorylated histone H3, a nuclear marker of active proliferation in cells (Figure 5A). In Hom E15.5 hearts, fewer cells positive for the marker were observed (Figure 5B). An analysis of dividing cells at higher magnification identified clear evidence of metaphase chromosome arrangement in dividing Hom embryonic cells, but these cells seemed to have a defect in myofibril disassembly, with residual sarcomeres observed (Figure S17). In order to gain insight into the disturbances leading to embryonic death, WT and Hom E15.5 heart protein samples were subjected to unbiased proteomics. Overall, 244 proteins were found to be upregulated and 133 proteins were downregulated in the Hom hearts when compared to WT (Figure 6A, Tables S6 and S7). Further, ingenuity pathway analysis (IPA) revealed dysregulation in a number of key canonical pathways, the most prominent being oxidative phosphorylation, mitochondrial dysfunction, sirtuin signalling and the citric acid (TCA) cycle (Figure 6B). Moreover, proteins belonging to the ubiquitination pathway and unfolded protein response were also enriched in the dataset (Figure 6B, Table S8). A deeper analysis of the data highlighted key mitochondrial protein complexes to be downregulated (Figure 6C and Figure S18), while proteins associated with protein processing and translation, including proteasomal activity, were upregulated (Figure 6B,C). A targeted interrogation of the proteomics dataset indicated that Actn2 protein levels were reduced by approximately 25% in the Hom hearts (p < 0.01, Table S6). To validate this, we probed for alpha-actinins 1–4 at the mRNA level. The transcripts for Actn2 and Actn3 were upregulated in Hom ventricles (Figure 7A and Figure S19A), while transcripts for other alpha-actinins, Actn1 and Actn4, were not affected. Equally, the expression of cardiac actin, Actc1, was normal, while skeletal muscle actin, Acta1, was downregulated. In contrast to the Actn2 upregulation observed at the transcript level, the protein levels for Actn2 were significantly reduced (Figure 7B). At the protein level, Actn3 expression was unchanged (Figure S19B,C). The reduced Actn2 levels in the Hom hearts suggested that the p.Met228Thr Actn2 is subject to protein degradation. Ubiquitin-proteasomal system (UPS) and autophagy are the main protein degrading machineries in cells [25]. We probed for autophagy makers (p62 and LC3, Figure S21), but failed to observe any changes in the Hom hearts. Furthermore, proteolytic activities and total ubiquitination showed no signs of changes in the Hom hearts (Figure S20). However, the fact that the UPS was enriched in the proteomics dataset (Figure 6C) argues for the UPS being responsible for the destabilisation of the Actn2 protein. In summary, the p.Met228Thr genetic variant renders the protein less stable. In homozygous animals, cell-cycle defects and mitochondrial dysfunction are observed; these impaired functions result in a range of morphological abnormalities of the embryonic hearts and are incompatible with life. The heterozygous ACTN2 p.Met228Thr variant was originally identified in a four-generation Italian family with atypical Hypertrophic Cardiomyopathy [9]. The ACTN2 variant showed co-segregation with cardiomyopathy, consistent with autosomal dominant inheritance, across 11 affected and 7 healthy family members, and was hence considered pathogenic. A detailed clinical investigation of affected family members revealed a range of cardiac features, including left ventricular hypertrophy, restrictive pathology, arrhythmias (including early-onset atrial fibrillation) and non-compaction. In order to gain insights into the disease mechanisms of the ACTN2 p.Met228Thr variant, we generated a mouse model. Young adult mice heterozygous for the variant—reflecting the genetic situation in the patients—had no cardiac phenotype. Despite normal cardiac dimensions and function on echocardiography, mature male mice displayed molecular transcript signatures compatible with early signs of cardiomyopathy [26]. These findings mirror previously studied mouse models of cardiomyopathy: often the genetic equivalent of the human disease is not sufficient to cause detectable phenotypes in mice [15,27,28]. Ageing can unmask disease features [15,29], which is in agreement with HCM being a late-onset disease in humans, with patients often presenting only as adults. For example, the index patient of the ACTN2 p.Met228Thr family was diagnosed with HCM in his 50s [9], which corresponds to approximately 10 months of age in mice. We can only speculate whether further ageing or stressors, such as adrenergic stimulation or a high-fat diet, might provoke a phenotype, as shown for other animal models [30,31,32]. Sex differences in cardiac phenotypes of C57Bl6 mice are well documented [33,34]. Of note, male mice have higher mean arterial pressure, and this sex difference increases between 3 and 12 months [35]. This could contribute to the observed molecular signs of cardiomyopathy in male, but not female, mature mice. The lack of an overt HCM phenotype in the heterozygous mouse model, equivalent to the human ACTN2 p.Met228Thr genetic situation, might be explained by the wildtype form dominating the alpha-actinin 2 protein. In support, we identified far more wildtype peptides in mass spectrometry than peptides carrying the p.Met228Thr variant; however, we were not able to detect the ubiquitination of alpha-actinin 2 by Western blotting (Figure S4C). Given the lack of an overt phenotype in heterozygous mice, the embryonic lethality of the variant in the homozygous setting was unexpected. Our detailed morphological analysis using HREM identified a broad range of abnormalities: The Hom E15.5 hearts had enlarged right ventricles, smaller left atria and regional decreases in the left ventricle. Three of the eight homozygous hearts had peri-membranous ventricular septal defects (VSD). Further, we observed aortic stenosis, a reduced volume of the pulmonary trunk and a thickening of the pulmonary valve leaflets, as well as an abnormal orientation of the hearts in the body cavity. While some of these defects (e.g., thinner wall, larger right ventricular lumen and VSD) might be attributable to reduced cell division and/or cell migration, others may be consequences of altered hemodynamics in the developing heart: alpha-actinin 2 has no noticeable expression in the aorta or pulmonary trunk, so changes in these structures are likely to be secondary to altered blood flow. It has been demonstrated for chicken embryos and zebrafish aortas that blood flow, or rather the lack of it, can induce remodeling [36,37]. However, the presence of a VSD did not affect other parameters measured, and there was no statistical difference between Hom hearts with and without VSD. Of note, the combination of morphological features resembles aspects of Noonan syndrome, in which a dysplastic pulmonary valve and Hypertrophic Cardiomyopathy are observed [38,39]. However, we observed no changes in ERK phosphorylation in the embryonic hearts. Hence, if there was a joint defect with Noonan’s syndrome, it is downstream of this point. Whether such a common disease pathway exists will be the subject of future investigations. Despite the indirect evidence of poor contractility in the Hom hearts (residual blood found in ventricular cavities after fixation), the sarcomeres appeared to be well formed and qualitatively normal in the hearts. Quantitative image analysis, however, revealed the reduced colocalisation of the titin Z-disk epitope with mutant alpha-actinin. Moreover, sarcomere length was substantially increased in the Hom hearts. At the developmental stage investigated (E15.5), cardiomyocytes undergo cycles of cell division [40], which requires them to disassemble and later reassemble sarcomeres [41]. Based on structural analysis, we hypothesise that ACTN2 p.Met228Thr, which maps to the interface between the CH1 and CH2 domains of the actin binding domain, might have an increased affinity to F-actin and quantitatively interfere with the breakdown of the sarcomeres required to undergo cell division. In support of this hypothesis, we identified defects in myofibril disassembly in cells undergoing division (Figure S17). As a consequence, the cells in the Hom hearts appear to have a shift towards fewer nuclei, and fewer proliferating cells were identified. In addition, the cell-cycle markers Anln, Cdkn1a and Wee1 were found to be upregulated in Hom hearts. All three control various cycle check points and their upregulation impairs the progression of cell division. This impairment of cell division in Hom hearts may also explain the reduced wall thickness observed in the LV wall and the lack of muscle growth, resulting in VSD in a proportion of Hom hearts. A striking feature of the Actn2 p.Met228Thr variant is protein destabilisation in vivo. At the protein level, alpha-actinin 2 was found to be reduced to approximately one-third in Hom E15.5 hearts. It appears that the Hom hearts try to compensate for this lack of protein by upregulating Actn2 at the transcript level. Nevertheless, this cannot make up for the decreased stability at the protein level and subsequent degradation. The mutation may lead to the partial mis-folding of the actin binding domain, which is supported by the enrichment of the ‘unfolded protein response’ pathway in the proteomics dataset (Figure 6B). Such mis-folded proteins are recognised and targeted for protein degradation. There are two major protein degrading pathways: the ubiquitin-proteasomal system (UPS) and autophagy [42]. We have no evidence to suggest that autophagy is involved. In contrast, unbiased proteomics revealed that UPS activity is more active in Hom hearts (Figure 6B,C and Table S8), so it is likely responsible for the increased turnover of the mutant protein. While the measurements of proteolytic activities (Figure S20A) do not support the increased activity of the UPS, future experiments using proteasomal or autophagy inhibitors on cultured cells, e.g., induced pluripotent stem cell-derived cardiomyocytes, will shed light on the specific role of the protein degrading pathways. The UPS has been implicated in other HCM-causing genetic variants, e.g., for MYBPC3 truncating variants and for CSRP3 C58G [27,32,43]. In support of a crucial role of protein stability in the pathogenesis of human HCM due to the p.Met228Thr variant, there are clear parallels to another HCM-associated ACTN2 variant (p.Thr247Met) located in the same domain [13]: Using an induced pluripotent stem cell-derived cardiomyocyte model, the authors observed a reduced stability of the mutant ACTN2 p.Thr247Met protein, with the activation of both UPS and autophagy. However, while their mutant protein formed aggregates in their cellular model upon exogenous expression, we did not detect any alpha-actinin 2 aggregates in the p.Met228Thr Hom hearts. It is worth noting that the tightly controlled UPS function is crucial for cell homeostasis and regulating many cellular processes, e.g., normal proteasomal activity is required for sarcomere disassembly during cell division in embryonic rat cardiomyocytes. If the proteasome is inhibited, alpha-actinin 2 fails to disassemble from sarcomeric structures [41]. Unbiased proteomics identified oxidative phosphorylation and mitochondrial dysfunction as the main consequences in the Hom hearts. During embryonic development at E11.5, glycolysis is the main source of energy while oxidative phosphorylation complexes begin to form and electron transport chain activity begins [44]. By E13.5, mitochondrial structure and function resemble those of mature mitochondria, and ATP is generated mainly through oxidative phosphorylation, with glycolysis becoming a secondary source [45,46]. Guo et al. [47] recently showed through the cardiomyocyte-specific mosaic expression of a hypomorphic mutation that Actn2 expression is important for cardiomyocyte maturation via serum response factor signaling. As one of the downstream pathways, mitochondrial expansion and organisation were found to be impaired. If dysfunctional mitochondria cannot facilitate maturation and metabolic shift in our model, energy deficiency might be a cause of embryonic lethality. Moreover, alpha-actinin 2 has been reported to more directly influence mitochondrial function. It controls the localization of the RNA transcripts required for oxidative phosphorylation via its interaction with the RNA binding protein IGF2BP2 [48]. However, an alternative explanation would be that mitochondrial defects occur secondary to cell death [49], however, this is less likely as mitochondrial impairment was also observed in a cellular model of the ACTN2 p.Thr247Met variant [13] in the absence of pronounced cell death. In summary, our heterozygous mouse model (in mature male mice) supports a pathological role of the ACTN2 p.Met228Thr variant but provides little insight into the disease mechanisms. Differences in physiology between humans and mice, the relatively young age of the mice and lack of stressors might explain this lack of an overt phenotype [50]. However, the detailed investigations of the Hom Actn2 p.Met228Thr embryonic hearts identified alpha-actinin protein destabilisation as a key feature of the model. This has several implications: firstly, it leads to an aberrant activity of the UPS, which has been implicated in multiple studies of HCM. Secondly, the lack of functional alpha-actinin has been suggested to interfere with serum response factor signaling [47], preventing cardiomyocyte maturation and consequently ensuing mitochondrial dysfunction. Together with the observed cell division defects, energetic deficiency is the likely cause of myocardial dysfunction. Moreover, the experiments identified a range of morphological abnormalities in the developing heart, suggesting that subtle functional abnormalities in alpha-actinin can have major consequences on the structure and function of sarcomeres as well as cell division, and consequently on the developing heart.

PMC10001376

Alice Varley,Andreas Koschinski,Mark R. Johnson,Manuela Zaccolo

cAMP Compartmentalisation in Human Myometrial Cells

24-02-2023

cAMP,myometrium,pregnancy,phosphodiesterases,signalling compartmentalisation,hTERT-HM cells

Preterm birth is the leading cause of childhood mortality and morbidity. A better understanding of the processes that drive the onset of human labour is essential to reduce the adverse perinatal outcomes associated with dysfunctional labour. Beta-mimetics, which activate the myometrial cyclic adenosine monophosphate (cAMP) system, successfully delay preterm labour, suggesting a key role for cAMP in the control of myometrial contractility; however, the mechanisms underpinning this regulation are incompletely understood. Here we used genetically encoded cAMP reporters to investigate cAMP signalling in human myometrial smooth muscle cells at the subcellular level. We found significant differences in the dynamics of the cAMP response in the cytosol and at the plasmalemma upon stimulation with catecholamines or prostaglandins, indicating compartment-specific handling of cAMP signals. Our analysis uncovered significant disparities in the amplitude, kinetics, and regulation of cAMP signals in primary myometrial cells obtained from pregnant donors compared with a myometrial cell line and found marked response variability between donors. We also found that in vitro passaging of primary myometrial cells had a profound impact on cAMP signalling. Our findings highlight the importance of cell model choice and culture conditions when studying cAMP signalling in myometrial cells and we provide new insights into the spatial and temporal dynamics of cAMP in the human myometrium.

cAMP Compartmentalisation in Human Myometrial Cells Preterm birth is the leading cause of childhood mortality and morbidity. A better understanding of the processes that drive the onset of human labour is essential to reduce the adverse perinatal outcomes associated with dysfunctional labour. Beta-mimetics, which activate the myometrial cyclic adenosine monophosphate (cAMP) system, successfully delay preterm labour, suggesting a key role for cAMP in the control of myometrial contractility; however, the mechanisms underpinning this regulation are incompletely understood. Here we used genetically encoded cAMP reporters to investigate cAMP signalling in human myometrial smooth muscle cells at the subcellular level. We found significant differences in the dynamics of the cAMP response in the cytosol and at the plasmalemma upon stimulation with catecholamines or prostaglandins, indicating compartment-specific handling of cAMP signals. Our analysis uncovered significant disparities in the amplitude, kinetics, and regulation of cAMP signals in primary myometrial cells obtained from pregnant donors compared with a myometrial cell line and found marked response variability between donors. We also found that in vitro passaging of primary myometrial cells had a profound impact on cAMP signalling. Our findings highlight the importance of cell model choice and culture conditions when studying cAMP signalling in myometrial cells and we provide new insights into the spatial and temporal dynamics of cAMP in the human myometrium. Preterm birth is the leading cause of mortality in children under the age of 5 years [1,2] and, due to its associated lifelong complications, it is a significant global health challenge [3,4,5]. Several complex molecular and cellular processes control the onset of spontaneous labour, but the precise mechanism is yet to be fully determined. As a result, there are no available interventions that can effectively stop labour once it has established at any stage of pregnancy. Critical changes have been identified in the expression of key components of the cAMP signalling pathway during pregnancy and the transition to uterine activity with the onset of term and preterm labour [6,7,8,9,10,11,12]. A switch in the cAMP effector system has been characterised whereby, during pregnancy, cAMP activates protein kinase A (PKA), promoting a relaxed myometrial phenotype, whilst, with the onset of labour, cAMP acts via the exchange factor activated by cAMP (EPAC), triggering a contractile response [6,13]. The altered expression of different signalling components and the switch in cAMP effector activity at the onset of labour indicates the existence of different cAMP signalling subnetworks with distinct functions during the progression of pregnancy and is suggestive of compartmentalisation of cAMP in the human myometrium [14]. The development of fluorescence resonance energy transfer (FRET)-based reporters that are genetically encoded and can be targeted to specific subcellular compartments has helped elucidate the intricate spatial and temporal regulation of cAMP signalling in several cellular systems [14,15,16]. Using this approach it has been possible to analyse selectively cAMP networks organised at distinct subcellular sites and to establish the critical role of the cAMP-hydrolysing enzymes phosphodiesterases (PDEs) in the compartmentalisation of cAMP [14]. FRET-targeted reporters have facilitated the mapping of subcellular cAMP signals [17,18] and their association with specific G-protein coupled receptors (GPCRs) [19,20]. This has contributed to our current understanding of how multiple, diverse but highly integrated multi-protein complexes, or signalosomes, effectively coordinate the diverse functions mediated by cAMP, while maintaining hormonal specificity. Several studies have shown that disruption in individual signalosomes and of cAMP compartmentalisation is linked with the development of diseases [21,22,23], and manipulation of local cAMP levels has been proposed as a novel modality for therapeutic intervention with subcellular precision [24,25]. There is no information currently available on the subcellular compartmentalisation of cAMP signalling in myometrial cells and on its relevance for the physiology of pregnancy and labour. The myometrial cAMP system has previously been targeted in the management of preterm labour with the successful use of beta-mimetics causing uterine relaxation [26]. These drugs, however, cause concerning maternal side effects due to their systemic action [27], and safer, more effective, treatments are highly desired. Here we used FRET imaging to investigate cAMP signalling in human myometrial cells in pregnancy. Using targeted FRET reporters expressed in human primary myometrial cells (HPMCs) and in the myometrial cell line hTERT-HM, a cell model frequently used for studies on myometrial physiology [28,29,30,31], we uncovered the presence of compartmentalised cAMP signals triggered by catecholamines and prostaglandins at different subcellular locations. Our analysis also demonstrated that hTERT-HM cells and HPMCs display profound differences in cAMP signalling and that the number of passages in culture significantly affects cAMP signalling in HPMCs. We further observed that the cAMP response is highly variable in HPMC from different individuals. Myometrial tissue biopsies (~0.5 × 0.5 × 0.5 cm) were taken from the upper portion of the uterine lower segment incision during Caesarean sections at Chelsea and Westminster Hospital between January 2018 and August 2019. Twenty women were included in the study who gave their written consent. Myometrial sample collection was conducted under the Preterm Labour (PREMS) study, which has approval from the Brompton and Harefield Research Ethics Committee (Reference number 10/H0801/45). The tissue samples were either snap frozen at −80 °C for extraction of mRNA and protein or stored in PBS at 4 °C for no longer than 3 h prior to preparation for cell culture. Inclusion criteria were as follows: singleton pregnancy, non-labouring, >37 weeks’ gestation, normal amniotic fluid levels, and preferably no more than two previous Caesarean sections. Caesarean section indications included fetal distress, previous Caesarean section, maternal request, or breech presentation. Patients were excluded if they had received an oxytocin infusion, or prostaglandins. See Table S1 for donor demographics. Following collection, the myometrial biopsy was placed into PBS (Life Technologies Ltd. (Gibco), Paisley, UK, 14190169) at 4 °C for no longer than 3 h prior to preparation for cell culture. In a sterile laminar flow hood, the biopsy was rinsed with PBS several times to remove excess red blood cells, minced, and digested in a 50 mL tube containing a DMEM solution (Life Technologies Ltd. (Gibco), Paisley, UK, 41966052) with 0.25 mg/mL collagenase 1A (Sigma-Aldrich, Dorset, UK, CS674), 0.25 mg/mL collagenase XI (Sigma-Aldrich, Dorset, UK, C9891), and 5 mg/mL BSA (Thermo fisher, Hemel Hempstead, UK, 12676029) for 20 min in a 37 °C water bath whilst being agitated. Following this first digestion/agitation step, half of the resulting cell suspension was filtered through a 500 μm cell strainer (pluriSelect, Cambridge, UK 43-50500-03) and the individual cells were collected via centrifugation at 180× g for 5 min. The resulting supernatant was then returned to the remaining cell suspension and the digestion step was repeated for another 20 min. For larger biopsies, this process was extended for 5–10 min to ensure that the majority of the tissue was digested. The combined cell suspension was filtered again through a 500 μm cell strainer, and the cells were collected via centrifugation at 180× g for 5 min. The cell pellet was resuspended in DMEM containing 10% FBS (Life Technologies Ltd. (Gibco), Paisley, UK, 10500064), 100 units/mL penicillin, 100 µg/mL streptomycin (Sigma-Aldrich, Dorset, UK, P-0781) and 2 mM L-glutamine (Life Technologies Ltd. (Gibco), Paisley, UK, 42430025). On average, between 5 × 105 cells to 7.5 × 105 cells were collected per biopsy. Depending on the cell yield and the experimental needs, cells were then seeded onto sterilised 15 mm diameter glass coverslips (VWR, Leicestershire, UK, 631-1579) in wells of 12-well plates (Corning Costar, Sigma-Aldrich, Dorset, UK, 3513) for FRET imaging experiments, or into 6-well plates (Corning Costar, Sigma-Aldrich, Dorset, UK, 3516) for RNA or protein extraction. Surplus cells were seeded into T75 flasks (Corning Costar, Sigma-Aldrich, Dorset, UK, 430641U) for subculture. HPMCs from 6 donors were used for quantitative PCR experiments. For western blotting, protein was extracted from HPMCs obtained from 7 donors. HPMCs were isolated from a total of 20 donors for FRET imaging experiments. All cells were kept in an incubator at 37 °C in a 5% CO2 humidified atmosphere for further experiments. The medium was refreshed every 2 days. Protein samples were prepared from lysed primary myometrial cells grown in a monolayer using cell lysis buffer (1x Laemmli buffer, Bio-Rad, Hertfordshire, UK, 1610737) containing 50 mM DTT (Bio-Rad, Hertfordshire, UK, 1610611). The detached and lysed cells including the supernatant were stored at −80 °C to be used for western blotting. Protein concentrations of the individual samples were determined using the Bio-Rad Protein Assay (Bio-Rad, Hertfordshire, UK, 5000112) and a Bio-Rad iMARK plate reader (Bio-Rad, Hertfordshire, UK, 1681130) measuring the absorbances at 660 nm. Concentrations were then recalculated from a measured BSA standard curve. The whole cell lysates in Laemmli buffer were heated to 96 °C for 5 min. Then, 20 μg of protein from each sample was loaded into each well on polyacrylamide gels (SDS PAGE precast gels, Bio-Rad, Hertfordshire, UK) alongside the Precision Plus Protein™ (Bio-Rad, Hertfordshire, UK, 1610374) pre-stained standard. The gels were subjected to SDS-PAGE electrophoresis and subsequently transferred onto polyvinylidene difluoride (PVDF) membranes (Rio-Rad, Hertfordshire, UK, 1704156) using the Trans-Blot® TurboTM Transfer System (Bio-Rad, Hertfordshire, UK, 1704150EDU). The membranes were blocked with 5% w/v fat-free milk powder in 1X TBS-T solution for 1 h at room temperature and then hybridised with the respective primary antibody (Table S2) overnight at 4 °C. The membranes were then washed for 1 h. Following this, they were incubated for 2 h at room temperature with the secondary antibody (Table S2). ECL plus Clarity Western Substrate (Bio-Rad, Hertfordshire, UK, 1705060) were used for antibody detection and the membranes were imaged using an iBright™ FL1500 Imaging System (Invitrogen, Paisley, UK, A44241). Total RNA was extracted and purified from primary myometrial cells grown in a monolayer using the RNeasy mini kit (Qiagen, Manchester, UK, 74004) as per manufacturer’s instructions. The concentration and purity of the RNA was quantified using a NanoDrop Nd-1000 spectrophotometer. The RNA was stored at −80 °C. Following quantification, 1.5 μg of RNA was reverse transcribed to cDNA with oligo DT random primers, PCR buffer, MgCl2 dNTPs, RNAse inhibitor, and MuLV reverse transcriptase using the QuantiTect Reverse Transcription kit (Qiagen, Manchester, UK, 205311). Primer sets were designed using the Primer 3 software and purchased from Invitrogen (Table S3). A nucleotide Blast search was conducted to ensure the primer sequences corresponded to the gene of interest. Quantitative real-time SYBR Green PCR assays were performed with a RotorGene Q thermocycler using a pre-programmed sequence. The cycle threshold (Ct) was used for quantitative analysis, which denotes the cycle at which the fluorescence emission reaches a predetermined threshold. The cycle threshold was fixed at a level whereby the exponential increase in amplicon yield was approximately equivalent between the samples. A standard curve was used that involved a ten-fold dilution series. The mRNA concentration data were normalised to the amount of GAPDH-mRNA, which was used as the housekeeping gene and expressed as relative amounts. A readily available hTERT myometrial cell (hTERT-HM) line was used [31]. The hTERT-HM cells were maintained in DMEM/Hams F12 (1:1) medium (Sigma-Aldrich, Dorset, UK D8327) supplemented with 5% charcoal stripped FBS (Life Technologies Ltd. (Gibco), Paisley, UK, 12676029), 100 units/mL penicillin, 100 µg/mL streptomycin, and 2 mM L-glutamine (refreshed every 2 days) at 37 °C in a 5% CO2 humidified incubator. At approximately 80% confluence, the hTERT-HM cells were passaged, and aliquots of 1.5 × 105 cells in suspension were seeded onto sterile 24 mm diameter borosilicate glass coverslips (VWR, Leicestershire, UK, 631-1583) for FRET imaging experiments. The cells on the coverslips were then kept for 16–24 h under the same culture conditions as above. Subsequently, the cells were infected with viral vectors encoding for either a cytosolic FRET-based sensor (EPAC-SH187) [32] or a FRET sensor that was targeted to the plasma membrane via fusion to the scaffolding protein AKAP79 (AKAP79-CUTie) [33]. A total of 4.5 × 107 virus particles of EPAC-SH187 or 3.8 × 107 virus particles of AKAP79-CUTie sensor were added in solution directly to the culture medium of each coverslip. The hTERT-HM cells were incubated for 3 h with the virus after which the media was replaced, and the cells were further incubated 18–24 h prior to imaging of the EPAC-SH187 sensor or 48 h for the AKAP79-CUTie sensor. Efficiency of infection was 90–100% for the EPAC-SH187 sensor and 70–80% for the AKAP79-CUTie sensor. Cells were imaged at approximately 40–50% confluency. A total of 2.2 × 104 cells were seeded onto 15 mm diameter sterilised borosilicate glass coverslips, which achieved a cell confluence of 30–40% for imaging of multiple cells per experiment, whilst assuring for a region devoid of cells to be used for background correction. 16–24 h after seeding, the coverslips were washed 2–3 times with PBS to remove excess red blood cells or residual tissue debris and the media was refreshed. Then, 4.5 × 107 virus particles of EPAC-SH187 or 3.8 × 107 virus particles of AKAP79-CUTie sensor were added in solution directly to the culture medium of each well. For both hTERT-HM cells and HPMCs, extensive trial experiments were performed to determine the optimum concentration of virus required for adequate sensor expression and infection efficiencies. A multiplicity of infection (MOI) of approximately 1000 virus particles per cell attained sufficient adenoviral transduction, which has previously been used in adult rat ventriculomyocytes [34]. Infection efficiencies of approximately 90% were achieved for the EPAC-SH187 sensor, and 70% for the cells expressing the AKAP79-CUTie sensor. After infection, the coverslips were kept at 37 °C in a humidified 5% CO2 atmosphere for 18–24 h prior to imaging of the EPAC-SH187 sensor or 48 h for the AKAP79-CUTie sensor. The HPMCs were typically imaged between day 3 and day 6 after plating. For all experiments, except when testing for the effect of multiple passages in vitro, the cells were used without further passage. Following isolation (deemed as passage 0 (P0)), HPMCs were grown to approximately 80% confluence in a T75 culture flask and sub-cultured for five subsequent passages (P1 to P5). At each passage, cells were seeded onto sterile 15 mm diameter borosilicate glass coverslips at a density of approximately 5.8 × 103 cells/cm2 for FRET experiments. In conjunction with FRET imaging experiments, at each passage cells were also seeded into 6-well plates for RNA isolation. These cells were harvested when they reached about 80% confluency. The remaining HPMCs were subcultured into a new T75 flask. All cells were grown in DMEM media supplemented with 10% FBS, 100 units/mL penicillin, 100 µg/mL streptomycin, and 2 mM L-glutamine. Cell viability and growth was monitored microscopically every day. Sensitised FRET experiments were performed with an inverted Olympus IX71 microscope using a PlanApoN 40× NA 1.42 oil immersion objective, 0.17/FN 26.5 (Olympus, Southend-on-Sea, UK). Cells expressing the sensors were excited at a wavelength of 436 +/− 10 nm, and the excitation/emission dicroic mirror was 455 nm LP. An optical beam-splitter device (Dual-view simultaneous-imaging system, DV2 Mag Biosystems, Photometrics, AZ, USA) and a CoolSnap HQ2 monochrome camera (Photometrics, Tucson, AZ, USA) were used to record the YFP and CFP emissions in real time. The emission filter wavelength was 535 +/− 15 nm for YFP emission and 470 +/− 12 nm for CFP emission, with a beam-splitter dicroic mirror of 495 nm LP (Chroma Technology, Olching, Germany). MetaFluor, Meta Imaging Series 7.1 software (Molecular Devices, San Jose, CA, USA), was used for acquisition, storage, and offline analysis of the FRET data. Ratio and FRET changes were calculated on background-corrected and, if applicable, drift-corrected emission intensities. These ratio- or FRET-change values correlated to changes in intracellular cAMP concentrations. Normalisation to the maximal response generated by forskolin (25 µM) and IBMX (100 µM) was conducted to allow for comparison of the two different FRET reporters used. The distribution of data was determined using the Shapiro–Wilk test. Normally distributed data were analysed using a t-test (paired or unpaired). In cases of multiple comparisons, a one-way ANOVA followed by a mixed-effects analysis and Turkey’s multiple comparison post hoc test were used. Data that were not normally distributed were analysed using a Wilcoxon matched pairs test for paired data or a Mann–Whitney test for unpaired data. In cases of multiple comparisons, a Friedman’s test with a Dunn’s multiple comparisons post hoc test was used for paired data, or for unpaired data, a Kruskal–Wallis test followed by a Dunn’s multiple comparisons post hoc test. Data were presented as the means +/− SEM. A value of p < 0.05 was considered statistically significant. The following symbol system was used to denote significance: * = < 0.05 < p < 0.01, ** = 0.01 < p < 0.001, *** = 0.001 < p < 0.0001, **** = p < 0.0001. GraphPad Prism 9.0 software was used to generate graphical representations of the data. In this study we set out to compare the cAMP response to catecholamines and prostaglandins, two stimuli that play a key role in the regulation of the myometrium during pregnancy [35,36]. Our initial aim was to compare the response in two distinct subcellular compartments, the bulk cytosol, and the sub plasmalemma space of HPMCs, and in the cell line, hTERT-HM. To this aim, we employed two genetically encoded, FRET-based cAMP probes, the cytosolic Epac-SH187 sensor [32] and the plasmalemma-anchored AKAP79-CUTie sensor [33]. We first established that, when expressed in HPMCs, the Epac-SH187 (Figure 1A) and AKAP79-CUTie (Figure 1B) sensors show the expected localisation. Correct localisation of the sensors was also confirmed in hTERT-HM cells (not shown). To compare the cAMP response in the bulk cytosol and at the plasmalemma, cells expressing the sensors were treated with the β-AR agonist isoproterenol (ISO) or with prostaglandin E2 (PGE2), and the cAMP response was monitored by measuring FRET changes in the two compartments. The non-selective PDE inhibitor IBMX (100 μM) was applied to assess the contribution of the PDEs to the regulation of the cAMP response to the two agonists. The adenylyl cyclase (AC) activator forskolin (25 μM) was subsequently applied to achieve maximal cAMP generation and sensor saturation. As shown in Figure 2, while application of 1 nM ISO generated a robust cAMP response both in the cytosol (Figure 2A) and at the plasmalemma (Figure 2C) in hTERT-HM cells, no cAMP increase was detectable in either compartment in HPMCs (Figure 2A,C). Even at 1 μM ISO, the cAMP response remained lower at the plasmalemma and in the cytosol of HPMCs compared with the response elicited by 1 nM ISO in the two compartments of hTERT-HM cells (Figure 2A,C). In contrast, treatment with 1 μM PGE2 resulted in a significantly larger response both in the cytosol (Figure 2B) and at the plasmalemma (Figure 2D) of HPMCs compared with the response measured in the two compartments in hTERT-HM cells (Figure 2B,D), a difference that was maintained even when HPMCs were treated with 30 nM PGE2 (Figure 2B,D). To investigate whether the differences between the two cell types may be due to a difference in local PDE hydrolytic activity, HPMCs and hTERT-HM cells expressing Epac-SH187 or AKAP79-CUTie were treated with ISO or PGE2 and subsequently exposed to the PDE inhibitor, IBMX (Figure 3). By subtracting the mean FRET responses observed after each agonist from the mean FRET change subsequently generated on application of IBMX, it was possible to assess the contribution of PDEs in regulating the cAMP levels achieved on agonist application. It is important to note that in no case was the FRET sensor saturated after addition of IBMX. The data show that, in hTERT-HM cells, the PDEs played a significantly different role in regulating the cAMP response to the two agonists. Specifically, we found that, both in the bulk cytosol and at the plasmalemma, the cAMP response to ISO was only minimally constrained by the PDEs, whereas the signal generated by PGE2 was significantly attenuated by PDE-dependent hydrolysis of cAMP (Figure 3A). In contrast, in HPMCs, the extent to which the PDEs constrained the cAMP response appeared to cover a wide range, resulting in an average value that was largely comparable for the two stimuli and the two compartments (Figure 3B). Comparison of the kinetics of FRET change also showed differences in the cAMP response to ISO and PGE2 between cell types, and between the cytosol (Figure 4) and plasmalemma (Figure 5). In the cytosol, the cAMP signal measured over time in the continuous presence of an agonist shows a peak-plateau response with a relatively fast decline or, more often, an oscillating behaviour (Figure 4) that was particularly pronounced in HPMCs treated with PGE2 (Figure 4E,F). This oscillatory behaviour was never observed when the agonist was applied in combination with the PDE inhibitor IBMX (not shown). By contrast, the time course of the cAMP signal measured at the plasmalemma showed, in general, a sustained or slowly declining response (Figure 5). Given the relatively limited availability of HPMCs, cell expansion through consecutive passages in culture is a common practice [37,38,39], although the possibility that such a procedure may impact on cell phenotype is recognised [40,41]. To assess whether in vitro passaging of HPMCs affects cAMP signalling, we set up subcultures from passage 0 (P0) to passage 5 (P5). At each passage, HPMCs expressing the cytosolic or plasmalemma sensor were treated with 1 µM ISO or 30 nM PGE2, followed by IBMX and forskolin, and the FRET changes recorded. We found that from P0 to P1 there was a significant increase in the cAMP response to 1 μM ISO detected in the cytosol (Figure 6A). This enhanced cAMP response was then sustained across subsequent passages (Figure 6A). A similar trend was observed in the plasmalemma, although in this compartment the difference between P0 and later passages did not reach statistical significance (Figure 6B). By contrast, no significant difference was observed in the cAMP signals generated by 30 nM PGE2 at either compartment across the different passages (Figure 6C,D). To investigate whether the larger response to ISO with passage was due to reduced PDE activity, we applied IBMX to the cells treated with the two agonists. As in previous experiments, in no case was the FRET sensor saturated after addition of IBMX. As shown in Figure 7, the PDE contribution to the regulation of cytosolic cAMP signals generated by ISO declined with increasing passages, reaching statistical significance at P4 and P5 relative to P0. This was not the case at the plasmalemma, where the PDE activity was comparatively unchanged in subsequent passages (Figure 7B). For cells treated with PGE2, there was no significant difference in the relative contribution of PDEs across passages in either compartment (Figure 7C,D). To investigate the cause of the enhanced cAMP response to ISO with increasing passages, we examined the gene expression profiles of PDE type 4B, β2-AR and EP2 receptor from P0 to P4. We found that, with increasing number of passages, the level of PDE4B gene expression significantly decreased by P4 compared with P0 (Figure 8A). Consistently, a decrease in the PDE4B protein level was also observed by P4, although the difference did not reach statistical significance (Figure 8B). The mRNA level for β2-AR (ADRB2) did not change with passage (Figure 8D), although a trend towards a decrease in protein level was observed (Figure 8E). There were no significant changes in the expression of EP2 receptor mRNA (PTGER2) (Figure 8G) or protein levels (Figure 8H) from P0 to P4. To further explore the effects of subculture on the HPMC phenotype, the expression level of the labour-associated genes connexin-43 (Cx43) and oxytocin receptor (OTR), and of the scaffold protein AKAP79, were also examined. We found no changes in the gene expression (GJA1) or protein levels of connexin-43 from P0 to P4 (Figure S1), while we found a significant increase in OTR gene expression (OXTR) (Figure S2A). A significant increase in AKAP79 gene expression (AKAP5) was also observed with passage (Figure S2B). The protein levels, however, were significantly decreased by P4 (Figure S2C). A striking variability in the cAMP response to agonist application both in the cytosol and at the plasmalemma was observed when analysing FRET changes in HPMCs obtained from individual donors (Figure 9). We found that, on application of 1 µM ISO, the cells from a small number of patients generated FRET changes of greater than 60% of the maximal response, whilst cells from other patients produced a much lower cAMP response (Figure 9A,B). Although the variability was particularly pronounced in responses to 1 µM ISO, a similar effect was evident also in cells treated with 30 nM PGE2 (Figure 9C,D). Notably, the amplitude of the cAMP response did not appear to be congruous in the two compartments or dependent on the specific agonist across different donors, with larger responses in the cytosol than at the plasmalemma in some donors or vice versa. The precise cellular processes involved in the initiation of spontaneous human labour have still not been fully determined. Solving this challenge would reduce the devastating complications and adverse perinatal outcomes of PTL [4]. A complex crosstalk of hormonal, biochemical, electrical, and mechanical influences are understood to activate and stimulate the myometrium to establish uterine contractions. Myometrial cAMP signalling is upregulated during pregnancy, promoting uterine quiescence [6,8,12,42,43,44]. Altered expression in its central signalling components and a switch in effector activity, in combination with the modulation of specific pro-labour genes such as OTR, are considered to promote the fundamental switch from a relaxed uterine state to a contractile one [6,8,12,13,45,46]. FRET imaging is a highly accurate and sensitive technique used to investigate, in real time, the cAMP response in specific subcellular compartments in living cells with unparalleled resolution in space and time [47]. In this study, we successfully expressed FRET-based reporters targeted at distinct subcellular sites in both hTERT-HM cells and HPMCs and established, for the first time, real-time imaging of cAMP in these cell models. One objective was to determine if the hTERT-HM cells are a reliable model to investigate cAMP signalling in the myometrium in pregnancy. Our results demonstrate significant disparities between hTERT-HM cells and HPMCs in both the cAMP response to β-AR and EP receptor stimulation and in the regulation of the cAMP signal by PDEs. We found that ISO generates a significantly larger cAMP response both in the bulk cytosol and at the plasmalemma in hTERT-HM cells than in HPMCs. By contrast, activation of EP receptors elicits a significantly smaller response in hTERT-HM cells compared with HPMCs. Of the four EP receptors, EP2 and 4 couple with Gαs, stimulate AC and promote uterine quiescence through increased cAMP production [48,49], while EP1 and 3 are associated with smooth muscle contraction, as they are coupled with Gq/11 and GI, respectively, resulting in the activation of phospholipase C and the IP3/calcium pathway and inhibition of AC [50,51,52]. The hTERT-HM cell line was developed from non-pregnant myometrial tissue [53]. A study by Duckworth et al. examining the effects of butaprost, an EP2 receptor agonist, on both non-pregnant and pregnant myometrial tissue found that the inhibition of myometrial activity was greater in the pregnant tissue samples [54], suggesting that indeed the hTERT-HM cells may express lower levels of Gs-coupled EP2 receptors compared with the HPMCs used in this study, which were obtained from pregnant women. Another consideration is that non-pregnant tissue biopsies are usually obtained from the anterior wall of the uterine fundus whereas pregnant samples are obtained from the lower uterine segment. Studies investigating EP receptor dominance in tissue from these different locations found that non-pregnant upper segment samples expressed mainly EP1 and 3 receptors, as opposed to lower segment pregnant tissue samples, where EP2 was predominant [55,56]. The finding of a larger cAMP response to ISO in the hTERT-HM cells compared with HPMCs may also be partly explained by differences in -receptor expression. Studies showed that in non-pregnant myometrial tissue the expression of 2-AR was approximately 50% higher, both at the mRNA and protein level, than in pregnant myometrial tissue [35,57]. Consistently, in functional studies, salbutamol was found to be more effective in inhibiting non-pregnant spontaneously contracting tissue strips than pregnant samples [35]. Analysis of the contribution of PDEs in determining the amplitude of the cAMP response to ISO and PGE2 suggests an additional mechanism that may account for the difference in cAMP responses observed between the two cell types. We found that application of IBMX to hTERT-HM cells resulted in a significantly larger cAMP increase in the presence of PGE2 than in the presence of ISO, indicating that the level of second messenger achieved in this cell line in response to PGE2 stimulation was markedly limited by PDE-dependent degradation, whereas the cAMP response to ISO stimulation was only minimally constrained by PDE activity. Thus, in hTERT-HM cells, a stronger coupling of PDE activity with EP2 receptors than with β-AR can explain, at least in part, the different cAMP levels observed in response to the two agonists. By contrast, in HPMCs, the extent to which the cAMP hydrolytic activity contributed in determining the cAMP response triggered by ISO or PGE2 appeared to be, on average, comparable for the two agonists, suggesting that, rather than a consequence of reduced degradation of cAMP by PDEs, the significantly larger response to PGE2 than to ISO observed in HPMCs may be due to more robust cAMP synthesis, consistent with higher expression levels of the EP receptors relative to 2-ARs in the HPMCs compared with the hTERT-HM cells. Further studies, however, are needed to confirm this hypothesis. Another important finding of the current study was the striking individual donor variability in the cAMP response in HPMCs. As the hTERT-HM cell line was generated from an individual donor [53], it is plausible that the differences observed between primary cells and the cell line reflected this individual variability, as exemplified by donor D41, which showed a response to agonist stimulation that was similar to the hTERT-HM cells. Inter-patient variability was demonstrated in contractility studies evaluating different 2 stimulants as potential tocolytics in term pregnant myometrial tissue strips [58]. A wide range of inhibitory effects to isoproterenol were observed across samples, which were correlated to the 2 receptor density [58]. Story et al. also detected variations in the inhibitory responses to isoproterenol in the relaxation of contracting term pregnant myometrial tissues, with 3 out of 11 samples exhibiting little or no effect [59]. The explanation for this insensitivity was not investigated but the authors speculated that it could be due to variations in receptor density or their affinity for the 2 agonist, altered GPCR coupling to Gs/AC, or a reduction in AC activity [59]. Due to limited availability, there were differences in the number of cells analysed per donor. As a result, for certain donors, the n number was small, and this, to some extent, could have influenced the results on individual donor variability described above. However, significant variations were present also when comparing samples where we had the opportunity to image larger number of cells (e.g., compare D50 with D55, D57 and D58), supporting variation between individuals (see Figure 9 and Table S4). As mentioned above, a major limitation of using primary cells is their limited availability. In vitro expansion and re-plating is a commonly used procedure that allows an extended use of these cells, albeit at later passages after isolation. A study investigating the phenotype of HPMCs cultured for ten passages, from P1 to P10, found no difference in the structural morphology of the cells with passage [40] and no significant change in the total transcription of key smooth muscle markers, in the level of the labour-associated protein OTR, or in the response to inflammatory stimuli [40]. However, the study did not compare the cultured cells to freshly isolated HPMCs at P0, or to the tissue of origin. To date, the majority of studies have used HPMCs at lower passages, usually P4 or less [37,38,39]. Here we found that a significant increase in the response to ISO occurred from P0 to P1 in the bulk cytosol and, to a lesser extent, in the sub plasmalemma compartments, with substantially higher cAMP levels maintained at subsequent passages. One possible explanation for the larger cAMP signals produced to ISO with increasing passage could be due to an increase in -AR expression. The protein levels of the -AR, which is the predominant receptor subtype in term pregnant myometrium [60], were investigated with passage. Surprisingly, the opposite was seen in that there was a significant reduction in receptor expression from P0 to P4. The reasons for this decrease are unclear and further studies will be required to confirm and explain these findings. Despite a reduced protein expression, mRNA levels for -AR remained unchanged with passages. This observation could be explained by increased protein turnover or effects due to altered efficiency of mRNA translation. Our results, however, do indicate that the enhanced cAMP response to catecholamine stimulation resulted, at least in part, from the progressive decline in PDE4B expression, which we documented both at the mRNA and protein level. Interestingly, the reduced expression of PDE4B did not impact on the cAMP response to PGE2, indicating a preferential coupling of β-ARs with this PDE, and suggesting a compartmentalised regulation that resulted in a cAMP signal that was specific for each individual GPCR. The differential regulation of the cAMP signal generated by different agonists was also observed in hTERT-HM cells, where we found that the response to PGE2 was more tightly regulated by the hydrolytic activity of PDEs compared with the response to ISO. A second observation from this study confirmed compartmentalisation of cAMP in myometrial cells. We found a marked difference in the way the cAMP signal was handled in the bulk cytosol compared with the sub plasmalemma compartment. In the continuous presence of an agonist, the cAMP response was sustained in the sub plasmalemma compartment, whereas cAMP showed an oscillatory behaviour in the bulk cytosol, particularly in response to EP receptor activation. The cAMP oscillations appeared to be dependent on the activity of the PDEs. It should be noted that PDE4 isoforms are activated by PKA [61], and a plausible mechanism explaining the cAMP kinetics in the cytosol could involve the following steps: an increase in cAMP activates PKA which, in turn, phosphorylates and activates PDE4, resulting in cAMP hydrolysis; the PDE4 is then dephosphorylated, allowing the cAMP level to increase again and the cycle repeats, producing cAMP oscillations. Further investigations will be necessary to confirm this hypothesis and to establish the functional role of these cAMP oscillations and how they impact activation of downstream targets. This study presents several novel findings concerning cAMP signalling in the human myometrium. The successful use of targeted FRET reporters uncovered the existence of compartmentalised cAMP signals in both HPMCs and hTERT-HM cells at distinct subcellular sites. Using this technique, we uncovered complex differences between the cell models in the cAMP pools generated to agonist stimulation, their unique regulation by PDEs and differential signalling kinetics in the cytosol compared with the plasmalemma. Marked changes were also identified in HPMCs after subculture, both in terms of amplitude of the cAMP response to specific agonist stimulation and in the expression level of key signalling markers. Overall, these data provide new insights into cAMP signalling in the human myometrium and set the foundation for future studies to define the role of compartment-specific cAMP signalling in pregnancy.

PMC10001383

Sílvia Soares,Cláudia Pereira,André P. Sousa,Ana Catarina Oliveira,Maria Goreti Sales,Miguel A. Correa-Duarte,Susana G. Guerreiro,Rúben Fernandes

Metabolic Disruption of Gold Nanospheres, Nanostars and Nanorods in Human Metastatic Prostate Cancer Cells

02-03-2023

beta-oxidation,glycolysis,gluconeogenesis,gold nanoparticles,internalization,nanomedicine,reactive oxygen species

Nanomaterials offer a broad spectrum of applications in biomedicine. The shapes of gold nanoparticles could modulate tumor cell behavior. Spherical (AuNPsp), stars (AuNPst) and rods (AuNPr) shapes of polyethylene glycol coated-gold nanoparticles (AuNPs-PEG) were synthesized. Metabolic activity, cellular proliferation, and reactive oxygen species (ROS) were measured and the impact of AuNPs-PEG in metabolic enzymes function was evaluated by RT-qPCR in PC3, DU145, and LNCaP prostate cancer cells. All AuNPs were internalized, and the different morphologies of AuNPs showed to be an essential modulator of metabolic activity. For PC3 and DU145, the metabolic activity of AuNPs was found to rank in the following order from lowest to highest: AuNPsp-PEG, AuNPst-PEG, and AuNPr-PEG. Regarding LNCaP cells, the AuNPst-PEG were less toxic, followed by AuNPsp-PEG and AuNPr-PEG, but it seems not to be dose-dependent. The proliferation was lower in AuNPr-PEG in PC3 and DU145 cells but was stimulated around 10% in most conditions (0.001–0.1 mM) in LNCaP cells (not statistically significant). For 1 mM, LNCaP cells showed a significant decrease in proliferation only for AuNPr-PEG. The outcomes of the current study demonstrated that different AuNPs conformations influence cell behavior, and the correct size and shape must be chosen considering its final application in the field of nanomedicine.

Metabolic Disruption of Gold Nanospheres, Nanostars and Nanorods in Human Metastatic Prostate Cancer Cells Nanomaterials offer a broad spectrum of applications in biomedicine. The shapes of gold nanoparticles could modulate tumor cell behavior. Spherical (AuNPsp), stars (AuNPst) and rods (AuNPr) shapes of polyethylene glycol coated-gold nanoparticles (AuNPs-PEG) were synthesized. Metabolic activity, cellular proliferation, and reactive oxygen species (ROS) were measured and the impact of AuNPs-PEG in metabolic enzymes function was evaluated by RT-qPCR in PC3, DU145, and LNCaP prostate cancer cells. All AuNPs were internalized, and the different morphologies of AuNPs showed to be an essential modulator of metabolic activity. For PC3 and DU145, the metabolic activity of AuNPs was found to rank in the following order from lowest to highest: AuNPsp-PEG, AuNPst-PEG, and AuNPr-PEG. Regarding LNCaP cells, the AuNPst-PEG were less toxic, followed by AuNPsp-PEG and AuNPr-PEG, but it seems not to be dose-dependent. The proliferation was lower in AuNPr-PEG in PC3 and DU145 cells but was stimulated around 10% in most conditions (0.001–0.1 mM) in LNCaP cells (not statistically significant). For 1 mM, LNCaP cells showed a significant decrease in proliferation only for AuNPr-PEG. The outcomes of the current study demonstrated that different AuNPs conformations influence cell behavior, and the correct size and shape must be chosen considering its final application in the field of nanomedicine. Nanomaterials have shown great promise in the fight against cancer, due to their unique properties and ability to interact with biological systems at the nanoscale [1]. In recent years, researchers have been exploring the use of nanomaterials in various cancer therapies and diagnostic tools, intending to improve the effectiveness and specificity of these treatments. Different nanoparticles can be used, including organic material (lipids, proteins, or polymers), hybrid (nanofoams) or inorganic material, such as metals or salts—gold, silver, magnetic. Although the use of gold for medical applications has a long history, there is an increasing interest in gold nanoparticles (AuNPs) in bioimaging and therapy of cancer [2]. Gold is the most stable noble metal, biocompatible, and its surface can be easily functionalized with various biomolecules [3,4]. Besides their unique chemical, optical and physical properties, AuNPs are easy to synthesize in different sizes (from 1 to 100 nm) and shapes (spheres, rods, stars, among others). The impact of AuNPs size has been extensively studied in the literature, but little is known about the AuNP shape effect in vitro. The spheres gold nanoparticles (AuNPsp) are the most known in the literature compared to rod gold nanoparticles (AuNPr) and star gold nanoparticles (AuNPst) or other shapes [2,5,6]. AuNPs have size- and shape-dependent physical and chemical properties [7]. In vitro studies have shown that AuNPs influence cellular uptake, cell-crosstalk, cell biodistribution, and optical properties [3,8,9]. However, their mechanism of action remains to be unveiled. Polyethylene glycol (PEG) was used as a surface coat of AuNPs to improve the monodispersity and their biocompatibility, to escape from the immune system surveillance [10]. Nanotechnology has been a trending area in medical applications, such as cancer. Chemotherapy and radiotherapy (RT) are two therapeutic approaches used against tumor cells but exhibited some constraints related to toxicity and treatment resistance [11]. Prostate cancer (PCa) is the most frequently diagnosed non-skin cancer and the leading cause of cancer death in men [12]. Consequently, patients with advanced or metastatic PCa do not have immediate and effective therapeutic interventions, presenting a 5-year survival rate of only 30% [13]. In addition, some patients do not respond to therapy protocols. Therefore, it is crucial further investigate new therapeutic strategies to target PCa. So, to overcome the lack of knowledge regarding AuNPs in PCa, we used three different shapes of AuNPs-PEG (AuNPsp, AuNPst, and AuNPr) in three metastatic PCa cell lines with different origins (bone, brain, and lymph node). This study will enable us to understand the effects of AuNP shape in terms of their physical and biochemical characteristics in pathological conditions. To our knowledge, this study is the first to evaluate the effect of these three AuNPs in PCa metastatic cell behavior. The results will clarify which nanostructure(s) is the most suitable for metastatic PCa treatment. Thiol-polyethylene glycol-amine (SH-PEG-NH2), molecular weight 2 kDa, trisodium citrate dehydrate (C6H5O7Na3·2H2O or NaCt), tetrachloroauric acid tetrahydrate (HAuCl4·4H2O, 99.99%), silver nitrate (AgNO4), sodium borohydride (NaBH4), hexadecyltrimethylammonium bromide (CTAB, ≥99%), L-ascorbic acid, ≥99%, fetal bovine serum (FBS), phosphate-buffered saline (PBS), trypsin-EDTA, and Antibiotic antimycotic solution from Sigma Aldrich®® LLC., St. Louis, MO, USA; Roswell Park Memorial Institute (RPMI-1640) and Minimum Essential Medium (MEM) media were purchased from Biowest (Nuaillé, France); cell proliferation ELISA, BrdU kit and PrestoBlue®® Cell Viability Reagent (PB) was obtained from Roche (Indianapolis, IN, USA) and Invitrogen Co. (San Diego, CA, USA) respectively. 2′,7′-Dichlorodihydrofluorescein diacetate (H2DCFDA) was acquired in Biotium (Hayward, CA, USA), QIAzol lysis reagent was purchased in QIAGEN Inc. (Valencia, CA, USA), EasyScript®® Reverse transcriptase in Transgen Biotech Co., LTD. (Beijing, China) and RT-qPCR NZYSpeedy qPCR probe kit in NZYTech (Lisbon, Portugal). AuNPsp were prepared according to Turkevich method and co-workers’ protocol using a HAuCl4·4H2O solution that is reduced and stabilized by trisodium citrate (NaCt) as illustrated in Figure 1A [14]. One hundred mL of 0.5 mM of HAuCl4·4H2O solution was prepared with ultrapure water in a triple-neck round-bottom flask and heated under vigorous stirring at 100 °C. Subsequently, 10 mL of 1% NaCt (w/v) was mixed with HAuCl4·4H2O solution. The solution was maintained for 15 min under heat until the red-wine colour was obtained. After, turn off the temperature and allow the solution to cool. After cooling, 1 mg/mL SH-PEG-NH2 was added to the AuNPsp solution and incubated at 4 °C overnight. Then, AuNPsp-PEG were purified by centrifugation at 4500 rpm for 40 min, and the pellets were resuspended in ultrapure water and stored at 4 °C. AuNPst were prepared according to the reported protocol of Tian and colleagues [15] as in Figure 1B. AuNPst were first synthesized using a seed solution obtained by adding 3 mL of 1% NaCt (w/v) to 100 mL of 1.0 mM HAuCL4. Then, 100 µL of seed solution was added to 10 mL of 0.25 mM HAuCl4 at room temperature. Forty µL of 0.01 M AgNO3 and 50 µL of 0.1 M L-ascorbic acid were added. To coat PEG on the AuNPst surfaces, 20 µL of SH-PWG-NH2 was added. The AuNPst-PEG were collated by centrifugation at 5200 rpm and redispersed in water. First, AuNPr seeds were prepared by mixing 25 µL of 50 mM HAuCl4 with 4.7 mL of 0.1 M CTAB solution in a water bath at 27–30 °C (Figure 1C). Next, 300 µL of 10 mM NaBH4 solution was added to the previous solution under constant stirring. To synthesize AuNPs, a seed growth solution was prepared based on Scarabelli and co-workers [16]. Ten mL of 100 mM CTAB were incubated with 100 µL of 50 mM HAuCl4 under gentle stirring. Then, 75 µL of 100 mM L-ascorbic acid was added to the mixture for a few seconds. Eighty µL of 5 mM of AgNO3 was added to the growth solution for a few seconds. Finally, 120 µL of seeds solution was added to the previous mixture and left undisturbed at 27 °C for 30 min. To remove the excess solution reagents, AuNPr was centrifugated twice at 7500 rpm for 30 min. The next step was PEGylation by adding 0.2 mM of SH-PEG-NH2 to the AuNPr solution. After stirring for 24 h, the solution was washed twice at 7500 rpm for 30 min. The UV-Visible (UV-Vis) absorption spectra of different solutions were measured in a 1 mm quartz cuvette at room temperature using an Evolution 200 Series spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The absorption values were used to determine the concentration of particles in the solution. The size and morphology of the samples were investigated using transmission electron microscopy (TEM). Ten µL of each sample was mounted on Formvar/carbon film-coated mesh nickel grids (Electron Microscopy Sciences, Hatfield, PA, USA). For experiments with PEG, prepared samples were contrasted with 10 µL of phosphotungstic acid (PTA) and placed on the grid. After, grids were observed in a JEM 1400 TEM (JOEL Ltd., Tokyo, Japan) with an accelerating voltage of 80 kV. Images were digitally recorded using a CCD digital camera Orious 1100 W Tokyo, Japan, and analysed using ImageJ software to create a size histogram based on representative images obtained. A scanning electron microscope (SEM) was used to confirm nanoparticle production and examine nanoparticle morphology. Ten µL of samples were deposited onto silicon wafers and left undisturbed until evaporating the solvent at room temperature. SEM images were acquired using a FEI Quanta 400 FEG ESEM/EDAX PEGASUS X4M equipment. Also, nanoparticles’ hydrodynamic diameter and zeta potential were measured by dynamic light scattering (DLS) using a Zeta Sizer Malvern Nano series (Malvern Instruments Ltd., Malvern, UK). All average particle sizes reported here are based on scattered light intensity weighted averages. Five DLS measurements were made for each sample suspension with a fixed run time of 30 s. The scattering/detection angle was set at 173°. The PCa cell lines used in this study were PC3 (ATCC®® CRL-1435™), LNCaP (ATCC®® CRL-1740™), and DU145 (ATCC®® HTB-81™) cells. PC3 and LNCaP cells used RPMI-1640 medium, and DU145 cells used MEM medium. Culture media were supplemented with 10% FBS and 1% penicillin/streptomycin [17,18,19]. Cells were maintained in culture at 37 °C with 5% CO2. For these experiments, cells were used between 8 and 15 passages. Cell lines were cultured and grown to ~80% confluence and sub-cultured for different assays. Cells (1 × 105 cells/well) were cultured in 96-well plates (VWR) for 24 h. Then, cells were washed with PBS and treated with AuNPs for 24 h at 37 °C with 5% CO2 in a humidified environment. Different concentrations of AuNPsp, AuNPst and AuNPr ranging from 0 to 1 mM were prepared in serum-free conditions. To evaluate the cellular uptake of different concentrations of AuNPs using TEM images and flow cytometry. Cells were treated with different AuNPs solutions and incubated for 24 h. Then, the cells were washed, trypsinized and resuspended in TEM fix solution (2.5% glutaraldehyde and 2% paraformaldehyde in 0.1 M sodium cacodylate) for three days. After, the fix solution was removed, and cells were washed in 0.1 M sodium cacodylate buffer. Next, a post-fix solution (2% osmium tetroxide in 0.1 M sodium cacodylate) was added to the samples. After 2 h, the samples were washed and centrifuged three times in water. Then, they were incubated with 1% Uranyl acetate for 30 min. The pellet was then included in HistogelTM (Thermo Fisher Scientific, Waltham, MA, USA, HG-4000-012). Finally, the samples were dehydrated in a graded series of ethanol solutions (50%, 70%, 80%, and 100%) and treated with propylene oxide (3×). Ultrathin sections of the samples were cut and observed with a JEM 1400 TEM (JOEL Ltd., Tokyo, Japan) with a CCD digital camera Orious 1100 W Tokyo, Japan. Then, the intracellular location of the AuNPs was analysed. Regarding flow cytometry, cells (1 × 106) were plated in 6-well and treated with each AuNPs solutions. The next day, the solution was removed, the cells were washed, and then the cells were collected using trypsin. Cells were examined using ATTUNE flow cytometer (Thermo Fisher Scientific, Waltham, MA, USA). Viable cells can metabolize resazurin into resofurin on mitochondria [20]. Cells were incubated with AuNP treatments for 24 h. Afterwards, 10 µL of resazurin was added directly into 90 µL of culture medium. Upon incubation for 1 h at 37 °C, 100 μL/well was transferred to a new 96-well plate. The absorbance was measured using a Spectra Max Gemini XS (Molecular Devices, San Jose, CA, USA) at excitation and emission wavelengths of 550 and 600 nm, respectively. After 24 h of treatment, the cells were incubated with BrdU solution at a fifinal concentration of 100 µM for 2 h. The cell proliferation assay was performed according to the manufacturer’s instructions [21]. The results were expressed as a percentage of control (100%) and tested in duplicates on two independent experiments. Molecular probe 2′,7′-Dichlorodihydrofluorescein diacetate (H2DCFDA) assay was dissolved in dimethyl sulfoxide (DMSO) at 10 mM stock solution. After plating cells, adherent cells were washed with buffer and stained with a 10 µM probe for 45 min at 37 °C in the dark. Next, cells were rewashed and were treated with AuNPs for 24 h. Cells were then analysed on a fluorescence plate reader (SpectraMax®® Gemini™ EM Microplate Reader, Molecular Devices, San Jose, CA, USA) at excitation/emission of 504/529 nm in endpoint mode. Cells (4–8 × 105 cells/well) were seeded in 6-well culture plates and grown overnight. Then, cells were treated with different concentrations of 0.1 mM of AuNPs for 24 h. Total RNA was isolated from different types of samples followed QIAzol (Qiagen, Crawley, UK). The amount of DNA and RNA was determined using a Thermo Scientific™ Multiskan SkyHigh Microplate spectrophotometer (Life Technologies Fisher Scientific, Waltham, MA, USA). The ratio of absorbance at 260 nm and 280 nm was used to assess the purity of DNA and RNA. RNA was reversely transcribed using EasyScript®® Reverse transcriptase (Transgen biotech, Beijing, China) and following manufacturer recommendations. RNA was subjected to RT-qPCR (NZYSpeedy qPCR probe kit, NZYTech, Lisbon, Portugal) using primer sets specific to hexokinase-2 (HK2), glucose-6-phosphatase (G6Pase), pyruvate kinase (PKM), pyruvate carboxylase (PCX), acyl-CoA dehydrogenase (ACADS) and mitochondrial fission 1 protein (FIS1, Table 1). Threshold cycle (CT) values from each sample were plotted with two experimental replicates following the manufacturer’s procedure. The melting curve analysis was used to monitor the specificity of primers and probes. The expression level of each gene was normalized to the expression of the GAPDH housekeeping gene, and gene relative expression was employed by the ΔCT expression/ΔCT control ratio. All data are presented in mean ± standard deviation (SD) of experiments repeated at least three times. Data were analysed through Prism 8.0 (GraphPad Software, Boston, CA, USA). Differences between treatments were evaluated by two-way ANOVA with Sidak multiple comparisons test, according to the number of conditions and treatments. Results were considered significant when p < 0.05. AuNPs with different conformations (AuNPsp, AuNPst, AuNPr) were used to compare their chemical, physical and biological effects. All AuNP conformations were functionalized with PEG to improve cellular uptake and overcome the immune system as described in the literature. AuNPs exhibited different surface plasmon resonance (SPR) bands in UV–Vis absorption spectra over 400–1000 nm, as shown in Figure 2D–F. AuNPs’ size and shape were observed by TEM and SEM analyses, respectively—Figure 2G–L. The average size for AuNPsp-PEG was 18.4 ± 2.1 nm, and a SPR peak was about ~522.3 nm. For AuNPst-PEG, the average size was 80.7 ± 18.9 nm, and a broad plasmon band mainly ranging from 480 nm to 1000 nm with a maximum at ~906.3 nm was observed. AuNPr-PEG were synthesized using the seed-mediated method to obtain 45.4 ± 4.5 nm × 11.6 ± 1.2 nm (length × width) by TEM (with an aspect ratio of around 3.9:1) and exhibit a dominant longitudinal SPR peak of ~763.6 nm and a minor transverse peak at ~513.6 nm. From the UV-Vis spectra, TEM, and SEM images, AuNPsp-PEG, AuNPst-PEG, and AuNPr -PEG had spherical, star and rod structures matching their designs. Finally, a histogram size was created using TEM images where over 50 particles were counted—Figure 2M–O. From DLS (Table 2), the average size for AuNPsp-PEG was about 47.31 ± 0.46 nm, AuNPst-PEG was 109.61 ± 1.27 nm, and AuNPr -PEG was 54.58 ± 0.34 nm. These AuNPs hydrodynamic size values were different from the ones obtained in TEM analysis, because on DLS the PEG chains layer was hydrated on the surface of nanoparticles [22]. According to the polydispersity index (PDI) of AuNPs, the AuNPst-PEG exhibited more monodispersity than AuNPsp-PEG and AuNPr-PEG. In addition, the zeta potential measurement demonstrated that AuNPs were successfully conjugated with PEG and all nanostructures were positively charged. AuNPsp-PEG, AuNPst-PEG, and AuNPr-PEG indicated a zeta potential of 6.7 ± 7.9, 33.1 ± 12.0, and 11.0 ± 18.9 mV, respectively. Cellular uptake of AuNPs-PEG involves highly regulated mechanisms with biomolecular interactions: shape, size, and capping dependents [23]. Also, AuNPs have multiple different cellular entry routes to cross the cell plasma membrane, including passive translocation across the cell membrane or through active endocytosis [23,24,25]. In the present study, cells were treated for 24 h with different structures of AuNPs at a concentration of 0.01 mM prior to TEM analysis to investigate cellular internalization. We performed a qualitative analysis of the cellular uptake of AuNPs using TEM images, and they revealed numerous high electron density-staining particles inside the cells incubated with AuNPs (Figure 3). AuNPs-PEG were not found in control groups (Figure 3A–C), whilst an interesting morphological phenomenon was found in treated groups. The three metastatic cell lines internalized the AuNPsp-PEG, AuNPst-PEG and AuNPr-PEG. TEM images showed AuNPs clusters distributed across the cytoplasm. Most AuNPs-PEG are trapped inside the endosome’s vesicles, most of which are in the proximity of mitochondria and the endoplasmic reticulum. The cell nuclei do not seem to be affected by AuNPs-PEG. TEM data demonstrated the cellular uptake of AuNPs in the three cell lines. Qualitatively, AuNPst-PEG appears to be more extensively accumulated than AuNPsp-PEG and AuNPr-PEG. Another complementary analysis was performed by flow cytometry using the forward-scattered light (FSC), proportional to the cell size and the side-scattered light (SSC) related to cell’s internal complexity. Results showed that after 24 h of incubation with AuNPs-PEG (Figure 4), the uptake was higher in case of AuNPsp-PEG following AuNPst-PEG and AuNPr-PEG in all cell lines. However, for DU145 cells, only some minor changes were found in AuNPsp-PEG and AuNPst-PEG. For LNCaP, modifications on complexity were identified only for AuNPsp-PEG. A broad spectrum of particle concentrations was tested to investigate the biological effect of AuNPs-PEG on cell viability of metastatic PCa cell lines—Figure 5A–C [26,27,28]. After 24 h treatments, all metastatic cell lines showed a reduction of cell viability compared to the control (cells without AuNP treatment). The results demonstrated that the cellular viability is independent of AuNPs concentration. PC3 and DU145 cells viability was between 50–100% compared to control upon treatment with 0.001 to 1 mM of AuNPsp-PEG or AuNPst-PEG. When treated with 0.001 to 0.1 mM of AuNPr-PEG, PC3 and DU145 cells viability was 70–80%. However, 1 mM AuNPr-PEG treatment revealed a higher decrease in cellular viability on PC3 and DU145 cell lines (52.5% and 52.9%, respectively for PC3 and DU145, p < 0.05). In the case of LNCaP, all treatments of AuNPs-PEG with different concentrations decreased the cellular viability. The cellular proliferation was performed using the BrdU cell assay—Figure 5D–F. When PC3 and Du145 cells were treated with 0.001–0.1 mM AuNPs-PEG concentrations of each shape, cell proliferation rate decreased compared to controls. Contrariwise, on LNCaP cells, the same treatments of AuNPs-PEG did not reveal a statistically significant difference in cell proliferation after 1 mM of AuNPr-PEG treatment (p < 0.001). TEM analysis has shown that all shapes of AuNPs-PEG can be internalized by PC3, DU145 and LNCaP cells and created ultrastructure changes. An increase in vacuolization and numerous autophagic vacuoles in the three cell lines were observed by TEM (Figure 3). Cells were treated with 0.1 mM of different shapes of AuNPs-PEG for 24 h, and then ROS levels were observed (Figure 6). AuNPsp-PEG decrease ROS levels when compared to control group in PC3 and DU145 cells. Remarkably, treatment with 0.1 mM of AuNPr-PEG only decreased ROS levels on DU145 cells. In LNCaP cells, the treatments did not alter the ROS levels when compared to the control group (p > 0.05). Changes in metabolic function can contribute to the growth and progression of PCa. Understanding these changes in metabolic function may provide new targets for the development of PCa therapies. So, the impact of different AuNPs-PEG in the expression of enzymes involved in metabolic pathways, such as HK2, G6Pase, PKM, PCX, and ACADS was evaluated (Figure 7 and Figure 8). Besides, mitochondria are highly dynamic organelles in cancer biology and are a crucial player on the altered cancer energy metabolism. To investigate the effect of AuNPs-PEG treatment on cancer cell energy metabolism, FIS1 mRNA levels, a critical checkpoint for mitochondria division involved in the genetic regulation of several metabolic pathways, such us, glycolysis, gluconeogenesis, and beta-oxidation was determined (Figure 7 and Figure 8). PC3 cells treated with AuNPsp-PEG and AuNPr-PEG presented an increase of mRNA expression of HK2 and a decrease of PKM, involved in the first and the last step of glycolysis, respectively. On the other hand, DU145 cells and LNCaP cells did not have statistically significant differences in these transcripts. Gluconeogenesis is another metabolic pathway that fully occurs in hepatocytes. All three cell lines express PCX and G5Pase mRNA, encoding the first and final gluconeogenesis step. PC3 cells treated with AuNPsp-PEG and AuNPr-PEG presented increased mRNA expression of these two mRNA enzymes. DU145 and LNCaP cells did not have statistically significant differences in gluconeogenesis mRNA expression genes upon ant treatment. Fatty acids and glucose can be used by the cells as energy sources through beta-oxidation and glycolysis pathways, respectively, resulting in acetyl-CoA. If acetyl-CoA increases, FIS1 ubiquitination can occur, decreasing mitochondria fission. On PC3 cells treated with AuNPsp-PEG and AuNPr-PEG, an increase of ACADS and FIS1 mRNA expression was determined. DU145 cells did not show statistically significant differences regarding enzymes expression for any treatment. However, a tendency to increased FIS1 was observed after AuNPr-PEG treatment. AuNPsp-PEG and AuNPst-PEG treatment increased the expression of ACADS mRNA in LNCAPs. No statistically significant differences were observed for FIS1 gene expression. Distinct methods were used to characterize the mean size of AuNPs-PEG, like TEM and DLS. The shape of AuNPs were confirmed by UV-Vis spectra, TEM and SEM image analysis. Considering particle size, data obtained from DLS measurement are usually bigger than those obtained from TEM due to the presence of the PEG chain and the layer hydration around the AuNPs solution [29,30]. Our synthesis process is in accordance with the literature, by the applied synthesis methods [14,15,16]. In our case, it was possible to characterize the three AuNPs-PEG with DLS. Still, by applying other techniques, such as depolarized dynamic light scattering (DDLS), it is possible to obtain results for specific anisotropic nanoparticles more similar to TEM results [31]. Regarding AuNPr-PEG, DLS measurements can provide a reasonably hydrodynamic diameter, which can be related to the length of AuNPr-PEG [32]. Regarding the shape, AuNPsp-PEG presented only one peak, AuNPr-PEG showed two peaks, and AuNPst-PEG exhibited a broad absorption band, which can be derived from the high density of surface spikes [22]. So, UV-vis showed different absorption patterns depending on the geometries, which agrees with the literature [26,33]. Additionally, the different morphologies were confirmed by TEM and SEM images. The validation of AuNPs-PEG was confirmed by positive values in zeta potential, increasing the stability of nanostructures, mainly AuNPst-PEG. The surface of AuNPs can be modified with several materials. Still, PEG is one of the biocompatible polymers most used in biomedicine because it improves the stability, internalization, and absorption of the AuNPs inside the cell. Besides, PEG contributes to reduced immunogenicity and elimination by clearance of AuNPs, increasing their circulation time in blood [28,34]. Also, PEG reduced the toxicity of AuNPs and improved their accumulation in tumor cells via the enhanced permeability and retention (EPR) effect [35,36,37]. Furthermore, Fytianos, et al. demonstrated that the cellular uptake of AuNPs modified with PEG-NH2 was higher than other functionalized surfaces, such as carboxylic acid—PEG-COOH [38]. Only a few publications analyzed the shape of AuNPs-PEG as an essential modulator of cytotoxicity, although extensive knowledge about AuNP’s cytotoxicity has been gathered. Our study allows evaluating at the same time different shapes of AuNPs-PEG using a concentration range to treat three metastatic cell lines of PCa [39]. These cell lines, PC3, DU145 and LNCaP, originated from different metastases of PCa, bone, brain, and supraclavicular lymph node, respectively [40]. Also, the LNCaP cell line is responsive to androgen and produces prostate-specific antigens (PSA). DU145 and PC3 cell lines are androgen-independent and have moderate and high metastatic potential, respectively [41,42,43]. Thus, analyzing different cell lines with other features, such as aggressiveness and hormonal dependence, provide a holistic overview of a wide range of PCa [42]. The uptake of different conformations of AuNPs-PEG by these three cell lines was analysed. Cells were treated with 0.01 mM AuNPs-PEG for 24 h. TEM findings revealed that all shapes of AuNPs-PEG suffered endocytosis in PC3, DU145, and LNCaP cells. We confirmed that AuNPs-PEG might be internalized by endosomes and vesicular bodies into PCa cells, as previously described [33,44,45]. AuNPst-PEG is the more captured nanoparticles by cells, appearing in clusters in all cell lines studied. They were detected in vesicles after 24 h of incubation. Remarkably, AuNPsp-PEG and AuNPr-PEG were found in sections after 24 h of incubation in all cell lines, but in less amount than AuNPst-PEG. It was demonstrated that citrate AuNPsp has a better internalization capacity when compared with AuNPr stabilized by citric acid ligands because AuNPsp has less contact area with cell membrane receptors, increasing the number of NPs that can be internalized in Hela cells [9]. Similarly, Lee and co-workers compared chitosan-capped AuNPsp, AuNPst, and AuNPr synthesized using green tea extract and concluded that AuNPsp exhibited the fastest internalization rate than other shapes (AuNPsp > AuNPr > AuNPst) and lower toxicity in human hepatocyte carcinoma cells HepG2 [8]. However, to better understand the shape effect of the AuNPs on cell interaction, more studies should be developed to contribute to more efficient therapeutic nanosystems, reducing the therapeutic resistance related to conventional treatments. In addition, our results showed a tendency to decrease the metabolic activity with increased concentration in AuNPsp-PEG, AuNPst-PEG, and AuNPr-PEG. Also, AuNPr-PEG showed a more significant decrease in metabolic activity than AuNPsp-PEG and AuNPst-PEG. The results are comparable to other outcomes of cytotoxicity in a similar range of concentrations, and the 0.1 mM concentration seems to be the safe dose of AuNPs-PEG [46,47]. LNCaP cells were not so sensitive, slightly reducing the viability and enhancing cell proliferation at the highest concentration compared to the other cell lines. This result can be due to their low growth rate observed by us and others [18]. In general, this study demonstrated that distinct morphologies have different cellular metabolic effects that can be caused by two factors—size or shape. Besides that, the results also suggest that AuNPs-PEG influence mitochondria functioning because using PrestoBlue®® assay showed their cytotoxicity. Moreover, it is known that cell cytotoxicity of AuNPs depends on the concentration used and the duration of the treatment [33]. Our findings indicated that cells respond in different manners to AuNPs treatment. Additionally, TEM images exhibited a loss of integrity of cellular membranes and morphological differences of mitochondria, showing a higher number of mitochondria and more condensed ones. Moreover, disruption of the cell membrane, oxidative stress, cytoskeleton destruction, autophagy, and lysosomal dysfunction are essential functions and potential explanations for the cytotoxicity of AuNPs [8]. More studies should be done to analyze the detailed mechanisms of the cytotoxicity effect. Ultimately, the decreased metabolic activity is likely related to the harmful effect of aggregates, as suggested by Connor et al. [48]. Based on the literature and as we mentioned before, metabolic activity can be influenced by several factors that difficult the comparison between studies, such as shape, size, physicochemical surface properties, concentration, exposure time, cell type, experimental design and implementation, and analytical methods, because of variety of bioapplication of AuNPs [43,49]. According to our knowledge, it is the first study comparing the cytotoxicity of different morphologies of AuNPs-PEG in three distinct metastatic PCa cell lines. Nevertheless, Favi et al. showed that AuNPsp (61.46 ± 4.28 nm) were more cytotoxic than AuNPst (33.69 ± 8.45 nm) in human skin fibroblasts and fat rat pad endothelial cells (RFPECs) [50]. Also, another study compared AuNPsp (~61.06 nm), and AuNPr (534 nm × 65 nm) negatively charged and concluded that AuNPsp presented more significant toxicity than AuNPr in fibroblast cells [51]. Tarantola and co-workers showed that AuNPsp (43 ± 4 nm) was more cytotoxic than AuNPr (38 ± 7 nm × 17 ± 3 nm) with identical surface functionalization with CTAB in MDCK II cells, and the authors related the cytotoxicity to a higher release of toxic CTAB upon intracellular aggregation [45]. Woźniak et al. compared different AuNPs size and shapes (AuNPsp, ~10 nm, nanoflowers, ~370 nm, AuNPr, ~41 nm, nanoprisms, ~160 nm, and AuNPst, ~240 nm) in both HeLa and human embryonic kidney cells (HEK293T) cell lines and showed that AuNPsp and AuNPr were more cytotoxic than nanoflowers, nanoprisms, and AuNPst [33]. One more time, the authors suggested that the tiny size of AuNPs and the aggregation process can influence the cytotoxicity of AuNPsp and AuNPr. More recently, Steckiewicz et al. compared the cytotoxicity of AuNPr (~39 nm × 18 nm), AuNst (~215 nm), and AuNPsp (~6.3 nm) in human fetal osteoblast (hFOB 1.19), osteosarcoma (143B, MG63) and pancreatic duct cell (hTERT-HPNE) lines. They showed that the cytotoxicity of AuNPs was shape-dependent, and AuNPst were the most cytotoxic against human cells, followed by AuNPr and AuNPsp [44]. Besides the biosafety and toxicity of AuNPs, there is a gap regarding the molecular mechanisms and factors that influence nanomaterial toxicity. Researchers have found that AuNPs could affect the expression of intracellular metabolites and consequently change the expression of the functional genome, transcriptome, and proteome [52,53,54]. Thus, metabolic reprogramming of tumor cells has emerged as a new therapeutic strategy. After the Warburg effect, where oxidative phosphorylation in proliferative cells was switched to glycolysis even in aerobic conditions, the metabolic changes in tumor cells began to be explored [55]. The sensitivity of tumor cells reveals different sensitivities to various molecules related to gluconeogenesis, glycolysis, or fatty acid synthesis pathway [56]. Although some studies explored the effect of AuNPs on tumor cell metabolism, is still a lot to uncover [57,58,59]. PC3 cells treated with AuNPsp-PEG and AuNPr-PEG presented increased gene expression involved in cell replication (Figure 7 and Figure 8). AuNPst-PEG triggers a global reduction in cellular metabolism and activity. DU145 cells treated with AuNPsp-PEG and AuNPst-PEG inactivate the whole central cell metabolism, as reflected in the decrease in cell viability, glycolytic pathways, oxidation of fatty acids and mitochondrial replication. Cells treated with AuNPr-PEG also showed increased mRNA expression of most enzymes implicated in energy metabolism. In LNCaP cells, AuNPsp-PEG prompted the reduction of gluconeogenesis enzymes and glycolytic enzyme HK2. However, there is an increased expression of beta-oxidation ACADS enzyme and an increase in PKM expression, resulting in increased acetyl-CoA concentrations that enter the TCA cycle. There was also a reduction in FIS1 mRNA, implying mitochondrial metabolic activity reduction. Treatment with AuNPst-PEG resulted in the upregulation of enzymes involved in glycolysis, beta-oxidation, and gluconeogenesis, suggesting the induction of energy metabolism and anabolic pathways required for proliferative cell activity. Furthermore, treatment with AuNPr-PEG led to FIS1 gene downregulation. Given that FIS1 is involved in mitochondrial replication, these findings led to the assumption that AuNPr-PEG induces cell metabolism inactivation. Additionally, AuNPr-PEG presented a slight stimulation of the first step of glycolysis and an inhibition of beta-oxidation. In general, AuNPsp-PEG and AuNPr-PEG tend to increase the expression of enzymes involved in glycolysis, such as HK2 and PKM in PC3 and LNCaP cells, suggesting they play a role in supporting cancer cell survival. Also, AuNPs slightly increased G6Pase in the PC3 cell line. It can be hypothesized that AuNPs may promote NADPH production, which plays a role in reductive synthesis (e.g., lipogenesis and cholesterol) and is a key regulator of the antioxidant defense. Overall, the effect of AuNPs on the expression of metabolic enzymes is complex and context dependent. While AuNPs may disrupt the energy production and biosynthesis pathways in cancer cells, they may also promote the production of NADPH and support cancer cells’ survival. Further studies are needed to fully understand the mechanisms behind the effects of AuNPs on metabolic enzymes and their potential implications for cancer therapy. Clinical development of treatments or therapeutic agents is essential to support an optimal management strategy for this challenging disease, the PCa. Until now, this was the first study to compare the cytotoxicity of different morphologies of AuNPs and to evaluate the effect of different AuNPs-PEG on cellular metabolic enzyme levels in three distinct metastatic PCa cell lines. The analysis of cellular metabolism should be considered to ensure safety is preserved whenever AuNPs are applied in the clinic. This study demonstrated that distinct morphologies of AuNPs influenced the metabolic activity in these three cell lines evaluated, being a potential modulator of cell viability, proliferation, and metabolic enzymes. Also, our study showed that AuNPs are concentration-dependent and cell-type-dependent. For PC3 and DU145, AuNPsp-PEG were less toxic, followed by AuNPst-PEG and AuNPr-PEG. We observed that for LNCaP cells, the AuNPst-PEG were the less toxic, followed by AuNPr-PEG and AuNPsp-PEG. In general, the AuNPr seem to be dose-dependent and the most efficient shape to destroy these two types of tumour cells with statistically significant results. Additional studies must be performed to properly quantify the cellular uptake efficiency of AuNPs and understand the effect of size and shape singly. After evaluating the effect of AuNPs on cell metabolism, AuNPsp showed opposite results between PC3 and DU145. We believe that the surface markers activated in each cell line differ due to the different membrane compositions. Regarding the effect of AuNPst-PEG and AuNPr-PEG, they seem to cause similar responses in more aggressive lines (PC3 and DU145) and to inactivate cell metabolism in more sensitive lines, such as LNCaP. So, this diverse response observed may be related to the different cell line characteristics, namely expressed markers on the membrane, and androgen receptor dependence, among others. However, more studies should be done to understand the mechanisms behind these differences.

PMC10001385

Weerachai Chantana,Rentong Hu,Songphon Buddhasiri,Parameth Thiennimitr,Payungsak Tantipaiboonwong,Teera Chewonarin

The Extract of Perilla frutescens Seed Residue Attenuated the Progression of Aberrant Crypt Foci in Rat Colon by Reducing Inflammatory Processes and Altered Gut Microbiota

26-02-2023

aberrant crypt foci,colon cancer prevention,inflammation,Perilla frutescens seed residue

Perilla frutescens (PF) seed residue is a waste from perilla oil production that still contains nutrients and phytochemicals. This study aimed to investigate the chemoprotective action of PF seed residue crude ethanolic extract (PCE) on the inflammatory-induced promotion stage of rat colon carcinogenesis and cell culture models. PCE 0.1 and 1 g/kg body weight were administered by oral gavage to rats after receiving dimethylhydrazine (DMH) with one week of dextran sulfate sodium (DSS) supplementation. PCE at high dose exhibited a reduction in aberrant crypt foci (ACF) number (66.46%) and decreased pro-inflammatory cytokines compared to the DMH + DSS group (p < 0.01). Additionally, PCE could either modulate the inflammation induced in murine macrophage cells by bacterial toxins or suppress the proliferation of cancer cell lines, which was induced by the inflammatory process. These results demonstrate that the active components in PF seed residue showed a preventive effect on the aberrant colonic epithelial cell progression by modulating inflammatory microenvironments from the infiltrated macrophage or inflammatory response of aberrant cells. Moreover, consumption of PCE could alter rat microbiota, which might be related to health benefits. However, the mechanisms of PCE on the microbiota, which are related to inflammation and inflammatory-induced colon cancer progression, need to be further investigated.

The Extract of Perilla frutescens Seed Residue Attenuated the Progression of Aberrant Crypt Foci in Rat Colon by Reducing Inflammatory Processes and Altered Gut Microbiota Perilla frutescens (PF) seed residue is a waste from perilla oil production that still contains nutrients and phytochemicals. This study aimed to investigate the chemoprotective action of PF seed residue crude ethanolic extract (PCE) on the inflammatory-induced promotion stage of rat colon carcinogenesis and cell culture models. PCE 0.1 and 1 g/kg body weight were administered by oral gavage to rats after receiving dimethylhydrazine (DMH) with one week of dextran sulfate sodium (DSS) supplementation. PCE at high dose exhibited a reduction in aberrant crypt foci (ACF) number (66.46%) and decreased pro-inflammatory cytokines compared to the DMH + DSS group (p < 0.01). Additionally, PCE could either modulate the inflammation induced in murine macrophage cells by bacterial toxins or suppress the proliferation of cancer cell lines, which was induced by the inflammatory process. These results demonstrate that the active components in PF seed residue showed a preventive effect on the aberrant colonic epithelial cell progression by modulating inflammatory microenvironments from the infiltrated macrophage or inflammatory response of aberrant cells. Moreover, consumption of PCE could alter rat microbiota, which might be related to health benefits. However, the mechanisms of PCE on the microbiota, which are related to inflammation and inflammatory-induced colon cancer progression, need to be further investigated. An inflammatory condition is a significant contributor to colon carcinogenesis’s onset and promotion [1]. Patients with inflammatory bowel disease (IBD) who have a family history of colorectal cancer have a higher risk of developing colon cancer [2]. In the tumor microenvironment, inflammation occurs due to the presence of immune cells that are in charge of clearing aberrant cells [3,4]. On the other hand, the presence of inflammatory cytokines in the tumor environment causes cell proliferation, resistance to cell death, tumor invasion, and metastasis [5,6,7,8]. Moreover, the gut microbiome is a key factor in inflammation-related multistage colon carcinogenesis [9]. The increase in beneficial colonic bacteria such as Lactobacillus and Bifidobacterium could reduce the incidence of colon carcinogenesis [10]. Therefore, controlling inflammation and modifying the gut microbiome in the colon by consuming natural products could modulate the progression of colon cancer. Perilla frutescens (Nga-kee-mon; Thai word) is a plant that is mostly found in Asian countries such as Korea, Japan, China, and Thailand [11]. Many studies have demonstrated that PF has antioxidant, anti-inflammation, and anti-cancer biological activities [12,13,14]. PF leaves contain high levels of bioactive compounds, including rosmarinic acid, apigenin, and luteolin [15]. In previous studies, PF leaf extraction showed an ability to inhibit the growth of colon cancer cell lines and suppress rat colon cancer progression [15,16]. Although the biological activity of Perilla seed oil has been widely reported [17,18,19], the activity of seed residue, a waste product from PF seed oil production, is limited. Therefore, PF seed residue is an interesting part of studying bioactive compounds, which have colon cancer-preventive properties. This study aimed to investigate the inhibitory activities of an ethanolic extract of Perilla frutescens seed residue on the promotion stage of rat colon carcinogenesis, general and local gut inflammation, and alteration of the gut microbiome. Moreover, the molecular mechanisms of PCE on inflammatory responses in both macrophage and human colon cancer cell lines were also determined. The seed residue used for this study was taken from perilla seed oil production in Baan San Khong, Dok Kham Tai district, Phayao, Thailand. The voucher number of plant material was deposited at the Queen Sirikit Botanic Garden Herbarium, Chiang Mai, Thailand (Code: QBG-93756). Dried PF seed residue was extracted with 70% ethanol (1:10 w/v) overnight at room temperature. After filtration, a concentrated extract was lyophilized to obtain PF seed residue crude ethanol extract (PCE) powder. All extracts were kept at −20 °C for further study. Total phenolic contents were measured using the Folin–Ciocalteu reagent assay [20] and presented in milligrams of gallic acid equivalent per gram of extract. Subsequently, flavonoid contents were measured using an aluminum chloride colorimetric assay [21] and presented in milligrams of catechin equivalent per gram of extract. PCE were subjected to determine known phenolic acids by HPLC, according to the report of Pintha et al. [22]. All samples were filtered and then separated by ODS-3-C18 column (4.6 × 250 mm, 5 µm particle diameters) (Agilent, Santa Clara, CA, USA). Mobile phases A and B were 0.1% trifluoroacetic acid and methanol, respectively. Separation and elution were performed as follows: 0–35 min, 90–10% mobile phase A, and 10–90% mobile phase B; 35–40 min, 10–90% mobile phase A, and 90–10% mobile phase B, with a flow rate of 1.0 mL/min. A UV detector at wavelength 320 nm was used for the measurement of phenolic content peaks. The concentration of compounds presented in the HPLC profile was calculated by phenolic acid standard curves. Male Wistar rats, aged four weeks, weighing 60–90 g, were obtained from the Siam Nomura. International Co., Ltd., Bangkok Thailand. The animal experiment protocol was authorized by the animal ethics committees at the Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand (Protocol No: 26/2563, approved date: 7 October 2020). The rats were kept in air conditioning set to 25 °C with a 12:12 h light/dark cycle. Rats were given a standard laboratory pellet diet and sufficient amounts of water. After 1 week of acclimatization, rats were randomly separated into five groups of six rats with comparable average weights (111.87 ± 2.09 g). In control groups (groups 1 and 5), rats received a subcutaneous (s.c.) injection of 0.9% normal saline once a week for two weeks and were fed with 10% DMSO (group 1; negative control) or 1 g/kg body weight of PCE (group 5; PCE control) in weeks five to 15. In positive control (group 2) and experimental groups (groups 3 and 4), rats were s.c. injected with 40 mg/kg body weight of dimethylhydrazine (DMH) at week 1 and week 2. At week 3, rats were then given 1% dextran sulfate sodium (DSS) daily instead of drinking water for one week. In week 5, rats were orally fed with 10% DMSO (group 2) and 0.1 or 1 g/kg body weight/day of PCE (groups 3 or 4, respectively) until week 15. Body weights were recorded once a week. Blood was collected from the lateral tail vein of the rats in week 3 (after DMH administration), week 5 (after DSS-induced inflammation), and week 10 (after five weeks of PCE intervention). The serum was subjected for the examination of pro-inflammatory cytokine concentration (IL-6, IL-1β, TNF-α) by ELISA kit (Thermo Fisher Scientific, Waltham, MA, USA ) following manufacturer’s instructions protocol. Fecal specimens were naturally collected (week 14, non-invasive, and sampled repeatedly) and subjected to intestinal microbiota studies. At the end of the experiments, all rats were sacrificed, and colons were collected for ACF evaluation (Figure 1A). Each rat colon was expanded by 10% formaldehyde in PBS (pH 7.4) and placed on ice for at least 30 min. After that, each colon was opened and divided into the rectum (2 cm from the anus), proximal segment, and distal segment. Then, colons were flattened between filter papers and kept in 10% formaldehyde in PBS for at least 24 h. Each piece of colon was stained with 2% methylene blue for 1–2 min. Under a light microscope, the quantity and size of ACF were graded in accordance with the Bird RP criteria [23,24]. Compared to normal crypts, aberrant crypts were bigger and had a thicker epithelial lining, and usually gathered into a focus of small (1–3 AC/f) or clusters of abnormally large crypts (>4 AC/f). Bacterial genomic DNA was extracted from the feces of each group of rats using the TIANamp Stool DNA Kit (Tiangen, Beijing, China). The bacterial 16S rRNA genes V4 region was amplified by polymerase chain reaction (PCR) using 515F (5’- GTGCCAGCMGCCGCGGTAA-3’) and 806R (5’- GGACTACHVGGGTWTCTAAT-3’) primers. Then, paired-end 2 × 250 bp sequencing was performed by the Illumina NovaSeq 6000 platform. Then, the Quantitative Insights Into Microbial Ecology 2 version 2022.8 (QIIME2) pipeline was used to analyze the sequence reads. The paired-end sequences were de-noised and merged using the DADA2 plugin within QIIME2. Taxonomic assignment of 16S rRNA sequences was performed using a Silva 138 99% taxonomy classifier [25,26]. Amplicon sequence variants (ASVs) were aligned using the mafft plugin in QIIME2. Shannon and Simpson indices were assessed for alpha diversity. Beta diversity was analyzed using the Bray-Curtis distance matrix and visualized by principal coordinate analysis (PCoA) in R software v4.0.1. Differential abundance analysis was assessed using linear discriminant analysis (LDA) effect size (LEfSe) in the Galaxy module (http://huttenhower.sph.harvard.edu/galaxy, accessed on 12 October 2022) [27] to identify significant bacterial taxa among groups. The relative abundances of the taxa were compared. Microbiome data were analyzed using the Kruskal–Wallis test with multiple comparisons and the Wilcoxon rank-sum test. Rats were randomly divided into five groups with four rats of similar average weights. The induction was performed in a similar way to the inhibitory activities of PCE in the ACF progression experiment. In week 4, rats were orally fed with 10% DMSO (groups 1 and 2) and 0.1 or 1 g/kg body weight of PCE (groups 3, 4, or 5, respectively). Body weights were recorded once a week. All rats were sacrificed at the indicated time, and then colon tissue was collected for the determination of pro-inflammatory cytokine mRNA levels (Figure 1B). Total RNA from scraped colonic mucosal cells was reverse transcribed to cDNA by the ReverTra Ace® qPCR RT kit (TOYOBO, Osaka, Japan), in line with the manufacturer’s instructions. Quantitative PCR was carried out using the Maxima SYBR Green qPCR Kit (Thermo Fisher Scientific; Waltham, MA, USA) and the ABI 7500 real-time PCR system. Primer sequences are listed as follows: TNF-α: 5′-TTCTCATTCCTGCTCGTGGC-3′(forward) and 5′-AACTGATGAGAGGGAGCCCA-3′ (reverse), IL-6: 5′-TCCTACCCCAACTTCAATGCTC-3′ (forward) and 5′-TTGGATGGTCTTGGTCCTTAGCC-3′ (reverse), IL-1β: 5′-CACCTCTCAAGCAGAGCACAG-3′ (forward) and 5′-GGGTTCCATGGTGAAGTCAAC-3′ (reverse). The set-up temperature and time are as follows: first denaturing for 10 min, followed by 40 cycles of 95 °C for 15 sec and 60 °C for 1 min. Observations of each gene were expressed compared to control cells after being adjusted to GAPDH. HCT-116 and HT-29 (human colorectal cancer cell lines) and RAW 264.7 (a murine macrophage cell line) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) with 10% fetal bovine serum (FBS), 100 units/mL penicillin, and 100 units/mL streptomycin at 37 °C in an incubator supplied with 5% CO2. Briefly, 2.5 × 103 cells/well of HCT-116 and HT-29 were cultured in serum-free media overnight. Then, 10 ng/mL of IL-6 was added, and plates were incubated for 24 h in an incubator. Afterward, 0–200 μg/mL of PCE were added and incubated for 0, 24, and 48 h. Cell viability was assessed by the MTT test at the set period compared to 100% cell viability at 0 h and calculated as relative cell growth. In 6-well plates, 5 × 105 cells/well of RAW 264.7 cells were cultured in DMEM with 1 µg/mL of LPS for 24 h. After that, cells were rinsed twice with PBS and continuously cultured with various concentrations of PCE for 24 h. Finally, a cell culture media was subjected to an evaluation of the concentration of IL-6, IL-1β, and TNF-α by ELISA kit (Thermo Fisher Scientific, Waltham, MA, USA) following manufacturer’s instructions protocol. The data from in vitro and in vivo experiments were presented as the mean ± standard deviation (SD) using Microsoft Excel. The statistical analysis for significant differences among all the data was conducted by one-way ANOVA (GraphPad Prism software, version 9.0.0 (121); San Diego, CA, USA). The significance of the difference or correlation between experimental groups was accepted at p < 0.05 and p < 0.01. One kilogram of perilla seed residue yielded 73.40 ± 6.50 g of lyophilized powder (7.34 ± 0.65 g%). The average of phenolic compounds was 49.65 ± 3.56 mg of gallic acid equivalents/gram extract, while the flavonoid content was 41.02 ± 2.68 mg of catechin equivalents/gram extract. Additionally, the concentrations of rosmarinic acid, luteolin, and apigenin were 21.06 ± 2.13, 11.11 ± 0.40, and 6.55 ± 1.78 mg in 1 g of PCE, respectively. The mean numbers of ACF and crypt/focus of experimental rats with percentages of inhibition are shown in Table 1. The ACF was not observed in the saline-treated group, while all rats administered by DMH exhibited ACF, which was distributed throughout the colon with a range of one to more than eight aberrant crypts per focus). The mean number of ACF in the positive control group was 388 ± 90.11 ACF/rat. Rats fed with 1.0 g/kg body weight of PCE after DMH and DSS administration for 10 weeks showed 66.46% lower mean number of total ACF (130 ± 31.78 ACF/rat) than DMH + DSS treated alone (p < 0.01). The inhibitory effects of PCE were strongly observed in the proximal part since the number of ACF was decreased to 77.07%, compared to the positive control group (p < 0.01). Moreover, the AC/f slightly decreased to 2.81 ± 0.40 AC/f in the low dose (15.12%) and to 3.09 ± 0.27 in the high dose (6.69%) of the PCE feeding group, respectively; nevertheless, the significant difference to the DMH + DSS treated group (3.31 ± 0.41 AC/f) was not observed. These results suggested that PCE could reduce the progression of ACF to a large amount and recover the aberrant crypt to normal crypt, which showed a lower number of ACF. The levels of cytokines, including IL-6, IL-1β, and TNF-α in rat serum, are shown in Figure 2A–C. At week 3, rats that received DMH resulted in significantly increased serum levels of IL-6 (1181.2 ± 0.8 pg/mL), IL-1β (668.4 ± 0.9 pg/mL), and TNF-α (241.8 ± 0.5 pg/mL) more than 2–3.5-fold when compared to the negative control group (770.7 ± 0.4, 343.7 ± 0.9 and 118.4 ± 0.3 pg/mL, respectively) (p < 0.05). At week 5, after one week’s DSS administration, the serum levels of these three cytokines were higher than in week 3, which was due to the inflammation in rat colons caused by DSS. In week 10, after five weeks’ oral gavage with 0.1 and 1 g/kg body weight of PCE, the serum levels of all cytokines were significantly lower (p < 0.05) than in the positive control group. This result demonstrated that DMH and DSS treatments induced the inflammatory process and were related to the promotion of colon carcinogenesis. After rats were fed with PCE, extenuated inflammatory cytokine production occurred, which could reduce pro-inflammatory cytokine production. The distribution of ACF from 1 to more than 8 AC/f throughout the colon was shown in Figure 3A. Moreover, the correlation between pro-inflammatory cytokines and ACF/rat was shown in Figure 3B, C. Taken together, PCE could suppress the mRNA expression of pro-inflammatory cytokines in colonic epithelial cells and reduce systemic cytokine production, which reduced aberrant colonic epithelial cell progression in rats. The fold change of mRNA expression is shown in Figure 2D–F. In the DMH + DSS group, a significant increase in the levels of IL-6, IL-1β, and TNF-α mRNA was observed (1.70 ± 0.16, 1.59 ± 0.00, and 2.08 ± 0.34-fold, respectively), compared to a negative control group. The mRNA levels of IL-6 and TNF-α in colonic epithelial cells were significantly decreased (1.23 ± 0.00, 1.29 ± 0.08-fold, respectively) (p < 0.05) in rats receiving DMH + DSS with a high dose of PCE (1 g/kg body weight), while the mRNA level of IL-1β was significantly decreased in both the low and high dose treatment of PCE (1.28 ± 0.07, 1.08 ± 0.02-fold, respectively). As with the serum cytokine levels, PCE had no effect on the expression of cytokines in colonic epithelial cells of untreated animals. The results suggested that PCE might have the potential to extenuate the inflammatory process by down-regulating pro-inflammatory cytokines, and reduced cytokines promoted aberrant crypt progression in rat colons. Firstly, PCE increased the dose-dependent gut microbiota diversity in the CRC rat model. In rats receiving DMH + DSS, gut microbiota diversity within the sample (alpha diversity) was significantly reduced compared to that of the healthy control group (Shannon entropy and Simpson’s diversity for Figure 4A,B, respectively). Interestingly, the rat’s gut microbiota beta diversity with Bray-Curtis dissimilarity could be observed only in rats fed with the high dose of PCE (1 g/kg rat body weight), not a low dose (0.1 g/kg rat body weight) (Figure 4C). Next, the relative abundance of bacterial biomarkers was determined and is expressed in Figure 5. There was no significant difference in the Firmicutes/Bacteroidota (F/B) ratio in any of the groups of rats. Interestingly, there was an increase in the relative abundance of Gammaproteobacteria, an important class of bacteria in the phylum Pseudomonadota (formerly named Proteobacteria) in DMH + DSS-treated rats (Figure 5B), which have been considered an important marker for gut dysbiosis in several medical conditions, including colorectal cancer [28]. On the other hand, consumption of PCE reduced the increase in Escherichia/Shigella, the representative bacteria belonging to a class of Gammaproteobacteria (Figure 5C). These data suggested that PCE consumption may attenuate the chronic inflammatory condition in the gut of DMH + DSS-treated rats and reduce Gammaproteobacteria levels. PCE also decreased the Gram-negative Paraprevotella (Phylum Bacteroidota) in the rat (Figure 5D). Interestingly, PCE increased the relative abundance of beneficial bacteria such as Muribaculaceae, Lactobacillus, and Oscillospiraceae (Figure 5E–G, respectively). The relative cell growth of HCT-116 and HT-29 at 24 and 48 h, compared with the starting time, is shown in Figure 6A,B. The treatment with IL-6 at 10 ng/mL resulted in a significant increase in the relative growth of HCT-116 and HT-29 at both 24 and 48 h when compared with a negative control group. Therefore, the growth rate of both types of colon cancer cells was increased in the presence of IL-6. When colon cancer cells were treated with PCE in the presence of IL-6, PCE at 100 and 200 µg/mL significantly inhibited the growth of both HCT-116 (about 12% (p < 0.05) and 25% (p < 0.01), respectively), and HT-29 (about 12% (p < 0.05) and 24% (p < 0.01), respectively), after 48 h, when compared to the positive control groups (Figure 6C,D). As expected, PCE alone did not affect the growth of both HCT-116 and HT-29. This result confirmed that PCE showed anti-proliferative properties on colon cancer cells in an inflammatory condition. The concentration of IL-6, IL-1β, and TNF-α was measured in the cell culture media of LPS-activated RAW 264.7 cells by an ELISA kit. The percentages of all cytokines produced are shown in Figure 7A–C. When RAW 264.7 cells were treated with 1 µg/mL of LPS resulted in a significant, more than 2–4 fold, increase in IL-6 (280.45 ± 9.09 pg/mL), IL-1β (284.57 ± 76.07 pg/mL), and TNF-α (758.36 ± 25.88 pg/mL), compared to the negative control (90.52 ± 3.74, 65.11 ± 11.48, and 345.21 ± 29.61 pg/mL, respectively) (p < 0.01). After 24 h of culturing with PCE (100 and 200 µg/mL), the concentration of cytokines in cell culture media significantly decreased to 178.91 ± 1.39 pg/mL (IL-6), 132.91 ± 1.15 pg/mL (IL-1β), and 88.56 ± 0.60 pg/mL (TNF-α) (80–200%, p < 0.01) compared to the positive control. The results demonstrated that PCE attenuated the inflammatory process by reducing the cytokine production in LPS-induced RAW 264.7 cells. The seed of Perilla frutescens is a great source of nutrients, especially essential fatty acids [22]. The biological activities of PF seed oil, including its anti-atherosclerosis and neuroprotective effects, are widely promoted [29,30]. Therefore, PF seed residue from PF oil production that has been used for animal feeds remains largely useless. Recently, it has been reported that PF seed residue contains a high amount of nutrients and phytochemicals [31]. After perilla seed oil production, the remaining 72.5% of PF seed residue still contains nutrients and bioactive compounds [19,31]. To increase the value of perilla waste product, its active compounds and biological activities were investigated in this study. The results revealed that the extract of PF seed residue (PCE) presented the same level of phenolic and flavonoid compounds, which were similar to the leaf extract of PF, but the concentration profile was different. HPLC analysis found 21.06 mg of rosmarinic acid, 11.11 mg of luteolin, and 6.55 mg of apigenin in one gram of PCE, compared to 148.0, 0.9, and 0.4 mg/g, respectively, in the leaf extract [15]. It was deduced that the residue of seed and leaves from PF, which contained the difference in the phytochemical ratio, might show biological effects that are similar or different. The effective dose of PCE was referred to our previous research, which suggests that a crude ethanolic extract of PF leaves containing high rosmarinic acid exhibited anti-inflammation in vitro and inhibitory effects on DMH-induced ACF formation in rats in the promotion stage [15]. Moreover, the crude ethanol extract and its partially purified fraction suppressed the growth of colon cancer cells through the alteration of the inflammatory signaling pathway [32]. Therefore, we hypothesized that PF seed residue may have the same properties as its leaves, especially anti-cancer activity. This study has firstly demonstrated the chemopreventive effect of PF seed residue on the progression of rat colon carcinogenesis, which had been induced by chronic inflammation. The inflammation-induced progression of aberrant crypt foci in the rat colon was used to determine the efficiency of PF seed residue. ACF growth is generally inhibited by inducing cell cycle arrest and apoptosis induction in colon epithelial cells [33] or by inflammatory regulation, which is the target in colon cancer prevention [34]. Chemoprevention can exert anti-inflammatory activity for applications in cancer prevention. Rosmarinic acid (containing seven metabolites such as trans-caffeic acid and trans-m-coumaric acid), a polyphenol compound that is discovered in many plants, especially P. frutescens, luteolin (containing eight metabolites such as luteolin-3′-O-β-d-glucuronide), and apigenin (whose major metabolite is luteolin) have been shown to possess anti-inflammatory properties, such as lowering TNF-α, IL-6, and IL-1β production or downregulating iNOS, via modulation of HMGB1/TLR4, NF-kB, AP-1, and TGF-β1/SMAD pathways [35,36,37]. Therefore, in this study, we investigated the effect of perilla seed residue extract on DSS-promoted ACF progression in DMH-treated rat colons. High-dose PCE strongly reduced the mean number of ACF, compared to the DMH/DSS-treated group (66.46%); while low-dose PCE slightly decreased the number of AC/focus (15.12%). It has been reported that DMH induces genetic alterations in colonic epithelial cells and that ACF is formed. Then, DSS treatment alters the colonic mucosal inflammation from various cytokines, leading to ACF progression [38,39]. Moreover, DMH/DSS promotes the expression of iNOS, COX-2, TNF-, and IL-1 in rat colonic mucosal cells [34]. Therefore, it could be summarized that PCE feeding after receiving DMH and DSS could suppress the progression of aberrant colonic epithelial cells. Next, we hypothesized that PCE might modulate the inflammatory environment in the rat lumen. Therefore, the inflammatory process, which is related to aberrant colonic epithelial cell progression, was investigated. The effect of PCE on the serum level of pro-inflammatory cytokines, consisting of IL-6, IL-1β, and TNF-α in rat serum, was determined by ELISA. The results revealed that DMH resulted in an increase in pro-inflammatory cytokine production in rat serum, which might be caused by colonic epithelial cell mutation and aberration of immune cell infiltration [40]. After rats were induced to develop colitis by DSS administration for one week, an increase in pro-inflammatory cytokine production occurred, leading to the promotion of aberrant colonic cell progression. By contrast, five weeks of PCE feeding in DMH + DSS rats resulted in a decrease in inflammatory cytokine production in a dose-dependent manner. Furthermore, an analysis of the effects of PCE on IL-6, IL-1β, and TNF-α expression in colonic epithelial cells of the rat colon carcinogenesis model showed an increase in IL-6, IL-1β, and TNF-α mRNA levels in the DMH + DSS group. When rats were fed with PCE after DMH and DSS administration for one week, the mRNA expression of IL-6 and TNF-α in colonic epithelial cells was significantly decreased in high-dose treatment of PCE, while IL-1β expression was significantly decreased in both low- and high-dose treatments of PCE. Consequently, these results suggested that PCE could suppress the expression of pro-inflammatory cytokines, leading to a reduction in aberrant colonic epithelial cell progression in rats. The inflammatory condition is present in the tumor microenvironment in many types of cancer, including colon cancer. It has been reported that the reduction in the inflammatory microenvironment can modulate the growth of cancer [41,42,43]. Many factors are related to the inflammation in the colonic lumen, which can be targeted with approaches to suppress tumor-promoting inflammation, such as the immune system, tumor metabolism, and gut microbiota [44]. An imbalance of gut microbiota is also a factor that leads to increased inflammation in the colonic lumen. Therefore, the alteration of the bacterial profile in the PCE-treated experimental rat was determined. It was found that rat gut microbiota diversity within the sample (alpha diversity) was significantly reduced in the model of DMH + DSS, compared to that of the healthy control group. Our findings showed that PCE increased the gut microbiota diversity in the CRC rat model. Moreover, there was an increase in the relative abundance of Gammaproteobacteria, an important class of bacteria in the phylum Pseudomonadota (formerly named Proteobacteria) in DMH + DSS-treated rats. High numbers of Proteobacteria have been considered an important marker for gut dysbiosis in several medical conditions, including colorectal cancer [28]. Gammaproteobacteria have a lipopolysaccharide outer membrane that causes chronic inflammation through toll-like receptor (TLR)-4 activation in the gut, which can be modulated by some bioactive compounds [45]. Interestingly, PCE reduced the increase in Escherichia/Shigella, the representative bacteria belonging to a class of Gammaproteobacteria. These findings suggested that PCE consumption might have the ability to attenuate the chronic inflammatory condition in the gut of DMH + DSS rats and reduce Gammaproteobacteria levels. Treatment of PCE also increased the relative abundance of beneficial bacteria such as Muribaculaceae, Lactobacillus, and Oscillospiraceae. The protective effects of Lactobacillus on the development of precancerous growths and colorectal carcinogenesis in the rat model have been revealed [46]. Moreover, Ruminococcaceae (Oscillospiraceae) is a family of strictly anaerobic bacteria normally present in the colonic mucosal biofilm of healthy individuals [47]. However, the interaction of gut microbiota, immune cells, inflammatory cytokines, and the response of aberrant colonic epithelial cells is the focal point for the prevention of inflammation-related colon carcinogenesis. One of the key factors in tumor microenvironments is the activated macrophage, which produces various inflammatory mediators to promote tumor development [48]. In addition, the production of an inflammatory microenvironment by aberrant colonic epithelial cells is also a crucial factor for carcinogenesis [1]. Therefore, the effect of PCE on inflammatory processes in LPS-activated macrophages was investigated. LPS, a major glycolipid presented on the outer membrane of Gram-negative bacteria, potently generates inflammation by activating and infiltrating immune system cells. It could act together with TLR4 (toll-like receptor 4) on the membrane of macrophages, which results in the triggering of MyD88 and TRIF pathways, leading to an increase in TNF-α and IL-6 production via NF-κB and/or MAPK signaling cascades [49]. Our results demonstrated that treatment with PCE could reduce cytokine secretion from LPS-activated RAW 264.7 cells. This model correlated with the inflammation observed in previous animal experiments, where PCE acted effectively against the inflammatory process. As a result, the suppression of aberrant crypt progression in rats is caused by the inhibition of pro-inflammatory cytokine production in macrophage cells. It has been reported that apigenin, luteolin, and other polyphenol compounds can inhibit cytokine production by suppressing the activation of macrophage cells via inhibiting NF-κB, MAPKs, especially the PI3K/Akt signaling pathway [50]. It is well documented that the presence of cytokines such as IL-1, IL-6, and TNF-α or chemokines such as CCL2 and CXL8 from white blood cells or tumor-associated macrophage (TAMs) generate cancer-related inflammation in tumors [51]. Therefore, this result indicated that the reduction in cytokine expression and/or secretion from macrophages might lessen the tumor’s inflammatory reaction. In the microenvironment, tumor cells also produce inflammatory cytokines and prostaglandins via coordination of transcription factors including STAT3, NF-κB, and HIF-1α [52], resulting in more cancer-related inflammations leading to the promotion of cell proliferation, cell invasions, angiogenesis, and metastasis. The up-regulation of the inflammatory genes in macrophages and epithelial cells is induced by IL-1β, IFN-γ, TNF-α, and LPS [53]. The previous study showed that treatment of the HT-29 colon cancer cell line with a combination of TNF-α, INF-γ, and LPS has resulted in the up-regulation of pro-inflammatory enzymes (COX-2 and iNOS) and cytokines (IL-1β and TNF-α) expression [54]. On the other hand, the modulation of the inflammatory tumor microenvironment controls the progression of carcinogenesis [41]. In vivo experiments found that most of the inflammation occurred at the systemic level; it might be caused by many immune cells, such as macrophage cells, and then we hypothesized that PCE might inhibit the cancer cell proliferation that is induced by the inflammatory process. Thus, the effect of PCE on colon cell proliferation was studied in inflamed HT-29 and HCT-116, induced by interleukin-6 (IL-6). IL-6 plays a key role in the promotion of cell proliferation and inhibition of apoptosis via its receptor (IL-6Rα) and co-receptor glycoprotein 130 (gp130), resulting in the activation of the JAK/STAT signaling pathway [55]. STAT belongs to a family of transcription factors closely associated with tumorigenic processes, including cancer initiations and progressions [56]. As expected, PCE at the indicated concentrations could inhibit the cell proliferation of IL-6-induced colon cancer cell lines HCT-116 and HT-29 but did not affect stimulated cancer cells. Therefore, the modulation of inflammatory signaling pathways that control their growth needs to be further investigated. Moreover, several reports have shown the application of anti-inflammatory compounds for cancer treatments, including all-trans-retinoic acid (ATRA), vitamin D, non-steroidal anti-inflammatory drugs (NSAIDs), and anti-inflammatory antibodies [42]. For example, ATRA (Vesanoid, Tretinoin) is the primary biologically active metabolite of vitamin A that possesses anti-inflammatory properties. It reduces the expression of immunosuppressive genes, including PD-L1 and IL-10, in advanced-stage melanoma patients [42]. Vitamin D (such as calcitriol) exhibits anti-inflammatory actions that contribute to its beneficial effects on many cancers via inhibition of the synthesis of prostaglandins, suppression of stress-activated kinase signaling, and suppression of NF-κB signaling [42]. Therefore, the mechanisms of inflammatory resolution are of vital importance for cancer prevention. Following in vitro studies, PCE could modulate cytokine secretion in macrophages and could alter the inflammatory microenvironment response of colon cancer cell lines, which was correlated to inflammation in the previous in vivo experiment, which showed a strong correlation between the number of ACF and each cytokine level (R2 ˃0.90). These findings could be used to determine the effect of PF seed residue on the progression of rat colonic aberrant crypts induced by DMH and DSS. Finally, for further application, the human equivalent dose (HED) was converted from the effective dose in rats. In this study, when rats were fed with 1 g/kg body weight of PCE, they received rosmarinic acid (about 21.06 mg/kg body weight), luteolin, and apigenin (about 11.11 and 6.55 mg/kg body weight, respectively). This concentration was equivalent to 9.73 g/day of PCE or 132.5 g of PF seed residue consumption in humans (adults weighing 60 kg), which this ingestion were equal to the receiving of rosmarinic acid (204.9 mg/day), luteolin (108.10 mg/day), and apigenin (63.73 mg/day). Similarly, 9.45 g/day of PF leaves receive rosmarinic acid (about 204.9 mg/day), luteolin, and apigenin (about 0.6 and 1.2 mg/day, respectively). However, the applicable concentration or the formulation of the extract will be further determined. The active components in PF seed residue could inhibit the inflammatory microenvironment generated from the activated immune cells and the modulation of gut microbiota, leading to the response suppression of aberrant cells for the tumor-promoting processes. There are limitations to this study. Although we found that PF seed residue suppressed the inflammation-induced ACF progression, the mechanisms of PCE on microbiota related to inflammation and inflammatory-induced colon cancer progression need to be further investigated. However, the consumption of food supplement products made from PF oil, seed residue, or leaves should be a good strategy for colon cancer prevention.

PMC10001387

Paula Annahi Menchaca-Tapia,Miguel Marín-Rosales,Diana Celeste Salazar-Camarena,Alvaro Cruz,Edith Oregon-Romero,Raziel Tapia-Llanos,José Francisco Muñoz-Valle,Claudia Azucena Palafox-Sánchez

Analysis of PTPN22 −1123 G>C, +788 G>A and +1858 C>T Polymorphisms in Patients with Primary Sjögren’s Syndrome

27-02-2023

PTPN22 polymorphisms,primary Sjögren’s Syndrome,PTPN22 expression

Background: Primary Sjögren’s syndrome (pSS) is an autoimmune exocrinopathy characterized by lymphocytic infiltration, glandular dysfunction and systemic manifestations. Lyp protein is a negative regulator of the T cell receptor encoded by the tyrosine phosphatase nonreceptor-type 22 (PTPN22) gene. Multiple single-nucleotide polymorphisms (SNPs) in the PTPN22 gene have been associated with susceptibility to autoimmune diseases. This study aimed to investigate the association of PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A), rs2476601 (+1858 C>T) with pSS susceptibility in Mexican mestizo subjects. Methods: One hundred fifty pSS patients and 180 healthy controls (HCs) were included. Genotypes of PTPN22 SNPs were identified by PCR-RFLP. PTPN22 expression was evaluated through RT–PCR analysis. Serum anti-SSA/Ro and anti-SSB/La levels were measured using an ELISA kit. Results: Allele and genotype frequencies for all SNPs studied were similar in both groups (p > 0.05). pSS patients showed 17-fold higher expression of PTNP22 than HCs, and mRNA levels correlated with SSDAI score (r2 = 0.499, p = 0.008) and levels of anti-SSA/Ro and anti-SSB/La autoantibodies (r2 = 0.200, p = 0.03 and r2 = 0.175, p = 0.04, respectively). Positive anti-SSA/Ro pSS patients expressed higher PTPN22 mRNA levels (p = 0.008), with high focus scores by histopathology (p = 0.02). Moreover, PTPN22 expression had high diagnostic accuracy in pSS patients, with an AUC = 0.985. Conclusions: Our findings demonstrate that the PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) are not associated with the disease susceptibility in the western Mexican population. Additionally, PTPN22 expression may be helpful as a diagnostic biomarker in pSS.

Analysis of PTPN22 −1123 G>C, +788 G>A and +1858 C>T Polymorphisms in Patients with Primary Sjögren’s Syndrome Background: Primary Sjögren’s syndrome (pSS) is an autoimmune exocrinopathy characterized by lymphocytic infiltration, glandular dysfunction and systemic manifestations. Lyp protein is a negative regulator of the T cell receptor encoded by the tyrosine phosphatase nonreceptor-type 22 (PTPN22) gene. Multiple single-nucleotide polymorphisms (SNPs) in the PTPN22 gene have been associated with susceptibility to autoimmune diseases. This study aimed to investigate the association of PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A), rs2476601 (+1858 C>T) with pSS susceptibility in Mexican mestizo subjects. Methods: One hundred fifty pSS patients and 180 healthy controls (HCs) were included. Genotypes of PTPN22 SNPs were identified by PCR-RFLP. PTPN22 expression was evaluated through RT–PCR analysis. Serum anti-SSA/Ro and anti-SSB/La levels were measured using an ELISA kit. Results: Allele and genotype frequencies for all SNPs studied were similar in both groups (p > 0.05). pSS patients showed 17-fold higher expression of PTNP22 than HCs, and mRNA levels correlated with SSDAI score (r2 = 0.499, p = 0.008) and levels of anti-SSA/Ro and anti-SSB/La autoantibodies (r2 = 0.200, p = 0.03 and r2 = 0.175, p = 0.04, respectively). Positive anti-SSA/Ro pSS patients expressed higher PTPN22 mRNA levels (p = 0.008), with high focus scores by histopathology (p = 0.02). Moreover, PTPN22 expression had high diagnostic accuracy in pSS patients, with an AUC = 0.985. Conclusions: Our findings demonstrate that the PTPN22 SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) are not associated with the disease susceptibility in the western Mexican population. Additionally, PTPN22 expression may be helpful as a diagnostic biomarker in pSS. Primary Sjögren’s syndrome (pSS) is an autoimmune disease characterized by lymphocyte infiltration to lachrymal and salivary glands and impaired secretory activity, leading to the most important manifestations of the disease, keratoconjunctivitis sicca and xerostomia [1]. The etiology of this disease is incompletely understood; however, a key element in the pathogenesis is T and B lymphocyte hyperactivity, leading to autoantibody production mainly against ribonucleoproteins (SSA/Ro and SSB/La) and consequent presence of hypergammaglobulinemia [2,3]. It has been suggested that pSS is a complex and multifactorial disease, with genetic, environmental and hormonal factors involved in the disease pathogenesis. The protein tyrosine phosphatase nonreceptor type 22 (PTPN22) gene encodes the cytoplasmic protein lymphoid tyrosine phosphatase protein (Lyp), a potent downregulator of T cells, by inhibiting signaling through dephosphorylation of several substrates [4]. PTPN22 is involved in calibrating the T cell activation threshold and terminating TCR signaling [5]. Diverse case-control studies have examined the potential contribution of PTPN22 SNPs and their haplotypes to susceptibility to different autoimmune diseases (AIDs); however, results are inconsistent, in part because of ethnic and racial differences [6,7,8,9]. For example, rs2488457 (−1123 C) has been associated with type 1 diabetes mellitus in the Korean population [10]. In the Chinese population, rs2488457 is associated with rheumatoid arthritis (RA) [11], latent autoimmune diabetes in adults [12] and ulcerative colitis (UC) [13], whereas it is reported to be associated with less risk of systemic lupus erythematosus (SLE) in the Mexican population [14]. In addition, Muñoz-Valle et al. found an association between rs2488457 and lower levels of anti-citrullinated antibodies in RA patients [15]. The SNP rs33996649 (+788 G>A) is located in region encoding the catalytic domain of Lyp and represents a change in arginine (R) to glutamine (Q) (R263Q). This amino acid alteration leads to loss of function through reduced phosphatase activity [7]. rs33996649GA has also been related to protection against autoimmune diseases in European and American populations [16,17]. Another functional SNP is rs2476601 (+1858 C>T), involving substitution of arginine for tryptophan at codon 620 (R620 W) in the first proline-rich domain (P1) of Lyp. This variation alters the Lyp/C-Src tyrosine kinase interaction domain and results in a gain of function Lyp (increased phosphatase activity) that inhibits TCR signaling [16]. This polymorphism has been related to SLE in North America [18], RA in Mexico [19], and pSS in Colombia [20]. In the present case-control study, we investigated whether there is an association between PTPN22 polymorphisms, their haplotypes and PTPN22 mRNA expression and susceptibility to pSS in a Mexican population. One hundred eighty healthy controls and one hundred fifty pSS patients were included in the present study. The pSS patients were classified according to the 2016 American College of Rheumatology (ACR) and European League Against Rheumatism (EURLAR) classification criteria for pSS [21]. The sample size was calculated according to the formula , and the minimum number of alleles was n = 283, based on the frequencies for PTN22 +1858C>T gene polymorphism previously published in Latin-American pSS patients [20]. This study was conducted in the Hospital General de Occidente, México, and Instituto de Investigación en Ciencias Biomédicas, Universidad de Guadalajara, México. All participants were born in western Mexico with a minimum of third-generation ancestry and a Spanish-derived last name [22]. We excluded HCs with a family history of autoimmune diseases. At the time of inclusion, the pSS patients were evaluated with Sjogrën’s Syndrome Disease Activity Index (SSDAI) and Sjogrën’s Syndrome Disease Index (SSDDI) [23]. All study subjects signed informed consent. The institutional ethics and research committees approved the study under approval number: 449/16. Peripheral blood was collected from pSS patients and HCs. Genomic DNA (gDNA) extraction was performed using Miller’s technique [24]. We used polymerase chain reaction (PCR) to identify rs2488457 (−1123 G>C), rs33996649, (+788 G>A), and rs2476601 (+1858 C>T) genotypes. The primers, enzymes, and digestive products to evaluate the SNP genotypes in our study are provided in Table 1. The forward primer for rs2488457 (−1123 G>C) contains a recognition site for the endonuclease Sac1 (GAGCTxC) with an A>G substitution (underlined) [14,25]. PCR was carried out in a final volume of 10 µL including 1× of 10× supplied buffer enzyme, 4 mM MgCl2, 2.5 mM of each dNTP, 3 mM of each primer, 0.04 units of Taq DNA polymerase (Invitrogen Life Technologies, Carlsbad, CA, USA) and 100 ng/μL of gDNA. The amplification protocol was as follows: initial denaturalization at 95 °C for 3 min, followed by 29 cycles of 94 °C for 30 s, 67 °C for 30 s and 72 °C for 30 s with a final extension of 72 °C for 3 min (Thermal cycler TechNet TC-5000, Cole-Palmer, Beacon Rode, ST, UK). The PCR products were digested with 3 U of SacI (New England Biolabs, Ipswich, MA, USA) at 37 °C for 3 h. The restriction fragments were assessed by 6% polyacrylamide electrophoresis and stained with 2% AgNO3. The products after digestion with SacI are shown in Table 1. For rs33996649 (+788 G>A), PCR was carried out in a final volume of 10 µL containing 1× of supplied 10× buffer enzyme, 2.5 mM of each dNTP, 3 mM of each primer, 0.2 units of Taq DNA polymerase (DONGCHEN Biotech, Guangdong, China) and 100 ng/μL of gDNA. The amplification protocol was as follows: initial denaturation at 95 °C for 5 min, followed by 35 cycles of 95 °C for 40 s, 53 °C for 40 s, and 72 °C for 40 s, with a final extension of 72 °C for 5 min (Thermal cycler TechNet TC-5000, Cole-Palmer, Beacon Rode, ST, UK). The PCR product was digested with 3 U of MspI (New England Biolabs, Ipswich MA, USA) at 37 °C for 3 h, and the restriction fragments were observed on a 6% acrylamide gel and stained with 2% AgNO3. Table 1 show digestion products with MspI. The PCR mixture for rs2476601 (+1858 C>T) was the same as for rs2488457 (−1123 G>C). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 3 min, 33 cycles of denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s and extension at 72°. The products were digested with 3 U of XcmI (New England Biolabs, Ipswich, MA, USA) at 37 °C for 3 h. The restriction fragments were separated by 6% gel polyacrylamide electrophoresis and stained with 2% AgNO3. The products after digestion with XcmI are shown in Table 1. Total cellular RNA was extracted from peripheral blood mononuclear cells (PMBCs) using TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol. Repeated phenol–chloroform extraction was performed for the RNA samples, which were subjected to isolation using the Chomiczyki and Sacchi method [26]. The 260/280 ratio was used to provide an estimate of purity. Low-quality and degraded RNA samples were excluded. According to the reverse transcriptase protocol (Promega, Madison WI, USA), Oligo-Dt primers and reverse transcriptase (MMLV) were used to synthesize complementary DNA (cDNA) from 1 μg of total RNA. PTPN22 mRNA expression was determined in twenty-eight pSS patients and twenty-eight HCs of different genotypes. Quantitative real-time polymerase chain reaction (qPCR) was carried out to quantify the expression of the gene of interest. The RT–qPCR protocol followed the guidelines of Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) [27] using a Nano Light Cycler 2.0 (Roche Applied Science, Branford, CT, USA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as a reference gene to determine relative quantification after it was shown to be stably expressed in the sample [28]. The primers and hydrolysis probes were designed with Roche Universal Probe Library (PTPN22: cat. no. 04689011001, GAPDH: probe cat. no. 05190541001). All samples were run as duplicates. After validation of PCR efficiency for both genes, the data obtained were analyzed. A comparative threshold cycle (Cq) method with a cutoff of 40 cycles was used to determine the PTPN22 mRNA copy number relative to GAPDH, and data are shown based on the 2−ΔΔCq method [29] and 2−ΔCq method [30]. Anti-SSA/Ro and anti-SSB/La serum levels were determined from serum samples stored at −80 °C until measurement using a commercially available ELISA kit (cat. no. ORG. 506 and ORG. 508, respectively, ORGENTEC Diagnostika GmbH Carl-Zeiss-Straße 49, 55129 Mainz, Germany) with a sensitivity of 1 U/mL and 0–200 U/mL standard range. A Multiskan GO spectrophotometer (Thermo Fisher Scientific Oy, Ratastie, PO, Finland) was employed to obtain the optical density of all samples. The concentration was calculated based on a standard curve, and the results are reported as U/mL. According to the ORGENTEC ELISA kit protocol, samples with values of >25 U/mL were considered positive. Concerning the evaluation of PTPN22 gene polymorphisms, Hardy–Weinberg equilibrium (HWE) was tested using the χ2 test or Fisher’s exact test. Genotypic and allelic frequencies were compared by a 2 × 2 contingency table, and a χ2 test was performed. The Lewontin normalized coefficient D0 was used for assessing linkage disequilibrium (LD) between pairs or markers. SHEsis software was applied for haplotype analysis [31], and haplotypes with a low frequency (<1%) were not included. Student’s t test, the Mann–Whitney U test, one-way ANOVA, the Kruskal–Wallis test and Dunn’s post hoc test were applied according to the data distribution. SPSS25 (IBM Corporation; Armonk, NY, USA) and GraphPad Prism 8.0 (GraphPad Software, Incorporation; La Jolla, CA, USA) software were used for all statistical analyses. Differences were considered significant at a p value < 0.05 and were corrected with Bonferroni’s method according to the case. Statistical analysis to determine the fold change in PTPN22 mRNA expression between pSS patients and HCs was performed by using the 2−ΔΔCq method, and statistically significant differences were determined through the 2−ΔCq method. Values were obtained using the following formulas: ΔCq = (CqPTPN22 average − CqGAPDH average) and ΔΔCq = (ΔCqpSS − ΔCqHC). Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to assess the performance of PTPN22 mRNA expression level as a diagnostic tool for pSS diagnosis. One hundred fifty pSS patients were included in this study. The average age was 55 (±10) years, and all patients were female. The disease duration was 2.3 years [interquartile range (IQR) 1–5.5], and the average lymphocytic infiltration obtained from biopsies of the minor saliva gland was 2.3 (±1.7) foci in 4 mm2. Anti-SSA/Ro autoantibodies were positive in 23.3% of the pSS patients and anti-SSA/La autoantibodies in 13%. SSDAI and SSDDI means were 3 (±1) and 1 (±1), respectively. The main clinical manifestations and treatments are shown in Table 2. The genotypic and allelic frequencies of the rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) PTPN22 polymorphisms in pSS patients and HCs and their comparison are shown in Table 3. All PTPN22 gene polymorphisms were in Hardy-Weinberg equilibrium. Overall, genotypic and allelic frequencies for rs2488457 (−1123 G>C) in the pSS patients were similar to those in HCs (GG 52%, GC 40.7% and CC 7.3% vs. GG 52.2%, GC 40% and CC 7.8%, respectively), with no significant differences (p > 0.05). Similarly, for rs33996649 (+788 G>A), there were no statistically significant differences in allele and genotype frequencies between the groups (GG 96.6%, GA 2.7% and AA 0.7% vs. GG 98.3% and GA 1.7%). Regarding rs2476601 (+1858 C>T), allele and genotype frequencies were similar in pSS patients and HCs (CC 98% CT 1.3% and TT 0.7% vs. CC 98.9%, CT 1.1% and TT 0%), with no significant differences between genotypic and allelic frequencies in pSS patients compared to HCs and a very low frequency of the T allele. rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T) were found to be in medium linkage disequilibrium (LD) (D’ = 0.70). On the other hand, the loci rs33996649 (+788 G>A) did not found in linkage disequilibrium with rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T). The most frequent haplotype in pSS patients and HCs was GGC (70.7% vs. 71%, respectively), which included the three wildtype alleles of the SNPs. CGC frequencies were similar in pSS (26.3%) and HC (27.73%) (p > 0.05) (Table 3). PTPN22 expression was determined in 28 pSS patients and 28 HCs. The pSS patients showed 17.9-fold higher PTPN22 gene expression than the HCs (Figure 1a) (p = 0.001, Figure 1b). When comparing PTPN22 gene expression according to rs2488457 (−1123 G>C) genotype in the pSS group, carriers of the GC genotype showed slightly higher expression (0.51-fold more) than GG carriers; however, no significant difference was found (p < 0.05; see Figure 1c). In addition, patients with active pSS expressed 1.94-fold higher levels of PTPN22 than patients with inactive pSS (Figure 1d). Quantitative expression of PTPN22 was higher in pSS patients with active disease (p < 0.05, Figure 1e) and in those positive for anti-SSA/Ro antibodies (p = 0.006, Figure 1f), and a positive correlation with SSDAI was also observed (r2 = 0.499, p = 0.008, Figure 1g). According to damage status and SSDDI score, PTPN22 expression was similar in pSS patients (Figure 1h) but higher than that in HCs (Figure 1i, p < 0.001), with no statistical correlation (r2 = −0.096, p > 0.05, Figure 1g). Regarding clinical manifestations and autoantibody profiles, SSDAI score had a positive correlation with anti-SSA/Ro (r2 = 0.200, p = 0.03, Figure 2a) and anti-SSB/La (r2 = 0.175, p = 0.046, Figure 2b) serum levels. Additionally, a significantly higher focus score for MSG biopsies and ANA titers was found in anti-SSA/Ro-positive patients (p < 0.05, Figure 2c and Figure 2d). Patients with high SSDAI hematological domain scores showed 2.58-fold higher expression than patients with quiescent disease (Figure 2e). Furthermore, PTPN22 expression displayed an AUC = 0.98 for accurate diagnosis of pSS (Figure 2f). pSS is a systemic autoimmune disorder characterized by focal lymphocytic infiltration into the exocrine glands, causing dry eyes and dry mouth [1]. It has been suggested that pSS etiology is complex; however, TCR dysregulation plays an important role in the pathogenesis of autoimmune diseases [32]. Lyp is a tyrosine phosphatase that regulates T cells through inhibitory signaling by dephosphorylating several substrates, including the Src family kinases Lck and Fyn, as well as ZAP-70, during TCR lymphocyte activation [4,33]. The Lyp protein is encoded by the PTPN22 gene on chromosome 1. rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) are functional polymorphisms of the PTPN22 gene associated with multiple inflammatory conditions, including autoimmune disorders such as pSS [7,20,33]. Our study analyzed the SNPs rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) in the PTPN22 gene and susceptibility to pSS development in a Mexican mestizo population. The minor C allele of rs2488457 was detected in 27.78% of HCs, which is a lower proportion than the frequencies reported in the Asian population (33% to 41%). Nevertheless, we found a similar frequency of the rs2488457 GC genotype (40% vs. 37–46.1%) and a lower percentage of the rs2488457 CC genotype (7.8 vs. 13.7–18.1%) [10,11,12,13]. The distribution of the major rs33996649 G allele and the rs33996649 GG genotype are similar in the Mexican population [34], and the absence of the rs33996649 AA genotype is consistent with reports for European and Argentine populations [16,17,35,36]. Additionally, the minor allele frequency of rs2476601 T in the western Mexico population (0.6%) is similar to that reported in Amerindian and African populations (<1%) [7] but lower than that in Northern European populations (15%) [9]. The rs2476601 (+1858CT) genotype frequency in our study was 2.2%, lower than in European and American populations [18]. However, the rs2476601 TT genotype was absent in the Occidental Mexican population, which is consistent with previous reports for the same population [14,15,19]. Previous studies have analyzed the distribution of all these SNPs in healthy unrelated Mexican Mestizo subjects, showing genotypic and allelic frequencies similar to those reported in our study [14,15,19,35]. In general, ancestry studies in Mexican mestizos from the west region (State of Jalisco), based on maternal ancestry (mtDNA haplogroups) underscore the predominance of the Native American contribution (87%), followed by European (9%), African (3%) and Eurasian (1%) contributions [37]. However, when the Mexican admixture are analyzed based on the paternal contribution (Y-STRs), the Native American contribution decrease (28%), followed by African (5%), while the European (67%) raised [38]. rs2488457 (−1123 G>C), rs33996649 (+788 GA) and rs2476601 (+1858 C>T) were not found to be associated with an increased risk of developing pSS in the Mexican mestizo population from western Mexico. In contrast, rs2488457 (−1123 G>C) has been associated with UC, RA, and autoimmune diabetes mellitus in Asians [11,13]. The genotypic and allelic frequencies observed in west Mexican pSS patients and HCs for rs2488457 (−1123 G>C) were similar to those reported for European population and the total allelic frequencies reported in the Phase 3 of the 1000 Genomes Project [39]. Additionally, the rs2476601 T allele is associated with a risk for developing pSS in the Colombian population [20], and with RA in west [19] and central Mexican AR patients [40]. rs33996649 (+788 GA) has been reported to have a protective role against SLE and RA in European populations [16,36]. This is the first study to investigate three SNPs, rs2488457 (−1123 G>C), rs33996649 (+788 GA) and rs2476601 (+1858 C>T), in the PTNP22 gene. The haplotype analysis showed a medium LD between rs2488457 (−1123 G>C) and rs2476601 (+1858 C>T) but not LD was found with the rs33996649 (+788 GA), and the haplotype frequencies were similar in both, pSS and HCs. Different studies evaluating PTPN22 haplotypes with polymorphic alleles have described an increased risk of developing RA in Norway and western Mexican populations [19,41]. In addition, PTPN22 gene polymorphisms have been associated with higher gene expression in RA and UC [13,35]. In this study, the pSS patients showed 17-fold higher mRNA expression than HCs. In another study by our group, patients with SLE showed similar PTPN22 mRNA expression levels as controls [14]. In general, polymorphisms might explain higher gene expression. Lyp1 is mainly present in the cytoplasm of active T lymphocytes, whereas Lyp2 is found in the nucleus, perinuclear membrane, and cytoplasm of inactive peripheral T lymphocytes [42]. The third isoform reported, named PTPN22.6, lacks the catalytic site and is reported to be predominant in RA patient carriers of the rs2476601 (+1858 C>T) R620W functional variant. PTPN22.6 leads to higher nuclear factor of activated T cells (NFAT) expression and elevated IL-2 levels, with uncontrolled autoreactive T cell clonal expansion, by exerting a dominant negative effect over Lyp 1. Additionally, expression of PTPN22.6 correlates with RA activity [43]. Similar to Chang et al., we found an association between PTPN22 mRNA expression and clinimetric indices and autoantibody profiles in RA patients, which is the most important finding of our study. T cell receptor dysregulation is a key factor in glandular tissue damage: it is associated with a higher concentration of inflammatory cytokines [2] and promotes B cell activation, class switching, the T cell-dependent autoantibody response and germinal center (GC) expansion [44]. GC expansion has also been associated with higher production of pSS autoantibodies, such as anti-SSA/Ro, anti-SSB/La, antinuclear antibodies, and rheumatoid factor. On the other hand, murine model studies have demonstrated that PTPN22 loss of function in myeloid cells results in an augmented inflammatory effector phase of autoimmune disease and GC generation by influencing the number and activity of Th follicular cells [44,45]. The presence of anti-SSA/Ro and anti-SSB/La correlates with severe lymphocytic infiltration of the salivary glands, a higher prevalence of extraglandular manifestations and recurrent swelling of the parotid glands [46]. In our patients with pSS, we observed a clinical association between pSS activity and damage indices, autoantibodies, and MSG infiltration. Anti-SSA/Ro and histopathological MSG focus scores are the only two diagnostic tools used to classify pSS patients. Therefore, we evaluated PTPN22 gene expression as a biomarker. The area under the curve of PTNP22 expression was 0.985 (the cutoff suggested was >60 relative expression units, with 100% sensitivity, 91.67% specificity, and likelihood ratio 12; data not shown), demonstrating high diagnostic performance for pSS, which is similar to the accuracy of anti-SSA/Ro autoantibody diagnosis [47]. In populations such as ours, with a low frequency of anti-SSA/Ro (25%) antibody positivity, PTPN22 expression may be helpful as a molecular biomarker for pSS diagnosis. This study has important limitations as small sample size, selective recruiting of the western Mexican population, lack of inclusion of patients with the homozygous rs2488457 (−1123 CC) genotype for analysis of PTPN22 mRNA expression, lack of inclusion of control disease for comparative analysis of PTPN22 mRNA, as well as heterogeneity in the treatment of pSS, which may reflect differences in PTPN22 mRNA expression. Moreover, the PTPN22.6 isoform was not evaluated. In summary, the rs2488457 (−1123 G>C), rs33996649 (+788 G>A) and rs2476601 (+1858 C>T) polymorphisms of the PTNP22 gene are not associated with the risk susceptibility of pSS in the Mexican population. We propose that PTPN22 expression could be used as a molecular biomarker in pSS, as PTNP22 expression is associated with autoantibody presence, disease activity index, and extraglandular manifestations. However, further studies are required to analyze interacting epigenetic factors, as well as the relationship between Lyp and the local environment of the germinal centers on exocrine glands.

PMC10001392

Li Wang,Tingting Wei,Li Zheng,Fangfang Jiang,Wentao Ma,Min Lu,Xiaomao Wu,Huaming An

Recent Advances on Main Active Ingredients, Pharmacological Activities of Rosa roxbughii and Its Development and Utilization

01-03-2023

R. roxburghii,nutritional composition,active ingredient,pharmacological activity,development,utilization

Rosa roxburghii tratt (R. roxburghii) is an important plant resource that is widely distributed in the southwest of China and favored by consumers due to its high nutritional value and healthy functions. Meanwhile, it is a traditional edible and medicinal plant in China. With the deepening research of R. roxburghii, more and more bioactive components and its health care and medicinal value have been discovered and developed in recent years. This review summarizes and discusses the recent advances on main active ingredients such as vitamin, protein, amino acid, superoxide dismutase, polysaccharide, polyphenol, flavonoid, triterpenoid and mineral, and pharmacological activities including antioxidant activity, immunomodulatory activity, anti-tumor activity, glucose and lipid metabolism regulation, anti-radiation effect, detoxification effect, and viscera protection of R. roxbughii, as well as its development and utilization. The research status and existing problems of R. roxburghii development and quality control are also briefly introduced. This review ends with some suggestions on the perspectives and directions for future research and potential applications of R. roxbughii.

Recent Advances on Main Active Ingredients, Pharmacological Activities of Rosa roxbughii and Its Development and Utilization Rosa roxburghii tratt (R. roxburghii) is an important plant resource that is widely distributed in the southwest of China and favored by consumers due to its high nutritional value and healthy functions. Meanwhile, it is a traditional edible and medicinal plant in China. With the deepening research of R. roxburghii, more and more bioactive components and its health care and medicinal value have been discovered and developed in recent years. This review summarizes and discusses the recent advances on main active ingredients such as vitamin, protein, amino acid, superoxide dismutase, polysaccharide, polyphenol, flavonoid, triterpenoid and mineral, and pharmacological activities including antioxidant activity, immunomodulatory activity, anti-tumor activity, glucose and lipid metabolism regulation, anti-radiation effect, detoxification effect, and viscera protection of R. roxbughii, as well as its development and utilization. The research status and existing problems of R. roxburghii development and quality control are also briefly introduced. This review ends with some suggestions on the perspectives and directions for future research and potential applications of R. roxbughii. Rosa roxburghii tratt (R. roxburghii) is a perennial wild deciduous shrub in the Rosaceae family, with a slightly sour state and astringent flavor, strong aroma and crisp texture. It is the third generation (3G) of premium fruit with homology of medicine and food in China. It is suitable for growing in the alpine and hilly areas of temperate and subtropical regions at an altitude of 500 m to 1500 m, especially in the areas with large temperature differences and some cold areas. R. roxburghii is an important resource of medicinal and edible origin, which is mainly distributed in southwestern China, especially in Guizhou Province. At present, R. roxburghii has been listed as one of the twelve characteristic and advantageous industries in Guizhou Province as a geographical indication protection product. Guizhou Province is the first region to develop and utilize the resources of R. roxburghii. It is the only province in China and even the world to carry out large-scale planting and industrial development of R. roxburghii. It started with artificial cultivation in the 1980s [1,2]. By the end of 2022, the planting area of R. roxburghii in Guizhou Province had reached 140,000 hm2, with 300,000 tons of fresh fruits output, realizing an output value of over 15 billion yuan. Although the history of R. roxburghii that was planted artificially is short, with the improvement of people’s understanding of the third generation fruit and the in-depth study on nutritional value of R. roxburghii fruit, the R. roxburghii industry has begun to enter a new stage of variety, cultivation, and comprehensive development. The plant of R. roxburghii is shown in Figure 1. This paper reviewed the recent research advances of the nutrients and active ingredients, components and various functional activities of R. roxburghii, summarized the development and problems of R. roxburghii products, and put forward some suggestions for the future research and development. It is hoped that this review can inspire investigations on R. roxburghii as a functional food. As early as more than 400 years ago, Miao and other ethnic minorities in Guizhou Province had discovered the value of R. roxburghii and began to use this resource [3]. The earliest literary record about R. roxburghii was written in the “Chronicles of Guizhou” by Tian Wen of the Ming Dynasty in 1640: “R. roxburghii resembles pomegranate but is smaller than it. It is an edible wild fruit with a slightly sour astringent flavor, and has the effect of aiding digestion”, and then its medicinal value was firstly record in “The Supplement to the Compendium of Materia Medica”, which was written by Zhao Xuemin in the Qing Dynasty [4]. In addition, it is also recorded in the “Dictionary of Traditional Chinese Medicine” that all the flowers, fruits, leaves, roots, and seeds of R. roxburghii can be used as medicine, which have effects of invigorating stomach, aiding digestion, and nourishing, and the root bark has the effect of stopping diarrhea [5]. Meanwhile, R. roxburghii has also been used in folk medicine to treat stomach distension, hemorrhoids, dysentery, and other diseases [6]. The most widespread traditional use of R. roxburghii is for brewing R. roxburghii wine. The origin of R. roxburghii wine in Guizhou was recorded very early in the Daoguang Years of the Qing Dynasty. The “Annals of Guiyang Prefecture” of the same year once wrote that “Today, people in Guizhou Province pick R. roxburghii fruits, steam them, dry them in the sun, wrap them in cloth, then brew them in a jar full of wine to get R. roxburghii wine, which tastes great”. This also reflects the traditional brewing process of R. roxburghii wine: Fresh R. roxburghii fruits washed and broken → juice → fermentation → clarification and filtration → canned sterilization → R. roxburghii wine [7]. By the 1940s, R. roxburghii wine in Guizhou province had reached a certain scale, and the R. roxburghii industry began to form. In 1951, the earliest R. roxburghii processing enterprise “State-owned Qingyan Distillery” (renamed as Guizhou Huaxi Rosa Roxburghii Wine Distillery in 1954) was established in Guizhou Province, China. However, the greatest impact of R. roxburghii today is the development of functional foods and dietary supplements, which may be widely used in complementary and alternative medicine in the future. Modern science has proved that the nutritional value of 3G fruit is hundreds or even thousands of times of the first two generations of fruit. At the same time, it also plays an important role in admiration, medicinal application, and soil and water conservation, suggesting that planting and developing 3G fruit become a new trend of fruit development in the 21 st century [1]. A large number of scientific studies have shown that R. roxburghii, as an emerging 3G fruit, contains in a variety of rich nutrients and active ingredients such as organic acids, superoxide dismutase (SOD), flavonoids, polyphenols, polysaccharides, triterpenoids and so on, which give it biological functions such as promoting gastrointestinal digestion, regulating immune function, delaying aging, anti-cancer, anti-radiation, anti-atherosclerosis, and protecting organs etc [8,9]. Its dual-use attribute of medicine and edible value was recognized, and was included in the Treasure Book of Ethnic Chinese Medicinal Materials of Guizhou Province in 2003 [4]. Products developed from R. roxburghii are becoming increasingly popular in the consumer market due to their good taste and healthy functions. R. roxburghii products have been widely covered in food, medicine, health care products, daily chemical products and other industries, and thus have broad market prospects [10]. Since the 1940s, scientists have conducted a series of studies on the nutritional components of R. roxburghii. They found that R. roxburghii fruits contained abundant nutrients and active components, such as vitamins, sugars, carbohydrates, organic acids, proteins and amino acids, dietary fibers, trace elements, and other nutrients, active components such as SODs, flavonoids, polyphenols, polysaccharides, triterpenoids, sterols, and glycosides, and volatile components such as nonanal, leaf alcohols, and ethyl oleates [11,12]. In addition, the flowers and leaves of R. roxburghii also contained many rich nutrients and active ingredients. Information on the content of important nutrients and active ingredients in different parts of R. roxburghii is detailed in Table 1. Vitamins and minerals are essential to the human body, as they play an essential role in a various of basic metabolic pathways that support fundamental cellular functions. In particular, they are involved in energy-yielding metabolism, DNA synthesis, oxygen transport, and neuronal functions, making them essential for brain and muscle function [21]. However, the human body cannot synthesize the vitamins and minerals needed for self-metabolism, and can only obtain them through daily diet. R. roxburghii can well meet the needs of the human body of vitamins and minerals, because it is rich in abundant vitamins and trace minerals. The content of vitamin C in R. roxburghii fresh fruit is 276.87~3716.19 mg/100 g [14], which is 455~800 times that of vitamin C in apple and 5~40 times that of vitamin C in kiwifruit. In addition, as seen in Table 2, its vitamin A content is 120 times that of apple and 44 times that of mulberry, vitamin E content is 6~7 times that of apple and 12 times that of banana, and carotene content is 145 times that of apple and 96~97 times that of mulberry. The mineral elements in the fruits of R. roxburghii are also abundant, among which the contents of Fe, Mn, Zn, B, Cu, P, K, and Ca are higher. In addition to the fruits, the flowers and leaves of R. roxburghii are also rich in vitamins and minerals. The content of vitamin C in the petals is 1.7 times of roses (89.57 mg/100 g), and the content of vitamin E is 2.2 times that of Hemerocallis citrina (4.79 mg/100 g). The vitamin C content in R. roxburghii leaves is 23 times that of Panax notoginseng leaves (8.79 mg/100 g). All minerals such as Fe, Mn, Cu, Zn, B, Mo, P, K, Ca, Mg, and Na were found in the flowers and leaves of R. roxburghii, and the contents of Fe, Mn, and Zn in the petals were 2.5, 14.6, and 38.7 times that of Fe (4.83 mg/100 g), Mn (0.48 mg/100 g), and Zn (0.18 mg/100 g) in roses, respectively [5,17]. Considering the rich content of vitamins and mineral elements in fresh fruits, flowers, and leaves of R. roxburghii, we can develop it into nutritious beverages, scented tea, tea drinks, and other health products to supplement human essentials and vitamins and trace elements under the premise of ensuring that pesticide residues and heavy metal residues are within the safety standards. Proteins can be digested and absorbed as amino acids (AAs) and short peptides, and AAs are the cornerstone of proteins, which have structural and metabolic functions in humans and other animals and are very important to human health. Plant protein is one of the important sources of amino acids required for human metabolic activities [23]. The soluble protein content in R. roxburghii fruits is 11.62~26.29% [14], and the total free amino acid content is 14.658~57.55 mg/100 g FW, the essential amino acid content is 2.29~12.93 mg/100 g FW, including eight essential amino acids such as threonine and serine and other non-essential amino acids, etc. In addition, there are seven amino acid metabolites found in R. roxburghii fruits, such as phosphoserine (P-ser), sarcosine (Sar), α-aminobutyrate (α-ABA), β-alanine (β-Ala), γ-aminobutyrate (GABA), ethanolamine (EOHNH2), and hydroxyproline (Hypro). Among them, GABA is an important inhibitory neurotransmitter in the nervous system, which plays an important role in analgesia, anti-anxiety, anti-arrhythmia, neuronutrition and regulation of hormone secretion, and its content in R. roxburghii fruits, 27.31~112.17 mg/kg FW, is significantly higher than that of other fruits such as apples, kiwifruits, and cherries. Studies have shown that the content of amino acid in R. roxburghii fruits is related to altitude and fruit maturity. The amino acid content of mature fruit is higher than that of half mature fruits, and the content of amino acid in mature fruits of the same variety decreased with the increase of planting altitude [18]. There were 18 protein amino acids and 15 free amino acids detected in the petals of R. roxburghii, including eight essential amino acids, and the percentage of essential amino acids to the protein amino acids was as high as 39.62%. The types of amino acids in the leaves of R. roxburghii are also abundant, including 18 protein amino acids and 15 free amino acids. The total free amino acid content in the R. roxburghii leaves is 6.7 times that of Eucommia ulmoides leaves, and its essential amino acid content is 17.7 times that of E. ulmoides leaves [5,17]. Organic acids usually refer to organic compounds containing carboxyl (—COOH) in molecular structure that can neutralize alkali. They are widely distributed in leaves and roots of plants, especially in fruits, and play significant roles in food nutrition, such as antioxidant, sterilization and anti-inflammatory, obesity prevention, regulation of intestinal flora, maintenance of acid-base balance, and resistance strengthening [24]. Common organic acids in fruits include citric acid, malic acid, tartaric acid, acetic acid, succinic acid, oxalic acid, and vitamin C, etc. Vitamin C is an organic acid, also known as ascorbic acid, because the two adjacent enol hydroxyl groups at the second and third positions in its molecule are easily dissociated and release H+, which has the property of acid. At present, the organic acids that have been identified in R. roxburghii fruits include vitamin C, lactic acid, malic acid, protocatechuic acid, citric acid, p-coumaric acid, gallic acid, syringic acid, 4-hydroxybenzoic acid, caffeic acid, 9, 12, 15—calendic acid, 9, 12—octadecadienoic acid, tartaric acid, oxalic acid, succinic acid, sorbic acid, linoleic acid, oleic acid, palmitic acid, stearic acid, citric acid, etc. It is found that the content of organic acid components in different parts of R. roxburghii was different. The seven main organic acids and their percentages in the total acid content in R. roxburghii ripening fruits in descending order were vitamin C (66.8%), malic acid (17.5%), lactic acid (9.9%), tartric acid (2.8%), citric acid (1.7%), oxalic acid (0.7%), and succinic acid (0.6%). The root of R. roxburghii mainly contains lactic acid and tartric acid, but almost no oxalic acid, and the high content of tartric acid may be the important reason that R. roxburghii root immersed in water had the function of treating diarrhea. The lactic acid content in stems and leaves was higher, especially in R. roxburghii leaves, which accounted for more than 45% of the total acid content. The flowers of R. roxburghii mainly accumulated succinic acid (accounting for 50% of the total of 7 acids) [25,26,27]. Due to the high proportion of vitamin C in organic acids in R. roxburghii fruits and its important medical valuem, most of the current studies on organic acids in R. roxburghii focus on vitamin C. These studies mainly include the extraction, purification, quantitative detection, pharmacological activity mechanism of vitamin C in R. roxburghii, the change rule of vitamin C content during growth process and the influencing factors of stability during storage and processing. Researchers have found that the contents of vitamin C in R. roxburghii fruits varied greatly depending on the origin, variety, maturity, and altitude, etc. The higher the maturity, the higher the contents of vitamin C, and the lower the area altitude, the higher the contents of it [18]. Moreover, the vitamin C contents of the wild fruit was higher than that of the artificial cultivated fruit [14]. The content of vitamin C in R. roxburghii fruits kept increasing during the growth and development, and reached the highest level when mature. The degradation rate of vitamin C in R. roxburghii fruits was not obvious at 60 °C, but accelerated when the temperature was higher than 60 °C. When the temperature reached 120C, the vitamin C loss reached 20% [28]. In addition, the loss of water is also an important factor affecting the stability of vitamin C in fresh fruits. The decrease in vitamin C content was significant when the moisture content of R. roxburghii fresh fruits was reduced from 85% to 60%, while the decrease in vitamin C content was not significant when the moisture content continued to decrease from 60% [29]. Xiang et al. processed R. roxburghii fresh fruits by hot air drying at 60 °C for 30 h and found that the vitamin C content was lost with the increase of temperature and evaporation of water during the drying process. The vitamin C content of dried fruits (7.2 × 103 mg/100 g) was significantly lower than that of freeze-dried fruits (10.8 × 103 mg/100 g), and there was a significant difference (p < 0.05). However, the content of vitamin C in dried fruit of R. roxburghii was still as high as 7.2% (7.2 × 103 mg/100 g), which did not affect the application quality of R. roxburghii [30]. Yan et al. dried the R. roxburghii at 37 °C in an oven until the moisture content was <5% (drying time is around 24 h), and compared relative contents of each type of compound in fresh and dried fruits, the result shows that both the total organic acids content and vitamin C content showed no significant difference between these two types of fruits (fold change < 1.5) [31]. The above two studies show that it is very important to find suitable drying and processing methods to reduce the loss of vitamin C and other active ingredients in R. roxburghii fruits. Moreover, vitamin C is a key circulating antioxidant and a cofactor of biosynthases synthetase and a gene-regulating enzymes family with anti-inflammatory and immune-supporting effects. It is essential for human beings to prevent scurvy, coronary heart disease, stroke, cancer, and other common and complex diseases. In addition, high doses of intravenous vitamin C have been found to be a low-cost and promising anticancer treatment option [32,33]. Therefore, the research on the qualitative, extraction, purification, quantitative detection, pharmacological activity mechanism, and stability during the processing of vitamin C in R. roxburghii is of great significance for the development of functional foods, dietary supplements and alternative medicine of R. roxburghii in the future. SOD is a kind of metal enzyme that can catalyze the dislocation of superoxide free radical (·O2−) into hydrogen peroxide (H2O2) and oxygen (O2), which is widely found in animals, plants, and microorganisms. It is the first line of defense for the body to remove active oxygen and help the body resist the damage caused by active oxygen species [34]. A large number of clinical studies have shown that SOD has a positive preventive effect on human cardiovascular diseases, neurodegenerative diseases, and metabolic diseases, including diabetes and its complications and obesity [35]. Due to its powerful activity, it has been widely used in the pharmaceutical industry, food industry, and cosmetics industry [36]. SOD is another active substance with a high content of 33,005~44,650 U/100 g in R. roxburghii fruits [13], which is 20~50 times of grape and apple [8] and makes R. roxburghii known as “the king of SOD” in the plants. There are many studies on SOD of R. roxburghii, mainly involving the extraction and preservation methods of SOD from R. roxburghii, and investigations on its enzyme activity, stability, and pharmacological activity, etc. Studies have found that the SOD activity in R. roxburghii was affected by temperature, pH, the content of vitamin C, the concentration of Cu2+ and Zn2+, and water content. During the growth of R. roxburghii fruits, the SOD activity showed a downward trend, and there was a certain degree of loss with the increase of postharvest storage time. High temperature during processing reduces the SOD activity, while low temperature can protect its activity well [37]. It was found that the SOD activity in R. roxburghii fruits was relatively stable at 40~60 °C and a pH value of 7.8~8.0, and the high concentrations of Cu2+ and Zn2+ had an inhibiting effect on the SOD activity, while 2~6 mmol/L Zn2+ had a stabilizing effect on it [28,38,39]. Tan et al. found that the freeze-drying of R. roxburghii juice into powder was highly profitable to the preservation of the SOD enzyme [40]. Fu et al. showed that the SOD activity of R. roxburghii increased about 1.7 times after sugar pickling [41]. In addition, studies have found that vitamin C has a protective effect on SOD and can significantly slow down the decline of its activity, which is strongly correlated with the content of vitamin C. As mentioned above, the vitamin C content of fresh R. roxburghii fruits decreased significantly when the water content decreased from 85% to 60%, however, the SOD activity began to decrease obviously only when the water content decreased from 75% and did not decrease significantly during the process of water content from 85% to 75% [29], which means the decrease of vitamin C content and SOD activity due to water content was not synchronized. It is extremely important for improving the nutritional value of R. roxburghii products that to guarantee the vitamin C content and SOD activity during the processing and utilization of R. roxburghii fruits. Therefore, how to control the above factors to ensure the vitamin C content and SOD activity of R. roxburghii in an optimal content range is an aspect needing attention in the development of high-value R. roxburghii products. Polysaccharides are ubiquitous in plants, animals, and microorganisms. Modern pharmacy studies have proved that polysaccharides are non-toxic, green, and safe, which not only provide the main energy needed for human life, but also have a variety of therapeutic effects, including anti-cancer, anti-tumor, anti-diabetes, anti-inflammatory, immune regulation and so on [42]. They are widely added to functional health products and functional foods. It has been confirmed that R. roxburghii is rich in polysaccharides, generally ranging from 1.12% to 1.43% [43]. Polysaccharides are a kind of macromolecular substance with extensive biological activities, which are connected by more than ten kinds of monosaccharides through glycosidic bonds, and their activities are related to their structures [44]. Therefore, researchers investigated the biological activity of polysaccharides by isolating them from R. roxburghii, and analyzing their molecular weight, monosaccharide composition, proportion, configuration, and the position of the glycosidic bond. There are many extraction methods of R. roxburghii polysaccharides at present, such as hot water extraction, alkali extraction, ultrasonic extraction, enzyme extraction, microwave extraction, and so on [45]. The polysaccharide components that have been isolated and studied about their biological activity including RRTP-1, PR-1, PR-2, RTFP, RTFP1-1, RTFP-3, RSPs-40, RSPs-60, RTFP-30, RTFP-50, RTFP-80, RRTFP-2 and RTFP-1, as shown in Table 3. These polysaccharides are mainly composed of mannose (Man), ascorbic acid (AsA), rhamnose (Rha), glucuronic acid (GlcA), galactose (GalA), glucose (Glc), galactose (Gal), arabinose (Ara), xylose (Xyl), fructose (Fru), glucosamine hydrochloride (GluH), fucose (Fuc), glucuronic acid (GluA) and other monosaccharides through different molar ratio and structures, which makes that most of them possess different molecular weight and have strong antioxidant, anti-aging activity and α-glucosidase, α-d-glucosidase and α-amylase inhibitory activity. Some of them have even stronger α-glucosidase inhibitory activity than the hypoglycemic drug acarbose [46,47,48,49,50,51,52,53,54]. These polysaccharide components can be used as a new source of natural antioxidants and hypoglycemic drugs for the development of functional food and dietary supplement products. In addition, studies have found that polysaccharide RRTP-1 has an obvious protective effect on the injury of neural stem cells induced by sodium thiosulfate [55], RRTFP-2 showed the never growth factor (NGF) like neurotrophic activity [56], and the properties (high water solubility and uronic acid content) of the acidic polysaccharide RTFP-3 can facilitate the reduction of AgNO3 into Ag nanoparticle composites RP3-AgNPs, which exhibit an excellent antimicrobial ability against Staphylococcus aureus and Escherichia coli compared with RTFP-3, and can provide a green and sustainable strategy for the development of antimicrobial products [57]. In conclusion, the polysaccharide contained in R. roxburghii can not only be developed into functional food and medicine, but also into food additives or preservatives, showing the great potential for development and utilization in many fields, and the further study and exploitation of R. roxburghii polysaccharide has high theoretical research and practical application value. Polyphenols are natural antioxidants and antimicrobial agents, which are effective against oxidative stress-related diseases. Polyphenolic compounds mainly include flavonoids and non-flavonoids such as phenolic acids, stilbenes, and tannins [58]. Flavonoids are a class of important polyphenols containing γ-pyran group in benzene ring, which have extensive therapeutic activities and can be used as raw materials for antibacterial, anti-inflammatory, immune regulation, heart protection, anti-tumor, anti-aging, and other drugs [59]. R. roxburghii is rich in polyphenols and flavonoids. At present, the studies on polyphenols and flavonoids in R. roxburghii mainly include the determination, extraction, purification, identification, antioxidant activity analysis, and pharmacological activity analysis, etc. Polyphenols and flavonoids in R. roxburghii can be extracted by semi-bionic method, ultrasonic-assisted extraction method, and ethanol reflux extraction method, etc. High-speed countercurrent chromatography was used to separate them with N-hexane:ethyl acetate:ethanol:water (1:20:1:20, v/v) as the solvent system [8,60]. Lu et al. found that R. roxburghii juice showed the strongest anti-oxidation activity, anti-tumor cell proliferation activity, and the lowest cytotoxicity among the five juices of R. roxburghii juice, seabuckthorn juice, lemon juice, blueberry juice, and citrus juice. The determination of total phenol content in these five juices showed that the total phenol content (gallic acid was used as the standard, and the results were expressed as gallic acid equivalent: g GAE/L) in R. roxburghii juice (13.06 ± 0.48 g GAE/L) was 40 times that of lemon juice (0.33 GAE/L), 27 times that of citrus juice (0.48 g GAE/L) and 7.2 times that of blueberry juice (1.82 g GAE/L), and the main phenolic compounds detected in R. roxburghii juice were gallic acid (5.48 mg/100 mL), rutin (53.61 mg/100 mL), and catechin (68.69 mg/100 mL). The high total phenol, rutin, catechin, and vitamin C contents in R. roxburghii juice are important factors for these strong activities of R. roxburghii juice [58]. Therefore, polyphenols and flavonoids in R. roxburghii have high development and utilization value. Fan et al. determined 10 phenolic acids and 13 flavonoids in the leaves, petals, and fruits of R. roxburghii (Table 4). It can be seen from the Table 4 that the total content of phenolic acids in leaves, petals, and fruits of R. roxburghii was 3928.20, 1389.61, and 3356.68 mg/100 g DW respectively, and the total content of flavonoids was 1864.95, 1962.55, and 4956.70 mg/100 g DW, respectively. It is worth noting that the contents of catechins in leaves, petals, and fruits are quite high, with 867.41, 720.7 and 1114.18 mg/100 g, respectively. The tannic acid content in R. roxburghii fruits was as high as 2624.98 mg/100 g, accounting for 78.2% of the total phenolic acid in the fruit [15]. While polyphenols in R. roxburghii fruits contained tannins, which lead to a certain astringency in the taste of fruits, and the removal of tannins is one of the bases for the taste of R. roxburghii products widely loved and accepted by consumers. Research in this area has been rarely conducted, and more in-depth studies are needed to improve the utilization rate and market share of R. roxburghii products. Triterpenoids are widely distributed in nature with various types and complex skeleton structures. They have attracted wide attention due to their anti-tumor, anti-virus, antibacterial, anti-inflammatory, and immunoregulatory activities [61]. The total triterpene content of R. roxburghii ripening fruit is 22.56–32.32 mg/g DW, which is significantly higher than that of apple, jujube, and other common fruits [18]. Fan et al. detected four triterpenoids including echinacoside, roseoside, rosolic acid, and ursolic acid from the leaves, petals, and fruits of R. roxburghii, and the contents of each component in different parts of R. roxburghii are shown in Table 5, where that the contents of four triterpenes in leaves were the highest, reaching 1423.64 mg per 100 g of dried leaves, followed by 1055.16 mg in fruits, and the contents of four triterpenes in flowers were relatively low. The total content of echinacoside was the highest among the four triterpene components [15]. In addition to the high content of triterpenes in R. roxburghii, its species are also abundant. The components that have so far been identified are polygalacic acid 3-O-β-D-glucopyranoside, 19α-hydroxyasiatic acid-28-O-β-D-glucopyranoside, kajiichigoside F1, 1-hydroxyeuscaphic acid, 2α,19α-dihydroxy-3-oxo-urs-12-en-28-oic acid and isomers, euscaphic acid, pomolic acid and isomers, ursolic acid, 2α, 3β-dihydroxylup-20(29)-en-28-oic acid, 1α, 2β, 3β,19α-tetrahydroxyurs-12-en-28-oic acid, aiiichigeside F1,potentilanoside B, rosamultin, and 2α,3α,19α-trihydroxy-olean-12-en-28-oic acid-28-O-β-D-glucopyranoside [8]. Studies have found that the total triterpene extraction yield of refluxing process was higher than other extraction methods such as boiling and precipitation with ethanol, macro absorption resin, impregnation method, and supercritical fluid extraction, and the extraction rate was as high as 72.4% [62]. Although triterpenoids are very important pharmacologically active substances, and their content is very high in the fruits and leaves of R. roxburghii, there are few studies on triterpenoids. It is necessary to further strengthen the research on the extraction, separation, and pharmacological activity of triterpenoids from R. roxburghii in the future, so as to fully develop the value of triterpenoids in the prevention and treatment of diseases. With the deepening studies on nutrients and active ingredients of R. roxburghii and mechanisms of various active functions in recent years, various pharmacological fuctions of R. roxburghii have been gradually discovered. The following is the detailed introduction of the research on several important pharmacological activities of R. roxburghii. Oxidative stress can cause the body to produce excessive reactive oxygen species, such as DPPH·, ABTS·, ·O2−, and OH·, which can cause damage to the body and lead to various diseases. Scientific studies have shown that the increase of SOD, glutathione peroxidase (GSH-Px), catalase (CAT), hydroxyproline (HYP), and hyaluronic acid (HA) in the human body can inhibit the oxidation and aging of the body. R. roxburghii is rich in vitamin C, SOD, vitamin E, polyphenols, flavonoids, β-carotene, polysaccharides, and other antioxidant substances, which can increase the content of plasma antioxidants, improve the activity of antioxidant enzymes (GSH-Px, CAT and SOD) in vivo, increase the levels of HYP and HA, and reduce the level of ROS in the body. In the meantime, they can improve the antioxidant capacity of plasma low density lipoprotein (LDL), reduce the accumulation of oxidized LDL and the lipid peroxidation product, malondialdehyde (MDA), protect cells from lipid oxidation, and improve the antioxidant and anti-aging ability of the body [63,64,65], regulate the Na+ and K+ levels of erythrocyte membrane and protect the activity of adenosine triphosphate (ATP), thereby inhibiting the activity of monoamine oxidase (MAO) in the brain and reducing the deoxygenation of MAO [9,66]. In addition, Zhang et al. found that R. roxburghii fruit powder can regulate the expression of nuclear factor E2-related factor 2 (Nrf2), B cell lymphoma factor-2 (Bcl-2), and heme oxygenase-1 (HO-1) protein in skeletal muscle tissue of overtraining rats, reduce cell apoptosis, and alleviate the oxidative stress injury of skeletal muscle movement [66]. Vitamin C, polyphenols, flavonoids, SOD, and triterpenoids are several important antioxidant substances in R. roxburghii, which play an antioxidant role by directly scavenging free radicals, regulating the activity of related enzymes, and chelating metal ions (Fe3+) involved in the formation of free radicals. Moreover, the free radical scavenging ability of polyphenols was significantly increased after purification by AB-8 macroporous resin, and the antioxidant activity of flavonoid-refined products was stronger than that of crude flavonoids [67,68]. All the leaves, flowers, roots, and fruits of R. roxburghii had strong antioxidant activity. Yan et al. evaluated and compared the constituents and in vitro antioxidant activities of fresh and dried R. roxburghii fruits for the first time. A total of 95 compounds, mainly including organic acids, phenols, and flavonoids were identified in fresh and dried fruits by using ultrahigh—performance liquid chromatography—quadrupole—time of flight mass spectrometry. It can be seen from Table 6 that the contents of phenols and acylamide in R. roxburghii fruits were significantly increased, while contents of flavonoids, organic acids, and terpenoids reduced after the drying process. However, the scavenging free radical and ferric reducing capacity assays indicated that the dried fruit showed stronger antioxidant activities. The high content of phenols in dried fruits might explain the above-mentioned results [31]. Other studies have also demonstrated the important role of phenols in the antioxidant activity of R. roxburghii. Yang et al. investigated and compared the phenolics in free and bound forms of two cultivars of R. roxburghii leaves, and their bioactivities, and found that the total amount of free phenols in the leaves of both cultivars was significantly higher than that of bound phenols, and free phenols in both cultivars showed significantly higher antioxidant activity and α-glucosidase inhibitory potency than bound phenols. The characterization and quantitative analysis of phenolic compounds in two leaves showed that the main active components of free phenols were ellagic acid, quercitrin, isoquercitrin, and quinic acid [69]. Tan et al. [19] found that the antioxidant activity of different medicinal parts of R. roxburghii was vitamin C > leaves > fruits > roots, which was positively correlated with the content of ellagic acid in different parts, meaning that the higher the content of free (total) ellagic acid was, the stronger the antioxidant activity of the corresponding medicinal parts was. Fan et al. revealed that the scavenging capacity of DPPH· and ABTS· and the reducing capacity of Fe3+ in leaves, flowers, and fruits of R. roxburghii were in the order of fruits > leaves > flowers, and the scavenging capacity of OH· and ·O2− was in the order of fruits > flowers > leaves. The reasons for the above differences were not only related to the differences of the contents of polyphenols, flavonoids, and triterpenes in leaves, flowers, and fruits, but also possibly related to the differences in vitamin C, vitamin E, and SOD contents. The total contents of vitamin C, polyphenols, flavonoids, SOD, and triterpenoids in R. roxburghii fruits were much higher than those in flowers and leaves and the synergistic effects of these antioxidants greatly enhanced the antioxidant capacity of R. roxburghii fruit extracts [15]. The results of principal component contribution rate analysis show that the contribution of the five active substances to the anti-oxidation ability was in the order of total phenol, vitamin C content > total triterpene content, SOD activity > total flavone content. In addition, it was found that the content of total triterpenoids in old leaves of R. roxburghii growing for 100~105 days was the highest, the content of total phenols and total flavonoids in mature leaves of R. roxburghii growing for 60~65 days was the highest, and the content of flavonoids in leaves was higher than that in fruits, while the content of vitamin C and SOD activity in mature leaves was significantly lower than that in fruits [70]. Therefore, mature leaves can be used as raw materials for total triterpenoids, polyphenols, and flavonoids in R. roxburghii, and the extraction and purification of vitamin C and SOD should be mainly based on fruits. In recent years, studies on the extraction and activity of R. roxburghii polysaccharides have found that many extracted polysaccharide components have scavenging effects on free radicals OH·, DPPH·, ABTS·, and ·O2− with different degrees (Table 3). The polysaccharides PR-1 and PR-2 extracted by Wang et al. showed significant DPPH· free radical scavenging activities, and the scavenging ability of PR-1 was equivalent to that of ascorbic acid [46]. The polysaccharides RSPs-40 and RSPs-60 extracted by Chen et al. had a certain scavenging effect on free radicals ABTS· and DPPH·, but both of them were weaker than ascorbic acid [50]. The new water-soluble polysaccharide RRTP1-1 extracted by Chen et al. not only showed strong OH·, ·O2−, and DPPH· scavenging ability, but also possessed obvious antioxidant activity in vitro [51]. At the dose of 200 mg/kg, it could significantly enhance the activity of antioxidant enzymes (SOD, CAT and GSH), and reduce the levels of lipid peroxidation and MDA in the serum of aging mice induced by D-galactosamine. The three polysaccharide components, RTFP-30, RTFP-50, and RTFP-80, isolated by Wang et al., showed a varying degree of scavenging effect on OH·, ABTS·, and DPPH· at a certain concentration, and scavenging ability of RTFP-50 was the strongest, which may be due to its strong electron or hydrogen atom donor [52]. The free radical scavenging activities of the above polysaccharide components are in direct proportion to the concentration of R. roxburghii polysaccharide in a certain concentration range. In addition, Cao et al. found that polysaccharide of R. roxburghii could not only increase the content of SOD, CAT and GSH, reduce the level of MDA, but also eliminate fatigue by providing energy substances such as blood sugar and muscle glycogen needed during exercise and reducing excessive metabolism of adverse substances such as lactic acid, lactate dehydrogenase, and creatine kinase [71]. The immune system is a barrier to maintain normal body function and can resist foreign matters. The disorders of immune system can lead to tumors, inflammation, infection, and other diseases, which will seriously threaten human life and health. Liu et al. found that R. roxburghii freeze-dried powder can reduce the expression of immune inflammatory factors in the kidney of model rats, regulate the immune microenvironment, and improve renal fibrosis in rats [72]. Lu et al. found that polysaccharide of R. roxburghii can increase the percentage of phagocytosis of chicken erythrocytes by mouse macrophages, prolong the half hemolysis value of serum, and enhance the non-specific immune function and humoral immune function of mice [73]. The experimental results on the immunity of total triterpenoids from R. roxburghii holded by Tian et al. showed that the total triterpenoids of R. roxburghii could alleviate the damage of cyclophosphamide (CTX) injection on thymus and spleen, improve the number of immune cells, enhance the anti-oxidative stress ability of mice and enhance the immune function of the body [74]. It could also regulate the activities of acid phosphatase (ACP) and lactate dehydrogenase (LDH) in mice, promote the proliferation of RAW264.7 macrophages in mice, and inhibit the secretion of NO by macrophages induced by lipopolysaccharide (LPS), which indicates it have potential anti-inflammatory and immune activities. In addition, clinical studies have shown that urine arsenic levels of patients with coal-burning arsenism were closely related to their immunosuppression, and R. roxburghii preparation could effectively improve the immune function of patients with coal-burning arsenism [75]. The balance of glucose and lipid metabolism is the basis of the body’s life activities, which plays an important role in the normal physiological function. Glucose and lipid metabolism disorder is an important reason for major chronic metabolic diseases such as diabetes, obesity, hyperlipidemia, and atherosclerosis [76]. Previous animal tests and clinical studies have found that R. roxburghii has good effects for regulating glucose and lipid metabolism. Diabetes is a metabolic disease characterized by hyperglycemia. The most common forms of diabetes are type 1 diabetes caused by the absolute deficiency of insulin, and type 2 diabetes caused by insulin resistance. Obesity is an important environmental factor for type 2 diabetes, and it is on the rise. Diabetic complications affect almost every tissue of the body, which is the main cause of cardiovascular disease and death, blindness, renal failure, and amputation [77]. Recent studies have revealed that polysaccharides, polyphenols, flavonoids, and triterpenoids in R. roxburghii have shown certain hypoglycemic activities, and there are mainly two ways that R. roxburghii exerts the hypoglycemic activity. The first way that R. roxburghii reduces the blood sugar levels in the body by inhibiting the activities of α-glucosidase, α-amylase, and α-D-glucosidase, then reduces the absorption rate of blood glucose, and achieves the purpose of preventing diabetes and alleviating the symptoms of diabetes. For example, both the polysaccharide components PR-1 and PR-2 extracted by Wang et al. showed a certain α-d-glucosidase inhibitory activity, and the inhibitory activity of PR-1 was stronger than PR-2, but both of them were weaker than acarbose [46]. RTFP-3 purified by Wang et al. [47], RSPS-40 and RSPS-60 extracted by Chen et al. [50], and RTFP-50, RTFP-80 isolated by Wang et al. [52] all had strong α-glucosidase inhibitory activity, which can reduce the absorption rate of blood glucose and prevent type 2 diabetes mellitus, and the effect of RSPS-40 and RSPS-60 was better than that of acarbose. In addition, RTFP, a polysaccharide component isolated by Wang et al. [49], had strong inhibitory activity against a-glucosidase and a-amylase, and its inhibitory activity was much higher than that of acarbose. Due to the strong activity of RTFP, Wang et al. further explored its potential hypoglycemic mechanism [48]. They found that RTFP could significantly improve the insulin resistance in DB/DB diabetic mice, increase their SOD, GSH, and catalase (CAT) activities in liver tissue, significantly reduce the weight, fat, liver hypertrophy, fasting blood glucose, serum insulin, and blood lipid levels of mice, enhance their glucose tolerance, and significantly improve the symptoms of hyperglycemia and hyperlipidemia. Zhu et al. found that polyphenols and flavonoids in R. roxburghii, especially the catechin, kaempferol hexose and rutin of them, had strong inhibitory activity on a-glucosidase, and the catechin, the crude extracts of flavonoid, had a good synergistic effect with acarbose, which had potential application value in reducing the clinical dosage of acarbose [78]. Qin et al. found that the triterpenes of R. roxburghii had great α-glucosidase inhibitory activity, which was much stronger than that of acarbose [62]. The other way is to regulate the expression of protease through multiple cellular signaling pathways to exert its hypoglycemic effect. For example, An et al. found that R. roxburghii fruit wine could shift glucose transporters 2 and 4 (GLUT2/4) by activating the phospholipid phthalinositol 3-kinase (PI3K) pathway of insulin pathway mediated by serum insulin (INS), promote the absorption and utilization of glucose by cells, thereby reduce the blood glucose level of type 1 diabetic mice [79]. It could also up-regulate the mRNA expression levels of AMP-activated protein kinase α (AMPK), glucose transporter 2 (GLUT2), acetyl-CoA carboxylases alpha (ACACA), fatty acid synthase (FASN) in liver tissue and down-regulate the mRNA expression levels of phosphoenolpyruvate carboxylase (PEPCK), glucose-6-phosphatase (G6Pase), HMG-CoA reductase (HMG-CoA) and cholesterol 7 α -hydroxylase (CYP7A1), significantly reduce the body weight, fat, liver hypertrophy and fasting blood glucose, serum insulin and lipid levels in db/db mice, and effectively alleviate the symptoms of type 2 diabetes [80]. Chen et al. isolated polyphenols-rich R. roxburghii extract (RP) from R. roxburghii fruits and separated four components (IRP1-4) from RP [81]. They found that both RP and IRP1-4 could regulate the expression of FOXO1 (a downstream protein of the P13K/AKT pathway) and p-GSK3 protein, control liver gluconeogenesis, improve liver glycogen storage insulin resistance, and relieve symptoms of type 2 diabetes by activating the expression of phosphatidylinositol 3-kinase (P13K)/thephosphorylation of protein kinase B (AKT) signaling pathway. It is worth noting that the cellular signal transduction pathway is very complicated, and different pathways often interact with each other. The same polysaccharide can often regulate multiple signal transduction pathways at different levels and at different links at the same time. In addition, Wang et al. firstly constructed selenium nanoparticles (SeNPs) functionalized with the RTFP-3, the novel polysaccharide extracted from R. roxburghii fruits mentioned above, via a facile, single -step, and green in situ synthesis method. They found that the RTFP-3-functionalized SeNPs (RP3-SeNPs) exhibited high dispersibility and stability, and could significantly inhibit the H2O2-induced apoptosis of INS-1 cells by attenuating oxidative stress and downregulating the expression of uncoupling protein-2 (UCP-2), which demonstrate that RP3-SeNPs may be a promising candidate for the treatment of ROS-mediated diabetes [54]. Hyperlipidemia is a manifestation of abnormal lipid metabolism. Hyperlipidemia related to lipid disorders are considered to be the cause of atherosclerotic cardiovascular disease. It is characterized by an increased levels of plasma lipid, such as total cholesterol (TC), triglyceride (TG), cholesterol ester, very low density lipoprotein cholesterol (VLDL-C), low density lipoprotein cholesterol (LDL-C), free fatty acids, and the reduced levels of high density lipoprotein cholesterol (HDL-C) [82]. Therefore, lowering blood lipids is an effective method to prevent and treat cardiovascular diseases. Zhang et al. found that flavonoids from R. roxburghii could significantly improve the activities of SOD and CAT in the pancreas, markedly reduce the level of MDA and the content of serum glucose, triglyceride and total cholesterol, increase serum insulin levels, and effectively protect the pancreas from oxidative damage of alloxan [83]. Zhou found that the aqueous extract of R. roxburghii could significantly reduce the triglyceride and cholesterol values of hyperlipidemia mice (p < 0.01), and speculated that its effect might be related to the water-soluble components such as organic acids [84]. Wu et al. found that the hydroalcoholic extract of R. roxburghii fruit (HRT) could significantly reduce body weight gain and decreased serum and liver lipid levels in the hyperlipidemicrats by improving the activities of antioxidant enzymes, lipoprotein lipase, hepaticlipase, and regulating the expressions of related mRNA and protein [85]. HPLC-MS analyses showed that the total phenolic acid content in HRT was 88.30%, including phenolic acids such as L-ascorbic acid, kaempferol, catechin, rutin, and isoquercitrin. Many previous studies have shown that phenolic acids are involved in lipid-lowering effects, such as ascorbic acid alleviating alcohol-induced hyperlipidemia, rutin preventing hypertriglyceridemia and inflammation, and green tea catechins effectively preventing obesity and hypercholesterolemia. These results indicate that phenolic acids played an important role in the hypolipidemic effect of hormone replacement therapy. The research of Wang et al. shows that polysaccharide of R. roxburghii also showed strong lipid-lowering activity. They compared in vitro binding characteristics of RTFP-30, RTFP-50 and RTFP-80, the three polysaccharide components isolated from R. roxburghii, and found that RTFP-30 and RTFP-50 showed strong binding ability to fat, cholesterol and bile acid, which had great potential in preventing obesity and hypercholesterolemia [52]. Intestinal flora is closely related to hyperlipidemia, which can regulate cholesterol and lipid metabolism in the host, and regulating intestinal flora and liver fat metabolism is an effective way to prevent diseases related to glucose and lipid metabolism disorders. Ji et al. found that the fermented R. roxburghii juice (FRRT) could alleviate hyperlipidemia induced by high fat diet in rats by regulating intestinal flora (prevotella, oscillospira, paraprevotellaceae prevotella and ruminococcus) and related metabolites (amino acid metabolites, bile acid metabolites and lipid metabolites) [86]. The chemical composition of FRRT is 13.49 mg/mL total flavonoids, 23.91 mg/mL total polysaccharides, 35.97 mg/mL total polyphenols, and 7.58 mg/mL vitamin C. It can be seen that polyphenols, polysaccharides, flavonoids, and vitamin C are the main active components of FRRT regulating intestinal flora in R. roxburghii juice. Except for lowering blood glucose, the polysaccharide component (RTFP) separated by Wang et al. can also be used as a natural anti-inflammatory agent to reduce chronic obesity-induced colitis [49]. It can significantly decrease gut inflammation and ameliorate the metabolic dysbiosis of intestinal microflora by decreasing the firmicutes/bacteroidetes ratio, reducing the levels of serum D-lactic acid and lipopoly-saccharides, inhibiting the TLR4/NF-κB signaling pathway, increasing the abundance of beneficial bacteria (ruminococcaceae, muribaculaceae, akkermansiaceae, etc.), and decreasing the abundance of pathogenic bacteria significantly [87]. There is a close relationship between inflammation of adipose tissue and obesity. In adipose tissue of obese patients, M1 phenotype macrophages account for the majority; in lean adipose tissue, M2 phenotype macrophages are the majority. Obesity leads to the transformation of adipose tissue macrophages from M2 phenotype to M1 phenotype. M1 phenotype macrophages secrete inflammatory factors, which aggravates adipose tissue inflammation, and eventually leads to obesity complications such as insulin resistance. Sui et al. found that the drug-containing serum of R. roxburghii produced in Guizhou could promote the transformation of adipose tissue macrophages from M1 phenotype to M2 phenotype by regulating the expression of related transformation factors and inflammatory factors, so as to achieve the effect of weight loss and inflammation and effectively prevent obesity complications such as insulin resistance [88]. Atherosclerosis (AS) is a kind of disease that mainly invades the great and middle arteries, thickens and hardens the inner wall of blood vessels, and narrows the lumen. Abnormal lipoprotein metabolism is the main cause of atherosclerosis, and both lipid excess and hyperlipidemia can lead to atherosclerosis. Individuals with high low-density lipoprotein (LDL) level and low high-density lipoprotein (HDL) level in plasma are prone to cardiovascular diseases [89]. Studies have shown that R. roxburghii can significantly reduce the contents of TC and triglyceride (TG) in serum, enhance the antioxidant activity of LDL, increase the level of HDL, reduce the damage of lipid metabolism and oxidation to arterial intima, adjust lipid metabolism of hyperlipidemia, and improve SOD activity, so as to prevent atherosclerotic plaques in arterial intima [90,91]. Zhang et al. showed that R. roxburghii can also inhibit the formation of foam cells induced by oxidized very low-density lipoprotein (Ox-VLDL) to achieve its anti-atherosclerosis function [92]. In addition, clinical studies have shown that oral administration of R. roxburghii juice in patients with cerebral infarction can effectively improve the symptoms of atherosclerosis and reduce the recurrence rate of patients [93]. Some scholars believe that these effects of R. roxburghii are closely related to the vitamin C in the fruit, but there is no more in-depth study on the related effective components and their mechanism of action. With the change of society and human lifestyle, diseases are also gradually changing and developing, including some diseases with unknown causes. Cancer is undoubtedly one of the biggest threats to human health and life. A large number of studies have shown that R. roxburghii can block the synthesis of N-nitroso compounds in vivo, induce apoptosis of cancer cells, inhibit the proliferation of various tumor cells, and thus play an anti-cancer role. For instance, the extract of R. roxburghii has obvious inhibitory effect on the growth of many human cancer cells, such as human esophageal squamous cell carcinoma CaEs-17, gastric cancer cell SGC-7901, lung cancer A549, human liver cancer SMMC-7721 cells, human CD34+ hematopoietic cells, human leukemia K562 cells, endometrial cancer, human ovarian cell line CoC2, etc [2,94,95,96]. Studies have shown that polysaccharides and triterpenoids are important anti-tumor active ingredients of R. roxburghii. Chen et al. found that the crude polysaccharide of R. roxburghii could inhibit the proliferation of ovarian cancer cell A2780 by inhibiting the expression of MMP-9 gene related to cancer cell proliferation through a dose-dependent manner [97]. Tang et al. found that polysaccharide of R. roxburghii extracted by optimizing microwave assistance had obvious anti-tumor activity and could significantly increase the number of white blood cells, thymus index, and spleen index of S180 tumor mice, which can be used as potential natural sources of functional food additives or anti-tumor drugs [98]. Chen et al. showed that polysaccharides of R. roxburghii could inhibit the proliferation of B16 melanoma cells in mice in vitro and in vivo, promote the apoptosis of mouse B16 cells in vitro, and improve the activation function of the immune system, thereby inhibiting tumor formation [99]. On the gene level, polysaccharides of R. roxburghii can reduce the expression of anti-apoptotic gene Bcl-2 and increase the expression of pro-apoptotic gene Bax in B16 cells in a dose-dependent manner. Jin et al. isolated and purified a novel polysaccharide RTFP-1 from R. roxburghii fruits, and found that RTFP-1 could inhibit the cell proliferation of human hepatocellular carcinoma cells by activating the apoptosis of HepG2 cells through ROS-mediated MAPK, STAT, and p53 apoptotic pathways [53]. Dai et al. found that triterpenoid of R. roxburghii had the effect of anti-human endometrial adenocarcinoma in vitro, and the mechanism might be related to its cytotoxicity, induction of cell differentiation, induction of apoptosis, and inhibition of cell proliferation [100]. Huang et al. reported that triterpenoid of R. roxburghii had an in vitro anti-proliferation effect on human liver cancer cell SMMC-7721, and its mechanism might induce cell differentiation by down-regulating the expression of bad mRNA, which is not related to inhibiting cell proliferation and inducing cell apoptosis [101]. The radiation protection is an important means to reduce the side effects of radiotherapy. Xu et al. found that flavonoids from R. roxburghii could regulate the Bcl-2 (Ca (2+))/Caspase-3/ PARP-1 and PARP-1/AIF apoptosis signaling pathways to reduce cell apoptosis and DNA damage, correct radiation-induced histopathological changes, promote the formation of splenic nodules, resist sperm distortion and protect the thymus, and had a significant anti-radiation effect [102,103]. Polyadenosyl diphosphate polymerase-1 (PARP-1) is the most abundant isomer in the PARP gene family, which is involved in many cellular functions, including DNA repair, post-transcriptional gene expression, regulation of inflammation, and cell death. Mitochondrial reactive oxygen species (ROS) are a DNA damage agent produced by cells due to radiation, which can cause base damage, single or double strand breaks, and stimulate mitotic cell cycle arrest. PARP-1 and ROS are two major participants of radiation injury. Xu et al. found that flavonoids from R. roxburghii could control the production of ROS by regulating PARP-1, thereby protecting DNA from radiation damage to play its anti-radiation effect [104]. Studies have shown that heavy metal poisoning by Pb, Mn, Cd, As, Hg, and F can inhibit the antioxidant function and immune function of the body, aggravating the damage of lipid peroxidation to the body serum, liver, kidney, and central nervous system, which in turn leads to skin mucosal lesions, polyneuritis, liver, and kidney function damage. R. roxburghii has obvious detoxification and detoxification effects on these heavy metal poisoning, and the mechanism may be related to its vitamin C, SOD, polysaccharide, and trace elements [105]. Studies have shown that R. roxburghii can promote the excretion of heavy metals, supplement trace elements in the body, enhance SOD activity in blood, liver, and kidney tissues, and promote the increase of T lymphocytes in the body, significantly improve the immune function and antioxidant capacity of the body, and reduce the lipid peroxide (LPO) and lipid peroxidation degradation product malondialdehyde (MDA) content, thereby lowering the total metal accumulation in blood, liver, and kidney tissues, and reducing the secondary damage caused by heavy metal poisoning [106,107,108,109,110,111]. The clinical trials have shown that the oral liquid of R. roxburghii had a Pb repellent effect similar to EDTA, and could reduce the Pb concentration of blood and cure the Pb poisoning without causing trace element disorder [112,113]. Human organs are important as well as fragile. Bad habits such as drinking, staying up late, getting angry, and overeating can upset the substance metabolism in the body, disrupt the water-liquid and acid-base balance, and affect the function of organs, thus posing a threat to human health. R. roxburghii has rich functional ingredients to protect human organs by regulating metabolic disorders, maintaining water-liquid and acid-base balance. Modern studies show that R. roxburghii has a good protective effect on human heart, liver, stomach, spleen, lung, kidney, and other organs. Flavonoids from R. roxburghii have protective effects on cardiovascular and myocardial cells, and also have protective effects on cardiotoxicity caused by chemotherapy drugs. Studies have shown that flavonoids from R. roxburghii have a good protective effect on the renal and cardiac function in rats with chronic renal heart syndrome. It can enhance the antioxidant damage ability of the body, improve the parameters of blood rheology, and regulate the excessive stress of endoplasmic reticulum of kidney and heart cells [114]. Molecular studies have shown that flavonoids from R. roxburghii play a role in protecting myocardial tissue in the recovery of heart function in rats with chronic heart failure by regulating the expression of integrin β1, FAK and apoptosis-related proteins in the integrin signaling pathway in myocardial tissue of rats with chronic heart failure [115]. In addition, Yuan et al. found that flavonoids from R. roxburghii could control the cardiotoxicity caused by adriamycin (DOX) by inhibiting autophagy, and down-regulate the autophagy of cardiomyocytes induced by DOX, thus protecting the cardiotoxicity induced by DOX [116]. Early research found that R. roxburghii fruits have effects of strengthening the spleen, aiding digestion, and having the analgesic effect of atropine; the root decoction of R. roxburghii has the medicinal value of preventing and treating gastric diseases and protecting gastric mucosa. The R. roxburghii root decoction can significantly reduce acute gastric mucosal injury, decrease the rise of lipid peroxide, and improve the activity of SOD without affecting the secretion of gastric acid [117]. In recent years, studies have shown that R. roxburghii juice has obvious therapeutic effects on gastric ulcer, and its mechanism may be related to inhibiting gastric acid, pepsin, MDA, and other damage factors, increasing the content of trefoil factor-2 (TFF-2), epidermal growth factor (EGF), and vasoactive substance NO, promoting protective factors such as SOD and prostaglandin E2 (PGE2), producing antioxidant free radicals and anti-inflammatory effects so as to repair and improve gastric mucosa [118,119]. R. roxburghii can improve alcohol-induced drunkenness and liver damage. Studies have shown that R. roxburghii has a good anti-alcoholism effect by increasing the activities of liver alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) to accelerate ethanol metabolism and reduce the blood ethanol concentration in acute drunken mice. Meanwhile, R. roxburghii can also improve the SOD activity and GSH content in the liver of acute hepatic injury mice, enhance the free radical scavenging capacity of the organism, reduce the alanine aminotransferase (ALT) activity, aspartate aminotransferase (AST) activity, and MDA contents in blood, inhibit liver swelling, and play a prominent role in protecting liver [120,121]. Yang et al. studied the anti-alcohol hepatoprotective mechanism of R. roxburghii on the gene level [122]. They found that R. roxburghii alleviated the overexpression of oxidative stress response genes (Hmox1, Gsta1, Gstm3, Nqo1, Gclc, Vldlr, and Cdkn1a), and promoted the expression of alcohol down-regulated metabolism genes (Angptl8, Slc10a2, Ces3b, Serpina12, C6, and Selenbp2), thereby reducing serum and liver triglyceride levels and effectively resisting chronic alcoholic liver injury. Furthermore, Zhou et al. found that polyphenols from R. roxburghii are important active components of R. roxburghii playing an anti-alcohol hepatoprotective effect, which can improve ethanol-induced drunkenness and liver injury by inhibiting oxidative stress and lipid peroxidation, and its anti-oxidative stress mechanism may be related to up-regulating the expression of nuclear factor E2-related factor 2 (Nrf2) in liver tissue Nrf2/ARE signaling pathway and activating its downstream antioxidant enzyme heme oxygenase-1 (HO-1) [123]. R. roxburghii can alleviate arsenic-induced liver injury. R. roxburghii can reduce the accumulation of arsenic in the body, improve the arsenic-induced element metabolism disorder, reverse arsenic-induced weight loss, antagonize arsenic-induced serum and liver oxidative damage and the inhibition of antioxidant enzymes, and effectively inhibit arsenic-induced liver toxicity, thereby alleviating the liver damage caused by coal-fired arsenic [124]. Molecular studies have shown that as can activate the Nrf2/GPX4 signal pathway, increase oxidative stress, and then promote As-induced liver injury of MIHA cells, while R. roxburghii can inhibit the Nrf2/GPX4 signal pathway, reduce oxidative stress, and thus reduce As-induced liver injury [125]. Clinical studies have shown that R. roxburghii can resist renal interstitial fibrosis by regulating the expression of “fibroblast growth factor 23 (FGF23)—Klotho protein axis” in patients, which has a good role in delaying the progress of chronic kidney disease, and has a better efficacy and safety for patients with spleen—kidney—deficiency CKD stage 3 [126]. Studies on unilateral ureteral obstruction (UUO) model rats show that R. roxburghii can alleviate renal fibrosis and injury in UUO rats by mediating TGF-β/Smads signaling pathway to prevent fibrosis and inhibit oxidative stress [127]. Guo et al. found that both the R. roxburghii lyophilized powder and SIRT1 agonist (resveratrol) could reduce the production of lipid peroxides in renal tissue of rats with renal interstitial fibrosis after unilateral ureteral ligation, and increase the contents of antioxidant enzymes, protect the damaged renal tubular epithelial cells, and effectively improve renal fibrosis. Its mechanism might be related to its being rich in vitamin C, SOD, flavonoids, and other antioxidants, which can activate SIRT1 in vivo, thus activating Smad7 to down-regulate the expressions of TGF-β1, TGF-βRI, Smda2, and Smad3 [128]. In addition to the pharmacological activities mentioned above, R. roxburghii and its extracts have the effects of improving sleep, anti-inflammatory and analgesic, anti-fatigue, anti-aging, and improving male fertility [65,129]. The side effects of R. roxburghii have not been found so far. Moreover, studies have found that R. roxburghii flowers, leaves, rhizomes, seeds, and pomace are rich in active substances, which have different edible and medicinal effects and have great development value. For example, R. roxburghii leaves are rich in flavonoids, and have strong antioxidant activity, which are the main medicinal ingredients of children’s digestion aperitif Granules [5]. R. roxburghii petals are rich in nutrients, especially vitamins, phenols, and anthocyanins, and other antioxidant substances, which have great potential for health food development [17]. R. roxburghii seeds can inhibit melanin synthesis by inhibiting tyrosinase activity, so as to play a whitening and skin care role. It can also induce apoptosis of human hepatoma cell HepG2 and effectively prevent and treat tumors [130]. The root decoction of R. roxburghii can treat ulcerative colitis, and triterpenoids, ellagic acids, flavonoids and oligosaccharide compounds in its rhizome are the main effective components of its anti-inflammatory effect [131,132]. R. roxburghii fruit pomace fermented by pleurotus ostreatus has good bowel soothing and defecating effects [133]. In general, the relevant pharmacological activities and their mechanism and relevant active ingredients of R. roxburghii are shown in Table 7. As the 3G fruit in China, R. roxburghii has a unique flavor and homology of medicine and food. Its medicinal and edible value is far higher than that of ordinary fruits. Due to its long-term wild or semi-wild status, it is in short supply in the international market and has gradually become a key industry for local development [1]. Since the 1980s, the new development and use of R. roxburghii has rapidly prospered in China. Guizhou, Yunnan, Guangxi, Sichuan, and other provinces have successively established factories and enterprises to develop a large number of R. roxburghii-related products. At present, there are a group of leading and demonstrating enterprises in China, which have cultivated a number of well-known and influential brands of R. roxburghii products, such as “the King of Cili”, “Golden Cili”, “King of Wild Fruits”, and so on. Brands such as “Panzhou Cili Candied Fruits”, “Golden Cili”, and “Longli Cili” in Guizhou Province have won the authorization of national geographic indication protection products. In addition, under the impetus of the government, R. roxburghii processing enterprises cooperate with China Agricultural University, Guizhou University, Taiwan Yilan University, and a number of biological companies to build laboratories and analytical test centers to develop R. roxburghii food, skin care products, health products, and drugs, and have successfully developed multiple products. Japanese and French research centers have also begun to import fresh fruits and juice of R. roxburghii from China for component test and high-end product development. At present, the developed R. roxburghii products in the market include: (1) Food and beverage, such as R. roxburghii fresh fruits, R. roxburghii beverage, R. roxburghii preserved fruits, R. roxburghii canned goods, R. roxburghii sugar, R. roxburghii jam, R. roxburghii lamb, R. roxburghii buccal tablets, R. roxburghii biscuits, R. roxburghii wine, R. roxburghii tea, R. roxburghii jelly, etc. (2) Health care products, such as R. roxburghii juice, R. roxburghii oral liquid, R. roxburghii vinegar, R. roxburghii refined powder, R. roxburghii freeze-dried powder capsules, etc. (3) Drugs, such as Jincishenjiuzheng Mixture, Kangaifuzheng Capsules, Xuezhiping Capsules, Yishenjianwei Oral Liquid and Cactus Weikang Capsules, etc. (4) Skin care products, such as R. roxburghii essence and so on. In addition, R. roxburghii medicinal products (e.g., natural VC, R. roxburghii SOD capsule) and cosmetics are in the stage of the further research and development [3,134,135]. Although as an important raw material for the domestic food and beverage, alcohol and nutrition, and health products industry, R. roxburghii has been developed in a variety of R. roxburghii products in China; the common R. roxburghii products in life are mainly the low-end products of the industrial chain, such as dried fruit, beverages, preserved fruit cake, R. roxburghii fruit juice, and R. roxburghii fruit wine, while the market share of the high-end products such as health products, drugs, nutritional supplements, food additives, cosmetics, and super foods is at a low level relatively. Moreover, few products have been researched and developed on the cheap and high-quality natural nutrition resources of R. roxburghii flowers, leaves, roots, seeds and pomaces, which have broad development prospect in health care products, tea, and medicine. Although the Chinese R. roxburghii industry has developed rapidly in recent years, its large-scale planting, product branding, and the share increase of high-end products remain need to be further strengthened. In summary, R. roxburghii is a potential plant constituent pool with a large number of antioxidants, antibacterials, anti-atherosclerosis, anti-diabetes, and anti-apoptosis compounds. These bioactive plant ingredients allow R. roxburghii not only to be developed into a variety of nutritious foods, but also as a unique framework to discover innovative health products, drugs, nutritional supplements, food additives, cosmetics, superfoods, and other high-end products. However, although R. roxburghii was included in the 2003 edition of “Quality Standard for Chinese Medicinal Materials and Ethnic Medicinal Materials in Guizhou Province”, the “Standard”only has a simple description of the character description, color reaction, and thin layer identification test of R. roxburghii, and lacks specific quantitative indicators. Up until now, there are some studies on the quality control of R. roxburghii, but the number of these studies is limited. In terms of qualitative identification, there are some qualitative identifications of fresh fruits, roots, and leaves of R. roxburghii. On the other hand, for quantitative determination, there have been some quantitative determinations of vitamin C, phenols, flavonoids, triterpenes and 2 triterpenoid components uscaphic acid and 1-β-hydroxy euscaphic acid in R. roxburghii fruits, gallic acid, polyphenols and total flavonoids in R. roxburghii leaves, total polyphenols, and ellagic acids in R. roxburghii roots, and the determination of four triterpenoids in R. roxburghii leaves, roots, stems, and fruits. In terms of quality marker screening, four triterpenoids, including rosolic acid, valeric acid, echinacoside, and rosamultin, were used as quality markers for the quantitative determination of R. roxburghii leaves, roots, stems, and fruits. Total polyphenols and ellagic acids were selected as quality markers for R. roxburghii roots [136,137,138,139,140,141,142,143]. Overall, although there are some progresses in the research on the quality control of R. roxburghii, there are many deficiencies. Firstly, although there are some studies on the quality control of R. roxburghii fruits, leaves, and roots, the research into R. roxburghii stems and flowers is almost blank. Secondly, there are few studies on quantitative determination and the active components involved are very limited, which are not enough for the reference of screening the quality markers of R. roxburghii. Finally, among the existing researches, there is no research to prove that the four triterpenoids of rosa acid, tormentic acid, rosaside, and rosamultin are closely related to any pharmacological activity of R. roxburghii; it thus is inappropriate to use them as quality markers. At the same time, the very high content of antioxidant active substance SOD in R. roxburghii fruits has not been studied as a quality marker of R. roxburghii fruits, which is very regrettable. In view of the current situation of research and development of R. roxburghii, we put forward some suggestions from the following aspects: Firstly, to focus on the qualitative and quantitative studies of organic acids, polyphenols, flavonoid triterpenoids, and polysaccharide components related to the efficacy of R. roxburghii, so as to provide the reference for the revision of R. roxburghii quality standards. Secondly, to develop a complex, enhanced nutritional complementary food and drug products based on the nutritional characteristics of R. roxburghii and in combination with the nutritional characteristics of other substances. At the same time, to strengthen the research on the pharmacological mechanisms of R. roxburghii alleviating or treating corresponding diseases and the development of the related preparations. Thirdly, research on tannin removal and flavor regulation of R. roxburghii fruits and related products should be strengthened to improve and enrich the taste of R. roxburghii food and its functional food, so as to improve consumers acceptance and broaden the market of them. Finally, besides fruits of R. roxburghii, more research on its flowers, leaves, roots, seeds, and pomaces should be strengthened to fully develop these cheap and high-quality natural nutrition resources, especially the development of polyphenols, flavonoids and triterpenoids in R. roxburghii leaves, which can be used as the source of natural medicines.

PMC10001410

Rajakumar Anbazhagan,Raghuveer Kavarthapu,Ryan Dale,Kiersten Campbell,Fabio R. Faucz,Maria L. Dufau

miRNA Expression Profiles of Mouse Round Spermatids in GRTH/DDX25-Mediated Spermiogenesis: mRNA–miRNA Network Analysis

27-02-2023

miRNAs,miRNA–mRNA network analysis,GRTH,round spermatids,spermatogenesis,transcriptome analysis

GRTH/DDX25 is a testis-specific DEAD-box family of RNA helicase, which plays an essential role in spermatogenesis and male fertility. There are two forms of GRTH, a 56 kDa non-phosphorylated form and a 61 kDa phosphorylated form (pGRTH). GRTH-KO and GRTH Knock-In (KI) mice with R242H mutation (lack pGRTH) are sterile with a spermatogenic arrest at step 8 of spermiogenesis due to failure of round spermatids (RS) to elongate. We performed mRNA-seq and miRNA-seq analysis on RS of WT, KI, and KO to identify crucial microRNAs (miRNAs) and mRNAs during RS development by establishing a miRNA–mRNA network. We identified increased levels of miRNAs such as miR146, miR122a, miR26a, miR27a, miR150, miR196a, and miR328 that are relevant to spermatogenesis. mRNA–miRNA target analysis on these DE-miRNAs and DE-mRNAs revealed miRNA target genes involved in ubiquitination process (Ube2k, Rnf138, Spata3), RS differentiation, and chromatin remodeling/compaction (Tnp1/2, Prm1/2/3, Tssk3/6), reversible protein phosphorylation (Pim1, Hipk1, Csnk1g2, Prkcq, Ppp2r5a), and acrosome stability (Pdzd8). Post-transcriptional and translational regulation of some of these germ-cell-specific mRNAs by miRNA-regulated translation arrest and/or decay may lead to spermatogenic arrest in KO and KI mice. Our studies demonstrate the importance of pGRTH in the chromatin compaction and remodeling process, which mediates the differentiation of RS into elongated spermatids through miRNA–mRNA interactions.

miRNA Expression Profiles of Mouse Round Spermatids in GRTH/DDX25-Mediated Spermiogenesis: mRNA–miRNA Network Analysis GRTH/DDX25 is a testis-specific DEAD-box family of RNA helicase, which plays an essential role in spermatogenesis and male fertility. There are two forms of GRTH, a 56 kDa non-phosphorylated form and a 61 kDa phosphorylated form (pGRTH). GRTH-KO and GRTH Knock-In (KI) mice with R242H mutation (lack pGRTH) are sterile with a spermatogenic arrest at step 8 of spermiogenesis due to failure of round spermatids (RS) to elongate. We performed mRNA-seq and miRNA-seq analysis on RS of WT, KI, and KO to identify crucial microRNAs (miRNAs) and mRNAs during RS development by establishing a miRNA–mRNA network. We identified increased levels of miRNAs such as miR146, miR122a, miR26a, miR27a, miR150, miR196a, and miR328 that are relevant to spermatogenesis. mRNA–miRNA target analysis on these DE-miRNAs and DE-mRNAs revealed miRNA target genes involved in ubiquitination process (Ube2k, Rnf138, Spata3), RS differentiation, and chromatin remodeling/compaction (Tnp1/2, Prm1/2/3, Tssk3/6), reversible protein phosphorylation (Pim1, Hipk1, Csnk1g2, Prkcq, Ppp2r5a), and acrosome stability (Pdzd8). Post-transcriptional and translational regulation of some of these germ-cell-specific mRNAs by miRNA-regulated translation arrest and/or decay may lead to spermatogenic arrest in KO and KI mice. Our studies demonstrate the importance of pGRTH in the chromatin compaction and remodeling process, which mediates the differentiation of RS into elongated spermatids through miRNA–mRNA interactions. Spermatogenesis is a highly organized dynamic process whereby male germ cells develop and differentiate serially into mature spermatozoa [1]. This process requires timely coordinated gene expression that is tightly regulated at the transcriptional and post-transcriptional levels. Post-meiotic haploid round spermatids (RS) formed during early spermatogenesis have unique and complex transcriptomes, and precise quality control mechanisms are necessary for the transcribed mRNAs with varied functions [2]. In addition, translational silencing/repression and storage of essential mRNAs occur at specific cytoplasmic sites called chromatoid bodies (CBs) [3,4]. Gonadotropin-regulated testicular RNA helicase (GRTH; DDX25) is a member of the DEAD-box family of RNA helicase first identified in our laboratory, which play essential roles in the completion of spermatogenesis [5,6]. GRTH is expressed exclusively in the Leydig cells and germ cells. It plays several functions as a post-transcriptional regulator of specific genes in germ cells. GRTH knock-out (KO) mice lack sperm and elongating spermatids (ES), making them infertile. These mice exhibit incomplete spermatogenesis due to failure of RS to elongate [5]. In germ cells, the GRTH protein exists in two forms: a 61 kDa phospho form and a 56 kDa non-phospho form. The phospho-GRTH (pGRTH) is found in the cytoplasm and CBs. The non-phosphorylated form is found in the nucleus, in the cytoplasm, and in the CBs. The pGRTH protein in the cytoplasm participates in the shuttling of specific mRNAs in and out of the CBs and becomes associated with polyribosomes for translation. The non-pGRTH protein is involved in the export of specific mRNAs from the nucleus to the cytoplasm of germ cells. Earlier studies from our group revealed a missense mutation (R242H) in exon 8 of GRTH gene in non-obstructive azoospermic men, which causes loss of pGRTH protein in COS-1 cells expressing the GRTH (R242H) mutant construct. The 61 kDa phospho-species from the cytoplasm [7] and CBs are absent in GRTH knock-in (KI) transgenic mice (human GRTH gene with R242H mutation), but the non-phospho form from the cytoplasm, nucleus, and CBs is unaffected [4]. Recent studies using GRTH KI and GRTH KO mice revealed an important role of pGRTH in acrosome biogenesis and its structural integrity during spermiogenesis [8]. There is a significant reduction in CB size and complete loss of pGRTH inside CBs, revealing the importance of pGRTH in maintaining the structural integrity of the CB and the associated miRNA pathways [4,7,9]. CBs are dynamic perinuclear organelles that temporarily store mRNAs transported by GRTH from the nucleus to cytoplasm and then finally to the CB [4,10]. During spermatogenesis, the germ cells utilize a large subset of small noncoding regulatory RNAs, such as microRNAs, to control the expression of an array of genes at transcriptional or post-transcriptional levels [1,11,12]. miRNAs are a class of small non-coding RNAs (18–25 nucleotides) that act as endogenous gene regulators and participate in a wide array of biological functions by controlling the stability of mRNAs or promoting target mRNA degradation and inhibition of translation [12,13,14]. miRNAs control posttranscriptional regulation of essential mRNAs to secure the correct timing of translation, which are critical for the final stages of spermiogenesis [10]. Several miRNAs were found to be involved in the regulation of mammalian spermatogenesis and any changes can lead to male infertility [15,16]. miRNAs, such as miR122-5p, can act as potential biomarkers of male infertility [17]. Each miRNA has the capacity to target several mRNAs from many genes at once, thereby tightly regulating gene expression in every organ. Sequence-specific base pairing in the RNA-induced silencing complex with the Argonaute proteins allows miRNAs to identify their target mRNAs (AGO). The Drosha–DGCR8 complex processes primary miRNA transcripts to produce precursor miRNAs, which are where majority of miRNAs originate (pre-miRNAs). These pre-miRNAs are carried to the cytoplasm, where Dicer-dependent or -independent pathways are used to produce mature miRNAs. Germ cells and Sertoli cells have been shown to contain the transcripts for the AGO proteins Drosha and Dicer [11,18]. At the mRNA and protein levels, GRTH controls the expression of numerous microprocessor complex proteins, Drosha, and DGCR8 (which is involved in miRNA synthesis) [11]. In haploid RS, it has been shown that miRNA biogenesis pathway proteins accumulate in the CBs, indicating that the CB and GRTH play a significant role in miRNA-dependent gene regulation [11,19]. The main components of CBs are mRNAs, short RNAs (including piRNA and miRNA), long non-coding RNAs, RNA-binding proteins (including DDX25 and the germ cell marker mouse Vasa homolog [MVH/DDX4]), and other proteins involved in RNA processing [3,4,9]. Owing to the relative importance of RNA regulatory and transport functions of GRTH and its involvement in miRNA biogenesis (at the mRNA and protein levels), it is imperative to study the functions of these components during spermatogenesis. To understand the precise role of pGRTH in the regulation of germ-cell-specific mRNA and implications of miRNAs during spermiogenesis, RS isolated from germ cells of WT, GRTH-KO, and GRTH-KI mice were analyzed using RNA-seq to compare their transcriptome (mRNA-seq) profiles with miRNA profiles. This study delineates the impact of pGRTH/DDX25 on putative mRNA–miRNA interaction in the regulation of chromatin compaction, remodeling, and ubiquitination processes, which are essential for the progression and completion of spermiogenesis. GRTH-KO and GRTH-KI transgenic mice were generated as described previously [5,7]. Briefly, the WT, GRTH-KO, and GRTH KI transgenic mice (10–12 weeks) were genotyped and used for all experiments. All animals were housed in pathogen-free, temperature- and light-controlled conditions (22 °C), with 14 h: 10 h light:dark cycle and ad libitum access to water and food. All animal experiments were performed in accordance with the guidelines established by the National Institute of Child Health and Human Development Animal Care and Use Committee. RS were isolated from the testes of GRTH-KO, GRTK-KI, and WT mice (75–85 days old) using a standardized procedure as described previously with minor modifications [20]. Briefly, testes (all genotypes) were decapsulated, seminiferous tubules mildly dispersed and digested using 1 mg/mL collagenase solution (in 1× Krebs buffer; Worthington, Lakewood, NJ, USA) at 37 °C for 3 min to remove Leydig cells. The tubules were washed with Krebs buffer (2 times) at RT and then digested with 0.6 mg/mL trypsin (in 1× Krebs buffer; Sigma-Aldrich, St. Louis, MO, USA) containing DNase I (Thermo Scientific, Waltham, MA, USA) at 34 °C for 15 min (~15 rpm). The obtained germ cell suspension was pre-chilled on ice (7 min) and filtered with a 40 μm filter (Falcon, Corning, NY, USA). The cells were centrifuged (600 g, 5 min, 4 °C) and washed with ice-cold Krebs buffer. The cell pellet was mixed with 3 mL of 0.5% BSA (in 1× Krebs buffer) and filtered again with a 40 μm filter to obtain single-cell suspension of germ cells. The germ cells (in 0.5% BSA) were added onto a BSA gradient (1% to 5% BSA-Krebs Buffer, top to bottom) and allowed to settle for 90 min on ice. Following sedimentation, 1 mL fractions were collected and washed in ice-cold Krebs buffer before cell viability was determined using an automated cell counter (Cell countess, Thermo Scientific). DAPI staining (Thermo Scientific) was used to confirm the purity of the RS cell fractions, which was then examined under a microscope (EVOS M-5000, Thermo Scientific). RNAeasy Plus micro kit was used to isolate total RNA from six RS samples obtained from the testes of WT, GRTH-KO, and GRTH-KI mice (N = 6; Qiagen, Germantown, MD, USA). RNA quantity and integrity were evaluated in an Agilent Bioanalyzer 2100 system using the RNA Nano 6000 Assay Kit (Agilent Technologies, Santa Clara, CA, USA). Sequencing libraries were prepared using a Takara Pico-RNA-seq kit (Takara) without the polyA selection step. The prepared libraries were quality-checked using a bioanalyzer (Agilent) and used for sequencing using an S1 reagent kit v1.5 (SR100 cycles flow cell (~60 million reads per sample) in a Novaseq 6000 (Illumina, San Diego, CA, USA) platform. The RNA-seq data have been submitted to the Gene Expression Omnibus (GEO; accession number GSE222626). Quality control was performed on paired-end reads using FastQC (Andrews), RseQC [21], Picard (Broad Institute), and MultiQC [22] both before and after trimming reads with cutadapt v3.4 [23] with arguments -q 20, -A, -a, and –minimum-length 25 (arguments for light quality trimming, adapter removal, and retains only reads >25 bp, respectively). Reads were aligned to the GRCm38 mouse reference genome using the STAR aligner v2.7.8a [24] with arguments --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverReadLmax 0.04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 to match ENCODE standard options for long RNA sequencing. To quantify reads in genes, the GENCODE release 18 annotation was used with featureCounts (in the subread package v2.0.1 [25]) in strand-specific mode (-s2 argument). Differential expression analysis between genotypes was conducted on gene counts using DESeq2 v1.34.0 using the model ~genotype. Genes were identified as differentially expressed if, when using lfcThreshold of 1 to test the null hypothesis that the LFC between conditions is different from 1, they had an adjusted p-value < 0.1. Note that the “ashr” shrinkage method [26] was used instead of the default “apeglm” method. All mRNA identified as differentially expressed in the KO vs. WT and/or KI vs. WT comparisons were further used for functional enrichment analysis, conducted with clusterProfiler v3.18.1 across the GO Biological Processes (BP), Cellular Component (CC), and Molecular Function (MF) databases. Genes were annotated with the three main GO categories (BP, CC, and MF) and were represented separately. A q-value threshold of 0.1 was imposed for the functional enrichment analysis. To validate selected differentially enriched mRNA from RNA-seq transcriptome analyses, real-time quantitative PCR (qRT-PCR) was used. Total RNA was extracted from RS (N = 3) of WT, GRTH-KO, and GRTH-KI mice testes using mRNeasy Micro Kit (Qiagen). Iscript’s first-strand synthesis kit was used to generate cDNA from one microgram of total RNA (Biorad, Hercules, CA, USA). qRT-PCR was carried out on a Quantstudio 3 Fast Real-Time PCR device (Thermo Scientific, CA, USA) with Fast SYBR green and a set of particular gene primers (Supplementary Table S1). All qRT-PCR reactions were performed in triplicates and 18srRNA was used as the reference gene for normalization. The comparative quantification of mRNA was performed using the 2−ΔΔCt method. Small RNAs (<200 bp) were purified from isolated RS (>94% purity) obtained from testes of GRTH-KI, GRTH-KO, and WT mice (N = 5) using miRNeasy Micro Kit (Qiagen). RNA quality and quantity were assessed using the RNA Nano 6000 Assay Kit in an Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Small RNA library construction was carried out using a small RNA-seq Library Kit (Qiagen). The prepared libraries were quality checked using a bioanalyzer (Agilent) and were sequenced using a Novaseq 6000 (Illumina) SP reagent kit v1.5 (100 cycles). The miRNAs were given a general name (e.g., miR26a) without specifying the strand name (e.g., miR26a-5p) for ease of use. All miRNAs identified and discussed in the study are -5p (strand) unless otherwise specified. The miRNA-seq data have been submitted to the Gene Expression Omnibus (accession number GSE222627). miRNA sequencing analysis was conducted similarly to the described mRNA-seq analysis, with the following modifications. Sequenced reads were obtained in single-ended fastq files. When trimming reads with cutadapt [23], only the -a argument was used to trim QIAseq miRNA 3′ adapters and –minimum-length was reduced to 15 to account for shorter reads following trimming. Reads were aligned to the GRCm38 mouse reference genome using Bowtie2 v2.4.2 [27] with arguments –local, –very-sensitive-local, –k 100 to match suggested parameters for optimized miRNA alignment [28]. Reads were counted in annotated miRNA genes according to the miRBase release 18 annotation, with the appropriate stranded mode (-s1 argument) indicated for featureCounts. When conducting differential expression analysis, two experimental batches were analyzed independently. The KI vs. WT contrast was performed using the first batch and the KO vs. WT contrast was conducted using the second batch. Due to the batch effect, the KI vs. KO contrast could not be confidently executed. No lfcThreshold was imposed for these contrasts. To validate the differentially enriched miRNA obtained from miRNA-seq transcriptome analyses, small RNA prepared from the RS (N = 3) of GRTH-KO, GRTH-KI, and WT were used. Small RNA (<200 bp) was extracted using miRNeasy Micro Kit (Qiagen); quantity and quality were checked prior to cDNA synthesis. Ten nanograms of small RNA (for miRNA quantification) was used to prepare cDNA using the miRCURY LNA RT Kit (Qiagen). qRT-PCR analysis was carried out with set of specific LNA gene probes (Qiagen; Supplementary Table S2) using with miRCURY LNA SYBR® Green (Qiagen) in a Quantstudio 3 Fast Real-Time PCR machine (Thermo Scientific, CA, USA). The following conditions were used: 95 °C for 30 s, followed by 40 cycles of 95 °C for 3s and 60 °C for 30 s. The cycle threshold (Ct) values were normalized to U6 small nuclear RNA as the reference gene, and each experiment was carried out in triplicate. The 2−ΔΔCt method was used to determine the relative miRNA quantification. A stringent set of miRNA–mRNA target pairs were compiled by taking the intersection of three miRNA databases, TargetScan v8.0 [29], TarBase v8 [30], and miRDB v6.0 [31]. This set of predicted miRNA–mRNA target pairs was further filtered to include only miRNAs identified as differentially expressed in this analysis, then subsequently reduced to miRNA–mRNA pairs for which the mRNA target was differentially expressed in the mRNA-seq analysis. This was performed separately for the KO vs. WT and the KI vs. WT. That is, for each of these contrasts, a miRNA–mRNA pair was considered to be supported by this study if all of the following conditions were true: (1) the miRNA–mRNA pair was known or predicted in all three databases; (2) the miRNA was differentially expressed; and (3) the target mRNA was differentially expressed. Note that multiple miRNAs can target one mRNA, and one miRNA can target multiple mRNAs. We did not restrict pairs to be exclusive one-to-one miRNA–mRNA pairs, and we did not restrict pairs to require the opposite direction of miRNA and mRNA so this analysis would include indirect effects. A directed graph was built using the miRNA–mRNA pairs that met the criteria described above, where each pair was further characterized by the following qualities and where “contrast” refers to a single comparison, such as KO vs. WT: (1) miRNA differentially expressed in both contrasts; (2) mRNA differentially expressed in both contrasts; (3) canonical (miRNA and mRNA have opposite log2 fold change sign) or not in a contrast; (4) magnitude of mRNA differential expression in a contrast; (5) identity of miRNA–mRNA targets based on criteria described above. These data were encoded into node and edge attributes of the graph using the networkx Python package and plotted using the matplotlib Python package. COS-1 cells (0.1 × 106 cells/well) were seeded in 12-well plates 24 h before transfection. Cells (70% confluence) were transfected with psiCHECK-2 construct carrying the TP2 coding region alone or together with its 3′ UTR. In addition to DNA constructs, miR122 miRNA mimic (5 nM) alone or miR122 miRNA mimic together with miR122 miRNA inhibitor (20 nM) or negative control (5 nM) were co-transfected using HiPerFect transfection reagent (Qiagen) in a serum-free medium. After incubation (8 h), the media with serum (10%) without antibiotic was changed and further incubated at 37 °C, 5% CO2. After 48 h of transfection, the cells were lysed with 1X passive lysis buffer (Promega, WI). Firefly and Renilla luciferase activities were measured with the Dual-Luciferase reporter assay system (Promega) using a Glomax navigator microplate luminometer (Promega). All data were obtained from three or more experiments and the results are presented as mean ± standard error of the mean (SEM). Significance of the differences between the groups was determined by a two-tailed Student t-test using the GraphPad Prism software program (GraphPad Software, Inc., San Diego, CA, USA) and Microsoft Excel (Microsoft). p < 0.05 was considered statistically significant. We initially performed mRNA-seq from the isolated RS of KO, KI, and WT mice (N = 6) to analyze the transcriptome changes in the KO and KI compared to WT. In total, we obtained more than 70 million reads in each RNA-seq library from the RS of KO, KI, and WT mice. Heterogeneity among these individual samples is highlighted in a principal components analysis (PCA) plot (Supplementary Figure S1). WT samples show a clear separation compared to KO and KI, while there is no distinction between KO and KI genotypes. An MA plot (log2 fold change vs. average of counts) was created using the DEGs from KO, KI, and WT groups. Significantly upregulated and downregulated DEGs in each RS of KO or KI compared to WT were shown as red dots (Figure 1A,B). A total of 255 genes downregulated and 114 genes upregulated were identified with a log2 fold change magnitude greater than one fold in the KO group compared to WT. There were 297 genes downregulated and 113 genes upregulated in KI compared to WT. Most enriched mRNAs in KO overlap with KI (Supplementary Figure S3). To further analyze and classify the biological function of identified DEGs, we performed functional enrichment analysis using GO in the biological process (BP), cellular component (CC), and molecular function (MF). BP revealed that the genes which play critical roles in germ cell development, spermatid differentiation, and development, such as Tnp1/2, Tssk3/6, Prm1/2, H2al2a, Tbc1d20, Fscn3, Spem1, H1f7, Neurl1a, and Paqr5, were altered significantly. DNA packaging and conformation (Tnp1/2, Prm1/2/3, H2al2a, H2bl1, Tssk6, H1f7, Naa60), histone exchange (Prm1/2/3, H1f7), and sperm motility (Tnp1/2, Prm3, Smcp, Cabs1, Gapdhs, Spem1, Tppp2, Efcab1, Neurl1a, Akap4) also demonstrated a significant downregulation in KO compared to WT. The BP of KI vs. WT was similar to the KO vs. WT. Changed genes are also enriched in CC categories, including DNA–protein complex, nucleosomes, DNA packaging complex (Tnp1/2, Prm1/2/3, H2al2a, H2bl1, H2ap), sperm flagella and cilia (Oaz3, Oxct2b, Spata18, Cabs1, Gapdhs, Camsap3, Atp1b3, Tppp2, Odf1, Efcab1, Akap4) in both KO and KI compared to WT (Figure 2A,B). Thus, differentially expressed genes appear to be related to spermatid differentiation, as would be expected from RS. To validate the mRNA-seq transcriptomic data, genes relevant to spermatogenesis and genes which were found to be interacting with identified miRNAs (mRNA–miRNA interaction studies) were selected. Their expression levels were confirmed by qRT-PCR analyses. Genes such as Rnf138, Ube2k, Csnk1g2, Hipk1, Pim1, Jag1, Mical3, Pdzd8, and Ppp2r5a show a significant downregulation (p < 0.05) in the RS of KO mice compared to RS of WT mice (Supplementary Table S3). In the RS of KI mice, the transcripts such as Rnf138, Ube2k, Csnk1g2, Hipk1, Pim1, Jag1, Mical3, Pdzd8, Akap1, Mbd2, and Prkcq downregulated significantly (p < 0.05) compared to RS of WT group. Rnf138 and Ube2k are involved in ubiquitination pathways which are critical for the later stages of spermatid development. Genes such as Jag1 (notch pathway), Akap1 (regulate cAMP levels), and Pdzd8 (acrosome stability) regulate spermatogenesis. Other genes such as Csnk1g2, Pim1, Prkcq, and Ppp2r5a mediate protein phosphorylation and dephosphorylation. The RNA-seq and GO analysis results suggest altered ubiquitination, phosphorylation, and dephosphorylation events which are comparable with the expression data of qPCR (Figure 3). The list of mRNAs (which are altered and show an interaction with altered miRNA profiles), together with their role in spermatogenesis and general cellular functions, is given in Table 1. Since each mRNA is regulated by more than one miRNA and one miRNA regulates more than one mRNA, it is essential to assess the entire RS population of miRNAs. miRNA-seq was performed from the isolated RS of KO, KI, and WT mice (N = 5). A total of around 80 million reads was obtained from each miRNA-seq library made from the RS of KO, KI, and WT mice. miRNA-seq was carried out in two different batches, KO vs. WT and KI vs. WT, and the overall results were compared between all the genotypes. A PCA plot of these samples shows a clear separation of WT compared to KO and KI, while there is no distinction between KO and KI genotypes (Supplementary Figure S2). Several miRNAs, such as miR32, miR184, miR335, miR140, miR141, miR1981, miR202, miR880, and miR669c, were downregulated in both KO and KI mice (Figure 4; Supplementary Table S4). These are involved in the positive regulation of spermatid differentiation, sperm motility, mRNA processing, and decay. The miRNAs miR150, miR196a-2, miR652, miR146, miR10b, miR379, miR122a, miR26a, miR27a, miR127, and miR328 were upregulated in both KO and KI mice (Figure 4; Supplementary Table S4), which negatively regulate the spermatid differentiation. Several enriched miRNAs in KO overlap with KI (Supplementary Figure S4). Expression analysis of selected miRNAs from RS of KO and KI groups were compared with RS of WT mice using qRT-PCR analysis. Bar graphs represent the fold change expression between KO vs WT or KI vs WT group (Figure 5A,B). The miRNAs such as miR140, miR141, miR32, miR184, and miR202 show a decrease in abundance and miRNAs miR150, miR146, miR122a, miR27a, miR328, and miR26a show an increase in abundance in the RS of the KO mice (Figure 5A). In KI, miR138, miR140, miR34a, miR202, miR32, miR335, and miR141 show a decrease in abundance and miR196a, miR223, miR485, miR322, miR24, miR26a, miR150 and miR146 show an increase in abundance in the RS of the KI mice (Figure 5B). The results of qPCR studies correspond to expression profiles from the miRNA-seq data. The miRNAs, miR322, and miR24 target Rnf138, miR322 target Pim1, miR26a target Ube2k, Jag1, Mical3, Pim1, Prkcq, and Hipk1 (Supplementary Table S5). All miRNA qRT-PCR experiments used LNA probes to obtain highly efficient expression data and validation. We selected the DEGs from mRNA-seq and miRNA-seq and then compared log2 fold changes between miRNA and putative mRNA targets based on the intersection of three published databases. The expected interaction relationship between a miRNA–mRNA pair should exhibit a negative correlation of expression (i.e., as miRNA expression increases, the expression of its mRNA target is expected to decrease). The mRNAs and miRNAs identified from the RS of KO, KI, and WT groups showing expected correlation (canonical, mRNA down = miRNA up or mRNA up = miRNA down) or not were highlighted in different color dots (Figure 6). Several target pairs are verified in this study to reveal the importance of these targets during late stages of spermiogenesis. All mRNA–miRNA pairs showing both canonical or expected (miRNA down and mRNA up or vice versa) and non-canonical (miRNA down and mRNA down) interactions were used for the network construction. We observe essential protein-coding genes such as Ube2k, RNF138, Pim1, Hipk1, Slc30a4, and Csnk1g2 regulated by miR26a (solid red lines indicate canonical for both KI and KO; solid black lines indicate just in KO or KI only) and other miRNAs mediate several essential pathways and intracellular function during spermatid differentiation and development. miRNAs (from the canonical mRNA–miRNA pairs) target multiple mRNAs and degrade them both in the cytoplasm and the CBs. The loss of mRNAs possibly resulted in the loss of essential proteins involved in ubiquitination, acrosome stability, and chromatin compaction that are required for spermatid differentiation and elongation events (Figure 7). The failure of these events resulted in spermatogenic halt and apoptosis of the RS. Temporal Tnp2 gene translation is critical for the spermatid chromatin compaction process, which precedes protamine transition. Even subtle changes will impact the downstream processes in spermatid development that result in infertility [7]. miR122a was found to be upregulated in the RS of KO mice. Modulation of translation of Tnp2 depends on the presence of regulators, such as specific miRNA, which binds to its 3′ UTR region and regulates it. To validate the interaction, a psiCheck2 luciferase reporter construct carrying the Tnp2 coding sequence with or without 3′ UTR was used to transfect COS-1 cells. In addition, miR122 mimic or miR122 mimic together with miR122 inhibitor or miR negative control was used for co-transfection. A schematic representation of the psiCheck2 reporter gene carrying the Tnp2 coding sequence with or without 3′ UTR is given in Figure 8A. The luciferase activity of Tnp2 with 3′ UTR was decreased significantly in the presence of miR122a mimic. In contrast, Tnp2 coding region (without 3′ UTR) did not alter the luciferase activity in the presence of miR122 mimic. To check the specificity of miR122a binding to the Tnp2 3′UTR, miR122a inhibitor was used together with miR122a mimic, which did not alter the luciferase activity (Figure 8B). This clearly shows that the miR122a binds to Tnp2 at its 3′UTR in a sequence-dependent manner and decreases its translation specifically, which altered the TP2 protein levels that directly impacted later stages of spermiogenesis. Spermatogenesis is a highly controlled serial developmental process resulting in the formation of functional spermatozoa. During spermiogenesis, RS undergoes 16 steps of development with elongating, condensing, and condensed spermatids. Regulation of spermatogenesis occurs at multiple levels, starting from post-transcriptional to translational regulation. Previously, we have shown that the KO and KI male homozygous mice are infertile due to the arrest of RS at step 8 of spermiogenesis resulting in the loss of ES and mature sperm [5,7]. Single-cell RNA-seq analysis of testicular germ cells revealed a crucial role of GRTH in round spermatid differentiation into elongated spermatids and acrosome biogenesis [8]. Given the intricacies of different germ cell heterogeneity and specific differentiation pathways, GRTH/DDX25 plays a critical role in mRNA regulation both directly and by regulating miRNA biogenesis [11]. In this study, we used mRNA-seq, miRNA-seq data, and mRNA–miRNA interaction studies to address the changing gene expression signatures and the role of miRNA regulation in the developing RS. This study provides clues on the role of pGRTH and mRNA–miRNA dynamics in the developing RS of WT, KO, and KI mice. In the current study, downregulation of several DEGs related to spermatogenesis, spermatid development, sperm motility, chromatin condensation, and DNA compaction were identified. Specifically, RNF138, UBE2K related to ubiquitination pathway and chromatin remodeling and transition proteins, Tnp1/2, Prm1/2/3, Spata 3/18, Tssk3/6, and several protein kinases and phosphatases altered during spermiogenesis. This current study demonstrated that the loss of pGRTH changed the transcriptomic profiles and may have indirectly impaired RNF138-, SPATA3-, and UBE2K-dependent histone modifications and nuclear transition protein-mediated chromatin remodeling [32]. This directly impacted the initiation of spermatid elongation at step 8 of spermiogenesis. Ring finger protein 138 (RNF138) is a member of an E3 ligase family that has been shown to be recruited to the regions of DNA double-strand breaks and repair them by homologous recombination [33]. Rnf138 is highly expressed in spermatogonia and spermatocytes, and Rnf138 deficiency promotes apoptosis of spermatogonia [34]. Histone ubiquitination and acetylation play a crucial role in chromatin remodeling, which is essential for the development of spermatids during spermiogenesis [35,36]. During step 8 of spermiogenesis, hyperacetylation of histones (H) leads to the replacement of histone followed by nuclear elongation and extension of the acrosome. Ubiquitin-conjugating enzyme E 2K interacts with Ring finger proteins, and its deficiency causes the failure of germ cells to undergo meiosis, resulting in male infertility. UBE2K is a component of the PRC1 complex that ubiquitinates histone H2A. UBE2K, in combination with RNF138, robustly induces the formation of ubiquitinated H3 [37]. The early-stage germ cells synthesize transcripts of these proteins and store them prior to nuclear condensation events. Differential expression of important mRNAs that mediate these events resulted in spermiogenesis halt. The final stages of spermiogenesis require several proteins that are essential for the differentiation of the RS, such as proteins involved in chromatin remodeling and compaction processes. During the chromatin remodeling process, 90% of the nucleosomal histones are replaced by testis-specific TP1/2. Subsequently it is replaced by sperm-specific PRM1/2 to form a highly condensed spermatid/sperm chromatin [38,39]. Differential expression analysis identified several genes such as Tnp1/2, Prm1/2/3, Spem1/2, Spata3/18, and Tssk3/6, which play critical roles in spermatid development, elongation, and chromatin compaction that were downregulated in KI and KO mice. SPATA 3 (spermatogenesis-associated protein 3), also known as Tsarg1, is expressed specifically at lower levels in pachytene spermatocytes and peaks in spermatids [40,41]. SPATA3 interacts with KLHL10, which is expressed exclusively in spermatids, and its inactivation leads to the disruption of spermiogenesis and complete male infertility in mice [42]. KLHL10 is a substrate-specific adapter that interacts with CUL3 (Cullin3), a core component of cullin-RING-based E3 ubiquitin-protein ligase complex functions specifically during spermiogenesis [43]. Our data shed light on the plausible role of SPATA3 and the involvement of GRTH in the SPATA3-, UBE2K-, and KLHL10-mediated protein ubiquitination pathway during spermiogenesis. The current study also identified mRNAs of several protein kinases and phosphatases, such as Csnk1g2, Hipk1, Prkcq, Pim1, and Ppp2r5a, which mediate several intercellular protein regulations inside developing spermatids which were targeted by miRNAs. Transition proteins and protamines are arginine-rich nuclear proteins that replace histones late in the haploid phase of spermatogenesis. Lack of these proteins leads to impaired nuclear condensation in the spermatid head, resulting in infertility. GRTH binding of Tnp2 mRNA decreased significantly in KI mice testis and inside the CB of germ cells [4]. Increased miR122a levels in the loss of GRTH in KO mice suggest that GRTH might have an intrinsic regulatory role in regulating the levels of miR122a. Another important miRNA identified in this study is miR26a, which targets several important mRNAs such as Ube2k, Rnf138, Pim1, Hipk1, Slc30a4, and Csnk1g2. These genes mediate several essential pathways and intracellular functions, which are critical for spermatid differentiation and development. miR26a regulates Jag1 (NOTCH ligand) expression, thereby regulating GDNF expression in Sertoli cells [44]. It also regulates sperm apoptosis by directly targeting PTEN and has a link with decreased sperm motility [45]. There is a significant overexpression of miR27a in infertile men with nonobstructive azoospermia [46], which target several genes, including H3K9 demethylase that regulates transcriptional suppression of Tnp1/Prm1, resulting in infertility in animal models and humans [47,48]. Prkar2a codes for cAMP-dependent protein kinase type II-alpha regulatory subunit (enzyme) and is regulated by miR322 in the RS of KO and KI mice. Prkar2a is involved in cAMP signaling and mediates membrane association by binding to the anchoring proteins, including the microtubule-associated protein 2 (MAP2) kinase. A-kinase anchoring proteins are functionally diverse polypeptides that compartmentalize PKA within the cell and are critical due to their unique ability to directly and/or indirectly interact with proteins that determine the cellular content of cAMP. The study identified miR130b as the targeting miRNA which acts on akap1 and decreases its expression. AKAP1 is a transcriptional target of Myc and supports the growth of cancer cells [49]. These are strong interactions and correlations between several kinases and phosphatases, and spermatid development. Transgenic mice studies showed that the 3′ UTR of Prm1/2 and Tnp1/2 is responsible for the correct temporal translation of their mRNAs during spermatogenesis. Putative functional response elements were identified within the coding region of Tp2 and Prm2 [11], and here we confirmed that miR122a targets Tnp2 by binding to 3UTR. Although there was no significant differential expression of miR122a in our miRNA-seq data, we found a significant reduction (4-fold) in their levels in qPCR expression analysis. This discrepancy between assays may be due to the fact that high-throughput sequencing libraries, by necessity, use the same number of PCR cycles for all transcripts, which may not be optimal for low-expression transcripts such as miR122a. In addition, luciferase reporter assays validated the interaction of miR122a and Tnp2 mRNA. miR122a binds to 3′ UTR of Tnp2 and modulates its translation in a site-specific manner which directly impacts later stages of the spermiogenesis process. miR122-5p was shown as a potential biomarker of male infertility [17]. Translation of Prm2 and Tnp2 mRNA were destined for ES stages (from steps 9–16 of spermiogenesis) but were actively transcribed early in the RS stage and stored temporarily inside the CB [4]. High levels of miR122a present during the RS stage result in their binding to Tnp2 mRNA and subsequently suppressing its translation. Furthermore, GRTH is one of the essential components of the CB, which also harbors miRNA–mRNA control mechanisms that mediate post-transcriptional regulation, including mRNA silencing or processing during spermiogenesis [9]. These findings could provide novel insights into the role of small RNAs and their interaction with a selective group of important mRNAs at the post-transcriptional level during spermiogenesis. Taken together, our studies indicate that the identified miRNAs target several mRNAs involved in ubiquitination, histone removal, and chromatin compaction processes, thereby controlling post-transcriptional regulation resulting in spermatogenic halt in KO and KI mice. In conclusion, pGRTH plays a critical role during RS development through miRNA-mediated mRNA regulation, thereby maintaining the overall regulation at the transcriptional, post-transcriptional, and translation levels.

PMC10001411

Divya Mishra,Ashish Mishra,Sachchida Nand Rai,Emanuel Vamanu,Mohan P. Singh

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis

03-03-2023

hepatocellular carcinomas (HCC),glioblastoma multiforme (GBM),metastasis,RNA-seq analysis,hub gene,cox regression analysis,GEPIA

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis. Cancer is a complex disease caused due to uncontrolled division and growth of cells, categorized according to the progression in organs such as breast cancer, blood cancer, colon cancer, liver cancer, etc. Liver cancer, also known as hepatocellular carcinoma (HCC), has nowadays become a common cancer type, with approximately 830,000 deaths in 2020 alone [1]. Tumorigenesis or the transformation of normal cells into cancerous cells often results in uncontrolled cell proliferation, metastasis, and apoptosis evasion [2]. Metastasis of cancer cells occurs through blood vessels and lymph nodes and accounts for the development of other types of cancers [3]. Its occurrence is common in hepatocellular carcinoma (HCC) patients undergoing surgery [4]. Most of the patients are diagnosed during the late phase of the disease. Significant advancements in early disease diagnosis through standard interventions such as radiation, surgery, personalized strategies, and chemotherapy have been developed in the past few decades. The aggregative five-year survival rate of HCC and GBM remains destructive due to their molecular heterogeneity and invasive behavior. It has been found in some studies that brain metastases from HCC are less frequent (0.2–2.2%) and resulted in poorer survival of patients [5]. Even though this rate is less, but still it is certain that 10–45% of different cancer types from liver, lung, and other body parts metastasize to the brain [6]. This process of metastasis can be dealt with using biomarkers. Biomarkers are biological molecules found in body fluids, blood samples, or tissues and these signify a particular condition or disease. In different cancer types, metastatic biomarkers help in detecting the early stages of tumor spread and recurrence probability and in predicting the favorable sites of metastasis [7]. Once metastatic cancer is detected, further we need to identify the DNA or RNA-based biomarkers that could allow for personalized therapy resulting in significantly positive survival outcomes in patients [8]. Some biomarkers such as CDK4, PTEN, and ERBB2 were found as potential indicators for the diagnosis and prediction of metastatic breast cancer [9]. Likewise, overexpression of EGFR in metastatic non-small cell lung cancer (NSCLC) makes it a prognostic biomarker [10]. In the past few years, the fast growth of in silico approaches such as next-generation sequencing has enabled insight into carcinogenesis and progression of distinct cancer [11]. High-throughput platforms have been extensively used in prognosis prediction, histological identification, early diagnosis, disease resistance analysis, and molecular classification. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), differentially expressed genes (DEGs), and differentially methylated CpG sites can potentially serve as valuable HCC biomarkers [12]. Few oncogenic lncRNAs such as LASP1-AS, MALAT1, HOTAIR, and NORAD acted as potential biomarkers in the case of HCC. Similarly, some tumor-suppressor genes viz. DGCR5, MIR22HG, and HOTAIRM1 are also found as potential biomarkers for HCC [13]. An example of miRNAs includes miR-125a-5p which was upregulated in patients having HCV-associated hepatocellular carcinoma [14]. Similarly, the overexpression of some differentially expressed genes such as CDC20, BUB1B, AURKA, CCNA2, and BUB1 was found responsible for the poor progression and high mortality of patients suffering from HCC [15]. The transcriptome analysis has disclosed the cancer molecular mechanisms. Meanwhile, few reports have been introduced to identify the candidate biomarkers related to HBV-HCC with combined datasets [12]. In this study, the transcriptome analysis was carried out using RNA-seq datasets to identify the differentially expressed genes (DEGs) that play a vital role as potential prognostic biomarkers in the case of metastatic HCC and GBM. One of the most important biological processes obtained from the DAVID database using DEGs, chromosome segregation, has a prominent role in tumorigenesis as any chromosomal instability causes genetic instability due to dysregulated chromosome segregation [16]. Similarly, the cell cycle, which is an important KEGG pathway, was obtained. Any aberrant change in this cycle may also result in tumorigenesis. Hence, its regulators could be treated as potential anticancer therapeutic targets [17]. Another predominant feature related to tumor development and progression is an alteration in DNA methylation. DNA hypomethylation is more prominent with tumorigenesis or malignancy than hypermethylation [11,18,19]. It has been found that genomic instability occurs due to DNA hypomethylation in the case of HCC [20,21,22]. This instability causes the activation of oncogenes such as antigen family A1 (MAGEA1) [23]. Genetic alterations in the form of mutations and DNA copy number alterations (CNAs) were also identified as critical features of HCC tumorigenesis and metastasis. A study found that missense mutation in the NUF2 gene was linked to cancer development and hence, its inhibition resulted in the suppression of tumor growth leading to cancer cells apoptosis [24]. Copy number alterations are present in 90% of solid tumors and play a prominent role in activating oncogenes and inactivating tumor suppressor genes by altering the dosage and structure of genes [25]. As CNAs outline pivotal genetic events that drive tumorigenesis, such genetic alterations have the potential as predictive factors [26]. Post-translational modifications (PTM) viz. phosphorylation, acetylation, Ubiquitination, methylation, sumoylation, etc., also play a vital role in the tumorigenesis of different cancer types, particularly in breast cancer [27]. Mutation in Aurora Kinase A (AURKA) in HCC through direct phosphorylation of Pkinase promoted tumorigenesis and subsequently metastasis [28]. Likewise, Ubiquitination, another PTM plays a vital role in administering the control of substrate degradation, which is required for the proper functioning of the cell cycle, and any aberrancy in this process will hamper normal cell functioning leading to cancer development and possibly metastasis [29]. In this study, aberrant Ubiquitination in Pkinase led to mutations in the AURKA gene and this abnormal overexpression resulted in tumorigenesis and later-stage metastasis of HCC. This study, therefore, involved the identification of differentially expressed genes that were overexpressed in both GBM and HCC. The 13 hub genes obtained were further validated through promoter methylation, mutation, and genetic alterations analysis proving their potential to be prognostic biomarkers. The survival analysis of all these hub genes showed poorer survival rates among metastatic HCC and GBM patients. The datasets for both GBM and HCC were taken from Gene Expression Omnibus (GEO). For GBM (normal samples-PRJNA494560 transcriptomic data with paired-end sequencing performed on Illumina HiSeq 3000 (Homo Sapiens) platform and tumor samples-PRJNA347513, transcriptomic data with paired-end sequencing performed on Illumina HiSeq 2000 platform) and for HCC (normal samples-PRJNA494560 transcriptomic data with paired-end sequencing performed on Illumina HiSeq 3000 (Homo Sapiens) platform and tumor samples-PRJNA414787 transcriptomic data with paired-end sequencing performed on Illumina HiSeq 2000 (Homo Sapiens) platform) were taken. The method that was followed for carrying out this study is shown below in flowchart (see Figure 1). The data pre-processing of the raw reads was performed on Galaxy, an open-source platform for analyzing genomic data [30]. Galaxy implements FastQC (Version 0.11.8), a quality control tool for high-throughput sequenced data, for conducting the quality assessment of raw reads and removing the adapter sequences, uncalled bases and low-quality reads for improving the sequence quality through filtering and trimming. For this purpose, Cutadapt tool (v 3.2) is implemented. After obtaining the high-quality data from pre-processing, the next step that follows is alignment of reads against human reference genome (GRch38/hg38). This is accomplished using STAR (Version 2.7.7a) which is an ultrafast universal RNA-seq alignment tool [31,32]. The mapped reads are subsequently quantified in a process called quantification, through featureCounts (subread Version 2.0.1) package [33]. This step provides read counts per annotated gene. The normalized read count is further taken and, eventually, the statistical analysis is performed to obtain the differentially expressed genes (DEGs) between control and treated groups. It provides the quantitative changes in expression levels of genes. DESeq2 (Version 1.22.1) is a tool that performs this normalization process and is based on negative binomial distribution [34]. DEGs having FDR (p-value (adj)) <0.05 and |Log2FC| > 2 are considered statistically significant. The protein-protein interaction network is critical for understanding the cellular processes in diseased and normal states. This network provides the mathematical representations of the physical contacts between different proteins. This network was obtained by taking DEGs as input in the STRING database [35]. The vertices constitute DEGs (proteins) and edges constitute the protein interactions. The network was subsequently visualized through Cytoscape software [36]. The confidence score was taken <0.4. The PPI enrichment value less than 1 × 10−16 indicated that the network has significant interactions. The modular analysis was obtained by implementing MCODE (Molecular Complex detection) plug-in of Cytoscape. The parameters included degree cut-off = 0.2, node score cut-off = 0.2, k-core = 2, and maximum depth = 100. The hub genes are then identified from the obtained module using cytohubba plug-in. Hub genes are hugely interconnected genes and play a critical role in PPI network. For this purpose, 5 different topologies i.e., Maximal Clique Centrality (MCC), Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), and Radiality were employed. The topmost 15 were considered as hub genes in all the 5 algorithms and the common 13 hub genes were then taken from these 5 topologies through venn diagram obtained from jvenn [37]. Both GO and KEGG pathway enrichment analysis was obtained by providing common DEGs between GBM and HCC as input to DAVID database which is an online tool for functional enrichment analysis [38]. For both GO term and KEGG pathway, the EASE value (modified Fisher Exact p-value), employed for measuring the gene enrichment in annotation terms, was set to 0.1 and the count threshold to 2 (default value). The lesser this p-value is, the more enriched the GO terms or KEGG pathways are. The cut-off value for any term or pathway to be significant was set at p < 0.05. REVIGO [39] was used subsequently for constructing the treemap for biological processes by entering GO ids of all the terms along with their respective p-values. DNA methylation is an epigenetic factor that plays a crucial role in gene regulation. It is a feature of different types of human diseases and is predominant in case of different cancer types. The epigenetic alterations have an effect on the genes participating in the tumorigenesis and metastasis of cancer [40]. In this study, UALCAN [41], which is an online web resource for the analysis of cancer OMICS data, was employed for obtaining the promoter methylation of hub genes through TCGA datasets for both GBM and HCC. The beta values indicated DNA methylation levels ranging from 0 (i.e., unmethylated) to 1 (i.e., fully methylated). For hypermethylation, the specified range of beta value was 0.5–0.7, and, for hypomethylation, this range was 0.05–0.3. The genetic alterations that mainly include mutations and DNA copy number alterations correspond to changes in the DNA sequences due to various factors. The accumulation of such genetic alterations may lead to cancer development, metastasis, growth, and resistance to therapy. This validation of genetic alterations in the hub genes was accomplished through cBioPortal [42], which is an open-source, open-access resource for interactively exploring multidimensional cancer genomics data sets. For this purpose, 592 TCGA samples were considered for GBM and 391 samples for HCC. Copy number data sets were generated via GISTIC (Genomic Identification of Significant Targets in Cancer) algorithms that identify those regions that are significantly altered across the sets of patients. OncoPrints are used for visualization of the genomic alterations (mutations and copy number alterations) and mRNA expression changes across a set of TCGA cases for the hub genes. In case of mutations, a splice site mutation occurs in an intronic region while splice region mutations take place near the exon/intron junction. The copy number analysis derived from GISTIC algorithms indicates the level of copy number per gene. In this case, −2 indicate deep deletion or deep loss and correspond to homozygous deletion. Notably, −1 corresponds to shallow deletion and indicates a heterozygous deletion. Notably, 0 is assigned to normal or diploid. Notably, 1 corresponds to gain which indicates low-level gain and 2 correspond to amplification which indicates a high-level amplification. The gene expression profiles of normal and cancerous TCGA samples related to all the 13 hub genes in both GBM and HCC were obtained through GEPIA (Gene Expression Profiling Interactive Analysis), an online web server [43]. Thereafter, the survival analysis of these hub genes was obtained via the web-based tool, SurvExpress [44]. The TCGA dataset in this case contained 148 patient samples of GBM and 361 patient samples of HCC. The univariate cox regression analysis was employed to obtain the risk score by grouping the patients into high- and low-risk groups. Further, the Kaplan-Meier plot was obtained for visualizing the survival analysis of all the 13 hub genes (potential biomarkers) in both GBM and HCC. There are a total of 3265 differentially expressed genes (1570 upregulated and 1695 downregulated) obtained from GBM datasets and 2321 differentially expressed genes (1444 upregulated and 877 downregulated) from HCC (see Supplementary Figure S1). The normal and cancerous tissues of the brain (GBM) and liver (HCC) cancer are taken from the Human Protein Atlas (HPA) (see Supplementary Figure S2). Out of these differentially expressed genes obtained for both GBM and HCC, there are 757 differentially expressed genes (452 upregulated and 305 downregulated) that are shared between both GBM and HCC. These 757 differentially expressed genes are considered for further analysis of network and pathway enrichment. These are common DEGs that are taken forward from the same NGS-analyzed data. The PPI network for the differentially expressed genes contained 757 nodes and 6628 edges (see Supplementary Figure S3). The PPI enrichment p-value was less than 1 × 10−16. Since this value is small, it indicates that the nodes are not random, and the observed number of edges is significant. The modules obtained from the MCODE plug-in and subsequently cytohubba plug-in provided 13 common hub genes in both GBM and HCC, viz Assembly Factor for Spindle Microtubules (ASPM), Aurora Kinase A (AURKA), BUB1 Mitotic Checkpoint Serine/Threonine Kinase (BUB1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cyclin A2 (CCNA2), Cyclin B2 (CCNB2), Kinase Family Member 2C (KIF2C), Maternal Embryonic Leucine Zipper Kinase (MELK), Non-SMC Condensin I Complex Subunit G (NCAPG), Non-SMC Condensin I Complex Subunit H (NCAPH), NUF2 Component of NDC80 Kinetochore Complex (NUF2), PDZ Binding Kinase (PBK), and DNA Topoisomerase II Alpha (TOP2A) (see Figure 2). All these genes had a function associated with chromosome and spindle behavior of mitotic cell division and showed an up-regulated expression level in both GBM and HCC. The pairwise correlation analysis of DEGs using Pearson correlation statistics showed a higher degree of positive correlations. The results obtained for biological processes from the DAVID database showed that the hub genes are enriched in chromosome segregation, cell division, cell cycle process, nuclear division, and antigen processing and presentation. Likewise, the KEGG pathway analysis showed the involvement of 13 hub genes in the cell cycle, DNA replication, oocyte meiosis, progesterone-mediated oocyte maturation, viral carcinogenesis, and Epstein-Barr virus infection signaling pathways (see Figure 3). Validation of promoter methylation using the UALCAN database revealed that the promoter methylation level of ASPM, AURKA, BUB1, KIF2C, NCAPG, NCAPH, and NUF2 was lower than normal samples in GBM that indicates higher expression of these hub genes as against that of BUB1B, CCNA2, CCNB2, MELK, PBK and TOP2A having higher promoter methylation level than normal samples (see Supplementary Figure S4a). In the case of HCC, the expression level of BUB1, CCNA2, CCNB2, KIF2C, MELK, NCAPG, NCAPH, NUF2, PBK, and TOP2A was higher due to their lower promoter methylation level against normal samples while ASPM, AURKA and BUB1B were lowly expressed (see Supplementary Figure S4b). The differential expression between normal and tumor cells obtained from the GEPIA database showed that the expression of hub genes was significantly higher in the case of GBM as compared to HCC. Moreover, among the 13 hub genes, the expression level of the TOP2A gene was significantly higher in both GBM and HCC (see Supplementary Figure S5). The aberrant expression of ASPM, AURKA, BUB1, BUB1B, MELK, NUF2, and PBK resulted in a poorer survival rate of GBM patients in the high-risk group with a survival rate of fewer than two years. The median survival rate was less than 2 years for all the 13 hub genes (see Figure 4). For each patient, the risk score was calculated and ranking was carried out accordingly in the TCGA dataset. Patients were then divided into a high-risk group and a low-risk group. The hazard ratio > 1 for these hub genes also showed a higher level of survival risk (see Table 1). The survival analysis of patients in the high-risk group showed a poorer median survival rate which was less than 3 years (see Figure 5). The risk score was calculated as shown below. The hazard ratio >1 for all the 13 hub genes also indicated a poorer survival rate of HCC patients (see Table 2). Tumorigenesis mainly occurs due to irremediable mutations in cell structures. These mutations could be identified through genetic alteration analysis. The alterations may be in the form of a missense mutation, splice mutation, deep deletion, truncating mutation, and amplification. In the case of GBM, the percentage alteration of all 13 hub genes varied from 0.3% to 2.1% (see Supplementary Figure S6). The corresponding copy number variations are shown in Supplementary Figure S7. The details of genetic alterations and copy number variations can be found in the table (see Supplementary Table S1). Most of the mutations in hub genes were found at phosphorylation, acetylation, and uniquitination PTM sites with the characteristics of missense mutation and diploid copy type alteration. In the case of HCC, the alteration percentage had variations between 0.3–10% for the 13 prognostic biomarkers (see Supplementary Figure S8). The copy number variations are shown in Supplementary Figure S9. The description of genetic alterations and copy number variations are summarized in Supplementary Table S2. The results show that mutations mainly occurred at phosphorylation and ubiquitination PTM sites with diploid copy number variations and missense mutations and these features were found to be enriched in the tumorigenesis and metastasis of cancers with markedly stronger accumulation and evolutionary conservation in protein domains [45]. Cancer development due to uncontrolled cell division is the leading cause of death worldwide. The most dangerous event that leads to cancer development is mitosis having the irreversible segregation of sister chromatids to daughter cells [46]. Abnormal chromosome segregation during mitosis results in tumorigenesis. This happens mainly due to failure in the mechanism of the spindle assembly checkpoint, as the checkpoint ensures proper chromosome segregation during mitosis [47]. This chromosomal instability that results in abnormal chromosome numbers produces uncontrolled cell division, leading to tumorigenesis and subsequently to the metastasis of cancer types [48]. Hepatocellular carcinoma (HCC) is one of the leading cancer types that metastasizes to the lungs, adrenal glands, lymph nodes, and brain [49]. The evidence of brain metastasis from HCC is rare but is nowadays becoming more frequent compared to the conditions in the past [50]. This study mainly focused on the metastasis of HCC to the brain that could potentially lead to the development of glioblastoma multiforme (GBM), the IV grade brain cancer. This progression and metastasis of this cancer resulted from the aberrant function of some genes and alteration in the patterns of gene expression. This dysregulation in the gene expression is mainly due to genetic alterations such as mutation, amplification, and copy number alterations [51]. Thirteen hub genes were obtained from the protein-protein interaction network analysis using differentially expressed genes. Similarly, the pathway enrichment analysis carried out using the DAVID database showed the involvement of these hub genes in processes such as cell cycle, cell cycle process, and oocyte meiosis. These signaling pathways actively participate in cancer development, leading to tumorigenesis and metastasis. Further validation of these genes was carried out using the UALCAN database which found them to be hypomethylated in both HCC and GBM. This resulted in their aberrant expression through the increased probability of undergoing mutations leading to tumorigenesis [52]. All the 13 hub genes, i.e., ASPM, AURKA, BUB1, BUB1B, CCNA2, CCNB2, KIF2C, MELK, NCAPG, NCAPH, NUF2, PBK, and TOP2A are oncogenes and were found to be upregulated in all the samples of HCC and GBM. ASPM gene had abnormality due to its overexpression in HCC and played a vital role in cell proliferation and metastasis (Lin et al., 2008). It promotes the progression of HCC through the activation of Wnt/β-catenin signaling [53]. This gene had missense mutations at eight different locations in two percent of the patients on phosphorylation and ubiquitination post-transcriptional modification (PTM) sites (Supplementary Table S2). These mutations happened due to diploid copy number alteration. Likewise, in the case of GBM, it had missense mutations on phosphorylation, acetylation, ubiquitination, and methylation PTM sites at 17 different locations (Supplementary Table S1). Aurora Kinase A (AURKA) also has tumorigenesis properties in different cancer types [54]. This gene was involved in cancer metastases in the case of HCC [55]. The missense mutation having diploid type alteration in 0.57% of the patients on phosphorylation PTM sites at 2 locations resulted in the abnormality in this gene. Likewise, in the case of GBM, this gene has missense mutations in 0.57% of the patients, with diploid copy number alterations on phosphorylation and acetylation PTM sites at 3 different locations. In one of the studies, it was found that AURKA inhibition suppressed the cell proliferation of GBM [56]. BUB1 overexpression promoted tumorigenesis and aneuploidy [57]. This resulted in poorer survival of patients suffering from HCC (Yang et al., 2019). It had missense mutations in 0.57% of the patients at 2 different locations with diploid copy number alterations. In GBM also, upregulated BUB1 was also responsible for cell proliferation resulting in tumorigenesis [58]. It has missense and splice mutations at five different locations. It has phosphorylation and ubiquitination PTM sites with diploid and shallow deletion type of copy number alterations. The next hub gene BUB1B was involved in the progression of hepatocellular carcinoma (HCC) by activating mTORC1 signaling pathway [59]. It has a missense mutation at a single location in 0.29% of the patients. It has diploid copy number alterations. In the case of GBM, BUB1B was found to promote tumor proliferation [60]. It has splice mutation in 0.29% of the patients at a single location and has diploid copy number alterations associated with it. CCNA2 is found to promote uncontrolled cell growth, resulting in tumorigenesis in the case of different cancer types [61,62,63]. The upregulated CCNA2 was involved in cell cycle progression that resulted in tumorigenesis and metastasis in the case of HCC [64]. It had a missense mutation in 0.29% of the patients with diploid copy number alterations. In the case of GBM, overexpressed CCNA2 resulted in a poor prognosis for patients [65]. It had missense and nonsense mutations in 0.29% of the patients on acetylation, phosphorylation, and ubiquitination PTM sites on A25V and E269. CCNB2 was also found to promote cell cycle progression resulting in tumorigenesis in cases of triple-negative breast cancer [66]. It was also identified in the cell cycle progression leading to poor prognosis of HCC [67]. In this study, amplification was found as genetic alterations in 0.57% of the patients. In GBM, CCNB2 acted as a potential biomarker and played a vital role in Cellular Senescence and cell cycle [68]. These have missense mutation at location P80S having phosphorylation PTM site and amplification in 0.57% of the patients and shallow deletion copy number alterations. The absence of mutation in HCC and the presence of one mutation in GBM showed that this mutation might have taken place due to metastasis of HCC in the brain leading to GBM. KIF2C resulted in tumorigenesis due to abnormal cell cycle progression and metastasis in cervical cancer [69]. In the case of HCC, it participated in the progression of HCC and could be a potential therapeutic target [69]. On the other hand, in the case of GBM, it had missense mutations at three different locations on phosphorylation, ubiquitination, acetylation, and methylation PTM sites with diploid and gain copy number alterations. According to a study, this MELK gene was found to possess therapeutic drug-like properties due to its role in cell proliferation and triggering of cell cycle arrest in different cancer types [70]. Its overexpression in the case of HCC strongly correlated with abnormal cell growth leading to early recurrence and poor prognosis of patients [71]. In the following study, it had missense mutations in 1.43% of the patients at 4 different locations on phosphorylation PTM sites having diploid copy number alterations. In GBM, MELK developed tumorigenesis and its inhibition could effectively suppress the abnormal growth of GBM [72]. Here, it was found to have missense mutations in 1.43% of the patients. It has phosphorylation PTM site and diploid copy number alterations. NCAPG gene was found to be responsible for the survival of tumor cells leading to tumorigenesis and metastasis in HCC [73]. It had missense and nonsense mutations in 0.86% of the patients at 3 different locations. It had diploid copy number alterations on phosphorylation and ubiquitination PTM sites. Similarly, NCAPG was responsible for promoting tumor progression in the case of GBM also [74]. It had missense, splice, and nonsense type mutations in 0.86% of the patients at 5 different locations on phosphorylation, ubiquitination PTM sites and diploid, gain, and shallow deletion copy number alterations. NCAPH was found to be overexpressed in different cancer types promoting tumorigenesis and possibly metastasis [75,76,77,78]. The upregulation of NCAPH resulted in the enhancement of cell proliferation, invasion, and migration in the case of HCC [79]. In this study, it had missense mutations in 0.29% of the patients having diploid copy number alterations. In the case of GBM, it had missense mutations in 0.29% of the patients with diploid copy number alterations. This gene played a regulatory role in cell proliferation and apoptosis in the case of HCC [80]. It had missense mutation at 3 locations in 0.86% of the patients having the gain type of copy number alterations. In GBM, NUF2 promoted tumorigenesis and its downregulation inhibited the growth of tumor cells and induced apoptosis [81]. It had a missense mutation in 0.79% of the patients at three different locations on phosphorylation and ubiquitination PTM sites having diploid copy number alterations. The overexpression of PBK in the case of HCC promoted metastasis through activating the ETV4-uPAR signaling pathway [82]. It had a missense mutation at E303V in 0.29% of the patients having diploid copy number alterations in HCC. Similarly, its overexpression resulted in a poorer survival rate in the case of GBM [83]. It had missense mutations in about 0.26% of the patients on phosphorylation PTM sites having diploid copy number alterations. TOP2A was associated with growth in HCC tumor cells resulting in metastasis [84,85]. It had nonsense and missense mutations in 1.14% of the patients at 4 different locations on phosphorylation PTM sites having diploid and gain copy number alterations. In the case of GBM also, TOP2A had missense mutations at 5 different locations in 0.79% of the patients on phosphorylation, sumoylation, acetylation, ubiquitination, and methylation PTM sites having diploid copy number alterations. These 13 hub genes that were discussed above were associated with the worst survival of the patients as studied through Kaplan-Meier survival plots in the case of both GBM and HCC. This survival rate was less than two years due to overexpression of these genes, and hence these could be potential prognostic biomarkers that could help in the suppression of metastasis of HCC. The present study identified 13 gene signatures, i.e., ASPM, AURKA, BUB1, BUB1B, CCNA2, CCNB2, KIF2C, MELK, NCAPG, NCAPH, NUF2, PBK, and TOP2A. These 13 hub genes could behave as potential biomarkers as their overexpression resulted in abnormal cell division leading to tumorigenesis and metastasis in HCC, and this cancer metastasized in the brain, causing GBM. This overexpression resulted in the poor survival of patients in both GBM and HCC. Proper design of suitable inhibitors for these overexpressed hub genes will help in reducing the tumorigenesis and metastasis of HCC, thereby increasing the overall survival outcomes of the patients.

PMC10001412

Yuhan Sheng,Baofang Zhang,Biyuan Xing,Zhao Liu,Yu Chang,Gang Wu,Yingchao Zhao

Cancer-Associated Fibroblasts Exposed to High-Dose Ionizing Radiation Promote M2 Polarization of Macrophages, Which Induce Radiosensitivity in Cervical Cancer

06-03-2023

cervical cancer,radiotherapy,radioresistance,tumor-associated macrophages,cancer-associated fibroblasts

Simple Summary Tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in the tumor microenvironment are critical factors in the curative effects of cancer therapies. The aim of our study was to explore the interactions between TAMs and CAFs in the context of ionizing radiation. We confirmed that M2 macrophages correlate with poor prognosis and induce radioresistance in cervical cancer. M2 polarization was increased after high-dose IR in both mouse models and patients with cervical cancer. M2 macrophage contents were increased in CAF-positive regions in patients with cervical cancer who relapsed after receiving radical radiotherapy. In addition, high-dose irradiated CAFs promote macrophage M2 polarization in cervical cancer through the secretion of chemokine (C-C motif) ligand 2 (CCL2). Our data provide a new insight into the relation between CAFs and TAMs under IR, which is of significance for further exploration of the mechanism of radioresistance in cervical cancer. Abstract Radiotherapy, including brachytherapy, is a major therapeutic regimen for cervical cancer. Radioresistance is a decisive factor in radiation treatment failure. Tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in the tumor microenvironment are critical factors in the curative effects of cancer therapies. However, the interactions between TAMs and CAFs in the context of ionizing radiation are not fully understood. This study was undertaken to investigate whether M2 macrophages induce radioresistance in cervical cancer and to explore the TAMs’ phenotypic transformation after IR and its underlying mechanisms. The radioresistance of cervical cancer cells was enhanced after being co-cultured with M2 macrophages. TAMs tended to undergo M2 polarization after high-dose irradiation, which was strongly associated with CAFs in both mouse models and patients with cervical cancer. Additionally, cytokine and chemokine analysis was performed to find that high-dose irradiated CAFs promoted macrophage polarization towards the M2 phenotype through chemokine (C-C motif) ligand 2. Collectively, our results highlight the crucial role that high-dose irradiated CAFs play in the regulation of M2 phenotype polarization, which ultimately induces radioresistance in cervical cancer.

Cancer-Associated Fibroblasts Exposed to High-Dose Ionizing Radiation Promote M2 Polarization of Macrophages, Which Induce Radiosensitivity in Cervical Cancer Tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in the tumor microenvironment are critical factors in the curative effects of cancer therapies. The aim of our study was to explore the interactions between TAMs and CAFs in the context of ionizing radiation. We confirmed that M2 macrophages correlate with poor prognosis and induce radioresistance in cervical cancer. M2 polarization was increased after high-dose IR in both mouse models and patients with cervical cancer. M2 macrophage contents were increased in CAF-positive regions in patients with cervical cancer who relapsed after receiving radical radiotherapy. In addition, high-dose irradiated CAFs promote macrophage M2 polarization in cervical cancer through the secretion of chemokine (C-C motif) ligand 2 (CCL2). Our data provide a new insight into the relation between CAFs and TAMs under IR, which is of significance for further exploration of the mechanism of radioresistance in cervical cancer. Radiotherapy, including brachytherapy, is a major therapeutic regimen for cervical cancer. Radioresistance is a decisive factor in radiation treatment failure. Tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) in the tumor microenvironment are critical factors in the curative effects of cancer therapies. However, the interactions between TAMs and CAFs in the context of ionizing radiation are not fully understood. This study was undertaken to investigate whether M2 macrophages induce radioresistance in cervical cancer and to explore the TAMs’ phenotypic transformation after IR and its underlying mechanisms. The radioresistance of cervical cancer cells was enhanced after being co-cultured with M2 macrophages. TAMs tended to undergo M2 polarization after high-dose irradiation, which was strongly associated with CAFs in both mouse models and patients with cervical cancer. Additionally, cytokine and chemokine analysis was performed to find that high-dose irradiated CAFs promoted macrophage polarization towards the M2 phenotype through chemokine (C-C motif) ligand 2. Collectively, our results highlight the crucial role that high-dose irradiated CAFs play in the regulation of M2 phenotype polarization, which ultimately induces radioresistance in cervical cancer. Cervical cancer is the most common gynecologic cancer and the main cause of cancer-related mortality among women in developing countries [1]. Worldwide, more than 5,000,000 women are diagnosed with cervical cancer and 300,000 die because of the disease each year [2]. Brachytherapy, in which radioactive materials are placed in or near the tumor and high-dose ionizing radiation (IR) is delivered directly to the lesion, is a routine cervical cancer treatment [3]. Therefore, both external-beam radiation therapy (EBRT) and brachytherapy with low segmentation and a high dose per fraction could lead to the tolerance to radiotherapy of cervical cancer. However, mainly because of local recurrences, three-year progression-free survival (PFS) after radiotherapy remains unsatisfactory [4,5,6]. Therefore, exploring effective methods to improve the radiosensitivity of cervical cancer is urgently needed. IR triggers treatment responses in components within the tumor microenvironment (TME) other than cancer cells [7]. On the one hand, the secretion of inflammatory cytokines and exposed antigens after IR promote the maturation and activation of dendritic cells (DCs), which mediate T-cell responses against cancer cells. On the other hand, IR stimulates immunosuppressive cells, such as the regulatory T cells and myeloid-derived suppressor cells [8]. Therefore, exploring the immunoregulatory effect of IR may help to shed light on radiosensitization [7]. Macrophages are among the most abundant cells in the TME and are classified into two categories: M1-type macrophages, which exert antitumor effects, and M2-type macrophages, which have pro-tumoral activities. The content of M2 macrophages has been found to be correlated with poor prognosis in various cancers, including cervical cancer [9,10]. Several studies have demonstrated that high-dose radiation induces the phenotypic transformation of tumor-associated macrophages (TAMs); however, the results are inconsistent [11,12,13]. Therefore, macrophage phenotype transformation after IR and its relationship with radiosensitivity in cervical cancer remains unknown. The mechanisms underlying radioresistance in cervical cancer may differ from those in other cancers because of the specific type of radiation used, i.e., internal radiation, which is usually performed in the later stages of treatment when the tumor burden is low. This indicates that the protection delivered from stroma to tumor tissue may be the reason for the tolerance of internal radiation therapy. Fibroblasts are a major component of the tumor stroma which is known to promote tumor growth and progression, and recent studies have shown a correlation between the distribution of fibroblasts and macrophages in various cancers [14,15], and that fibroblasts can induce M2 macrophage polarization through the secretion of interleukin 6 and stromal cell-derived factor 1 [16]. To our acknowledge, the effect of cancer-associated fibroblasts (CAFs) on TAMs under IR in cervical cancer has not been reported. Our objective was to assess the effect of M2 macrophages on prognosis and radioresistance in cervical cancer and to assess the TAMs’ phenotypic transformation after IR and its underlying mechanisms. In total, 151 tumor biopsy specimens were obtained from patients with cervical cancer at the Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, between March 2014 and June 2018. All patients received radical external radiation therapy at a dose of 45–50.4 Gy/25–28 F followed by brachytherapy at a dose of 21–35 Gy/3–5 F alone or combined with chemotherapy, and outcomes were followed up. Samples were collected from all patients before they received radiation therapy, and five samples were obtained from relapse lesions after radiotherapy. All patients provided informed consent. The study was approved by the institutional review board of Wuhan Union Hospital. Tumor tissues were fixed with 4% paraformaldehyde immediately after resection and embedded in paraffin. Immunohistochemical staining was performed according to standard procedures, and the locations of CAFs and TAMs were evaluated in serial sec-tions. The primary antibodies used were anti-α-SMA (ab5694; Abcam, Cambridge, UK), anti-CD163 (ab182422; Abcam), and anti-CD206 (ab64693; Abcam). For M2 macrophage quantification, the samples were viewed at high magnification (400×) in five randomly selected regions. Macrophages were counted using ImageJ software, and averages were calculated. For the quantification of M2 macrophages around CAFs, the samples were viewed at low magnification (100×), and the region with the largest area of positively stained CAFs (hotspot area) was selected. M2 macrophages in this region were quantified as described above. The human cell lines HeLa, SiHa, CaSki, and THP-1 were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and were cultured in Dul-becco’s modified Eagle’s medium (DMEM) or RPMI-1640 culture medium supplemented with 10% fetal bovine serum. To generate macrophages, cells were isolated from the bone marrow of wild-type C57BL/6 mice or human peripheral blood (mononuclear cells were isolated from buffy coats by density gradient using Ficoll and suspended for 1 h to allow monocyte adherence and remove non-adherent cells. The cells were cultured in the presence of 20 ng/mL macrophage colony stimulating factor (M-CSF; PeproTech, Rocky Hill, NJ, USA) for 7 days to generate M0 macrophages. For THP-1 derived macrophages, 5 * 105/mL THP-1 cells were treated with 200 ng/mL phorbol 12-myristate 13-acetate (PeproTech) for 24 h to become M0 macro-phages. M0 macrophages were treated with 100 ng/mL lipopolysaccharide (Sigma, St. Louis, MO, USA) + 20 ng interferon-γ (PeproTech) or 20 ng/mL IL-4 (PeproTech) + 20 ng/mL IL-13 (PeproTech) for 24 h to generate M1 and M2 macrophages, respectively. The reagents used for the experiments included rm-CCL2, rh-CCL2 (PeproTech), pe-roxisome proliferator-activated receptor (PPAR)γ antagonist (GW9662; MCE), CCL2 re-ceptor (CCR2) antagonist (INCB3344; MCE), and CCL2-neutralizing antibody and isotype control IgG (Clone2H5; eBioscience, Vienna, Austria). Tumor tissues were washed with sterile phosphate-buffered saline to remove necrotic tissue and blood. Then, they were cut up with scissors as much as possible, completely minced, and digested in culture medium supplemented with 0.1% collagenase at 37 °C. The suspension was passed through a 0.4-μm filter to obtain a single-cell suspension. The cells were incubated at 37 °C for 30 min to allow CAF adherence. The supernatant containing non-attached cells was discarded, and the remaining cells were used as pure CAFs. Cells of less than 10 passages were used in the experiments. A co-culture system was established using 0.4-μm Transwell inserts (Corning, NY, USA) in a 6-well culture plate. For the co-culture of macrophages and cancer cells, macrophages were seeded on the upper layer and cancer cells were seeded on the bottom layer (at 60–70% confluence), and the cells were cultured for 24 h. For the co-culture of macrophages and CAFs, CAFs were seeded on the upper layer (at 30–40% confluence) and macrophages were seeded on the bottom layer, and the cells were cultured for 3 days. Cervical cancer cells were seeded in 6-well plates at different concentrations according to the indicated IR dose and were cultured in fresh culture medium for 10–14 days. Clones were fixed with 4% paraformaldehyde for 30 min and stained with a 0.1% crystal violet solution. The adherence rate was calculated as the number of clones/number of cells seeded × 100%. The survival fraction was calculated as the adhesion rate at dose Y in group X/adhesion rate at 0 Gy in group X. Apoptosis was assayed using the Annexin V-FITC Apoptosis Detection Kit (C1062; Beyotime, Shanghai, China) following the manufacturer’s instructions. The cells were assessed by flow cytometry within 1 h. Cells were collected and fixed in 75% alcohol at −20 °C overnight. The cells were incubated with propidium iodide and RNase (Beyotime) in the dark for 30 min and as-sessed by flow cytometry. Cells were stained with LIVE/DEAD Cell Viability Assay kit agents (65-0868-14; eBioscience) and blocked with 5 μg/mL anti-mouse CD16/32 (14-0161-81; eBioscience) before surface staining. Cells were stained for surface markers F4/80 (clone BM8) (11-4801-81; eBioscience), Gr-1 (108427; BioLegend, San Diego, CA, USA), CD45 (25-0451-81; eBioscience), CD11b (101263; BioLegend), and CD86 (105007; BioLegend) on ice. Then, the cells were fixed with 2% paraformaldehyde (Sigma-Aldrich), permeabilized with permeabilization buffer (BioLegend), and stained with directly labeled antibody to CD206 (MMR) (141707; BioLegend). Lewis cancer cells were incubated with 5 nM carboxyfluorescein succinimidyl ester (CFSE) at 37 °C in the dark for 20 min. After washing away free CFSE, the CFSE-labeled Lewis cells were collected, and the cell concentration was adjusted. The collected cells were added to bone marrow-derived macrophages (BMDMs) at a tumor cell:macrophage ratio of 1:4 and cultured for 2 h. The cells were then incubated with F4/80-FITC (clone BM8, BioLegend) for flow cytometry analysis. The supernatant of CAFs and cervical cancer cells was assayed for cytokines and chemokines using the Mouse XL Cytokine Array (Ary028; R&D Systems) and CCL2 ELISA kits (mouse, EK0568; human, EK0441; BOSTER) according to the manufacturers’ instructions. Total RNA was isolated using TRIzol (TaKaRa, Tokyo, Japan) and reverse transcribed into cDNA using PrimeScript™ RT Master Mix (TaKaRa). qPCRs were run in a 20-μL reaction mixture using TB Green™ Premix Ex Taq™ II (Tli RNaseH Plus) (TaKaRa). GAPDH was used as an endogenous control. The primers used are listed in Table S1. Cells were lysed using RIPA NP-40 RIPA Lysis buffer containing phenylmethylsulfonyl fluoride and protease inhibitor. The proteins (20–50 μg) were separated by 8–10% precast sodium dodecyl-sulfate polyacrylamide gel electrophoresis at 80 V and then blotted onto polyvinylidene difluoride membranes at 200 mA for a suitable time. The membranes were blocked and probed with primary antibodies, including anti-GAPDH (5174; Cell Signaling Technology (CST)), anti-CD163 (ab182422; Abcam), anti CD206/MRC-1 (ab64693; Abcam), anti-Akt (4685; CST), anti-pAkt (Ser473) (4060; CST), anti-Erk1/2 (4695; CST), anti-pErk1/2 (4370; CST), and anti-PPARγ (ab45036; Abcam) antibodies. After incubation with HRP-conjugated antibodies specific for rabbit or mouse IgG, the blots were developed using ECL chemiluminescence reagent. Bands were quantified using ImageJ. Cells cultured on slides were fixed with 4% paraformaldehyde. After permeabilization with 0.25% Triton X-100 for 10 min, the cells were incubated with primary anti-bodies (anti γ-H2AX (2718; CST) and anti Ym-1 (192029; Abcam) at 4 °C overnight. Then, the cells were incubated with secondary antibodies diluted in blocking buffer in the dark at room temperature. Finally, the cells were stained with DAPI and observed using a fluorescence microscope. Siha (5 × 105) or HeLa (5 × 105) cervical cancer cells were implanted subcutaneously into 6–8-week-old female BALB/c-Nu mice. Tumor volumes were measured every 3 days (tumor volume = length × width2 × 0.5). When tumor volumes reached approximately 100 mm3, the mice were randomly assigned to indicated groups (n = 8 per group) before treatment initiation. The study was approved by the Animal Management Committee of Huazhong University of Science and Technology. Cells seeded in culture dishes were irradiated with a single dose of 2, 4, 6, and 8 Gy. Mice in IR groups were anesthetized with sodium pentobarbital (60 mg/kg intraperitoneally), and the right posterior limbs (with tumors) were subjected to local IR using an X-ray irradiator (Varian, Palo Alto, CA, USA) at a beam energy of 6 MV and dose rate of 6 Gy/min. All experimental data were processed using SPSS 23.0 and GraphPad Prism 7 software and are presented as mean ± standard deviation (SD). The data were statistically analyzed using Student’s t-test, one-way ANOVA or chi-square test as appropriate. Kaplan–Meier analysis was conducted, using the log-rank test to determine statistical significance. A value of p < 0.05 was considered statistically significant. To evaluate the role of M2 macrophages in cervical cancer, we first detected the expression of CD163, which is considered an M2 macrophage marker [17], in 151 biopsy specimens from patients with cervical cancer who received radical radiotherapy. The CD163 expression levels varied among patients (Figure 1A). A receiver-operating characteristic (ROC) curve was drawn to determine a suitable cut-off value for high-CD163 and low-CD163 expression groups (Figure S1). The cut-off was set to > 8.96 macrophages per high-power field (HPF) of view (sensitivity = 58.2%, specificity = 58.3%). Clinico-pathological features (age, tumor size, pathological type, clinical stage, and lymph node metastasis) were compared between the two groups (Table S1). The lymph node metastasis rate in the high-CD163 expression group (36.1%) was significantly higher than that in the low-CD163 expression group (21.5%) (p < 0.05). Furthermore, PFS was lower in the high-CD163 expression group than in the low-CD163 expression group (p < 0.05) (Figure 1B), suggesting that M2 macrophages may play a role in the radiosensitivity of cervical cancer. To further demonstrate the role of M2 macrophages in the radiosensitivity of cervical cancer, we co-cultured human mononuclear cell line THP-1 cell-derived M2 macrophages (Figure S2A) with cervical cancer cell lines. After radiation, the M2 macrophage co-culture group showed lower levels of G2/M cell cycle arrest (Figure 1C) and apoptosis (Figure 1D), enhanced colony formation ability (Figure 1E), and a lower foci formation frequency (Figure 1F) than cancer cells cultured alone, indicating that the M2 macrophages induced radioresistance in the cervical cancer cells. Further, the phosphorylation levels of Akt and Erk1/2 in the M2 macrophage co-culture group were significantly increased after IR compared to those in the cancer cell cultured alone (Figure 1G), indicating that M2 macrophages may induce cervical cancer cell radioresistance via these pathways. To investigate phenotypic changes in macrophages in the TME after different doses of IR, we established subcutaneous xenograft mouse models using HeLa and SiHa cervical cancer cells. When tumor volumes reached 100 mm3, 2 Gy or 8 Gy IR was administered. Tumor tissues were collected for flow cytometry analysis on the 3rd or 8th day after ra-diation. In HeLa model mice, the proportion of CD11b+F4/80+ macrophages was signifi-cantly increased after 8 Gy IR in vivo. Macrophages displayed elevated expression of the M2 marker CD206 on the 3rd day after 8 Gy IR, but not on the 8th day after 8 Gy IR or after 2 Gy radiation (Figure 2A). Similar results were obtained in the SiHa xenograft mouse model (Figure 2B). To explore the mechanism of phenotypic transformation of TAMs after radiotherapy, we irradiated macrophages alone (Figure S3A) or after co-culture with irradiated HeLa (Figure S3B) and SiHa (Figure S3C) cervical cancer cells. However, no obvious change in M2 marker expression was observed. We suspected that interstitial components in cervical cancer tissue may be involved in this process. Immunohistochemical staining of serial mouse tumor sections revealed that M2 macrophages accumulated in CAF-positive areas after high-dose radiotherapy (Figure 3A). Similar findings were made in clinical specimens; the numbers of M2 macrophages in CAF-positive areas in tumors of recurrent cervical cancer patients after radical radiotherapy were significantly higher than those before treatment (Figure 3B). CAFs, one of the most important components of the inter-stitial tissue in cervical cancer, express α-SMA and are able to promote M2 polarization. We found that CAF markers, such as fibroblast activation protein (FAP), ACTA2 (α-SMA), and beta-type platelet-derived growth factor receptor (PDGFRB) were associated with the infiltration of macrophages in cervical cancer in the Tumor Immune Estimation Resource (TIMER) database (Figure 3C). RNA-sequencing data were extracted from The Cancer Genome Atlas (TCGA) cervical cancer database (307 patients), normalized using RSEM and log2-transformed. Correlations between macrophage phenotypic molecules and FAP levels were determined using Spearman’s rank correlation test. FAP levels were positively correlated with M2 macrophage markers, including mannose receptor c-type 1 (MRC1), CD163, CCR2, and CD209 (Figure 3D). On the basis of these results, we hypothesized that CAFs may be more potent in driving the phenotypic transformation of macrophages in the context of high-dose radiotherapy. CAFs cultured in vitro were subjected to different doses of IR and then co-cultured with M0 macrophages. CD206 expression was higher in M0 BMDMs co-cultured with 8 Gy irradiated CAFs than in M0 BMDMs co-cultured with non-irradiated CAFs, whereas there was no significant change in CD86 expression after IR (Figure 4A). The expression levels of M2 markers Arg-1, CCL22, and FN1 in THP-1 cell-derived macrophages after co-culture with 8 Gy CAFs were higher than those in the control cells (Figure 4B). In addition, Arg-1 expression in M0 BMDMs was increased after co-culture with 8 Gy irradiated CAFs (Figure 4C). MRC-1 expression in M0 peripheral blood mononuclear cell (PBMC)-derived macrophages was increased after co-culture with 8 Gy irradiated CAFs (Figure 4D). Furthermore, high-dose irradiated CAFs suppressed the phagocytic ability of macrophages (Figure S4). Therefore, we hypothesized that CAFs play an important role in high-dose IR-induced macrophage reprogramming in cervical cancer. To validate the above hypothesis and unravel the underlying mechanisms, cytokine and chemokine assays were performed to analyze changes in secretory molecules from CAFs after high-dose IR. We found that CCL5, coagulation factor III, and CCL2 were significantly increased in the supernatant of CAFs after high-dose IR (Figure 5A). Treatment of macrophages with exogenous CCL5 or coagulation factor III had no effect on the macrophage phenotype. We then focused on CCL2. Western blot (Figure 5B) and ELISA (Figure 5C) results revealed elevated secretion of CCL2 from CAFs rather than cervical cancer cells after IR with 8 Gy (Figure 5B). After treatment with 20 ng/mL CCL2, CD206 expression in macrophages was increased (Figure 5D). These results indicated that CCL2 promotes macrophage M2 transformation. CCR2 expression was not altered in macrophages co-cultured with irradiated CAFs (Figure S5A), indicating that the M2 phenotypic transformation was mainly due to increased secretion of CCL2 from the CAFs. To confirm whether CCL2 is required for M2 transformation, we used a CCL2-neutralizing antibody and a CCR2 antagonist to abolish the effect of CCL2 in the co-culture system. Flow cytometry analysis revealed that treatment with these agents partially reversed the effect of irradiated CAFs on macrophages (Figure 5E,F). Ym-1 (chitinase 3) is a known M2 macrophages marker. Immunofluorescence staining showed that Ym-1 expression in co-cultured macrophages was decreased after CCR2 inhibition (Figure 5G). PPARγ is a nuclear receptor that has potent anti-inflammatory properties. Activation of PPARγ primes macrophage M2 polarization. After exogenous administration of CCL2, BMDMs and THP-1-derived macrophages had increased PPARγ expression (Figure S5B,C). Inhibition of PPARγ reversed the effect of CCL2 on macrophage M2 polarization (Figure S5D). Therefore, we concluded that CCL2 mediates high-dose irradiated CAF-mediated M2 transformation via PPARγ. In brief, the results indicate that high-dose irradiated CAFs regulate M2 phenotype polarization via increased secretion of CCL2, which subsequently induces radioresistance in cervical cancer (Figure 6). Radiotherapy has known immunomodulatory effects and is a critical treatment for cervical cancer, with brachytherapy being an essential element for curative-intent treatment. In the present study, we investigated the effect of M2 macrophages on the radiosensitivity of cervical cancer cells and unveiled the time window and the cause of the phenotypic transformation of TAMs in cervical cancer in the context of radiation. TAMs have distinct functions depending on the environmental stimuli that affect their phenotype. In past decades, researchers found that M2 macrophage contents correlate with the prognosis of various cancers. Consistent with findings in other studies on breast cancer and head and neck cancer [18,19], we found that M2 macrophages reduced the radiosensitivity of cervical cancer cells. Therefore, the effect of radiation on the macrophage phenotype is crucial for the development of radioresistance in cervical cancer. However, the effects of radiotherapy on macrophage polarization have been inconsistent among studies. After a single high dose of ablation radiotherapy, the innate immune system is activated by inflammatory cytokines such as IL-1, TNF and M-CSF and profibrotic factors such as TGFβ, which may impair T-cell functions and promote tumor progression and recurrence by recruiting M2 macrophages [7,11]. After low-dose radiation, iNos+CD68+ cells (M1 macrophages) in tumor tissue increase, contributing to vascular normalization, T-cell recruitment, and the inhibition of tumor progression [20]. We reported that TAMs tended to be M2 polarized in the xenograft mouse models of cervical cancer after high-dose irradiation, but not after 2 Gy radiation. Moreover, the phenotypic transformation was dynamic, and its time window was focused on the third day after radiation. We also demonstrated that CAFs play a crucial role in the process of phenotypic transformation of TAMs after radiation. It has long been recognized that wound healing is closely related to the mechanism of tumorigenesis, and malignant tumors are described as “non-healing wounds” [21]. In the TME, CAFs play a key role in wound healing. TAMs and CAFs interact with each other and ultimately promote the progression of neuroblastoma and prostate cancer [14,16,22]. However, the immune-modulatory effects of CAFs on macrophages after radiation have rarely been discussed. Berzaghi et al. [23] demonstrated that CAFs isolated from non-small-cell lung cancer tumors promoted changes in M0 macrophages that were in line with both M1 and M2 phenotypes and inhibited pro-inflammatory features of M1 macrophages by suppressing the production of nitric oxide and pro-inflammatory cytokines, migration, and M1 surface marker expression (both in co-culture and in the presence of CAF-conditioned medium). Moreover, CAFs maintained their immunoregulatory effect on macrophages after 1 × 18 Gy or 3 × 6 Gy IR. In accordance with American Brachytherapy Society guidelines, the total high-dose rate of brachytherapy treatment for cervical cancer is 25–30 Gy in 4–5 separate fractions [24]. Therefore, we applied an 8 Gy radiation dose to be consistent with clinical practice. We found that CAFs had an enhanced capacity for promoting the M2 polarization of TAMs after 8 Gy radiation in cervical cancer. Our results diffed from previous findings to some extent, which is likely due to differences in the samples, cancer types and radiation regimens studied. Therefore, further studies are needed to elucidate the relationship between TAMs and CAFs in the context of radiation. It has been shown that disrupting the interactions between CAFs and TAMs may be useful for improving the efficacy of radiation therapy. As for the detailed molecular mechanisms, it has been reported that CAFs can promote macrophage M2 polarization through immunomodulatory factors, such as M-CSF [25], endosialin [26], IL-6, and GM-CSF [27]. We reasoned that CCL2 may be a key regulator of M2 transformation induced by high-dose irradiated CAFs. CCL2 is an effective chemokine in monocytes, T cells, and NK cells. CCL2 is overexpressed in various cancers and is associated with poor prognosis in breast, colorectal, and thyroid cancers [28], and a lack of CCL2 is associated with increased survival in patients with cervical cancer [29]. The recruitment of TAMs in the TME is generally mediated via the CCL2-CCR axis. As for its effect on macrophage polarization, Roca et al. showed that CCL2 could shift human peripheral blood CD11b+ cells toward a CD206+ M2-polarized phenotype [30], and Sierra-Filardi et al. disclosed an important role for the CCL2-CCR2 axis in regulating macrophage polarization [31]. Consistent with these study findings, we demonstrated that high-dose irradiated CAFs promoted M2 polarization through the secretion of CCL2. Though studies have reported that CCL2 is an important factor in inducing tumor progression, clinical trials targeting CCL2 did not yield promising results [32,33]. The limited curative effect may be ascribed to the negative feedback of CCL2 and the compensation of other cytokines or chemokines after CCL2 inhibition. Therefore, the detailed molecular mechanisms involving CAFs, TAMs and cancer cells remain unraveled. This study had some limitations. Immunological studies were performed in xenograft models established using BALB/c-Nu mice, which may differ from syngeneic immunocompetent model mice. Cervical cancer is associated with human papillomavirus infection, and a mature immunocompetent mouse cervical cancer model is currently lacking. Therefore, to investigate TAMs, we used xenograft mouse models, which are widely adopted by researchers [34]. Our study demonstrated that M2 macrophage associates with the poor prognosis and radioresistance of cervical cancer. Notably, our data indicate that high-dose irradiated CAFs showed an enhanced immunosuppressive effect on macrophage reprogramming via CCL2. Focusing on the relationship between TME components under radiation provides us with a new perspective that may aid in the resolution of radioresistance.

PMC10001413

Lana Kupershmidt,Moussa B. H. Youdim

The Neuroprotective Activities of the Novel Multi-Target Iron-Chelators in Models of Alzheimer’s Disease, Amyotrophic Lateral Sclerosis and Aging

27-02-2023

Alzheimer’s disease,amyotrophic lateral sclerosis,oxidative stress,monoamine oxidase,iron,iron chelator,neuroprotection,erythropoietin,amyloid precursor protein,tau protein

The concept of chelation therapy as a valuable therapeutic approach in neurological disorders led us to develop multi-target, non-toxic, lipophilic, brain-permeable compounds with iron chelation and anti-apoptotic properties for neurodegenerative diseases, such as Parkinson’s disease (PD), Alzheimer’s disease (AD), age-related dementia and amyotrophic lateral sclerosis (ALS). Herein, we reviewed our two most effective such compounds, M30 and HLA20, based on a multimodal drug design paradigm. The compounds have been tested for their mechanisms of action using animal and cellular models such as APP/PS1 AD transgenic (Tg) mice, G93A-SOD1 mutant ALS Tg mice, C57BL/6 mice, Neuroblastoma × Spinal Cord-34 (NSC-34) hybrid cells, a battery of behavior tests, and various immunohistochemical and biochemical techniques. These novel iron chelators exhibit neuroprotective activities by attenuating relevant neurodegenerative pathology, promoting positive behavior changes, and up-regulating neuroprotective signaling pathways. Taken together, these results suggest that our multifunctional iron-chelating compounds can upregulate several neuroprotective-adaptive mechanisms and pro-survival signaling pathways in the brain and might function as ideal drugs for neurodegenerative disorders, such as PD, AD, ALS, and aging-related cognitive decline, in which oxidative stress and iron-mediated toxicity and dysregulation of iron homeostasis have been implicated.

The Neuroprotective Activities of the Novel Multi-Target Iron-Chelators in Models of Alzheimer’s Disease, Amyotrophic Lateral Sclerosis and Aging The concept of chelation therapy as a valuable therapeutic approach in neurological disorders led us to develop multi-target, non-toxic, lipophilic, brain-permeable compounds with iron chelation and anti-apoptotic properties for neurodegenerative diseases, such as Parkinson’s disease (PD), Alzheimer’s disease (AD), age-related dementia and amyotrophic lateral sclerosis (ALS). Herein, we reviewed our two most effective such compounds, M30 and HLA20, based on a multimodal drug design paradigm. The compounds have been tested for their mechanisms of action using animal and cellular models such as APP/PS1 AD transgenic (Tg) mice, G93A-SOD1 mutant ALS Tg mice, C57BL/6 mice, Neuroblastoma × Spinal Cord-34 (NSC-34) hybrid cells, a battery of behavior tests, and various immunohistochemical and biochemical techniques. These novel iron chelators exhibit neuroprotective activities by attenuating relevant neurodegenerative pathology, promoting positive behavior changes, and up-regulating neuroprotective signaling pathways. Taken together, these results suggest that our multifunctional iron-chelating compounds can upregulate several neuroprotective-adaptive mechanisms and pro-survival signaling pathways in the brain and might function as ideal drugs for neurodegenerative disorders, such as PD, AD, ALS, and aging-related cognitive decline, in which oxidative stress and iron-mediated toxicity and dysregulation of iron homeostasis have been implicated. The etiology of neurodegenerative diseases is not yet well understood, although cerebrovascular atrophy that leads to brain ischemia may be a potential pathogenic factor for age-related dementia [1]. However, accumulating evidence has shown that iron-dependent oxidative stress (OS), increased iron levels, and monoamine oxidase (MAO)-B activity, as well as reduced antioxidant levels and activities in the brain, may be major pathogenic factors in neurodegenerative diseases [2]. It is well established that iron is an essential cofactor for many key proteins involved in the normal function of neuronal tissues and is normally involved in oxygen transport, storage and activation, electron transport, and many important metabolic processes [3]. In the central nervous system (CNS), iron is essential for multiple functions, including gene expression, DNA synthesis, neurotransmission, myelination, and mitochondrial electron transport (Figure 1) [4]. Iron incorporation and transport in the brain are regulated by the interaction between the endothelial cells and astrocytes: the transferrin receptor 1 (TfR1) in the luminal membrane of endothelial cells binds Fe3+-loaded transferrin and internalizes this complex in endosomes, where Fe3+ is reduced to Fe2+. The latter is transported to the cytosol by the divalent metal transporter-1 (DMT1) and exported into the extracellular fluid by the iron exporter, ferroportin [5]. Alternatively, it has been proposed that the transferrin-TfR1 complex may be transported from the luminal to the abluminal surface by an iron release [6]. Ceruloplasmin, expressed in the astrocyte, oxidizes newly released Fe2+ to Fe3+, which binds to transferrin in the brain interstitial fluid [5,6,7]. Fe2+ can also bind to adenosine triphosphate (ATP) or citrate and be transported as non-transferrin-bound iron (NTBI), which is the source of iron for astrocytes and oligodendrocytes, which do not express TfR1 [5,6]. Yet, there is increasing evidence that iron accumulation and deposition can cause a vast range of neurodegenerative disorders of the CNS [8,9,10]. Free iron can induce OS because of its interaction with H2O2 in the Fenton reaction, thus resulting in an increased formation of hydroxyl free radicals. Free radical-related OS causes molecular damage that can then lead to a critical failure of biological functions, protein modification, misfolding and aggregation and ultimately cell death [3,11,12,13]. Indeed, in aging and various neurodegenerative diseases, such as Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), iron was shown to accumulate at the site of the lesion, thus suggesting having a role in neuronal death processes [8,10,14,15,16,17]. Iron also has been shown to accumulate in the aged brain mainly in the form of ferritin, in the microglia, astrocytes, oligodendrocytes, and the various regions, including the globus pallidus, substantia nigra, putamen, caudate nucleus, dentate nucleus and frontal cortex [4]. AD is the most prevalent neurodegenerative disease in the elderly population, and it has been estimated that about 5% of adults over 65 years are affected by this devastating disease [18]. Its predominant clinical manifestation is progressive memory deterioration and other changes in brain function, including disordered behavior and impairment in language, comprehension, and visual-spatial skills [19]. The neuropathology of AD is characterized by several features, including extracellular deposition of amyloid β (Aβ) peptide-containing plaques in the cerebral cortical regions, accompanied by the presence of intracellular neurofibrillary tangles (NFTs) and a progressive loss of basal forebrain cholinergic neurons leading to reductions in cholinergic markers, such as acetylcholine levels, choline acetyltransferase (ChAT) and muscarinic and nicotinic acetylcholine receptor binding [20,21]. Additionally, there is accumulating evidence demonstrating that many cytotoxic signals in the AD brain can initiate apoptotic processes, including OS, inflammation, and iron accumulation [22,23,24]. Iron is significantly concentrated around amyloid senile plaques and NFTs, leading to alterations in the pattern of the interaction between iron regulatory proteins 1 and 2 (IRP1 and IRP2) and their iron-responsive element (IRE) and disruption in the sequestration and storage of iron [25,26]. Additionally, high levels of iron have been reported in the amyloid plaques of the Tg2576 mouse model for AD, resembling those seen in the brains of AD patients [27]. In addition to iron accumulation in senile plaques, it was demonstrated that the amount of iron present in the AD neuropil is twice that found in the neuropil of non-demented brains [25]. Further studies have suggested that iron accumulation could be an important contributor to the OS damage of AD pathology, and thus, the neurons in AD brains experience high oxidative load [28,29,30,31]. Indeed, it was found that NFTs and senile plaques contain redox-active transition metals and may exert pro-oxidant/antioxidant activities, depending on the balance among neuronal antioxidants and reductants [32]. Post-mortem analyses of AD patients’ brains have revealed activation of two enzymatic indicators of cellular OS: heme oxygenase (HO-1) [33] and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase [34]. In addition, HO-1 was greatly enhanced in neurons and astrocytes of the hippocampus and cerebral cortex of Alzheimer’s subjects, co-localizing to senile plaques and NFTs. It was reported that ribosomal RNA provided a binding site for redox-active iron and served as a redox center within the cytoplasm of vulnerable neurons in the AD brain prior to the appearance of morphological changes indicating neurodegeneration [29]. Many studies suggested that iron homeostasis is disrupted in AD [25,35,36]. Thus, abnormal localization of the IRP1, and IRP2, which was shown in AD, might be linked to impaired iron homeostasis in AD [37]. However, this is likely to be a secondary effect via another process, such as increased HO-1 activity in response to cellular OS [38] or a decrease in heme bioavailability resulting from Aβ binding to heme, increasing free iron levels [39]. The location of the iron-transport protein, transferrin, in senile plaques, instead of its regular location in the cytosol of oligodendrocytes, may indicate that transferring becomes trapped within plaques while transporting iron between cells [35]. Indeed, Loeffler and collaborators [40] reported on diminished transferrin/iron ratios in various brain regions of AD patients compared with normal elderly controls, indicating dysregulation of iron homeostasis. In addition, higher transferrin C2 allele occurrence has been described in AD compared with normal controls [41]. Previous studies assessing the effect of certain genes coding for proteins involved in iron metabolism, such as hemochromatosis (HFE) [42] and transferring C2 gene variants, showed a high fold risk of developing AD in aged bicarriers [43]. In addition, the mediator of iron uptake by cells, melanotransferrin and the iron-storage protein ferritin, are altered in AD and are expressed within reactive microglial cells that are present both in and around senile plaques [42]. At the biochemical level, iron was demonstrated to facilitate the aggregation of Aβ and induce aggregation of the major constituent of neurofibrillary tangles (NFTs), hyperphosphorylated tau protein [28,44]. It was suggested that the toxicity of Aβ is mediated, at least in part, via redox-active iron; neuronal toxicity was significantly attenuated when Aβ was pretreated with the natural prototype iron chelator/radical scavenger, desferrioxamine (DFO), while conversely, toxicity was restored to original levels following incubation of Aβ with excess free iron [45]. In addition, previous in vitro studies demonstrated that Aβ has a high affinity for iron, and the iron-binding sites are in the hydrophilic N-terminal part of the peptide [46]. Furthermore, the presence of redox-available iron in association with pathological lesions and increased OS strongly support the notion that oxidative damage plays an important role in the pathogenesis of AD [31]. Iron regulates amyloid precursor protein (APP) translation via functional IRE. Another molecular link between iron metabolism and AD pathogenesis was provided by Rogers et al. [47], who described the presence of an IRE-type II in the 5′ untranslated region (5′UTR) of the APP transcript encoding the Alzheimer’s APP (51 to 94 from the 5′-cap site). The APP mRNA IRE is located immediately upstream of the interleukin-1 responsive acute box domain (101 to 146) [23]. Thus, APP 5′UTR is selectively responsive to intracellular iron levels in a pattern that reflects iron-dependent regulation of intracellular APP synthesis. This signifies the APP molecule as metalloproteinase, which resembles that of the iron-associated protein ferritin, a central iron-storage molecule, and the iron-regulated transporter protein-1 (IREG-1), which transports iron from enterocytes into the bloodstream [48,49,50]. Indeed, iron levels were shown to regulate mRNA translation of APP holo-protein in astrocytes [47,51] and neuroblastoma cells by a pathway like iron control in the translation of the ferritin-L and -H mRNAs by IREs in their 5′UTRs [50]. An additional study demonstrated that IRP1, but not IRP2, selectively bound the APP IRE in human neural cells [52]. Recently, Duce and coworkers [53] have identified APP as a functional ferroxidase like ceruloplasmin. Ferroxidases prevent OS caused by Fenton and Haber–Weiss reactions by oxidizing Fe2+ to Fe3+. Both holo-APP and soluble forms of APP (sAPP) species were found to have interactions with ferroportin to regulate iron transport from neurons [53]. The authors suggested that APP, by discharging neurons of Fe2+, may play an important role in preventing iron-mediated OS through two alternative domains: a heme-oxygenase-inhibitory domain that prevents the release of Fe2+ from heme and the ferroxidase domain, which oxidizes Fe2+ to Fe3+ [53]. In addition, iron has been shown to facilitate Aβ and hyperphosphorylated tau aggregation. At the biochemical level, Aβ is defined as a redox-active metalloprotein that is precipitated by interactions with neocortical metal ions, especially zinc, copper, and iron resulting in its auto-aggregation and oligomerization [54,55]. Three histidine and one tyrosine residues at the hydrophilic N terminus of the peptide are crucial for metal ion binding to the peptide [54,55]. Regarding the interaction with iron, it was found that Fe3+ influences the formation of amyloid fibrils of Aβ1–42 [56] and Aβ25–35 [57]. The interaction of redox-active Fe3+ with Aβ fibrils results in their chemical reduction and concomitant generation of reactive oxygen species (ROS), causing lipid membrane peroxidation, DNA breakdown, and protein oxidation [44,58,59,60]. Partial aggregated and oligomerized intracellular Aβ was documented to be cytotoxic and synaptotoxic in cell culture and in vivo [45,61,62,63,64]. In vitro, iron was demonstrated to enhance Aβ toxicity since the removal of iron from the culture medium or the inclusion of adequate iron chelators reduced or blocked Aβ-induced toxicity while overloading cells with iron-potentiated neuronal toxicity [45,65]. Thus, Fe3+ in culture media may bind to soluble Aβ and, given its amphiphilic nature, may subsequently attach to the cell membrane, thereby inducing oxidative injury [66,67]. Iron may enhance Aβ toxicity by preventing the formation of mature well-ordered fibrillar aggregation of Aβ [68]. Consequently, it has been suggested that abnormal iron deposition, such as in AD plaques, can interact with Aβ to mediate free radical-induced neurotoxicity and may account, in part, for the widespread oxidative damage in AD brains, accelerating the pathological process [31]. In addition, iron was found to accumulate in NFTs [31,69]. Fe3+ can bind hyperphosphorylated tau and induce its aggregation in vitro, leading to the formation of NFTs in AD. Currently, numerous clinical trials have demonstrated the safety and efficacy of acetylcholinesterase inhibitors (AChEIs) in the treatment of AD. Yet, their benefits in AD as symptomatic drugs are likely to be more complex than a simple replacement of lost acetylcholine [70,71,72,73]. As reviewed previously, there is growing preclinical evidence that AChEIs have minor therapeutic effects and may block some of the fundamental neurodegenerative processes involved in AD [74]. Thus, much of pharmacology efforts are allocated towards other therapeutic approaches for AD, such as targeting iron neurotoxicity. Previous studies have demonstrated that metal-chelating compounds confer a potential to prevent metal-induced ROS, OS, and Aβ peptide aggregation [75]. Recently, several studies have reported a survey of various metal chelators, including restoring iron homeostasis compounds, that are potentially useful for the treatment of AD [76,77,78,79,80]. The iron chelator, DFO, has been reported in a single clinical study to slow down the progression of AD dementia [81], to prevent the formation of β-pleated sheets of Aβ1–42 and dissolve preformed β-pleated sheets of plaque-like amyloid in vitro [56]. Some clinical success has also been achieved with another metal-complexing agent, clioquinol [82]. Nonetheless, the long-term treatment of clioquinol (first generation, PBT1) caused side effects ending up with subacute myelopathic neuropathy and no significant effect on the cognition of AD patients [83,84,85]. However, a Phase IIa clinical trial reported positive results with the safe use of the second generation of clioquinol, PBT2, which significantly reduced Aβ plaque deposits and improved the cognitive behavior of AD patients [86,87,88]. In preclinical experiments, oral administration of clioquinol was reported to inhibit Aβ accumulation in an AD transgenic mouse model via its actions as a metal chelator [89], and clioquinol was recently demonstrated to chelate metal ions from metal-Aβ species and cause conformational transformation of Aβ aggregates [90]. The identification of an IRE in the 5′UTR of the APP transcript led to a novel therapeutic approach aimed at reducing amyloidosis by several FDA-pre-approved drugs targeted to the IRE in the APP mRNA 5′UTR [91,92]. For example, DFO, tetrathiomolybdate (Cu2+ chelator), and dimercaptopropanol (Pb2+ and Hg2+ chelator) were found to suppress APP holoprotein expression and lower Aβ peptide secretion [91,92]. In addition, the bi-functional molecule XH-1, which contains both amyloid-binding and metal-chelating moieties, was shown to reduce APP expression in SH-SY5Y cells and attenuate cerebral Aβ in APP and PS1 double-transgenic mice [93]. Additional drug classes were also reported to suppress the APP 5′UTR and limit APP expression, including antibiotics, selective serotonin reuptake inhibitors (SSRIs), and other selective receptor antagonists and agonists [92]. Up to date, available drug treatments are symptomatic with no disease-modifying effects on the underlying progressive processes in AD. A new field of therapeutic strategy is poly-pharmacology and multiple-target molecules, suggesting that drugs acting at a single target may be insufficient for the treatment of AD, which is characterized by the coexistence of several pathologies [94,95,96,97,98,99]. Aging of the brain has been demonstrated to be the main risk factor for AD. The association between brain aging and AD is continuously discussed. One observation holds that AD results when brain aging goes beyond a threshold. According to recent criteria, cerebrospinal fluid (CSF) Aβ changes are the primary link between AD and brain aging [100]. Evidence for the role of iron in aged-related pathologies has been demonstrated by studies showing that concentrations of non-heme iron increase in the putamen, motor cortex, prefrontal cortex, sensory cortex, and thalamus during the first 30–35 years of life [101]. Recent studies have also shown that levels of ferritin, the major iron storage protein, in older individuals were higher than in younger controls in the frontal cortex, caudate nucleus, putamen, substantia nigra, and globus pallidus [10,102]. A study comparing the cellular and regional distribution of ferritin and iron between young and aged rats has indicated that in the normal aging brain, there is an intracellular accumulation of iron in neurons [103]. A recent study involving human subjects demonstrated the correlation between iron content, as measured by quantitative magnetic resonance imaging (MRI), and cognitive impairments in elderly participants. Accordingly, the R2 MRI parameter affected by changes in brain iron concentration and water content was different in elderly participants with mild to severe levels of cognitive impairment compared with healthy controls [104], suggesting that iron misregulation might play a role in the decline in cognitive function observed in aged individuals. Recently, DFO and other metal-chelating agents have also been investigated as possible therapeutic agents for age-related neuropathology. For example, the effect of DFO was evaluated on age-related recognition memory deficits in aged Wistar rats. DFO-treated rats showed normal recognition memory in a novel object recognition task, while the saline group showed long-term recognition memory deficits. The results showed that DFO was able to reverse age-induced recognition memory deficits and reduced oxidative damage to proteins in the cortex and hippocampus, indicating that iron chelators might prevent age-related memory dysfunction. ALS, commonly referred to as Lou Gehrig’s disease, is a relentlessly progressive neurologic disorder with an estimated prevalence of four to six cases per 100,000 [105]. The onset of ALS is most common in midlife (usually between ages 45 and 60), with a typical disease course of 1 to 5 years. Most ALS cases (90%) are of unknown etiology and are classified as sporadic. The remaining 10% of cases are familial, and 20% of them are attributed to mutation in the superoxide dismutase-1 (SOD1) gene [106]. Both sporadic and familial forms of ALS are clinically and pathologically similar, suggesting possible common pathogenesis and the final pathway of neurodegeneration. The molecular pathogenesis of ALS is poorly understood, contributing to the lack of effective system-based therapies to treat this disease. Investigations have implied that ALS is a multifactorial and multisystemic disease that arises through a combination of several mechanisms that act by concurring damage inside motor neurons and their neighboring non-motor cells, including protein misfolding and aggregation; genetic factors; OS damage and mitochondrial dysfunction; defective axonal transport; excitotoxicity, and neuroinflammation [107,108]. In ALS patients, an imbalance in ROS production, either caused directly by mutant SOD1 or indirectly by other mechanisms, could be responsible for an altered iron homeostasis [109]. How this is accomplished is not known. An intriguing hypothesis is based on the observation that superoxide radicals, if not detoxified appropriately, can inactivate enzymes containing Fe–S clusters by oxidizing one Fe and causing its release from the cluster [110]. The increased Fe demand of the SOD-defective cells may reflect its aim to continuously reconstitute the “missing” Fe in the Fe–S clusters. An early indication of the role of iron in the pathogenesis of ALS was provided by the elevated iron levels in the CNS of both sporadic and familial forms [111,112,113]. In addition, the expression of ferritin was induced at the last stages of the disease in the SOD1-G93A transgenic mouse (which develops symptoms and pathology like those of ALS patients), indicating high iron concentrations [107]. In line with this, it was reported that transferrin is localized in Bunina bodies of spinal cord neurons from ALS patients [114,115], suggesting the involvement of transferrin in the formation of these inclusions. Interestingly, in transgenic mice expressing the wild-type SOD1 or SOD1-active mutant enzyme, G93A-SOD1, the expression of TfR and IRP1, a positive transcriptional regulator of TfR, were positively modulated in response to increased SOD1 mutation [114]. Jeong and collaborators [116] recently described the dysregulation of the iron homeostasis mechanism in the CNS in the G37R-SOD1 transgenic mice model of ALS, suggesting that iron chelation therapy might be useful for the treatment of ALS. A defect in the HFE gene, which was previously associated with iron-overload diseases, hemochromatosis, and AD, is currently associated with ALS [117]. The protein normally made by the HFE gene is thought to limit the uptake of iron by cells, protect against OS, and possibly dampen inflammatory reactions. An increased incidence of HFE mutation was reported in ALS patients [118]. The presence of this mutation was shown to disrupt the expression of tubulin and actin at the protein levels, potentially consistent with the disruption of axonal transport seen in ALS and associated with a decrease in SOD1 expression [118]. Treatment of ALS has been fueled in part by frustration over the shortcomings of the symptomatic drugs available because these are incapable of slowing the progression of the disease and neuronal degeneration. Regrettably, the single drug approved for use in ALS, riluzole, a membrane-stabilizing drug, only slightly prolongs survival [119]. Currently, >150 different potential therapeutic agents or strategies have been tested in transgenic ALS mice, according to published trials [108]. This list involves 108 pharmacotherapies, 14 gene or antisense therapies, nine cell transplantations, three immunizations, and seven dietary or lifestyle regimens. The pharmacotherapy spectrum encompasses antioxidants, anti-excitotoxins, anti-aggregation compounds, antiapoptotic, anti-inflammatories, and neurotrophic agents. Unfortunately, therapeutic modifiers of murine ALS have failed to translate in patients successfully, probably because most of these trials tested single agents that affect only one mechanism or because of delivery limitations. Given the multiplicity of pathologic mechanisms implicated in ALS, new ALS therapies may consider a simultaneous manipulation of multiple targets. Combination treatments or polypharmacy targeting different disease mechanisms have consistently shown superior efficacy in transgenic ALS mice [120,121,122]. Several designed synthetic and natural multipotent compounds were investigated and described by Youdim and collaborators [17,98,123,124] to hit two or more targets implicated in AD. In a series of novel multifunctional iron chelators, the compound M30 (5-[N-methyl-N-propargylaminomethyl]-8-hydroxyquinoline) was found to be the most potent, nontoxic, lipophilic, and brain-permeable selective iron chelator (compared with zinc and copper) [123,124]. M30 and another multimodal iron-chelating compound, HLA20 (5-[4-propargylpiperazin-1-ylmethyl]-8-hydroxyquinoline) (Figure 2), were designed from the prototype brain-permeable iron chelator, VK28 (Figure 2) (Varinel Inc., West Chester, PA, USA) (5-[4-(2-hydroxyethyl) piperazine-1-ylmethyl]-quinoline-8-ol) and chemically attached to the propargyl moiety of the anti-Parkinsonian MAO-B inhibitor, rasagiline (Azilect®) [125] (Figure 2) thus inheriting some of their neuroprotective/neurorestorative properties [123,124,126,127,128,129,130]. In the series of multifunctional iron chelators, the compound M30 [5-(N-methyl-N-propargylaminomethyl)-8-hydroxyquinoline] (Figure 2) was found to be a most potent iron chelator, displaying highly effective inhibition of booth MAO-A and MAO-B activities, as well as iron-dependent lipid peroxidation in vitro and in vivo [123,124,128,131]. M30 possessed solubility and selective iron-chelating properties (compared with zinc and copper) [123,124] and found to be non-cytotoxic, as shown by the genotoxicity assay performed in three different cell lines, A549, SH-SY5Y, or HepG2; inhibition of cytochrome p450 isozymes and voltage-dependent potassium channel–blocking test (Varinel, Inc., West Chester, PA, USA). M30 was demonstrated to be an effective inhibitor of lipid peroxidation with higher IC50 value, comparable with that of the prototype iron chelator, DFO [123,124,128]. It is well established that strong iron chelators could form inert complexes with iron and interfere with the Fenton reaction, leading to a decrease in hydroxyl free radical production and thus block lipid peroxidation. M30, which has been shown to possess high iron-binding capacity [123], may also be active through this mechanism to inhibit free radical formation. In addition, M30 may act as a radical scavenger by directly blocking the formation of free radicals, as confirmed in the spin trapping of the hydroxyl radical by 5.5-dimethyl-I-pyrroline-N-oxide (DMPO), measured in the electron paramagnetic resonance (EPR) spectra (Varinel Inc., West Chester, PA, USA). It was shown that M30 could significantly reduce the DMPO-hydroxyl radical signal generated by the photolysis of H2O2 (Varinel Inc., West Chester, PA, USA). In in vitro neuroprotective studies, M30 was found to invoke a wide range of pharmacological activities, including a neurorescue response; a protective potency against OS insults, H2O2, and SIN-1 (peroxynitrite generator, 3-morpholino sydnonimine), and a regulatory action on neuronal differentiation and neurite outgrowth in various neuronal cell lines (125,126,129) [126,127,130]. M30 was found to suppress the translation of a luciferase reporter mRNA through the APP 5′UTR sequence [127]. This effect may account, at least in part, for the observed downregulation of membrane-associated holo-APP levels in the mouse hippocampus and in SH-SY5Y neuroblastoma cells, presumably by chelating intracellular iron pools [126,127]. Furthermore, M30 markedly reduced the levels of the amyloidogenic Aβ in the medium of CHO cells, stably transfected with the APP “Swedish’’ mutation [126,127] and protected primary cultured neurons against Aβ toxicity [132]. Our recent in vitro studies in pancreatic β-cells demonstrated a decreased formation of intracellular ROS after H2O2 exposure and enhanced activity of the antioxidant detoxifying enzyme, catalase, in the protective effect of both M30 and HLA20. This cytoprotective effect was suppressed by pre-treating with a catalase inhibitor, suggesting a crucial role of catalase in the defensive action of the multifunctional iron-chelating drugs [133]. In in vivo studies, M30 was previously shown to prevent the loss of mouse tyrosine hydroxylase (TH)-positive neurons induced by post-intranigral injection of lactacystin (proteasome inhibitor); improve behavioral performances, and attenuate inhibition of ubiquitin-proteasome activity, iron increase, and microglial activation in the ipsilateral substantia nigra [134]. Moreover, we demonstrated that M30 prevented 1-methyl 4-phenyl 1,2,3,6-tetrahydropyridine (MPTP)-induced striatal dopamine depletion [128], as well as restored nigrostriatal dopaminergic neurons in the post-MPTP mouse model of PD [129,134] (Figure 3). Current therapeutic approaches suggest that drugs acting at a single target may be insufficient for the treatment of multifactorial neurodegenerative diseases such as PD, AD, and ALS, all characterized by the coexistence of multiple etiopathology (e.g., OS and ROS formation, protein misfolding, and aggregation, mitochondrial dysfunction, inflammation, metal dyshomeostasis and accumulation at the sites of neurodegeneration). Based on this reasoning, the working hypothesis of the current study was that multimodal chimeric compounds, synthesized by amalgamating the propargyl moiety of the neuroprotective/neurorestorative drug, rasagiline, into the antioxidant-chelating skeleton of an 8-hydroxyquinoline derivative of the iron chelating compound VK-28 might provide a new powerful new strategy for combating multifactorial neurodegenerative disorders. The major aim of this study was to explore whether the novel multifunctional iron-chelator, M30, exhibits beneficial effects on cognitive impairments and pathological alterations in APP/PS1 Tg mice. The effect of long-term M30 treatment (1 and 5 mg/kg for 9 months, initiated when the mice were 3 months old) on spatial learning deficits in the transgenic mice was investigated. The abilities of the mice to learn and process spatial information were evaluated by the Morris water maze test, one of the most widely accepted behavioral tests of hippocampus-dependent spatial learning and memory [135]. All mice were tested on both the visible and hidden platform versions of the Morris water maze test. Figure 4 shows the results of water maze acquisition training of all mice. The visible platform tests showed that the escape latency decreased significantly across the five days of visible platform sessions for all groups. As shown in Figure 4A, despite the significant initial spatial learning impairment exhibited by vehicle-treated APP/PS1 mice, they were able to locate the visible platform proficiently by the fifth day of the visible platform session. In the hidden platform version, vehicle-treated APP/PS1 mice showed impaired acquisition of spatial learning, compared with the non-Tg mice, as indicated by much slower improvements in the escape latency across consecutive trials. M30 treatment ameliorated the performance deficits in APP/PS1 mice during the testing period with the invisible platform, compared with the vehicle-treated group. This was followed by a probe trial performance, showing that M30 treatment not only significantly promoted the acquisition phase of place learning but also significantly improved memory retention during the probe trial (Figure 4B). Taken together, these data indicate that M30 treatment led to spatial learning-memory improvement in APP/PS1 mice. Y Maze spontaneous alteration is a particular test of memory function, navigation behaviors, and the willingness of rodents to explore new environments. Y maze has been shown to be extremely sensitive to hippocampal damage, as well as many other parts of the brain (e.g., septum, basal forebrain, and prefrontal cortex) have been shown to be involved in this task. Y-maze task revealed that vehicle-treated APP/PS1 mice were impaired in this task, compared with the non-Tg mice, while APP/PS1 mice given M30 (5 mg/kg) showed a significantly higher proportion of spontaneous alternations (Figure 5). There was no significant difference in the general activity, measured as the total number of arm entries between the groups (data not shown). In the next experiment, the effect of M30 on cognitive function was examined by the Hebb–Williams maze test. The Hebb–Williams maze is an incentive-based exteroceptive behavioral model useful for measuring the spatial working memory of rodents. Each mouse was trained for two sessions per day, three trials each, in practice maze A (Figure 6A). Each mouse was run through the practice maze until reaching the criterion of completing the 3-trial session in less than a total of 60 s for two consecutive sessions. The test phase began after all mice met the acquisition criterion. Our results revealed that, compared to non-Tg counterparts, the APP/PS1 vehicle-treated mice were impaired in the total number of errors, initial entry errors, and repeat errors in both (number 8 and 12) problems performed (Figure 6A). M30-treated mice at both concentrations showed improved performance in both problems, making significantly fewer total and repeat errors, as compared with vehicle-treated Tg controls (Figure 6B). The novel taste neophobia test that is sensitive to the amygdala and hippocampal damage was used as a measure of anxiety and memory for a novel food. Figure 7 shows that in the vehicle-treated APP/PS1 group, food intake was significantly reduced after the initial exposure compared to the non-Tg mice. M30 (1 and 5 mg/kg)-treated APP/PS1 mice consumed significantly more novel food during the second encounter compared with vehicle-treated Tg mice. The nest construction test permits an evaluation of the effect of experimental therapies on reversing apathy, as well as deficits in planning and multistep problem-solving. It is considered to reflect a type of step-by-step planning analogous to dysexecutive symptoms seen in AD [136] and, like human cases, is sensitive to lesions of the prefrontal cortex [137]. In addition, this test may reflect apathy, the most common neuropsychiatric symptom reported among individuals with AD, manifested by a lack of interest in surroundings, social withdrawal, and a loss of motivation to improve or work [138]. In the present study, the nesting behavior test, followed by the analysis of nesting scores, revealed that nesting was impaired in vehicle-treated APP/PS1 mice in comparison with the non-Tg group. A significantly improved nesting was observed in M30-treated APP/PS1 mice at both given concentrations (1 and 5 mg/kg) (Figure 8). The effect of M30 on the non-cognitive behavior of APP/PS1 mice was analyzed by the rotarod task, routinely used to study motor coordination and balance, and screen tests, as an indicator of general muscle strength [139]. The results of both tests revealed that vehicle-treated APP/PS1 mice displayed similar performance when compared to non-Tg mice (data not shown). In addition, as mentioned above, APP/PS1 vehicle-treated mice were able to locate the visible platform by the end of the visible platform session and demonstrated similar general activity during Y-maze testing as non-transgenic mice. Therefore, the reduced spatial learning ability of vehicle-treated APP/PS1 mice was not caused by motor or sensory deficiencies. Chronic M30 treatment at both concentrations (1 and 5 mg/kg) also had no effect on both rotarod and screen tests performances of APP/PS1 mice versus vehicle-treated APP/PS1 mice (data not shown), indicating that the observed effects of M30 on cognitive-based tasks presumably cannot be due to non-cognitive effects of M30 on sensorimotor function. Weekly body weight monitoring showed that vehicle-treated APP/PS1 mice were consistently and significantly lower in body weight gain than the non-Tg mice. Compared to vehicle-treated APP/PS1 mice, M30 (1 and 5 mg/kg)-treated APP/PS1 mice started to gain more body weight at 6 months of age, and this trend was observed for the rest of the experiment (Figure 9). At the end of the experiment, body weights of M30 (1 and 5 mg/kg)-treated APP/PS1 mice were slightly higher than those of vehicle-treated APP/PS1 mice (33.2 ± 1.6 g and 31.8 ± 1.7 g, respectively, vs. 30.6 ± 0.8 g), like the non-Tg mice (32.8 ± 1.4 g). Relative brain weights did not significantly differ between the non-Tg and APP/PS1 vehicle-treated mice (1.9 ± 0.11% vs. 1.8 ± 0.09%) and were not significantly affected by M30 treatment (1.8 ± 0.1% and 1.9 ± 0.12% for 1 and 5 mg/kg-treated groups, respectively). After the behavioral assessment, we studied the effect of M30 on various pathological features of AD, including cerebral iron levels, changes in fibrillar amyloid deposition, and Aβ levels in the brain. A qualitative examination of Perl’s-DAB-stained brain sections revealed an increase in iron concentration in vehicle-treated APP/PS1 mice (n = 4) as compared with virtually undetectable levels in vehicle-treated non-Tg mice (n = 3) (Figure 10). The iron levels were more pronounced in the striatum (1.9 ± 0.21) than in the cortical and hippocampal areas (0.52 ± 0.21 and 0.29 ± 0.12, respectively), as determined by OD analysis. M30 (1 and 5 mg/kg)-treated APP/PS1 mice showed notably reduced levels of iron staining in all brain regions studied, compared with the vehicle-treated group (Figure 10). The cortical and hippocampal iron levels in M30 (1 mg/kg)-treated APP/PS1 mice (n = 3) were 0.22 ± 0.06 and 0.16 ± 0.09, respectively, representing a 57% decrease in cortical (p < 0.05) and a 44% decrease in hippocampal (p < 0.05) iron levels. There was a 24% decrease in striatal iron level, but this was not found to be statistically significant (0.22 ± 0.14 vs. 0.29 ± 0.1; p = 0.1). The cortical, hippocampal and striatal iron levels in M30 (5 mg/kg)-treated APP/PS1 mice (n = 3) were 0.18 ± 0.1, 0.14 ± 0.02, and 1.12 ± 0.16, respectively. This represents a 65% decrease in cortical (p < 0.05), a 51% decrease in hippocampal (p < 0.05), and a 41% decrease in striatal iron levels (p < 0.05). Further biochemical and immunohistochemical studies detected fibrillar amyloid deposits in brain slices by Thioflavin S staining (Figure 11A,B) and total Aβ plaque load, including diffuse and compacted fibrillar plaques, by a specific anti-Aβ-amyloid antibody (6E10, corresponding to amino acids 1-17 of Aβ peptide) (Figure 11C,D). We found that vehicle-treated APP/PS1 mice exhibited high levels of Aβ and fibrillar load, consistent with previous observations (Figure 11) [140]. In contrast, long-term oral administration of M30 resulted in a significant decrease of Thioflavin S-positive plaque deposition (Figure 11A,B) and total Aβ plaque burden (Figure 11C,D) in the frontal cortex, hippocampus, and parietal cortex, compared with vehicle-treated APP/PS1 mice, indicating that M30 was capable to reduce both fibrillar and nonfibrillar/diffused Aβ plaques. Consistent with these findings, high-resolution Western blot analysis showed that Aβ levels were reduced in the frontal cortex, hippocampus, and parietal cortex of the M30-treated APP/PS1 group (Figure 12). Next, we examined the effect of M30 on the levels of cerebral Aβ in APP/PS1 mice by a sandwich ELISA. As shown in Figure 13, the M30 treatment caused a significant decrease in brain concentrations of Aβ-40 and Aβ-42 in the TBS-soluble and guanidine-soluble brain homogenates. This indicates that the reduction in Aβ levels could account for the decrease in Aβ deposition observed in M30-treated APP/PS1 mice. We further assessed the impact of M30 on cerebral levels of the full-length APP and α- and β-CTFs of APP in APP/PS1 mice. Figure 14A shows that M30 treatment has led to a reduction in holo APP levels in all brain regions assessed (frontal cortex, hippocampus, and parietal cortex), as indicated by Western immunoblotting using the anti-APP antibody 22C11, which recognizes an epitope located between amino acids 60 and 100 in the N-terminal part of the ectodomain of APP. Similarly, immunoblot analysis using an anti-APP C-terminal (676–695) antibody showed that compared with the vehicle-treated group, M30 treatment reduced the levels of holo APP in the frontal cortex, hippocampus, and parietal cortex (Figure 14B). M30 treatment also caused a significant reduction in the CTFs of APP, produced by α- and β-secretases, C83 and C99, respectively (Figure 14B). These results complemented the decrease in Aβ levels. We explored a possible effect of M30 treatment on phosphorylation levels of APP and tau in the brain of APP/PS1 mice, using specific antibodies against phospho-Thr-668 of APP and phospho-tau at Ser-202. As shown in Figure 15 and Figure 16, a significant reduction in phospho-APP (Thr-668) and phospho-tau (Ser-202) was observed in the frontal cortex, hippocampus, and parietal cortex of M30-treated APP/PS1 mice, compared with the vehicle-treated group. Given the importance of CDK5 and GSK-3β/AKT in the regulation of APP, as well as the phosphorylation of tau, we determined the effect of M30 treatment on the phosphorylation levels of these kinases in the brain of APP/PS1 mice. Increased phosphorylation of Ser-9 in GSK-3β reflects the decreased activity of GSK-3β, whereas phosphorylation of AKT at Ser-473 and CDK5 at Ser-159 reflects the increased activity of AKT and CDK5. Quantification of Western blots revealed that M30 (5 mg/kg) treatment significantly increased the ratio of phospho-GSK-3β (Ser-9)/GSK-3β and decreased phospho-AKT (Ser-473)/AKT and phospho-CDK5 (Ser-159)/CDK5 ratios, compared with vehicle-treated APP/PS1 mice (Figure 15 and Figure 16). The levels of total GSK-3β, AKT, and CDK-5 were unchanged by M30 treatment (Figure 15). The effect of M30 treatment on brain levels of microtubule-associated protein 2 (MAP2, a marker for neuronal cell bodies and dendrites) [140] was examined in the brains of APP/PS1 mice. Since global neocortical neuronal loss is not apparent in this mouse model at this age, and only local neuronal loss in the hippocampal region has been observed [140], we performed the immunohistochemical analysis in the dentate gyrus, CA1, and CA3 hippocampal areas. Consistent with previous reports on the APP/PS1 mouse model of AD [140,141], we found a marked reduction in the MAP2 immunoreactivity in the CA3 hippocampal region (Figure 17) but only an insignificant decrease in dental gyrus and CA1 (data not shown). However, in M30-treated APP/PS1 mice, significant preservation of MAP2 expression was observed in CA3 regions, accompanied by a significant improvement of the integrity of the neuronal fibers and increased neuronal body volume as compared with the vehicle-treated APP/PS1 mice. These data indicate that M30 treatment might decrease the rate of neuronal degeneration in the APP/PS1 mouse model. To investigate the possibility that the novel multifunctional brain permeable iron-chelator, M30, could attenuate age-related cognitive deficits and β-amyloid deposition, the drug was chronically administrated by oral gavage to 15 months-old mice at concentrations of 1 and 5 mg/kg 4 times a week for 6 months. It was found that at the end of chronic M30 treatment, the average body weight was not significantly different between M30 (1 and 5 mg/kg)- and vehicle-treated mice (28.21 ± 1.25 g and 30.51 ± 2.25 g vs. 29.21 ± 2.1 g, respectively). To analyze the effect of M30 on a broad behavioral profile, we used the modified SmithKline Beecham, Harwell, Imperial College, Royal London Hospital, Phenotype Assessment (SHIRPA) analysis, a basic semi-quantitative behavioral and functional battery that includes measures of muscle function and cerebellar, sensory, neuropsychiatric, and autonomic performances [142,143]. No significant differences were observed between M30- and vehicle-treated mice in the majority of the SHIRPA variables related to general behavior, motor control, muscle tone, reflexes, cerebellar, sensory, and autonomic functions (data not shown). However, M30 significantly reduced the levels of anxiety and aggression, as compared with vehicle-treated aged mice in a dose–response manner (Table 1). Specifically, M30 at 1 mg/kg significantly affected two anxiety-like measures, and M30 at 5 mg/kg significantly decreased five measures of neuropsychiatric functions in aged mice compared to vehicle-treated aged mice (Table 1). Data are expressed as median followed by score range in parentheses. Results are presented only for those SHIRPA tests in which significance was noted. Shaded boxes represent statistically significant differences (p < 0.05) among M30 (1 or 5 mg/kg)-treated and vehicle-treated aged mice. In the object recognition test, vehicle-treated aged mice showed significantly lower preference towards the novel object, as compared with vehicle-treated young mice in both short-term (Figure 18A) and long-term (Figure 18B) memory retention tests. However, M30 (1 and 5 mg/kg)-treated animals exhibited a significantly higher preference in exploring the novel object during the short-term (Figure 18A) and the long-term (Figure 18B) memory retention trials than vehicle-treated aged mice, as their recognition index was significantly higher than the vehicle-treated aged group. Nesting behavior studies, followed by the analysis of nesting scores, revealed that nesting was impaired in vehicle-treated aged mice in comparison with vehicle-treated young mice (Figure 18). A significantly improved nest behavior has been observed in aged mice at both given concentrations of M30 (1 and 5 mg/kg) (Figure 19). Results for open field exploration behavior in aged mice treated with vehicle or M30 (1 and 5 mg/kg) demonstrated that M30 at both concentrations given did not affect the number of rearings (Figure 20B), latency to start locomotion (Figure 20C), or defecation (Figure 20D), compared with vehicle-treated mice. Significant induction of locomotor activity was observed in the groups treated with M30 at both concentrations, as indicated by a higher number of crossings, compared with the vehicle-treated aged group (Figure 20A). After the behavioral assessment, we studied the effect of M30 on various age-related pathological alterations, including regulation of cerebral iron levels and β-amyloid plaque deposition. A qualitative examination of Perl’s-DAB-stained brain sections revealed an increase in cortical iron concentration in vehicle-treated aged mice, as compared with virtually undetectable iron levels in vehicle-treated young mice (Figure 21). M30 (1 and 5 mg/kg)-treated aged mice showed notably reduced levels of iron staining compared with the vehicle-treated group (Figure 21). Loid plaques by Thioflavin S staining (Figure 22) and by monoclonal anti-Aβ amyloid (17-24) antibody (4G8) immunohistochemistry (Figure 23) in brains of mice that received M30 (1 and 5 mg/kg), or vehicle. Figure 22 shows a notable induction of Thioflavin S-positive plaque deposition in cortical and hippocampal areas of vehicle-treated aged mice, as compared with young mice. M30 (1 and 5 mg/kg)-treated aged mice showed significantly reduced levels of Thioflavin S staining in both cortical and hippocampal regions vs. vehicle-treated aged mice (Figure 22). Furthermore, 4G8 antibody immunoreactive Aβ deposits were significantly reduced in the frontal cortex and hippocampus of M30-treated aged mice, as compared with the vehicle-treated (Figure 23). As M30 has been previously shown to be a potent irreversible brain mitochondrial MAO-A and -B inhibitor [128], we finally examined the effect of M30 administration on MAO-A and -B activities in the cerebellum of aged mice. As shown in Table 2, M30 (5 mg/kg) caused a significant inhibition of both MAO-A and -B activities in the cerebellum of aged mice, compared to vehicle-treated aged control mice. We evaluated the neuroprotective effect of the novel multimodal iron chelating drugs, M30 and HLA20, against H2O2- and SIN-1-induced neurotoxicity in NSC-34 cells, a widely used motor neuron-neuroblastoma fusion line. This cell line was chosen as it expressed many of the morphological and physiological properties of motor neurons [144]. In these experiments, M30 treatment (1–10 µM) significantly reduced cell mortality induced by H2O2 (Figure 24A) and SIN-1 (Figure 24B), analyzed by an apoptotic cell death detection ELISA, based on the use of mouse monoclonal antibodies to detect free histones and fragmented DNA. A similar neuroprotective effect was obtained with HLA20 (1–10 µM) (data not shown). In these concentrations, M30 and HLA20 alone, in the absence of the neurotoxins, had no effect on cell viability relative to control. TfR is known to be induced by iron chelators through IRP-mediated mRNA stabilization [145]. Thus, if M30 and HLA20 act as iron chelators, levels of TfR should be increased in response to drug treatment. As shown in Figure 25A, at 5 and 10 µM, M30 and HLA20 induced a dose-dependent increase in TfR levels in NSC-34 cells, as indicated by Western blot analysis. Immunofluorescence staining of TfR (Figure 25B) confirmed this increase, further indicating the iron chelation effect of these drugs in NSC-34 cells. Previous studies have proposed that the protective effects of iron chelators are not exclusively the result of suppression of the Fenton chemistry. Another potential therapeutic effect of iron chelators is based on the inhibition of the iron-dependent HIF prolyl 4-hydroxylases (PHDs) that regulate HIF stability, leading to transcriptional upregulation of a cassette of protective genes [146,147]. Thus, we next tested the effect of the novel multifunctional iron chelators on HIF-1α levels in NSC-34 cells. As shown in Figure 26, the levels of both mRNA (Figure 26A) and protein expression (Figure 26B) of HIF-1α were increased in M30- and HLA20-treated NSC-34 cells, indicating that the drugs not only stabilized HIF-1α protein, but influenced its regulation at the transcriptional level. Immunohistochemical analysis confirmed these data, and further revealed that M30 and HLA20 caused not only accumulation but also nuclear translocation of HIF-1α (Figure 26C). The expressed HIF-1α in control was almost exclusively distributed in the cytosol and rarely found in the nucleus. In contrast, NSC-34 cells treated with 10 µM M30 and HLA20 for 48 h expressed high levels of HIF-1α in both the cytosol and the nucleus. In addition, real-time RT-PCR analysis revealed that exposure of NSC-34 cells to M30 or HLA20 significantly increased mRNA levels of VEGF (Figure 27A) and enolase 1 (Figure 27B), two known HIF-1 regulated genes [146,147,148,149,150]. It has been reported that phosphatidylinositol-3-kinase (PI3K)/AKT signaling plays an important role in regulating HIF-1α expression [149,150]. Therefore, we investigated the activation of AKT by monitoring Ser-473 phosphorylation in NSC-34 lysates. Figure 28 demonstrates that both M30 (10 µM) and HLA20 (10 µM) markedly increased the amount of phospho-AKT at 30 min drug exposure. GSK-3β is a well-characterized downstream substrate of AKT [150,151]. Previous studies have demonstrated that the phosphorylation of GSK-3β at Ser-9 by AKT results in the inhibition of GSK activity [151]. In our hands, M30 and HLA20 significantly increased the phosphorylation of GSK-3β at Ser-9 in NSC-34 cells (Figure 28), suggesting that both drugs induced activation of AKT followed by phosphorylation (inactivation) of GSK-3β. Next, we demonstrated that while untreated control cells appear to be characterized by short cell processes, NSC-34 cells treated with M30 (2.5–10 µM) or HLA20 (2.5–10 µM) were characterized by a higher number of cell processes, displaying a marked neuron-like phenotype (Figure 29). These neurites appear to be very well extended from the cell body, with a mean length ranging from 5–10 times the soma size. The morphological modifications were accompanied by a significant increase in the expression of the neuronal-specific axonal marker of differentiation GAP-43, as determined by immunofluorescence (Figure 29A,B), Western blot analysis (Figure 29C), and real-time PCR (Figure 30A). Moreover, treatment with M30 (10 µM) and HLA20 (10 µM) appeared to significantly increase mRNA expression of BDNF (Figure 30B), consistent with the presence of both pro- and mature forms of BDNF in NSC-34 cell lysates [152] and with a possible autocrine role of BDNF for motor neurons. These results are in line with previous studies implicating the importance of iron in DNA replication, and indeed, cellular depletion of iron has been shown to induce cell differentiation and cell-cycle regulation [153,154,155]. We further examined the effect of M30 and HLA20 on the levels of cyclin D1, which plays a critical function in G1 progression by interacting with cyclin-dependent kinases and can be regulated post-transcriptionally by iron-depletion [155]. Figure 31 shows that both M30 and HLA20 at concentrations of 5 and 10 µM, markedly reduced the levels of cyclin D1, as determined by Western immunoblotting, suggesting an inhibitory effect of these iron-chelator compounds on cell cycle reentry [127]. As many reports have suggested the involvement of extracellular signal-regulated kinases (ERKs) in the neuronal differentiation [156,157], we examined whether ERKs may play a role in M30- and HLA20-stimulated neurite outgrowth. To block the activation of ERKs, we used PD98059, a selective inhibitor of MEK. PD98059 non-competitively blocks the activation of MEK by Raf-1 without affecting other known serine/threonine and tyrosine kinases [158]. Figure 31 demonstrates that pre-treatment of NSC-34 cells with PD98059 (10 µM) almost completely abolished M30- and HLA20-induced neurogenesis. In addition, prior exposure to the PKC-specific inhibitor, GF109203X (2.5 µM), significantly attenuated neurite outgrowth induced by M30 and HLA20, indicating the involvement of both ERK and PKC activation (Figure 32). Indeed, M30 (10 µM) (Figure 33A) and HLA20 (10 µM) (Figure 33B) caused rapid phosphorylation of PKC and ERK1/2 in NSC-34 cells at 10 min, reaching a maximum of 20 min. Nonconfluent cultured G93A-SOD1 NSC-34 cells were shifted into a growth medium containing 1 mg/mL doxycycline to induce mutant G93A-SOD1 expression 48 h before administration of the drugs for a further 48 h. Figure 34 shows that cell viability was markedly reduced after induction of mutation (48 h), compared to empty vector-transfected NSC-34 cells, while M30 and HLA-20 (5 and 10 µM) significantly increased G93A-SOD1 NSC-34 cell viability, as determined by the MTT reduction analysis. The positive outcome of our study, conducted in motor neuron cell cultures, has encouraged us to investigate the effect of the multifunctional iron chelating compound M30 in the fast-progressing strain of G93A-SOD1 mutant ALS transgenic mice. Oral administration of M30 (1 mg/kg) four times a week, starting from the 70th day after birth and continuing until death, resulted in delaying the onset of motor dysfunction (Figure 35). The plot of cumulative probability of the symptom onset against the age of animals (Figure 35A) shows a significant shift to the right by M30 treatment, compared with the vehicle-treated group. The average age of onset was 107 ± 3 days in the control group and 112 ± 4 days in the M30-treated group (n = 14–16; p < 0.001; log-rank Mantel–Cox test) (Figure 35B). Next, we examined the effects of M30 treatment on mice survival and life span. Kaplan–Meyer curve illustrates an increase in survival by M30 treatment (Figure 35C) from 124 ± 6 to 134 ± 12 days (n = 14–16; p < 0.025; log-rank Mantel–Cox test) (Figure 35D). In addition, the effect of M30 treatment on overall deficit scores of motor dysfunction was assessed by four independent behavioral tests and plotted against the age of the ALS transgenic mice. Figure 35E shows that the curve of dysfunction scores was shifted to the right by the M30 treatment. Body weight was also evaluated in the course of disease progression. As demonstrated in Figure 35F, M30 treatment slightly attenuated the weight loss of G93A-SOD1 mice. Considering the observed neuroprotective ability of M30 in the transgenic mouse models of AD and ALS and age-related cognitive decline, we intended to provide further insight into the various endogenous molecular mechanisms and pro-survival signaling pathways, activated in the brain following M30 systemic administration that might mediate neuroprotection. HIF-1α, a master regulator of cellular oxygen homeostasis, is stabilized and activated by hypoxia or treatment with heavy metals such as cobalt chloride or iron chelation with DFO and deferasirox, and modulates the expression of several target genes, which could contribute to neuroprotection [125]. To investigate the systemic effects of the multifunctional iron chelator M30 on HIF-1α levels and HIF-1-dependent target genes in the brain, the drug was chronically administrated by oral gavage to adult C57BL/6 mice. At the end of the chronic 30 d treatment with M30, the average body weight was not significantly different between M30- and vehicle-treated mice (26.8 ± 5.3 g vs. 28.1 ± 4.9 g, respectively). Serum iron levels of M30-treated (n = 12) and vehicle control mice (n = 11) were 110.5 ± 16.4 μg iron/dL and 172.4 ± 26.2 μg/dL, respectively (p < 0.05). Liver iron levels of M30-treated and vehicle control mice were 110.49 ± 20.6 μg iron/100 mg tissue and 145.7 ± 15.8 μg/100 mg tissue (p > 0.05). Western blotting for HIF-1α and real-time RT-PCR analysis of selected HIF-1-target genes was performed in the frontal cortex, hippocampus, striatum, and spinal cord tissue samples. In agreement with previous results, HIF-1α protein levels were virtually undetectable in the vehicle controls [159]. Treatment with M30 revealed a marked induction of HIF-1α protein levels in all brain regions assayed (cortex, hippocampus, and striatum) and spinal cord (Figure 36B) with no effect on its mRNA levels (Figure 36A), indicating that the drug-mediated HIF-1α induction is primarily at the post-translational level. Gene expression analysis revealed that M30 differentially upregulated mRNA levels of selected downstream HIF-1-related genes (e.g., vascular endothelial growth factor (VEGF), erythropoietin (EPO), Enolase-1, transferrin receptor (TfR), heme oxygenase-1 (HO-1), inducible nitric oxide synthase (iNOS), and glucose transporter (GLUT)-1)) in brain regions (frontal cortex, hippocampus, striatum) and spinal cord (Figure 36). For example, VEGF and iNOS were significantly induced in the cortex, striatum, and spinal cord; GLUT-1 in the cortex, striatum, and hippocampus; Enolase-1 only in the cortex; and HO-1 in all brain regions and spinal cord (Figure 37). These observations indicate that M30 possesses the ability to activate the HIF-1 pathway in the central nervous system (CNS) in vivo. Those differences, demonstrated in the regulatory effect M30 on HIF-1α target genes between various CNS regions, might reflect a differential pattern of PHDs tissue distribution, enzyme activity, or regulation [130,160]. Previous studies have implicated that among other protective mechanisms (e.g., stabilization of mitochondrial membrane; induction of pro-survival Bcl-2 protein); the neuroprotective effect of propargylamine derivatives is ascribed to induction of neurotrophic factors and antioxidant enzymes [161]. Thus, we next tested the in vivo regulatory effect of M30 on mRNA expression levels of the neurotrophic factors, brain-derived neurotrophic factor (BDNF) and glial cell-derived neurotrophic factor (GDNF), and antioxidant enzymes, catalase, superoxide dismutase (SOD)-1 and glutathione peroxidase (GPx) in various brain regions and spinal cord. Real-time RT-PCR revealed that M30 increased mRNA expression levels of BDNF in the cortex and striatum and GDNF in the hippocampus and spinal cord (Figure 38). Additionally, M30 administration resulted in a significant increase in mRNA levels of catalase in all brain regions and spinal cord, SOD-1 in the cortex and spinal cord, and GPx in the cortex and striatum (Figure 39). To evaluate the regulatory effect of M30 on CNS signaling cascades implicated in neuronal survival molecular processes, Western blotting analysis was performed using a panel of specific relevant anti-phosphorylated PKC, ERK1/2, AKT, and GSK-3β antibodies. Figure 40 demonstrates that M30 treatment significantly increased the levels of phospho-PKC, -ERK1/2, -AKT (Ser-437), and -GSK-3β (Ser-9) in the cortex and striatum of mice. In addition, pAKT (Ser-437) was elevated following M30 treatment in mice hippocampus and spinal cord (Figure 40). Taken together, these results show that M30 regulates the phosphorylation status of pro-survival signaling pathways in the CNS. To determine whether the regulatory effects observed following M30 treatment in adult mice CNS occur in periphery organs, we studied the effect of the drug on the expression of HIF-1α and the same HIF-1-target genes in the heart and liver. In addition, the regulation of antioxidant enzymes and signaling pathways in these periphery organs, following treatment with M30, was examined. As shown in Figure 41A, M30 administration significantly induced HIF-1α protein levels in both the heart and liver. Moreover, in response to M30 treatment, organ-specific regulation of several HIF-1-related genes and antioxidant enzymes was evident (Figure 41B,C), pointing to a common regulatory mechanism for CNS and periphery organs. Gene expression analysis in liver samples showed upregulation of VEGF, Enolase-1, TfR, iNOS, and GLUT-1 mRNA levels, while heart samples showed significant upregulation of EPO and Enolase-1. This may represent a common mechanism of drug-induced HIF-1 signaling for the brain and other organs. mRNA expression levels of SOD-1, as well as GPx, were significantly higher in the liver of M30-treated mice, and no significant effect was observed in the heart (Figure 41C). Analysis of signaling pathways revealed that in the liver of M30-treated mice, the levels of phospho-PKC, pAKT (Ser-437), and pGSK-3β (Ser-9) were significantly higher, and in the heart than those of pAKT (Ser-437) and pGSK-3β (Ser-9) (Figure 41D). In the present study, we have examined the potential therapeutic utility of our novel multi-target iron-chelating drug, M30, in the treatment of AD, age-related cognitive decline, and ALS. Our results suggest that the multifunctional iron chelator compounds can upregulate several neuroprotective-adaptive mechanisms and pro-survival signaling pathways in the brain and might function as ideal drugs for neurodegenerative disorders, in which oxidative stress and iron-mediated toxicity and dysregulation have been implicated. The potential therapeutic effect of M30 on AD-related neuropathology and cognitive deficits was investigated in APP/PS1 double Tg mice, a well-established AD mouse model [97,140,162,163]. The APP/PS1 mouse co-expresses a chimeric mouse/human APP containing the K595N/M596L Swedish mutations and a mutant human PS1 gene with the exon 9 deletion under the control of mouse prion promoter elements [164,165]. These transgenes co-segregate in the APP/PS1 mice, which develop AD-like cognitive deficits, as well as brain amyloid plaques, in a similar pattern to human AD, by around 6 months of age [164]. The current study demonstrates that chronic M30 treatment improved cognitive impairment and attenuated Aβ accumulation and tau phosphorylation in various brain regions of APP/PS1 Tg mice, compared with vehicle treated Tg mice. The observed beneficial response of M30 on cognitive functions may be associated with the inhibitory effect of the drug on Aβ levels and tau phosphorylation since a clear relationship has been demonstrated between Aβ accumulation and tau hyperphosphorylation and the cognitive deficits of AD Tg mouse model [166,167,168]. This inhibitory effect of M30 on Aβ levels may be attributed, at least partly, to the reduction observed in the levels of APP and APP/CTFs, which are the precursors of Aβ. Consistent with these findings, in vitro studies have previously described the regulatory effect of M30 on APP expression/processing, resulting in reduced APP expression levels and Aβ generation in SH-SY5Y neuronal cells and CHO cells, stably transfected with the “Swedish” mutation [127]. Regarding the APP processing pathway, M30 was found to activate the non-amyloidogenic pathway within the Aβ sequence, resulting in induced soluble APP, thus precluding the formation of Aβ, as also shown previously for other propargyl-containing compounds [169,170]. In addition, M30 may improve spatial memory by directly protecting the neurons from deteriorative processes. Accordingly, in vitro studies have recently demonstrated that M30 attenuated tau phosphorylation and protected cultured neurons against Aβ-induced toxicity [130]. Moreover, it has been described that M30 possesses neuroprotective/neurorescue activities, including a reduction in the pro-apoptotic proteins, Bax and Bad, and inhibition of the apoptosis-associated phosphorylated H2A.X protein and caspase-3 activation [127]. The increased level of OS in AD brain is reflected by high levels of iron, which can stimulate free radical formation (e.g., hydroxyl radicals via the Fenton reaction), enhanced lipid peroxidation, increased DNA and protein oxidation and glycation end product and decreased cytochrome C oxidase [171,172]. Thus, it is reasonable to assume that the neuroprotective action of M30 is mediated by a reduction in OS due to the iron-chelating properties of the drug. Indeed, we observed that the iron staining decreased in the cortex, striatum, and hippocampus after M30 treatment, compared with the respective brain regions in the vehicle-treated APP/PS1-treated group, indicating that the drug may prevent and/or modify the progression of neuronal degeneration by reducing excessive iron and its redox activity. It is established that iron chelators could form inert complexes with iron and interfere with the Fenton reactions, leading to a decrease in hydroxyl-free radical production, thus blocking the lipid peroxidation [10]. M30, which has been shown to possess iron-binding capacity [123], may be active through this mechanism to inhibit free radical formation. Moreover, M30 may act as a radical scavenger by directly blocking the formation of free radicals, as confirmed in the spin trapping of the hydroxyl radical by 5.5-dimethyl-I-pyrroline-N-oxide (DMPO), measured in the electron paramagnetic resonance (EPR) spectra (Varinel Inc., West Chester, PA, USA). It was shown that M30 can significantly reduce the DMPO-hydroxyl radical signal generated by the photolysis of H2O2 (Varinel Inc., West Chester, PA, USA). Studies have revealed that M30 has a lower affinity for iron than that of the prototype iron chelator, DFO, although it is highly inhibitory against iron-induced lipid peroxidation, with an IC50 value of 12 µM, comparable to that of DFO [123,124]. It is likely that the very high iron chelating property of DFO contributes to its cytotoxicity, which limits its application for long periods of time in pathological conditions unrelated to systemic iron overload. By contrast, a brain-permeable compound with moderate iron chelating affinity may be a more appropriate and promising agent for AD therapy in which iron is selectively accumulated in various brain regions. Further evaluation of the effect of M30 treatment on the phosphorylation levels of APP, tau, GSK-3β/AKT, and CDK5 revealed that the drug attenuated tau phosphorylation reduced phospho-CDK5 and enhanced phospho-AKT and phospho-GSK-3β in the frontal cortex, hippocampus and parietal cortex of APP/PS1 mice. These data are consistent with our recent studies showing that M30 enhanced the AKT and GSK-3β phosphorylation pathway and attenuated tau phosphorylation in cultured cortical neurons [130]. The mechanism through which the drug modulates these kinases will require further investigation. Because increased GSK-3 activity has been previously demonstrated to be linked to spatial learning deficits in AD transgenic mice [173,174], it may be speculated that activation of the AKT/GSK-3β pathway contributes, at least partly, to the improved cognitive abilities demonstrated following the M30 treatment, in APP/PS1 mice. In addition, M30 treatment reduced the levels of phospho-APP (Thr-668), which has been shown to be upregulated in the pathological process of APP/PS1 Tg mice [140]. Previously, it has been reported that phospho-APP (Thr-668) possesses various regulatory effects on neurodegeneration and APP processing cascades, indicating that APP is mainly phosphorylated by CDK5 and GSK-3β [173,174,175,176]. Finally, we examined the effect of the drug on the expression of the neuronal marker MAP2, as previous studies have demonstrated a significant degeneration of neurons, characterized by damage/loss of neuronal fibers surrounding the plaques in APP transgenic mice [177]. Our study shows that M30 treatment attenuated the loss of the immunoreactivity of MAP2 seen in vehicle-treated APP/PS1 mice. This is concordant with previous findings that M30 has a profound impact on neuronal differentiation in SH-SY5Y and PC12 cell lines [127]. In this regard, it has been previously demonstrated that the multimodal drug, M30, and several other propargyl derivatives significantly upregulated mRNA expression of various growth factors (e.g., BDNF, nerve growth factor (NGF), and glial cell-derived neurotrophic factor (GDNF) [161,178,179,180], suggesting that the stimulation of this neuronal pathway may provide an important step in their neuroprotective activity. Since aging of the brain has been demonstrated to be the main risk factor for AD [181,182], which is one of the most prevalent neurodegenerative disorders in the elderly population, we have further examined, in the present study, the potential beneficial effects of M30 in aged mice. Our findings showed that systemic chronic treatment of aged mice with M30 had a significant positive impact on neuropsychiatry functions and cognitive age-related impairment. These beneficial responses of M30 might be attributable, at least partly, to the following mechanisms: first, given the evidence supporting a role for free radicals’ production and OS in brain dysfunction during the aging process [183,184], the neuroprotective action of M30 may be mediated by a reduction in OS, due to its iron chelating properties. Here, we showed that cerebral iron staining decreased after M30 treatment in aged mice, compared with the vehicle-treated aged group, indicating that the drug can prevent or attenuate the progression of neuronal degeneration by the reduction in excessive iron and its redox activity. Indeed, previous studies have shown that M30 is a hydroxyl radical scavenger and an effective inhibitor of lipid peroxidation with a high IC50 value [124]. Accordingly, a recent report has shown that the iron chelating agent, DFO, was able to reverse age-induced recognition memory deficits and reduce protein carbonylation in the cerebral cortex and hippocampus in rats, further supporting the view that age-related cognitive deficits might be associated with iron accumulation in the brain [185]. Second, M30 may beneficially influence cognitive deterioration through its effect on attenuating MAO activity. This is consistent with our previous studies showing that M30 is a potent, selective brain MAO-A and B inhibitor [128]. Like other propargylamine-containing MAO inhibitors, M30 is a potent irreversible inactivator of the enzyme and as such, it is expected to make a covalent interaction with the FAD cofactor at the active site of the enzymes [186]. Given that products of MAO-catalyzed reaction, such as aldehydes and H2O2, are major inductors of lipid peroxidation, it is assumed that activation of MAO is associated with age-related disturbances of the homeostasis and generation of free radicals in involution of the nervous tissue [187]. Indeed, it was shown that in the brain of mice, MAO-A activity remained stable between 2 and 24 months, while MAO-B activity increased significantly between 2 and 16 months [188]. Quantitative radiography studies also showed an age-related increase in MAO-B in various brain structures [189]. Previous studies have demonstrated that a series of propargylamines, including rasagiline, deprenyl, and R-2HMP, all increased superoxide dismutase (SOD) and catalase activities in several brain regions of dopaminergic nature and in systemic organs such as heart and kidney [190]. Other studies have shown that deprenyl significantly prolonged the life span of aging rats [191], although the magnitude of the effect was quite variable, depending on individual studies [192,193,194]. In the hippocampus of aged rats, rasagiline was shown to reverse several age-related mitochondrial and key regulator genes that are involved in neurodegeneration, cell survival, synaptogenesis, oxidation, and metabolism [195]. Another interesting finding of this study is the observation that M30 treatment reduced the levels of β-amyloid plaques in the cortex and hippocampus of aged mice, compared with vehicle-treated aged animals. As discussed above, this effect may be associated with the previously observed reduction in the levels of APP in CHO cells, stably transfected with the “Swedish” mutation by M30 [126,132]. It was suggested that metal chelators could reduce APP levels by modulating APP translation via an IRE in the 5′ untranslated region of the APP transcript [23,47,92,196]. Indeed, M30 was found to suppress the translation of a luciferase reporter gene fused to the APP mRNA 5′ untranslated region [127]. In addition, M30 was shown to enhance the non-amyloidogenic pathway of APP processing and increase sAPPα levels [169,170]. In aged animals, these effects may be beneficial in the face of previous findings demonstrating increased amyloid deposition early in the aging process in various species [197,198,199,200,201]. The accumulation of Aβ with aging seems to be a combination of decreased efflux transport of endogenously generated Aβ and increased influx transport from the vascular compartment, and it is likely that the proportions from each Aβ source change with time [202]. Furthermore, it has been demonstrated that the interaction of APP and beta-site amyloid cleavage enzyme 1 (BACE1) is enhanced with aging [203,204]. Increases in APP, γ-secretase, and BACE1 have also been observed in correlation with age in cells and in vivo [181,182]. Indeed, aging is viewed as the most significant risk factor for AD and is closely correlated with AD neuropathology and there is presumably a continuum in Aβ accumulation from normal aging to AD, although the mechanism underlying this transition is not yet clarified. M30 and HLA20 were shown to possess multiple pharmacological activities in NSC-34 cells, a widely used mouse motor neuron hybrid cell line [205]. These include improvement of neuronal survival, activation of HIF-1α and induction of its expression and downstream target genes, promotion of neuronal differentiation and induction of various neuroprotective signaling pathways. We also demonstrated the ability of M30 to significantly extend the survival of G93A-SOD1 ALS transgenic mice and delay the disease onset. Initially, we have demonstrated the protective potency of M30 and HLA20 after exposure of cultured NSC-34 cells to the OS insults, H2O2, and peroxynitrite generator, SIN-1, previously shown to be associated with motoneuron degeneration in ALS [206,207]. This observation is consistent with our previous studies showing that M30 decreased apoptosis of SH-SY5Y neuroblastoma cells when given after depriving the cells of serum support (neurorescue paradigm) via various protective mechanisms, including reduction in the pro-apoptotic proteins, Bad and Bax, and inhibition of the apoptosis-associated phosphorylated H2A.X protein (Ser-139) and caspase-3 activation [127]. Thus, considering the mechanism of action of the novel multifunctional drugs, it can be assumed that the neuroprotective effect demonstrated in NSC-34 cells may be associated with their propargyl moiety since N-propargylamine and rasagiline conferred neuroprotection/neurorescue via activation of PKC and MAPK pathways, coupled to pro-survival Bcl-2 family members and mitochondrial membrane stabilization [208,209]. On the other hand, the iron complexing moiety embedded in the drugs may favorably influence cell survival by reducing the levels of ROS and reactive nitrogen species (RNS) because of the neutralization of excessive free-reactive Fe2+. An alternative pathway of protection by iron chelators may include the inhibition of the iron-dependent HIF-PHD, an enzyme that regulates HIF stability. Indeed, PHDs have been suggested as an additional target for neuroprotection in various neurodegenerative diseases. Inhibition of HIF-PHDs prevents the hydroxylation and subsequent degradation of HIF-1α [146]. Stabilization and subsequent nuclear localization of HIF-1α results in heterodimerization with its partner HIF-1β, binding to the hypoxia-response element in the gene regulatory regions, and subsequent transcriptional upregulation of established protective genes, such as erythropoietin, VEGF, p21waf1/cip1 and glycolytic enzymes (e.g., aldolase and enolase 1) [146,147,148]. Siddiq et al. [146] used an in vitro model of OS to correlate the protective effects of iron chelators and small molecules and peptides that do not bind iron but do inhibit the PHDs, with their ability to activate HIF-1. This model has been further supported by the observation that DFO was neuroprotective in hippocampal neuronal culture exposed to oxygen and glucose deprivation, in addition to OS and excitotoxicity damage, while this protection was prevented by blockade of HIF-1α with antisense oligonucleotide transfection [210]. In accordance, here we showed that M30 and HLA20 induced mRNA expression of HIF-1α and enhanced the protein levels of HIF-1α and its nuclear translocation in NSC-34 cells. Previously, several in vivo studies showed regulation of HIF-1α at the transcription level, in addition to the regulation of HIF-1α by protein stabilization [147,211,212]. Further, M30 and HLA20 significantly increased the levels of the endogenous HIF-1-dependent genes, enolase 1, VEGF, and BDNF in NSC-34 cells. Although VEGF was once considered to be only a specific angiogenic factor, emerging evidence indicates that it also has direct effects on neuronal cells and protects motoneurons from cell death induced by various insults, such as oxidative stress, hypoxia/hypoglycemia, glutamate-excitotoxicity and serum deprivation [213]. Deletion of the hypoxia-responsive element in the promotor region of the VEGF gene can cause motor degeneration in mice, and low-VEGF-producing alleles of the VEGF gene are associated with motoneuron degeneration in human ALS, suggesting that VEGF is a modifier of motoneuron degeneration in human ALS [214,215,216]. Although there is evidence for and against the role of VEGF in ALS etiopathogenesis, the literature has widespread interest in developing VEGF-based therapies for motoneuron degenerative disorders, raising new hope for the treatment of ALS and other neurodegenerative diseases. Besides regulating VEGF, HIF-1 has also been shown to promote glycolytic enzyme gene expression and consequent aerobic glycolysis [217,218]. Recent studies have demonstrated that a shift in energy generation from glucose oxidation in mitochondria to aerobic glycolysis is associated with resistance to OS [219]. Thus, by inducing glycolytic enzyme expression, iron chelators may reduce the ambient free radical burden of neurons by enabling the cell to generate more energy glycolytically and minimize deleterious consequences of mitochondrial glucose oxidation. Taken together, it can be suggested that the regulation of HIF-1α expression and its related genes may constitute an additional pathway underlying the neuroprotective effect of M30 and HLA20. Another finding of this study is the ability of M30 and HLA20 to induce differentiation of NSC-34 motoneuron cells. Both drugs were found to induce cell elongation and stimulate neurite outgrowth. These morphological modifications were accompanied by an increase in the immunoreactivity of the neuronal marker GAP-43 and a decrease in cyclin D1 expression, in accordance with results of previous studies, demonstrating that M30 induced a neuritogenic effect and triggered cell cycle arrest in G0/G1 in PC12 and SH-SY5Y cell lines [127]. Indeed, many cell cycle-regulating factors require iron for their function [155,220], suggesting that the cell cycle-blocking activity of iron chelators may trigger the process of differentiation through various iron-associated biological events [221]. In addition, the effect of the multifunctional drugs on motoneuron differentiation may be associated with their propargyl moiety since N-propargylamine and rasagiline were shown to upregulate BDNF and GDNF gene expression in PC12 cells [209,222]. Here, we show the ability of M30 and HLA20 to induce mRNA levels of BDNF, which is a well-recognized neurotrophic factor for motoneurons [223,224,225,226]. Motoneuron differentiation, induced by M30 and HLA20, was modulated by inhibitors of ERK/MAPK and PKC signaling pathways. In results complementary to inhibition studies, we found that the drugs significantly increased the immunoreactivity of phosphorylated MAPK and PKC in NSC-34 cells. In vivo studies demonstrated that treatment with M30 provides clear benefits in G93A-SOD1-transgenic mice, significantly increasing their survival and delaying the onset of neurological dysfunction, even when the treatment was initiated at a relatively advanced stage of the disease [227]. Complementary studies conducted in the laboratory of Dr. Moussa Youdim’s collaborator, Dr. Weidong Le [228], demonstrate a significant attenuation by M30 in the elevated iron level and transferrin receptor expression, oxygen free radicals, and microglial and astrocytic activation levels in the spinal cords of the SOD1 G93A mice. These results provide further evidence that iron is involved in the pathogenesis of ALS. This may be of significant relevance for the further development of M30 for the treatment of ALS since almost all ALS patients are diagnosed after symptom onset. Considering the neuritogenic effect of M30 demonstrated in the motoneuron NSC-34 cells, it is possible that the in vivo action of M30 is mediated through the regeneration process of motor nerves, inducing neurodifferentiation and sprouting of axons, leading to the reinnervation of muscle fibers. In conclusion, the neuroprotective/neurorescue potential of the novel rasagiline derivatives/iron-binding compounds may result from their multifunctional activities: (a) similar to rasagiline and other propargyl-containing molecules, such as ladostigil, they activate the canonical survival pathways, MEK and PKC, associated with elevation of BDNF; (b) the promotion of neurite sprouting and extension may result from activation of the above pathways, combined with the ability of iron-complexing molecules to interfere with cell cycle progression (like VK28) via deactivating cell cycle regulators, such as cyclins D1 and E, thus triggering differentiation through various iron-associated biological events. Since increased expression of the cyclin system may be involved in the mechanism of motor neuronal death at the late stage of ALS, it is reasonable to suggest that drugs directed towards cell cycle inhibition might be of value for disease treatment; (c) activation of HIF-1 and induction of its pro-survival/neuroprotective target genes (e.g., VEGF and enolase 1), an action that has been ascribed to iron chelation and inhibition of PHDs. Considering the observed neuroprotective ability of M30, we intended to provide further insight into the various endogenous molecular mechanisms and pro-survival signaling pathways activated in the brain following M30 systemic administration that might mediate neuroprotection. The results in adult mice chronically treated with M30 have demonstrated that the treatment produced a significant upregulation of HIF-1α protein expression in the brain (cortex, striatum and hippocampus) and spinal cord. In addition, real-time RT-PCR revealed that M30 differentially induced the transcription of a broad range of downstream HIF-1-related protective genes within the brain, such as those involved in erythropoiesis (EPO), angiogenesis (VEGF), glycolysis (GLUT-1), and oxidative stress (HO-1), indicating a biological HIF-1 activation in the brain in response to M30 administration in vivo. This mechanism of HIF-1α upregulation is consistent with previous studies demonstrating that iron chelators may function as hypoxia mimetic regulators; stabilizing and transactivating HIF-1α, thus leading to the regulation of HIF-1-responsive genes [146,229,230,231]. This may support adaptive mechanisms, which protect the brain from a hypoxic injury through the regulation of cerebral metabolism and blood flow, promotion of angiogenesis, and induction of cytoprotection [125,160,232,233]. Previous studies have demonstrated that iron chelation by DFO enhanced HIF-1 activity and prevented neuronal death in both in vitro and in vivo models of ischemia via HIF-PHDs inhibition [132,134,229,231,234,235]. Indeed, it was found that the protective effect of DFO against neuronal death after oxygen- and glucose deprivation could be reversed by blockade of HIF-1α with an antisense oligonucleotide transfection [134]. Thus, the activation of the brain HIF-1 signal transduction pathway and consequent expression of HIF-1-target genes possessing pro-survival properties may implicate a link between M30-induced HIF-1-driven gene expression and neuroprotective capacities. In accordance, our in vitro findings demonstrated the ability of M30 to upregulate HIF-1α and several HIF-1α-target genes (e.g., enolase-1, VEGF, EPO, and p21) in cultured cortical neurons and NSC-34 cells, accompanied by protective effects against Aβ25-35- and mutant G93A-SOD1-induced toxicity, respectively [130]. Activation of the HIF-1 signaling pathway by M30 was also achieved in peripheral organs (liver and heart). For example, of the HIF-1 target genes examined in the liver, VEGF, Enolase-1, TfR, iNOS, and GLUT-1 were significantly increased. Accordingly, activation of HIF-1α was recently shown to play a role in the effect of iron depletion by DFO on glucose metabolism in hepatocytes in vitro and in vivo [236]. In HepG2 cells, DFO stabilized HIF-1α and increased the expression of GLUT1 and insulin receptors. In addition, it was shown that DFO consistently increased the phosphorylation status of AKT/PKB and its targets FoxO1 and GSK-3β, which mediate the effect of insulin on glucogenesis and glycogen synthesis and upregulated genes involved in glucose uptake and utilization. In vivo, iron depletion increased hepatic HIF-1α expression, GLUT-1 mRNA levels, and AKT/PKB activity [236]. The specific activation of HIF-1 signaling and upregulation of HIF-1-related genes in the liver may be also associated with hepatic cytoprotection, as it was shown in various models of injury that stimulation of the HIF system could protect the liver against apoptosis. Another interesting finding is the differential upregulation of BDNF and GDNF in the CNS following M30 treatment. These data complement previous observations showing the ability of M30 and HLA20 and promote neuronal differentiation, including cell body elongation, stimulation of neurite outgrowth, and triggering cell cycle arrest in G0/G1 phase [126]. Additionally, in the current study, we showed that M30 induced mRNA expression levels of the major antioxidant defense system, comprising the detoxifying enzymes, catalase, SOD-1, and GPx, in various brain regions. The transcriptional upregulation of neuronal growth factors and antioxidant enzymes are presumably associated with the propargyl moiety embedded in the M30 molecule. Indeed, previous studies reported that several propargyl derivatives upregulated mRNA expression of BDNF and NGF and increased protein levels of BDNF [161,190,195,237], suggesting that the stimulation of these neuronal survival pathways may provide an important step in their neuroprotective activity. In line with this, it was previously shown that propargylamines possess an antioxidant action and suppress the formation of free radicals by increasing the activity of the antioxidant enzymes, SOD, and catalase in rat brain dopaminergic regions [161,190,237]. By inducing antioxidant enzymes and decreasing the formation of ROS, propargilamine-containing drugs may combat an oxidative challenge, implicated as a common causative factor in neurodegenerative diseases. Finally, M30 treatment induced a significant increase in brain expression of phosphorylated PKC, ERK1/2, AKT, and GSK-3β. Regarding the role of these signal pathways in the regulation of neuroprotection, it has been reported in many studies that MEK/ERK and PI3K/AKT/GSK-3β pathways can promote cell survival, especially neuronal survival by both enhancing the expression of anti-apoptotic proteins and inhibiting the activity of pro-apoptotic ones [238,239,240]. In addition, these signaling pathways are well documented to play a key role in the regulation of HIF-1α [125] and, thus, might be involved in the increased expression of HIF-1α following M30 treatment. It cannot be ruled out that these signaling cascades are activated in the brain of M30-treated mice as a secondary phenomenon by a HIF-1α-dependent gene product. Thus, considering the mechanism of action of M30, it can be assumed that the neuroprotective effects of the drug may be also associated with the activation of these pro-survival signaling cascades. Indeed, N-propargylamine and rasagiline confer neuroprotection/neurorescue effect via activation of PKC and MAPK pathways, coupled with pro-survival Bcl-2 family members and mitochondrial members stabilization [125,208]. Although misregulation of the HIF pathway is only one component of a spectrum of reactions occurring in neurodegeneration, HIF-1 is a “master switch” being an important physiological response mechanism, likely resulting in several reproducible neuroprotective effects [241]. Given the wide range and diversity of cellular functions regulated by the whole spectrum of HIF-1-target genes, it is suggested that this compensatory pathway can mediate neuroprotection and is crucially involved in many physiological processes within the brain. Thus, the novel therapeutic approach of pluripotential iron-chelating compounds, such as M30, that target several pharmacological sites involved in neurodegenerative processes and activates the HIF pathway and downstream neuroprotective genes will broaden the current strategies for the treatment of neurological disorders and overall will open a new window for future development of drugs possessing a profound impact on neuron preservation. Currently, simultaneous modulation of multiple targets by one multifunctional compound is the most promising approach for the multi-dysfunctional molecular conditions observed in neurodegeneration. The design of M30 and its series counterparts was to address multiple CNS etiologies in various neurodegenerative disorders, in particular AD and age-related dementia. The several targets and diverse pharmacological properties of M30 make this compound potentially valuable for a therapeutic strategy to delay neurodegeneration, as shown in the following illustration (Figure 42).

PMC10001429

36909794

William M. Vanderheyden,Micah Lefton,Carlos C. Flores,Yuji Owada,Jason R. Gerstner

Fabp7 Is Required for Normal Sleep Suppression and Anxiety-Associated Phenotype following Single-Prolonged Stress in Mice

13-05-2022

fear,stress,lipid signaling,blbp,glia,anxiolytic

Humans with post-traumatic stress disorder (PTSD) exhibit sleep disturbances that include insomnia, nightmares, and enhanced daytime sleepiness. Sleep disturbances are considered a hallmark feature of PTSD; however, little is known about the cellular and molecular mechanisms regulating trauma-induced sleep disorders. Using a rodent model of PTSD called “Single Prolonged Stress” (SPS) we examined the requirement of the brain-type fatty acid binding protein Fabp7, an astrocyte expressed lipid-signaling molecule, in mediating trauma-induced sleep disturbances. We measured baseline sleep/wake parameters and then exposed Fabp7 knock-out (KO) and wild-type (WT) C57BL/6N genetic background control animals to SPS. Sleep and wake measurements were obtained immediately following the initial trauma exposure of SPS, and again 7 days later. We found that active-phase (dark period) wakefulness was similar in KO and WT at baseline and immediately following SPS; however, it was significantly increased after 7 days. These effects were opposite in the inactive-phase (light period), where KOs exhibited increased wake in baseline and following SPS, but returned to WT levels after 7 days. To examine the effects of Fabp7 on unconditioned anxiety following trauma, we exposed KO and WT mice to the light–dark box test before and after SPS. Prior to SPS, KO and WT mice spent similar amounts of time in the lit compartment. Following SPS, KO mice spent significantly more time in the lit compartment compared to WT mice. These results demonstrate that mutations in an astrocyte-expressed gene (Fabp7) influence changes in stress-dependent sleep disturbances and associated anxiety behavior.

Fabp7 Is Required for Normal Sleep Suppression and Anxiety-Associated Phenotype following Single-Prolonged Stress in Mice Humans with post-traumatic stress disorder (PTSD) exhibit sleep disturbances that include insomnia, nightmares, and enhanced daytime sleepiness. Sleep disturbances are considered a hallmark feature of PTSD; however, little is known about the cellular and molecular mechanisms regulating trauma-induced sleep disorders. Using a rodent model of PTSD called “Single Prolonged Stress” (SPS) we examined the requirement of the brain-type fatty acid binding protein Fabp7, an astrocyte expressed lipid-signaling molecule, in mediating trauma-induced sleep disturbances. We measured baseline sleep/wake parameters and then exposed Fabp7 knock-out (KO) and wild-type (WT) C57BL/6N genetic background control animals to SPS. Sleep and wake measurements were obtained immediately following the initial trauma exposure of SPS, and again 7 days later. We found that active-phase (dark period) wakefulness was similar in KO and WT at baseline and immediately following SPS; however, it was significantly increased after 7 days. These effects were opposite in the inactive-phase (light period), where KOs exhibited increased wake in baseline and following SPS, but returned to WT levels after 7 days. To examine the effects of Fabp7 on unconditioned anxiety following trauma, we exposed KO and WT mice to the light–dark box test before and after SPS. Prior to SPS, KO and WT mice spent similar amounts of time in the lit compartment. Following SPS, KO mice spent significantly more time in the lit compartment compared to WT mice. These results demonstrate that mutations in an astrocyte-expressed gene (Fabp7) influence changes in stress-dependent sleep disturbances and associated anxiety behavior. Post-traumatic stress disorder (PTSD) develops rapidly, and is associated with long-term alterations in sleep and brain physiology. PTSD can elicit sleep abnormalities shortly after a traumatic event, with persistent changes in anxiety behavior. According to the American Psychiatric Association, sleep impairments are a diagnostic criterion for PTSD (2013). Poor sleep following trauma exposure is considered a predictor of subsequent PTSD severity [1–3]. However, very little is known regarding mechanisms that intersect sleep–wake processing and PTSD etiology. Therefore, understanding molecular and cellular events that interact between the regulation of sleep and symptomatology of PTSD will be important for targeting therapeutic strategies in treating this disorder. This underscores the importance of the use of pre-clinical animal models that recapitulate hallmark traits of PTSD and associated changes in sleep [4]. Single prolonged stress (SPS) is a well-validated rodent model of PTSD where a subsequent 7-day isolation period is necessary for a PTSD-like phenotype to develop [5–10]. Previous studies showed that sleep alterations occur after SPS [11], and that sleep during this 7-day window following SPS exposure predict fear-associated memory impairments [12]. Further, optogenetic increases in sleep post-SPS exposure were associated with enhanced fear conditioning [13]. While these data indicate a relationship between sleep and the development of PTSD, specific molecules or cell-types that may integrate them, remain largely unexplored. The astrocyte-enriched brain-type fatty acid binding protein, Fabp7, has been described to be associated with PTSD-like behaviors, as has the neuronal expressed Fabp3 [14]. For example, Fabp7-null mice exhibit enhancement of fear memory and anxiety [15]. We have previously shown that Fabp7 follows a circadian rhythm in gene expression broadly throughout mammalian brains [16,17], which is regulated by the core clock gene BMAL1 [18] and circadian transcriptional repressor REV-ERBα [19]. Fabp7 has also been shown to regulate sleep across phyla, including flies, mice, and humans [20–22]. Taken together, this suggest that Fabp7 may represent a functional node that regulates sleep and the etiology of PTSD. In order to test this, we examined changes in sleep and anxiety behavior in Fabp7 knock-out (KO) mice compared to C57BL/6N wild-type (WT) genetic background mice following SPS exposure. We observed a day-night reversal of sleep–wake phenotype following a full 7-day window post-SPS that is associated with cognitive disruption in a light–dark box anxiety test. These studies indicate that a genetic deletion of an astrocyte-enriched gene impairs sleep and cognitive processing, and provides a novel therapeutic target in lipid signaling pathways for treating PTSD. All animal procedures were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and approved by the WSU Institutional Animal Care and Use Committee (IACUC; ASAF# 6459 “Astrocyte Involvement in Stress Induced Sleep Alterations”). Male, C57BL/6N (WT) and Fabp7 KO mice (provided by Dr. Owada) were used for all experiments to eliminate the known impact of estrous cycle hormones on sleep and behavior [12,13,23]. Mice (60–90 days old) were housed in temperature (21–24 °C) and humidity-controlled (30–50%) rooms on a 12:12 light–dark cycle, and given ad-libitum access to food and water. Mice destined for the EEG studies (N = 15) underwent one survival surgery to implant electroencephalographic (EEG) and electromyographic (EMG) recording electrodes (described below). Mice were given at least 10 days to recover from surgery prior to beginning the experiment. Animal well-being was assessed daily during the surgical recovery period. Any sign of illness or pain, including decreased motility and responsiveness, vocalizations, lack of appetite, decreased grooming, etc. was noted and treated in consultation with veterinary staff. Mice were housed singly following surgical procedures. Survival stereotaxic surgery was performed on WT (n = 8) and Fabp7 KO (n = 7) to implant the EEG/EMG sleep recording electrodes as previously published [13,21]. Aseptic surgeries were performed under isoflurane anesthesia. A midsagittal incision was made on the top of the skull, and the skin was retracted. After cleaning the surface of the skull, 4 holes were drilled through the cranium, and bare wire electrodes were inserted bilaterally over the frontal area and hippocampal area of the mouse brain for EEG recordings. Two flexible wire electrodes were threaded through the dorsal neck muscles for EMG recordings. All electrodes were connected to a six-pin connector which was attached to the skull via light-curable dental acrylic. Electronic connections were finalized through the six-pin connector to a Tucker Davis Technology (TDT) (Alachua, FL, USA) electrophysiology recording device. Following recovery from surgery, animals were housed individually and connected to the TDT recording system via a lightweight, flexible tether attached to a commutator (Sparkfun.com, Slip Ring) for free movement within the cage. The recording system was used to sample signals at 333 Hz, filtered between 0.1–100 Hz and amplified. Prior to analysis, signals were down sampled to 250 Hz. The four EEG electrodes were differentially referenced to obtain two independent EEG channels (frontal and hippocampal). Two EMG channels were also differentially referenced to obtain the EMG signal. Animals were given 48 h to acclimate to the tethers prior to beginning baseline recordings. During the acclimation period, animals were supplied food to last the duration of the EEG/EMG experimental recordings. While connected to tethers, animals were monitored daily for food, water, and health via visual inspection by a video monitoring system to avoid disturbing the animals. Collected data were transferred from the recording PC, stored onto disk, and scored off-line in 10-s epochs to determine sleep/waking state using Sleep Sign software (version 3.3.6.1602) (Nagano, Japan). Three vigilance states were assigned: Wake, REM sleep and NREM sleep. Wake consists of visible EEG theta activity and high EMG activity; REM sleep consists of clear, sustained EEG theta activity and phasic muscle twitches on a background of low EMG; and NREM sleep consists of high amplitude, synchronized EEG and low EMG activity. EEG and EMG signals were recorded for 24 h of baseline, after which the animals were unhooked from the recording system; single prolonged stress (SPS, described below) was performed. Following SPS, animals were reconnected to the recording system and seven subsequent days were recorded and scored. Data collected after SPS were compared to the baseline recording day using Graphpad Prism software. Sleep states were quantified as an average duration spent in state per hour (in seconds) over the light phase (ZT0–12) and dark phase (ZT12–0). Single prolonged stress was performed similarly to previously published work [10,13,21]. Briefly, animals were exposed to 3 successive stressors at the start of the dark phase. First, physical restraint was performed for 2 h in custom-built plexiglass restraining devices. Next, the animals were placed in a (20 W × 20 D × 10 H cm) plastic bin containing 30 °C water and were forced to swim in groups of 6–8 for 20 min. Following a 15-min recuperation period in a towel-lined bin, the animals were exposed to 30 mL of ether vapors in a 2000 cc isolation chamber until fully anesthetized (<5 min). Ether exposure is a critical component in the development of the PTSD phenotype in rodents; substitution of an alternative anesthetic, such as isoflurane for ether, is insufficient to cause extinction retention deficits in fear-associated memory processing [23]. Afterward, the animals were returned to their EEG/EMG recording-cages where they were isolated for the following seven days (as shown in Figure 1). To assess baseline anxiety, animals underwent light–dark box testing. All animals, WT (n = 7), WT/SPS (n = 7), Fabp7 KO (n = 8), and Fabp7 KO/SPS (n = 8), were housed individually in the experimental room on a 12:12 light:dark schedule for seven days prior to beginning testing. SPS animals were then trauma exposed at ZT12 and allowed to rest for another seven days. The testing chamber consisted of two compartments, each measuring 7” W × 7” D × 12” H, separated by a 3” H × 3” W door with a metal bar floor (Coulbourn Instruments, Allentown, PA, USA). The right chamber was illuminated with white light and the left chamber was dark. The position of the animal was determined using infrared beams placed along the floor of the entire chamber. All testing was performed at ZT12. The animal was started in the illuminated compartment and allowed to freely explore both sides of the chamber for 300 s. Time spent on each of the light side and the dark side was measured, along with the number of transitions between compartments, and initial escape latency to the dark side. Supplementary methods can be found in Supplementary materials. In order to determine whether Fabp7 plays a role in the interaction between sleep–wake behavior and PTSD-like phenotype, we measured sleep and wakefulness from EEG data of Fabp7 KO and WT control mice prior to treatment (baseline), immediately following SPS, and after the 7-day window post-SPS. The experimental outline is shown in Figure 1. During the active-phase (dark period), we did not observe any differences in baseline wake time, or immediately following SPS (Figure 2a). However, after the 7-day window when SPS treatment is known to cause sleep-associated long-lasting effects on fear-associated behavior [12], we observed an increase in wake in Fabp7 KO versus WT mice (Figure 2a). These effects were reciprocal in the inactive-phase. Fabp7 KO mice had an increase in wake during the light phase in baseline and immediately following SPS treatment (Figure 2b). After the 7-day window post-SPS, there were no differences in wake time between Fabp7 KO and WT mice (Figure 2b). These data suggest that the astrocyte-expressed Fabp7 impairs the normal sleep-wake responses to single-prolonged stress in mice in a time-of-day dependent manner. While we observed a change in active-phase versus inactive-phase wakefulness between Fabp7 KO and WT mice after the 7-day window post-SPS, we also were interested in determining whether there were specific changes in sleep staging based on genotype. During the active-phase (dark period), there were no differences in baseline amounts of non-rapid eye movement (NREM) or REM sleep (Table 1). However, we observed a decrease in NREM sleep after the 7-day window post-SPS (Table 1), that was associated with an increase in wake observed during the active phase, in Fabp7 KO versus WT mice (Figure 2a). During the inactive-phase (light period), we observed differences in REM sleep on baseline and immediately following SPS in Fabp7 KO versus WT mice, but not after the 7-day window post-SPS (Table 2). Similarly, no differences between Fabp7 KO and WT were observed during the inactive-phase for NREM during baseline or after the 7-day window post-SPS, just immediately following SPS (Table 2). Taken together, these data suggest that the effects of SPS on sleep suppression post-trauma are dependent on normal levels of Fabp7, and that Fabp7-associated differences in sleep may in turn influence post-trauma behavior. The light–dark box anxiety test is an assay for determining anxiety behavior in rodents [24–27]. The light–dark box anxiety test is based on an unconditional aversion of rodents to brightly lit areas in response to mild stressors (e.g., novel environment and light). Here, we were interested in determining whether Fabp7 KO mice show changes in innate anxiety following SPS treatment, compared to WT controls. We did not observe any baseline differences between Fabp7 KO and WT mice in time spent in the lit compartment, however, Fabp7 KO mice did have a significant increase in time spent in the lit compartment following SPS compared to WT controls (Figure 3). No differences were observed in the number of transitions (Appendix A) or in latency to the dark box (Appendix B). These data suggest that the normal anxiety response following SPS is dependent on Fabp7 expression. Clinical and animal studies have shown that fear-associated neuronal circuits are closed tied to the development and retention of PTSD [28,29]. In addition, sleep disturbances are tightly associated with PTSD, and may represent a point of intervention [30–32]. The neural circuits that have been implicated in PTSD include fear learning, emotional processing, arousal, and context processing circuitry [33,34]. However, much less is known about how glial cells, and in particular, astrocytes, may be playing a role in PTSD; however, the role of glia in sleep processing is beginning to be understood [22,35–37]. How non-neuronal cells such as astrocytes may be implicated in the relationship between sleep behavior and PTSD etiology remains poorly understood. Fabp7 is an astrocyte-expressed molecule that has been implicated in fear and anxiety-like behavior [15], cognitive processing [20,21,38] and sleep–wake regulation [20–22]. Here we show that Fabp7 is required for normal sleep suppression following trauma, using the SPS paradigm in mice. We also observed Fabp7-dependent disruption of anxiety-related phenotypes post-SPS. Our studies show that the trauma-induced sleep–wake phenotype in Fabp7 KO compared to WT mice changes between the active-phase and inactive-phase (Figure 1). Whether these differences can be partially accounted for by NREM and REM sleep stages over the light–dark cycle (Figure 2) will require future investigation. For example, optogenetic enhancement of sleep was shown to improve fear memory following SPS in rats [13]. Previously, we showed that SPS blocks sleep homeostasis; however, pre-trauma sleep deprivation did not exacerbate trauma-induced fear-associated memory impairments [23]. Here, we observed time-of-day-dependent differences in pre- and post-SPS sleep stages between Fabp7 KO and WT mice (Tables 1 and 2). During the dark phase, we only saw differences in NREM sleep between Fabp7 KO and WT mice following the 7-day window post-SPS (Day 7). However, during the light phase, differences in REM sleep were observed between Fabp7 KO and WT mice during baseline and SPS, but these normalized by Day 7. The light phase post-trauma increases in REM sleep observed in Fabp7 KO mice may be relevant for testing potential treatments for PTSD and cognitive function. The phosphodiesterase-4 (PDE4) inhibitor rolipram was shown to have anxiolytic effects in mice [39]. Rolipram treatment is known to rescue cognitive deficits following REM sleep deprivation for spatial working memory [40], or for contextual fear conditioning following total sleep deprivation [40,41]. Whether PDE4 represents a mechanistic pathway in neural or glial cells in the relationship between changes in sleep and cognitive processing [42] in our model will require more experimentation. In addition, future work in disrupting sleep or disrupting different stages of sleep (i.e., either NREM or REM) at various time-windows post-SPS will be needed to determine the role sleep plays in cognitive processing after trauma exposure. Previous studies showed that Fabp7 KO mice exhibit increased anxiety-like behavior [15]. Here, we observed that Fabp7 KO mice spend approximately the same amount of time in the lit compartment as WT mice under baseline conditions, but spend significantly more time in the lit compartment following SPS. While this effect may appear to contradict findings by Owada et al. [15], it may mean that Fabp7 KO mice have anxiolytic-like effects on cognitive processing following traumatic stress, or have an enhanced ‘freezing-like’ behavior when placed in the lit compartment. Therefore, future studies will be important to identify the precise mechanism that Fabp7 may be playing in astrocytes to affect sleep and anxiety following trauma in our model. Astrocyte loss was shown following SPS exposure in the hippocampus of rats [43]. Chemogenetic technology targeting dorsal hippocampal astrocyte activation was also shown to be sufficient to attenuate stress-enhanced fear learning (SEFL), a PTSD-like behavior [44]. Using SEFL, hippocampal astrocyte expression of interleukin-1β (IL-1β) was shown to be increased [45]. SEFL exposure reduced immunoreactivity for the dorsal hippocampal postsynaptic density 95, PSD95, a synaptic maker, which was co-localized with astrocytes [44]. Previously, we showed that Fabp7 protein and mRNA oscillated in a circadian phase in the fine perisynaptic processes of astrocytes, and the mRNA is trafficked within the hippocampus based on time of day [46]. Fabp7 inhibition was previously shown to limit cytokine production and secretion of TNF-α and IL-1β [47,48], suggesting that a signaling cascade may exist between astrocyte Fabp7 activity and IL-1β post-trauma. The astrocyte Fibroblast Growth Factor 2 (FGF2), a mitogen that is involved in the signaling pathways for memory extinction and relapse [49,50], was shown to block PTSD behavior following SPS in rats [51]. Xia et al., also observed that intraperitoneal FGF2 administration inhibited SPS-induced hyperarousal and anxiety behavior [51]. Fabp7 transfection in U87 astrocytoma cells was shown to increase FGF2 expression [52], suggesting that FGF2 and Fabp7 may be linked in the development of PTSD behavior. Future studies determining the relationship of sleep and Fabp7 with other previously identified astrocyte factors that mediate PTSD-related phenotypes will be important for generating clinical therapeutic strategies for the treatment of PTSD.

PMC10001432

Nourhan Hassan,Janes Efing,Ludwig Kiesel,Gerd Bendas,Martin Götte

The Tissue Factor Pathway in Cancer: Overview and Role of Heparan Sulfate Proteoglycans

28-02-2023

tissue factor,platelets,proteoglycans,cancer,syndecans

Simple Summary Tissue factor is a protein that is important for the regulation of blood coagulation. New research has highlighted the important roles of tissue factor in cancer. This review summarizes recent work that shows how a special class of glycoproteins called heparan sulfate proteoglycans regulate tissue factor function in a cancer context. These findings provide new ideas for future anti-cancer therapies. Abstract Historically, the only focus on tissue factor (TF) in clinical pathophysiology has been on its function as the initiation of the extrinsic coagulation cascade. This obsolete vessel-wall TF dogma is now being challenged by the findings that TF circulates throughout the body as a soluble form, a cell-associated protein, and a binding microparticle. Furthermore, it has been observed that TF is expressed by various cell types, including T-lymphocytes and platelets, and that certain pathological situations, such as chronic and acute inflammatory states, and cancer, may increase its expression and activity. Transmembrane G protein-coupled protease-activated receptors can be proteolytically cleaved by the TF:FVIIa complex that develops when TF binds to Factor VII (PARs). The TF:FVIIa complex can activate integrins, receptor tyrosine kinases (RTKs), and PARs in addition to PARs. Cancer cells use these signaling pathways to promote cell division, angiogenesis, metastasis, and the maintenance of cancer stem-like cells. Proteoglycans play a crucial role in the biochemical and mechanical properties of the cellular extracellular matrix, where they control cellular behavior via interacting with transmembrane receptors. For TFPI.fXa complexes, heparan sulfate proteoglycans (HSPGs) may serve as the primary receptor for uptake and degradation. The regulation of TF expression, TF signaling mechanisms, their pathogenic effects, and their therapeutic targeting in cancer are all covered in detail here.

The Tissue Factor Pathway in Cancer: Overview and Role of Heparan Sulfate Proteoglycans Tissue factor is a protein that is important for the regulation of blood coagulation. New research has highlighted the important roles of tissue factor in cancer. This review summarizes recent work that shows how a special class of glycoproteins called heparan sulfate proteoglycans regulate tissue factor function in a cancer context. These findings provide new ideas for future anti-cancer therapies. Historically, the only focus on tissue factor (TF) in clinical pathophysiology has been on its function as the initiation of the extrinsic coagulation cascade. This obsolete vessel-wall TF dogma is now being challenged by the findings that TF circulates throughout the body as a soluble form, a cell-associated protein, and a binding microparticle. Furthermore, it has been observed that TF is expressed by various cell types, including T-lymphocytes and platelets, and that certain pathological situations, such as chronic and acute inflammatory states, and cancer, may increase its expression and activity. Transmembrane G protein-coupled protease-activated receptors can be proteolytically cleaved by the TF:FVIIa complex that develops when TF binds to Factor VII (PARs). The TF:FVIIa complex can activate integrins, receptor tyrosine kinases (RTKs), and PARs in addition to PARs. Cancer cells use these signaling pathways to promote cell division, angiogenesis, metastasis, and the maintenance of cancer stem-like cells. Proteoglycans play a crucial role in the biochemical and mechanical properties of the cellular extracellular matrix, where they control cellular behavior via interacting with transmembrane receptors. For TFPI.fXa complexes, heparan sulfate proteoglycans (HSPGs) may serve as the primary receptor for uptake and degradation. The regulation of TF expression, TF signaling mechanisms, their pathogenic effects, and their therapeutic targeting in cancer are all covered in detail here. Blood coagulation generally serves as a host defense mechanism against bleeding. The corresponding coagulation cascade is triggered upon vessel wall perforation or activation of the endothelium by chemicals, cytokines, or inflammatory processes [1,2,3,4,5,6]. A vascular lesion is initially occluded by a platelet plug whose formation is coordinated through the coagulation system response [6]. The blood coagulation system presents as a complex, highly regulated process with the participation of more than a dozen plasma proteins, at least one tissue protein, phospholipid membrane surfaces, Ca2+ ions, and platelets [7]. In normal physiology, the hemostatic system maintains the circulating blood in a fluid state; several anticoagulant mechanisms ensure careful control of coagulation by prevailing the procoagulant forces explained in the following that allow a rapid response to vessel wall rupture [6,8]. Tissue factor (TF), or coagulation factor III, is a cell surface glycoprotein of 47 kDa comprising a 23 residue transmembrane domain flanked by a short cytoplasmic tail and a relatively large N-terminal extracellular domain (ECD) [9,10,11,12]. Its function as the primary initiator of physiological hemostasis is closely related to its particular expression pattern within the human body. TF is not expressed equally in all tissues; the highest expression was found in the brain, lung, epithelial cells of the skin, heart, and testis [13,14,15,16,17,18]. Besides the expression levels in these specific organs, TF levels can be found on subendothelial surfaces, in particular, adventitial fibroblasts of large blood vessels, and pericytes surrounding smaller vessels [17]. Cells directly exposed to flowing blood, such as endothelial cells, normally do not express any TF [10]. Following this circumstance, the ‘hemostatic envelope’ theory was postulated, which emphasizes the restriction of TF expression to certain cell types to allow initiation of the coagulation cascade only when vascular integrity is breached [13]. Besides the subendothelial TF populations, there are also circulating TF molecules known as blood-borne TF [19]. They circulate within the bloodstream in at least three different pools: (1) associated with white blood cells and platelets [20,21], (2) located on cell-derived microparticles (a subclass of extracellular vesicles) [22], or (3) as a soluble protein [23]. The latter—also known as alternative spliced TF (asTF)—lacks the transmembrane domain as well as the cytoplasmic tail, and therefore displays no membrane association [23]. Healthy individuals’ plasma mean level of blood-borne TF ranges between 149 and 172 pg/mL [24,25]. The blood clotting cascade can be subdivided into two major pathways: the extrinsic and the intrinsic. The nomenclature of the former originates from the fact that its initiation involves direct contact of blood plasma with ‘something extrinsic’ with respect to the vasculature [26]. The extrinsic pathway initially involves a high-affinity association of subendothelial TF with plasma protein FVII and its activated form (FVIIa), respectively [27]. FVIIa displays a serine protease whose activity is heavily enhanced (2 × 107-fold higher enzymatic activity rate) [28] when associated with TF due to active site region rearrangements [29,30,31] (Figure 1). FVIIa is synthesized in the liver and circulates in human blood at concentrations of approximately 10 nM [32], to a great extent in its inactivated form. Exposure of TF following vessel injury and blood extravasation leads to complexation with plasma FVII. Subsequent activation of the protease [33] affects two major downstream substrates in the coagulation cascade via limited proteolysis [26]. In the first place, the protein complex promotes the conversion of minute quantities of FX, another plasma serine protease, to its activated form (FXa). Secondly, the circulating FIX serine protease zymogen is also converted to an active conformation (FIXa) [34,35]. However, besides the physical proximity, the full activity of the TF/FVIIa tandem to activate its downstream targets requires the presence of a membrane surface as well as Ca2+ ions [10]. FXa and FIXa must assemble on a suitable membrane surface together with their cofactors: FVa and FVIIIa, respectively [26]. The so-generated small amounts of FXa participate in the prothrombinase complex with its cofactor FVa and serve as the primary activator of prothrombin by catalyzing the locally restricted proteolytic cleavage of prothrombin to thrombin [6,11,36,37,38,39]. Feedback amplification at several levels is crucial for the efficiency of the cascade: TF-bound FVII gets activated to FVIIa, FIXa, and FXa. FXa itself, when membrane-bound, in turn, can also activate its own cofactor FV as well as FVIII (Figure 1) [40]. Additionally, thrombin-dependent activation of FXI, FVIII, and FV occurs, ultimately resulting in a rapid burst in thrombin generation [6,41,42,43,44,45]. All components participating in the alternative intrinsic pathway can be found solubilized in blood plasma. Thus, its initiation requires no extravascular molecule, making it TF-independent; consequently, activation of the intrinsic blood coagulation pathway does not necessarily involve any trauma [46]. Initially, FXIIa converts FXI to its activated form (FXIa). FXIa, in the presence of Ca2+, increases FIXa level, which is membrane-associated due to the binding of its cofactor FVIIIa. Activated FIX in turn is capable of proteolytically activating FX, finally leading to the same downstream result of thrombin generation [28,46,47]. However, despite the similarities in downstream signaling to its extrinsic counterpart, the intrinsic pathway is not inevitably essential for normal blood coagulation [46]. FXa integrates the two pathways and therefore shows up as a critical point of the coagulation process: the serine protease receives extrinsic and intrinsic upstream signals and orchestrates downstream responses [27]. Several protease components are partially involved in both pathways, emphasizing them to be highly interconnected. Generally, the plasma concentrations of the particular coagulation proteins relate to their specific role in the corresponding pathway; while the early components of the pathway circulate in a less great abundance, factors more downstream can be found in higher quantities [48,49]. Both the extrinsic and the intrinsic pathways finally result in the locally restricted generation of thrombin from its zymogen prothrombin. Thrombin exhibits multifaceted functions and is regarded as the key enzyme in the coagulation cascade due to the affection of several downstream targets [50]. It displays a Na+-activated allosteric serine protease that is assigned to the chymotrypsin family [51] with its zymogen predominantly synthesized in the liver, the major site of clotting factor synthesis [50,52,53,54]. Thrombin is mainly responsible for the initiation and propagation of clot formation: its procoagulant effect relies on its capability to cleave plasma fibrinogen into an insoluble fibrin clot that directs and anchors activated platelets to the site of the lesion [46,55,56]. The establishment of the clot composed of fibrin molecules and activated platelets is further reinforced by the activation of FXIII, also by thrombin. FXIIIa activity results in covalent fibrin crosslinking, which in turn stabilizes the assembled clot [11,48,57]. In addition to its role as a blood coagulation trigger, TF associated with FVIIa can activate members of the protease-activated receptors (PAR) family [58]. This activity seemingly depends on the catalytical activity of FVIIa and has been considered with respect to several pathophysiological scenarios [58]. The unique, cleavage-triggered activation of PARs is normally associated with thrombin receptor activity. In these terms, thrombin can activate platelet via PARs on platelet surfaces. Thus, thrombin initiates various intracellular signaling events finally resulting in the transformation of the normally mobile, non-adhesive platelets into central participants of hemostatic clot growth primarily via morphological changes [55,59,60]. Apart from their participation in clotting, these transformed platelets also provide binding sites for the different serine protease–cofactor complexes that assemble due to the propagation of the coagulation cascade [43]. Interestingly, besides its procoagulant role in blood clotting, thrombin once generated in blood also shows some opposing anticoagulant activities. Upon binding to endothelial membrane receptor thrombomodulin, the proteases’ ability to cleave fibrinogen and PAR1 is suppressed, while simultaneously its specificity toward zymogen protein C is massively enhanced (>1000-fold) [61,62]. The activated protein C proteolytically cleaves and thereby inactivates FVa and FVIIIa, the essential cofactors of the coagulation cascade proteins FXa and FIXa, respectively, required for efficient thrombin generation [63]. By this mechanism, downregulation of both the amplification and progression of the coagulation cascade occurs, emphasizing the versatile role of thrombin in coagulation [64]. In normal hemostasis, the thrombin–thrombomodulin tandem with subsequent protein C activation constitutes a natural anticoagulant pathway to prevent severe intravascular emergence of a fibrin clot upon thrombin hyperactivity [64,65]. Generally, regulation of the coagulation cascade occurs at each level, either by enzyme inhibition or modulation of the cofactor activity. Endogenous inhibition of the TF pathway has been known since the 1950s [66] and the accountable molecule that inhibits the initiation phase of extrinsic blood coagulation was identified approximately 30 years later [67,68,69,70,71]. Tissue factor pathway inhibitor (TFPI) appears as a high-affinity inhibitor of relatively low abundance in blood plasma [67,72]. It directly suppresses FXa activity and further also TF/FVIIa activity in an FXa-dependent manner [72,73,74,75,76,77]. There are two major isoforms of TFPI based on alternative splicing: TFPIα and TFPIβ [71,78,79]. They differ in their tissue expression pattern [80,81,82] and their cell surface association mechanisms [79,83,84,85], as well as regarding their ability to affect TF/FVIIa and prothrombinase activity, respectively [76,86]. While TFPIα is mainly associated with plasma membranes of the corresponding target cells via heparan sulfate proteoglycans [87,88] or with low-density lipoprotein (LDL) when solubilized in blood plasma [6], TFPIβ comprises a GPI anchor sequence and thus is directly bound to the surface of endothelial cells [78,89]. In humans, the existence of a third minor isoform, namely, TFPIδ, has been discovered [90]; however, its role is much less explored. Besides protein C and TFPI, a third major inhibitor warrants the tight regulation of blood clotting: antithrombin (antithrombin III). Antithrombin acts as a serine protease inhibitor suppressing the activity of FXa and other protease components of the cascade [91]. It is postulated to be the main inhibitor of thrombin and its generation [42] 4. However, to efficiently fulfill its inhibitory function, antithrombin needs to act in consultation with heparin and heparin-like molecules on the surface of endothelial cells [92,93,94,95,96,97]. Heparin also accelerates the action of TFPI [98]. In vitro, TFPI and heparin have been shown to have synergistic inhibitory activity on TF-induced coagulation [99,100]. These different inhibitory mechanisms partially act in synergy with the consequence of thrombin generation being a threshold-limited process. They also feature some interconnections [36,101,102,103]. For instance, protein S acts as a cofactor for both protein C and TFPI [104]. A final and not less important anticoagulant mechanism comprises the fibrinolytic system whose enzymes finally dissolve the clot after it accomplishes its function [46]. The importance of the numerous inhibitory components for the health of individuals is undisputed. First, this is substantiated by the fact that there is no common human TFPI deficiency, suggesting it results in embryonic lethality [105]. Furthermore, deficiencies of other essential inhibitory components of the coagulation system such as protein C or antithrombin are known as one main cause of thrombosis due to excessive thrombin generation [106]. On the other hand, thrombosis might also occur as a consequence of elevated concentrations of particular clotting factors common for certain liver diseases [107,108]. On the other hand, in various cell types and tissues, many miRNAs have been found to directly modulate TF expression and thus its functions. This can have an impact on both physiological and pathological processes [109]. MiR-19b and miR-20a, for example, suppress TF expression in vitro and are observed to be reduced in monocytes obtained from patients with systemic lupus erythematosus and antiphospholipid syndrome. This suggests that miRNA control of TF may play a role in these disorders [110]. According to one study, miR-126 has antithrombotic characteristics by affecting the hemostatic equilibrium of the vasculature in diabetes mellitus by modulating TF expression. MiR-126 can be a predictive biomarker for diabetes mellitus progression and complications [111]. MiR-145 levels were restored in thrombotic animals via in vivo miR-145 mimic treatment, which resulted in lower TF levels and activity, as well as lower thrombogenesis. MiR-145 levels were likewise lowered in individuals with venous thromboembolism (VTE) and were linked to higher TF levels. Notably, when endothelial cells (ECs) were transfected with a miR-223 mimic or inhibitor, TF expression was changed accordingly at both the mRNA and protein levels. On damaged atherosclerotic plaques, TF initiates thrombosis, which is important during the diagnosis of acute coronary syndromes (ACS). In atherosclerosis, overexpression of miR-223 decreased TF procoagulant activity. The potential of the TF-FVIIa complex to initiate coagulation is defined as TF procoagulant activity (PCA). Detergents, Ca2+ ionophores, and oxidants are among the agents that can greatly increase TF PCA [112,113,114]. Previous studies reported that the elevation in TF PCA associated with oxLDL treatment also stimulates the transcription of TF mRNA, whereas treatment with hydrogen peroxide (H2O2) only elevates TF PCA [115]. Since so much expressed TF is accessible on the cell surface, many regulatory pathways are required to avoid spontaneous broad coagulation [116]. TF serves as a signaling receptor in addition to its action in coagulation [58]. Rottingen et al. reported this evidence in 1995 when they found that adding FVIIa to a variety of TF-expressing cell types triggered Ca2+ oscillation [117]. Both TF/FVIIa complex and TF/FVIIa/FXa complex can mediate TF signaling. By activating integrins and transactivating or proteolytically cleaving certain members of the receptor tyrosine kinase (RTK) family, signaling can be dependent or independent of PAR2 and PAR1 (Figure 2) [118]. Mitogen-activated protein kinases (MAPKs) including p44/42, p38, and C-Jun N-terminal kinase (JNK), which are responsible for cell cycle control, and PI3K/AKT, which are mainly essential for cell survival, are among the signaling components identified to be activated by TF/FVIIa [119,120]. Src-family kinases are involved in the upstream processes of both p44/42 and PI3K/AKT [121,122]. Since cell survival and migration, as well as inflammation and angiogenesis, are key processes in cancer cell progression and tumor development, the impact of TF/FVIIa signaling in oncology is apparent [123,124,125,126,127] and will be discussed in detail in this review. When the thrombin receptor was cloned in 1991, the PAR family of G protein-coupled receptors (GPCRs) was discovered [128]. In humans, four recognized PARs are extensively expressed in a variety of cell types. While GPCRs are usually activated by endogenous extracellular agonists, PARs are cleaved particularly near the N-terminus by proteases unmasking a tethered ligand, which subsequently folds back to activate the receptor (Figure 2A) [128]. Although PAR1 is the most common thrombin receptor, thrombin can also cleave and activate PAR3 and PAR4. The TF/FVIIa/FXa complex can cleave and activate PAR1 in addition to thrombin. In a process independent of the TF cytoplasmic domain, the cleavage of PAR1 by TF/FVIIa/FXa can result in Ca2+ influx and p44/42 MAPK activation [127,129,130]. PAR2 is thrombin-insensitive, unlike the other PARs, but can be induced by direct cleavage by both TF/FVIIa or the TF/FVIIa/FXa complex. Furthermore, the cytoplasmic domain of TF may be required for PAR2 signaling [131,132]. While low concentrations of FVII are required for cleavage of PAR1/PAR2 via TF/FVIIa/FXa complex as for coagulation activation, much greater amounts of TF/FVIIa complex are required to activate PAR2 (Figure 2A) [133,134]. Signaling can also be dependent on the phosphorylation of the TF cytoplasmic domain when PAR2 is activated by the TF/FVIIa complex [135]. The activation of PAR2 by TF/FVIIa results in the release of cytokines and angiogenic factors that are important in inflammatory, gastrointestinal, respiratory, cardiovascular, metabolic, and neurological illnesses, as well as malignancies [136]. Furthermore, insulin resistance and inflammation in adipose tissue have also been linked to TF-PAR2 signaling in hematopoietic and myeloid cells [137]. RTKs are extracellular ligand receptors that are distinguished by their intrinsic tyrosine kinase activities. Ligand interaction causes receptor subunit dimerization and tyrosine kinase moiety activation, followed by the transphosphorylation of tyrosine residues in the cytoplasmic domain of the receptor [138]. These tyrosines serve as binding sites for the adapter proteins with SH2- and PTB-domains that induce downstream signaling via the Ras/MAPK or PI3K/Akt pathways [139]. Growth factors and hormones, which are required for cell differentiation, proliferation, and motility, serve potential RTK ligands. RTK signaling is essential for cell development and function, and also plays a role in the pathophysiology of many human disorders [140]. The uncontrollable proliferation of tumor cells is driven by deregulated growth factor signaling, which constitutes a fundamental event in many malignancies [141]. The Eph tyrosine kinase receptors constitute the biggest RTK family in the human genome, comprising 14 members [142]. Thereof, EphB2 and EphA2 have been identified as TF/FVIIa proteolytic targets. The ectodomains of these receptors can be cleaved by TF/FVIIa, affecting Eph-mediated cell division and segregation (Figure 2C) [143]. In addition, TF can act as a molecular ligand directly interacting and thus activating RTKs. EGF receptor (EGFR), PDGF receptor β (PDGFRβ), and insulin-like growth factor 1 receptor (IGF1R) are known to be transactivated by TF/FVIIa [144,145,146]. The triple membrane-spanning model (TMSP) describes how the EGFR is transactivated by stimulating metalloproteinases, which then release heparin-bound EGF to activate the receptor. Intracellular protein kinases may also play a role in transactivation. A unique pattern of phosphorylation at four tyrosines in the PDGFRβ cytodomain has been identified as a result of TF/FVIIa-induced transactivation of the PDGFRβ. PAR-2 is also required for this process (Figure 2D) [145]. On the other hand, the transactivation of the IGF-1R is not dependent on PARs [144]. The magnitude of the tyrosine phosphorylation responses by TF/FVIIa is lower than that of the native ligands of the receptor, indicating the receptors to be only partially activated and thus activation to be selective. Although the physiological consequences of EGFR transactivation are less obvious, TF/FVIIa transactivation of PDGFRβ and IGF-1R has a significant influence on the target cells. Due to the activation of Src-family kinases and the transactivation of PDGFRβ, TF/FVIIa stimulates smooth muscle cells, fibroblasts, monocytes, and endothelial cells to migrate towards a 100-fold lower concentration difference of PDGF-BB than cells without TF/FVIIa complex formation [146]. The insulin and IGF-1 receptor families have a common ancestor and display a lot of similarities. Their roles have been merged over time, where the insulin receptor mainly regulates glucose metabolism and the IGF-1R regulates cell proliferation and growth [147]. The ligands IGF-1 and IGF-2, as well as insulin at high doses, activate the IGF-1R [148]. Most cells in the body express IGF-1R, and it is also frequently (over)expressed in neoplastic cell lines and human tumors [149]. IGF-1R is a tetramer made up of two α- and two β-subunits that are linked together by disulfide bonds [150]. When a ligand binds to the α-subunit of the receptor, three tyrosine residues Tyr1131, Tyr1135, and Tyr1136 in the activation loop of the kinase domain are auto-phosphorylated. Phosphorylation of other residues in the β-subunit serves as docking sites for several proteins including insulin receptor substrate (IRS), which mediates the signaling cascades generated by IGF-1 stimulation (Figure 2E) [150,151]. IGF-1R signaling has also recently gained attention as a potential therapeutic target in human cancer, with IGF-1R inhibition expected to diminish tumor cell survival [152,153]. Different cell types, including human breast cancer cells, human aortic smooth muscle cells with basal expressions of TF, human monocytes conditioned to express TF, and porcine aortic endothelial cells overexpressing human TF showed dose-dependent transactivation of the IGF-1R upon treatment with full functioning FVIIa, but not after treatment with an active site-inhibited FVIIa. IGF-1R inhibition or ablation inhibited FVIIa-mediated activation of AKT and prevented the protection of cancer cells from TRAIL-induced apoptosis [127,133,154,155,156,157,158,159]. A SUMOylated IGF-1R was also translocated to the nuclei after the treatment with FVIIa, where it worked as a transcriptional enhancer. When porcine cells were transfected with human TF containing a mutant cytoplasmic domain, TF was shown to not need phosphorylation for transactivating IGF-1R [144]. However, it is still unclear how the transmission of anti-apoptotic signals happens between TF/FVIIa and IGF-1R. It was found that there is an association between both TF and IGF-1R with domains called caveolae, which are protein-rich, persistent invaginations of the plasma membrane that operate as signaling nodes [160,161]. Recently, Åberg and colleagues demonstrated that Cav1 Tyr14 activation by Src-family kinases is caused by TF/FVIIa-dependent ITGβ1 activation. The ability of TF/FVIIa to protect cancer cells from TRAIL-induced apoptosis and stimulate IGF-1R-dependent protein synthesis was eliminated by inhibiting ITGβ1 or Src, or by Cav1 activation [162]. Integrins are transmembrane cell surface receptors that play a role in various essential biological processes including cell adherence to the extracellular matrix (ECM), cell-to-cell adhesion, and cell migration. Integrins are heterodimers of non-covalently linked transmembrane α and β subunits. There are multiple distinct α- and β subunits, which are combined to form roughly 24 different integrins [163]. They play a crucial role in cell signaling by influencing multiple pathways, including the IGF-1R-induced one often resulting from their binding functions [164]. Integrins that are located on the surface of platelets are responsible for fibrin attachment within a growing thrombus during coagulation [165]. TF/FVIIa can also communicate with integrins (Figure 2B). As previously mentioned, TF/FVIIa-dependent PARs and Eph receptor signaling are activated by proteolytic cleavage. β1 integrins (ITGβ1), on the other hand, are not activated by TF/FVIIa proteolytic cleavage. However, a more direct physical connection between the complexes appears to be required for ITGβ1 activation by TF/FVIIa. The FVIIa protease domain contains an integrin-binding motif that is known to be necessary for the interaction of TF/FVIIa with ITGβ1, causing conformational changes that can lead to ITGβ1 activation. The TF/FVIIa/ITGβ1 signaling has been linked to tumor development and angiogenesis [166,167]. MAPK p42/44 is documented to have a role in cell proliferation, differentiation, and survival. The MAPK p42/44 can phosphorylate a broad range of proteins, of which some immediately translocate into the nucleus. P42/44 controls several transcription factors [168]. Elk-1 is phosphorylated by p42/44, which stimulates immediate-early gene transcription. MAPK p42/44 phosphorylation promotes smooth muscle cell proliferation [169]. It was observed that upon FVIIa binding to TF, MAPK p42/44 is activated. The p42/44 inhibitor was used to inhibit phosphorylation [123,170]. However, in BHKtf cells (stable TF-transfected baby hamster kidney cells), the same mechanism is mediated through PKC (protein kinase C). Blocking Raf kinase, which is part of the traditional signaling pathway between Ras and p42/44, stopped p42/44 phosphorylation in both cell lines [170]. The PKC or Src activation by FVIIa receptor remained incompletely understood. Another study found that stimulation with FVIIa induced the phosphorylation of epidermal growth factor (EGFR) and proline-rich tyrosine kinase 2 (PYK 2) in HaCaT cells [146]. EGFR kinase inhibitors could prevent TF/FVIIa complex-mediated activation of p42/44. These inhibitors also blocked the induction of the EGR-1, hb-EGF (heparin-binding epidermal growth factor), and IL-8 genes [171]. In fibroblasts, TF was found to impact gene expression by transient activation of p44/42 MAPK and other familiar proteins such as p38 MAPK [172]. The MAPK pathway has also been linked to the overexpression of TF by VEGF, as MAPK inhibitors reduced this process [173]. In the past two decades of clinical research, a view of TF and its downstream pathway has emerged beyond its central role as a simple blood coagulation initiator to TF serving as a versatile signaling receptor affecting several cellular processes, including apoptosis [118], gene and protein expression [174], proliferation [175], and angiogenesis [176,177]. TF signaling events can arise both dependently or independent of its short cytoplasmic tail region. TF signaling recently has gained more and more attention due to its implication in several human malignancies. The first evident indication of a tight linkage between the blood coagulation cascade and pathogenesis of certain malignancies is represented by the significantly elevated thrombotic risk in advanced-stage cancer patients: 90% of metastatic cancer patients are affected by some kind of coagulopathy [178,179,180] and this characteristic prothrombotic state is responsible for a not negligible proportion of cancer-associated deaths [181]. Furthermore, TF was found to be frequently expressed and consistently upregulated in a broad range of human malignancies, pre-eminently adenocarcinomas [182]. Elevated TF expression was found inter alia in carcinomas of the bladder [183,184,185], brain [186,187,188,189,190], colon [191,192,193,194,195], ovary, and breast [196,197,198,199], and various other tissues such as lung, liver, or pancreas [200,201,202,203,204,205,206]. Additionally, research revealed a correlation between cancerous TF expression levels and the malignant potential as well as the aggressiveness of tumor cells—high TF levels were shown to be associated with a poor prognosis in a multitude of different types of cancer [185,196,207,208,209,210]. Clinical evidence underscores these findings by revealing a correlation between high TF expression levels in tumor tissues and metastasis in a variety of these types of cancer [182,194,195,211]. Together, these studies emphasize the importance of TF and its downstream targets for cancer pathogenesis. There are various potential causes for increased TF expression levels in cancer cells. First, oncogenic changes may impact TF expression. The upregulation of TF parallels the expression of several oncogenes in different types of cancer. For instance, TF levels were found to be elevated upon K-ras activation in human colorectal cancer cells [212]. Similarly, activation of certain members of the EGFR protein family-like EGFR and HER-2 was found to induce TF expression in multiple carcinoma cell lines [209,213,214,215] and the corresponding ligands, such as EGF and TGFα, further increased TF expression [216,217,218]. Additionally, the inactivation of the p53 tumor suppressor also led to an enhanced TF expression in human colorectal cancer cell lines [212,219]. In analogy, loss of PTEN resulted in induced TF gene expression via Akt-mTOR and Ras-MEK-ERK signaling in human glioma cell line 23.11 [219]. Clinical data from colorectal and lung cancer patients further support the correlation between mutant K-Ras and p53 and changes in TF expression [220,221]. TF levels might also be affected by the actions of particular transcription factors. Early growth response gene-1 as well as hypoxia-inducible factor-1α independently induced TF gene expression under hypoxic conditions in the human breast cancer cell lines MDA-MB-231 and MDA-MB-435 [222]. MET signaling is also involved in the link between cancer and blood coagulation. The MET gene regulates invasive growth by encoding the tyrosine kinase receptor for hepatocyte growth factor/scatter factor [223]. Under hypoxic conditions, which are common in the central areas of solid tumors, this oncogene is activated [224]. The transcription of the hemostasis genes including plasminogen activator inhibitor-1 (PAI-1) and cyclooxygenase-2 (COX-2) is induced by activated MET oncogene, which results in the polymerization of fibrin around tumor cells, providing a scaffold for angiogenesis [223]. Thrombo-hemorrhagic syndrome can also be caused by an activated MET oncogene, as well as activated PAI-1 and COX-2 [223]. Epithelial–mesenchymal transition (EMT) is a critical step during the establishment of metastases in advanced disease stages. Centrally involved transcription factors Snail and ZEB1 also were shown to induce TF expression in breast cancer cells [225]. Additionally, both AP-1 and NFκB transcription factors were shown to induce TF expression in the human breast cancer cell line MDA-MB-231 [226]. Notably, the fact that both mutant K-Ras as well as p53 inactivation, the two most common genetic alterations in human malignancy [212], can induce and elevate TF expression, respectively, emphasizes the frequency of this condition and therefore underlines its significance for cancer pathogenesis. There is growing evidence suggesting TF as a key regulator rather than an incidental participant regarding several cellular events in cancer progression. Since the dawn of the new millennium, numerous studies have attributed the role of TF to cancer progression and tumor growth. As discussed in detail above, the activation and action of the coagulation cascade comprise various proteases. Besides their canonical role for proper blood clotting following vessel injury, these proteases are also able to cleave the extracellular domains of certain PAR proteins, ultimately triggering G protein- and β-arrestin-coupled signaling, respectively [227,228,229]. Additionally, PAR1 might be partially activated also by FXa as well as aPC, MMP-1, and MMP-13 [230,231,232]. In cancer cells, PAR2 activation by TF/FVIIa was found to induce Akt phosphorylation and inactivation of glycogen synthase kinase-3b (GSK-3b), finally resulting in the upregulation of the Wnt pathway (Figure 3) [126,233,234]. These results are further encouraged by studies on breast carcinoma cells [196,235]. In another study, the promigratory effect as a consequence of TF/FVIIa-dependent PAR2 signaling was found to be more indirect due to the release of interleukin-8 (IL-8) [236]. The cytokine binds G protein-coupled receptors (GPCRs) CXCR1 and CXCR2 [237,238] and affects several cellular processes. Effects on cell migration are postulated to be partially mediated by CXCR-dependent activation of particular members of the Rho-family of GTPases, ultimately resulting in actin cytoskeleton rearrangements [239,240]. Beside GPCR signaling, PAR cleavage might also induce a G protein-independent pathway via the recruitment of β-arrestin. This intracellular molecule takes a prominent role in membrane receptor internalization [241] and is postulated to promote breast cancer migration through the cofilin pathway [242,243]. Blocking either PAR2 or specifically, the signaling functions of the TF/FVIIa binary complex suppressed tumor growth in a xenograft model in immunodeficient mice, while inhibition of the TF-initiated coagulation only exhibited a minute effect [167,244], accentuating the importance of TF-PAR2 signaling for cancer progression [245,246]. In cancer patients, hypercoagulability increases the risk of venous thromboembolism (VTE), pulmonary embolism (PE), disseminated intravascular coagulopathy (DIC), and bleeding [247]. Furthermore, hypercoagulable conditions can foster carcinogenesis by inducing angiogenesis. As documented in human breast cancer cell lines, TF is required for hypercoagulability as the clot formation is FVIIa-dependent and could be inhibited by anti-TF antibodies [248]. Upregulation of TF is also linked to VTE in pancreatic cancer and serves as an independent predictor of VTE in ovarian cancer [198,204,249]. Additionally, overexpression of TF has been shown to result in the systemic production of TF-positive extracellular vesicles, which can be used as a predictive marker for deep vein thrombosis (DVT) [250]. It was also reported that TF-positive extracellular vesicles can be correlated with D-dimer levels which significantly predict VTE in cancer patients [191]. Patients with cancer are more likely to experience thromboembolic events, particularly those receiving chemotherapy and radiotherapy. Every year, 11% of cancer patients undergoing chemotherapy develop VTE, and hormone treatments (such as tamoxifen) raise the risk by two- to three-fold [251]. Furthermore, anti-angiogenic medications are unexpectedly linked to a high rate of thrombosis, although these findings could be biased because several clinical trials combined antiangiogenics and chemotherapeutics [252]. Despite the cancer treatments appearing to influence the hypercoagulability of the patients, the mechanisms are still uncertain. It was previously demonstrated that an elevation in the tumor-secreted extracellular vesicles and cell-free DNA can contribute to this condition [253]. In vitro treatment of endothelial cells with a VEGFR-2 inhibitor in combination with chemotherapy (especially cisplatin and gemcitabine) resulted in a significant elevation in surface TFPI expression, indicating extracellular procoagulant transformation; however, this transformation was diminished when the chemotherapeutic treatments were prescribed at a lower dose [254,255]. TF-related angiogenesis arises both indirectly and directly through the coagulation or release of proangiogenic factors [256,257,258]. TF-induced thrombin production is partially responsible for the coagulation-associated indirect control of angiogenesis. The fibrin clot that is formed provides a proangiogenic matrix that promotes blood vessel invasion [257]. Moreover, coagulation causes proangiogenic factors to be released from the granules of active platelets. This activates other angiogenic pathways that are reliant on FXa production, thrombin, and the signaling of PARs [259]. Additionally, there has recently been emerging experimental and early clinical evidence indicating TF-dependent PAR signaling to forward tumor progression by promotion of proangiogenic processes either through upregulation of proangiogenic factors such as VEGF or downregulation of anti-angiogenic molecules like thrombospondin (Figure 3) [212,219,245,256,258,260]. The evocation of new blood vessels enabling cancer cells to access the circulatory system and simultaneous regression or even collapse of the pre-existing vasculature in close vicinity are critical steps during tumor growth due to the reliance of cancer cells on metabolic exchange and oxygen as well as access to vascular and lymphatic channels for efficient intravasation [261,262,263]. In breast cancer cells, TF/FVIIa-mediated PAR2 cleavage and subsequent signaling events were shown to induce proangiogenic factors such as VEGF [264], Cyr61, CXCL1, and IL-8 [136,236]. Biopsies of newly diagnosed invasive breast cancer patients revealed a significant increase in TF and PAR2 compared to non-invasive ductal carcinoma in situ paralleled an elevated VEGF expression, emphasizing the link between tumor angiogenesis and TF-dependent PAR2 signaling in a more clinical model and further in vivo results manifest this impression [228,265]. Besides the initial TF/FVIIa complex, the thrombin molecule also settled more downstream in the blood clotting cascade, positively affecting tumor angiogenesis [266,267,268]. Local thrombin generation as a consequence of the action of the TF pathway may upregulate VEGF receptor expression through paracrine PAR1 activation in stromal cells or tumor cell PAR1 signaling in an autocrine manner [269], potentially enhancing responsiveness of these cells to VEGF stimulation. Further evidence for TF-mediated effects on cancerous angiogenesis was provided by animal studies in which specific inhibition of TF/FVIIa suppressed tumor growth and in vivo angiogenesis [256]. Other studies indicate that the expression of anti-angiogenic thrombospondins is substantially repressed in TF-positive tumor cells [212,258], which might additionally enhance the actions of proangiogenic growth factors. Since fibrinogen serves as a scaffolding molecule for cell migration as well as the binding site for promigratory and angiogenic factors such as VEGF, ECM remodeling events following thrombin activity might also impact angiogenesis in the tumor tissue and its microenvironment [180]. TF/FVIIa promotes p44/42 MAPK as well as Akt/protein kinase B phosphorylation in breast cancer cells, which in turn triggers the mTOR pathway that is involved in the regulation of several cancer-related processes such as cell survival, growth, proliferation, and motility [270]. Apoptosis resistance is a well-known strategy for malignant cell survival, as well as the induction of anti-apoptotic pathways, and the occurrence of defects in apoptosis promote carcinogenesis and metastasis [271]. As mentioned previously, FVIIa is a well-known activator of the anti-apoptotic signaling pathways involving P42/44 MAPK and protein kinase B [58]. Interestingly, previous studies, performed on TF-overexpressing human breast cancer cells (MCF-7), showed that the formation of the TF-FVIIa complex inhibited apoptosis in a thrombin-dependent manner by affecting the phosphorylation of both P42/44 MAPK and protein kinase B/Akt and causing stimulation in anti-apoptotic survivin expression (Figure 3) [270]. Additional findings demonstrated that supplying FVIIa to serum-depleted baby hamster kidney and Chinese hamster ovary cells expressing TF improved cell viability [162]. Furthermore, since TF-FVIIa signaling has been documented to be involved in the creation of STAT5-dependent BclxL and Jak2-dependent protein kinase B activation, lack of adhesive ability may be the cause of the anti-apoptotic characteristics mediated by FVIIa [272]. Protection against immune response and cytotoxicity is another strategy by which TF improves tumor cell survival. The TF cytoplasmic domain was found to be responsible for a 40% invasion frequency from peripheral blood monocytes in TF-expressing colon cancer cells [273,274]. Although the exact mechanism is still unknown, this could contribute to enhanced cell survival and metastasis. The coagulation cascade, particularly thrombin release, is critical for metastasis. TF has been shown to have an important function in attenuating the attacks of natural killer cells to forming or still established micrometastases by fibrinogen-dependent and platelet-dependent mechanisms [275]. Moreover, fibrinogen displays an important role in metastasis due to its central involvement in tumor cell dissemination to establish metastases via the lymphatic or hematogenous system. Remarkably, non-metastatic breast cancer cells have limited TF expression, whereas metastatic breast cancer cells display abundant cell surface TF. However, the mechanisms remain unclear [273]. Perhaps the downstream coagulation factors affect the production of TF-FVIIa and play a role; nonetheless, a promising idea implies that TF-FVIIa signaling enhances cell motility. TF-FVIIa signaling has been linked to cell motility in numerous studies. In vitro, cleaving of the extracellular domain of TF caused the TF cytoplasmic domain to interact with actin-binding protein 280 (ABP-280), which is implicated in cell migration and adhesion [276]. Furthermore, the PARs have been linked to tyrosine kinase receptors in a previous study, correlating TF-FVIIa signaling to the migration caused by the platelet-derived growth factor-BB [126]. In invasive breast tumors, PAR1 is extensively expressed and activated by thrombin to increase their invasiveness, but it is not found in healthy breast tissue or non-invasive malignancies. In addition, thrombin-dependent PAR1 signaling was also found to influence tumor cell motility. The cytoplasmic domain of TF is required for migration in human bladder carcinoma [172]. Experimental tumor cell metastasis, besides TF/FVIIa-driven thrombin generation, was dependent on the signaling properties of the TF intracellular domain (Figure 3) [277,278]. However, further studies with contradictory results have been published. PAR1 signaling in both tumor and host cells was credited essentiality for TF-dependent lung metastasis of breast cancer cells and the PAR signaling pathway was postulated to enhance breast cancer cell invasiveness and tumorigenesis [279]. PAR2 partially contributes to thrombin-PAR1 signaling-dependent tumor cell metastasis via cross-activation [280]. On the other hand, by cleavage of the tyrosine kinase receptors EphA2 and EphB2, TF/FVIIa complex can increase cell migration [143]. MiR-19 decreases TF expression in breast cancer cells similarly, and the data suggest that miRNA control of TF may alter tumor-associated processes [281]. In colon cancer cells, miR-19a decreased TF production and thus migration and invasion, demonstrating a more serious influence of miRNA on TF activity [282]. The metastatic potential of melanoma cells implanted into severe combined immunodeficiency mice was found to be correlated with the level of TF [277]. Furthermore, suppressing TF with anti-TF antibodies, siRNA, and the Fab fragment of TF antibodies all were documented to decrease melanomas and breast cancer metastasis [283,284,285]. Notably, both cytoplasmic TF signaling and the cell surface proteolytic activity of TF-FVIIa were considered to be significant in this context [277,284]. Apart from TF-dependent effects on cancer progression via cleavage of particular PAR proteins, the transmembrane receptor might also influence certain tumor-associated processes more directly. First, the large extracellular domain of the TF is known to impact pathways relevant to cell adhesion processes, primarily through interaction with several integrins through which it might influence migratory and invasive properties as well as signaling pathways of cancer cells [286,287]. Surprisingly, despite its brevity of just 21 residues, the cytoplasmic tail region is said to affect certain processes with an essential role in cancer progression and tumor growth. TFt provides two potential phosphorylation sites at Ser253 and Ser258, which can be phosphorylated by PKC [132,286] and MAPK p38 [288], upon extracellular binding of FVIIa and resulting conformational changes [289,290]. Phosphorylated TF cytoplasmic tail is postulated to serve as a binding site for actin crosslinking protein filamin A whose recruitment is dependent on the complexation of TF and FVIIa [276,291]. Filamin A is predicted to have a tumor-promoting role when localized to the plasma membrane or cytoplasm due to its diverse role in cell migratory and adhesive processes following the interaction with various signaling molecules [292,293]. This relationship is further manifested by another study finding that the short cytoplasmic region may act as a positive regulator of tumor growth under certain conditions [245]. Numerous studies implicate FVIIa-dependent TF cytoplasmic tail signaling in tumor metastasis [277], angiogenesis, and corresponding vascular remodeling events [172,265], as well as inflammatory and immune responses [12,294]. Deregulated TF phosphorylation contributes to the aggressive behavior of invasive tumor cells [295]. Cytoplasmic tail signaling may also interplay or even synergize with coagulation cascade component-mediated PAR signaling [17]. Phosphorylation of the TF cytoplasmic region was evidenced to contribute to TF/FVIIa-dependent PAR2 signaling and tumor growth in a murine model system [135]. In this regard, clinical data from patients with recurrent breast cancer indicated an association between TF cytoplasmic tail phosphorylation and PAR2 expression [296]. Data from numerous studies indicate the assembled TF/FVIIa tandem contributes to tumor growth and tumor-favoring alterations in the TME in a variety of different types of cancer in experimental but also preclinical models [167,256,297] orchestrated by a plethora of cellular mechanisms [212]. Notwithstanding all these studies, the question remains how TF and its effector FVIIa may even be complex in tumor tissues in the first place since TF is still located extravascularly while its downstream proteases circulate in the blood. First, coagulation factors such as FVIIa or FXa might be able to readily enter the tumor from the blood due to the leaky tumor vasculature, which allows complex assembly on the tumor cell surface [298]. Moreover, following migration and intravasation, cancer cells might also gain access to the bloodstream allowing direct interaction of surface TF with circulating effector proteases. Additionally, oncogenic pathways might also stimulate the release of TF-containing microvesicles (MVs) from cancer cells into the circulatory system [215,299]. Last but not least, circulating TF can also be found in its soluble form as a cleavage product of full-length TF or asTF [23,300,301,302,303], in both cases lacking the transmembrane domain and the cytoplasmic tail. Circulatory asTF, despite the absence of the regulatory cytoplasmic region as well as displaying no membrane association, has also been shown to enhance tumor growth and cancer angiogenesis [302,304,305,306], although the responsible mechanism has not yet been fully elucidated. In accordance with these potentially tumor-promoting properties of TF, its natural inhibitors TFPI-1 and TFPI-2 have been described to act as tumor suppressors [307]. It was reported that the downregulation of TFPI decreased the apoptosis in breast cancer cells, but ectopic overexpression of TFPI increased apoptosis [308]. Moreover, stable downregulation of both isoforms of TFPI was found to increase the metastatic growth of breast cancer cells by boosting cell proliferation, motility, and invasion [309]. Enhanced MMP-2 and MMP-9 activity were observed, which could clarify the increase in cell invasion. Elevated integrin levels may indeed promote the tumor cells to be more aggressive. Downregulation of TFPI improved integrin-mediated adhesion and cell proliferation in MDA-MB-231 cells, as evidenced by increased cell adhesion and stress fiber formation in response to an ECM constituent [309]. Furthermore, it was assumed that TFPI knockdown increased the expression and activity of collagen-binding integrins, which was contrary to Sum102 in terms of which there was no difference in adherence to collagen I [310]. Notably, the downregulation of TFPIβ but not TFPIα was observed to enhance the cell motility in MDA-MB-231 cells [309]. Overexpression of TFPI was recently known to trigger apoptosis in SK-BR-3 breast cancer cells [308]. TFPI-2 is frequently downregulated in the vast majority of aggressive tumors such as glioma [311], breast cancer [312], melanoma [313], colorectal cancer [314], and pancreatic cancer [315], just to name a few. Downregulation mainly relies on epigenetic alterations in cancer cells, primarily hypermethylation of TFPI-2 promoter or histone deacetylation [312,316,317,318]. Low or absent TFPI-2 expression in breast cancer patients was associated with increased metastatic growth and angiogenesis and thus with advanced disease progression, recurrence, and poor survival outcome [307,309]. Similar correlations were found in a multitude of other different types of cancer such as gastric cancer [319], melanoma [320], and glioma [321], as well as nasopharyngeal [322] and oesophageal carcinoma [317]. Following these findings, it was suggested that restoration of TFPI might potentially be of antitumoral value due to the diminishment of cancer aggressiveness. Kondraganti and colleagues found that the reintroduction of TFPI-2 inhibits tumor invasion and growth in vitro and in vivo in a malignant melanoma cell line [323]. Similar results were published for prostate cancer cells [324] and glioblastoma cells [325], as well as oesophageal and pancreatic carcinoma cells [326]. TFPI-2 antitumoral effects are further strengthened by its capability to decelerate cell proliferation by inducing apoptosis in small-cell lung cancer (SCLC) [327] and a glioblastoma cell line [325]. Peritumoral TFPI injection further was demonstrated to suppress tumor growth in melanoma, even though this effect was only temporary [256]. Additionally, intravenous injection of recombinant TFPI in mice directly after injection of B16 mouse melanoma cells significantly reduced experimental lung metastasis [320]. These gathered data together indicate a potential therapeutic value of TFPI regarding substantial inhibition of invasiveness and aggressiveness of certain different types of cancer and thus deceleration of cancer progression. Nevertheless, the role of TFPI in cancer progression and tumor growth is not as unambiguous as the first impression suggests it to be. Its understanding becomes more challenging when taking into account that TFPI was shown to bind ECM components, thus supporting the TF/FVIIa complex to promote tumor cell adhesion and migration [328]. Therefore, it remains to be tested whether the clinical applicability of TFPI is a potential antimetastatic strategy [329]. Proteoglycans (PG) are glycoproteins characterized by the covalent attachment of one or more carbohydrate chains of the glycosaminoglycan (GAG) type [330]. Among the different classes of these highly negatively charged non-branched linear chains of repetitive disaccharide units, heparan sulfate (HS; N-acetylglucosamine-α-L-iduronic acid/β-D-glucuronic acid) is of particular relevance for this review due to its structural similarity to the anticoagulant heparin. PGs, collagens, and additional non-proteoglycans are known to make up the vascular ECM, which is thought to isolate the layer of endothelial cells from circulating blood [331]. However, the mechanism by which heparin enters ECM and quickly redistributes TFPI from ECM into circulating blood is still unknown. TFPI released by heparin can be found in vascular beds with fenestrated endothelium, such as the liver and bone marrow [332]. Heparin perfusion, on the other hand, released TFPI confined to the ECM surrounding the umbilical vein, demonstrating that the release also happens in circulatory beds without fenestrated endothelium [332]. It was also reported that in vivo infusion of heparin induced a two- to four-fold increase in the level of circulating TFPI [333]. The lack of increase in TFPI concentration ex vivo, and the addition of heparin to plasma, suggests the release of TFPI from intracellular or extracellular stores, such as to HS or other GAGs of the endothelium, for instance, dermatan sulfate or chondroitin sulfate C to be released [334]. This bound form of TFPI of complete length appears to possess, in principle, a higher inhibitory capacity to FXa, which can be additionally enhanced by heparin than the “COOH-cleaved” TFPI forms circulating in plasma [332]. GAGs appear to be the favorite molecules for TFPI accumulation at the endothelial cell surface. The following facts support this hypothesis suggesting that TFPI interacts with heparin agarose [335]; heparin and sulfated polysaccharides improve the anticoagulation capability of TFPI [336]. TFPI plasma levels rise several-fold following intravenous heparin administrations, and heparin contends for numerous binding sites of TFPI on the cell surface [84,337]. TFPI-1 is secreted on stimulation by thrombin and is thus able to participate in the control of thrombus formation by its increased concentration at the site of vascular injury [338]. Inflammatory mediators induce TF in vessel wall cells and monocytes but have no significant effect on the synthesis of TFPI-1 [339,340]. Proteoglycan receptors shown to bind TFPI-1 include the transmembrane ryudecan/syndecan 4 (SDC-4) and the glycosylphosphatidylinositol (GPI)-anchored glypican 3 (GPC-3), with the type of membrane anchoring critically influencing the respective localization of surface receptors on specific regions of the cell membrane [341]. GPC-3 in liver cells and SDC-4 isolated from endothelial cells have been found to bind to TFPI [87,88]. TFPI-1 is associated with HSPGs because of the Kunitz domain 3 and the C-terminal end, which are essential for effective binding to cell surfaces of endothelium and hepatoma cells [342,343]. Moreover, the evidence stating that endogenous TFPI-1 is mainly anchored on the surface of ECV304 cells via a GPI linkage and investigations employing the primary HUVECs in culture came up with similar results. Furthermore, a large pool of TFPI-1 is non-covalently linked to endothelial cell HSPGs such as syndecan-1 (SDC-1) in quantities two to four times the plasma concentration [344]. SDC-1 levels in patients suffering from thermal injuries were found to be potentially considered as a marker of endothelium damage associated with age and increased 24 h fluid needs in trauma patients, but not with burn size or death, according to a previous study [345,346]. Furthermore, it was shown that burn injury causes SDC-1 shedding, and that plasma-based restoration can reduce vascular leakage in a rat model of burn injury [347]. Plasma TFPI levels may be raised as a result of endothelial binding site loss associated with endothelium SDC-1 shedding, platelet release, or both [348]. Furthermore, heparinase or heparitinase, but not chondroitinase treatment, significantly inhibited FXa-stimulated-TFPI uptake and degradation. Growing the cells in chlorate-containing media inhibited GAG sulfation, which reduced FXa-stimulated-TFPI breakdown. These findings imply that HSPGs are necessary for TFPI/FXa complex uptake and breakdown [349]. On the other hand, Nassar et al. observed that silencing of SDC-1 reduced HUVEC tubule network formation in triple-negative breast cancer (TNBC) cells. Moreover, in the SDC-1-silenced secretome, the angiogenesis array indicated lower levels of VEGF-A and TF. SDC-1 depletion resulted in lower secreted endothelin-1 (EDN-1) and TF levels, while stimulation with TFPI inhibited angiogenesis. These findings imply that SDC-1 may control the angiogenesis of cancer cells through the TF pathway in addition to other angiogenic pathways [350]. As mentioned in the previous chapter, experimental evidence for TF and TFPI interactions with GAGs and HSPGs exist, but a generalization or a valid basis for this finding is still not fully understood. TF interacts with a range of signaling receptors, which trigger the activity of several signaling pathways and biological processes ranging from coagulation to malignancy including cell survival, inflammation, and angiogenesis. HSPGs as part of the ECM play a role in both physiological and biochemical processes, such as signaling during inflammation and the stimulation of cell proliferation, adhesion, motility, angiogenesis, and tumor growth [351]. The capability of HSPGs to control the expression and activity of growth factors, cytokines, chemokines, and adhesion molecules is one of their most significant features. This is owing to their ability to function as co-receptors for a variety of signaling molecules, including FGF, VEGF, integrins, Wnts, IL-6/JAK-STAT3, NFkB, and others [351]. In light of the diversity of findings and the diversity of approaches, we aimed to generate a general virtual approach using STRING analysis. We explored the protein interactions in silico using the free online STRING database to display the interaction network between HSPGs and factors involved in the TF pathway [352]. The STRING database serves as an online tool that exports identified pathways, interaction predictions, and protein networks from curated resources to obtain protein–protein interactions [352]. Figure 4A demonstrates the interaction of targets of tissue factor pathway (highlighted blue box) with cell surface HSPGs, showing that (a) TF and other targets involved in thrombosis and coagulation process along with TFPI are highly and closely interconnected whereas TFPI directly and strongly interacts with SDC-4 cell surface HSPG; and (b) almost all HSPGs including syndecans (SDC-1, 2, 3, 4), glypicans (GPC-1, 2, 3, 4, 5, 6), and CD44, betaglycan (TGFBR3), CSPG4 and phosphacan (PTPRZ1) are strongly and directly interconnected. As shown in Figure 4B, this complicated protein network is engaged in various key molecular and biochemical functions, cellular components, and biological processes (green, orange, and blue bars, respectively). Particularly, the receptor and co-receptor activity, thrombin-activated receptor activity, and coreceptor activity were observed to be involved in Wnt signaling planar cell polarity pathways within the molecular function category. Regarding the cellular component category, the HSPGs and TF pathway-related proteins are associated with the Golgi and lysosomal lumen, cell surface, extracellular matrix, collagen-containing extracellular matrix, an intrinsic component of the plasma membrane, extracellular region, and anchored component of the plasma membrane. On the other hand, the biological process category to which TF pathway-related proteins and HSPGs are related include glycosaminoglycan catabolic and biosynthetic processes, retinoid metabolic processes, cell and leukocyte migration, wound healing, macromolecule metabolic processes, blood coagulation regulation, organonitrogen compound metabolic processes, and blood coagulation (extrinsic pathway). KEGG pathway findings show biological pathways related to proteoglycans in cancer, and atherosclerosis, as well as complement and coagulation cascades, cell adhesion, ECM-receptor interaction, and fluid shear stress, confirming the analysis of the gene ontology (GO) (Figure 4C). It appears that the network of interactions between HSPGs and TF pathway-related proteins, as well as the potential effects of those interconnections, are important not only for maintaining normal cell function but also for inducing carcinogenesis and malignancy. As a result, understanding more about the relationships of these HSPGs with TF pathway-related proteins may enhance our insight into the mechanisms through which these molecules might be useful targets for cancer therapy. We can also demonstrate from these analyses that TFPI emphasizes as the maestro of this interconnection, due to its direct interaction with SDC-4, linking the HSPGs and the TF pathway together. In previous experimental work, it was already shown that HSPGs on endothelial cells might function as receptors for the internalization of TFPI-FXa complexes, contributing to the anticoagulant TFPI activity. On another hand, Tinholt et al. observed the association between SDC-3 and endogenously expressed TFPI. FGF, heparin-binding growth-associated molecule, EGFR, and notch signaling ligands are considered to act as SDC-3 co-receptors for various growth factors and ECM components [353]. Since a reduction of cell surface-related-TFPI antigen was found in GPC-3 knockdown cells, the GPI-anchored molecule might be engaged with TFPI binding [354]. This result is supported by the previous finding that TFPI binds to GPC-3 in the HepG2 tumor liver cell line [355]. The connection between TFPI and GPC-3 could be a mechanism for eliminating the TFPI-FXa complex from circulation. In the last decades, the role of TF has been expanded beyond its central role as a simple blood coagulation initiator. Notably, the dysregulation of TF pathway constituents in several tumor entities and TF-associated signaling functions suggest an important mechanistic role for this pathway in tumor progression. Cancer-induced hypercoagulability, apoptosis (resistance), tumor angiogenesis, and metastatic spread are important clinicopathological processes affected by the TF pathway. While complex context-dependent effects exist, TF exhibits many tumor-promoting properties, whereas its natural inhibitors TFPI-1 and TFPI-2 have been associated with tumor suppressor activity. Notably, HSPGs add to the mechanistic complexity, as they are necessary for TFPI/FXa complex uptake and breakdown. Moreover, recent results suggest a role for some HSPGs in regulating the expression of TF constituents, resulting in altered tumor angiogenesis. These findings stimulate new ideas and may expand the possibility of targeting of selected TF pathway functions in malignant diseases in the near future.

PMC10001462

Chenhao Jiang,Zijian Liu,Jingsheng Yuan,Zhenru Wu,Lingxiang Kong,Jiayin Yang,Tao Lv

Construction of Two Independent RAB Family-Based Scoring Systems Based on Machine Learning Algorithms and Definition of RAB13 as a Novel Therapeutic Target for Hepatocellular Carcinoma

22-02-2023

hepatocellular carcinoma,Rab GTPase,tumor microenvironment,immune response,prognostic evaluation,risk model

Hepatocellular carcinoma (HCC) remains a global health challenge with a low early diagnosis rate and high mortality. The Rab GTPase (RAB) family plays an essential role in the occurrence and progression of HCC. Nonetheless, a comprehensive and systematic investigation of the RAB family has yet to be performed in HCC. We comprehensively assessed the expression landscape and prognostic significance of the RAB family in HCC and systematically correlated these RAB family genes with tumor microenvironment (TME) characteristics. Then, three RAB subtypes with distinct TME characteristics were determined. Using a machine learning algorithm, we further established a RAB score to quantify TME features and immune responses of individual tumors. Moreover, to better evaluate patient prognosis, we established a RAB risk score as an independent prognostic factor for patients with HCC. The risk models were validated in independent HCC cohorts and distinct HCC subgroups, and their complementary advantages guided clinical practice. Furthermore, we further confirmed that the knockdown of RAB13, a pivotal gene in risk models, suppressed HCC cell proliferation and metastasis by inhibiting the PI3K/AKT signaling pathway, CDK1/CDK4 expression, and epithelial-mesenchymal transition. In addition, RAB13 inhibited the activation of JAK2/STAT3 signaling and the expression of IRF1/IRF4. More importantly, we confirmed that RAB13 knockdown enhanced GPX4-dependent ferroptosis vulnerability, highlighting RAB13 as a potential therapeutic target. Overall, this work revealed that the RAB family played an integral role in forming HCC heterogeneity and complexity. RAB family-based integrative analysis contributed to enhancing our understanding of the TME and guided more effective immunotherapy and prognostic evaluation.

Construction of Two Independent RAB Family-Based Scoring Systems Based on Machine Learning Algorithms and Definition of RAB13 as a Novel Therapeutic Target for Hepatocellular Carcinoma Hepatocellular carcinoma (HCC) remains a global health challenge with a low early diagnosis rate and high mortality. The Rab GTPase (RAB) family plays an essential role in the occurrence and progression of HCC. Nonetheless, a comprehensive and systematic investigation of the RAB family has yet to be performed in HCC. We comprehensively assessed the expression landscape and prognostic significance of the RAB family in HCC and systematically correlated these RAB family genes with tumor microenvironment (TME) characteristics. Then, three RAB subtypes with distinct TME characteristics were determined. Using a machine learning algorithm, we further established a RAB score to quantify TME features and immune responses of individual tumors. Moreover, to better evaluate patient prognosis, we established a RAB risk score as an independent prognostic factor for patients with HCC. The risk models were validated in independent HCC cohorts and distinct HCC subgroups, and their complementary advantages guided clinical practice. Furthermore, we further confirmed that the knockdown of RAB13, a pivotal gene in risk models, suppressed HCC cell proliferation and metastasis by inhibiting the PI3K/AKT signaling pathway, CDK1/CDK4 expression, and epithelial-mesenchymal transition. In addition, RAB13 inhibited the activation of JAK2/STAT3 signaling and the expression of IRF1/IRF4. More importantly, we confirmed that RAB13 knockdown enhanced GPX4-dependent ferroptosis vulnerability, highlighting RAB13 as a potential therapeutic target. Overall, this work revealed that the RAB family played an integral role in forming HCC heterogeneity and complexity. RAB family-based integrative analysis contributed to enhancing our understanding of the TME and guided more effective immunotherapy and prognostic evaluation. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fifth most common malignancy [1,2]. Moreover, HCC has been recognized as the primary cause of death in patients with liver cirrhosis [3]. Although many treatment options have been proposed in recent years, the prognosis of HCC patients is still unsatisfactory [4,5]. There is an urgent demand to discover early diagnostic markers and therapeutic targets, especially those that could be applied to modulate the tumor microenvironment (TME) and inhibit angiogenesis to improve the quality of life and prognosis of HCC patients. HCC is a morphologically heterogeneous malignancy with variable structural growth patterns and several distinct histological subtypes [6,7]. In recent years, large-scale attempts have been made to identify targeted genomic alterations in HCC [8,9]. However, translating genomic features into clinically personalized management remains a challenge for precision oncology. The Rab GTPase (RAB) family is the most prominent in the Ras superfamily of small GTPases and comprises more than 60 members of humans [10]. Similar to other small GTPases, the RAB family is present intracellularly in the GTP-bound or GDP-bound form and regulates the transport of intracellular substances [11]. Some members of the RAB family are known to function in specific cells, where they control the trafficking of specialized vesicles [12]. Accumulating evidence has well-characterized the roles played by certain members of the RAB family in the progression of HCC. RAB40B and RAB11A promote HCC progression by regulating the PI3K/AKT signaling pathway and the expression of matrix metallopeptidase 2 (MMP2) [13,14]. You et al. reported that the hepatitis B virus X protein upregulates the oncogene RAB18, resulting in the dysregulation of lipogenesis and the proliferation of hepatoma cells [15]. Sui et al. emphasized that RAB31 promoted HCC progression by inhibiting cell apoptosis induced by the PI3K/AKT/Bcl-2/BAX pathway [16]. Nevertheless, their enormous number restricts the possibility of a comprehensive and thorough study of RAB family members. With the development of multiomics technologies, utilizing diverse gene expression profiles and bioinformatics approaches has provided the opportunity to define the expression patterns and clinical significance of RAB family members in HCC. In this study, we characterized the expression landscapes of RAB family members across multiple datasets and summarized the biological characteristics of HCC with distinct expression patterns of the RAB family. Utilizing unsupervised clustering methods, RAB family-related molecular subtypes with distinct TME characteristics were determined based on a pooled HCC cohort. We further constructed a RAB score using the principal component analysis (PCA) score algorithm to predict the response to immunotherapy in HCC [17]. Moreover, to better guide the prognostic evaluation of patients, we constructed a RAB risk score using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm [18]. The predictive power of both risk models for the therapeutic efficacy of immune checkpoint inhibitors and the long-term prognosis were validated in independent HCC cohorts and distinct HCC subgroups. We further validated the role of RAB13 expression in cell proliferation and metastasis, and identified its potential downstream signaling pathways. Furthermore, we found that sorafenib could induce glutathione peroxidase 4 (GPX4)-dependent ferroptosis in RAB13-knockdown HCC, underscoring its potential as a therapeutic target for HCC. The workflow of this study is depicted in Figure 1A. To exhibit expression alterations of the RAB family in HCC, we visualized the expression landscape of 64 RAB family members (Table S1) available in The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) cohorts. According to the criterion of p < 0.05, we observed that 42 RAB family genes in the TCGA cohort were markedly overexpressed, while five genes were significantly expressed at lower levels in HCC tissues than in paracancerous tissues (Figure 1B). However, in the ICGC cohort, 43 RAB family genes were highly expressed, and 8 genes were expressed at low levels in HCC tissues relative to paraneoplastic tissues (Figure S1A). Moreover, the receiver operating characteristic (ROC) curve of the RAB family indicated that RAB24, RAB6B, RAB10, and RAB13 were excellent diagnostic predictors of HCC due to their area under the ROC curve (AUC) greater than 0.9. Meanwhile, the AUC values of 11 RAB members were greater than 0.8 (Figure 1C and Table S2). These data indicated that RAB family members might be strictly associated with HCC initiation. Using univariate Cox regression analysis, we further investigated whether the expression of RAB family genes could predict the overall survival (OS) of HCC patients. As expected, in the TCGA cohort, we found that 26 RAB family genes were tightly associated with OS in HCC patients, and 2 of them (RAB10 and RAB29) were identified as “high-risk” factors for OS with a hazard ratio greater than 2 (Figure 1D). We further validated the critical roles of 23 RAB family genes in predicting OS in HCC using the ICGC dataset (Figure S1B). Subsequently, we screened 15 critical genes of the RAB family based on the criteria of AUC values >0.7 and hazard ratio (HR) values of prognosis >1.0. Differential expression analysis revealed that all 15 genes were markedly overexpressed in HCC relative to paraneoplastic tissue (Figure 1E). Moreover, the expression correlations among the 15 RAB genes are shown in Figure 1F exhibiting a strong positive correlation with each other. The powerful hierarchical properties of the 15 RAB family genes in the diagnosis and prognosis prediction of HCC patients prompted us to further investigate their association with biological characteristics. First, we used the “Combat” algorithm to remove the batch effects of nontechnical bias between the hepatocellular liver carcinoma (LIHC) cohorts of TCGA and ICGC databases (Figure 2A) and named this combined gene expression profile the pooled HCC cohort to simplify subsequent analysis. Next, the nonnegative matrix factorization (NMF) algorithm was used to analyze the 15 RAB genes to characterize 2 RAB clusters in the pooled HCC cohort (Figure 2B). The silhouette width plots indicated that the silhouette width values of RAB cluster 1 and cluster 2 were 0.65 and 0.94 (Figure 2B), respectively, indicating good classification effectiveness of the NMF algorithm. Moreover, these 15 RAB family genes also differed markedly in distinct RAB clusters (Figure 2C,D). Kaplan–Meier survival analysis revealed that HCC patients in RAB cluster 1 had better OS than HCC patients in RAB cluster 2 (Figure 2B). To investigate the biological characteristics of distinct RAB clusters, we further performed a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the gene expression profiles of each RAB cluster in the pooled HCC cohort. Notably, both the gene ontology (GO)-biological processes and KEGG pathway enrichment analyses suggested that HCC in RAB cluster 1 was markedly associated with abnormal tumor metabolism, including metabolic pathways, drug metabolism, lipid metabolism, and amino acid metabolism, whereas HCC in RAB cluster 2 was significantly associated with aberrant activation of oncogenic signaling pathways, including PI3K/AKT signaling pathways, pathways in cancer, focal adhesion, and tight junction (Figure S2A). Gene set enrichment analysis (GSEA) also revealed that metabolism-related signals and oncogenic signals were concentrated in RAB cluster 1 and RAB cluster 2, respectively, in HCC (Figure 2E). Interestingly, gene set variation analysis (GSVA) indicated that HCC of RAB cluster 1 not only had the most remarkable correlation with metabolic pathways such as xenobiotic metabolism, bile acid metabolism, and fatty acid metabolism, but also had a strong association with immune signals such as the interferon-gamma/alpha response, while RAB cluster 2 in HCC was significantly associated with cell cycle regulation, the TGF-β signaling pathway, the PI3K/AKT/mTOR signaling pathway, and epithelial-mesenchymal transition (EMT) (Figure S2B). Significant progress has been achieved recently using immunotherapy for the treatment of HCC [19,20], highlighting the importance of the immune microenvironment in the treatment of HCC. According to previously reported algorithms [17], we further analyzed the differences in the proportion of immune cell infiltration between RAB clusters in the pooled HCC cohort. Interestingly, the analysis indicated that the HCC of RAB cluster 1 had higher levels of B cells, CD8 T cells, dendritic cells (DCs), activated DCs (aDCs), cytotoxic cells, eosinophils, and neutrophils, whereas RAB cluster 2 in HCC contained a higher proportion of macrophages, mast cells, natural killer (NK) cells, and T helper cells (Figure 2F). We further performed a tumor immune dysfunction and exclusion (TIDE) analysis on the pooled HCC cohort to evaluate the association of RAB clusters with immunotherapy response in HCC. As expected, RAB cluster 1 had a lower TIDE score and a better response to immunotherapy than RAB cluster 2 in HCC (Figure 2G,H). Moreover, our TIDE analysis revealed that RAB cluster 1 in HCC had lower levels of cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs), as well as a higher microsatellite steady state (MSI), whereas HCC of RAB cluster 2 was directly associated with immune exclusion (Figure 2G). However, the levels of PD-L1, CD8, interferon-gamma, and CD8 and T-cell inflammation (Merck18) were not markedly different between the two RAB clusters in HCC (Figure 2G). The above results suggested that RAB family genes may play critical roles in the progression and TME cell infiltration of HCC. The absence of differences in immune checkpoints and partial immune cells indicated that the 15 RAB family genes alone failed to cluster HCC well (Figure 2G). Thus, we further attempted to perform unsupervised clustering for differentially expressed genes (DEGs) in the two RAB clusters to identify the subtypes of HCC. According to the criteria of log | fold change (FC)| > 1 and p < 0.05, a total of 830 DEGs between the 2 RAB clusters from the pooled HCC cohort were obtained, which were named RAB-associated gene signatures, including 304 positively correlated genes and 526 negatively correlated genes (Figure S2C and Table S3). Subsequently, the patients with different TME patterns in the pooled HCC cohort were classified based on the expression of RAB-associated gene signatures using the R package ConsensusClusterPlus. Notably, three distinct RAB subtypes were eventually identified using unsupervised clustering, including 326 cases in subtype-1, 69 cases in subtype-2, and 213 cases in subtype-3 (Figure 3A). Prognostic analysis for the three main RAB subtypes revealed a particularly prominent survival advantage in subtype-1 (Figure 3B). To investigate the biological features of these distinct RAB subtypes, we performed a GO enrichment analysis. As shown in Figure 3C, the results of the GO biological process analysis implied that RAB subtype-1 was markedly enriched in metabolic pathways, RAB subtype-2 presented enrichment pathways associated with carcinogenic activation, and RAB subtype-3 was prominently associated with immune activation. GSVA further indicated that adipogenesis, cholesterol homeostasis, fatty acid metabolism, and bile acid metabolism were markedly activated in RAB subtype-1 but were remarkably inhibited in RAB subtype-2. Additionally, immune response-related signals such as the interferon-gamma/alpha response, IL6/JAK/STAT3 signaling pathway, and IL2/STAT5 signaling pathway were significantly activated in RAB subtype-3 (Figure 3D). For further quantitative comparison, we performed GSVA for metabolic-, immune-, and carcinogenesis-related signaling pathways. Consistently, glycolysis, heme metabolism, adipogenesis, and fatty acid metabolism had the highest enrichment scores (ES) in RAB subtype-1 compared with RAB subtype-2 or -3, while RAB subtype-3 had the highest ES in the immune response-related signaling pathways (Figure 3E). Meanwhile, RAB subtype-2 had a higher ES in oncogenic-related signaling, including cell cycle-related signaling, the PI3K/AKT/mTOR signaling pathway, Notch signaling, and the P53 pathway (Figure 3F). Interestingly, the analysis of TME cell infiltration revealed that the three RAB subtypes had distinct immune cell infiltration characteristics. Specifically, RAB subtype-3 had the highest abundance of adaptive immune cells, including B cells, T cells, NK cells, and neutrophils (Figure 3G). Together with the activation status of its immune signaling, RAB subtype-3 was classified as an immune-inflamed phenotype, a previously reported model of immune classification characterized by immune activation and adaptive immune cell infiltration [21]. However, RAB subtype-2 was remarkably abundant in innate immune cell infiltration, including aDCs, immature DCs (iDCs), macrophages, mast cells, T helper cells, central memory T cells (Tcm), effector memory T cells (Tem), follicular helper T cells (Tfh), and Th1 cells (Figure 3G). However, patients with RAB subtype-2 HCC did not exhibit a matching survival advantage (Figure 3B). Previous studies have demonstrated that stromal activation suppresses the antitumor effects of immune cells [21]. GSEA analysis revealed that stromal activity was markedly enhanced in RAB subtype-2, including the activation of apical surfaces and junctions, TGF-β signaling pathways, and EMT (Figure 3F). Thus, RAB subtype-2 was classified as an immune-excluded phenotype characterized by stromal activation and innate immune cell infiltration. Notably, malignant tumors with immune exclusion also exhibited the presence of abundant immune cells, but these cells remained in the stroma surrounding tumor cell nests rather than penetrating the parenchyma and were considered T-cell suppressive. Furthermore, RAB subtype-1 in HCC had only plasmacytoid DC, eosinophil, and T regulatory cell (Treg) infiltration (Figure 3G), which was classified as an immune-desert phenotype characterized by the suppression of immunity. We further found that the HCC of RAB subtype-2 had the highest mRNAsi index compared with RAB subtype-1 and -3, representing the strongest tumor stemness in RAB subtype-2 in HCC (Figure 3H). Conversely, RAB subtype-2 had the lowest ferroptosis index, indicating that this subtype of HCC had the weakest ferroptosis vulnerability, while subtype-1 had the highest ferroptosis index (Figure 3I). Moreover, a tumor mutational burden (TMB) analysis revealed that the overall TMB was significantly higher in RAB subtype-1 than in the other subtypes (Figure 3J), with mutations mainly originating from catenin beta 1 (CTNNB1), whereas the mutations in RAB subtype-2 and -3 were primarily derived from P53 (Figure 3K). Based on the above analysis, we realized that HCC could be classified into three subtypes with distinct TME characterization based on RAB-associated gene signatures, namely: RAB subtype-1, oncogenic signal suppression, metabolic activation, immune-desert, tumor stemness, ferroptosis sensitivity, and high TMB; RAB subtype-2, oncogenic signal activation, metabolic suppression, immune-excluded and ferroptosis tolerance; and RAB subtype-3, oncogenic signal suppression, metabolic suppression, and immune-inflamed. The above results indicated that RAB-associated gene signatures played a nonnegligible role in shaping distinct TME landscapes in HCC. Next, we further evaluated whether RAB-associated gene signatures could predict TME characteristics and prognosis in individual patients. According to the criteria of log |FC| > 2 and p < 0.05, we further screened 100 DEGs between 2 RAB subtypes from the pooled HCC cohort to narrow the gene number of RAB-associated gene signatures and facilitate subsequent analysis (Figure 4A). Then, based on these 100 phenotype-related DEGs, we constructed a scoring system to quantify the TME characteristics of individual patients with HCC, which was termed the RAB score. Notably, the Kruskal–Wallis test showed considerable differences in RAB scores between RAB subtypes (Figure 4B). RAB subtype-1 exhibited the highest median score, while RAB subtype-2 had the lowest median score, which implied that a high RAB score could be closely associated with metabolic activation-related signatures, whereas a low RAB score could be related to oncogenic signal activation-related signatures. Consistently, as shown in Figure 4C, GSVA revealed that metabolism-related signals were markedly activated in HCC patients with high RAB scores, including xenobiotic metabolism, bile acid metabolism, and fatty acid metabolism, while oncogenic-related signaling pathways were remarkably activated in HCC patients with low RAB scores, including cell cycle-related signaling, EMT, the PI3K/AKT/mTOR signaling pathway, and the TGF-β signaling pathway. Interestingly, the immune-related interferon alpha response was positively correlated with the RAB score, while the IL2/STAT5 signaling pathway was negatively correlated with the RAB score. Next, patients in the pooled HCC cohort were divided into low or high RAB score groups with a median as the cutoff value. Notably, patients with high RAB scores demonstrated a moderate survival benefit (p < 0.001, 95% CI: 0.36–0.66), with an HR value of 0.49 (Figure 4D). Moreover, the mRNAsi index confirmed that a high RAB score HCC was markedly correlated with lower tumor stemness (Figure 4E). Meanwhile, the RAB score and ferroptosis index also exhibited a noticeable positive correlation (Figure 4F). Subsequently, the analyses of TME cell infiltration indicated that HCC with a higher RAB score was correlated significantly with a high proportion of Th17 cell, B cell, DC cell, eosinophil, and neutrophil infiltration (Figure 4G), which meant that these patients were characterized by an immune-inflamed phenotype with a better clinical outcome. However, HCC patients with a lower RAB score were strongly correlated with the proportion of NK cells, Tem cells, Tfh cells, T helper cells, macrophages, and mast cell infiltration (Figure 4G), which also indicated that these HCC patients tend to have an immune-excluded phenotype with a poorer clinical outcome. TIDE analysis was further performed in the pooled HCC cohort to evaluate the capability of the RAB score in predicting the response to immunotherapy. Notably, studies confirmed that high TIDE scores were associated with poorer responses to anti-PD1 and anti-Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy [22]. Our TIDE quantitative analysis indicated that HCCs with high RAB scores usually had lower TIDE scores, suggesting that HCC patients with high RAB scores responded better to immunotherapy (Figure 4H). Moreover, a low RAB score was directly associated with immune exclusion, which further emphasized that HCC with a low RAB score was closely related to the immune exclusion phenotype. Meanwhile, the correlation analysis further indicated that HCC with a low RAB score contained more abundant immunosuppressive cells, including MDSCs and CAFs (Figure 4H). These data all support the essential indicative and predictive role of the RAB score in the TME of HCC. To further validate the validity of the RAB score for predicting immunotherapy response, multiple HCC datasets from the Gene Expression Omnibus (GEO) database were used as test cohorts to validate our above results. Pathway enrichment analysis of GSE14520 indicated that the RAB score was positively correlated with metabolism-related signals but adversely correlated with oncogenic signaling pathways such as the cell cycle, EMT, and inflammatory responses (Figure 5A, upper left panel). Moreover, the RAB score was significantly positively correlated with the proportion of Th17 cell, DC cell, eosinophil, and neutrophil infiltration but negatively associated with NK cell, Tfh cell, T helper cell, macrophage, and mast cell infiltration levels in HCC (Figure 5A, lower right panel). The analysis of major histocompatibility complex (MHC) molecular and adhesion molecule levels further indicated that HCC with a high RAB score has higher levels of PDCD1, CD40, and ICAM4 factors (Figure 5B), suggesting that this type of HCC may have better immune responses than HCC with a low RAB score. More importantly, a low RAB score demonstrated significant clinical benefit and significantly prolonged OS and recurrence-free survival (RFS) compared with a high RAB score in HCC (Figure 5C). Furthermore, consistent with the TIDE analysis of the pooled HCC cohort, the TIDE analysis of GSE14520 also suggested that a high RAB score was indeed associated with low TIDE scores and a higher proportion of patients with immune responses in HCC (Figure 5D), and these results were also consistently validated by TIDE analysis of GSE5975, GSE25097, and GSE124751 (Figure 5E–G). Notably, the previous prognostic analysis showed that the RAB score could well predict the prognosis of HCC patients with a p value significantly less than 0.05 (Figure 4D and Figure 5C). However, the HR values of the prognostic analysis were all less than 0.6, which indicated that the prognostic stratification ability of the RAB score was not very reliable. To further obtain an ideal prognostic prediction model based on RAB-associated gene signatures (Figure S2C), we applied an iterative LASSO Cox regression algorithm. Interestingly, we obtained 26 genes with independent prognostic significance in patients with HCC (Figure 6A). Then, the RAB risk score was calculated based on the expression values and regression coefficients of these 26 genes (Figure 6B and Table S4). Importantly, using the pooled HCC cohort, we found that these 26 genes could well predict the prognosis of HCC patients with a 5-year AUC value of 0.765 and an HR value of 3.78 (Figure 6C,D). The RAB risk score allowed patients in the pooled HCC cohort to be divided into high-risk (n = 304, score value > 1.013) and low-risk (n = 304, score value < 1.013) score groups based on median values. Consistently, the number of deaths in HCC patients increased significantly with increasing RAB risk score (Figure 6E), which also reflected that the high-risk score group had a significantly higher mortality rate than the low-risk score group. Subsequently, we attempted to determine whether the RAB risk score could serve as an independent prognostic factor in HCC patients by univariate and multivariate Cox regression analyses. As expected, the univariate analysis demonstrated that tumor stage, tissue grade, RAB score, and RAB risk score were all prognostic factors for HCC patients (Figure 6F). Multivariate Cox regression analysis further indicated that RAB risk score and tumor stage were independent factors that could be used to predict the prognosis of HCC patients (Figure 6G). To provide clinicians with a relatively quantitative tool for predicting mortality risk in HCC patients, we constructed a nomogram using these prognostic factors (Figure 6H). By adding the points for each prognostic factor, each patient was assigned a total prognostic score. A higher total prognostic score corresponds to a worse OS outcome in patients with HCC. The calibration curves suggested good consistency between the prediction by the nomogram and actual OS outcomes at three and five years (Figure 6I). More importantly, the time-dependent AUC values of the RAB risk score for predicting the 1- to 8-year survival rates were all greater than 0.75, which was much better than the time-dependent AUC value of the RAB score for predicting OS (Figure 6J). To determine whether the RAB risk score is robust, we further evaluated the predictive effect of the RAB risk score on the prognosis of HCC patients in different clinical cohorts and subgroups. Here, the median value was used as the cutoff value for different HCC cohorts. First, we validated the prognostic stratification ability of the RAB risk score using the TCGA and ICGC cohorts. Kaplan–Meier survival curves of the TCGA cohort indicated that HCC patients with high RAB risk scores had worse OS than those with low RAB risk scores (p < 0.001, HR = 3.25, 95% CI = 2.27–4.65), with a time-dependent AUC value greater than 0.75 at 1, 3, 5, and 8 years (Figure 7A). Consistent Kaplan–Meier analysis outcomes were obtained from the ICGC cohort (p < 0.001, HR = 5.13, 95% CI = 1.76–9.56). The time-dependent AUC values of the RAB risk score for the prediction of one- to four-year survival rates in the ICGC cohort all exceeded 0.8 (Figure 7B). Subsequently, all patients in the pooled HCC cohort were grouped by age and then ranked by the RAB risk score into high- and low-risk subgroups. Kaplan–Meier survival analyses indicated that the OS in the high-risk subgroup was markedly worse than that in the low-risk subgroup (age >60: p < 0.001, HR = 4.34, 95% CI = 2.95–6.39; age ≤60: p < 0.001, HR = 3.11, 95% CI = 1.91–5.06) (Figure 7C). Our above analysis suggested that tumor stage, tissue grade, and RAB score were prognostic factors in patients with HCC. Likewise, a high RAB risk score was correlated with dramatically worse OS regardless of whether the patient exhibited early- (p < 0.001, HR = 3.52, 95% CI = 2.27–5.47) or advanced-stage (p < 0.001, HR = 3.51, 95% CI = 2.11–5.81), well-differentiated (p < 0.001, HR = 3.34, 95% CI = 2.20–5.07) or poorly differentiated (p < 0.001, HR = 5.91, 95% CI = 3.32–10.53), and high- (p < 0.001, HR = 4.03, 95% CI = 2.63–6.17) or low-RAB score (p < 0.001, HR = 3.81, 95% CI = 2.06–7.04) HCC (Figure 7D-F). Tumor mutation is also a malignant burden factor of HCC. Consistently, the RAB risk score provided a statistical stratification of OS regardless of whether the HCC was CTNNB1 wild-type (WT) (p < 0.001, HR = 3.14, 95% CI = 2.06–4.80), CTNNB1 mutant (MUT) (p < 0.001, HR = 3.48, 95% CI = 1.64–7.38), P53 WT (p < 0.001, HR = 3.09, 95% CI = 2.01–4.74), or P53 MUT (p < 0.001, HR = 5.56, 95% CI = 2.87–10.78) (Figure 7G,H). Furthermore, we further validated the predictive power of the RAB risk score for OS and RFS in HCC patients using the GSE14520 dataset. As expected, Kaplan–Meier survival curves of the GSE14520 dataset also indicated that HCC patients with high risk scores had worse OS (p < 0.001, HR = 2.44, 95% CI = 1.59–3.75) and RFS (p < 0.001, HR = 1.98, 95% CI = 1.38–2.86) than those with low risk scores with a time-dependent AUC value greater than 0.60 at 1, 3, and 5 years (Figure 7I,J). These data demonstrated that the RAB risk score is a reliable and stable model for predicting the prognosis of patients with HCC. The above data proposed and validated a RAB score for immune response prediction and a RAB risk score for prognosis prediction in HCC. Here, Pearson correlation analysis further indicated an inverse correlation between the RAB score and the RAB risk score (Figure 8A). Next, we attempted to further screen critical RAB family genes to ascertain their roles in HCC. We performed TIDE analysis on 15 previously screened RAB family members (Figure 1E), and the results indicated that the expression of seven RAB family genes (RAB11A, RAB13, RAB1B, RAB35, RAB5B, RAB5C, and RAB6B) had remarkable differences in the immune response of HCC (Figure 8B). Notably, RAB13 was used as a target for subsequent studies, as its roles were not fully explored in HCC. Further analysis revealed that RAB13 exhibited a positive correlation with the RAB score and a negative correlation with the RAB risk score (Figure 8C,D). GSVA showed that RAB13 was positively correlated with multiple oncogenic signaling pathways, including the PI3K/AKT signaling pathway, EMT, and cell cycle-related signaling pathways, while it was negatively associated with metabolism-related signaling pathways. Moreover, RAB13 also exhibited a marked negative correlation with immune-related signals, including the IL2/STAT5 signaling pathway, IL6/STAT3 signaling pathway, inflammatory response, and interferon alpha/gamma response (Figure 8E). We further analyzed the correlation between RAB13 and the immune microenvironment. Interestingly, the expression of RAB13 was markedly positively correlated with immune exclusion (Figure 8F). Meanwhile, RAB13 expression was positively associated with MDSC, TAM M2, and Th2 cell levels and remarkably negatively correlated with neutrophil, eosinophil, DC cell, cytotoxic cell, CD8 T cell, and B-cell infiltration levels in HCC (Figure 8F,G). In addition, the level of RAB13 was negatively correlated with the immune checkpoint PDL1 (Figure 8F). Furthermore, our clinical samples indicated that RAB13 protein expression was markedly elevated in HCC tissues compared to paired non-cancer liver (NCL) tissues (Figure 9A). Using the Human Protein Atlas, we also verified that the protein expression of RAB13 was markedly higher in HCC than in paracancerous tissues (Figure 9B). Next, we further investigated the potential role of RAB13 in HCC using cytological assays. RAB13 was markedly knocked down by transfection with siRNA targeting RAB13 sequences (siRAB13) in Huh7 and Hep3B cells compared to the control siRNA (siCTL) (Figure 9C,D). The Cell Counting Kit-8 (CCK-8) and EdU assays revealed that RAB13 knockdown markedly inhibited the proliferation and DNA replication of HCC cells (Figure 9E,F). Moreover, wound healing and transwell assays demonstrated that RAB13 silencing significantly inhibited the metastasis of HCC cells (Figure 9G,H). Based on previous analysis, we further investigated the precise relationship of RAB13 expression with the PI3K/AKT signaling pathway, cell cycle regulation, and EMT. As expected, RAB13 silencing markedly suppressed the protein levels and phosphorylation levels of the PI3K/AKT signaling pathway (Figure 10A). CDK1 is pivotal in regulating the G2-phase transition of the cell cycle, while CDK4 manages the G1 phase to enter the S phase of DNA synthesis [23,24]. Interestingly, our results indicated that RAB13 knockdown significantly restrained CDK1 and CDK4 expression (Figure 10B). In addition, inhibition of RAB13 expression restricted the EMT process (Figure 10C). Notably, our data implied that RAB13 levels are inversely correlated with the IL2/STAT3 signaling pathway (Figure 8E). Consistently, RAB13 knockdown promoted the expression and activation of JAK2/STAT3 signaling (Figure 10D). Moreover, we found that RAB13 silencing enhanced the expression of interferon regulatory factors-1 (IRF1) and IRF4 (Figure 10E), which are vital factors mediating tumor immunity. These data indicate that elevated RAB13 expression is critical for the malignant progression of HCC. Our data suggested that activation of metabolism-related signaling pathways was associated with a better prognosis for patients with HCC (Figure 3B,E). Notably, dysregulation of metabolic signaling regulates ferroptosis vulnerability. Therefore, we wondered whether RAB13 expression could alter the ferroptosis vulnerability of HCC cells. Correlation analysis indicated a significant negative relationship between RAB13 expression and the FPI index (Figure 10F), implying that RAB13 overexpression may impair ferroptosis vulnerability in HCC. In addition, sorafenib, a ferroptosis inducer, markedly restrained the proliferation of RAB13-knockdown HCC cells (Figure 10G). To further demonstrate that RAB13-inhibited HCC cells suffered ferroptosis following sorafenib treatment, we observed the Phen Green SK diacetate (P-GSK) probe and examined the variation in malondialdehyde (MDA) levels. As expected, the P-GSK probe indicated that sorafenib markedly promoted the accumulation of iron in HCC cells after RAB13 knockdown (Figure 10H). Meanwhile, sorafenib promoted lipid oxidative damage in RAB13-knockdown HCC cells (Figure 10I). Finally, we detected alterations in GPX4 and ferroptosis suppressor protein 1 (FSP1) expression. Interestingly, our Western blot results indicated that RAB13 knockdown suppressed GPX4 protein expression but not FSP1 expression (Figure 10J). These data illustrate that RAB13 is a crucial target for boosting GPX4-dependent ferroptosis vulnerability in HCC. The RAB family acts as molecular switches that localize to different intracellular membranes, providing spatiotemporal control of organelle maintenance and trafficking [10,11,12]. However, a comprehensive and thorough investigation of RAB family genes in HCC is still lacking. Here, we comprehensively characterized the landscape of RAB family genes and constructed RAB gene-related models for the clustering and evaluation of HCC, which has tremendous clinical implications. In our study, we first attempted to cluster the gene expression profiles of HCC according to RAB family genes using the NMF algorithm. Although the results suggested that RAB cluster 1 and RAB cluster 2 could well stratify the prognosis of HCC patients, the two clusters were more similar to a summary of the biological characteristics for the gene expression patterns with different levels of RAB family genes and failed to exhibit good stratification in describing TME differences. Furthermore, the mRNA transcriptome differences between distinct RAB expression levels have been demonstrated to be dramatically associated with metabolic-, oncogenic-, and immune-related biological pathways. Thus, these DEGs were considered RAB-associated signatures. Interestingly, three genomic subtypes with distinct TME patterns were revealed based on RAB-associated signatures utilizing unsupervised clustering analysis. RAB subtype-1 was characterized by the activation of metabolism and the suppression of oncogenic signaling corresponding to the immune-desert phenotype. In addition, RAB subtype-1 had the highest TMB and the weakest ferroptosis vulnerability. Notably, RAB subtype-1 exhibited greater prognostic survival than RAB subtypes-2 and -3, indicating that immune status is not an independent predictor for assessing patient prognosis. Moreover, we hypothesize that the worst prognosis in RAB subtype-2 is associated with its oncogenic signaling activation, ferroptosis tolerance, and immune-excluded phenotype. Furthermore, our analysis revealed that patients with RAB subtype-2 are optimal candidates for immune checkpoint therapy, as RAB subtype-2 was characterized by activation of adaptive immunity, corresponding to an immune-inflamed phenotype, also known as an immune hot tumor [25,26], manifested by a prominent infiltration of immune cells in the TME. Considering individual heterogeneity, to further quantify individual tumor characteristics and facilitate clinical application, we attempted to establish a scoring system—the RAB score—to evaluate the immunological features and prognosis of individual HCC patients. RAB subtype-1, characterized by an immune-desert phenotype, exhibited a higher RAB score and was associated with a better prognosis. RAB subtype-2, which is characterized by an immune-excluded phenotype, showed a lower RAB score and was associated with a poorer prognosis. Moreover, we validated this model in several distinct HCC cohorts. This finding indicated that the RAB score was a robust and reliable tool to comprehensively assess the TME characteristics for individual HCC, which could be used to further determine the tumor immunophenotype. However, the integrated analysis revealed that the RAB score was not an independent prognostic biomarker for HCC. To this end, we further constructed the RAB risk score using the iterative LASSO regression algorithm, whose predictive power of prognosis was also validated in various HCC cohorts and subgroups. Notably, the RAB risk score was not comparable to the RAB score in evaluating the TME characteristics of HCC, so we did not present and interpret these results. We speculated that this status was mainly caused by the fact that the RAB score recombined the crucial genes of the RAB family-related DEGs through the PCA score method, which contains multiple gene patterns and could well characterize the TME features according to the expression of crucial genes, but not all of these essential genes were prognostic stratification genes; thus, the RAB score was not suitable for prognostic assessment of HCC. Conversely, the RAB risk score incorporated genes with significant prognostic stratification, but its limited number of genes restricts the capacity of a robust depiction of TME for HCC. Therefore, the two scoring models could complementarily guide clinicians in the management of HCC, which has potential clinical significance. The role of RAB13 in tumors has been widely reported. Wang et al. elucidated that RAB13 sustains breast cancer stem cells by supporting tumor-stromal crosstalk [27]. Hinger et al. reported that RAB13 regulates the secretion of small extracellular vesicles in mutant KRAS colorectal cancer cells [28]. Zhang et al. demonstrated that RNF115 inhibits the postendoplasmic reticulum trafficking of Toll-like receptors (TLRs) and TLR-mediated immune responses by catalyzing K11-linked RAB1A and RAB13 ubiquitination [29]. However, the role of RAB13 in HCC has not been reported. In our constructed models, we found that RAB13 had significant weights in both the RAB score predicting immune response and the RAB risk score predicting prognosis. Therefore, we further investigated the function of RAB13 in HCC cells using cytological studies. Interestingly, we found that RAB13 could be involved in modulating HCC cell proliferation through the PI3K/AKT signaling pathways and cell cycle regulation. Meanwhile, we demonstrated that RAB13 promotes the metastasis of HCC cells through EMT. Moreover, we found that the promotion of the immune-excluded phenotype by elevated RAB13 expression may be associated with the inhibition of interferon-regulated signaling (IRF1/IRF4) and the JAK2/STAT3 signaling pathway. These data all indicated that RAB13 might be a potential target for HCC therapy. To validate this hypothesis, we further tested whether RAB13 could regulate ferroptosis due to the relevance of RAB13 to metabolism-related signaling and ferroptosis vulnerability. As expected, RAB13-silenced HCC had increased sensitivity to sorafenib, and this phenomenon was associated with the accumulation of intracellular iron and increased levels of lipid oxidation. More importantly, our Western blotting results confirmed that RAB13-induced alterations in ferroptosis vulnerability were dependent on GPX4 expression. A retrospective analysis of resected HCC samples at West China Hospital of Sichuan University from May 2014 to December 2020 was performed. Thirty fresh human HCC and paired NCL tissues were collected. Immunohistochemistry (IHC) staining was performed as described previously [30,31]. Anti-RAB13 (ABclonal, A10571, Wuhan, China, 1:200) was used. The IHC results were evaluated by two independent observers based on the percentage of positively stained cells (scored from 0 to 3 points) and intensity of staining (scored from 0 to 3 points), and a final immunoreactivity score (range 0–9 points) was obtained by multiplying the two scores. RAB13 expression levels were classified as low if the score was less than five and high if the score was ≥ five [30,31]. This study was approved by the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (2020, No 385). Informed consent forms were signed by all involved patients or their families. Huh7 and Hep3B cell lines were purchased from the National Collection of Authenticated Cell Cultures (Shanghai, China) and were cultured in complete medium containing Dulbecco’s modified Eagle’s medium (HyClone, Logan, UT, USA) supplemented with 10% fetal bovine serum (Gibco, Grand Island, NY, USA), 1000 U/mL penicillin, and 100 μg/mL streptomycin (HyClone, Logan, UT, USA), and were grown in a humidified air atmosphere containing 5% CO2 at 37 °C. All cell lines were analyzed by STR profiling for cell line authentication and routine mycoplasma detection. Sorafenib (S7397) was purchased from Selleckchem (Houston, TX, USA). Transfection was performed as previously described [30,31]. Additional information about siRNAs is available in Table S5. qRT–PCR and Western blot analysis were performed as previously described [30,31]. The primers and the primary antibodies used in this study are listed in Tables S6 and S7, respectively. Wound healing assays were performed as previously described [31]. For the transwell assay, transfected HCC cells resuspended in an FBS-free medium were added to the top chamber (Corning-Costar; pore size 8 μm), and the bottom chamber was filled with 30% FBS as an inducer. After 48 h, the cells that failed to invade from the top of the membranes were erased, and then the invaded cells on the bottom of the membrane were fixed and stained. Invaded cells from five random fields were counted and photographed under a light microscope. CCK-8 proliferation assay was performed as previously described [30]. Additionally, to examine the inhibitory effect of sorafenib on the indicated cells, the processed cells (1 × 103 cells per well) were inoculated in 96-well plates for 24 h. Sorafenib was then administered at a concentration of 5 μM and incubated for 72 h. Then, 10 μL of CCK-8 solution was added to the wells and incubated for 4 h. Finally, the absorbance at 450 nmol was recorded, and the results were analyzed. EdU assays were performed using a BeyoClick™ EdU Cell Proliferation Kit with Alexa Fluor 594 (Beyotime, Wuhan, China) according to the manufacturer’s instructions. A P-GSK probe was used to monitor the iron content in the indicated HCC cells using a Phen Green SK Reagent Kit (Thermo, Waltham, MA, USA) in accordance with the manufacturer’s instructions. The levels of MDA (A003-1-2) were measured to assess the level of lipid oxidative damage using commercially available kits from Nanjing Jiancheng Bioengineering Institute (Nanjing, China) in accordance with the manufacturer’s instructions. The LIHC clinical information and raw fragment per kilobase (FPKM) values were taken from the ICGC and TCGA datasets. We then transformed FPKM values into transcripts per kilobase million (TPM) values. The series matrix files of the Affymetrix and Illumina-generated microarray for GSE14520, GSE5975, GSE25097, and GSE124751 were directly downloaded from the GEO database. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 was used for the KEGG pathway analysis. According to previously published expression methods, we further performed the GSEA on the specified set of transcripts. Moreover, the gene set “c5.all.v6.2. symbols” was downloaded from the MSigDB database, and another published pathway gene set is summarized in Table S8. GSVA enrichment analysis was further used to estimate the pathway and biological process variations using these two gene sets. To correlate the survival status of patients with gene expression values, we employed a consensus clustering method, NMF, to perform clustering analysis based on the expression of RAB genes and the OS of HCC patients [32]. The principle of consensus clustering is to perform two-dimensional resampling of the original dataset and then repeatedly cluster the perturbation subsets, and the final clustering results are obtained by clustering the consensus matrix. Pearson’s correlation coefficient was used to measure the distance, and “average” was used as the linkage method, with 100 repetitions. The performance of these clustering methods was evaluated with three frequently utilized measures as previously reported [33]: (1) Survival analysis to evaluate the prognostic values between subtypes; (2) average silhouette width, a measure of cluster coherence, to assess the similarities across subtypes; and (3) clustering heatmap to intuitively visualize the effect of sample clustering. Based on the expression of RAB-associated gene signatures, unsupervised clustering analysis was conducted using the pooled HCC cohort to identify distinct HCC subtypes for further research. The number of clusters and their stability were determined by a consensus clustering algorithm. The above steps were repeated 1000 times using the “ConsensuClusterPlus” R package to ensure the strength of the classification. To quantify the RAB expression patterns of individual tumors, the PCA score method was used to construct a scoring system named the RAB score [17]. In addition, the iteration LASSO Cox regression model was used to screen for the best genes for prognostic assessment in HCC [18]. The RAB risk score could be calculated using the following formula: RAB risk score = Σ (Coef i × Exp i), where I is the member involved in the gene signature. The TIDE algorithm was used to predict HCC responsiveness to immunotherapy [23]. We used the GSVA method to quantify the relative abundance of each infiltrating cell in a single sample. The immune cell markers used in this study were extracted from a previously published authoritative study. To assess the stemness of cancer cells, a one-class logistic regression algorithm, mRNAsi, was used to calculate the stemness index for each HCC sample using the workflow available on a previously established database [34]. In addition, an index representing ferroptosis vulnerability was found from the expression data of ferroptosis core machine genes according to a previously published algorithm [35]. Based on the clinical risk factors and multivariate Cox regression coefficients, a prognostic nomogram was built using the “rms” R package, and the predictive accuracy of this nomogram was assessed using the calibration curve and the concordance index. All statistical analyses were performed using R software (version 3.6.1). Analysis of differentially expressed genes (DEGs) between different defined groups was performed using the “limma” R package. DEGs between the two RAB clusters were obtained with significance criteria set as adjusted p value < 0.05 and log2 |FC| > 1, while the criteria of p value < 0.05 and log2 |FC| > 2 were set for screening DEGs between two RAB subtypes. DEGs were visualized as heatmaps in R using the packages “pheatmap” and “ggplot2”. To calculate the TMB per megabase, the total number of mutations counted was divided by the size of the coding region of the targeted territory in the TCGA-LIHC cohort. The mutation landscape oncoprint was generated using the R package “ComplexHeatmap”. The comparison of normally distributed variables between the two groups was performed using an unpaired t-test, and the statistical significance of the nonnormally distributed variables was estimated using the Mann–Whitney U test (Wilcoxon rank-sum test). Spearman’s correlation analysis was performed to calculate the correlation coefficient between the two factors. Based on the correlation between gene expression and patient survival, the optimal cutoff point for each dataset was determined using the “survminer” R package, and the “surv-cutpoint” function was used to repeat all potential cutoff points to obtain the maximum rank statistic, divided into two groups: high and low. Survival curves for prognostic analysis were generated using the Kaplan–Meier method, and significant differences were determined using the log-rank test. The false discovery rate (FDR) method was used to adjust the p value for multiple comparisons, and statistical significance was set at p < 0.05; that is, the FDR was less than 0.05. The asterisks represent the statistical p value (* p < 0.05; ** p < 0.01; *** p < 0.001). In conclusion, this work highlighted the potential importance of RAB family genes in the TME of HCC. Aberrant expression of RAB family genes is a nonnegligible factor in the TME heterogeneity and complexity of HCC. The models constructed based on RAB-associated signatures will contribute to improving our understanding of the characteristics of cell infiltration in the TME, and guide more effective immunotherapy strategies and prognostic assessments.

PMC10001466

Jelizaveta Lamceva,Romans Uljanovs,Ilze Strumfa

The Main Theories on the Pathogenesis of Endometriosis

21-02-2023

endometriosis,pathogenesis,immune regulation,oestrogen,progesterone,stem cells,metaplasia,epigenetics,carcinogenesis

Endometriosis is a complex disease, which is defined by abnormal growth of endometrial tissue outside the uterus. It affects about 10% of women of reproductive age all over the world. Endometriosis causes symptoms that notably worsen patient’s well-being—such as severe pelvic pain, dysfunction of the organs of pelvic cavity, infertility and secondary mental issues. The diagnosis of endometriosis is quite often delayed because of nonspecific manifestations. Since the disease was defined, several different pathogenetic pathways have been considered, including retrograde menstruation, benign metastasis, immune dysregulation, coelomic metaplasia, hormonal disbalance, involvement of stem cells and alterations in epigenetic regulation, but the true pathogenesis of endometriosis remains poorly understood. The knowledge of the exact mechanism of the origin and progression of this disease is significant for the appropriate treatment. Therefore, this review reports the main pathogenetic theories of endometriosis based on current studies.

The Main Theories on the Pathogenesis of Endometriosis Endometriosis is a complex disease, which is defined by abnormal growth of endometrial tissue outside the uterus. It affects about 10% of women of reproductive age all over the world. Endometriosis causes symptoms that notably worsen patient’s well-being—such as severe pelvic pain, dysfunction of the organs of pelvic cavity, infertility and secondary mental issues. The diagnosis of endometriosis is quite often delayed because of nonspecific manifestations. Since the disease was defined, several different pathogenetic pathways have been considered, including retrograde menstruation, benign metastasis, immune dysregulation, coelomic metaplasia, hormonal disbalance, involvement of stem cells and alterations in epigenetic regulation, but the true pathogenesis of endometriosis remains poorly understood. The knowledge of the exact mechanism of the origin and progression of this disease is significant for the appropriate treatment. Therefore, this review reports the main pathogenetic theories of endometriosis based on current studies. Endometriosis is a chronic gynaecological condition, which is characterized by abnormal presence of endometrial glands and stroma outside the uterus accompanied by chronic inflammation. Most commonly it affects organs of the pelvic cavity: ovaries, fallopian tubes, urinary bladder, intestines or peritoneum [1,2]. Rarely, it is localized in other organs outside the pelvis—diaphragm, pleura, abdominal wall, central or peripheral nervous system [2]. Endometriosis is mainly found in girls and women of reproductive age. According to the World Health Organization data, there are approximately 10% of reproductive-aged women (190 million) globally who are diagnosed with this condition [1]. The peak age of patients is in the time frame between 25 and 45 years [3]. Usually it takes up to 8 to 10 years to reach the diagnosis of this disease [4]. Endometriosis has a considerable impact on worldwide economics as well—it costs the world over 80 billion USD per year [5]. Endometriosis has variable range of manifestations—from accidentally found asymptomatic lesions to severe condition, which does not depend on the size of the lesion [3]. Most often the first symptoms show up before the age of 20 [3]. The main symptoms caused by endometriosis are chronic pelvic pain, severely painful menstrual periods, dyspareunia, dysuria and/or painful defecation, abdominal bloating and constipation. It may also increase the risk of mental health issues, such as anxiety and depression. The other manifestation of endometriosis is infertility without any other symptoms: 40–50% of infertile women are diagnosed with endometriosis [2,5]. There are different mechanisms how endometriosis can influence fertility: distorted anatomy of the pelvic cavity, development of the adhesions, fibrosis of the fallopian tubes, local inflammation of the pelvic structures, systemic and local (i.e., endometrial) immune dysregulation, changes in hormonal environment inside the uterus and/or impaired implantation of the embryo [3]. In addition, the disease has a significant negative impact on quality of life and social well-being of patients—due to pain and other symptoms, e.g., fatigue, severe bleeding or mood swings, women have to skip their studies or work and might tend to avoid sex. The main classification of endometriosis is based on its localization and histopathology; there are three subtypes: superficial peritoneal endometriosis, ovarian endometriotic cysts and deep infiltrating endometriosis. Superficial peritoneal endometriosis rarely causes severe clinical symptoms. It is found on the surface of organs of the pelvic cavity and often attaches to the peritoneum. Ovarian endometriotic cysts appear on the ovaries and form cystic structures known as endometriomas or “chocolate cysts”. These cysts are filled with fluid and vary in size. This subtype is associated with infertility and ovarian cancer. Deep infiltrating endometriosis can invade visceral organs to a depth of 5 mm or more within or outside the pelvic cavity and distort local anatomy. It is a rare form of endometriosis, and the cause of significant symptoms, so it requires surgical treatment frequently [6]. The pathogenesis of endometriosis still has many questionable aspects, that is why it is a relevant topic for the research. Nowadays, there are many theories and evidence for each of them. The full understanding of the mechanism of this condition would help to develop the most effective treatment, which is still limited now. In the present article we summarized the main theories on the pathogenesis of endometriosis according to the relevant research data. The theory about retrograde menstruation is well-known as Sampson’s theory. It remains relevant since it was described for the first time in 1925. The main idea of it is that menstrual blood containing endometrial cells regurgitate via patent fallopian tubes into the peritoneal cavity, where the implantation of these cells might occur [7,8]. After implantation, development and growth of the lesion is supported by angiogenesis [9]. It is possible because of activated peritoneal macrophages, which produce angiogenic factors, e.g., vascular endothelial growth factor (VEGF) [10]. The problem of this theory is that retrograde menstruation might explain ovarian and superficial peritoneal endometriosis, but not deep infiltrating endometriosis or lesions outside the peritoneal cavity [6,7,8,9]. However, several studies show that reflux of menstrual blood is physiological for women with patent fallopian tubes, and most of them (76–90%) experience retrograde menstruation without further endometriosis development [9,11]. The cases, when endometriosis develops in women, who have retrograde menstruation, could be explained by epidemiological studies which expose the risk factors of endometriosis—short menstrual cycle, longer menstrual flow and uterine outflow obstruction. These factors increase the quantity of retrogradely flushed cells [11,12]. Researching this theory, the baboon model has been used for some studies, because of ethical reasons on women’s examination. Serial diagnostic laparoscopies have been performed to evaluate the amount and composition of menstrual blood in the peritoneal cavity in different phases of menstrual cycle. Results on experiments with baboons show the correlation between retrograde menstruation and the development of endometriosis, but researchers admit that most likely this is not the single one pathogenetic reason for the disease [11]. In 1927 Sampson suggested one more pathogenetic mechanism—theory of metastatic endometriosis. This theory assumes that a small amount of the endometrial tissue can be disseminated through the uterine-draining lymph vessels during menstruation. This theory is based on his finding: there was an endometrial polyp projecting into the lumen of a lymph vessel [13]. The benefit of a benign metastasis theory is that this pathogenetic mechanism can explain the occurrence of endometriosis in lymphatic nodes and distant locations such as lungs, because lymphatic capillaries are found in almost all organs [14]. Nowadays, there are some reports of lymph node endometriosis, confirmed by histopathological examination that shows the presence of endometrial glandular and stromal cells in lymph node, and immunohistochemistry that is positive for oestrogen receptor, progesterone receptor, PAX8 and CD10 [14,15,16,17]. Research on lymphangiogenesis has discovered that there is a dysregulation of the expression of lymphangiogenic growth factors and their receptors in the eutopic endometrium of ladies diagnosed with endometriosis. The main promoters of lymphangiogenesis in endometrium are VEGF-C and VEGF-D [14,18,19], which are upregulated by proinflammatory cytokines interleukin 1β (IL-1β), tumour necrosis factor α (TNFα), IL-7 and CD74 [14]. In addition, the density of lymphatic microvessels of eutopic endometrium of patients is increased, too. So, these changes together could facilitate the entry of endometrial tissue into the lymphatic circulation [14,19]. However, it is still unclear how this dysregulation actually affects the development of endometriosis. Inflammation, caused by immune dysregulation, is one of the main mechanisms that takes part in diseases where cell proliferation and infiltration occur. In case of endometriosis, proinflammatory pathways block functions of apoptotic mechanisms, and potentially harmful cells adhere to distant sites [20]. Immune cells involved in formation and further development of endometrial lesions are macrophages, neutrophils, NK cells, dendritic cells and T cells (Figure 1). Macrophages detect and phagocytose pathogens and foreign cells, act as antigen-presenting cells to activate T cells and participate in tissue regeneration of healthy endometrium [20,21,22]. Normally, macrophages represent approximately 10% of total immune cell population in the proliferative phase in endometrium. They change in number according to the menstrual cycle phase, regulated by oestradiol and progesterone. During menses, their number is significantly increased in accordance with their phagocytic function—clearing apoptotic cells and cell debris during endometrial shedding [22]. In endometriosis, number of macrophages is increased in eutopic endometrium [21,22] and peritoneal fluid [10] across all phases of menstrual cycle and without cyclic changes [23]. In contrast, phagocyte function is decreased because of the reduced expression of CD3, CD36 and annexin A2 [10,21,24]. It results in incomplete endometrial shedding, presence and survival of desquamated tissue in the peritoneal cavity [21]. Peritoneal macrophages release proinflammatory cytokines TNFα, IL-6, IL-8, IL-1β, which recruit neutrophils, provoke inflammation and support the development of endometrial lesions [3,10]. Macrophages also produce VEGF, which promotes angiogenesis in endometriosis [10]. Moreover, some studies found the predominance of M2 macrophage subtype in endometriotic lesions and peritoneal cavity [10,25]. This subtype classically promotes development of the tumours, e.g., colorectal cancer and osteosarcoma. It means that endometriosis shares some characteristics, e.g., inflammation and tissue invasion, with neoplastic processes, but is still classified as a benign disease. In addition, M2 promotes nerve fibre growth, so predominance of this subtype of macrophages could be related to severe pain, experienced in women with endometriosis [21,22]. In healthy endometrium, neutrophils are involved in endometrial repair and regulation of cyclic vascular proliferation. In women with endometriosis, neutrophil counts in peritoneal fluid are increased. This could be attributable to the locally increased concentration of chemoattractants secreted by epithelial cells such as IL-8, epithelial neutrophil-activating peptide 8 (ENA-78) and human neutrophil peptides 1-3 (HNP1-3), which attract neutrophils to the peritoneal cavity [10,21]. According to the results of the mouse model study, depleting of the neutrophils with anti-Gr-1 antibody in the early stage of endometriosis significantly decreased the number of endometrial lesions [26]. In contrast, this antibody had no effect in advanced disease, which suggests that neutrophils do not take part in endometriosis progression, but only in induction [10]. However, neutrophils express cytokines, e.g., VEGF, IL-8, C-X-C chemokine motif ligand 10 (CXCL10), which cause progression of the disease [10]. Role of the NK cells in the immune system is the following: they produce cytokines, which control tumour immunity and microbial infections. Regarding endometriosis, their cytotoxic function is suppressed by the IL-6, IL-15 and transforming growth factor β (TGF-β) [10,27]. Therefore, endometrial cells, which enter the peritoneal cavity, tend to stay there. However, the amount of the NK cells shows no differences in women with and without endometriosis. Dendritic cells are responsible for antigen presentation to T cells and, therefore, are involved in immune responses in mucosal surfaces [22]. There are two types of dendritic cells—plasmocytoid dendritic cells and myeloid dendritic cells. Plasmocytoid dendritic cells are involved in recognition of viruses and produce interferons, while myeloid dendritic cells are involved in T cell activation and represent the most relevant cells to endometriosis. In healthy individuals, the amount of the dendritic cells increases to clear endometrial debris during menstruation. In women affected by endometriosis, there is significantly reduced density of myeloid dendritic cells in endometrium [28]. In the peritoneal cavity, numbers of dendritic cells are increased and may promote neuroangiogenesis, causing and enhancing pain sensation [22]. One of the important factors, which maintains the development of endometriosis, is imbalance between type 1 T lymphocytes (Th1) and type 2 T lymphocytes (Th2). These two types have different functions in the immune system: Th1 lymphocytes produce cytokines and promote cellular responses, but Th2 lymphocytes influence differentiation of B lymphocytes and suppress cellular and humoral responses [3]. In endometriosis, Th2 lymphocytes represent the main population of T cells, so potentially harmful cells stay unrecognized. On the other hand, the immune response of CD4+ Th1 lymphocytes in peritoneal fluid is suppressed due to an increased expression of IL-10 and IL-12 [29]. Moreover, the peripheral concentration of cytotoxic (CD8+) T cells and activated (HLA-DR) T cells in healthy women increases in luteal phase compared with the follicular phase of the menstrual cycle, but there are no such fluctuations of cytotoxic and activated T cells in patients with endometriosis [30]. Recently, the association between regulatory T cells (Treg cells) and endometriosis was reported. The main function of regulatory T cells is modulation of the immune system, maintaining tolerance to self-antigens and preventing autoimmune diseases [29]. In endometriosis patients, there is an increased amount of Tregs in the peritoneal fluid and decreased—in the peripheral blood. These changes can lead to the development of autoimmune reactions and suppress local cellular immune response [29]. In 1924 Robert Meyer proposed coelomic metaplasia theory. It is based on the female reproductive tract development: it develops from a pair of Müllerian ducts, which arise from coelomic epithelial cells of mesodermal origin [31]. This theory assumes that the original coelomic membrane undergoes metaplasia and forms endometrial stroma and glands. It is the most suitable explanation for cases of endometriosis in men, who have received high doses of oestrogen for prostatic carcinoma treatment, and Rokitansky-Kuster-Hauser syndrome patients who does not have functioning endometrial tissue because of congenital aplasia of the uterus and the upper part of the vagina [9,31,32]. In both these clinical groups, endometriosis cannot be explained by Sampson’s implantation theory due to lack of eutopic endometrium. The most common form of endometriosis, which could be explained by this theory is ovarian endometrioma. The mesothelium, which derives from the coelomic epithelium covering the ovary, has great metaplastic potential and can invaginate into the ovarian cortex [9,32]. These mesothelial inclusions could be transformed into endometriosis by metaplasia [9]. Growth factors, which influence this phenomenon, are still unknown. Embryonic rest theory is a sort of a metaplasia theory. It states that remnants of embryonic cells of Wolffian or Müllerian duct origin may differentiate into endometriotic lesions [12,33]. In the coelomic metaplasia theory, the transformation occurs only to mesothelium, but there is no such restriction in the embryonic rest theory [33]. In this theory, it is supposed that some changes in cell differentiation or relocation of the Müllerian ducts during embryogenesis of the fetus can maintain the spreading of embryonic cells—primordial endometrial cells [34]. Generally, these cells are located in the posterior pelvic floor and remain inactive until puberty, and then the process of formation of endometriotic lesions starts with oestrogen stimulation [12]. Recently, as a proof of this theory, Signorile et al. published their results on autopsy of female fetuses, where they found the presence of ectopic endometrium in the posterior pelvic floor structures: Douglas pouch, recto-vaginal septum, rectal tube and posterior wall of uterus [12]. These places are pretty common for diagnosed endometriosis cases. This theory is suitable not only for endometriosis cases in women, but men too, because Wolffian ducts also contain embryonic cells. Stem cells represent a minor fraction of multipotent cells with high replicative potential, having unlimited ability to renew themselves and capability to produce more differentiated daughter cells [35]. Many studies have targeted the impact of stem cells on endometriosis in recent years. They show that there are several populations of somatic stem cells in endometrium, including epithelial, mesenchymal and mixed side population [6,36]. The main functions of these cells are remodelling, regeneration and homeostasis of the tissue. Epithelial stem cells are found in the basal layer, and are responsible for regeneration of the functional layer during the proliferative phase, but mesenchymal stem cells are localized in the perivascular area of the basal and functional layers and are responsible for generation of functional stroma [6,12]. The migration of endometrial stem cells remains hypothetical. Some of the above-listed theories could be used to explain the mechanism. Firstly, endometrial stem cells are also found in menstrual blood [36]. This blood containing stem cells can reach the peritoneal cavity via patient fallopian tubes as Sampson’s retrograde menstruation theory considers [12]. The second theoretical mechanism of the migration of stem cells to the ectopic sites is abnormal cell migration during organogenesis of the female reproductive tract, which is associated with aberrant expression of WNT and HOX genes [36]. The last mechanism is an ability of endometrial origin stem cells to enter the angiolymphatic space passively during menstruation and move around the circulation [6]. After the migration phase, stem cells adhere and start to form endometrial lesion. The stem cell potential of lesion formation has been proven by Cervelló et al. in 2011, when endometrial side population cells were implanted beneath the kidney capsule in immunocompromised NOD-SCID mice and this experiment resulted in endometriosis [37]. This theory is an important finding because it can explain the pathogenesis of all three subtypes of endometriosis and its ectopic localization outside the abdominal cavity [6]. This variant of stem cell recruitment theory is based on another source of stem cells—bone marrow. These cells are able to incorporate themselves into the endometrium to regenerate the tissue [36]. Several populations of cells take part in endometrial regeneration—mesenchymal, hematopoietic and endothelial progenitor cells [6]. The conception of theory is the following: bone marrow stem cells, which circulate via blood vessels, are settled in soft tissue instead of going to the endometrium, while reduced number of cells is recruited to eutopic endometrium [6,12]. Recent studies suggest that the CXCL12/CXCR4 axis is involved in recruiting bone marrow-derived stem cells, so the malfunction of this axis can cause the misplacement of stem cells [6]. The advantage of bone marrow-derived stem cell theory is its capability to explain extrapelvic endometriosis without the concept of “benign metastasis”. In the healthy endometrium, progesterone and oestrogen signalling is strictly coordinated and menstrual cycle phase-dependent. This is important to maintain a normal menstrual cycle, embryo implantation and development of the pregnancy [38]. Oestrogen induces epithelial proliferation during the proliferative phase, while progesterone inhibits the action of oestrogen and initiates the secretory phase, when stromal cells begin the decidualization [20,38]. The dysregulation of these two hormones—resistance to progesterone and oestrogen dominance [38,39]—leads to endometriosis development (Figure 2). Endometriosis is often called an “oestrogen-dependant” disease. The reason for this statement is simple—endometriosis mostly affects women of reproductive age, but it also appears in women in postmenopausal age if the lady has high oestrogen level or undergoes oestrogen-replacement therapy [40]. The main functions of oestrogen in healthy endometrium include stimulation of epithelial proliferation and induction of leukaemia inhibitory factor (LIF), an IL-6 family cytokine, which is important for successful embryo implantation and decidualization of the endometrium [38]. In endometriosis, studies report higher levels of oestradiol—oestrogen steroid hormone—in menstrual blood and abnormal expression of enzymes involved in oestrogen metabolism, which can lead to increased oestrogen concentration and suppressed inactivation of oestrogen synthesis [40]. There are two oestrogen receptors—ERα and ERβ, which are coded by different genes: ESR1 and ESR2, respectively [38,39]. ERα and ERβ normally work together, but in endometriosis patients, expression of the receptors is changed—the ERα:ERβ ratio is significantly reduced due to high ERβ levels [41]. The main problem caused by abnormal expression of ERα is increased synthesis of inflammatory cytokines, prostaglandins, tumour-promoting and angiogenic factors [20,29]. On the other hand, ERβ overexpression leads to inhibition of TNFα-induced apoptosis and also promotes the inflammation [27,41]. As a result, synthesized prostaglandins induce inflammation and prevent cell apoptosis; tumour-promoting and angiogenic factors support the progression of the endometrial lesions and inhibition of apoptosis promotes cell proliferation and lesion growth [20,27]. In addition, oestrogen is able to stimulate growth of peripheral nerve fibres by upregulating nerve growth factors (NGF) causing nociceptive pain [27]. The expression of the progesterone receptor (PGR) is induced by oestrogen action through its receptor ERα. PGR has two isoforms: PR-A and PR-B, expression of which increase during the proliferative phase and decrease after the ovulation [41]. Expressed PGR inhibits ERα expression, establishing a feedback system. In endometriosis, as a result of low ERα:ERβ ratio and high oestrogen levels, progesterone resistance develops: PR-B is undetectable and PR-A levels are significantly lower than in the endometrium of healthy individuals [41]. Progesterone resistance manifests as a decreased responsiveness to progesterone of endometrial stromal cells [2]. Moreover, mutation of PGR causes sterility in mice due to reduced or absent ovulation, uterine hyperplasia, lack of decidualization of the endometrium and limited mammary gland development [38]. Therefore, to compensate the lack of progesterone, progestin therapy is one of the options of hormonal therapy for endometriosis. This therapy reduces endometriosis-related pelvic pain and eliminates laparoscopically visible endometrial lesions [41]. Due to the significant impact of oestrogen and progesterone on endometriosis development, the treatment mainly aims at the balance of these hormones. Therapy options that are currently in use include combined oral contraceptives, progestins, gonadotropin-releasing hormone agonists, danazol and aromatase inhibitors. The other options of treatment are still under development, e.g., gonadotropin-releasing hormone antagonists, selective oestrogen receptor modulators and selective progesterone receptor modulators [3,38]. However, hormonal therapy is associated with systemic adverse effects including weight gain, fluid retention, acne, hot flashes, decreased libido, insomnia and vaginal dryness [3], which might decrease the compliance to long-term hormonal treatment. In recent years, growing body of evidence suggests that epigenetic changes have a certain role in the development of endometriosis. Epigenetic changes are alterations of gene expression without any changes in DNA sequence. They are represented by the alterations in DNA methylation, histone acetylation, RNA transcription, chromatin remodelling, etc. [42]. The epigenome can be influenced by environmental factors, e.g., social behaviour, metabolism and nutritional deficiencies [39]. The enzymes DNA methyltransferases are responsible for DNA methylation. Normally, the expression of DNA methyltransferases in endometrium is regulated by oestrogen and progesterone and varies depending on the cycle phase [42]. These enzymes are important for decidualization of the endometrium [42]. In endometriosis patients, hypermethylation of DNA of the local cells occurs due to increased expression of DNA methyltransferases DNMT1, DNMT3A and DNMT3B [39,43]. Changes in methylation of the Human Homeobox A10 (HOXA10) genes are important because the dysregulation in some of these genes can lead to endometriosis. HOXA10 expression is regulated by oestrogen and progesterone [39]. These genes are important for the endometrial changes throughout the normal menstrual cycle—they regulate endometrial growth, differentiation, and embryo implantation [39,44]. In patients affected by endometriosis, the expression of HOXA10 is decreased during the secretory phase, and, as the result, uterine receptivity is decreased and endometriosis-related infertility occurs [39,43]. Probably, HOXA10 gene expression is reduced due to hypermethylation of the HOXA10 gene promoter in the endometrial tissue [39,43]. The enzymes called histone deacetylases are responsible for histone modulation and acetylation. In endometriosis, activity of histone deacetylases HDAC1 and HDAC2 is increased. It leads to the hypoacetylation of cyclins, which causes cell cycle induction and propagation [42,43]. Micro-RNAs are short non-coding RNA molecules that regulate translation of post-transcriptional mRNA by repression and mRNA degradation, acting as large-scale molecular switches [45]. According to recent findings, endometriosis is characterised by abnormal spectrum of micro-RNAs, further influencing the expression of the relevant target mRNAs [45]. Wide spectrum of micro-RNAs are involved in different steps of endometriosis. For example, miRNA-135a/b, regulating HOXA10, is upregulated in endometriosis and cause progesterone resistance [42,46]. MiR-199 is downregulated, so COX-2 translation is not suppressed, and it leads to pro-inflammatory prostaglandin synthesis such as IL-8 [42,45]. MiRNA-96b is also downregulated, and it is the cause of increased proliferation of the endometrial lesions [42]. MiR-126 increases VEGF and fibroblast growth factor (FGF) signalling in endothelial cells, resulting in neoangiogenesis and the development of a mature vasculature [45]. MiRNA-223 also showed a significant impact on endometriosis. This micro-RNA is involved in signal transduction, regulation of transcription, cell growth and development, modulation of inflammation and tumorogenesis [47]. In 2022, Xue et al. found that miRNA-223 is decreased in eutopic and ectopic endometrial stromal cells in women with endometriosis [47]. They also have proved that in case of upregulation of miRNA-223 proliferation, invasion and migration of endometrial stromal cells could be suppressed and epithelial-to-mesenchymal transition could be reversed as well [47]. These findings give miRNA-223 a potential of new therapeutic target. The other micro-RNA, which promotes the growth, proliferation and angiogenesis of ectopic stromal cells, is miRNA-21 [46,48]. In the research by Wu et al. are presented three micro-RNAs with significant impact on endometriosis development. Expression of miR-26b-5p and miR-215-5p was downregulated, but miR-6795-3p (Table 1)—upregulated in serum of the patients, and their expression was stage related [48]. In addition, it has been found, that these three micro-RNAs are involved in such signalling pathways as MAPK and PI3K-Akt. MAPK signalling pathway is important in regulation of inflammation and the following cell processes: differentiation, division, proliferation, stress response, metabolism and apoptosis [48]. PI3K-Akt signalling pathway is also involved in cell processes and angiogenesis. These two pathways are significant for endometriosis development and further progression, that is why they could be used as therapeutic target too. Despite the fact that endometriosis is classified as a benign disease, it still has a potential to transform into malignancy. This transformation is rather rare—it develops in 1% of all endometriosis patients [39,45]. Endometriosis-related malignant transformation most frequently affects ovaries, and the most common types of the ovarian malignancy are ovarian endometrioid carcinoma and ovarian clear cell carcinoma, which are found in 76% of all cases [39,49]. Recently, several carcinogenetic pathways have been reported for endometriosis-related malignant transformation. It is supposed that uncontrolled cell division, infiltration of surrounding tissues, neoangiogenesis and escape of apoptosis might be caused by the demethylation of oncogenes and the hypermethylation of tumour suppressor genes [39]. For example, the following events are involved in malignant transformation of the endometriosis: the hypermethylation of the human mutL homolog 1 (hMLH1) gene promoter which causes a decrease in DNA mismatch repair gene expression, the hypomethylation of long interspersed element-1 (LINE-1), inactivation of the tumour suppressor genes runt-related transcription factor 3 (RUNX3) gene and Ras-association domain family member 2 (RASSF2) gene by their promoter hypermethylation [39]. Also, in case of endometrioid cancer, there has been found an impact of activation of the KRAS oncogene and inactivation of the PTEN tumour suppressor gene [44,49]. Loss of PTEN activity is supposed to be an early event in malignant transformation of endometriosis, and is related to the mutation of PTEN gene itself [45]. In addition, Anglesio et al. found that in deep infiltrating endometriosis there are somatic mutations in cancer driver genes ARID1A, PIK3CA, KRAS, and PPP2R1A [50]. Factors of the environment definitely have an impact on risks of endometriosis development. External factors can cause crucial changes in women organism under certain circumstances. But this section still has lack of evidence. The main factors of the lifestyle that can provoke dysregulation in normal functioning of the organism are: lack of physical activity, smoking, caffeine and alcohol intake, diet. It is supposed that physical activity helps to reduce the risk of endometriosis, because it decrease menstrual flow and normalize oestrogen balance [46]. Tobacco smoking increase the expression of pro-inflammatory mediators, disrupts synthesis of prostaglandin E2 and natural steroids [46]. Disrupted steroidogenesis leads to increased oestrogen and decreased progesterone synthesis [51]. Caffeine reduces production of Sex-Hormone Binding Globulin, it decrease the amount of bio-available testosterone, and as a result the levels of oestrogen reduce too [46]. It is suggested that caffeine has a protective potential due these changes, but the results of researches are still conflicting. On the other hand, Kechagias et al. found a correlation between endometriosis and caffeine intake in high quantities (>300 mg/day), admitting that it could be the risk factor for the disease [52]. Alcohol has the opposite effect on synthesis of hormones compared to caffeine. It has an impact on pituitary luteinizing hormone and activates the enzyme aromatase, resulting in increased oestrogen production and increased testosterone conversion to oestrogen, but it depends on certain dose of the alcohol [46,51]. The main dietary factor which could negatively affect on endometriosis development is increased red meat consumption, because of content of saturated fat [51,53]. Dioxins and polychlorinated biphenyls (PCBs) are organic pollutants produced by industrial processes. The most toxic environmental pollutant is called 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) [53]. PCB and TCDD have an ability to disrupt an endocrine system. In context of endometriosis, it was found that increased amount of PCB and TCDD have been accumulated in adipose tissue of patient with deep infiltrating endometriosis [51,54], but the mechanism of this changes remains uncertain. Endometriosis is a chronic disease which affects a significant number of women all around the world. Their quality of life is strongly reduced by disturbing symptoms and negative emotional experience which is associated with general health status, diagnosis and adverse effects of the treatment. It is clear that the pathogenesis of endometriosis is complex and involves many factors and processes which occur simultaneously. There are multiple interactions of the immune system, hormones, genes, local and stem cells—everything has an impact on endometriosis development and its further progression. In recent years, many theories have been studied, but there is no single theory which could explain all aspects of endometriosis. The future concept of endometriosis is likely to incorporate the elements from all the listed pathogenetic theories.

PMC10001477

Sunyoung Jeong,Sungryong Bae,Eui-Cheol Shin,Jong-Hwa Lee,Jung-Heun Ha

Ellagic Acid Prevents Particulate Matter-Induced Pulmonary Inflammation and Hyperactivity in Mice: A Pilot Study

03-03-2023

particulate matter,ellagic acid,inflammation,hypoxia,hyperactivity

The inhalation of fine particulate matter (PM) is a significant health-related environmental issue. Previously, we demonstrated that repeated PM exposure causes hyperlocomotive activity in mice, as well as inflammatory and hypoxic responses in their lungs. In this study, we evaluated the potential efficacy of ellagic acid (EA), a natural polyphenolic compound, against PM-induced pulmonary and behavioral abnormalities in mice. Four treatment groups were assigned in this study (n = 8): control (CON), particulate-matter-instilled (PMI), low-dose EA with PMI (EL + PMI), and high-dose EA with PMI (EH + PMI). EA (20 and 100 mg/kg body weight for low dose and high dose, respectively) was orally administered for 14 days in C57BL/6 mice, and after the eighth day, PM (5 mg/kg) was intratracheally instilled for 7 consecutive days. PM exposure induced inflammatory cell infiltration in the lungs following EA pretreatment. Moreover, PM exposure induced inflammatory protein expression in the bronchoalveolar lavage fluid and the expression of inflammatory (tumor necrosis factor alpha (Tnfα), interleukin (Il)-1b, and Il-6) and hypoxic (vascular endothelial growth factor alpha (Vegfα), ankyrin repeat domain 37 (Ankrd37)) response genes. However, EA pretreatment markedly prevented the induction of expression of inflammatory and hypoxic response genes in the lungs. Furthermore, PM exposure significantly triggered hyperactivity by increasing the total moving distance with an increase in moving speed in the open field test. On the contrary, EA pretreatment significantly prevented PM-induced hyperactivity. In conclusion, dietary intervention with EA may be a potential strategy to prevent PM-induced pathology and activity.

Ellagic Acid Prevents Particulate Matter-Induced Pulmonary Inflammation and Hyperactivity in Mice: A Pilot Study The inhalation of fine particulate matter (PM) is a significant health-related environmental issue. Previously, we demonstrated that repeated PM exposure causes hyperlocomotive activity in mice, as well as inflammatory and hypoxic responses in their lungs. In this study, we evaluated the potential efficacy of ellagic acid (EA), a natural polyphenolic compound, against PM-induced pulmonary and behavioral abnormalities in mice. Four treatment groups were assigned in this study (n = 8): control (CON), particulate-matter-instilled (PMI), low-dose EA with PMI (EL + PMI), and high-dose EA with PMI (EH + PMI). EA (20 and 100 mg/kg body weight for low dose and high dose, respectively) was orally administered for 14 days in C57BL/6 mice, and after the eighth day, PM (5 mg/kg) was intratracheally instilled for 7 consecutive days. PM exposure induced inflammatory cell infiltration in the lungs following EA pretreatment. Moreover, PM exposure induced inflammatory protein expression in the bronchoalveolar lavage fluid and the expression of inflammatory (tumor necrosis factor alpha (Tnfα), interleukin (Il)-1b, and Il-6) and hypoxic (vascular endothelial growth factor alpha (Vegfα), ankyrin repeat domain 37 (Ankrd37)) response genes. However, EA pretreatment markedly prevented the induction of expression of inflammatory and hypoxic response genes in the lungs. Furthermore, PM exposure significantly triggered hyperactivity by increasing the total moving distance with an increase in moving speed in the open field test. On the contrary, EA pretreatment significantly prevented PM-induced hyperactivity. In conclusion, dietary intervention with EA may be a potential strategy to prevent PM-induced pathology and activity. Air pollution continues to threaten public health in many cities and endangers the basic right to breathe. Diesel exhaust particles (DEPs) consist of a carbon core that adsorbs a mixture of sulfate, nitrate, metals, and organic chemicals, including polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. DEPs are one of the major components of urban air pollution [1]. DEPs comprise mainly fine particulate matter (PM) with diameters less than 2.5 µm (PM 2.5), including nanoparticles, which can reach the lower lobe of the lung and even systemic circulation. Exposure to air pollutants, including DEPs, is inevitable, and there is cumulative evidence that indicates that continuous exposure to DEPs triggers detrimental effects on the pulmonary [2,3], renal [4,5], hepatic [6], cardiovascular [7,8], and nervous [9,10] systems. In particular, DEP inhalation significantly induces pulmonary inflammation, oxidative stress, and malfunction in mammals [11,12]. In addition, increased exposure to air pollutants triggers behavioral disorders in humans and experimental animals. The worsening degree of air pollution is closely intertwined with the early onset of attention deficit hyperactivity disorder in Taiwan [13,14]. In the US and Denmark, increased inhalation of air pollutants increases the incidence of psychiatric disorders, such as depression, bipolar disorder, and schizophrenia [15]. Although the exact developmental mechanisms of direct pathological causes of behavioral disorders are poorly understood, environmental challenges may be significant initiators of behavioral disorders [16,17]. In addition, in experimental mice, exposure of dams to air pollutants during pregnancy triggered hyperactivity in the pups [18,19]. Furthermore, we have previously demonstrated that PM instillation in relatively young adulthood (8~10 weeks) triggers hyperactivity in mice [20,21]. Interestingly, dietary intervention with phenolic components successfully prevented PM-induced hyperactivity in experimental mice [21]. If exposure to air pollution is inevitable, then dietary intervention with functional materials may be an excellent preventive means to attenuate and/or prevent air-pollutant-induced physiological disturbances [22,23,24,25]. Polyphenolic components are strong candidates for coping with exposure to air pollutants, given that polyphenols are abundant in plants and possess multiple biological functions, including anti-inflammatory [26,27,28], antiendoplasmic reticulum stress [29,30], and antioxidative effects [31,32,33]. Among polyphenols, ellagic acid (EA) may be a promising candidate to mitigate and/or prevent air-pollutant-induced pathophysiological responses in humans. EA is a conjugated form of two distinctive gallic acids, known as strong antioxidants, bridged by two lactone rings [34]. Plants (e.g., berries, grapes, and pomegranates) produce EA as a metabolite of tannin hydrolysis [34]. EA attenuates dyslipidemia [35], weight gain [36], insulin resistance [37], carcinogenesis [38,39], inflammatory responses [40,41], and oxidative stress [42,43]. Therefore, owing to its biological functionalities, EA may be a promising polyphenol candidate that can mitigate the effects of air pollutant inhalation. EA is an excellent candidate for controlling pathophysiological phenomena during the inhalation of air pollutants. Inhalation of air pollutants directly induces pulmonary disturbances such as inflammation. Therefore, dietary supplements against exposure to air pollutants should be effective in mitigating pathological events in the lungs. According to previous reports, EA significantly ameliorated pulmonary damage triggered by various toxicants to the pulmonary system, such as hydrochloric acid [40], carbon tetrachloride [44], elastase [45], bleomycin with cyclophosphamide [46], and ovalbumin-induced asthma [47] in multiple animal models. The protective role of EA against pulmonary toxicants mainly relies on its anti-inflammatory and/or antioxidant effects [40,44,45,46,47]. Pretreatment with EA significantly attenuated LPS-induced acute pulmonary pathology and significantly reduced inflammatory cell infiltration and cytokine production (TNFα, IL-1β, and IL-6) in experimental mice [48]. Based on a literature review, EA may have protective functions against the effects of exposure of mammals to air pollutants (i.e., PM). However, animal models of PM exposure by instillation have only been established recently; therefore, robust experimental data are not yet available. Moreover, the preventive role of EA against pulmonary PM exposure has not yet been fully elucidated. In this study, to understand the protective effects of EA against acute pulmonary PM exposure, EA was orally administered at 20 and 100 mg/kg for 7 days before initiation of PM instillation. After 1 week of EA administration, PM (5 mg/kg) was instilled for 7 consecutive days while maintaining the aforementioned EA administration. To determine the beneficial effects of EA on PM exposure, pulmonary immune cell infiltration, PM loading, cytokine secretion, and mRNA expression were analyzed. Moreover, behavioral alterations caused by PM exposure and EA pretreatment were examined using an open field test (OFT). All experimental animal procedures were previewed and approved by the Institutional Animal Care and Use Committee (protocol # 2002-0023) of the Korea Institute of Toxicology and accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). Seven-week-old male C57BL/6NCrlOri mice (Orient Bio Inc., Seongnam, Republic of Korea) were acquired, acclimatized for 7 days, and maintained in a controlled room at a temperature of 22 °C and humidity of 50% with a 12 h light/dark cycle. The mice were allowed free access to a purified diet (PMI Nutrition International LLC, St. Louis, MO, USA) and filtered distilled water. After the acclimatization period, the experimental mice were weighed and randomly assigned to four groups (n = 8/group) as follows: (1) Control (CON): 5% dimethyl sulfoxide (DMSO; Sigma-Aldrich, St. Louis, MO, USA) was administered orally for 14 days, and after the eighth day of DMSO administration, distilled water was instilled for another 7 days. (2) PM-instilled (PMI): PM (5 mg/kg; standard reference material 2975; National Institute of Standards and Technology, Gaithersburg, MD, USA) was instilled for 7 days. (3) Low-dose of EA with PMI (EL + PMI): EA (20 mg/kg; Sigma-Aldrich) was administered orally for 14 days, and after the eighth day of EA administration, PM (5 mg/kg) was instilled for another 7 days. (4) High-dose of EA with PMI (EH + PMI): EA (100 mg/kg) was administered orally for 14 days, and after the eighth day of EA administration, PM (5 mg/kg) was instilled for another 7 days. Fifteen days after the first EA administration, the mice were euthanized by isoflurane inhalation. After sacrifice, the final body, liver, and lung weights were measured. PM instillation was performed 1 h after EA treatment in the EL + PMI and EH + PMI groups. The left lung was fixed in 10% (v/v) neutral-buffered formalin (Sigma-Aldrich) and further processed for hematoxylin and eosin staining as previously described [20,21]. BALF was collected as previously described [20,21], and cells were counted using a cell counter (NC-250; ChemoMetec, Gydevang, Denmark). In addition, cell types in the BALF were distinguished after smearing with the cytospin slide (Thermo Fisher Scientific, Waltham, MA, USA) and staining with Diff-Quik solution (Dade Diagnostics, Aguada, Puerto Rico) as previously described [20,21]. Mouse TNFα (Invitrogen, Waltham, MA, USA), IL-6 (Invitrogen), and H2O2 (Biovision, Milpitas, CA, USA) levels in the BALF were analyzed using commercially available ELISA kits. Serum corticosterone levels were determined using an ELISA kit (Abcam, Cambridge, MA, USA). All ELISA procedures were performed in accordance with the manufacturer’s instructions. Total RNA extraction, cDNA synthesis, and relative qRT-PCR analyses were performed as previously described [21]. Primers used for qRT-PCR are indicated in Table 1 and previous literature [20,21]. An open field test (OFT) was performed 1 h after the last PM instillation. Experimental mice were individually placed in the center of a plexiglass container (42 cm width × 42 cm depth × 42 cm height). The illumination of the plexiglass container was controlled by placing a 100 W lamp 2 m above the floor. The mice were acclimatized for 10 min in an OFT environment, and behavioral indices were recorded continuously for 10 min. Mice movements were recorded using an automated computer system (Ethovision, Noldus, The Netherlands). The distance, duration, and velocity of movements were calculated and expressed in inches, seconds, and inch/s, respectively. All data from the experiments are summarized and expressed as the mean ± standard deviation for each group. Equal variance of experimental data was assessed using the D’Agostino and Pearson omnibus test. If the datasets were normally distributed, one-way analysis of variance (ANOVA) with Tukey’s post hoc test was applied. Otherwise, Kruskal–Wallis with Dunn’s post hoc test was executed. Statistical significance was set at p < 0.05. Statistical analysis was performed using GraphPad PRISM 5 (GraphPad Software, San Diego, CA, USA). Pulmonary PM exposure (PMI, EL + PMI, and EH + PMI) did not significantly alter the final body weight at sacrifice (Figure 1A). In contrast, delta body weight and relative lung weight were significantly increased by PM instillation (Figure 1B,D). Interestingly, EA treatment significantly attenuated the delta body weight against PM exposure regardless of the EA concentration (Figure 1B). However, PM-induced lung weight did not change with EA treatment (Figure 1D). There were no significant differences in the relative liver weight among the experimental groups (Figure 1C). EA treatment did not affect the final body weight or the relative lung and liver weights. The overall changes in patterns of delta body weight and relative lung weight exhibited similar trends to our previous studies on quercetin treatment [21]. In our previous findings, PM instillation increased delta body weight [20,21]; however, quercetin treatment inhibited the PM-induced increase in delta body weight [21], similar to the effect exerted by EA treatment in the current study. According to our previous findings, PM instillation directly induces pulmonary inflammatory responses in rodents [20]. Similar to our previous findings, in this study, PM exposure resulted in black particles or black pigment-laden alveolar macrophages in the alveolar areas (Figure 2B, blue arrows). In addition, infiltrated inflammatory cells were noted in the peribronchiolar, perivascular, and interstitial regions (Figure 2B, red arrows), similar to our previous reports [20]. Pulmonary PM loading induces infiltration of immune cells and cytokine secretion in the BALF [20], and we postulated that EA treatment would attenuate the recruitment of immune cells and cytokine secretion in the BALF. As expected, PM exposure markedly increased the total number of immune cells and inflammatory cytokines in the BALF. After PM exposure, total immune cells in the BALF were increased ~3-fold compared with CON with induction of the absolute number of neutrophils and macrophages (Figure 3A–C). In addition, a significant increase in the number of eosinophils and lymphocytes was induced in the PMI group compared with the CON group (Figure 3D,E). However, the total number of immune cells was not ameliorated in the EL + PMI and EH + PMI groups compared with the PMI group (Figure 3A–C), similar to the results in our previous study on quercetin treatment [21]. In contrast, the number of eosinophils and lymphocytes gradually decreased in the EA-treated groups compared with that in the PMI group in an EA dose-dependent manner (Figure 3D,E). PM exposure also upregulated pulmonary cytokine secretion in the BALF. After PM instillation, pulmonary TNFα and IL-6 protein secretion was remarkably elevated, consistent with our previous findings [20,21]. PM exposure elevated pulmonary TNFα and IL-6 secretion in the BALF by approximately 2.6- and 19-fold, respectively, compared with the CON group (Figure 4A,B). However, EA treatment did not prevent the induction of pulmonary inflammatory cytokine secretion in the BALF (Figure 4A,B). In our previous study, quercetin treatment also failed to prevent PM-induced recruitment of immune cells and cytokine secretion in the BALF [21]. Therefore, PM instillation may directly and strongly induce pulmonary inflammation, and EA and quercetin [21] may not fully prevent inflammatory events such as physical PM loading and inflammatory cytokine secretion in the BALF. Moreover, we measured hydrogen peroxide levels in the BALF to determine whether PM exposure may induce pulmonary oxidative stress. PM exposure for 1 week did not increase hydrogen peroxide secretion in the BALF (Figure 4C), consistent with our earlier findings [20,21]. EA treatment did not reduce the recruitment of immune cells or cytokine secretion in the BALF. However, in our previous study, we observed that quercetin exerted anti-inflammatory effects by decreasing pulmonary cytokine mRNA expression [21]. Similarly, pulmonary cytokine mRNA expression was increased in the PMI group compared with that in the CON group. The mRNA expression of pulmonary cytokines such as Tnfα, Il-1b, and Il-6 increased 6.2-, 3.1-, and 1.4-fold, respectively, in the PMI group compared with the CON group (Figure 5A–C). However, EA treatment decreased PM-induced pulmonary Tnfα mRNA expression by 1.9-fold and 2.9-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5A). In addition, EA treatment remarkably reduced PM-induced pulmonary Il-1b mRNA expression by 1.6-fold and 2.1-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5B). Furthermore, EA treatment also reduced PM-induced pulmonary Il-6 mRNA expression by 0.8-fold and 0.9-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5C). Inflammatory and hypoxic responses are often coincidental physiological events that occur in a site- and cell-type-specific manner [49]. To understand whether (1) PM exposure induced pulmonary hypoxic responses and (2) EA treatment prevented PM-induced hypoxic responses, mRNA expression of hypoxic response genes (e.g., Vegfα and Ankrd37) was assessed in the lung tissue by qRT-PCR. As expected, pulmonary Vegfα mRNA expression in the PMI group was elevated 4.2-fold compared with that in the CON group; however, pulmonary Vegfα mRNA expression was markedly attenuated by 1- and 1.7-fold in the EL + PMI and EH + PMI groups, respectively, compared with that in the PMI group (Figure 5D). In addition, pulmonary Ankrd37 mRNA expression in the PMI group increased by 2.1-fold compared with the CON group; however, pulmonary Ankrd37 mRNA expression was significantly attenuated by 1.2- and 1.4-fold in the EL + PMI and EH + PMI groups, respectively, compared with the PMI group (Figure 5E). Similar trends were also observed in a previous experiment in which PM exposure elevated the mRNA expression of genes for an inflammatory and hypoxic response, which was significantly reduced by quercetin treatment [21]. To evaluate the behavioral effects of EA on PM-exposed mice, an OFT was implemented. In this study, mice in the PMI group exhibited hyperactivity compared with the CON group. The total moving distance, including both the outer and central parts of the plexiglass container, were increased in the PMI group compared with the CON group (Figure 6A–C). Interestingly, PM-treated mice spent a significantly increased amount of time in the central area, whereas quercetin treatment decreased [21]; however, in this experiment, all treatments did not significantly alter the time spent in the central part of the plexiglass container (Figure 6F). Although there were no statistical differences, PM exposure increased the time spent in the central part of the plexiglass container by approximately 30.4% (p = 0.19) compared with the CON group, while EL + PMI and EH + PMI decreased the central staying time by approximately 75.5% and 85.6%, respectively, compared with the PMI group. In conjunction with, in all groups, no significant changes were noted in the staying time on the border of the plexiglass container (Figure 6E). The total movement speed of the PMI group was significantly elevated with hyperactivity (mean and maximum speed) at the border of the plexiglass container (Figure 6G,H,J). For the PMI group, the maximum speed at the central area did not significantly increase (Figure 6K); however, the mean speed at the central area significantly increased (Figure 6I). In contrast, in the EL + PMI and EH + PMI groups, the hyperactivity observed in the PMI group was reduced. Subsequently, we proposed that amelioration of PM-induced hyperactivity with EA treatment may involve serum corticosterone, a gold standard to assess stress levels. To answer our extended research question, serum corticosterone levels were analyzed using an ELISA kit. However, there were no distinguishable differences in serum corticosterone levels among the experimental groups (Figure 6L), as in our previous study [21]. In this study, we investigated the potential protective effects of EA against PM-induced pulmonary pathology and locomotor hyperactivity in experimental rodents. PM exposure is an inevitable and chronic event; therefore, dietary intervention with supplementation of functional phenolic compounds may be an ideal means to prevent and/or attenuate PM-induced pulmonary pathology and behavioral alterations. To understand whether EA pretreatment effectively attenuated PM-induced pulmonary pathology and hyperactivity, we used our previous pilot experimental conditions [20,21]. Briefly, mice were supplemented with vehicle control or EA (20 or 100 mg/kg) for 7 days, and then PM was instilled with continuous dietary interventions for the following 7 days. Pulmonary PM loading was a physical and inevitable event because EA pretreatment failed to prevent pulmonary PM accumulation and recruitment of immune cells in the BALF. EA pretreatment partially prevented PM-induced pulmonary cytokine and hypoxic mRNA expression and hyperactivity. PM instillation significantly elevated PM loading in the lung and pulmonary inflammatory responses, similar to our previous findings [20,21]. Based on the histological evaluation, black materials from the PM were markedly accumulated in the alveolar lumen and interstitial tissue in all PMI groups, regardless of the EA pretreatment. In the BALF, PM instillation significantly induced infiltration of immune cells such as neutrophils and macrophages, as noted in previous publications [20,21]. Moreover, cytokine secretions in the BALF, such as those of IL-6 and TNFα, were remarkably elevated in all PMI groups. EA treatment did not significantly prevent IL-6 and TNFα induction in the BALF. Jeong et al. also reported that dietary intervention with quercetin did not prevent PM loading in the lung and cytokine secretion in the BALF [21]. Probably, the PM loading concentration in our experimental protocol was in excess, as evidenced by pulmonary PM loading; therefore, dietary intervention may not be sufficient to prevent pulmonary cytokine secretions in BALF. However, EA pretreatment significantly attenuated PM-induced pulmonary cytokine and hypoxic mRNA expression in our experiments. As expected, PM instillation significantly induced the mRNA expression of pulmonary cytokines (Il-1b, Tnfα, and Il-6), as increased cytokine secretion was observed in the BALF. Moreover, the expression of hypoxic response genes (e.g., Ankrd37 and Vegfα) was markedly elevated in the PMI group. The induction of inflammatory and hypoxic responses verified our previous results [21]. However, EA treatment significantly reduced PM-induced inflammatory and hypoxic changes in mRNA expression. Key regulatory proteins for inflammatory and hypoxic responses are NF-κB and HIF1α, respectively, which are closely intertwined at the molecular level [50]. The NFκB and HIF1α pathways share a common molecular denominator, the IKK complex; therefore, the induction of NFκB by phosphorylation may trigger hypoxic signal induction of HIF1α, and vice versa. Our previous [21] and current findings suggest that dietary intervention with phenolic compounds (e.g., quercetin and EA) may attenuate PM-induced pulmonary inflammatory and hypoxic mRNA expression. In future studies, the expression of NFκB and HIF1α pathways should be scrutinized to understand whether dietary intervention can prevent PM-induced pulmonary inflammation and/or hypoxic events. EA pretreatment significantly attenuated PM-induced locomotor hyperactivity in experimental mice. The PMI group had increased total, border, and center moving distances and mean speeds and increased maximum speed at the border compared with the CON group. Interestingly, EA pretreatment decreased the distinctive PM-induced hyperactivity by attenuating moving distances in total (EH + PMI), border (all EA treatments), and center (EH + PMI), mean speeds in total (EH + PMI), border (EH + PMI), and center (all EA treatments), and maximum speed in the border (EH + PMI). Previous findings using cohort studies have also demonstrated that PM exposure in early developmental periods triggers attention deficit hyperactivity disorder-like hyperactivity [14,51]. In addition, high-DEP exposure prenatally and 1 week after birth led to increased hyperactivity in experimental mice [18]. Moreover, maternal PM exposure significantly triggered hyperactivity in pups in a mouse model [19]. In this study, we demonstrated that PM instillation in relatively young adulthood (8~10 weeks) also increased locomotor activity in mice, consistent with our previous findings [20,21]. Interestingly, dietary intervention with phenolic components, such as EA and quercetin [21], successfully prevented PM-induced hyperactivity in experimental mice. An increased chance of inhalation of air pollutants is closely intertwined with an elevation in abnormal behaviors, such as depression, bipolar disorder, and schizophrenia [15]. Therefore, finding and applying functional dietary resources (e.g., EA and quercetin) as preventive measures against air pollutants may be a possible and sustainable strategy to maintain normal health. Our current findings have significant advantages and disadvantages when extrapolating to the clinical field. Our experimental conditions included limited dietary intervention, PM exposure time, and PM concentration. Exposure of humans to PM may be long-term; however, our experimental protocol was executed in a relatively short-term period (14 days of dietary intervention and 7 days of PM exposure) with relatively higher concentrations of PM. Dietary intervention with phenolic compounds (EA and quercetin [21]) did not significantly prevent inflammatory cytokine secretion in the BALF. It seems that our experimental conditions may not fully account for potential pathological events and dietary interventions in humans. In addition, we detected hydrogen peroxide to gauge the pulmonary oxidative stress level in the BALF because prolonged inflammation may induce oxidative stress. Under hypoxic conditions, oxidative stress is generally elevated by ROS induction of reactive oxygen species [52]. Therefore, we postulated that hydrogen peroxide would be increased by PM exposure because of the induction of hypoxic Ankrd37 and Vegfa mRNA expression in the lungs. However, hypoxic mRNA expression in the lung and hydrogen peroxide secretion in the BALF did not match because hydrogen peroxide concentrations in the BALF were similar among all experimental groups. In future studies, we need to optimize the experimental conditions to make robust conclusions regarding whether PM exposure triggers pulmonary hypoxic responses. In addition, hyperactivity was noted in the PMI group, but EA pretreatment significantly normalized hyperactivity in mice. Our previous study used an identical experimental setting; quercetin also prevented PM-induced hyperactivity [21]. Therefore, we hypothesized that the stress hormone corticosterone would be altered by PM exposure; however, serum corticosterone levels were unchanged among all treatments, regardless of dietary intervention or PM treatment. Therefore, in the future, we may try to find any behavior-related hormones that are controlled by PM exposure and dietary intervention. Although there are restrictions, there are numerous advantages to our experimental setting. In our current and previous experiments [21], in a relatively short period of time, we remarkably observed the preventive potency of EA and quercetin [21] against pulmonary inflammatory and hypoxic mRNA expression induced by PM exposure. Therefore, in the future, the application of optimized and lower PM concentrations to reflect current air pollution with longer experimental periods may result in the positive suppression of PM-induced infiltration of inflammatory cells and cytokine secretion in the BALF. Another promising finding was the behavioral alterations observed in our mouse model. Similar to other PM exposure models in the early life phases [18,19,53], we also found that PM exposure in early adulthood induced hyperactivity in mice. A relatively short period of dietary intervention with EA and quercetin [21] effectively normalized hyperlocomotive activity. Therefore, dietary intervention may be an acceptable approach for maintaining normal behavior amidst PM exposure. EA is a widely accepted dietary polyphenol with multiple beneficial effects, especially in reducing biological inflammatory reactions [41,54,55,56]. In our experiments, EA pretreatment prevented PM-induced pulmonary cytokine mRNA expression over a relatively short period (14 days). Other studies have demonstrated that EA has significant efficacy in attenuating pulmonary inflammation, oxidative stress, and fibrosis (Table 2). In an acute lung injury (ALI) mouse model triggered by hydrochloric acid, oral EA treatment reduced neutrophil recruitment in the BALF and the lungs [40]. In this model, EA decreased the proinflammatory cytokine IL-6 and increased the anti-inflammatory cytokine IL-10 in the BALF [40]. In addition, EA treatment exerted an anti-inflammatory effect in an LPS-induced ALI model [48]. EA treatment also attenuated elastase-induced immune cells and cytokine secretion in the BALF in an emphysema model [45]. In a murine asthma model, EA treatment also prevented pulmonary inflammation by suppressing pulmonary NFκB activation [47]. Furthermore, EA has anti-inflammatory, antioxidative [44,46], and antifibrosis effects [46] in experimental rodents. In this study, EA pretreatment significantly prevented PM-induced pulmonary inflammatory and hypoxic mRNA expression, along with the normalization of hyperlocomotive activity. However, inflammatory cytokine and hydrogen peroxide secretion in the BALF did not alter with either PM exposure or EA pretreatment. Our study is a novel endeavor in at least two aspects: (1) investigating the pulmonary pathophysiology of PM instillation and (2) investigating whether dietary intervention with EA could thwart PM-induced pathology. To date, dietary preventive means in PM-exposed animal experiments have just begun [21]; therefore, there is limited information on which experimental settings are suitable for potential clinical application. Our experimental period may have been relatively short, considering PM exposure in humans has a longer incidence. We also used a relatively higher PM concentration compared with those that humans are practically exposed to. Therefore, in future studies, we may optimize our experimental protocols by increasing the PM exposure duration and using lower PM concentrations. Although our experimental setting has some limitations, prevention of pulmonary inflammatory and hypoxic mRNA expression by EA pretreatment may also prevent PM-induced protein expression and function. Another obvious finding was that dietary intervention with EA pretreatment normalized PM-induced hyperactivity. This study investigated the effectiveness of EA, a natural polyphenolic compound, in preventing the adverse effects of PM exposure in C57BL/6 mice. Four groups of mice were assigned (CON, PMI, EL + PMI, and EH + PMI); EA was orally administered for 14 days in C57BL/6 mice, and after the eighth day, PM (5 mg/kg) was intratracheally instilled for 7 consecutive days. The experimental results demonstrated that EA pretreatment with EA prevented PM-inducible pulmonary inflammatory and hypoxic mRNA induction, as well as hyperactivity in the experimental mice. This study suggests that EA may be a promising approach for mitigating the pathophysiological impacts of PM exposure.

PMC10001496

Linhui Ji,Yu Xin,Dufa Guo

Soil Fungal Community Structure and Its Effect on CO2 Emissions in the Yellow River Delta

26-02-2023

CO2 emissions,fungal community,structure characteristics,adaptation mechanisms,different salinity gradients

Soil salinization is one of the most compelling environmental problems on a global scale. Fungi play a crucial role in promoting plant growth, enhancing salt tolerance, and inducing disease resistance. Moreover, microorganisms decompose organic matter to release carbon dioxide, and soil fungi also use plant carbon as a nutrient and participate in the soil carbon cycle. Therefore, we used high-throughput sequencing technology to explore the characteristics of the structures of soil fungal communities under different salinity gradients and whether the fungal communities influence CO2 emissions in the Yellow River Delta; we then combined this with molecular ecological networks to reveal the mechanisms by which fungi adapt to salt stress. In the Yellow River Delta, a total of 192 fungal genera belonging to eight phyla were identified, with Ascomycota dominating the fungal community. Soil salinity was the dominant factor affecting the number of OTUs, Chao1 index, and ACE index of the fungal communities, with correlation coefficients of −0.66, 0.61, and −0.60, respectively (p < 0.05). Moreover, the fungal richness indices (Chao1 and ACE) and OTUs increased with the increase in soil salinity. Chaetomium, Fusarium, Mortierella, Alternaria, and Malassezia were the dominant fungal groups, leading to the differences in the structures of fungal communities under different salinity gradients. Electrical conductivity, temperature, available phosphorus, available nitrogen, total nitrogen, and clay had a significant impact on the fungal community structure (p < 0.05). Electrical conductivity had the greatest influence and was the dominant factor that led to the difference in the distribution patterns of fungal communities under different salinity gradients (p < 0.05). The node quantity, edge quantity, and modularity coefficients of the networks increased with the salinity gradient. The Ascomycota occupied an important position in the saline soil environment and played a key role in maintaining the stability of the fungal community. Soil salinity decreases soil fungal diversity (estimate: −0.58, p < 0.05), and soil environmental factors also affect CO2 emissions by influencing fungal communities. These results highlight soil salinity as a key environmental factor influencing fungal communities. Furthermore, the significant role of fungi in influencing CO2 cycling in the Yellow River Delta, especially in the environmental context of salinization, should be further investigated in the future.

Soil Fungal Community Structure and Its Effect on CO2 Emissions in the Yellow River Delta Soil salinization is one of the most compelling environmental problems on a global scale. Fungi play a crucial role in promoting plant growth, enhancing salt tolerance, and inducing disease resistance. Moreover, microorganisms decompose organic matter to release carbon dioxide, and soil fungi also use plant carbon as a nutrient and participate in the soil carbon cycle. Therefore, we used high-throughput sequencing technology to explore the characteristics of the structures of soil fungal communities under different salinity gradients and whether the fungal communities influence CO2 emissions in the Yellow River Delta; we then combined this with molecular ecological networks to reveal the mechanisms by which fungi adapt to salt stress. In the Yellow River Delta, a total of 192 fungal genera belonging to eight phyla were identified, with Ascomycota dominating the fungal community. Soil salinity was the dominant factor affecting the number of OTUs, Chao1 index, and ACE index of the fungal communities, with correlation coefficients of −0.66, 0.61, and −0.60, respectively (p < 0.05). Moreover, the fungal richness indices (Chao1 and ACE) and OTUs increased with the increase in soil salinity. Chaetomium, Fusarium, Mortierella, Alternaria, and Malassezia were the dominant fungal groups, leading to the differences in the structures of fungal communities under different salinity gradients. Electrical conductivity, temperature, available phosphorus, available nitrogen, total nitrogen, and clay had a significant impact on the fungal community structure (p < 0.05). Electrical conductivity had the greatest influence and was the dominant factor that led to the difference in the distribution patterns of fungal communities under different salinity gradients (p < 0.05). The node quantity, edge quantity, and modularity coefficients of the networks increased with the salinity gradient. The Ascomycota occupied an important position in the saline soil environment and played a key role in maintaining the stability of the fungal community. Soil salinity decreases soil fungal diversity (estimate: −0.58, p < 0.05), and soil environmental factors also affect CO2 emissions by influencing fungal communities. These results highlight soil salinity as a key environmental factor influencing fungal communities. Furthermore, the significant role of fungi in influencing CO2 cycling in the Yellow River Delta, especially in the environmental context of salinization, should be further investigated in the future. Saline soil accounts for more than 7% of the earth’s land surface, and approximately 70% of saline soil is used for agricultural production [1]. However, the amount of saline soil increases by 10% every year [2], which is one of the most compelling environmental problems on a global scale. High salinity in soil inhibits plant growth, changes the soil’s physical and chemical properties, and even causes the degradation of soil quality [3]. Fungi are the main members of soil microorganisms and are widely distributed in terrestrial ecosystems. Fungi play a key role in organic matter decomposition [4] and nutrient cycling [5] in the soil ecosystem, and the fungal community structure is often used as an important parameter for measuring the change in soil quality [6]. Understanding the response of the soil fungal community to the change in salinity is essential for rehabilitating salinized soil. Previous studies have reported the effects of salinity on soil microbial biomass [7], microbial activity [8], diversity [9,10], community composition [11,12], soil enzyme activity [13,14], and soil physical and chemical properties [15]. However, studies regarding the structural fluctuation and interactions with the soil fungal community among the natural salinity gradient during the process of regional evolution remain scarce. Moreover, soil fungi are active participants in the soil carbon and nutrient cycles; organic matter is decomposed, altered, and modified by soil fungi. The products of these processes are typically greenhouse gases such as CO2 that are released into the atmosphere [16]. However, plants can form symbiotic relationships with mycorrhizal fungi that firmly anchor carbon in the soil, and scientists have learned that a special type of mycorrhizal fungus, i.e., ectomycorrhizal fungi, is helping plants to take up carbon dioxide more quickly [17]. Therefore, the relationship between soil fungal communities and CO2 emission fluxes needs to be dissected to provide a fungal perspective for the future development of efficient microbial management strategies to mitigate greenhouse gas emissions [18]. In recent years, many scholars have been studying the changing characteristics of microbial salt stress in different regions. For example, Rath et al. assessed the microbial communities along two naturally occurring salt gradients located around Lake O’Connor in Western Australia and found that estimates of fungi were less affected by salinity than bacteria [19]. Zheng et al. evaluated soil prokaryotic microbial communities in Bohai Bay located in China and found that salinity altered the composition and structure of the prokaryotic microbial communities and enhanced their interactions [20]. This shows that the effect of salinity on microbial composition and structure is still a research hotspot at present; however, most studies have only focused on this, and the correlation between microorganisms and CO2 emissions in salinized soils has been inadequately studied, especially for soil fungi. The Yellow River Delta (YRD), a land regressive and transgressive area, has formed soil with different salinization degrees due to different distances from the sea, land forming times, and soil desalination degrees. The soil salinity shows a tendency to decrease from sea to land and increase outward from both sides of the river [21]. It is a natural laboratory for studying the relationship between the saline soil microbial community and the salinity gradient [22]. In recent years, studies on soil microorganisms in the YRD have mainly concerned the structure and diversity of the bacterial community in saline or oil-contaminated soils [23], the effects of environmental factors on the bacterial community [24], and the relationship between the bacterial community and halophytic vegetation succession [25]; however, research on the changes in the structure of soil fungal communities under different salinity gradients in this area is almost non-existent. Microorganisms in the natural environment do not exist as independent individuals [26]; interactions among microbial species have a strong influence on their community stability [27], and the importance of network interactions for ecosystem processes and functions may exceed species diversity. In this work, we used high-throughput sequencing technology to explore the characteristics of the structure of soil fungal communities under salinity gradients in the Yellow River Delta and then combined this with the Partial Least Squares–Path Model to reveal whether the fungal communities influence CO2 emissions. We sought to provide a theoretical microbial perspective for future restoration efforts in wetland environments. The purposes of this study were (1) to investigate the effects of salinity on the fungal community structure and fungal community interactions and (2) to investigate whether fungal community diversity significantly affects CO2 emissions. The YRD, located on the south bank of Bohai Bay and the west bank of Laizhou Bay (118°44”14.1” E–118°55”10.3” E, 37°26”16.7” N–37°32”41.4”), has a warm temperate, semi-humid continental monsoon climate with four distinct seasons. The annual average temperature is 12.9 °C, annual average rainfall is 596 mm, and annual evaporation is 1900–2400 mm. The main soil types in the YRD are fluvo-aquic soil and saline soil, and the soil parent material is Yellow River alluvium. The vegetation types are few, and the structure is simple. There are halophytes, such as Suaeda salsa (L.) Pall., Aeluropus sinensis (D.) Tzvel., Tamarix Chinensis Lour., Imperata cylindrica (L.) Beauv., and Artemisiacapillaris Thunb, with different salt tolerance levels. The changes in soil salinity and soil type in the Yangtze River Delta were fully considered through several surveys. In October 2018, soil samples were collected in the Yellow River Delta at a distance of no less than 1 km between every two sampling points, as shown in Figure 1. The 30 soil samples were divided into three levels according to the salinity gradient (10 sampling points per level): high-salinity, medium-salinity, and low-salinity. Considering the distance of each sampling point in the same level, each level was divided into two groups: high-salinity (H1 and H2), medium-salinity (M1 and M2), and low-salinity (L1 and L2). In other words, a total of 30 soil samples were collected, with sampling points within each group (5 sampling points) that were relatively close to each other; the sampling points between groups were relatively far apart. The surface vegetation and cover were removed, and 0–20 cm of the soil was collected using a soil auger according to the diagonal five-point sampling method. After removing the gravel and roots, we placed the soil into two sealed bags (approximately 200 g each). One part was placed in an icebox, brought back to the laboratory within 24 h, and frozen at −80 °C until it was used for molecular biology research; the other part of the fresh sample was processed according to experimental requirements to determine the soil physicochemical properties and to perform laboratory incubation experiments. Soil electrical conductivity (EC) was measured using an electrical conductivity meter (DDS-307A, China) with a soil–water suspension (soil/water ratio = 1:5). Because EC has always been regarded as a representative index of soil total soluble salt, the EC was used to characterize the soil salinity [28]. The sample soils were classified into three categories according to their salinity status: high-salinity soils (EC1:5 > 3 dS·m−1), medium-salinity soils (1.5 dS·m−1 < EC1:5 < 3 dS·m−1), and low-salinity soils (EC1:5 < 1.5 dS·m−1). Soil texture was measured using a laser particle size analyzer (Mastersizer 3000, Britain). The soil total nitrogen (TN) and organic matter (SOM) were determined using a macro element analyzer (Vario MACRO Cube, Germany). The alkali solution diffusion method was used to determine the soil available nitrogen (AN). The Olsen method was used to determine the soil available phosphorus (AP). Soil moisture content (MC) was determined by a drying-weighing method at 105 °C. The rate of soil CO2 emissions was evaluated using a laboratory incubation method. Fresh soil equivalent to 60 g of dry soil was weighed into a 250 mL culture bottle and pre-incubated at 25 °C for 7 days to activate the soil microorganisms. Taking into account the frequent inundation of the coastal areas of the Yellow River Delta by seawater and the evaporation of soil moisture in the incubator, the soil water–soil ratio was adjusted to 1:1 with sterile water during the formal incubation process to approach the original water content; this was followed by an unsealed incubation at 25 °C in the dark for a total of 14 days. During the culture period, the water in the bottle was supplemented by a weighing method, and the CO2 concentration was measured on day 1, 2, 3, 4, 7, 10, 13, and 14. The bottle mouth was sealed with a flip stopper when collecting gas; the gas was pushed into the bottle three times with a 10 mL syringe to ensure that the gas was evenly mixed, a 5 mL gas sample was drawn, and its concentration was measured with an Agilent gas chromatograph and then extracted with a syringe. We added 5 mL of air to the culture bottle, kept the pressure in the bottle consistent, and after 40 min, measured the concentration of the gas sample again; we then used the concentration difference between the two measurements to calculate the CO2 emission rate on that day. The CO2 emission rate for each site was calculated by averaging the CO2 emission rates for 8 days. The CO2 emission rate was calculated as where F’s unit is in μg kg−1-d, ΔC is the difference between the two CO2 concentrations, V is the volume of the incubation flask, T is the incubation temperature, t is the incubation time, and M is the sample weight [29]. The total DNA was extracted from 0.5 g of soil per sample using a Fast DNA Kit (MP Biomedicals, Santa Ana, CA, USA). The detailed steps were carried out according to the instructions of the kit. The primers 528F (GCGGTAATTCCAGCTCCAA) and 706R (AATCCRAGAATTTCACCTCT) were used to amplify the V4 region of the 18S rRNA genes. The PCR reaction system included 10 ng of Genomic DNA, 5.0 μL of 10 × PCR Buffer, 1 μL of 50 μM each primer, 0.5 μL of 10 μM dNTPs, and 0.5 μL of 5 U/μL Plantium Taq DNA; this was replenished to 50 μL with sterilized, double-distilled water. The PCR reaction conditions were as follows: pre-denaturation for 30 s (94 °C), 30 cycles of denaturation for 20 s (94 °C), annealing for 20 s (60 °C), and extension for 20 s (72 °C), followed by a final extension for 5 min (72 °C) and preservation at 10 °C. The PCR products were purified and recovered by a Gene JET Kit (Thermo Scientific, Waltham, MA, USA) and were subjected to high-throughput sequencing on a Thermofisher Ion S5TMXL titanium platform. Cutadapt software (V1.9.1) was used to remove the low-quality reads and to trim the barcode and primer sequences [30]. The operational taxonomy units (OTUs) were clustered at a 97% identity threshold using the UPARSE algorithm (v7.0.1001) (Edgar, 2013). The UCHIME algorithm (v4.2.40) was used to detect and remove the chimerisms [31]. The RDP classifier (v2.0) was used to compare the OTU sequences with the SILVA132 database, with 80% similarity, to classify the sequences at the phylum, class, order, family, genus, and species levels [32]. In order to compare soil physicochemical properties and fungal community alpha diversity between different saline gradient classes, we performed a one-way analysis of variance (ANOVA) using SPSS (v20.0) software [21]. In order to investigate Pearson’s correlation between the soil salinity and the alpha diversity of fungal communities, we used SPSS software. In order to determine the influence of soil physical and chemical factors on the fungal community structure, we performed a redundancy analysis (RDA) using CANOCO 5.0 software. In order to compare the differences between the salinity gradient classes in the structure of soil fungal communities, we performed a UPGMA clustering analysis using R software [33]. In order to analyze the contribution of the major fungal genera to the community differences, we performed a SIMPER analysis and ANOSIM test using PAST (V1.0) software [34]. Molecular ecological networks of soil fungi with high, medium, and low salinity gradients were constructed using online tools available on the MEAN website (http://ieg2.ou.Edu/mena/ (accessed on 13 January 2022)) [35]. Gephi software (V0.9.2) was used to visualize the networks [36]. Models were constructed using the “plspm” package in the R language, and goodness-of-fit statistics were used as a predictive power for the path models [37]. The physical and chemical property data of soil samples are represented as the means ± SE, as shown in Table 1. According to the texture classification standard established by the United States Department of Agriculture (USDA), the soil in these six sites belonged to silt loam. The soil salinity ranged from 0.28 to 4.65 dS·m−1, and according to the level of soil salinity at the sampling points, the soils were classified with a high- (H1 or H2), medium- (M1 or M2), or low-salinity (L1 or L2) gradient. TN, SOM, and AN were the highest at the M2 site and the lowest at the H1 site, while AP was the highest at the M2 site and the lowest at the L2 site. However, there were no significant differences in the soil physicochemical properties between the two groups of the same salinity class, suggesting that distance has little effect on the soil environment within the YRD. These results show that there were significant differences in soil physical and chemical factors along the salinity gradient, forming a certain ecological gradient. The highest soil SOM content was found in the medium-salinity soil, the second highest was found in the low-salinity soil, and the lowest was found in the high-salinity soil. The fungal community’s coverage in the six sites was greater than 96%, indicating that the sequencing depth can reasonably represent the situation of the samples (Table 2). The number of fungal OTU increased as the salinity gradient decreased. The Shannon index of the fungal community at the H2 site was the largest, so the fungal diversity in the H2 site was the highest, followed by the H1, L1, L2, M2, and M1 sites. Abundances of fungal communities under different salinity gradients are ordered from highest to lowest as follows: L2, L1, M2, M1, H2, and H1. Taken together, the number of OTUs and abundance of the soil fungal communities increased as the soil salinity gradient decreased, and there was no significant difference in the distance on the alpha diversity in the same salinity class. In addition, a Pearson’s correlation test between soil salinity and fungal α-diversity showed that the number of OTUs, Chao1 index, and ACE index had the highest correlation coefficient with soil salinity, i.e., −0.66, 0.61, and −0.60, respectively; this indicates that soil salinity was the dominant factor affecting the number of OTUs, Chao1 index, and ACE index of the fungal communities, whereas the soil salinity had an insignificant effect on the Shannon index of fungal communities. A total of 192 fungal genera belonging to eight phyla were identified by high-throughput sequencing in the YRD. The soil fungal communities mainly included Ascomycota, Basidiomycota, Mucoromycota, and Chytridiomycota at the phylum level (Figure 2a). Ascomycota was widely distributed in various sites, and their relative abundance was 54.81–74.65%, which was the dominant fungal phylum in the YRD. The Mucoromycota relative abundance was 4.90–25.74%, which was the subdominant fungal phylum in the YRD. The relative abundance of the top 30 fungal genera is shown in Figure 2b. Chaetomium was the dominant fungal genus at L1 and L2, with a relative abundance of 19.95% and 25.56%, respectively. Alternaria (13.68%), Cephaliophora (16.97%), Fusarium (6.56%), and Alternaria (9.46%) were dominant at H1, H2, M1, and M2, respectively. Taken together, the fungal community composition was similar under the same salinity class and varied more under different salinity classes, indicating that soil salinity has a greater effect on the fungal community structure than distance in the YRD. The analyses of the ANOSIM test and UPGMA clustering based on the Bray–Curtis distance value showed that the soil fungal communities had significantly different distribution patterns under different salinity gradients (r = 0.504, p < 0.01, Figure 2). The UPGMA clustering analysis showed that the fungal communities under the same salinity gradient were grouped into one cluster, while the fungal communities under different salinity gradients were scattered. This indicates that soil salinity has a greater influence on soil fungal communities than geographic distance, and fungal community structure was quite different under different salinity gradients in the YRD. A SIMPER analysis was used to further find the different species that lead to different distribution patterns of fungal communities. Table 3 listed the contribution rates of the major fungal genera to spatial dissimilarity. The difference in the fungal community structure between the H and M groups mainly came from Fusarium, Chaetomium, and Alternaria, and the total contribution rate was 31.39%; Chaetomium, Mortierella, and Malassezia greatly contributed to the difference in the fungal community structure between the H and L groups, with a total contribution rate of 63.85%; the difference in the fungal community structure between the M and L groups was mainly due to Chaetomium, Mortierella, and Fusarium. For further insight into the relationship between fungal communities and soil environmental factors under different salinity gradients, a redundancy analysis (RDA) was conducted. As shown in Figure 3, RDA axes 1 and 2 explained 86.74% of the total variation. The correlation between fungal genera and environmental factors was expressed by the cosine of the angle between them, and the longer the arrow of environmental factors, the greater the influence on the fungal community structure. The results of the RDA forward selection showed that EC, T, AP, AN, TN, and clay caused significant differences in the fungal community distribution patterns under different salinity gradients (F = 6.6, p < 0.01), and Monte Carlo permutations tests indicated that all six factors were significantly correlated with the soil fungal community structure (p < 0.05). The long arrows of EC, AN, and AP factors show that they had a great influence on the fungal community distribution, and the explanation rate of EC was the highest, which was the dominant factor affecting the fungal community distribution patterns. TN, AN, and clay were positively correlated with most fungal genera, while EC, T, and AP were negatively correlated with most fungal genera; this indicates that TN, AN, and clay played a promotive role in the growth of most fungi, while EC, T, and AP played an inhibitory role in the growth of most fungi. To investigate the interactions between the soil fungal OTUs under different salinity gradients and to determine the key species in the soil fungal communities, fungal OTUs that appeared in more than 50% of the samples were selected to construct molecular ecological networks (Table 4; Figure 4). Among them, each node signified a fungal OTU, the node size represented a node degree, different colors of the nodes represented different modules, and lines between nodes were colored based on the modules. In addition, 100 random networks with the same number of nodes and connections as the original networks were constructed by the random method, and their topological characteristics were calculated; in contrast, the cohesion of empirical networks was generally higher than that of random networks, which indicated that the interactions between fungal OTUs in the molecular ecological networks were significant. The decrease in salinity changed the network structure of fungal communities and increased the complexity of the networks. According to Table 4 and Figure 5, the node quantity, edge quantity, and modularity coefficients of the networks increased as the salinity gradient decreased. In high-salinity soil, there were 12 network modules with 2–14 nodes, and the modularity coefficient was 0.63 (Table 4, Figure 5a); in medium-salinity soil, there were 10 network modules with 2–28 nodes, and the modularity coefficient was 0.73 (Table 4, Figure 5b); in low-salinity soil, there were 19 network modules with 2–25 nodes, and the modularity coefficient was 0.76 (Table 4, Figure 5c). The average degree (Avg K) and average clustering coefficient (Avg CC) values of the medium-salinity network were the highest, while the average path distance (GD) was the shortest. Therefore, in medium-salinity soil, the information, energy, and material among fungal OTUs had the highest transmission efficiency and closest connection. At the same time, the response speed of microorganisms was the fastest, and the microbial community was more prone to change in medium-salinity soil. In this study, OTU_107 (Ascomycota; Prussia), OTU_52 (Ascomycota; Talaromyces), and OTU_515,778 (Ascomycota; Aspergillus, Ascomycota; Scopulariopsis) from Ascomycota had the highest number of connections, which were the core nodes of the molecular ecological network of high-, medium-, and low-salinity soil fungi, respectively. The relative abundance of Ascomycota was the highest in the soil fungal community in the YRD, indicating that Ascomycota occupied an important position in the saline soil environment and played a key role in maintaining the stability of the fungal community. To investigate the effect of fungi on CO2 emissions, a partial least squares regression analysis of the CO2 emission rate of soils in the study area was performed with the soil fungal and soil environmental factors (Figure 6a). It was found that the CO2 emission significantly increased with decreasing soil salinity, and the effect of geographical distance on soil CO2 emission rate was not significant. The PLS-PM goodness-of-fit was 0.6223, which proved that the PLS-PM was reasonably constructed and statistically significant (Figure 6b). The results showed that the fungal diversity had an effect on the CO2 flux (estimate: 1.08, p < 0.05). Salinity was significantly negatively correlated with the amount of fungal diversity (estimate: −0.58, p < 0.05), while AP, AN, TN, and SOM were significantly positively correlated with the fungal diversity (estimate: 0.41, p < 0.05; estimate: 1.55, p < 0.05; estimate: 0.58, p < 0.05; estimate: 0.40, p < 0.05). This confirms that soil environmental factors indirectly affect CO2 emissions by influencing fungal communities, and fungal communities significantly affect CO2 emissions. Ascomycota was the predominant phylum with the highest abundance in the present study, which was similar to the results obtained by Wang et al. (2020) using molecular biology methods to study fungal samples in a saline environment [27]. The vast majority of Ascomycota fungi are saprophytes, which can decompose refractory organic substances such as lignin and keratin and play an important role in nutrient cycling [38]. Mucoromycota had the highest abundance in low-salinity soil, but Cryptomycota had the highest abundance in high-salinity soil. This suggests that Cryptomycota is more salt-tolerant than Mucoromycota. With the decrease in salinity in the soil, plant growth and species [39], plant rhizosphere exudates [40], and other factors could cause changes in the microbial composition and structure, which may be the reason for the decreased distribution of Chaetomium in high-salinity soil. Salinity had a significant effect on microbial diversity [9]. The results of the diversity analysis of fungal communities showed that the number of OTUs and abundance of the soil fungal communities increased as the soil salinity gradient decreased. Yang et al. highlighted in their study of soil fungi in the YRD that significantly lower values of the Chao1 richness index were observed in extreme salinity soil [21], which was also confirmed in the present study. The Pearson’s correlation test showed that the number of OTUs, Chao1 index, and ACE index were significantly negatively correlated with soil salinity, which could be attributed to the increase in the extracellular osmolarity of fungi caused by the accumulation of salt in the soil and that the fungi that were not adapted to osmotic stress were inhibited or even died [41], thus reducing the number of fungal OTUs, Chao1 index, and ACE index. Consistent with the findings of Yang et al., salinity altered the fungal community structure [21]. The UPGMA cluster analysis showed that there were obvious similarities in the structures of fungal communities with the same salinity gradient, but there were great differences in the structures of fungal communities under different salinity gradients, which could be due to the similar soil environments and similar effects on fungal communities under the same salinity gradient, but also due to different soil environments under different salinity gradients, so that the effects on fungal communities were different. The RDA analysis indicated that EC, T, AP, AN, TN, and clay had a significant effect on the fungal community structure. EC had the greatest influence, which was the main factor leading to the difference in the distribution patterns of fungal communities under different salinity gradients. Chowdhury et al. (2011) found that soil salinity affected the composition of soil microbial communities through osmotic potential, and fungi were more sensitive to salinity than bacteria [42]. Rajaniemi and Allison (2009) demonstrated that the effect of soil salinity on the composition of soil microbial communities is greater than that of soil C and N, which is consistent with the conclusion of this paper [43]. The SIMPER analysis showed that Chaetomium, Mortierella, and Fusarium were the fungal groups with the highest contribution to the difference in community structure, with an average relative abundance of 5.32%, 2.31%, and 0.89%, respectively. Among these, Mortierella, which was significantly correlated with EC, T, and AN (p < 0.05), could degrade the toxic organic compounds in soil, prevent soil degradation, and improve soil health [44]. Fusarium was significantly correlated with EC, TN, AN, and clay (p < 0.01). Chaetomium, part of the Ascomycota phylum, was significantly correlated with EC, T, AN, TN, and AP (p < 0.05); it could produce a large number of cellulolytic enzymes and plays an important role in the carbon cycle of the natural ecosystem and soil improvement. In the natural environment, microorganisms often form complex network structures through various interactions rather than as independent individuals [45]. The positive correlations between microorganisms may mean that there are positive ecological interactions, such as commensalism or mutualism [46]. The negative correlations may be attributed to competition or amensalism [47]. Zheng et al. found that all six networks had positive association percentages above 98% in their study of soil microbial responses to salt stress in Bohai Bay, China, which is similar to the results of the present study [20]. In the three fungal networks with different salinity in this study, the percentages of the positive connections among fungal OTUs were all above 90%, which could be because the high-salinity habitat forced the fungi to strengthen their cooperation in response to salt stress, or because the fungi in the high-salinity environment had mutualism in the long-term co-evolution process. The module is a closely connected area in the network, which is usually interpreted as a niche [48]. The low-salinity network had the largest number of modules (19) and the highest modularity (0.76); therefore, the niche differentiation degree of microorganisms in the low-salinity soil was the highest, and the community structure was the most complex. The nodes with the highest number of connections in the network were identified as the core nodes [49]. The absence of the core nodes may cause module and network decomposition [50], so they play an important role in maintaining the stability of the microbial community. Core nodes were usually interpreted as key species [51]. OTU_107, OTU_52, OTU_515, and OTU_778 from Ascomycota had the most connections, which were identified as the core nodes of the fungal molecular ecological networks. Ascomycota also had the highest relative abundance in the fungal community in the YRD, which indicated that Ascomycota occupied an important position in the saline soil environment and played a key role in maintaining the stability of the fungal community. Soil properties influence C cycling by altering wetland microbial diversity, and this is an important but previously underestimated indirect pathway [52]. Soil CO2 emission is an important indicator that responds to the participation of soil microorganisms in the carbon cycle process and converts organic matter [53]. Soil fungi not only release CO2 during the metabolic decomposition of organic matter but also participate in the carbon sequestration processes to reduce CO2 emissions [54]. It was found that increased soil salinity indirectly reduces CO2 emissions by reducing soil fungal diversity. This is mainly because increased salinity has a strong negative effect on fungal community activity. For example, elevated salinity in the soil increases the extracellular osmotic pressure rate of fungi, which inhibits or even kills fungal activity and ultimately leads to a decrease in fungal diversity [28]. Increases in soil organic matter and TN increase soil CO2 emissions because soil with higher organic matter and TN content tend to have higher soil C and N content, resulting in strong soil respiration and high CO2 emissions [55]. Moreover, the decomposition process of organic matter by saprophytic fungi releases CO2 [56], and a study by Suvendu et al. found a significant positive correlation between the saprophytic fungus Mortierella and CO2 emissions [57]. In the present study, Mortierella was one of the genera that contributed most to the differences in soil fungal community structure at different salinities and was significantly correlated with EC. This suggests that salinity can influence CO2 emissions by affecting fungal communities. Therefore, it can be inferred that CO2 emissions from the Yellow River Delta are closely related to the existence of soil fungal communities, while soil environmental factors mainly affect soil CO2 emissions indirectly by influencing the fungal communities. Our study found that the soil fungal abundance increased as the soil salinity decreased. EC had the greatest, most significant impact on the fungal community structure, which was the dominant factor leading to the difference in the distribution patterns of the fungal communities under different salinity gradients. Chaetomium was the dominant fungal genus in the low-salinity soil, while Aspergillus was the dominant fungal genus in the high- and medium-salinity soil. The SIMPER analysis showed that Chaetomium, Fusarium, Mortierella, Alternaria, and Malassezia were the dominant fungal groups leading to the difference in the structures of fungal communities under different salinity gradients. In the molecular ecological networks, the decrease in salinity changed the reticulation of fungal communities and increased the complexity of the network. Moreover, fungal community diversity affects CO2 emissions, soil environmental factors also affect CO2 emissions by influencing fungal communities, and increased soil salinity decreases soil CO2 emissions.

PMC10001503

Orsolya-Zsuzsa Akácsos-Szász,Sándor Pál,Kinga-Ilona Nyulas,Enikő Nemes-Nagy,Ana-Maria Fárr,Lóránd Dénes,Mónika Szilveszter,Erika-Gyöngyi Bán,Mariana Cornelia Tilinca,Zsuzsánna Simon-Szabó

Pathways of Coagulopathy and Inflammatory Response in SARS-CoV-2 Infection among Type 2 Diabetic Patients

21-02-2023

COVID-19,thrombosis,coagulopathy,vasculopathy,inflammation,diabetes mellitus

Chronic inflammation and endothelium dysfunction are present in diabetic patients. COVID-19 has a high mortality rate in association with diabetes, partially due to the development of thromboembolic events in the context of coronavirus infection. The purpose of this review is to present the most important underlying pathomechanisms in the development of COVID-19-related coagulopathy in diabetic patients. The methodology consisted of data collection and synthesis from the recent scientific literature by accessing different databases (Cochrane, PubMed, Embase). The main results are the comprehensive and detailed presentation of the very complex interrelations between different factors and pathways involved in the development of arteriopathy and thrombosis in COVID-19-infected diabetic patients. Several genetic and metabolic factors influence the course of COVID-19 within the background of diabetes mellitus. Extensive knowledge of the underlying pathomechanisms of SARS-CoV-2-related vasculopathy and coagulopathy in diabetic subjects contributes to a better understanding of the manifestations in this highly vulnerable group of patients; thus, they can benefit from a modern, more efficient approach regarding diagnostic and therapeutic management.

Pathways of Coagulopathy and Inflammatory Response in SARS-CoV-2 Infection among Type 2 Diabetic Patients Chronic inflammation and endothelium dysfunction are present in diabetic patients. COVID-19 has a high mortality rate in association with diabetes, partially due to the development of thromboembolic events in the context of coronavirus infection. The purpose of this review is to present the most important underlying pathomechanisms in the development of COVID-19-related coagulopathy in diabetic patients. The methodology consisted of data collection and synthesis from the recent scientific literature by accessing different databases (Cochrane, PubMed, Embase). The main results are the comprehensive and detailed presentation of the very complex interrelations between different factors and pathways involved in the development of arteriopathy and thrombosis in COVID-19-infected diabetic patients. Several genetic and metabolic factors influence the course of COVID-19 within the background of diabetes mellitus. Extensive knowledge of the underlying pathomechanisms of SARS-CoV-2-related vasculopathy and coagulopathy in diabetic subjects contributes to a better understanding of the manifestations in this highly vulnerable group of patients; thus, they can benefit from a modern, more efficient approach regarding diagnostic and therapeutic management. Diabetes mellitus (DM) is a major and common public health issue in both developed and developing countries, due to its high mortality rate and induced disabilities. COVID-19 is caused by the SARS-CoV-2 virus, and since its appearance it became a public health problem due to its rapid spread, the severity of the symptoms, and increased mortality, causing a pandemic, with serious medical, social, and economic consequences globally [1,2,3]. Chronic inflammation, present in DM, enhances the synthesis of several cytokines. This chronic inflammatory state is preceded by a subclinical inflammatory response, represented by elevated IL-1β and IL-6 before the onset of T2DM [4]. Multiple studies reported during the pandemic that severe forms of COVID-19 are associated with elevated inflammatory markers, and comorbidities [5,6,7,8]. Endothelial dysfunction is also a consequence of DM and leads to micro- and macroangiopathy, and concomitantly to hypercoagulability [9]. Scientists had reported from the beginning of the pandemic the association of thrombosis and hypercoagulability with COVID-19, and the urgent need to understand the underlying mechanism for adequate management [10,11,12]. COVID-19-associated coagulopathy (CAC) is potentially lethal and can lead to disabilities [13,14,15,16]. To prevent severe complications and reduce the mortality of COVID-19 patients, targeted therapies for the associated pathologies are required. The authors have undertaken to write a review that integrates the mechanisms of COVID-19 coagulopathy in patients with type 2 diabetes mellitus (T2DM). To synthesize the paper, a comprehensive literature search on PubMed, Embase, and Cochrane Library was performed, using the following keywords: “SARS-CoV-2”, “T2DM”, “COVID-19 and diabetes mellitus”, “coagulopathy and T2DM”, “mechanism”, “inflammation” “cytokine storm”, “gene polymorphism”, “COVID-19 and coagulopathy”, “hypercoagulability and endothelial dysfunction”, “role of MASP-2 and COVID-19 hypercoagulability”, “complement activation and COVID-19”. Open-access, full-text English language articles published between the 1st of January 2020 and the 2nd of December 2022 were accessed. The first search included clinical trials, meta-analyses, and randomized controlled clinical trials, and returned 1126 results. In the second step, we narrowed our search area by screening the articles using titles and abstracts, reducing the number of articles to 200. After eliminating duplicates, full-text analysis and further reduction occurred, and finally, 101 manuscripts were selected and integrated into this review, without taking into consideration the scientific impact or citation numbers of each article. The authors aimed to assess the links between the altered molecular pathways of coagulation on the background of chronic low-grade inflammation of diabetes mellitus and the pathomechanisms of COVID-19-associated coagulopathy and extreme inflammatory response. The secondary goal was to identify possible mechanisms that may be responsible for the higher risk of severe COVID-19 progression in diabetic patients. The originality of the article is derived from the multiple interactions presented, which are involved in the pathomechanism of COVID-19-related vasculopathy and coagulopathy in type 2 diabetic patients. Novel research results are included, based on the latest articles in scientific literature, and the connections between different pathways are presented in the text and on a complex, original diagram. A limitation of the study is the lack of long-term experience in basic research related to COVID-19 mechanisms, taking into consideration the relatively recent occurrence of this special epidemiological situation of the coronavirus pandemic. Another limitation is that exclusively open-access articles were used and the authors used only articles written in English, so data that were published in other languages or not in open access were not included in this review. The most common form of diabetes is T2DM, a heterogeneous disorder, characterized by relative insulin deficiency, and insulin resistance in target tissues. Insulin resistance could be the key mechanism in the development of T2DM and other pathologies, such as hypertension, obesity, coronary artery disease, and metabolic syndrome [17]. The lack of insulin response is the result of the decrease of insulin receptors on the target cell’s surface. Some authors have reported that altered endothelial cell signaling and activation of redox regulated transcription factors are contributors as well [18,19]. Normally, insulin binding to its receptors activates two major signaling pathways: the phosphatidylinositol 3-kinase (PI3K)-dependent insulin signaling pathway and the mitogen-activated protein kinase (MAPK)-dependent insulin signaling pathway. PI3K is responsible for metabolic changes and is regulating glucose transporter type 4 (GLUT4) translocation in adipose cells, while MAPK regulates mitogenesis, growth, and differentiation [17]. Endothelial production of nitric oxide (NO) is regulated by a PI3K-dependent insulin signaling pathway, with a vasodilator effect, also enhancing glucose uptake of skeletal muscles [17]. It was also described that insulin stimulates endothelin-1 (ET-1) secretion via the MAPK signaling pathway, leading to vasoconstriction. In T2DM, the overproduction of advanced glycation end products (AGEs) and inflammatory cytokines contribute to the development of macroangiopathy, and its main form, atherosclerosis. It was also described that oxidative stress and excess production of reactive oxygen species (ROS) are the consequences of the activated major pathways involved in the development of diabetes- related complications: polyol pathway, protein kinase C (PKC) isoforms, excess formation of AGEs, increased expression of AGEs receptor and its activating ligands, and overactivity of the hexosamine pathway [20,21,22]. Hyperglycemia in T2DM is also responsible for endothelial dysfunction as the consequence of insulin resistance and excessive production of ROS [17]. Oxidative stress will lead to decreased antioxidant effect and excess synthesis of hydrogen peroxide anion, which directly deactivates NO, resulting in decreased NO activity [20]. ROS in excess can induce epigenetic changes. All these mechanisms can be the common links between the development of diabetes, chronic inflammatory response, and cardiovascular diseases (CVD). Cardiovascular complications are present in approximately 80% of T2DM patients [18]. Vascular complications of T2DM include macrovascular, microvascular, cerebrovascular lesions, and peripheral artery disease [18,23]. Macrovascular arteriopathy can affect the central and peripheral arteries, while microvascular diseases affect the small blood vessels in multiple organs, leading to chronic kidney disease (CKD), retinopathy, and the most common type, peripheral neuropathy [24]. The vascular endothelium secretes vasoactive substances to maintain vascular homeostasis by regulating vasoconstriction and vasodilation. Angiotensin II (AT-II), thromboxane A2, and ET-1 have vasoconstrictor effects, while prostaglandin I2 and NO are vasodilators under physiological conditions [20]. The homeostasis of vascular function, especially blood pressure and volume control, is under the regulation of the renin–angiotensin–aldosterone system (RAAS), but RAAS is also known to be involved in local tissue homeostasis, with anti-inflammatory, anticoagulant, antiproliferative, antifibrotic, and antiapoptotic effects on epithelial cells via the ACE2 activated Mas-receptor axis [25]. In T2DM, vascular homeostasis is disturbed by endothelial dysfunction, oxidative stress, platelet hyperreactivity, and inflammation [26], causing alteration in the physicochemical properties of the vascular wall, and enhance the development of atherosclerosis. All these events will aggravate thrombosis and hypercoagulability [27]. SARS-CoV-2 infects human cells using the ACE2 receptor and a specific transmembrane serine protease 2 (TMPRSS2), for the priming of the spike protein [28]. ACE2 is expressed in various tissues and organs, including endothelium, lung, heart, intestine, kidney, pancreas, and on the epithelial cells of oral mucosa and the tongue [29]. Reportedly, in T2DM patients the ACE2 receptors are upregulated. It has been hypothesized by many that overexpression of ACE2 receptors in T2DM potentially increases the susceptibility to COVID-19 [30,31]. Once SARS-CoV-2 binds to ACE2 receptors and blocks their activity, the RAAS will be affected. Consequently, accumulation of angiotensin 2 (AngII) will occur, which triggers intracellular signaling pathways (caspase 3, p83 MAPK, ROS, cytochrome C) [32], and leads to vasoconstriction, increased oxidative stress, inflammation, cellular damage, and fibrosis. The regulation of RAAS is influenced by the interaction between ACE2 and bradykinin (BK). Normally, BK acts as a negative regulator of RAAS by dilating blood vessels via local NO release. BK is known for its anti-inflammatory and antioxidant properties and has a role in regulating cytokine production and blood vessel permeability. It also has a stimulating effect on plasminogen secretion and thrombus formation. In COVID-19, the internalization of ACE2 will enhance the activation of different types of BK receptors, leading to increased inflammation and local vascular hyperpermeability. Indirectly, it may activate the coagulation cascade through the resulting endothelial damage [33]. The activation of p83 MAPK can contribute to inflammation and oxidative stress, and the activation of caspase 3 can lead to cellular death [33]. ROS formation will induce oxidative damage to cells and tissues and will release cytochrome C from mitochondria, which can trigger the activation of apoptotic signaling pathways and contribute to cell death as well [34]. Nuclear factor kappa B (NF-κB) is a key molecule involved in the nuclear translocation and activation of controlled genes. Overactivation of NF-κB will lead to the extensive synthesis of proinflammatory mediators, uncontrolled inflammatory response, and eventually to cytochrome storm, as observed in COVID-19 patients [35]. After the endothelial infection by the SARS-CoV-2, von Willebrand factor (vWF) is released into the circulation. The vWF is stored in the Weibel–Palade bodies of the endothelial cells. Platelet aggregation initiated by the increased release of vWF [14] will generate a deposition of platelet-rich clots in the lung microcirculation. This event is the key mechanism leading to respiratory failure [36]. Hypercoagulability will be sustained because of the associated release of factor VIII [14], but it is also the consequence of the virus replication within the endothelial cells. The infection causes endothelial cell death and consequently, the endothelial damage will launch the procoagulant reaction [37]. CAC is characterized by clot formation in the lungs, and elevated D-dimer levels at an early stage of COVID-19, but after the systemic activation of the coagulation and the development of disseminated microthrombosis, multiple organs will be affected [38]. Post-mortem autopsy of severe COVID-19 patients found diffuse alveolar damage, and inflammatory infiltrations with hyaline membrane formation in the lung, but also inflammation of the myocardium, focal pancreatitis, axon injury, glomerular microthrombosis, macrophage accumulation in the brain, and lymphocyte infiltrations of the liver [39]. COVID-19 infection determines endothelial activation by angiopoietin-2, a mediator stored also in the Weibel–Palade body, which is released as well, showing elevated circulating levels in COVID-19 and an association with the induction of procoagulant and proinflammatory reactions [40]. The development of inflammatory processes is a key pathological feature of SARS-CoV-2 infection. From the early beginning of the pandemic, several studies have suggested that massive inflammatory cytokines and chemokines are released in COVID-19 [41]. The innate immune system plays an important role, so proinflammatory cytokine production is a desired phase of the immune response against a pathogen. However, in some cases of COVID-19 infection, proinflammatory cytokine release and synthesis are rapidly overactivated, leading to multisystemic damage to the infected host. Interleukin (IL) 2 and 6 (IL-2, IL-6), tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), macrophage inflammatory protein (MIP), and monocyte chemoattractant protein 1 (MCP-1) are among many other cytokines that are present in seriously ill COVID-19 patients [42,43]. During inflammation, IL-6-induced tissue factor is released by macrophages [44]. IL-6 is a proinflammatory cytokine that can stimulate the release of other cytokines and activate immune cells, contributing to the overall systemic inflammation observed in severe COVID-19 cases [45]. AngII triggers NF-κB activation, leading to hyperinflammation, mostly through increased synthesis of IL-6 and IL-1b, and subsequently enhancing the transcription of proinflammatory cytokines. These interleukins presented extremely elevated levels in case of severe COVID-19 [46,47]. The exaggerated expression of IL-6 and IL-6 receptor in COVID-19 leads to endothelial cell hyperactivation and a large amount of tissue factor is released, both processes leading to infection-induced coagulopathy. This event plays an important role in thrombocytopenia, although the cytokine storm is the trigger of thrombocytosis. IL-6 is also participating in the production of some coagulation factors (fibrinogen, factor VIII). Acting on the endothelium, IL-6 enhances the synthesis of vascular endothelial growth factor (VEGF), leading to vascular hyperpermeability and hypotension [48]. Additionally, other cytokines, such as TNF-α, IFN-γ, and IL-1β, have also been implicated in the cytokine storm observed in COVID-19 patients and can contribute to increased inflammation and hypercoagulability. IL-6 and IL-1α have the crucial role of linking inflammation with the coagulation system. During the proinflammatory phase, these cytokines are present on activated platelets, monocytes, and endothelial cells. IL-1α has the role of activating the inflammatory cascade in thrombo-inflammatory conditions, but is also a key element of thrombogenesis, through its granulocyte recruitment effect, prolongation of clot-lysis time, and increasing thrombocyte activity [43]. Combined with TNF, IL-1 is the most important mediator of endogenous coagulation cascade suppression [44]. The exact mechanisms and interplay between cytokines in diabetes with SARS-CoV-2 infection remains an area of active research. The activation of the complement system following infection with SARS-CoV-2, as the main participant of innate immunity plays an important role in thrombotic events, combined with endothelium disturbances, thrombocytopenia, and bleeding, all representing risk factors of poor clinical outcome [34]. The literature describes three pathways of complement activation (host–antigen contact, antigen–antibody complex trigger, lectin pathway), all in the defense of the host, leading to the synthesis of C3 and derivatives and activation of plasma proteins [49]. The host–antigen contact will activate the first pathway, the activation of the second pathway is caused by antigen–antibody complexes, and the third one is activated by the lectin pathway, which will bind polysaccharides on antigen surfaces to host cells [50]. At this point, the virus will invade host cells that express the ACE2 receptor and damage them, causing a thrombotic–inflammatory response, which further activates the complement system [51]. The particularity of COVID-19 is related to the lectin pathway component, the mannose-associated serine protease 2 (MASP-2), with a key function of thrombin activation and fibrin mesh formation. Complement cascade participants dysregulate the endothelial cells, affecting the action of clotting cascade proteins [50]. In diabetic patients, the complement system, as an innate humoral defense, will become dysregulated, with the consequences of chronic low-grade inflammation and increased risk of infections [52]. Factor XII (FXIIa) activation has a trigger effect on complement complex C1. A further procoagulant effect of complement activation is the initiation of thrombocyte aggregation [53]. These pathomechanisms reveal a close relationship between complement and coagulation cascade, leading to the reciprocal up-regulation of both processes. In COVID-19 the complement (C3 and C5) is the mediator for developing inflammation [54]. The terminal C5b-C9 complement activation leads to a release of C3b and C5b fragments, with a proinflammatory role [50]. C3b is involved in the opsonization process, marking the SARS-CoV-2, to be destroyed by immune cells. It is important as well for recruiting macrophages and neutrophils, which can release cytokines, signaling molecules that coordinate the immune response. This will induce prostaglandin and leukotriene synthesis, boosting further proinflammatory cytokine production. In some cases, the release of cytokines can become excessive, leading to an overactive immune response [54]. Normally, these processes occur with the purpose of self-defense of the host. However, uncontrolled complement activation results in exaggerated inflammation and systemic procoagulation status with the installation of disseminated intravascular coagulopathy and cellular damage [50]. In the later stages of the complement cascade activation, C5b is involved in the formation of the membrane attack complex (MAC). MAC formation has a role in direct cell lysis. In COVID-19 patients, activation of C5b and formation of the MAC can contribute to inflammation and tissue damage in the lungs and can induce the development of severe symptoms and complications [55,56,57]. Neutrophil extracellular trap (NET) release is a mechanism of the innate immune response, as a result of the interaction with activated platelets. It occurs through the explosive intravascular destruction of neutrophils and the release of nucleic substances in the extracellular space, providing a source of extracellular histones with significant cytotoxicity [58]. With the ability to trigger inflammation and thrombosis, NETs release into the extracellular space oxidizes enzymes (NADPH oxidase, nitric oxide synthase) [44,59]. It has also been reported that NETs are among the main drivers of immune-thrombosis in severe COVID-19 cases [60]. Some authors hypothesize that SARS-CoV-2 can directly activate platelets through interaction with its surface spike protein [61], which triggers the release of platelet granules containing proinflammatory and procoagulant factors. Additionally, cytokine release as the result of the immune response to infected cells can also contribute to platelet activation. In COVID-19, platelets are activated and play a role in microvascular thrombosis, leading to serious complications such as acute respiratory distress syndrome (ARDS) and multi-organ failure (MOF) [62]. From the outbreak of the SARS-CoV-2 pandemic, Nicolai et al., among others, hypothesized that activated platelets might induce severe forms of NETosis in some COVID-19 patients with severe symptoms, leading to immune-thrombosis and higher mortality. The team superfused neutrophils isolated from healthy patients with platelet-rich plasma from severe COVID-19 patients and healthy subjects, revealing that the thrombocytes of severe COVID-19 patients adhered at a significantly increased rate to neutrophils compared to the controls [63]. The release of prothrombotic FXII, vWF, TF, and fibrinogen by NETosis will also lead to a procoagulant microenvironment. Circulating histones will also activate further platelets through their Toll-like receptors, resulting in clot formation [34]. NETosis is considered by a few authors to be a prothrombotic risk factor in COVID-19. Thus, a possible therapeutic option for thrombosis risk mitigation is proposedly NET inhibition using neutrophil elastase inhibitors and adenosine receptor agonists [64]. The hypercoagulability present in T2DM will be enhanced by SARS-CoV-2′s binding to the ACE2 receptor and the receptor’s internalization will alter ACE2 functionality. Normally the enzyme binds to AngII, transforming it into angiotensin 1-7 (Ang1-7) peptide with anti-inflammatory effects. Ang1-7 binds and activates a MAS-related transmembrane G-protein coupled receptor (MRGPCR) [34], this reaction will assure anti-inflammatory, antioxidant, and antithrombotic effects. The downregulation of ACE2 receptors will alter RAAS leading to the above-mentioned hypercoagulability, but also hyper-inflammation, hypertension, hypertrophy, and apoptosis [33]. Moreover, platelet dysfunction can also lead to hypercoagulable states [49]. Platelet activation occurs through the initiation of angiotensin II type 1 receptor (AT1R) and its release of plasminogen activator inhibitor 1 (PAI-1). Platelets are also triggered by the altered ACE2R function [65]. Another important aspect is that platelets have MRGPCRs that modify thrombosis via NO release, and this also contributes to clot formation. This may be the explanation for the importance of platelet activation in COVID-19 coagulopathy [65]. The interrelation between different factors and pathways involved in the development of arteriopathy and coagulopathy in diabetic patients is presented in Figure 1. Hypercoagulability assessment using routine laboratory parameters in COVID-19 and T2DM is listed in Table 1. In the clinical practice, for rapid assessment of CAC, a highly performant, point-of-care laboratory method was introduced. Rotational thromboelastometry (ROTEM) is a point-of-care viscoelastic method for whole blood analysis, providing real-time information about clot formation, firmness, and fibrinolysis in severely ill patients and it is useful to identify a hypercoagulable state related to sepsis, COVID-19 [66,67,68]. Several ROTEM tests can be performed depending on the added substrate along with phospholipids and calcium. The extrinsic coagulation pathway is assessed using rotational thromboelastometry (EXTEM), which is initiated by adding a tissue factor. To assess fibrinolysis, fibrinogen rotational thromboelastometry (FIBTEM) is used, and it differs from EXTEM by using cytochalasin D, which inhibits the platelet cytoskeleton, so the whole clot formation depends on fibrinogen [66]. Point-of-care hemostasis assessment in severe COVID-19 with ROTEM is represented in Table 2 and Figure 2, containing measured parameters, definitions, and levels in severe COVID-19. The markers of the hypercoagulable state are as follows: shortened CT and higher MCF EXTEM and FIBTEM, shorter than normal EXTEM CFT, and higher alpha-angle [69]. Hypercoagulability could be assessed by ROTEM in 61% of cases [70]. ROTEM would be an appropriate point-of-care method for adequate assessment of coagulopathy, facilitating the work of clinicians to choose the most suitable therapy, applied individually based on the bedside results [66,67,68,70]. Furthermore, according to Schrick D. et al., ROTEM assays could also reveal platelet reactivity to antiaggregant therapy, and a lower reactivity was found to be associated with higher rates of lethal outcomes in severe COVID-19 cases [71]. The novel coronavirus breakout and pandemic have intensified the need for genetic investigations related to gene expression for a better understanding of the underlying pathomechanisms of SARS-CoV-2 and its genetic association with different diseases [72,73]. SARS-CoV-2 is a coronavirus of bat origin, causing a disease with various symptoms, from mild fever, cough, and sore throat in some patients or severe pneumonia, ARDS, and even septic shock or MOF in other individuals [74]. The intracellular pathogenicity of viruses makes them dependent on host cells, but it also suggests a virus–host protein–protein interaction (PPI). These PPIs have been the focus of recent analyses. The identification of the most common human proteins known to interact with coronavirus could provide a better understanding of the mechanism of COVID-19 and may suggest therapeutic strategies or drug combinations [75,76]. Using a network-based strategy, which incorporates gene expression profiling, gene ontologies, and PPI analysis, RNA-Seq scientists can identify molecular interactions between virus and host during the development of the infection and establish adequate treatment methods. RNA-Seq is a next-generation sequencing technology to measure gene expression with a high level of accuracy [76]. According to a study conducted by Islam et al. in 2021, cytokine activity and cytokine-mediated signaling pathways were predominant in COVID-19-associated T2DM. Similarly, TNF signaling pathway and cytokine–cytokine receptor interaction were found “enriched” [73]. The most frequently reported pathways were TNF and IL-17 signaling pathways, cytokine–cytokine receptor interactions, and photodynamic therapy-induced NF–κB survival. According to Ouyang et al., the TNF pathway is hyperactivated in severe COVID-19 [77]. On a background of T2DM, there is a direct involvement of TNF-α, through reduction of the insulin metabolism-related GLUT4 expression. Moreover, this phenomenon is also concomitant with insulin receptor inhibition by the serine phosphorylation of insulin receptor substrate-1 (IRS-1) [73]. IL-17, one of the principal triggers of cytokine storm in COVID-19, is released by the activation of the T-helper 17 lymphocyte (Th17) [55,78]. The IL-17 pathway has an insulin resistance-promoting effect, which is worsening the cytokine storm through AT1R excitation, leading to enhanced NO synthesis in diabetes [79,80]. CCL20 was found to have increased levels in the COVID-19-related cytokine storm, obesity, and insulin resistance. In pancreatic β-cells, CCL20 is regulated by NF–κB subunits. FOSL1 TF protein downregulates type I interferon (IFN-1) response, effective in the protection against viral infections [81], thus leading to viral susceptibility. A study by Islam et al. identified 11 micro-RNAs (miRNAs) with shared pathogenetic potential between COVID-19 and diabetes: miR-1-3p and miR-20a-5p [73]. The miR-34a-5p miRNA decreases the antiapoptotic BCL2 protein, leading to increased glucose-mediated cardiomyocyte apoptosis [82]. Up-regulated miR-34a-5p is related to acute myocardial infarction causing heart failure [73]. In COVID-19, some prevalent miRNAs have been found to be associated with asthma (miR-155-5p, miR-16-5p) and other lung diseases (let-7b-5p). As a response to infection, miR-146a expression is induced by NF-κB. This will negatively affect IL-1 and TNF-α receptors so they attenuate inflammation. It has been shown that β-cell miRs are causing islet inflammation, leading to miR-146a-5p expression, which has down-regulated islet inflammation and beta-cell death by impairing NFκB and MAPK signaling [83]. The consequence of downregulated circulating miR-146 is hyper-inflammation in different organs [84]. Donyavi et al. have found that some miRNA can be used as biomarkers for the diagnosis of acute COVID-19 and to distinguish the acute phase from the post-acute form of COVID-19. The identified and suggested miRNAs as biomarkers were: miR-29a-3p, miR-155-5p, and miR-146a-3p. Thus, the connections between transcription factors and miRNAs with the pathogenesis of COVID-19 and diabetes may provide a better understanding of severe COVID-19 in diabetic patients [85,86]. The products of several genes involved in hyperglycemia, cytokine release, hormonal signals, receptor binding, and enzyme activities are interconnected and can influence the pathomechanism of COVID-19 and its complications in diabetic patients [87]. Genetic polymorphisms affecting ACE2 receptors, the cytochrome p450 system, or the cytoprotective heme oxygenase can complicate the treatment of COVID-19 by enhancing a proinflammatory and prooxidant state, increasing the cytokine storm and inducing a prothrombotic state [88]. Iessi et al. suggested that sex-related differences in the immune response may be transmitted via mitochondrial DNA, which could be responsible for the inferior function of male mitochondria and the observed reduced immune response in males [89]. The difference in immune response related to sex may also be explained by the bi-allelic expression [90] of X-linked genes encoding inflammatory mediators or receptors. Viveiros et al. suggested that due to the presence of the ACE2 gene on the X chromosome, which is considered as an X-gene escape, theoretically, women would have a double dose of ACE2, compensating for virus-mediated membrane ACE2 loss. However, ACE2 regulation is under the control of proteolytic cleavage and miRNAs; thus, the expression of ACE2 may not correlate with enzyme activity [90]. Gemmati et al. also hypothesized that women, due to the presence of two X-chromosomes, may have an advantage compared to men, based on their better adaptability to infectious diseases, such as COVID-19 [41]. The role of hyperglycemia in the development of cardiovascular diseases can be partially related to genetic background, although there is limited evidence. More than 150 loci showed association with coronary artery disease in the general population, and some of these loci (such as 9p21) were clearly demonstrated to be involved in increased cardiovascular risk of diabetic patients, especially in case of poor glycemic control [91]. Certain pathomechanisms of diabetes are closely related to specific genetic and biochemical features, causing inflammation, fibrosis, apoptosis, and the release of ROS enhancing factors. Modified histone proteins, methylation of the genetic material, and modulation of microRNA expression are epigenetic changes which can regulate diabetic vascular complications despite adequate glucose control, or major signaling pathways in T2DM [18,92]. A bidirectional genetic interaction is described in the scientific literature between the human and viral genome during COVID-19. SARS-CoV-2 viral microRNAs can target different genes of the host organism (such as the ADIPOQ gene, playing an important role in metabolic syndrome) and human microRNAs were suggested to potentially target viral genes [93]. COVID-19 is characterized by coagulopathy and hemostatic imbalance. Scientific evidence allows us to formulate the hypothesis that COVID-19-induced coagulopathy in T2DM develops more likely based on pre-existing vascular and metabolic disturbances through the pathomechanisms of the viral infection (intense cytokine release, endothelial dysfunction associated to infection, hyperinflammation, and hypercoagulable state). Glycemic control will be a priority, not only for CVD protection, but also because ACE2 is present on pancreatic beta-cells as well, and pancreatic inflammation, induced by the cytokine storm, can lead to insulin resistance [94]. Several studies reported higher ACE2 expression in females, and decreasing ACE2 expression in elderly patients, which will be severely altered in the presence of DM. It was also reported that the cytokine storm has a repressing effect on ACE2 leading to severe outcomes [95]. It has been reported that chronic inflammation in T2DM will promote platelet activation leading to hypercoagulability. In T2DM the underlying condition, i.e., excessive level of proinflammatory cytokines and low level of anti-inflammatory cytokines, leading to an immunocompromised state, together with metabolic imbalance may be the explanation for the severe outcome of T2DM patients infected with SARS-CoV-2 [96]. The ROTEM method is a proper diagnostic tool for rapid evaluation of hypercoagulable state in COVID-19-related sepsis, which could be applied on a large scale. Β-cell proliferation is observed in obese patients due to insulin resistance. It occurs as a compensatory response to nondiabetic obesity, when the organism is trying to counteract the insulin resistance by increasing insulin-secreting β-cells [97]. In some cases, the first sign of the onset of DM is in the form of ketoacidosis concomitant with COVID-19, leading to a severe outcome [98]. It has also been reported that those patients who are diabetics at the time of infection with SARS-CoV-2 have a better outcome than those with concomitantly installed DM and COVID-19. Certain studies also reported that patients with DM have a two-fold risk of intensive care unit hospitalization and a two–three-fold risk of hospital mortality than non-diabetic patients [99,100]. Several studies have shown that the infectivity of COVID-19 is not higher in patients with associated diabetes. The prevalence of diabetes in the COVID-19 patient population is not significantly different from the prevalence of diabetes in the general population [101]. Early diagnosis and identification of gene modifications that can influence the course of the disease is a desired aim to prevent further complications and to recognize risk levels for each patient. The authors concluded that the mechanism of COVID-19, with the virus binding to the ACE2 receptor, might be different in patients in accordance with the individual genetic background or developed susceptibility. We consider that human genetics plays an important role (inherited predispositions) in COVID-19 management, due to the possibility of identification of gene modifications that contribute to poor prognosis, even more, when pre-existing comorbidities and acquired risk conditions are present. Comprehensive knowledge of the underlying pathomechanisms of coagulopathy and inflammatory response in diabetes mellitus contributes to a better understanding of the manifestations of angiopathy in this very vulnerable group of patients; thus, they can benefit from a modern, more efficient management.

PMC10001540

Omar Rodrigo Guadarrama-Escobar,Pablo Serrano-Castañeda,Ericka Anguiano-Almazán,Alma Vázquez-Durán,Ma. Concepción Peña-Juárez,Ricardo Vera-Graziano,Miriam Isabel Morales-Florido,Betsabe Rodriguez-Perez,Isabel Marlen Rodriguez-Cruz,Jorge Esteban Miranda-Calderón,José Juan Escobar-Chávez

Chitosan Nanoparticles as Oral Drug Carriers

21-02-2023

chitosan,chitosan nanoparticles,oral administration

The use of nanoparticles as drug delivery systems has increased in importance in the last decades. Despite the disadvantages of difficulty swallowing, gastric irritation, low solubility, and poor bioavailability, oral administration stands out as the most widely used route for therapeutic treatments, though it may not always be the most effective route. The effect of the first hepatic pass is one of the primary challenges that drugs must overcome to carry out their therapeutic effect. For these reasons, controlled-release systems based on nanoparticles synthesized from biodegradable natural polymers have been reported to be very efficient in enhancing oral delivery in multiple studies. Chitosan has been shown to have an extensive variability of properties and roles in the pharmaceutical and health fields; of its most important properties are the ability to encapsulate and transport drugs within the body and enhance the drug interaction with the target cells, which improves the efficacy of the encapsulated drugs. The physicochemical properties of chitosan give it the ability to form nanoparticles through multiple mechanisms, which will be addressed in this article. The present review article focuses on highlighting the applications of chitosan nanoparticles for oral drug delivery.

Chitosan Nanoparticles as Oral Drug Carriers The use of nanoparticles as drug delivery systems has increased in importance in the last decades. Despite the disadvantages of difficulty swallowing, gastric irritation, low solubility, and poor bioavailability, oral administration stands out as the most widely used route for therapeutic treatments, though it may not always be the most effective route. The effect of the first hepatic pass is one of the primary challenges that drugs must overcome to carry out their therapeutic effect. For these reasons, controlled-release systems based on nanoparticles synthesized from biodegradable natural polymers have been reported to be very efficient in enhancing oral delivery in multiple studies. Chitosan has been shown to have an extensive variability of properties and roles in the pharmaceutical and health fields; of its most important properties are the ability to encapsulate and transport drugs within the body and enhance the drug interaction with the target cells, which improves the efficacy of the encapsulated drugs. The physicochemical properties of chitosan give it the ability to form nanoparticles through multiple mechanisms, which will be addressed in this article. The present review article focuses on highlighting the applications of chitosan nanoparticles for oral drug delivery. During the last decades, alternative ways of medication administration have gained attention. Several delivery route options are outlined in this review, along with their strengths and weaknesses; fundamental and physicochemical criteria features that would make a drug an appropriate candidate for pharmaceutical formulation; and methods to evaluate delivery viability, toxicity at the place of delivery, and feasibility [1]. There are numerous studies on novel drug delivery approaches, but oral administration remains the most effective and easiest to administer, and it induces minimal side effects [1,2]. However, the main disadvantage of oral administration is poor bioavailability [3]. To overcome these limitations, the use of nanocarriers as drug delivery systems by oral route has become known, thanks to the development of nanotechnology and, more specifically, nanomedicine. Nanomedicine is a subdivision of nanotechnology, which uses nanometric particles [4]. Nanoparticles (NPs) are capable of functioning as pharmaceutical carriers for a variety of delivery systems. Studies have shown that NPs have been applicable for use in the pharmaceutical and biomedical sectors to treat illnesses including diabetes, cancer, and HIV [5]. Furthermore, NPs can interact with the immune system in many ways, particularly by eliciting inflammation and interacting with dendritic cells. In addition, NPs have been developed to increase therapeutic limitations and membrane crossing, and with the development of personalized therapies, their therapeutic efficacy has been improved [6]. NPs can be comprised of a multitude of substances, but some existing NPs may be toxic to humans [5]. Polymeric NPs, such as the chitosan NP, are commonly 10–1000 nm in dimension and, being formulated from polymers, have a natural bioadaptability, biocompatibility, and biodegradability [4]. Chitosan NPs have the benefit of slowing and controlling the release of drugs, improving their solubility and stability, and decreasing their toxicity [7]. This review focuses on the development, importance, and impact that chitosan-based polymer NPs have acquired in the fields of pharmacology and health, highlighting the novel alternatives for drug delivery, the reduction in adverse effects, and the upgrading of bioavailability, efficacy, and acceptance by patients. Drug administration depends on the individual’s physiology and the formulation [8]. The absorption mechanism and characteristics of the drug are the essential aspects that define the proper delivery system for optimal bioavailability and efficacy. In contrast to drug prescription, which mostly lies in the hands of health care personnel, drug administration is an everyday practice for almost all humans [9,10]. Drug delivery systems are engineered technologies that perform the targeted delivery and/or controlled release of therapeutic agents [11,12]. Many of the pharmacologic properties of free drugs can be improved using drug delivery systems such as nanocarriers, which are made primarily of lipids or polymers and their associated therapeutics [11]. Drug delivery systems are designed to either alter the pharmacokinetics (PK) and biodistribution (BD) of their associated drugs, function as drug reservoirs (i.e., as sustained-release systems), or can sometimes perform both functionalities. Nanotechnology is a novel pharmaceutical technology pertaining to nanosized particles of a variety of materials, and it opens a new avenue for drug delivery methods. According to the U.S. Environmental Protection Agency (EPA), nanotechnology is defined as “the creation and use of structures, devices, and systems that have novel properties and functions because of their small size” [12,13]. In comparison with the oral route, the intramuscular route avoids the gastrointestinal tract. However, it presents pain and risk of injury, and it requires adequate personnel (Table 1) [9]. Other administration routes, such as ocular, transdermal, subcutaneous, or nasal delivery, have also been developed for localized drug administration with the avoidance of undesirable systemic effects [11]. The aim of the transdermal route is to deliver the medication across the skin layers to the blood tissue. Drug absorption in this case occurs through the intercellular, transcellular, and transappendageal pathways [9,11]. Transdermal drug delivery systems present many inconveniences, including skin irritation or contact dermatitis, risk of allergic reactions, poor permeability of some drugs through the skin, and insufficient skin absorption of drugs with large particle sizes [12]. Possible toxic effects and drug uptake limitation are important in transdermal systems as skin conditions change with age [13]. Subcutaneous drug administration is used due to the simplicity of the injection method, the ability to deposit large volumes of medication, and the freedom of choosing a specific injection site. However, with this administration route, the rate and extent of bioavailability are dependent on a large number of biopharmaceutical and biological factors [14]. An alternative route that presents significant challenges is the ocular drug delivery route. The anatomy, physiology, and biochemistry of the eye render this organ highly impervious to external substances. In ocular drug delivery systems, the primary limitation is the rapid and extensive elimination of conventional eye drops, resulting in extensive loss of the medication. Only 5%–10% of the total administered medication reaches the target tissue, causing an extremely poor intraocular bioavailability [15]. Inhaled medications are one of the cornerstones of pharmacological treatment. Several inhaler devices exist, and each device has particular attributes to get the optimal inhalation of drugs; however, the correct use of the inhaler device is not guaranteed and is prone to patient error. Moreover, eliminating the toxicity of NPs, polymers, and other excipients is critical for the development of safe inhalable formulations [16,17]. Despite some disadvantages, oral administration is the natural route of drug administration, which has advantages (Table 2) such as sustained release, ease of administration, and ease of use. In addition, the GI tract’s large surface area (>300 m2) lined with a mucosal layer paves the way for drug attachment and subsequent absorption [9,18]. Many developments have been made to optimize oral drug absorption, including the use of absorption enhancers, enzyme inhibitors, enteric coating, and microparticles or nanoparticles [19]. The main function of the GI tract is food digestion and protection against microbial agents [20]. The oral administration of drugs is desirable, but there occurs the enzyme degradation of these drugs, and of the drug compounds (Table 3) [21]. Oral ingestion remains the preferred mode of delivery for most drugs, largely due to simplicity [22]. The gastrointestinal tract has an area of 300–400 m2 for drug absorption by enterocytes, and the gastrointestinal tract contains specialized cells such as Peyer cells, M cells, and goblet cells for this function [19]. In the GI tract, any drug will encounter a series of barriers before it reaches the capillaries in the subepithelial tissue [20]. The function of the GI tract includes digestion, excretion, and protection. The GI tract has many sophisticated and autonomous functions coordinated over a range of length and time scales [23]. The GI tract can be divided into upper and lower portions. The oral cavity, pharynx, esophagus, stomach, and the initial portion of the small intestine, known as the duodenum, are the parts that make up the upper GI; on the other hand, the lower GI tract includes the rest of the small intestine, consisting of the jejunum and ileum, as well as the large intestine, consisting of the cecum, colon, and rectum. The structure of the GI tract is similar throughout all its segments [9]. The GI tract developed to enable the transport of nutrients throughout its length. The small intestine measures approximately 1.5 m in length, with a diameter of 6–7.5 cm. The surface area of the small intestine is significantly enlarged by the existence of villi and microvilli, which increase the intestinal surface area 30-fold and 600-fold, respectively (Table 4). Furthermore, drug molecules trapped within the GI tract mucus are protected against the shearing forces caused by flowing gastric juice [9,24]. Absorption is helped by the increased mucosal surface area provided by elongated villus folds lined by absorptive enterocytes. Each enterocyte has microvilli that comprise a fine apical brush border that further increases surface area [25]. Intestinal mucus is the main barrier through which ingested NPs must pass. Surface charge can play a critical role in absorption. A net neutral or positive surface charge prevents mucoadhesion and allows penetration, while a negative surface charge in hydrophilic and lipophilic compounds hinders penetration. Small NPs penetrate easier than bigger ones [25]. The small intestine is the main site of nutrient absorption. The pH of the duodenum is 6–7 in humans, 4 in mice and 5 in rats. The physicochemical behavior of the intestine is complex. The intestinal epithelium is the major barrier limiting the absorption of macromolecules [26]. This epithelial cell layer is made of enterocytes, goblets cells, and M cells. The most abundant in the intestine is the enterocyte cells, which specialize in transporting nutrients by active transport or passive diffusion [27]. The oral administration of drugs has limitations, such as low stability in the GI tract, as well as low permeability and solubility. That is the reason for the application of pharmaceutical biotechnology to improve the physicochemical and biopharmaceutical properties of pharmaceuticals [28]. The devices developed for oral drug administration can be classified as intestinal patches, GI microneedles, and particulate carriers, which include microparticles, NPs, micelles, and liposomes [9]. Swelling polymers increase their weight (10–1000 times) in aqueous media. Swelling polymers have been used to develop and generate swellable matrices or devices and super disintegrants [29]. Associated with chemical approaches, physical strategies are more feasible in improving the pharmacodynamic and pharmacokinetic properties of drugs. These approaches are based on covalent and noncovalent interactions of drugs with absorption enhancers, enzyme inhibitors, or colloidal carrier systems such as microparticles, NPs, or nanoemulsions [30]. Surfactants, chelating agents, and fatty acids are regular absorption enhancers that increase drug penetration due to their capacity to alter the membrane fluidity of the intestine’s lipid bilayer [31]. Enzyme inhibitors contribute to protecting peptides from degradation while in the GI tract. To improve the oral bioavailability of peptides and proteins in drugs, enzyme inhibitors are usually used in combination with absorption enhancers [32,33]. Enhanced oral delivery includes the improvement of the physicochemical properties of pharmaceuticals as well as nanocarriers [28]. Erosion of polymers is a complex phenomenon, as it involves swelling, diffusion, and dissolution. Erosion occurs in two ways: homogeneously and heterogeneously. Homogenous erosion occurs at the same rate throughout the matrix, whereas heterogeneous erosion occurs from the polymer’s surface toward its inner core [34]. The intake of exogenous, engineered nanoparticles primarily results from hand-to-mouth contact in the workplace. Nanoparticles can be ingested directly via food, drinking water, drugs, or drug delivery systems [35]. Both the biological characteristics of the GI tract as well as the properties (particle size, coating, aggregation, among others) of the NPs impact ingestion and bioavailability studies [25]. Another challenge in oral drug administration is short gut residence time and poor mucosal contact; a method to overcome this is the use of adhesion promoters such as linear or tethered polymer chains to promote bio-adhesion, which has been well documented [36]. The translocation of particles through the intestinal barrier is a multistep process that requires diffusion across the mucus layer, contact with enterocytes and M cells, and uptake via cellular entry or paracellular transport. The most common mechanism for the uptake of NPs into intestinal epithelial cells appears to be endocytosis [25]. One way to overcome absorption barriers is the generation of gene NPs. These protect the gene or drug and enhance cellular uptake through endocytosis, of which a promising polymer for these systems is chitosan [21]. One benefit of NPs’ formulation is the potential for providing targeted and localized drug delivery. Several studies have shown that NPs can increase the oral bioavailability of drugs through different mechanisms [22]. The generation of orally targeted NPs is essential to understand the disease as well as the physiological barriers and specific receptors presented by the different regions of the GI tract [22]. The convenience and other advantages of oral delivery also make NP formulation a promising strategy for mass vaccination programs [9]. NPs as a delivery system have achieved advantages by overcoming the challenges of typical dosage methods [36]. According to the definition from the National Nanotechnology Initiative (NNI), NPs are structures with sizes ranging from 1 to 100 nm in at least one dimension. However, the prefix “nano” is commonly used for particles that are up to several hundred nanometers in size [37]. Nanotechnology is the science of the nanoscale [38]. NPs are made up of three layers: (a) the surface layer, (b) the outer layer, and (c) the core (Figure 1) [39]. The nanocarriers must be able to integrate into the biological system and must not cause any negative or toxic effects [40]. NPs have also been used in immunotherapy (vaccines), which, by containing foreign substances and being in contact with the immune system, generates a specific immune response. Properties such as size, charge, and rigidity, among others, determine their interaction with the immune system [41]. Polymeric NPs can deliver drugs and overcome biological barriers, as well as target drugs to specific cells [42]. Most polymeric NPs are now biodegradable and biocompatible due to the accomplishments of researchers in the last few decades in developing NPs for drug delivery systems. These biodegradable NPs are coated with controlled-release polymers that can release proteins, peptides, antigens, DNA transporters, and target specific sites [43]. Polymeric systems have become popular due to their ability to provide a sustained release of the associated active compounds [44]. For more than five decades, the pharmaceutical industry has been using procedures such as coating and encapsulation to incorporate polymers with bioactivity [45]. Various polymers have been used for site-specific drug delivery while minimizing side effects [46]. Biodegradable and bioerodible polymers are a crucial group of materials for drug delivery [47]. Biopolymers are of natural origin (vegetable, animal, bacterial, fungi). They include networks of polysaccharides, cellulose, starch, gelatin, and collagen among others, with potential applications in the pharmaceutical industry [48]. In recent years, biodegradable polymers have garnered considerable attention as potential drug delivery devices, given their applications in the controlled release (CR) of drugs, their ability to target particular organs and tissues, their potential as carriers of DNA in gene therapy, and their ability to deliver proteins, peptides, and genes through a peroral route of administration [46]. The objective of a delivery system is to release at the desired site for a specified time to exert the therapeutic effect [45]. The purpose of nanotechnology is the delivery drugs to target sites so that the pharmacologically desired effect of the drug is maximized and the limitations and drawbacks that would hinder the required effectiveness are overcome [49]. NPs consist of macromolecular materials and can be used therapeutically as adjuvants in vaccines or drug carriers [50]. The challenges of biological barriers such as the passage of substances through the blood-brain barrier have been overcome with the use of NPs [49]. Polymeric NPs, which possess better reproducibility profiles than liposomes (Figure 2), have been used as alternative drug carriers to overcome many drug delivery problems. Polymers used to form NPs can be either synthetic or natural polymers [50]. Natural polymers such as chitosan, albumin, and heparin have been used for the delivery of oligonucleotides, DNA, and proteins, as well as drugs. Polymeric NPs are popular due to their ability to deliver drugs, as well as for their biodegradability. Chitosan is one of the most widely used cationic polymers [51]. Environmentally responsible polymers are a class of smart polymers consisting of linear, cross-linked copolymers (Table 5). Their characteristic feature is their ability to undergo physicochemical change in response to external stimuli such as pH, temperature, etc. [45]. Polymeric microparticles and NPs have been applied to gene delivery, and particularly in vaccine design (e.g., DNA vaccine). Synthetic vectors based on polycation enable gene delivery by cell-targeted ligands [51]. There are two varieties of NPs, depending on the preparation process: nanospheres or nanocapsules (Figure 3) [55]. Nanospheres have a matrix structure where the drug is located. The nanocapsules have a membrane and contain the drug inside [50]. Polymeric NPs have been widely explored in the pharmaceutical fields since their launch [56,57]. Polymeric NPs can be prepared by different methods, such as solvent evaporation, nanoprecipitation, salting out, dialysis, and supercritical fluid technology [43]. Among the available families of nanocarriers, polymeric vectors have been widely researched, owing to several beneficial properties, including biocompatibility, biodegradability, non-immunogenicity, and nontoxicity. Progress has also been made in designing potent, stable NPs for tissue and cell targeting by conjugating ligands in the polymeric NPs. Altogether, these advancements have improved NP performance [56]. The synthesis of metal NPs includes the processes of spray pyrolysis, liquid infiltration, rapid solidification, and others. The synthesis of ceramic nanocomposites includes the powder process, polymeric precursor process, and sol-gel process. Finally, the fabrication of polymeric nanocomposites includes intercalation, in situ intercalative polymerization, melt intercalation, template synthesis, mixing, in situ polymerization, and the sol-gel process [58]. In this technique, an ionic cross-link is conducted by the aggregation of chitosan or its derivates with oppositely charged macromolecules or in the presence of an ionic cross-link agent (Figure 4) [59]. A cross-link is formed through a chemical reaction, such as van der Waals forces, which link two polymers together [60]. Ionically cross-linked chitosan NPs are based on the formation of complexes with the amino group and a polyanion (tripolyphosphate TPP) [61]. Covalent cross-linking is more attractive than the ionic gelation method. Bodnar et al. [61] reported that the synthesis of chitosan NPs covalently cross-linked with tartaric acid resulted in particles 60–280 nm in size [62]. Covalent cross-linking enhances the chemical and mechanical properties of the material [63]. In the reverse micellar formation of NPs, the surfactant is dissolved in an organic solvent forming micelles and chitosan is added under continuous stirring; to this transparent solution, a crosslinker is added. The limitations are the use of organic solvents as well as the washing steps [61]. NPs are prepared by alkaline precipitation (pH > 6.5). Chitosan is injected into the organic solvent by employing a nozzle. The NPs are obtained by filtration or centrifugation, then rinsed with water, and a crosslinking agent is added to modulate the release of the substances [61]. The NPs are formed by the emulsification of an organic polymeric solution in an aqueous phase, after which the organic solvent is evaporated. The organic solution is poured into the aqueous phase. Emulsification is carried out under high-shear force conditions to reduce the size of the emulsion droplet. The evaporation of the solvent leads to the formation of NPs [61]. Chitosan is a cationic polysaccharide and has been considered a promising nanomaterial [64]. Chitosan offers outstanding biological properties, including biocompatibility, biodegradability, and nontoxicity, that make it increasingly important in various applications in the pharmaceutical and biomedical fields [65]. The shellfish industry makes very common use of the meat, and the head and shells are discarded as waste (80,000 tons of waste per year) [66]; shell waste is recycled to obtain commercially viable products such as chitin [67]. Chitosan is the N-acetyl derivate of chitin obtained by N-deacetylation. Chitosan is widely used in the encapsulation of active food ingredients, enzyme immobilization, controlled drug delivery, and plant growth promotion in agriculture. Chitosan has properties such as biodegradability, biocompatibility, antimicrobial, bioactivity, nontoxicity, and a polycationic nature [68]. Chitosan (Figure 5) is well-known for its hydrophilic, biocompatible, biodegradable, and nontoxic properties. The use of chitosan NP for oral and nasal drug delivery routes has been reported in previous studies [69]. NP technology is an increasingly accepted formulation technique as it overcomes the limitations of conventional oral drug delivery. The positively charged chitosan will bind to cell membranes and is reported to decrease the trans-epithelial electrical resistance (TEER) of cell monolayers, as well as to increase paracellular permeability. Chitosan solutions have been shown to increase transcellular and paracellular permeability in a reversible, dose-dependent manner that depends on the molecular weight and degree of deacetylation of the chitosan. Low-molecular-weight chitosan possesses the ability to penetrate cells, where it is suspected of binding to cell DNA, prohibiting mRNA synthesis and causing the termination of cell multiplication [60]. The mechanism of action, which includes interaction with the tight junction proteins and ZO-1 proteins, redistribution of F-actin, and slight destabilization of the plasma membrane, appears to be mediated by the positive charges on the chitosan. Thus, the ability of chitosan to enhance permeation is influenced by the pH of the environment [21]. Chitosan is biodegradable and, due to its low molecular weight, is eliminated by the kidneys, and if it is of higher molecular weight, it can be degraded into smaller fragments for renal elimination. Mucoadhesive NPs are able to have their surface coated with mucoadhesive polymers such as chitosan or Carbopol [66]. Mucus is a blend of molecules including salts, lysozyme, and mucins, which are highly hydrated glycoproteins primarily responsible for the viscoelastic properties of mucus. Sialic acid residues on mucins have a pH of 2.6, making them negatively charged at physiological pH [21]. In addition, the formation of chitosan into microparticles and NPs also preserves mucoadhesion [21]. The application of biodegradable nanosystems is one of the most successful advancements in the pharmaceutical industry [40]. Chitosan can be used as an oral gene carrier due to its adhesive properties. On the other hand, researchers have found that in vitro, chitosan-mediated transfection depends on the cell type, serum concentration, pH, and chitosan molecular weight [51]. The protection offered by NPs has generated the development of systems with macromolecules and proteins, among others, since these promote the absorption of therapeutic substances [61]. The process of NP formation is based on electrostatic interactions between the amine group of chitosan and a negatively charged group of polyanions such as tripolyphosphate (Figure 6). This method is easy in aqueous media [51]. One study has shown that the intratumoral administration of interleukine-12 co-formulated with the biodegradable polysaccharide chitosan could enhance the anti-tumor activity of interleukine-12 in mice bearing established colorectal (MC32a) and pancreatic (Panc02) tumors [51]. Chitosan nanospheres have applications for drug delivery in the gastrointestinal, ophthalmic, nasal, sublingual, transdermal, and vaginal tract [51]. The absorption-promoting effect of chitosan has been extensively studied by the combination of mucoadhesion and the transient opening of tight junctions in the mucosal cell membrane, which have been experimentally verified both in vitro and in vivo [61]. The mechanism of chitosan NP transport across the GI tract is likely through adsorptive endocytosis. Chitosan NP internalization is higher in the jejunum and ileum than in the duodenum [61]. Chitosan NPs can be applied to mucosal delivery (pulmonary, nasal), where peptides and proteins can be administered [66]. Oral formulations are considered a desirable alternative to intravenous drug administration due to the advantage of offering adaptability to tune the dosing schedule to individual patient responses based on efficacy and toxicity. Oral formulations of NPs can increase the number of patients treated [70]. Recently, the use of chitosan in pharmaceutical development has increased due to its compatibility with other components such as surfactants, starches, etc. Chitosan increases cell membrane permeability, both in vivo and in vitro. Chitosan has the potential of serving as an absorption enhancer across intestinal epithelia, prolonging the residence time of delivery systems at absorption sites and relaxing the tight junctions of cell membranes [71]. The cationic nature of chitosan permits it to form complexes with oppositely charged drugs and excipients, thereby altering the physicochemical characteristics of the formulation. Reacting chitosan with controlled amounts of multivalent anions results in cross-links between chitosan molecules (Table 6) [72]. In gene therapy, transfection is hindered by the orientation of the system to the target cell as well as the degradation of endolysosomes and intercellular trafficking of plasmid DNA [95]. Gene carriers have the disadvantage of low transfection and toxicity, and they even provoke severe immune responses [7]. Nucleic acids are being developed for gene therapy and vaccination [21]. As a non-virus carrier, chitosan has exceptional compatibility and biodegradability [7,95]. Dastan and Turan [96] improved chitosan microparticles and reported a sustained release profile of DNA with a high potential transfer of DNA tested in different cell lines such as human embryonic kidney, Swiss 3T3, and HeLa [97]. Proteins and peptides usually have a high molecular weight and low lipophilicity, which is why they are usually administered subcutaneously. However, NPs have been shown to administer peptides and proteins orally. Chitosan NPs are gaining increased attention for their ability to serve as carriers for oral protein and peptide delivery [80,87]. Antiviral, antiallergic, and hormone drugs can be loaded in chitosan NPs through an ionic cross-link method [7,98]. For example, in the research performed by Shailender et al. [89], the ionic gelation method was used in the preparation of tenofovir disoproxil fumarate chitosan NPs [50,99]. Cancer has become one of the most lethal and prevalent conditions throughout the world. The success of current therapies is primarily limited by tumor recurrence, metastasis, acquired resistance, and the presence of side effects [100]. The cancer treatment doxorubicin, for example, produces side effects such as cardiotoxicity. To minimize these side effects, the drug has been encapsulated in chitosan NPs. This has caused several advantages, including better delivery, improved cell- or tissue-targeted drug delivery, and enhanced absorption in the entire small intestine [101,102]. Tuberculosis continues to be the leading cause of mortality worldwide, and it is also an occupational disease in health care. Noncompliance is the primary limitation of treatment, largely because treatment involves continuous, frequent, and multidrug dosing. Chitosan NPs could improve a long-duration drug formulation, releasing the antitubercular agents in a slow and sustained manner [92]. The effect of chitosan as an adjuvant for the generation of vaccines makes it a safer therapy [71]. Oral vaccination is a highly promising application of chitosan NPs. Food allergy is a common and often fatal condition with no effective treatment. Orally administering NPs prepared by complexing plasmid DNA with chitosan has been shown to result in a transduced gene expression in the intestinal epithelium of patients with food allergies [103]. Olivera et al. [104] developed a vaccine for the control of schistosomiasis, which is recognized as the most important human helminth infection in terms of morbidity and mortality. They described that chitosan NPs with plasmid DNA encoding the Rho1-GTPase protein of Schistosoma mansoni were able to induce high levels of the modulatory cytokine IL-10. It resulted in a significant reduction in liver pathology. Mice immunized with only chitosan NPs presented with 47% protection against parasitic infection. The oral route of administration continues to be the first choice of both patients and doctors; although, as mentioned, the administered medications must cross many barriers and physiological processes that reduce their bioavailability and, consequently, their efficacy. For this reason, nanoparticulate or nanometric drug delivery systems based on biocompatible polymers have acquired great relevance. Their physicochemical characteristics have allowed them to be included within the unconventional forms of administration, and they have enabled the vectorization of active ingredients with low solubility or bioavailability, increasing their interaction with the target organs or cells through different routes of administration. Due to their easy preparation and high capacity to encapsulate peptides, drugs, and genes without interfering with their biological activity, chitosan-based NPs are the first choice when treating diseases through non-conventional methods. Chitosan NPs enable medications to cross physical and biological barriers, increasing bioavailability and leading to a more powerful effect with fewer adverse effects, which can be achieved without requiring invasive or painful routes of administration by using the oral route.

PMC10001542

Caroline Carlé,Yannick Degboe,Adeline Ruyssen-Witrand,Marina I. Arleevskaya,Cyril Clavel,Yves Renaudineau

Characteristics of the (Auto)Reactive T Cells in Rheumatoid Arthritis According to the Immune Epitope Database

21-02-2023

rheumatoid arthritis,memory T cells,shared epitope,neoepitopes,peptides

T cells are known to be involved in the pathogenesis of rheumatoid arthritis (RA). Accordingly, and to better understand T cells’ contribution to RA, a comprehensive review based on an analysis of the Immune Epitope Database (IEDB) was conducted. An immune CD8+ T cell senescence response is reported in RA and inflammatory diseases, which is driven by active viral antigens from latent viruses and cryptic self-apoptotic peptides. RA-associated pro-inflammatory CD4+ T cells are selected by MHC class II and immunodominant peptides, which are derived from molecular chaperones, host extra-cellular and cellular peptides that could be post-translationally modified (PTM), and bacterial cross-reactive peptides. A large panel of techniques have been used to characterize (auto)reactive T cells and RA-associated peptides with regards to their interaction with the MHC and TCR, capacity to enter the docking site of the shared epitope (DRB1-SE), capacity to induce T cell proliferation, capacity to select T cell subsets (Th1/Th17, Treg), and clinical contribution. Among docking DRB1-SE peptides, those with PTM expand autoreactive and high-affinity CD4+ memory T cells in RA patients with an active disease. Considering original therapeutic options in RA, mutated, or altered peptide ligands (APL) have been developed and are tested in clinical trials.

Characteristics of the (Auto)Reactive T Cells in Rheumatoid Arthritis According to the Immune Epitope Database T cells are known to be involved in the pathogenesis of rheumatoid arthritis (RA). Accordingly, and to better understand T cells’ contribution to RA, a comprehensive review based on an analysis of the Immune Epitope Database (IEDB) was conducted. An immune CD8+ T cell senescence response is reported in RA and inflammatory diseases, which is driven by active viral antigens from latent viruses and cryptic self-apoptotic peptides. RA-associated pro-inflammatory CD4+ T cells are selected by MHC class II and immunodominant peptides, which are derived from molecular chaperones, host extra-cellular and cellular peptides that could be post-translationally modified (PTM), and bacterial cross-reactive peptides. A large panel of techniques have been used to characterize (auto)reactive T cells and RA-associated peptides with regards to their interaction with the MHC and TCR, capacity to enter the docking site of the shared epitope (DRB1-SE), capacity to induce T cell proliferation, capacity to select T cell subsets (Th1/Th17, Treg), and clinical contribution. Among docking DRB1-SE peptides, those with PTM expand autoreactive and high-affinity CD4+ memory T cells in RA patients with an active disease. Considering original therapeutic options in RA, mutated, or altered peptide ligands (APL) have been developed and are tested in clinical trials. Rheumatoid arthritis (RA) is a chronic, inflammatory, systemic autoimmune disease of the joints leading to cartilage destruction and bone erosion [1]. RA affects 0.5–1% of the population, which makes it the most common inflammatory arthropathy in Western countries. Multiple triggering factors are involved, half of which are genetic factors and half are environmental and sex-related factors, as supported by epidemiological studies and the use of Mendelian randomization approaches testing direct associations [2,3,4,5,6]. The RA immune response development is suspected to start several years before RA onset in the inflamed mucosa of the lung, mouth, and/or gut with the emergence of neo-epitopes from cell death and post-translational modifications (PTM), including citrullination, carbamylation, and/or acetylation [7]. This leads to neo-epitope intake by antigen presenting cells (APC), their migration into lymphoid organs, and lymphocytes’ activation with the production of anti-modified protein antibodies (AMPA), including anti-citrullinated protein antibodies (ACPA). As a consequence, an immune response can take place in the mucosa against bacterial species (e.g., Porphyromonas, Aggregatibacter, Prevotella) and enzymes (e.g., peptidylarginine deiminase or PAD) implicated in the neo-antigen process. These neo-antigens can be derived from mucosal bacteria membranes, histones from the uncontrolled formation of neutrophil extracellular traps (NETs), and extracellular matrix or cellular proteins [8,9,10,11]. An immune response also takes place in inflamed and arthritic joints at the preclinical stage of the disease due to the accumulation of neo-antigens present on histones and extracellular matrix proteins such as fibrinogen, collagen, tenascin, and another cell component like enolase and vimentin. The autoantibody (autoAb) spectrum in RA is completed by the presence of rheumatoid factor (RF), an anti-immunoglobulin G (IgG) autoantibody, which is associated with disease severity [12,13]. RA is considered as a prototypic CD4+ T cell disease that drives inflammation and autoAb production. CD4+ T cells are predominant in the synovial tissue of RA patients [14], and similarly to synovial CD4+ T cells, peripheral blood CD4+ T cells are able to recognize auto-antigens and to induce a cellular response. Accordingly, it has been proposed that naïve T-cell differentiation into Th1 cells is associated with the production of pro-inflammatory cytokines such as interferon gamma (IFN-γ, specific to T cells and NK cells), TNF-α, and lymphotoxin, leading to chronic inflammation and destruction of bone and cartilage [15]. However, this paradigm suffers from major limitations that include the non-response of monoclonal antibodies (mAb) against IFN-γ and CD4+ T cells in RA patients, the presence of Th17-IL-17 instead of Th1-IFN-γ positive CD4+ cells within the rheumatoid synovium, and a paradoxical IFN-γ anti-arthritis effect reported in RA-prone murine models as compared to the promotion of arthritis flares by IFN-γ in normal mice [16,17,18,19]. This Th1 model is further complicated by the report of an immunosenescence of the cytotoxic CD8+/CD4+ T cells and regulatory T cells (Treg), the pro-inflammatory contribution of Th17 cells, the presence of extra-follicular lymphoid structures in synovium, and the presence of recently described ‘peripheral helper’ T cells inducing plasma cell differentiation (in vitro) and antibodies production [20]. Considering murine models of RA, it is shown that CD4+ T cell depletion strategies abrogate T cell development, while a depletion in CD8+ T cells increases the severity of the disease [21]. In a model of severe combined immunodeficiency (SCID), mice develop arthritis after an adoptive transfer of primed autoreactive CD4+ Th1-cells followed by a challenge with the auto-reactive target [22]. Overall, these results highlight the pathophysiological importance of T cells in RA that is further reinforced by the observation that autoantigen presentation to CD4+ T cells is restricted in human to the human leukocyte antigen (HLA) class II DRB1 shared epitope (DRB1-SE) alleles and in mice to the major histocompatibility (MHC) class II H2 alleles. Accordingly, and to improve our understanding regarding T cell functions in RA, we took advantage of the Immune Epitope Database (IEDB) to better understand the T cell responses in RA. Given the importance of the T cell responses in RA, an RA-related antigen list was generated from the IEDB web site (http://www.iedb.org/, accessed on 7 February 2023) using the following filters: any epitope, no MHC restriction, any host, and “rheumatoid arthritis” [23]. From this analysis, and as presented in Table 1 for T cell antigens, 1390 epitopes were found corresponding to 79 antigens from various origins (human, other mammalian origin, and infectious agent), 865 assays, and 103 references. The same analysis was performed with MHC and B cell antigens. From this list and since not all epitopes and antigens are relevant to RA, publications were individually reviewed in depth in order to establish a list of major epitopes/antigens based on their relevance to RA. Moreover, and when available, information regarding HLA class I/II restriction, T cell activation, the phenotype of the T cell population expanded, origin of peripheral blood or synovial fluid T cell, the capacity to enter the docking site of the DRB1-SE, cross-reactivity, the importance of PTM in generating neo-epitopes, antagonist effect, and TCR molecule usage were further collected from the database and/or from publications. From these sources, two tables were generated based on HLA I/II restriction usage and antigen characteristics for the latter. To help the reader, the main techniques used to characterize RA-associated (auto)reactive T cells are reviewed below. T cell activation requires two signals: first, a T cell receptor (TCR) recognition of specific peptides presented by MHC class II for CD4+ T cells or by MHC class I for CD8+ T cells, and second, simultaneous co-signals induced by CD28 or CD40L receptor engagement. Both signals can be provided by circulating monocytes present in peripheral blood or, better, after monocyte differentiation into dendritic cells (DCs) [24]. Due to the capacity of these APC to process and present relevant immunodominant peptides to T cells, full-length proteins can be used instead of peptides. Immunodominant peptide identification requires the use of overlapping and single peptides coupled with the use of in silico predictive tools. RA-associated T cell characteristics (Table 2) can be appreciated by measuring lymphocyte proliferation after 3H-thymidine incorporation into new strands of chromosomal DNA during mitosis, flow cytometric analysis of activation markers (e.g., CD69, CD40L/CD154), and the cytokine-secreting profile using, among other techniques, enzyme-linked immunospot (ELISpot) assays. The most used cytokine marker for T cell activation is IFN-γ, but other cytokines can also be explored to investigate Th1 cell types (IFN-γ, TNF-α, IL-2, IL-6, IL-12, and IL-21), Th2 cell types (IL-4, IL-5, IL-10 and IL-13), Th17 cell types (IL-17), T regulatory (Treg) cell type (IL-10), and monocyte/macrophage activation markers such as the monocyte chemoattractant protein 1/2 (MCP-1/CCL2) and the macrophage inflammatory protein 1 alpha (MIP-1α/CCL3) [25,26]. MHC multimers (usually tetramers) are valuable tools for monitoring (auto)antigen-specific T cell frequencies and phenotypes during RA development or disease progression or in response to treatments. Initially developed to follow clonally expanded antigen-specific CD8+ T cells in blood and synovial fluid [27], the technique is based on the use of streptavidin–fluorochrome conjugates associated with mono-biotinylated tetramer class I MHC-peptide complexes. For antigen-specific CD4+ T cells, the use of tetramer-class-II–peptide complexes are more challenging, mainly due to their low frequency. For this reason, dextramers, based on a dextran backbone, bearing multiple fluorescein and streptavidin moieties can be used to allow multibinding and selection of low-affinity antigen-specific T cells. The multimer technology further allows antigen-specific T cell purification. Autoreactive clonal T cell expansion can be assessed by testing TCR repertoire diversity in peripheral blood and synovial fluid of RA patients using purified CD4+ or CD8+ T cells and TCR DNA amplification through multiplex RT-PCR or next-generation sequencing (NGS). When coupled with a single T cell analysis, TCR sequencing can be paired with a transcriptomic analysis, providing information regarding αβTCR chain association, immune phenotype, cell cycle, and metabolism [28,29]. The main limitations are related to the fact that autoreactive T cell prevalence is low and that cytokines used in long-term tissue culture can cause bias in the TCR repertoire. These effects can be limited by isolating high-affinity T cells directly ex vivo with MHC multimers but again with a risk of overlooking autoreactive T cells with lower affinity. According to the established CD8+ T cell IEDB RA-list (Table 3), the main RA-associated class I epitopes are limited and derive from viral antigens and cryptic epitopes. In humans, an important part of the immune T cell response is directed against bacteria and viruses that produce a flurry of foreign antigens. Immunization takes place in lymphoid organs where antigen-driven naïve T cells expand and differentiate into effector and memory T cells. Next, differentiated T cells migrate through a chemokine gradient into inflamed tissue. Part of these cells can persist in the tissue as resident memory T (TRM) cells to provide immune protection but also to drive local inflammation [29,37,38,39,40]. Analysis of the synovial fluid and tissues from RA patients reveals an increased number of cytotoxic effector T cells and TRM cells. Among them, an anti-viral response is reported in RA patients with an oligoclonal enrichment of Epstein–Barr virus (EBV)- and cytomegalovirus (CMV)-specific CD8 TRM cells together with an HLA class I restriction in up to 15% in synovial fluid as compared to ~1% in peripheral blood [27,30,31,32,33,34]. In line with a persistent antiviral response fueled by viral reactivation, the synovial CD8+ TRM cell response is directed against HHV active lytic proteins (e.g., BZLF1, BMLF1, pp65) rather than latent proteins (e.g., EBNA, LMP2) and non-latent viruses such as influenza [32,33,34]. The contribution of members of the human herpes viridae (HHV) family as risk factors that may trigger RA is debated, as well as any HHV contribution to disease severity [3,41,42]. However, several reports support an exacerbated HHV reactivation from their latent forms at the early stages of RA for herpes simplex virus (HSV)1/2 [43,44], and following introduction of immunosuppressive drugs for herpes zoster, CMV and EBV [45]. This reactivation does not necessarily take place in the joints, but synovial inflammation promotes the attraction and survival of the synovial pool of resident anti-HHV CD8+ TRM cells [5,46]. Expansion of this pool in the joints is associated with disease activity and contributes to extra–articular involvement as CD8+ TRM cells possess the capacity to migrate into inflamed tissues [18,47]. Such an effect has been named TRM memory inflation and is described in numerous types of chronic inflammatory arthritis including RA [48]. Latent HHV including CMV infections enhance CD8+ effector and TRM cells’ immunosenescence characterized by increased capacity to release IFN-γ, loss of the CD28 co-stimulatory signals, up-regulation of the inhibitory NK cell receptor leukocyte immunoglobulin-like receptor 1 (LIR-1 or CD85), migration toward the fractalkine gradient into inflamed tissues such as the synovium due to the expression of the chemokine receptor CXCR3, and an oligoclonal TCR repertoire [30,31,49]. As a consequence, and in addition to improperly controlling latent HHV infections, CD8+ CD28-negative T cells are suspected to perpetuate the inflammatory signal with an increased burden [50]. During chronic viral infections, a bystander CD8+ T cell response may be directed to cryptic self-antigens generated by caspases and unveiled during cellular apoptosis [51]. Using a multimer approach to characterize them, it was reported that HLA-A2 restricted apoptotic epitopes (AE) target specific CD8+ (CD107a+) TM cells [35,36]. This subset is significantly increased in RA patients, and its level is correlated with disease activity and is sensitive to anti-TNF-α therapies within RA responders. Moreover, AE-CD8+ TM cells express granzyme B, produce high levels of TNF-α and selectively contact Treg and control their expansion (Figure 1). The antigen-binding groove of the HLA class II molecule binds a limited repertoire of peptide fragments 9–15 amino acids in length, which constitutes a specific ligand for the TCR of CD4+ T cells to elicit a response. The stabilization of the peptide–HLA complex depends on four pockets (P1, P4, P6, and P9) that determine which peptides the receptor can interact with. In the third hyper-variable region of the DRB1 chain, the P4 pocket contains the SE motif at positions 70–74, and at its base the amino acids 11 and 13 that allow the binding of both negatively charged (Asp and Glu) immunodominant peptides. Pockets P1/P9 are further implicated in peptide stabilization [52]. The DRB1-SE motif accounts for the highest RA genetic risk factor with an odds ratio (OR) ranging from 2.17 (95% CI:1.94–2.42) for DRB1*01:01/02 to 4.44 (CI95: 4.02–4.91) for DRB1-SE*04.01, which benefits from the additive effect of a valine in position 11 [53]. As a consequence, and according to the population studied, 50–70% of RA patients and up to 95% of erosive RA patients possess one or two copies of the DRB1-SE, as compared to 20–50% within the healthy control group. Of note, this genotype confers higher risk for the development ACPA-positive RA disease [54]. A long list of peptides with the capacity to enter the docking site of RA-associated DRB1-SE alleles have demonstrated their ability to promote CD4+ T cell activation. According to the immunodominant CD4+ T cell RA-IEDB list (Table 4), the main DRB1-SE restricted peptides eluted from synovial tissues derive from highly conserved antigens such as molecular chaperones, synovial peptides, cross-reactive bacterial antigens, and extracellular matrix or cellular peptides with PTM modifications, mainly citrullination. One of the best-known antigens triggering a T cell response in patients with RA is related to heat-shock proteins (HSPs). HSPs are molecular chaperones overexpressed in response to pro-inflammatory cytokines, which is the case in the synovial tissue of RA patients [64,94]. HSPs are ubiquitous and conserved proteins present in both prokaryotic and eukaryotic organisms that possess an agonist or antagonist DRB1-SE-dependent effect in their peptide structure. The agonist or effector effect leading to T cell proliferation is carried by cross-reactive epitopes with similarities reported between the mycobacterial (myc)HSP70/DNAk peptide 287–306 (DRTRKPFQSVIADTGISVSE) and the human chaperone binding immunoglobulin protein 336–355 (BiP, RSTMKPVQKVLEDSDLKKSD) [58]. DRB1-SE effector peptides are also retrieved from GroEL2, the bacterial homolog of the HSP60; DnaJ, the bacterial homolog of the HSP4055; the 19 kDa lipoprotein (LpqH), extracted from M. tuberculosis lysate; and influenzae antigens such as hemagglutinin (Hae, 307–319) and matrix protein 1 (MP1, 17–29). The rat matrix metalloproteinase (MMP)-3-derived peptide 446–460, which presents molecular mimicry with mycHSP65 178–186, is effective in inducing T cell proliferation in PBMC from RA patients and proinflammatory cytokine release [63]. The DRB1-SE’s restricted and effector effect is counterbalanced by an antagonist or tolerogenic effect, as the BiP peptide (456–475, DNQPTVTIKVYEGERPLTKD) contributes to the proliferation of IL-10 secreting Treg cells both in vitro and in vivo within the collagen-induced arthritis (CIA) model, and the antagonist BiP peptide (456–475) is further effective in reversing the agonist BiP peptide’s (336–355) capacity to induce T cell proliferation in vitro [55]. This tolerogenic effect is common and also reported with peptides from mycHSP70/DNAk, mycHSP60/GroEl2, and pan-DR HSP60 [58,62,95]. As a consequence, atypical mycobacteria such as M. avium can be found in RA patients’ lungs [96], and RA patients present an elevated risk of mycobacterial infections, exacerbated under anti-TNF-α therapies [97]. The dual role of DRB1-SE’s restricted peptides has led to the development of competitive peptide analogs to dampen T cell activation in RA, and some of them are in clinical development, as described below [98]. Among notable autoantigens found in synovial articulations, collagens, which are one of the main constituents of cartilage, and in particular type 2 collagen (Col2), have demonstrated a pathogenic role in the CIA mouse model [99]. In humans and in opposition to mice, a weak T cell reactivity towards Col2 (259–273) is reported in its native form as compared to a strong activation in its galactosylated form among patients with DRB1-SE*04:01 [67]. The MHC–Col2 interaction is similar when comparing RA patients and healthy subjects carrying the DRB1*04:01 allele; however, the difference for RA patients resulted in a higher IFN-γ, IL-17, and IL-2 T cell response and an antigen specific repertoire that is correlated with clinical activity [66,69,70], and this effect is higher in early RA than in established RA [68]. Highly abundant in the extracellular matrix from synovial tissues, cartilage proteoglycans (CPG) are not directly immunogenic as the native form induces a low T cell-stimulation response. However, and particularly during inflammation, CPG fragments can be released from proteolysis, and portions of them (CPG 16–39 and 263–282) can trigger T cell proliferation, T cell activation (CD69), and cytokine release [62,100]. Other released synovial autoantigens overexpressed in RA are effective in inducing a proliferative T cell response with pro-inflammatory cytokine production; this list included the heterogeneous nuclear ribonucleoprotein A2 (hn-RNP-A2/RA33), G1 aggrecan, and the peptidylarginine deiminase (PAD) 4 [74,75,76]. Using a structural approach, Col2 261–273 (AGFKGEQGPKGEP) presented a homology with the virulence-associated trimeric transporter (Vta 755–766) from Haemophilus parasuis (AGPKGEQPKGE) and CPG 263–282 (YLAWQAGMDMCSAGW) with the Yersinia outer membrane protein (Yop) 68–82 from Yersinia sp. (QKQLGWQAGMDEART) [71,100]. According to molecular mimicry theory, these two bacterial peptides, Vta 750–766 and Yop 68–82, have further demonstrated their capacity to induce a T cell response similar to their human counterparts in RA patients. PTM and in particular the conversion of arginine to citrulline generates “altered-self” peptides that become electropositive, allowing for insertion in the electronegative P4 pocket of the DRB1-SE, whereas the electropositive P4 pocket of the RA-protective/resistant DRB1-SE*04:02 compromises this insertion [101]. Therefore, citrullination expands the repertoire of peptides from the extracellular matrix and cellular components, and these peptides may interact with DRB1-SE and further enhance the T cell response. This effect of citrullinated peptides on T cells is suspected to result from a direct contact with the TCR from autoreactive CD4+ T cells rather than a higher affinity to the MHC, as initially stated for Col2 [79]. The citrullination process that is catalyzed by PAD4, an enzyme with increased activity during inflammation, cell death, and stress, has been reported in joints in RA patients [102] and in lung epithelial cells in tobacco smokers. Consistent with this is a report that smoking increases the risk of developing ACPA among DRB1-SE positive individuals [103]. The comparative analysis of the T cell response between non-citrullinated and citrullinated (Cit) peptides recognized by ACPA (fibrinogen, vimentin, α-enolase, Col2, aggrecan, human cartilage (HC), HCgp39, tenascin, and cartilage intermediate layer protein (CILP)) revealed [77,78,80,83,84,85,86,89] (i) that citrullinated peptides induce a T cell proliferation that can be associated with an activation when testing the native counterparts or not, as reported for aggrecan and tenascin; (ii) that T cell response is related to the presence of DRB1-SE (*04:01-04-05, *01:01-02, and/or *10:01); (iii) that a biased TCR usage was reported for tenascin 1012–1026; (iv) that differences in cytokine production are observed between peptides (elevated IL-6 levels with Cit-aggrecan and elevated IL-17 levels with Cit-fibrinogen); and (v) that Cit-peptide response number increases with established RA. Class II multimers that covalently couple immunodominant peptides to DRB1-SE can be used to stain autoreactive CD4+ T cells. This strategy was applied to characterize RA-associated autoreactive CD4+ T cells by using tetramers containing TCR high affinity citrullinated peptides derived from enolase, aggrecan, fibrinogen, CILP, and vimentin. These high-affinity autoreactive CD4+ T cells are expanded in RA patients, which is not the case when using low-/medium-affinity tetramers harboring native immunodominant peptides (e.g., Col2 and HCgp39) [59,60,73]. This supports the concept that an elevated MHC-TCR affinity is required to break tolerance [82]. Moreover, high-affinity autoreactive CD4+ T cell expansion is related to memory T cells, and their level is correlated with disease activity and ACPA level [84,88,101]. High-affinity memory CD4+ T cells are oligoclonal and polarized into Th17 cells, while Th1 cells decline, and this pool is controlled by anti-TNF-α biotherapies [81]. The peptide repertoire or immunopeptidome recognized by autoreactive CD4+ T cells can be appreciated by mass spectrometry following MHC elution in an unbiased manner. This strategy was applied first in a pioneering study conducted in 1995, in which 14 HLA-DR endogenous peptides were eluted from the spleen of an RA patient with Felty syndrome, and among them, a peptide derived from the human serum albumin peptide (106–120) was effective for binding DRB1-SE*04:01 [90]. Next, in 2011, two RA patients harboring DRB1-SE (*01:01 and *04:01) or not (*04:02 and *11:04) were selected, and 166 synovial peptides were eluted from DRB1 showing common peptides derived from intracellular, membrane, extracellular, and plasma proteins [104]. Some of the proteins corresponding to the 166 synovial DRB1 eluted peptides were recognized by RF (immunoglobulins) or ACPA (vimentin, fibrinogen, fibronectin, and collagen). More recently, Maggi et al. characterized synovial peptides presented by DRB1-SE from three RA patients and reported that the corresponding parental proteins were increased in the synovial fluid from RA patients (20%), recognized by autoantibodies (10%), and/or known to elicit an RA T cell response (5%) [79]. Next, authors reported the capacity of both native (gelsoline, histone H2B/H4, myeloperoxidase (MPO)) and citrullinated peptides (Histone H2B and proteoglycan 4) to trigger IFN-γ and CD40L expression on circulating CD4+ T cells from 29 RA patients as compared to 12 healthy controls. Prevotella copri is considered a pathogenic taxon in RA. Indeed, gut microbiome variations (dysbiosis) including P. copri are associated with the presence of DRB1-SE in healthy controls [105], with RA at early stage [106], but also with other metabolic syndromes such as insulin-resistant type II diabetes [107]. In synovial fluid from an RA patient harboring two copies of DRB1-SE, the detection of Prevotella sp. 16S rRNA was associated with the isolation of the HLA-DR-restricted peptide from a 27 kDa protein of P. copri (Pc-27) in peripheral blood mononuclear cells (PBMC) [91]. Two other self-peptides derived from N-acetylglucosamine-6-sulfatase (GNS) and filamin A (FLNA) sharing 67% and 80% homology with P. copri, respectively, were further eluted from the synovium [92]. In addition, the same team reported four new HLA-derived peptides from P. copri that were identified in new RA patients [93]. P. copri-derived peptides and self-GNS and -FLNA peptides were effective for inducing a Th1 response with IFN-γ release in PBMC from RA patients at new onset, and IgA P. copri antibody levels were correlated with ACPA values. The development of ectopic or extrafollicular germinal centers occurs in RA synovial tissues and drives immune responses. In these structures, B cells contribute to T cell activation through the expression of MHC class II and co-stimulatory molecules (APC role), to pro-inflammatory cytokine production, and to the local production of AMPA and RF. In this context, the simultaneous activation of B-cells and T-cells can be promoted by overlapping and shared immunogenic epitopes. As reported in Figure 2, among the long list of T and B cell antigens associated with RA, a limited number of epitopes are recognized by the two cell types: Cit-tenascin [87], HSP40/DNAj [65], PAD4 [76], and vimentin [77]. Then, these major overlapping T/B-cell epitopes can amplify B cell-dependent antigen presentation (via MHC), autoantibody production, and autoreactive CD4+ T cell selection and expansion. Accordingly, T/B cell cooperation in RA was illustrated in the “hapten carrier model” that suggests that B cells harboring DRB1-SE present PAD4 peptides to T cells, which in turn help ACPA-producing B cells. In this model PAD4 is the carrier and citrullinated peptides are the haptens [76]. Altered peptide ligands (APL) were initially developed as controls for CD4+ T cell-immunodominant peptides, but it was quickly observed that APL were effective in (i) repressing proliferation without altering cytokine and antibody production by fostering T-B cell cooperation, (ii) promoting Treg development instead of T helper polarization, or (iii) acting as a super-agonist, as recently reviewed by Candia et al. [108]. Then the possibility of creating APL opens up new and attractive therapeutic options for RA, and for that purpose, a three-step strategy has been initiated. First, APL has been screened in vitro to test partial agonist effects, as observed following amino acid substitution in Col2 (256–271) and in HCgp39. Both peptides conserved DRB1-SE-binding capacity but failed to induce a T cell response [109,110]. Another mechanism is described for HSP60 E18 mutants (90–109, HSP60 APL1:L109, Hsp60 APL2:L103) that are effective in both increasing the proportion of Treg and suppressing IL-17 level, and APL-1 is further effective in inducing apoptosis in autoreactive CD4+ CD25+ T cell and increasing IL-10 levels when incubated with PBMC from RA patients [111,112,113] (Figure 3). Second, APL’s capacity to reverse the disease has been tested in animal models. To this end, the synthetic Col2 (245–270, A260, B261, N263) is effective in suppressing CIA directly by increasing the Th2 response toward Th1/Th17 responses or indirectly when APC-trained T cells are passively transferred [114,115,116]. Both HSP60 APL-1 and APL-2 mutants reduce adjuvant arthritis in ill rats treated with Mycobacterium tuberculosis [112,113]. Similarly, the prophylactic use of APL-12 derived from human glucose 6 phosphate isomerase (hGPI 325–339) improves the severity of arthritis via Treg induction in the GPI-induced arthritis mouse model [117]. Finally, and third, phase I clinical trials have started, conducted for HSP60 APL-1 (CIGB-814) in order to evaluate its pharmacological characteristics, and good tolerance with clinical improvement has been reported in 20 RA patients [118,119]. RA is a chronic immune and inflammatory disorder for which no cure exists, and this results from a lack of understanding of the mechanisms leading to T cell hyperactivation, which in turn initiates and sustains the disease. Analysis of the peptides leading to T cell hyperactivation supports a complex process that includes a combination of disease-provoking microorganisms, genetic mutations, inflammation, and environmental factors. The common denominators are molecular mimicry, neoantigens, MHC restriction, TCR engagement, and overlapping T/B epitopes. Consequently, a better characterization of the hyperactivated autoreactive T cells in RA allows for personalized perspectives regarding disease prediction, disease follow-up, therapeutic response, and the development of new therapeutics to prevent and control the development hyperactivated autoreactive T cells.

PMC10001544

Gheorghe Paltanea,Veronica Manescu (Paltanea),Iulian Antoniac,Aurora Antoniac,Iosif Vasile Nemoianu,Alina Robu,Horatiu Dura

A Review of Biomimetic and Biodegradable Magnetic Scaffolds for Bone Tissue Engineering and Oncology

21-02-2023

bone tissue engineering,magnetic scaffolds,magnetic nanoparticles,regenerative medicine,cancer therapy,magnetic hyperthermia,photothermal therapy

Bone defects characterized by limited regenerative properties are considered a priority in surgical practice, as they are associated with reduced quality of life and high costs. In bone tissue engineering, different types of scaffolds are used. These implants represent structures with well-established properties that play an important role as delivery vectors or cellular systems for cells, growth factors, bioactive molecules, chemical compounds, and drugs. The scaffold must provide a microenvironment with increased regenerative potential at the damage site. Magnetic nanoparticles are linked to an intrinsic magnetic field, and when they are incorporated into biomimetic scaffold structures, they can sustain osteoconduction, osteoinduction, and angiogenesis. Some studies have shown that combining ferromagnetic or superparamagnetic nanoparticles and external stimuli such as an electromagnetic field or laser light can enhance osteogenesis and angiogenesis and even lead to cancer cell death. These therapies are based on in vitro and in vivo studies and could be included in clinical trials for large bone defect regeneration and cancer treatments in the near future. We highlight the scaffolds’ main attributes and focus on natural and synthetic polymeric biomaterials combined with magnetic nanoparticles and their production methods. Then, we underline the structural and morphological aspects of the magnetic scaffolds and their mechanical, thermal, and magnetic properties. Great attention is devoted to the magnetic field effects on bone cells, biocompatibility, and osteogenic impact of the polymeric scaffolds reinforced with magnetic nanoparticles. We explain the biological processes activated due to magnetic particles’ presence and underline their possible toxic effects. We present some studies regarding animal tests and potential clinical applications of magnetic polymeric scaffolds.

A Review of Biomimetic and Biodegradable Magnetic Scaffolds for Bone Tissue Engineering and Oncology Bone defects characterized by limited regenerative properties are considered a priority in surgical practice, as they are associated with reduced quality of life and high costs. In bone tissue engineering, different types of scaffolds are used. These implants represent structures with well-established properties that play an important role as delivery vectors or cellular systems for cells, growth factors, bioactive molecules, chemical compounds, and drugs. The scaffold must provide a microenvironment with increased regenerative potential at the damage site. Magnetic nanoparticles are linked to an intrinsic magnetic field, and when they are incorporated into biomimetic scaffold structures, they can sustain osteoconduction, osteoinduction, and angiogenesis. Some studies have shown that combining ferromagnetic or superparamagnetic nanoparticles and external stimuli such as an electromagnetic field or laser light can enhance osteogenesis and angiogenesis and even lead to cancer cell death. These therapies are based on in vitro and in vivo studies and could be included in clinical trials for large bone defect regeneration and cancer treatments in the near future. We highlight the scaffolds’ main attributes and focus on natural and synthetic polymeric biomaterials combined with magnetic nanoparticles and their production methods. Then, we underline the structural and morphological aspects of the magnetic scaffolds and their mechanical, thermal, and magnetic properties. Great attention is devoted to the magnetic field effects on bone cells, biocompatibility, and osteogenic impact of the polymeric scaffolds reinforced with magnetic nanoparticles. We explain the biological processes activated due to magnetic particles’ presence and underline their possible toxic effects. We present some studies regarding animal tests and potential clinical applications of magnetic polymeric scaffolds. Bone tissue engineering (BTE) is an important topic in orthopedic [1] and craniofacial surgery [2,3]. To restore defects due to prosthetic implants and degenerative diseases such as osteoarthritis, osteoporosis, Paget’s disease, or osteogenesis imperfecta, biomimetic scaffolds can be involved [4,5,6,7,8]. Bone reconstruction is a complex process starting from inflammation, regeneration, and remodeling, each with its unique physical and biological mechanisms. Within this process, an important aspect is the action of the stem cells combined with growth factors or cytokines. Mesenchymal stem cells (MSCs) usually differentiate into osteoblasts, and hematopoietic stem cells (HSCs) are directly linked to osteoclast formation [9]. These two types of cells are essential for the formation and remodeling process of new bone. In the case of small bone defects, the healing phenomenon is spontaneous, but some supplementary interventions are required in the case of large defects [10,11,12]. Schemitsch [13] proposed a classification of bone defects. He considered that small defects are characterized by 50% cortical circumference loss and a defect size of less than 2 cm, intermediate defects consist of a cortical circumference loss higher than 50% and a defect size between 2 and 6 cm, and large defects exhibit a size greater than 6 cm [14]. Autografts or allografts are harvested using an invasive procedure associated with high risk of infection or disease transmission; even graft rejection can occur [15,16]. Usually, bone from the iliac crest, autologous vascularized fibular graft, or allograft is used. Another classical solution consists of the use of bone transport fixators; however, this method is linked to an increased healing time and pain. Autografts represent the gold standard in orthopedy, but this method is not always safe due to donor site morbidity effects and limited availability [17,18]. Regarding allografts, immunogenic responses or vascularization graft absence should be taken into account [19]. Grafting techniques are expensive, and due to high graft demand on the worldwide market, problems in bone defect treatments are foreseen [20,21,22,23]. Bone scaffolds can be defined as artificial platforms dedicated to supporting and repairing a defect. A scaffold is necessary when an organ or a tissue is damaged, and in these cases, a three-dimensional (3D) structure is indicated. The most important properties and design features for a scaffold are biocompatibility, mechanical properties, biodegradability, pore size and interconnectivity, osteoinductivity, porosity, stability, antimicrobial effects, osteoconductivity, osteointegration, and osteogenesis, as depicted in Figure 1 [24,25,26,27]. For 3D scaffolds, a few criteria must be met before qualifying as an ideal implant. First, the scaffolds should have sufficient porosity to allow for tissue growth, adequate signaling, cellular ingress, and vascularization [28,29,30]. However, it is important to note that the mechanical properties of scaffolds are inversely proportional to their porosity. Therefore, recent studies [31,32] have recommended that scaffolds with a porosity of 200–350 μm are suitable for bone tissue regeneration. In the case of small bone defects, two-dimensional structures can be used as scaffolds to facilitate better interaction between cells and implant biomaterials [33]. After the scaffold is implanted, it is expected to provide a structure that is beneficial for cell proliferation, adhesion, and differentiation to create an adequate biomechanical medium for tissue regeneration, permit the dissemination of oxygen and nutritional substances, and to allow for the encapsulation of cells that will be released and combined with growth factors [34,35]. Scaffolds can be very useful for delivery of drugs and cells and, in the case of organ disease or failure, can sometimes be used to restore normal functionality. In bone tissue engineering, it is well known that bone-like porous structures ensure blood circulation, nutrient movement, and a combination of osteogenic cells and bioactive substances, which promote mineralization and angiogenesis in the transplanted graft. Chemical composition and topological aspects strongly influence scaffold surface properties, which are essential in cell adhesion and proliferation. The implant surface is the main boundary between biomaterial and tissue [36,37,38]. Surface roughness is considered a critical factor in osteoblasts’ adhesion and differentiation, and the mechanical properties of the scaffold must be similar to those of human bone to ensure successful and healthy bone grafting [39,40]. The implant must support the bone ingrowth process until the new bone can sustain itself. The efficacity of the regenerative process plays an important role through pore distribution, exposed surface area, the material’s porosity, the rate of cell penetration within the scaffold volume, and the extracellular matrix (ECM) architecture [41]. Research has been focused on different types of scaffolds that show biological components [42,43]. These implants are, unfortunately, expensive, so scaffolds that do not contain so-called “biologics” provide significant advantages. They are adequately manufactured to collect and recruit cells from the tissue placed in the scaffold vicinity to enhance new bone formation. The use of adequate bioactive agents can sometimes be helpful for recruitment of cells with osteogenic characteristics [42,44]. As a result, the of mineralized matrices occurs in the entire implant structure [45,46]. A high amount improves the regenerative process. Angiogenesis is another essential aspect that sustains the needs of the new tissue [47]. Bioactive materials such as bioglass and calcium-phosphate-based ceramics are usually used in bone tissue engineering. They interact with natural tissue through an ion-exchange reaction, which leads to the formation of an active apatite layer on the scaffold [48,49]. Hydroxyapatite and tricalcium phosphate are biodegradable and begin to dissolve when introduced into human or animal bodies. Due to an increased similarity with human bone, scaffolds can be manufactured from bioactive ceramics that are corrosion-resistant, osteoconductive, and biocompatible [42]. Their main disadvantage is related to the fact that they are brittle and porous, and an increased risk of fracture can be foreseen. The most commonly used ceramics in BTE are hydroxyapatite, calcium phosphates, and β—tricalcium phosphate. Another important class of materials is bioactive glasses. They have a composition based on SiO2, P2O5, and B2O3. The silanol groups result from SiO2 dissolution and precipitate into a silica layer that sustains the migration of phosphate and calcium ions, leading to the appearance of a layer of calcium phosphate [50,51]. In some bioactive glasses, partial replacement of SiO2 with B2O3 generates borosilicate or borate glasses that exhibit a controllable biodegradation rate. A faster degradation rate of the scaffold was noticed in the case of phosphate glasses that include Na2O and CaO. The advantages of bioactive glasses are controlled resorbability and osteoconduction. The main drawbacks are that the mechanical properties of the glass have values that differ from those of human bone and that the material must be tuned to control its degradation rate and ion release to avoid toxicity. Another disadvantage of bioactive glass was observed when 3D porous scaffolds were made; a crystallization phenomenon was identified during the sintering step [52]. As a result, the reduced compressive strength of the implant was put in evidence, which makes these types of materials suitable for scaffolds dedicated to use in low-load defect locations or as part of a composite structure with polymers or bioactive ceramics [53]. One of the most used materials in scaffold manufacturing is polymers, which can have natural or synthetic origins. The main polymeric implant properties are divided into three categories based on processing conditions, their intrinsic nature, and the final product. The intrinsic properties, such as density, solubility, crystallinity, transition temperature, mechanical properties, transparency, electromagnetic behavior, etc., depend on chemical composition and structure [54,55]. The viscosity, the melt strength, and melt flow index are considered the main processing characteristics and put in evidence the material behavior during the production process. The product’s properties combine those mentioned above and include esthetic properties, environmental behavior, and degradation conditions [56]. Polymeric scaffold biodegradability is very important and is defined as a gradual breakdown process of the material. There are two main biodegradable polymers: stepwise polycondensation and ring-opening polymerization materials. The first group includes polysaccharides and proteins [57], and the second contains aliphatic and aromatic polyesters. Most natural polymers are degraded by different enzymes. Polysaccharide-based biomaterials are degraded by amylases and lysosomes inside the human body. Many synthetic polymers are degraded by a hydrolytic process. The most common non-biological degradation processes are hydrolysis and erosion. The mechanical properties of polymers are influenced by molecular weight and crystallinity grade, which are directly linked to the degradation process of the material [58,59,60]. To obtain a successful treatment in the case of biodegradable polymers, it is important to maintain adequate mechanical strength to reconstruct load-bearing tissues such as bone. Rheological parameters such as Young’s modulus, flexural modulus, maximum strain, and tensile/compressive strength are always measured when a new implant enters the market [61]. The advantages of synthetic polymeric implants are that they can be manufactured under controlled conditions, and as a direct consequence, their degradation rate, mechanical properties, and porosity can be modified in accordance with different medical applications [62,63]. They can be produced in large quantities and can exhibit a homogenous structure. Better interaction with cells characterizes natural polymers, but they are found in limited quantity [64]. Their main drawback is that their properties cannot be controlled, as in the case of synthetic polymers, their toxicity must be carefully addressed [65]. Magnetic nanoparticles (MNPs) can be incorporated into scaffolds manipulated in situ under electromagnetic forces [66,67,68]. Due to the influence of magnetic field, these implants offer the possibility of increased osteogenesis and angiogenesis at large bone defect sites [69]. Many literature studies have proven that scaffolds reinforced with MNPs support the differentiation and proliferation of osteoblasts in the presence and absence of a magnetic field by activating dedicated signal pathways [44,70,71,72,73,74,75]. Treatment of bone tumors with methods such as magnetic hyperthermia or photothermal therapy is also possible. This review focuses on biodegradable magnetic polymeric scaffolds by providing insight into the biomaterials used in implant manufacturing; mechanical, thermal, and magnetic properties of the scaffolds; the influence of magnetic field on cells; biocompatibility; and osteogenic effects. Furthermore, we discuss issues related to the toxicity of magnetic nanoparticles, in vitro and in vivo analysis, and potential clinical applications of magnetic scaffolds. The main magnetic scaffold components are presented in Figure 2 [76,77], taking into account the scaffold geometry and shape and its combination with stem cells [78,79], growth factors or bioactive molecules [80], chemical compounds, and drugs [81]. Biomaterials for scaffolds must possess the ability to present biomimicry by taking into account the properties mentioned above, as explained in Section 1.2. Many studies have been conducted to determine the best material combinations to obtain an enhanced osteogenic and angiogenic effect of the scaffold when implanted in the human body [2,42,69,82,83]. In this direction, magnetic nanoparticles can be successfully combined with polymer scaffolds, obtaining an increased osteogenic effect on the stem cells [44,70,71,72,73,75]. Through MNPs, drugs or bioactive agents can be directly guided to the defect site to help in bone regeneration [84]. Figure 3 shows several types of biomaterials and scaffolds used in bone tissue engineering. Table 1 presents studies conducted on magnetic scaffolds based on different biopolymers and magnetic nanoparticles [85,86,87,88,89,90,91,92,93,94,95,96,97]. The scaffold manufacturing technique is chosen according to the following criteria. The chemical properties of the material must not be modified during the production process to negatively influence the implant’s clinical use or alter its biocompatibility [98]. There are two types of manufacturing technologies: conventional and the advanced techniques. Conventional techniques are based on subtractive routes that consist of material removal from an initial bulk volume to obtain the desired shape of the implant. The main drawback is that a random architecture of the scaffolds results [98]. These technologies imply the use of organic solvents, which may harm cell functions and viability [99]. On the other hand, advanced methods permit the control of scaffold geometry and pore size. Tunable mechanical properties characterize the implants, in accordance with the surrounding tissue attributes. These methods allow for compositional variation of different materials across the interface, surface, or volume of the scaffolds. In addition, they do not use toxic organic solvents, which is directly linked to increased scaffold biocompatibility [100]. Some of the most important techniques for the manufacture of polymeric scaffolds using conventional technologies are freeze drying, electrospinning, gas foaming, solvent-casting particulate leaching, and thermally induced phase separation. Freeze drying is based on polymeric slurry production. After that, it is poured into a mold and frozen. The resulting ice crystals generate the scaffold pores, and lyophilization occurs once the slurry undergoes solidification. Scaffolds manufactured through freeze-drying exhibit a porous structure with low stiffness and small pores. The main disadvantages of this method are high energy consumption, the use of cytotoxic solvents, and the long duration of the procedure [100,101,102]. The electrospinning technique consists of an electric charge liquid jet used to generate, with the help of a syringe pump, fine polymeric fibers, creating a collector on a nanofibrous architecture. The system’s main components are a high-voltage power supply, a syringe pump, a spinner with a metallic needle, and a collector connected directly to the ground. The electric field strength overcomes the surface tension of the material droplet, and a charged liquid jet, which is continuously deformed by the electrostatic repulsion phenomenon, is deposited on the collector. Fibrous polymeric scaffolds are manufactured using this technology [103]. A drawback of this method is that it is linked to organic solvent use. Sponge-like scaffolds based on inert gases that pressurize molded polymers with fluoroform and water are obtained using gas foaming. The material becomes saturated and is characterized by gas bubbles. An advantage of this technique is the avoidance of toxic solvents, and the disadvantages are the heat developed during the compression molding process, isolated pores, and a continuous skin layer [104]. Solvent-casting particulate leaching requires a solvent containing a dissolved polymer solution to be mixed with specific diameter-sized salt particles. By evaporating the solvent, an embedded salt matrix is obtained. Using water, the salt leaches out, generating a highly porous structure. The advantages of this method are high porosity and a controllable pore diameter through salt particle size. The main drawbacks include residual solvent presence and scaffolds with a simple geometry [105,106]. In the case of the thermally induced phase separation method, the polymer solution is subjected to a low temperature, so a liquid–liquid phase separation is obtained. Two phases result: a polymer-rich phase and a polymer-poor phase. The polymer-poor phase is eliminated during solidification. A highly porous nanoscale structure is obtained [107]. The advanced methods are part of the class of rapid prototyping technologies that include selective laser sintering (SLS), selective laser melting (SLM), stereolithography (SL), fused deposition modeling (FDM), and binder jetting (BJ). SLM and SLS are derived from the powder bed fusion class and can be used to obtain scaffolds with desired shape architecture and controlled porosity; however, small details such as sharp corners or complicated boundaries cannot be designed [108]. Through SLS, powder particles are bonded in thin layers under a high-power laser effect. The last formed layer is bounded to the previous layer as indicated in a predefined computer-aided design (CAD) file. The main drawbacks of this technology are the high operating temperature and the fact that residual powder must be removed [109,110,111]. SL includes a tank with a photosensitive liquid polymer placed in a thin layer on a movable built platform. The desired geometry layer is defined using an ultraviolet (UV) laser, the platform is lowered, and the process is repeated. This method is fast and provides a high resolution. Its drawbacks are brittleness and low mechanical strength of the scaffold [108]. FDM implies a molten thermoplastic material extruded through a nozzle to form a continuous thin filament printed on an imposed CAD pathway in a layer-by-layer procedure. Through this technique, a controlled porosity can be obtained [112]. The method does not require toxic solvents [113,114,115]. BJ technology is based on a deposited powder bed on which, using a printing head, a liquid binder solution that describes the required geometry is placed. The advantages of this method are the manufacture of scaffolds adapted to the patient’s anatomy or multilayered implants used for hybrid tissue regeneration. The unbounded powder removal, the limited pore size configuration, and the possibility of the binder being dissolved are the main drawbacks [2]. Three-dimensional bioprinting technology offers the possibility of including cells and differentiation or growth factors in the scaffold geometry. Its main drawback is that during the post-fabrication stages, the solvent must be entirely removed [116,117]. Figure 4 shows some of the conventional and advanced preparation methods and examples of obtained polymeric scaffolds. Table 2 summarizes the advantages and disadvantages of scaffold manufacturing techniques. As previously mentioned, MNPs can be inserted into polymeric scaffolds to enhance cell adhesion and differentiation or to apply regenerative or oncological treatments. They are fabricated using solvothermal, hydrothermal, coprecipitation, sol–gel, electrochemical, and laser pyrolysis methods [77]. Other preparation technologies reported in the literature are powder metallurgy, evaporation synthesis, laser ablation, and microbial methods [118]. The most used technology is precipitation from a solution. Magnetite (Fe3O4) is prepared based on an aqueous solution of Fe3+ and Fe2+ chloride combined with a base. Coprecipitation consists of a ferric and ferrous hydroxide suspension that is oxidized through different chemical substances (i.e., Fe2+ salt, nitrate ions, and a base) or a mixture of stoichiometric ferric and ferrous hydroxides that are aged in aqueous media. Other important methods are based on microemulsions, which generate nanoparticles with tunable sizes and distributions, reverse micelle solutions, or polyols, as explained in detail in [77]. Different methods have been reported in the literature to obtain magnetic scaffolds. Lu et al. [86] added SrFe12O19 nanoparticles prepared by the molten salt method and MBG microspheres in CS solution. After a stirring procedure, the samples were frozen under an external magnetic field, and finally, a freeze-drying step was applied. Cojocaru et al. [87] made magnetic scaffolds from natural biopolymers combined with Fe3O4 MNPs using a biomimetic coprecipitation method. Before this process, different concentrations of MNPs were added to the polymer solution, and after that, freeze-drying technology finished the production method. Samal et al. [89] designed silk scaffolds based on a salt-leaching procedure in which they infused MNPs under a static magnetic field effect generated by a permanent magnet. Dankova et al. [91] made a mixture of polycaprolactone, adding MNPs based on the dispersion method. Then, fibrous scaffolds were generated through the electrospinning method. De Santis et al. [92] designed, through rapid prototyping, 3D fully biodegradable magnetic scaffolds made from polycaprolactone reinforced with iron-doped hydroxyapatite (FeHAp) nanoparticles processed through a 3D fiber deposition method. They investigated the influence of FeHAp nanoparticle concentration to adapt the implants to dedicated medical applications such as advanced bone tissue engineering or magnetic hyperthermia. The choice of the scaffold production method must consider the geometry of the required implant (because it should be patient-oriented), the toxicity grade of the MNPs, and its cost. Magnetic scaffolds represent an innovative approach for large bone treatments, and they are applicable for drug delivery, cell guidance based on magnetic force action, and cancer therapy. Magnetic nanoparticles exhibit unique material properties because they can be manipulated using an externally applied magnetic field. These nanomaterials are made from a magnetic core, which contains a different oxide of ferromagnetic metals such as iron (Fe), cobalt (Co), or nickel (Ni) that can be coated with a biocompatible material with unique properties for medical applications [77,119,120]. Figure 5a shows a classic scheme of a coated MNP that can be used for ligand transport, responsive elements, and fluorophores. Figure 5b,c show scanning electron microscopy (SEM) images and elemental analyses of Fe3O4 uncoated MNPs functionalized with chitosan. The most common applications of MNPs are magnetic hyperthermia, magnetic drug targeting therapy, and as magnetic resonance imaging (MRI) contrast agents. The size of the magnetic particle represents the main parameter that separates ferromagnetic and superparamagnetic behavior. Below a critical diameter, the MNPs present a so-called superparamagnetic state characterized by a magnetic single-domain configuration with a sigmoidally shaped magnetization curve. High magnetic susceptibility and saturation magnetization values characterize superparamagnetic iron oxide nanoparticles (SPIONs). These nanoparticles are special MNPs extensively used in biomedicine [121,122]. They are biocompatible and chemically stable and exhibit environmentally friendly behavior. Superparamagnetic behavior is directly linked to the magnetic anisotropy of the MNPs measured along the easy magnetization axis of the particle, which is a direction characterized by a minimal value of magnetic anisotropy energy. In the case of spherical magnetic nanoparticles, the total magnetic anisotropy can be considered a barrier in the magnetization direction change [123]. At very low values of the particle diameter, the anisotropy energy is almost equal to the heat activation energy [124], and when the latter is increased, there is no preferential direction for the magnetic moment orientation. The behavior of SPIONs could be assimilated to that of paramagnetic atoms [125,126]. The value of the temperature at which the thermal activation is higher than the magnetic anisotropy energy is denoted in the literature as the blocking temperature [127,128]. It was previously shown that particles with a large diameter are more toxic than smaller particles when an alternating low-frequency magnetic field is applied [129]. At a diameter lower than 200 nm, the nanoparticles are not trapped in the sanguine system and are expelled through the mononuclear phagocyte system and hepatic filtration function [130,131]. The material surface must be modified to improve the drawbacks of magnetic nanoparticles, such as poor biodegradability, chemical instability, and moderate biocompatibility. One of the most used methods is MNP functionalization with different materials. Biofunctional molecules such as ligands, antibodies, or receptors can link different nanostructures of the human body to the magnetic core, making some treatments more efficient [132]. Another technology consists of the integration of SPIONs or, in general, MNPs with other metallic nanoparticles, which leads to so-called heterodimer structures. These unique materials permit the attachment of functional molecules to a specific surface part of the heterodimer that can bind to different receptors or act as agents in imaging techniques [133]. A direct application is a platform for bacterial detection [134,135]. The chemical stability and solubility of MNPs must be carefully controlled in biological media. It is well known that by incorporating MNPs into biodegradable polymeric scaffolds, owing to their hydrophilic nature, the implant wettability is improved, and increased cell adhesion and proliferation are observed [93]. The MNP concentration also plays an important role in the improvement of mechanical properties. Some studies have evidenced well-established MNP concentrations beyond which a decrease in mechanical strength is reported [136,137]. However, as an overall finding, the addition of MNPs can be linked to a reduction in the porosity percentage of PCL scaffolds and an increase in the porosity grade of chitosan or collagen implants [85,138]. When SPIONs are incorporated into polymeric scaffolds, because each particle is a single magnetic domain, the implant exerts a magnetic influence on the receptors placed on the cell membrane, activating the intracellular signaling pathways [88,137]. Due to the presence of magnetic induction, the cell cycle is accelerated, and osteogenic differentiation is put in evidence [91]. Modern magnetic scaffolds consist of a matrix made from different materials and magnetic nanoparticles chemically doped or physically loaded into the implant structure. The matrix is usually made from bioceramics, polymers, or hydrogels, and it is a suitable tool in regenerative medicine and anticancer therapy because the magnetic hyperthermia effect can be combined with the osteoinductive properties of the MNPs [139]. Table 3 shows the magnetization values of some MNPs incorporated in polymeric or composite matrices of different types of scaffolds used in bone tissue engineering. Biodegradable polymers can be used to manufacture the scaffold matrix in BTE. In the case of medical applications, increased attention is devoted to the cellular environment and the interaction between materials and cells [141,142]. Due to their specific properties, such as biodegradability, high porosity, important surface-to-volume ratio, and favorable mechanical properties, polymeric scaffolds have become among the most used implants for BTE [143,144]. As a function of structure and monomeric units, there are three important polymer types: polysaccharide-based (e.g., chitosan, chitin, hyaluronic acid, and alginate), polypeptide- and protein-based (e.g., collagen, silk, and gelatin), and polynucleotide-based [25] polymers. Members of the polysaccharide-based group are made from disaccharide or monosaccharide chains. Chitin and chitosan are characterized by non-toxicity, biocompatibility, and biodegradability properties [145]. They owe reactive species as hydroxy and amino groups, high charge density and exhibit broad hydrogen-bonding capabilities and a single chemical structure. Due to their reactive species, chitosan and chitin can be easily linked to different biomolecules to increase the biocompatibility of scaffolds. The biodegradation rate of these biopolymers depends on the acetyl content, and their in vivo breakdown occurs as a result of lysozymes. If chitosan is modified in an appropriate manner, scaffolds for bone regeneration can be produced [146]. Zhao et al. [85] prepared magnetic bioinspired micro/nanostructured composite scaffolds based on a chitosan/collagen organic matrix. They incorporated nanohydroxyapatite (nHAp) and Fe3O4 nanoparticles into scaffolds. The matrices were prepared by in situ crystallization and freeze-drying technique. In vitro analyses including physicochemical and biocompatibility tests proved that [CS/Col]/[Fe3O4/nHAp] magnetic implants were characterized by good structural and mechanical properties and were beneficial for cell adhesion and proliferation. Enhanced osteogenic differentiation due to the presence of MNPs was also noticed. Mineralization tests showed that the magnetic scaffolds have a very good in situ biomimetic mineralization process. Lu et al. [86] fabricated magnetic nanoparticles of SrFe12O19 that were incorporated in modified MBG/CS porous scaffolds. These implants proved to have beneficial properties against tumors with excellent bone regeneration effects. SrFe12O19 nanoparticles had an improved photothermal conversion property. Cojocaru et al. [87,117] made biopolymer–calcium phosphate composites with the inclusion of MNPs. As biopolymers, they used chitosan, hyaluronic acid, bovine serum albumin, and gelatin, and as MNPs, they used magnetite nanoparticles prepared by the coprecipitation method. The morphology of the magnetic scaffolds was investigated using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction, and energy-dispersive X-ray spectroscopy. In vitro degradation analysis showed evidence of a slow degradation rate, and a biocompatibility test revealed no adverse effects on osteoblast cells. The mechanical properties of the scaffold were improved by increasing the MNP content. Hyaluronic acid is a linear polysaccharide that can be found in many parts of the extracellular tissue [147]. It can be used in hydrogel or solution form to repair different body sites because HyA is a part of the connective tissue with an essential role in lubrification, cell differentiation, and growth. Hydrogels from HyA can be easily produced due to functional groups such as alcohols or carboxylic acids. It is well known that innovative scaffolds can be made from HyA, and they exhibit biodegradable and bioactive properties, showing non-specific protein adsorption. These scaffolds are very effective in tissue repair and growth via cell receptors [148]. Zheng et al. [149] provided a comprehensive review of hyaluronic-acid-based materials used in bone regeneration. Composite hydrogel systems have proven their efficiency due to good mechanical properties, high biocompatibility, and biodegradability. They can also be combined with MNPs for drug delivery and an enhanced osteogenesis process. HyA stimulates extracellular matrix microenvironments, promotes cellular activities, and can realize crosslinking action with other polymers, and MNPs help deliver drugs or growth factors. Three-dimensionally printed HyA scaffolds have proven to have a strong influence on the bone formation process. Proteins and peptides are derived from α—L amino acids. The main drawbacks of polypeptide- and protein-based materials consist of their lack of processability and immunogenicity, but good biological properties and low mechanical strength characterize these biopolymers. Collagen scaffolds are already implemented in clinical treatments, and some are in trials [150]. Collagen degradation occurs as a result of two enzymes, i.e., collagenases and metalloproteinases, which produce an amino acid [151]. Mechanical properties of type I collagen must be tuned. Its limited chondrogenic capacity and significant shrinking must be improved for use in scaffold manufacturing. Usually, collagen is combined with HyA, CS, or chondroitin sulfate [152]. Collagen scaffolds are not used for load-bearing areas of the human body. Bianchi et al. [153] investigated the nanomechanical properties of newly formed bone four weeks after implantation surgery. This was performed through magnetic scaffold insertion into the trabecular bone of rabbit femoral condyles. The developed magnetic scaffolds contain NdFeB magnets combined with HAp/Col, with MNPs directly nucleated on the collagen fibers in the manufacturing process or introduced later. It was concluded that the second production technique led to better results because the mechanical properties of the neo-bone had similar values to those of native bone. The addition of MNPs to the final product has an important influence on the osteogenesis process. Through disintegration or denaturation, insoluble collagen results in a gelatin degradation compound. Gelatin has poor mechanical properties and a high biodegradability rate, and due to active chemical groups such as amino and carboxyl acids, the degradation time can be increased through different chemical treatments [154,155]. Dashnyam et al. [88] prepared a novel magnetic scaffold based on gelatin–siloxane for bone tissue engineering by incorporating magnetite magnetic particles using the sol–gel process. Porous scaffolds were manufactured based on the freeze-drying method. With the addition of MNPs, the mechanical properties were highly improved, the scaffolds exhibited superparamagnetic behavior, and the saturation magnetization increased directly proportionally to the MNP content. The developed implants showed good bone bioactivity. The rapid growth of the apatite minerals crystals and osteogenic differentiation were put in evidence. Cellular mineralization increased when MNPs were added. Silk is a natural protein-based biopolymer characterized by good mechanical properties, a controllable degradation rate, and high biocompatibility. Silk fibers have high strength, good durability, low weight, and high elasticity. Silk is made from two proteins. The first is called fibroin, which consists of a fibrous portion; the second is named sericin, which is soluble in water and contains 18 amino acids [156,157]. Samal et al. [89] developed biomimetic magnetic silk fibroin protein scaffolds. These implants were intended to be used in magnetic-field-assisted BTE. MNPs were introduced into scaffolds through the dip-coating technique. Good magnetic hyperthermia, improved osteogenic effects, cell adhesion, and proliferation were reported. Synthetic biopolymers are characterized by tunable properties and well-established structures and can be produced in different forms. They are much more easily manufactured than natural polymers but have a main drawback, i.e., bioinertia [158]. Many modern synthetic materials have mechanical and physiochemical properties similar to those of human bone. The main classes of synthetic biopolymers include poly(α—hydroxy esters) (poly(ε—caprolactone) (PCL), polylactic acid (PLA), polyglycolic acid (PGA), poly(L-lactide-co-glycolide) (PLGA)) and poly(ethers) (poly(ethylene oxide) (PEO), polyvinyl alcohol (PVA), poly(ethylene glycol) (PEG), and polyurethane (PU)). These are the most used materials, and they have an imposed Young’s modulus, degradation rate, and mechanical strength. The abovementioned synthetic biopolymers exhibit different levels of biocompatibility, biodegradability, and mechanical properties, and no single material envisages all the ideal properties for scaffold manufacturing [159]. Poly(ε—caprolactone) (PCL) is a biocompatible aliphatic and semicrystalline polymer that is very tough and hydrophobic [160]. The initial degradation process consists of non-enzymatic bulk hydrolysis of ester connections that are catalyzed with the help of carboxylic acid end groups. PCL can induce foreign body responses, which are evidenced by the giant cells and the presence of macrophages. To increase its biocompatibility, solutions such as surface functionalization or a blended formulation must be considered. The rate of deterioration is relatively slow and can be longer than two years [161]. Ganesh et al. [90] incorporated a multimodal contrast agent with HAp nanocrystals inside a poly(caprolactone) nanofibrous scaffold produced through electrospinning. Magnetic resonance was used to analyze the scaffold’s influence on tissue regeneration. The implant biocompatibility was put into evidence through in vitro tests with the help of human MSCs. Incorporating multifunctional hydroxyapatite nanoparticles (MF-nHAp) within the PCL nanofibers leads to the increased strength of the scaffold, good protein adsorption, proliferation, and differentiation of the cells. Dankova et al. [91] developed a nanofibrous scaffold through electrospinning from PCL and MNPs. The biocompatibility of the scaffold was put into evidence by taking into account the biomaterial influence on fibroblasts and MSCs. When the MNP percentage increases, a more critical stimulation of cell adhesion, proliferation, and differentiation were noticed. A gradual rise in the saturation magnetization was observed, and it was concluded that up to 10% wt. MNPs, the effects were contained in the biological range. De Santis et al. [92] used rapid prototyping (RP) technology to obtain 3D magnetic nanocomposite scaffolds made from a PCL matrix reinforced with iron-doped hydroxyapatite. It was noticed that by adding magnetic properties to biopolymers, an enhanced osteointegration process is obtained. Kim et al. [93] studied a classical magnetic scaffold made of PCL and MNPs. MNPs were produced by a surfactant mediation process and distributed in the PCL matrix. Superparamagnetic behavior was observed, and it was concluded that the incorporation of MNPs leads to high hydrophilicity and water swelling of scaffolds. Using acellular apatite-forming ability tests, a high mineral induction potential of the implant was revealed. The mechanical stiffness increased directly proportionally to the MNP content, and high cell adhesion and proliferation were observed during in vitro tests. Unfortunately, PCL exhibits hydrophobic behavior, leading to reduced cell affinity and a low rate of tissue regeneration. To address this critical limitation, PCL can be combined with different polymers such as PLA or PLGA, and cell proliferation and adhesion can be improved [162]. The synthetic polymer PLGA was approved by the Food and Drug Administration (FDA) for clinical use. Scaffold structures made from this material have been developed, which have proven to be efficient if they have an adequate porosity grade characterized by precise contour geometrical dimensions and internal morphologies, which sustain cellular attachment and structure colonization [96]. In this case, the scaffold surface can be functionalized with bioactive substances or chemicals, and plasma treatment can be applied to increase implant efficacity. Chen et al. [94] optimized the interaction between seed cells and scaffold to ensure beneficial conditions for cell growth under natural biomimetic conditions. They have reported the manufacture of a magnetic [PLGA/PCL] scaffold made using electrospinning technology and layer-by-layer assembly of superparamagnetic iron oxide nanoparticles. These composite scaffolds exhibited increased hydrophilicity and a high value of elastic modulus. They have a good influence on the osteogenesis process of stem cells. It was concluded that the magnetic properties of implants are a key factor in enhancing osteogenic differentiation, which is important as a bioactive interface between cells and scaffolds. The results were compared with those obtained in the case of gold nanoparticles, and the authors concluded that using MNPs in scaffold production leads to an increased osteogenic effect and a high application potential in BTE. Zhang et al. [95] developed 3D MNPs combined with mesoporous bioactive glass/polycaprolactone ([MBG/PCL]/[Fe3O4]) composite scaffolds. In vitro bioactivity, chemotherapeutic drug delivery, mechanical strength, and magnetic heating effect were put in evidence. The produced scaffolds had uniform macropores of 400 μm, a high porosity grade of 60%, and good compressive strength of about 14 MPa. The incorporation of MNPs did not disturb the apatite mineralization process but provided the scaffolds with a high magnetic heating ability and enhanced osteogenesis-related gene expression. The authors concluded that these medical devices are essential in cancer therapy, and they can also stimulate new bone formation and angiogenesis. PLA is a semicrystalline polymer with high biocompatibility, hydrophobic properties, biodegradability, and easy processability [163]. The degradation products that result are carbon dioxide and water, which are not harmful to the human body [164]. This polymer can be used in clinical practice as poly(L-lactic acid) (PLLA), poly(D,L-lactic acid) (PDLLA), and poly(D,L-lactide) (PDLA). Shuai et al. [96]. elaborated a PLLA/PGA scaffold made using the laser sintering method incorporated with Fe3O4 magnetic nanoparticles. A rigid enhancement effect of MNPs was put in evidence through an increase in compressive strength and modulus of about 70%. After in vitro and in vivo tests, the obtained results indicated enhanced angiogenesis and osteogenesis effects, fibrous tissue formation, and new bone development. PGA is a linear aliphatic polyester not soluble for organic solvents because it has a high degree of crystallinity. PGA can break into glycolic acids, which can be combined with the tricarboxylic acid cycle, and expel products such as water and carbon dioxide [165]. PLGA is a well-known ring-opening copolymer of PGA and PLA that is biodegradable, has a low toxicity level, good mechanical properties, a controllable degradation rate, and favored cell adhesion and multiplication. In the BTE domain, PLA, PGA, and PLGA are used for scaffold manufacturing to restore the function of damaged organs or tissues. The FDA has already approved PLA and PGA uses for different medical implants due to the safe elimination process of lactic and glycolic acid secondary products [166]. Jia et al. [97] developed a scaffold for oral bone defect restoration. Three-dimensional composite scaffolds made of PLGA and superparamagnetic iron oxide nanoparticle coatings were implanted in rat animal models to analyze the palate–bone regenerating effects and their interaction with the oral microbiota. These special MNP-coated implants induced an excellent bone regeneration effect. Regarding oral bacteria, a decrease in the Clostridium spp. population and a dominant flora consisting of Proteobacteria were put in evidence. Although MNPs had a beneficial effect on bone regeneration, they altered the oral microbiota in rats. MNPs upregulated hepcidin and the concentration of iron serum. In scaffold design for the BTE domain, different structural parameters should be considered because the implant must mimic the ECM of the tissue, which has to be replaced. The cellular response is strongly influenced by parameter modification [167]. As mentioned in the Introduction section, surface roughness, wettability, and pore scaffold characteristics such as shape, size, and density are directly linked to cell differentiation, proliferation, and gene expression. Another main parameter that influences the structural and morphological aspects of the scaffolds is the fiber diameter and alignment. The main structural and morphological scaffold parameters that affect the cell behavior are presented in Figure 6. Zhao et al. [85] prepared a composite matrix from CS/Col in which they introduced nanohydroxyapatite and magnetite nanoparticles. The manufactured scaffolds were characterized by high porosity with interconnected pores with sizes between 100 and 300 μm. The authors concluded that the implant structure and surface roughness were similar to those of human bone and facilitated the proliferation and adhesion of cells and the circulations of nutrients. Lu et al. [86] developed composite porous magnetic scaffolds for cancer treatment and bone regeneration. A mixed solution containing CS, MBG microspheres, and SrFe12O19 magnetic nanoparticles was prepared. Finally, 3D scaffolds characterized by interconnected macropores with an average size of 200 μm were obtained (Figure 7). Cojocaru et al. [87] carefully investigated the porosity of composite magnetic scaffolds based on innovative biopolymer combinations prepared using a biomimetic coprecipitation method. They found that the implant porosity varied as a function of polymeric matrix composition, and the average pore size was about 994 μm for the CS 3% scaffold and 1115.25 μm for the CS-HyA 3% implant (Figure 8). As an overall conclusion, all the scaffolds were characterized by a 3D structure with interconnected pores and included calcium phosphate and MNPs. Dashnyam et al. [88] made hybrid magnetic scaffolds based on gelatin–siloxane with interconnected microporous morphology and uniform distribution of pores. The MNPs were homogeneously distributed in the polymeric matrix, and no particle agglomerations were present. Samal et al. [89] produced biomimetic magnetic silk scaffolds and noticed that the morphology of the implants was not affected by the magnetization process. The pore size was almost the same in the case of the silk implant and the magnetic scaffold. Very little aggregation of MNPs as clusters of 50–200 nm was microscopically visualized, and it was concluded that the silk interacted very well with the magnetic nanoparticles, and homogenous biomaterial adequate for bone regeneration and tumor treatment was developed. Ganesh et al. [90] developed PCL-based nanofibrous scaffolds doped with nanohydroxyapatite and gadolinium particles. Regarding the scaffold morphology, randomly oriented nanofibers with a diameter between 100 and 500 nm were put in evidence, and it was concluded that the fiber diameter decreased due to the inclusion of HAp and gadolinium particles. The sample wettability was tested using water at 25 °C and 65% humidity. It was noticed that the contact angle decreased from 146° for the PCL scaffold to 130° for the composite PCL-based scaffold. Dankova et al. [91] made 2D poly-ε-caprolactone nanofibers incorporating MNPs. A nano/microfibrous morphology was put in evidence. A dominant nanofibrous fraction with a mean fiber diameter of 216 nm and a microfibrous fraction with a mean diameter of 1138 nm were components of the scaffold mesh. Images obtained through scanning electron microscopy showed that an important quantity of MNPs was placed inside the polymeric fibers. De Santis et al. [92] fabricated 3D composite implants from a PCL matrix in which iron hydroxyapatite particles were inserted. Based on scanning electron microscopy combined with energy-dispersive spectroscopy (SEM/EDS), aggregates of FeHAp uniformly distributed in the matrix were put in evidence. Transmission electron microscopy showed an amorphous calcium phosphate matrix with Fe particles. The calcium phosphate particles exhibited a needle shape, and their sizes ranged between 5 and 20 nm in width and between 50 and 80 nm in length. Kim et al. [93] made magnetic scaffolds based on PCL with an average porosity of 74.6% for 5% wt. MNPs and 70.9% for 10% wt. MNPs. It was noticed that since the porosity percentage was almost the same for all the scaffolds, the density level increased directly proportionally to the MNP concentration. Using X-ray diffraction (XRD) investigations, characteristic peaks for magnetite were observed, and an average particle size of about 10.7 nm was computed using the Scherrer equation. The addition of MNPs resulted in a decrease in the contact angle from 85° for pure PCL scaffold to 61° measured in the case of 5% wt. MNPs and 47° for 10% wt. MNPs. Safari et al. [168] manufactured biofunctional phosphorylated polycaprolactone combined with a gelatin magnetic scaffold. SEM investigations showed that all the scaffolds exhibited a 3D porous structure with interconnected and open macropores. The average pore size for the PCL/G samples containing MNPs was about 240 μm. It was noticed that the addition of MNPs resulted in a decrease in the porosity percentage of the implant. Singh et al. [140] investigated the potential of magnetic nanofibrous scaffolds of poly(caprolactone). Transmission electron microscopy and XRD characterization point out an average size for the MNPs of 11 nm. Nanofibrous scaffolds were made through electrospinning for different MNP concentrations. Microscopy images put in evidence continuous and smooth fibers with different average diameters as a function of MNP concentration. For 5% wt. MNPs, the measured fiber diameter was about 864 nm, and in the case of 15% wt., the MNP diameter decreased to an average value of 200 nm. The apparent contact angle decreased directly proportionally to the MNP increase. For the 10% wt. MNPs, it was measured at 68°, which decreased to 47° in the case of 20% wt. MNPs. Structural and morphological aspects of polymeric scaffolds loaded with MNPs cannot be uniquely established for all cellular responses. To promote individual cell proliferation and adhesion, the scaffold design must be adapted as a function of targeted application characteristics. The most important properties of polymeric scaffolds reinforced with MNPs are mechanical and thermal. The implant must present adequate stability to substitute the missing hard or soft tissue and can be useful in tumor treatment as required. Many literature studies have provided valuable information regarding topography, morphology, the existence of adhesion sites for living cells, and mechanical properties, putting in evidence that the abovementioned factors are significant with respect to scaffold integration inside the human body [169,170,171]. The most investigated mechanical properties are tensile strength, Young’s modulus, and fracture toughness because they impact cell proliferation [172]. It is of great interest to develop a scaffold that sustains a proper mechanical microenvironment favorable to a physiological medium, which determines cell development [173]. Biomechanical signals emitted by cells are linked to increased stem cell differentiation for implants with rough surfaces [174]. Applying mechanical forces can guide the differentiation and proliferation of the cells, leading to tissue formation under well-controlled conditions. The ECM is important because it facilitates cell viability through biochemical interactions such as adhesive motifs and growth factors and mechanical characteristics such as stiffness and deformability. Vogel and Sheetz [175] and Wang et al. [176] proved that mechanical signals significantly impact cell proliferation, adhesion, and death. Studies regarding the mechanical properties of natural and synthetic polymeric scaffolds loaded with MNPs are summarized in Table 4. Even if the implant suffers a decrease in mechanical properties due to its degradation in the biological medium, the cells can strengthen the scaffold body because they produce ECM and reconstruct the surrounding tissue. Different physiological loads act on an implanted scaffold, dependent on cellular traction forces and/or host tissue, resulting in tissue deformation near the implantation site. Traction forces appear during the cell attachment process, while in cell seeding, scaffold contraction manifests. The implant stiffness must have similar values to those of host tissue, and the scaffold elasticity has to be adequate to absorb the forces due to cell movements [177]. Scaffold thermal properties are fundamental in oncological tumor treatment. Based on MNP properties through hyperthermia treatment (HT), which consists of a local temperature increase above 42 °C for a duration between 30 and 60 min, the deoxyribonucleic acid of cells is damaged [178]. During the HT process, cellular protein denaturation, extracellular pH increase, and free radical apparition of can be reported [179]. Magnetic hyperthermia enhances the body’s immunomodulation through the release of heat-shock protein. Usually, MNPs incorporated into scaffold material exposed to an alternating magnetic field through magnetocaloric effect transform magnetic energy into heat, which is released in the implant vicinity. The scaffold must be adequate for tumor-related bone defect treatment, and it also has the ability to eliminate malignant tumor recurrence due to residual cell existence. Another treatment of oncological pathologies consists of photothermal therapies, which involve materials containing MNPs. It was observed that under the effect of near-infrared light (NIR), the temperature can increase up to 42–50 °C, which permits tumor hyperthermia ablation. The bifunctional character of magnetic scaffolds, including the bone regeneration process combined with systematic MNP treatment, such as bone-targeting nanoparticles for tumors, is depicted in Figure 9. Lu et al. [86] incorporated M-type hexagonal ferrites (SrFe12O19) into an MBG/CS polymeric matrix. They applied NIR conditions and investigated the effect of MNP-based implants on the MG63 cell line. After 6 min of irradiation, a temperature increase of about 45 °C was attained. Results consisting of osteosarcoma cell death were reported in the case of scaffolds containing MNPs. The authors also conducted in vivo tests and proved increased necrosis of about 84.6% in the tumor region. They concluded that SrFe12O19 nanoparticles have a strong antitumoral effect when NIR light is applied. In [89], through an infusion technique, silk scaffolds with low (50 μL/mL) and high concentrations (250 μL/mL) of MNPs were developed. The application of an external magnetic field permitted the evaluation of the thermal response of implants. It was concluded that because the scaffolds exhibited a low saturation magnetization value, they were characterized by good magnetic hyperthermia properties, producing an increase in temperature of about 8 °C above the 37 °C level. Zhang et al. [95] investigated the magnetic hyperthermia properties of composite MBG/PCL scaffolds dopped with Fe3O4 magnetic superparamagnetic nanoparticles. It was noticed that the samples with 5% wt., 10% wt., and 15% wt. SPIONs exhibited an increase in temperature when an alternating magnetic field with a maximum value of magnetic flux density of 180 Gs and a frequency of 409 kHz was applied. For 15% wt. SPIONs, the temperature increased from 20 °C to 43 °C in 2 min. The specific absorption rate (SAR) index for the scaffolds had values between 1.4 W/g for 5% SPIONs and 4.7 W/g measured in the case of 15% wt. SPION implants. Lodi et al. [139] analyzed “if and how” the MNP concentration affected the hyperthermia treatment of residual cancer cells. They developed a non-linear multiphysics problem in which they set the magnetic induction at 30 mT and a frequency to 300 kHz. By choosing two selected loading values, they concluded that the scaffolds exhibited well-defined behavior when the temperature increased. The main finding of this simulation was that different therapeutic results could be estimated and investigated through a clear visualization of the material temperature patterns, resulting in a high dependence on the MNPs’ loading characteristics and condition. Espinosa et al. [180] investigated the duality of iron oxide nanoparticles in cancer treatment. They observed the amplification of the heating effect through magnetic hyperthermia combined with photothermal therapy. Iron oxide nanocube suspension was used to measure the magnetic hyperthermia effect on three cancer cell lines, i.e., PC3 (prostate cancer), SKOV3 (ovarian cancer), and A431 (epidermoid cancer). The magnetic field strength varied between 5 and 24 kA/m, and the frequency ranged from 320 kHz to 1.1 MHz. This condition was combined with the effect of laser hyperthermia induced by an NIR continuous laser at 808 nm. For the in vivo tests, 22 female Naval Medical Research Institute (NMRI) immunodeficient nude mice were involved, and different solid tumors were artificially created by injecting human cancer cells. A complete tumor remission was noticed in the case of all animal models when the bimodal treatment was applied. Future research in the magnetic scaffold domain must include these two thermal properties of MNPs to achieve fast and efficient treatment of different oncological pathologies. MNPs are characterized by unique properties such as high surface-to-volume ratio and magnetic responses, which are influenced by small particle diameters and differ from bulk materials. Two physical quantities are the most important part of MNP use in biomedical therapies. A higher magnetic moment is helpful in magnetic imaging and biosensing applications, while a higher magnetic field strength value is sought in the case of the magnetic theragnostic domain [181]. It was observed in [182,183,184,185] that a surface spin-canting effect, which determines the differentiation of magnetic properties between the surface layer and the core of MNPs, can generate a decrease in saturation magnetization followed simultaneously by an increase in the anisotropy constant (Figure 10a). This fact is considered the opposite of the bulk material magnetic phenomenon. In the case of a spherical magnetic nanoparticle, the saturation magnetization (Ms) can be expressed as a function of magnetic core diameter (D), the thickness of the spin-canting layer (δ), and the saturation magnetization of the bulk material (Msb) (Equation (1)). For the effective anisotropy constant, in [186] Keff was determined to depend on the bulk (Kb) and surface (Ks) anisotropy constants through a shape parameter (Φ) and magnetic core diameter (D) (Equation (2)): The magnetization processes in the case of MNPs depend on the particle diameter, so a critical value (Dcrit) can be considered a boundary between the single magnetic domain state and the multidomain configuration (Figure 10b) [77]. The highest magnetic moment value characterizes single-domain MNPs because the particle magnetization vector is oriented in only one direction and equal to saturation magnetization. Stoner-Wohlfarth’s model [187] considered that the magnetization of a monodomain particle rotates as if it were only one giant magnetic moment. This phenomenon is called “macro-spin approximation”. Superparamagnetic behavior occurs in the case of small-diameter ferro- or ferrimagnetic nanoparticles. It is well known that in the case of a single-domain particle, there are two antiparallel preferred orientations of the magnetic moment along the easy magnetization axis. Between these directions is an energy barrier (Eb), which prevents the switch of the magnetic moment from one stable equilibrium position to the other minimum-energy state (Figure 10c). Another critical particle diameter size (Dsp) at which it is possible to transition from the monodomain state to superparamagnetic behavior must be considered. If this geometrical dimension is reached at a given temperature at which the energy barrier becomes comparable to thermal energy (kBT, where kB is the Boltzmann constant), the magnetic moment flips from one preferred direction to another. The fast reversal of the magnetic moments exhibits a null magnetic moment without an externally applied magnetic field. In opposition, the magnetic moments align along the external magnetic field, so as a consequence, a net magnetization value is attained. The particles present an anhysteretic behavior when different magnetic field values are considered. The magnetization curve has a reversible S shape that the Langevin model can approximate according to Equation (3) as follows [188]: where L(x) = coth(x) − 1/x represents the Langevin function, μ0 is the vacuum magnetic permeability, H is the magnetic field strength, and μ is the absolute value of the particle magnetic permeability. Superparamagnetic nanoparticles are usually included in polymeric scaffolds for tissue engineering due to their stability over time and their increased biocompatibility. Lu et al. [86] analyzed the influence of MNP content on the magnetic properties of SrFe12O19 nanoparticles incorporated into MBG/CS porous scaffolds. They developed two samples with SrFe12O19 masses of 0.125 g (MBCS1:7) and 0.25 g (MBCS1:3) and MBG masses of 0.875 g and 0.75 g. These mixtures were added to CS solutions. The saturation magnetization depended on a high MNP content, so for the MBCS1:7 sample, it was measured at a value of 4.44 emu/g, and in the case of MBCS1:3, a value of 7.68 emu/g was obtained. The experimental coercivities were found to be 4120 Oe and 5102 Oe, respectively. It can be noticed that this particular type of M-ferrite nanoparticle exhibited a hysteresis cycle that showed that they are not in a superparamagnetic state. Cojocaru et al. [87] investigated the magnetic properties of different polymeric matrices (CS, CS-HyA, and CS-BSA), in which they integrated 1% wt., 3% wt., and 5% wt., respectively, of Fe3O4 nanoparticles coated with CS. For the 5%wt MNPs, the magnetization was found to be equal to 10.14 emu/g (CS), 12.53 emu/g (CS-HyA), and 8.16 emu/g (CS-BSA). Dashnyam et al. [88] reinforced hybrid porous scaffolds from GS with Fe3O4 up to 3% wt. The magnetic properties were investigated using superconducting quantum interference device (SQUID) magnetometry, and the MNPs showed typical superparamagnetic behavior with S-shaped magnetization curves. The saturation magnetization increases proportionally with the MNP content from 0.24 emu/g (1% wt. MNPs) to 0.64 emu/g (3% wt. MNPs). Samal et al. [89] determined the magnetic properties of silk scaffolds in which they diffused 50 μL/mL or 250 μL/mL MNPs. The magnetic measurements were performed at 37°, and a superparamagnetic-like response characterized by saturation magnetization values of 2.7 emu/g and 13 emu/g was obtained. The coercive field was about 15 Oe, a value considered negligible. In [91], PCL scaffolds with MNPs (γ-Fe2O3) were prepared through the electrospinning method. The magnetization curves showed a typical trend for iron oxide nanoparticles with a diameter above 20 nm. The saturation magnetization of the samples was found to be about 6.1 Am2 at 300 K, with an estimated content of MNPs of 7.9% wt. Kim et al. [93] prepared magnetic scaffolds from PCL and MNPs with contents of 5% wt. and 10% wt., respectively. Experimentally determined saturation magnetizations of 1.6 emu/g and 3.1 emu/g were obtained. The coercivity and remanence points were impossible to determine, so the superparamagnetic state of the MNPs was confirmed. It was noticed that 80% magnetic saturation was attained at 0.5 kOe in a linear field dependence. Zhang et al. [95] developed composite magnetic scaffolds with a matrix of MBG/PCL polymers through additive manufacturing technology. They inserted Fe3O4 nanoparticles in 5% wt., 10% wt., and 15% wt. The saturation magnetization was between 1.01 emu/g (5% wt. MNPs) and 2.90 emu/g (15% wt. MNPs). Shuai et al. [96] manufactured PLLA/PGA scaffolds that incorporated Fe3O4 MNPs with 2.5% wt., 5% wt., 7.5% wt., and 10% wt. through the SLS method. According to vibrating sample magnetometry (VSM), the samples showed no measurable coercive field and remanent induction values for each MNP percent. A saturation magnetization value proportional to the MNP content was obtained, starting with 1.66 emu/g to 8.51 emu/g. Singh et al. [140] made PCL nanofibrous scaffolds incorporating 12 nm diameter Fe3O4 in concentrations of 5% wt., 10% wt., and 20% wt. It was concluded that the saturation magnetization increased from 1 emu/g to 11.2 emu/g relative to the mass fraction of MNPs placed into the polymer matrix. The specific hysteresis energy losses at the maximum applied field of 20 kOe were estimated to be 2.4 × 103 erg/g (5% wt. MNPs), 5.5 × 103 erg/g (10% wt. MNPs), 12.5 × 103 erg/g (15% wt. MNPs), and 22.3 × 103 erg/g (20% wt. MNPs). The MNP percentage in scaffolds can have an important influence on the therapeutic properties of implants in osteogenesis and cancer treatments. Lodi et al. [139] showed that spatial loading influenced the saturation magnetization of the samples in a direct manner. The magnetic properties of scaffolds can be tuned as a function of the preparation method, and magnetic cluster formation must be avoided because non-linear magnetic effects appear and negatively influence the treatment. Including MNPs in biodegradable polymeric matrices is of great importance because innovative therapies strongly dependent on a well-established percentage of MNPs can be applied in clinical applications. MNPs have proven to be a very efficient tool in tissue engineering due to the fact that they produce an intense cell induction effect by generating an intrinsic magnetic field [189,190]. Nanoparticles are internalized by cells, so as a consequence, activation of intracellular pathways that enhance osteogenesis can occur [71,191]. When MNPs or SPIONs are reinforced inside a scaffold, the resulting magnetization sustains the substance changes that occur between receptors placed on the cell membrane and ion channels, which is directly linked to improved osteogenic proliferation and differentiation [76]. Cell adhesion is promoted, and the mechanical properties of the scaffold are improved, as shown in Section 4. MNPs are essential in angiogenesis when an external magnetic field is applied. Under the effect of an alternating magnetic field, MNPs can transport different drugs or even mesenchymal stem cells (MSCs) to a bone defect site [192]. In the case of MSCs, magnetic stimuli can be recognized through the cytoskeleton or membrane of the cells that transmit chromosomal responses with a significant influence on gene expression and protein synthesis [193,194]. SPIONs can provide mechanical stimulation to the membrane of MSCs; furthermore, the intrinsic magnetic field of the particle can act on the mitogen-activated protein kinase (MAPK) pathway, even in the absence of an external magnetic field [195,196]. This phenomenon determines an overexpression of runt-related transcription factor (RUNX2), defined as an early osteogenesis differentiation marker, and an upregulation of bone morphogenetic protein 2 (BMP2), which activates the Smads proteins. These types of proteins are the principal signal transducers involved in the transforming growth factor β (TGFβ) receptors and enhance the expression of RUNX2 [197]. The upregulation of INZEB2, which is of high importance in the osteogenesis process, has a direct consequence in the downregulation of zinc finger transcription factor 2 (ZEB2), which suppresses the BMP2/Smads/RUNX2 pathway [137,198]. As a final result, alkaline phosphatase (ALP), osteocalcin expression, and collagen type I (COL-1) increase and have a positive impact on the osteogenesis process (Figure 11). Safari et al. [168] tested the biocompatibility capabilities of biofunctional phosphorylated magnetic scaffolds for BTE. They used human dental pulp stem cells (hDPSCs) seeded at 37 °C in 96-well plates at a density of 5 × 103 cells/well. The MNP effect consisted of increased ALP activity and higher expression level of RUNX2 and BMP2 osteogenetic biomarkers. The use of phosphorylated polycaprolactone determined a very good implant osteoconductivity due to the upregulation of COL1, RUNX2, BMP2, and osteocalcin (OCN) genes. They concluded that the developed scaffolds exhibited high biocompatibility. In the case of magnetic nanofiber scaffolds [140], MSCs derived from rat bone marrow were used to analyze osteogenesis. It was noticed that osteoblastic cell adhesion was amplified by the MNPs, and good penetration through the implant nanofibers was put in evidence. To analyze osteoblastic differentiation, the ALP activity was determined after cell culture for 7 and 14 days. By adding samples to the ALP reaction medium, an upregulation of this parameter was observed. This result was supported by analysis of the expression of mRNA levels of bone-associated genes such as COL1, osteopontin (OPN), and bone sialoprotein (BSP). Biocompatibility investigations proved that a magnetic nanofiber implant is an excellent candidate for BTE. Another approach in scaffold manufacturing based on additive manufacturing technology was presented in [96]. The magnetic microenvironment generated by PLLA/PGA/MNPs 3D scaffolds exhibited good biocompatibility in the case of MG63 cells. The cells were incubated in Dulbecco’s modified eagle medium (DMEM) supplemented with sodium pyruvate, 10% fetal bovine serum (FBS), and antibiotics under standard testing conditions. The sterilized magnetic scaffolds were placed in 24-well culture plates, and 4 × 105 MG63 cells were used for each scaffold. Good cell adhesion was evidenced through SEM investigations, and high cell viability was obtained after a cell counting kit-8 (CCK-8) assay was performed. The qualitative staining and quantitative activity of ALP showed high osteoblastic differentiation of cells placed in the scaffold vicinity. Zhang et al. [95] tested the biocompatibility of other 3D-printed magnetic composite scaffolds (Fe3O4/MBG/PCL) on hBMSCs. It was noticed that integration of MNPs into a polymeric composite matrix had no significant effect on the mineralization ability of the implant but upregulated the ALP activity and the osteogenesis-related gene expression (OCN, RUNX2, BSP, BMP2, and COL-1), putting in evidence an increased osteogenesis effect. In the case of the 15% wt. MNPs scaffold, the determined values of osteogenic expression were found to be almost double those for the 5% wt. and 10% wt. MNPs. Chen et al. [94] prepared a magnetic cell–scaffold interface by incorporating SPIONs. The scaffolds’ biocompatibility was investigated on Spraque–Dawley rat adipose-derived mesenchymal stem cells (ADSCs). Using confocal laser scanning microscopy, cell morphology was visualized at 6 and 24 h after cell adhesion. The viability was measured at 1, 4, 7, and 10 days with the help of a CCK-8 kit. It was concluded that the highest cell viability and adherence were obtained in the case of MNP-containing scaffolds. ALP activity analysis was used to evaluate osteogenic differentiation. The magnetic scaffolds presented the highest values of this indicator, proving an intense osteogenic process. Dankova et al. [91] presented a practical approach to in vitro MSC proliferation based on PCL/MNP nanofibrous scaffolds. The MSCs were extracted from the ilium bone marrow of miniature pigs and sterilized at 37° by ethylene oxide. The cells were seeded on scaffolds in 96-well plates at a density of 63 × 103/cm2. The cell proliferation, as well as metabolic and ALP activities, as observed for 21 days. Based on the MTS assay, it was concluded that the cell metabolic activity and viability were improved when MNPs were added. Regarding the ALP activity, a significant increase was put in evidence in the case of MSCs cultivated on magnetic scaffolds on days 7 and 21. Cojocaru et al. [117] investigated the biocompatibility of microporous biomimetic scaffolds loaded with MNPs. They used sarcoma osteogenic (SaOS-2) cells and performed all measurements following ISO 10993 standard [199] by quantifying the cell viability through lactate dehydrogenase (LDH) release. It was concluded that because they developed a very complex implant with different MNP concentrations, the cell behavior and viability were strongly influenced by various parameters such as particle diameter and shape, the chemical composition of the scaffold matrix, and implant morphologic and magnetic properties. Lu et al. [86] incorporated M-type ferrite nanoparticles in MBG/CS porous scaffolds. The hBMSC cell line was used to investigate the implant biocompatibility. Well-spread cell morphology was observed in the case of MNP-based scaffolds. They concluded that SrFe12O19 nanoparticles are highly biocompatible and that the scaffolds promoted cell proliferation and adhesion. Osteogenic differentiation was analyzed using real-time polymerase chain reaction (PCR) and Western blotting. The highest level of osteogenic gene expression was found in the case of a higher percentage of MNPs. Table 5 provides examples of biological response (i.e., cell viability and proliferation and bone markers) for polymeric magnetic scaffolds. In the case of an external magnetic field presence, the magnetic stimulation applied by the MNPs to the cells is enhanced, so osteogenesis and angiogenesis are improved [70,200]. It was noticed that an electromagnetic field (EMF) might lead to a high amount of cell migration, adhesion, and differentiation, as a direct consequence of which the tissue regenerates faster than in the absence of an EMF [201,202]. The combination of magnetic polymeric scaffolds for BTE and EMF exposure has recently received considerable scientific interest; some such studies are presented in Table 6 [69,203,204,205,206]. It can be noticed that the existence of an external electromagnetic field produces beneficial effects on angiogenesis and osteogenesis, but it adds many complications regarding the type of devices that can be clinically employed. The devices must be easy to use, adequate for specific patient anatomy, with facile follow-up, and preferably with low-cost EMF-generating components. More research is needed to determine whether the application of an external EMF is necessary in the case of magnetic scaffolds because MNPs with sufficiently strong magnetization that sustains increased osteogenic and angiogenic effects can be developed. Magnetic nanoparticles can be internalized by stem cells because they promote osteogenic differentiation. The percentage of MNPs is an important parameter in biomedical applications because they tend to accumulate in the kidney, bone marrow, spleen, and liver [210]. The macrophage cells in the reticuloendothelial system (RES) internalize MNPs with reduced diameter that are subjected to acid-induced degradation [211,212]. Numerous investigations involving in vitro and in vivo studies have revealed that the toxicity is dose-dependent and that the maximum quantity of MNPs should be chosen by taking into account the type of nanoparticles and living cells [213,214,215,216,217]. Free ionic iron, which results from the degradation of iron oxide nanoparticles, is stored in ferritin protein and is used in normal cellular functions. Another transmembrane protein is ferroportin, which is involved in iron export from cells, from where it is transported into the bloodstream through transferrin [218]. Usually, cells control the level of ferritin and ferroportin and maintain a balance between stored and exported ionic iron simultaneously with the downregulation of transferrin receptors, which limit the assimilation of iron from the blood [218]. The toxic effect of MNPs appears when free ionic iron that remains unbound takes part in a Fenton reaction, resulting in the appearance of reactive oxygen species (ROS) [77,219]. Fan et al. [220] noticed that in the case of an intracellular iron content of about 13 pg, the osteogenesis process decreased for BMSCs labeled with citric-acid-coated SPIONs. They concluded that this high iron concentration led to ROS species formation, which reduced cell viability. In another study conducted by Andreas et al. [221] using the same type of SPIONs, an intracellular iron content of 70 pg was reported, which did not have an important influence on human stem cells. Animal stem cells did not support a higher grade of MNP toxicity compared to human cell lines. When MNPs are incorporated into scaffolds, their toxicity grade is reduced. All cytotoxicity analyses must be conducted in accordance with the ISO 10993 standard, which states that a material exhibits non-cytotoxic effects in the case of cell viability higher than 70% in comparison with control samples [199]. Cojocaru et al. [117] analyzed the cytotoxicity of a magnetic polymeric scaffold by quantifying the LDH release. They found that on day 7 in some culture plates, the LDH release decreased below 60%, indicating that the samples with 50% COL, 50% CS, and Ca/P between 1.57 and 1.72, and 5%wt. MNPs exerted important cytotoxicity over the SaOS-2 cells. In [87], magnetic scaffolds with a natural polymer-based matrix and 1% wt., 3% wt., and 5%wt. MNPs showed cell viability higher than 96%, indicating that the increased MNP percentage did not result in significant toxicity. A [CS/BSA]/[3% wt. Fe3O4] scaffold was chosen for a live/dead staining assay performed for fibroblasts. It was noticed that after 96 h of incubation, the cell viability increased, proving that this MNP concentration is suitable for BTE. Sometimes, MNPs form aggregates that can impact cell adhesion and proliferation. De Santis et al. [92] made PCL/FeHAp scaffolds in which they seeded BMSCs that were magnetically labeled with MNPs in a concentration between 1.04 and 8.33 μg/mL. An MNP/cell ratio of 16.6 μg/1000 cells was set for the cytotoxicity tests. They concluded that in the case of higher MNP loading, the number of cells in the scaffolds was reduced due to magnetic agglomerate formation. In [168], [PCL/G]/[MNPs] scaffolds were prepared, and due to the hydrophilic character of the MNPs, hDPSC growth and adhesion properties improved. This fact can be attributed to the phenomenon whereby, through the intrinsic peroxidase-like activity of the MNPs, a diminution of intracellular H2O2 occurred, and accelerated cell cycle activity was induced [222]. Most of the presented studies put in evidence that MNPs reinforced into polymeric scaffolds do not exhibit toxic effects against the cell. Still, implant biocompatibility and cytotoxicity analysis must be carefully considered before introducing such scaffolds in animal testing or clinical trials. Scaffolds must be biologically compatible with the animal model tissue to permit proliferation, adhesion, and differentiation of the host cells. As explained in Section 6, the biocompatibility and toxicity of the implant must first be investigated in vitro, and if the safety requirements are met, it can be implanted in the animal body. Figure 12 shows a schematical representation of the main functions of magnetic scaffolds regarding in vivo studies that involve rodents. Zhao et al. [85] used male SD rats weighing 300 g to investigate the in vivo behavior of magnetic [CS/Col/nHAp] scaffolds reinforced with Fe3O4 nanoparticles. They induced an osseous defect with a 5 mm diameter on the middle ridge of the rat skull. Sample scaffolds with an area of 5 × 1 mm2 were implanted inside the defect of some animals. For the analysis, a control group was considered, including rats with a bone defect in the skull and no implant use. Post-surgical antibiotic treatment was provided, and no complications were reported. All the animals were euthanized after 12 weeks, and microcomputed tomography tests were conducted to evaluate bone growth in the calvarium, with histological assessment to investigate the histological repair process. The group with magnetic scaffolds exhibited good osteointegration and a gradual implant degradation that supported new bone formation. The inclusion of MNPs in the polymeric matrix favored a magnetocaloric effect, which produced dynamic bone growth. It was concluded that the best osteogenesis phenomenon was observed when magnetic properties were added to the polymeric scaffold. Lu et al. [86] investigated the bone regeneration properties and photothermal therapy efficiency of [MBG/CS]/[SrFe12O19] scaffolds implanted in 12 male SD rats with an average weight of 310 g. Bilateral calvarial defects were made, and implants with a thickness of 2 mm and a diameter of 5 mm were inserted. All the animals were injected with fluorescent dye under general anesthesia conditions at 2, 4, and 6 weeks to observe new bone formation at an interval of 12 weeks. After micro-CT investigations, it was proven that SrFe12O19 resulted in a high percentage of differentiation and proliferation of stem cells combined with increased new bone formation. The photothermal properties of the scaffold were analyzed, and under an NIR laser effect (808 nm, 4.6 W/cm2) applied for 6 min, a temperature of about 43 °C was obtained. It was concluded that photothermal therapy was successfully used and that it induced cell apoptosis and ablations, resulting in a volume reduction in the cancer tumor. Necrosis of the oncological tissues was also put in evidence. As a general conclusion, the authors stated that these types of implants are suitable for BTE and cancer therapy (Figure 13). Cojocaru et al. [117] conducted in vivo tests using [Cs/Col/HyA]/[Fe3O4] scaffolds with an area of 1 cm2 on 40 male Wistar rats with a mean body weight of 165 g to investigate the inflammatory effect of the magnetic implant. All animal procedures were conducted in accordance with ISO 10993-2 [223]. The surgery generated a 1 mm diameter pocket between the hypodermis and dermis, where the scaffold was inserted. A foreign body reaction was observed on day 2 after the implantation of the samples. The presence of leucocytes, collagen fibers, and fibroblasts characterized the inflammatory process. On day 12, fibroblast development was accompanied by angiogenesis, and on day 64, rare leukocytes, resorption, and integration of the scaffold were put in evidence and led to the appearance of new blood vessels. Kim et al. [93] made magnetic [PCL]/[Fe3O4] composite scaffolds and implanted four small samples on the lateral back side of the spine of three ten-week-old SD rats with an average weight of 300 g. The animals were sacrificed after 14 days. All the harvested biological samples showed mild or moderate fibrosis and angioblastic differentiation. The MNPs were considered safe because no foreign body reaction occurred. Shuai et al. [96] implanted magnetic [PLLA/PGA]/[Fe3O4] scaffolds into rabbit radius bone defects (Figure 14). They selected 18 NZW rabbits, and after the surgery, the animals were sacrificed at 1 or 2 months post intervention. No infections were reported, and in the case of implants with 7.5% wt. MNPs, an important quantity of new bone linked to the host bone combined with new blood vessel apparition was noticed. It was concluded that the incorporation of MNPs is strongly related to cell proliferation and adhesion and that the developed scaffolds are an ideal candidate for BTE. Singh et al. [140] developed [PCL]/[MNPs] composite scaffolds with a volume of 1.5 cm × 1.5 cm × 300 μm and surgically inserted them in pouches placed near the spine of SD rats. After 4 weeks, the animals were euthanized. Connective tissues formed along the collagen fibers and localized between the scaffold and neighborhood host tissue, and neovascularization was put in evidence. The scaffolds with the highest percentage of MNPs showed traces of degradation, with the missing areas replaced by fibroblasts. It was concluded that the MNPs promoted angiogenesis because signs of blood vessel formation were present in the histological investigations. Yun et al. [207] investigated the osteogenic properties of [PCL]/[Fe3O4] scaffolds under the effect of a static magnetic field. They implanted samples with 10% wt. MNPs and MNP-free samples into 5 mm diameter calvarial defects surgically induced in female 6-to-8-week-old ICR mice. After the procedure, the mice were placed in cages that contained two permanent magnets set in opposition. The generated magnetic field had an average magnetic induction of 15 mT with variable values ranging from 0.05–0.2 mT in the middle of the cage to 15–25 mT in the vicinity of the magnets. After 6 weeks, the animals were euthanized. Microcomputed tomographic and histological analyses showed that the combined effect of the external and internal magnetic fields stimulated new bone formation and proved an adequate strategy in regenerative medicine for bone. Meng et al. [224] prepared [PLA/nHAp]/[γ-Fe2O3] composite nanofibrous scaffolds using electrospinning. Scaffolds were introduced in lumbar transverse defects in 24 NZW rabbits. Their cages were equipped with permanent magnets to create static magnetic field stimulation. Micro-CT measurements on samples harvested on day 110 after the implantation surgery put in evidence well-organized and homogenous new bone tissue. It was noticed that the external magnetic field accelerated bone remodeling and new bone development. The in vivo biocompatibility was evaluated through measurements of biochemical parameters such as creatinine kinase (CK), creatinine (CR), alkaline phosphatase (ALP), and alanine aminotransferase (ALT). All the markers had normal values, showing that the developed scaffolds did not exhibit harmful effects on the surrounding tissues and animal organs. Russo et al. [68] created a defect in rabbit femoral condyle and investigated bone regeneration through magnetic activation. They developed [Col/HAp]/[7% wt. MNPs] based on freeze drying and infiltration methods, which were implanted in vivo, together with a cylindrical sample of NdFeB characterized by a height of 8 mm, a diameter of 2 mm, and a magnetic flux density of 1.2 T. A permanent magnet was introduced in a titanium capsule with a thickness of 200 μm to increase the biocompatibility of the device. After careful biomechanical, histological, and histomorphometric investigations conducted 12 weeks after surgery, a pellicular bone structure with interconnected trabeculae oriented perpendicular to the magnetic field lines was put in evidence. This effect occurred because fibrin and collagen can be orthogonally oriented under the influence of an applied magnetic field. This investigation proved that the applied scaffold is an efficient tool for accelerating bone healing. All the in vivo studies mentioned above present necessary steps in BTE and underline the effect of the intrinsic magnetic field of MNPs or an external static magnetic field in bone remodeling and new bone formation. All the presented strategies have been proven very efficient and showed high in vivo compatibility. The main potential clinical applications of magnetic polymeric scaffolds is in the bone tissue engineering domain. Increased adhesion, differentiation, and growth of osteoblasts and fibroblasts were obtained when they were subjected to the effect of a magnetic field, which can be due only to the presence of MNPs or the effect of an external EMF. As mentioned before, tissue engineering is a real challenge because it is still very difficult to manufacture large and complex functional tissues such as bone, heart, kidney, parts of the bloodstream, and muscles [225]. The prevascularization of scaffolds represents an important challenge, and preliminary in vitro and in vivo studies are necessary to investigate cell growth at a required density and metabolic activity [226,227]. Another challenge is loading of implanted scaffolds with different biological agents [228,229,230,231]. Furthermore, the addition of MNPs to scaffolds can attract cells or growth factors that can be linked to MNPs. Elfick et al. [232] investigated the role of stem cells used in the tissue repair process. They put in evidence a transgenic approach suitable for MSC magnetization. This cell line was modified based on a transfection procedure with the mms6 gene, with Magnetospirillum magneticum AMB-1 as its origin. After this process, the intracytoplasmic MNPs were bioassimilated into modified MSCs. In this way, cellular processes such as proliferation, differentiation, and migration become visible through magnetic resonance devices. Soon, biological MNPs can be included in the scaffold because they represent a non-toxic alternative to the classical FDA-approved iron oxide MNPs. Russo et al. [233] presented an interesting approach regarding the internal fixation of magnetic scaffolds using magnetic forces. Different configurations were proposed and analyzed based on finite-element modeling. The system comprised a magnetic ring positioned around the leg, four small magnetic pins implanted under the scaffold in the bone, and four stainless-steel pins introduced in the same fashion and magnetized under the influence of an external field; the authors concluded that this system is very efficient, with a beneficial effect in large defect regeneration. This direction must be considered in the near future for magnetic scaffold fixation. Porous scaffolds are among the most important templates for cell growth and tissue development. Tampieri et al. [234] developed porous bio-hybrid scaffolds made from HAp/Col in which they directly attached MNPs based on impregnation with the ferrofluid method during HAp nucleation. This approach increased the implant biocompatibility because MNPs became an internal component of the scaffold. Magnetically guided tissue development can be foreseen from the mentioned study. Although many synthetic or natural polymers have been used in scaffold manufacturing, challenges such as limited cell density and active cell growth control must be underlined. As mentioned in Section 6 and Section 7, magnetic scaffolds can be sensitive to mechanical stimulation due the interaction between MNPs and magnetic fields. Furthermore, the application of an EMF can increase the osteogenesis and angiogenesis processes by a significant amount. Other important applications of magnetic polymeric scaffolds are drug delivery and enzyme immobilization. A pulsatile release of drugs that mimics the profile of a given hormone or peptide can be linked to the ideal zero-order release of the active substance over a long time [235]. De Paoli et al. [236] investigated the release of dextran from magnetic [Col/MNPs] scaffolds. They concluded that applying a low-frequency alternative magnetic field enhanced the drug effect. Thermosensitive scaffolds were reinforced with MNPs to achieve controlled drug release due to the thermal properties of the particles that can change the implant temperature. Two directions are reported in the literature with respect to dendrimers and hydrogel solutions [237]. In both cases, a molecular collapse around an average temperature of 42 °C was reported. It was noticed that under the influence of a high-frequency magnetic field, a potential remote release of chemical factors stimulative for tissue regeneration from MNP-loaded drugs and scaffold matrix occurs. Meikle et al. [238] analyzed the functionalization of MNPs with thermoresponsive poly(epsilon-lysine) dendrons tethered with carboxy betaine. They succeeded in delivering vascular endothelial growth factors important in angiogenesis. This process was directly linked to the mild magnetic hyperthermia pulses of MNPs obtained due to an external alternating magnetic field. It was shown in [239] that magnetic alginate scaffolds that suffered a large deformation and a volume increase of 70% due to a medium-frequency magnetic field provided a controlled release of mitoxantrone and chemokine. Magnetic scaffolds can be used to immobilize and release different enzymes that are covalently bonded with a polymeric matrix or MNPs. Under the effect of an alternating magnetic field that generates an imposed temperature value, the enzymes are released. Magnetic scaffolds can exhibit multiple interactions with immobilized enzymes that can improve their thermal stability and restrict the modifications suffered by the molecules during the heating process [240]. Magnetic polymeric scaffolds can be populated with natural or gene-engineered stem cells or signaling molecules. The most used types of cells are multipotent, pluripotent, and progenitor stem cells [241,242,243,244]. Unfortunately, this therapy is incipient for safety reasons because some studies showed contradictory effects regarding anti- and protumor development [245]. Another critical potential clinical application of magnetic polymeric scaffolds is bone tumor treatment. This process is challenging due to a vicious cycle between new bone formation and tumor cell proliferation [246]. Regarding chondrosarcoma, osteosarcoma, and chordoma, which are the most frequently encountered oncological diseases, a release of osteoblast transmembrane molecule (RANKL) directly linked to osteoclast differentiation and activation of the osteolysis process determines healthy bone destruction combined with cancer cell proliferation [83,247,248,249,250]. As mentioned in Section 4, hyperthermia is a direct application that can be used alone or in combination with chemotherapy to increase oncological tissue sensitivity to chemotherapeutic drugs [251,252]. When MNPs were mixed with HAp, the tumor dimensions were drastically reduced due to locally induced heat generation. This effect was amplified by an external magnetic field or a laser light [253]. Matsumine et al. [254] proved that hyperthermia is similar to radiotherapy in treating bone tumor apparition after surgery. Many in vitro and in vivo studies have demonstrated the efficiency of hyperthermia treatments in cancer therapy. The main challenge associated with this method is exploiting its advantages to adjust the Curie temperature of MNPs to a level superior to the hyperthermia temperature [255,256,257,258] in order to avoid the transition of the MNPs’ magnetization state from superparamagnetic or ferromagnetic to a paramagnetic state. All the potential clinical applications described in this paper are part of a new research area dedicated to the design of magnetic polymeric scaffolds that can enhance osteogenesis and angiogenesis or be involved in hard tissue regeneration after surgical intervention in the absence of or under the effect of an external electromagnetic field. Previously developed applications of the implants mentioned above are presented in Figure 15. This review presents a new perspective regarding the incorporation of MNPs in BTE scaffolds, providing implants with increased in vitro cell performance, in vivo efficacy, and good mechanical properties. We showed that a direct consequence of the interaction between MNPs and cells consists of improved osteogenic and angiogenic differentiation. Many in vitro studies put in evidence that endothelial cells and osteoblasts can internalize SPIONs or MNPs, leading to new bone formation and blood vessel apparition. We conclude that innovative cell-based regenerative strategies can be applied when an external EMF is applied. These treatments are suitable for bone cancer therapies, such as photothermal and magnetic hyperthermia effects. In vivo analysis proved the efficiency of the abovementioned methods by highlighting the significant reduction in tumors and the formation of new bone in their place. The use of highly biocompatible and biodegradable polymers represents an important advantage because secondary surgeries for scaffold removal become unnecessary, and the chemical compounds of the implant matrix are directly related to increased osteogenesis. The literature reports that, from a biological point of view, the incorporation of MNPs in polymeric scaffolds leads to implants with superior properties. However, additional research must be conducted to completely elucidate the magnetization and demagnetization processes of the MNP effect on cell proliferation and differentiation. In this review paper, we state that magnetism is a key factor, but no existing study defines the intracellular pathways that are influenced by it in a clear and detailed manner. This could represent a future perspective in magnetic-assisted biological environmental analysis. We investigated whether the concentration of MNPs is an essential factor related to their well-known toxicity. Our literature review revealed that the magnetic properties of scaffolds, which considerably influence osteointegration or cancer cell-death treatment, must be carefully controlled because, in some cases, in vitro studies showed a decrease in cell viability, putting in evidence the existence of ROS species and so-called ferroptosis. Although the latter process is considered beneficial in cancer treatments, it can also induce damage to healthy tissues, as a direct consequence of which the influence of the MNP must be further investigated. Additional analyses are also necessary regarding magnetic scaffolds populated with stem cells or growth factors because these can be magnetically controlled to differentiate and aid in the restoration and regeneration of large bone defects in specific cell lines when autografts or allografts are not a feasible treatment strategy. The current study is subject to some limitations, such as the safety limit of MNPs’ toxicity for each biomedical application, owing to the hyperthermia effect activated through the thermal properties of MNPs or SPIONs. We consider that a local and personalized treatment can be applied to address the oncological problem or to assist in the patient’s osteogenesis and angiogenesis. Additionally, the lack of in vivo studies in the literature means that there is a dearth of information regarding the angiogenic properties of magnetic scaffolds. Exploring the effect of magnetic scaffolds on endothelial cell differentiation and proliferation represents a crucial future perspective that must be taken into account. Potential clinical applications of magnetic polymeric scaffolds that can be combined with magnetically labeled stem cell therapies and small and portable devices, which generate a static or an alternative EMF to enhance the treatment effect, must be further investigated.

PMC10001583

Xinyan Zhang,Jinxian Xu,Brendan Marshall,Zheng Dong,Yutao Liu,Diego G. Espinosa-Heidmann,Ming Zhang

Transcriptome Analysis of Retinal and Choroidal Pathologies in Aged BALB/c Mice Following Systemic Neonatal Murine Cytomegalovirus Infection

21-02-2023

cytomegalovirus,age-related macular degeneration,RNA sequencing,latency,inflammation,degeneration

Our previous studies have shown that systemic neonatal murine cytomegalovirus (MCMV) infection of BALB/c mice spread to the eye with subsequent establishment of latency in choroid/RPE. In this study, RNA sequencing (RNA-Seq) analysis was used to determine the molecular genetic changes and pathways affected by ocular MCMV latency. MCMV (50 pfu per mouse) or medium as control were injected intra-peritoneally (i.p.) into BALB/c mice at <3 days after birth. At 18 months post injection, the mice were euthanized, and the eyes were collected and prepared for RNA-Seq. Compared to three uninfected control eyes, we identified 321 differentially expressed genes (DEGs) in six infected eyes. Using the QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA), we identified 17 affected canonical pathways, 10 of which function in neuroretinal signaling, with the majority of DEGs being downregulated, while 7 pathways function in upregulated immune/inflammatory responses. Retinal and epithelial cell death pathways involving both apoptosis and necroptosis were also activated. MCMV ocular latency is associated with upregulation of immune and inflammatory responses and downregulation of multiple neuroretinal signaling pathways. Cell death signaling pathways are also activated and contribute to the degeneration of photoreceptors, RPE, and choroidal capillaries.

Transcriptome Analysis of Retinal and Choroidal Pathologies in Aged BALB/c Mice Following Systemic Neonatal Murine Cytomegalovirus Infection Our previous studies have shown that systemic neonatal murine cytomegalovirus (MCMV) infection of BALB/c mice spread to the eye with subsequent establishment of latency in choroid/RPE. In this study, RNA sequencing (RNA-Seq) analysis was used to determine the molecular genetic changes and pathways affected by ocular MCMV latency. MCMV (50 pfu per mouse) or medium as control were injected intra-peritoneally (i.p.) into BALB/c mice at <3 days after birth. At 18 months post injection, the mice were euthanized, and the eyes were collected and prepared for RNA-Seq. Compared to three uninfected control eyes, we identified 321 differentially expressed genes (DEGs) in six infected eyes. Using the QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA), we identified 17 affected canonical pathways, 10 of which function in neuroretinal signaling, with the majority of DEGs being downregulated, while 7 pathways function in upregulated immune/inflammatory responses. Retinal and epithelial cell death pathways involving both apoptosis and necroptosis were also activated. MCMV ocular latency is associated with upregulation of immune and inflammatory responses and downregulation of multiple neuroretinal signaling pathways. Cell death signaling pathways are also activated and contribute to the degeneration of photoreceptors, RPE, and choroidal capillaries. Human cytomegalovirus (HCMV) is a common virus which infects 40 to 80% of individuals in the human population [1]. The virus is usually acquired during early life when the innate and adaptive immune systems are not fully mature [2], with the eye being one of the major target organs. Thus, the incidence of HCMV chorioretinitis is reported to be 25% in infants with symptomatic congenital HCMV infection [3,4,5,6]. Furthermore, the choroid/RPE may be a site of HCMV latency, since recent studies in our laboratory of ocular tissue from human cadavers have revealed that HCMV DNA was present in 17% (4 of 24) of choroid/RPE samples [7]. In order to investigate the effects of lifelong infection with cytomegalovirus, we established an in vivo mouse model in which systemic neonatal murine cytomegalovirus (MCMV) infection of BALB/c mice spreads to the eye, with subsequent establishment of latency in the choroid/RPE and the development of retinal and choroidal pathologies that appear in aged, infected mice, including deposits at both basal and apical aspects of the RPE (basal lamina deposits and subretinal deposits), together with degeneration of the choriocapillaris, RPE, and photoreceptors [8]. These pathologies exhibit some features of human age-related macular degeneration (AMD), which is characterized in its early stages by lipoprotein deposits at the basal and apical aspects of the RPE and in its advanced forms by CNV [9,10] or geographic atrophy (GA) of the outer retinal tissue, RPE, and choriocapillaris [11,12,13]. AMD is a complex, multifactorial, progressive disease and a leading cause of severe, permanent vision loss in older individuals [9,10,14]. Although the exact events which contribute to the development of AMD remain uncertain, studies have implicated various immunological and inflammatory mechanisms [15,16,17]. Many studies support correlations between oxidative stress, persistent low-grade inflammation, and AMD pathogenesis [18,19,20,21,22,23]. Inflammation and oxidative stress are correlated physiological processes [18,24,25,26,27]. Superoxide radicals are formed by NADPH oxidase from activated immune cells during inflammatory reactions, while oxidants are activators of the NF-κB pathway triggering inflammation/immune response [28]. Our studies have indicated that MCMV most likely remains latent in eyes of aged mice, since replicating virus was never recovered and expression of virus genes m83 and m54, which, respectively, encode a virion protein expressed late in the viral replication cycle [29] and a virus DNA polymerase, [30] was not detected in any of the eyes of the infected mice. However, expression of several other virus genes was detected in the eyes of aged mice [8]. The majority of these genes function in either immune-modulation, such as m04 and m138 [31], or inhibition of cell death, including m36, m38.5, m41, and m45 [32], which could facilitate the establishment of ocular virus latency. Furthermore, other genes with the potential to stimulate immune responses, such as IE1, m80, and m18, were also expressed in the eyes of latently infected, aged mice [8]. IE1 can trigger a proinflammatory host transcriptional response via a STAT1-dependent mechanism [33,34], and m80 encodes a MCMV assembly protein protease [35], while m18 encodes a protein which is an antigenic peptide recognized by CD8 T cells [36] and also drives expression of the RAE-1 family of NKG2D ligands leading to subsequent activation of NK cells [37]. Therefore, activation of these genes could stimulate an inflammatory/immune response, which could, in turn, induce retinal and choroidal pathologies observed in infected aged BALB/c mice. In this study, we analyzed eye samples from aged, infected mice and age-matched uninfected controls by performing RNA sequencing (RNA-Seq) to identify molecular pathways involved in immune/inflammatory responses as well as others affected by ocular MCMV latency. Six eyes from the MCMV latently infected mice at 18 months p.i. and three eyes from age-matched uninfected controls were used for sequencing analysis. Prior to removal of the eyes, SD-OCT examinations were performed, and retinal thickness was calculated using a Leica Envisu R2210 system (Bioptigen, Leica, Morrisville, NC, USA). Compared to age-matched uninfected control eyes, a significantly reduced retinal thickness was observed in all six eyes of the infected mice (Figure 1B) as previously reported [8], while severe photoreceptor (PRC) degeneration, including disappearance of the entire outer nuclear layer (ONL) in some areas, was observed in three of six eyes (Figure 1A). In addition, one choroidal neovascularization (CNV)-like lesion was observed in one of these three eyes with severe PRC degeneration (not shown). Each sample generated 80 to 107 million paired reads with 100% passing filter (PF) clusters, >96% perfect barcode, >95% bases with Q30, and >39 mean quality scores. The number of genes (≥1 normalized count) expressed in each sample ranged from 12,875 to 14,230 with a cutoff of |fold change| ≥ 2 and q ≤ 0.05 used to identify differentially expressed genes (DEGs). Compared to the uninfected control eyes (samples c1, c2, c3), 321 DEGs (208 downregulated and 113 upregulated, Supplementary Table S1) were identified in the six virus latently infected eyes. The top 15 upregulated and downregulated genes are listed in Table 1, together with the fold changes and FDR values. Among virus-infected eyes, 48 DEGs (38 downregulated and 10 upregulated, Supplementary Table S2) were identified in three eyes with severe retinal degeneration (samples h1, h2, h3), compared to three infected eyes with milder retinal degeneration (samples m1, m2, m3). A heat map for all nine samples is shown in Figure 1D. RNA-seq transcriptome data were validated by qRT-PCR analysis and expression levels of eight genes were analyzed in three control and six virus-infected samples. As shown in Figure 1C, similar expression trends were observed by both qRT-PCR analysis and RNA-Seq for all eight genes. A correlation (R2) of 0.8904 was observed between the log2FC of ΔCT values derived by qRT-PCR analysis and the log2FC value derived by RNA-seq analysis. QIAGEN IPA analysis was used to identify involvement of canonical pathways with a p-value of less than 10−2. These pathways, together with the differentially expressed genes in each canonical pathway, are listed in Table 2. Among a total of 17 canonical pathways identified, 10 function in neuroretinal signaling, with the majority of differentiated regulated genes involved in these pathways being downregulated in infected eyes. As shown in Figure 2, for example, all 22 differentially expressed genes linked to the Phototransduction Pathway of rod and cone cells, were downregulated. These include genes involved in cAMP and PKA-mediated signaling, which participate in the modulation of presynaptic GABA release [38] and are themselves regulated by Relaxin signaling in a biphasic manner [39]. cAMP signaling also plays a role in regulating glutamatergic transmission via phosphorylation of certain ionotropic glutamate receptors [40] through dopamine-DARPP32 feedback. Since DARPP-32 is localized in horizontal cells, amacrine cells, and Müller glial cells, this may result in the disfunction of these important retinal cell types. Expression of genes involved in RNA splicing was also downregulated in infected eyes, consistent with previous results which have shown that RNA processing defects are associated with many diseases of the neuron, including retinal degeneration [41]. Other pathways affected include the Endothelin-1 (ET-1) pathway, which was also downregulated, and G beta gamma signaling. The neuropeptide ET-1 is expressed in multiple retinal cell types, including RPE, photoreceptors, the inner plexiform layer, and ganglion cell layer [42] and functions in neuromodulation and neurotransmission [42,43]. However, ET-1 could also activate ET receptors in the retinal and choroidal vasculature, suggesting an important role in regulating in situ blood flow [42]. G beta gamma signaling plays a critical role in rod cell function in low light conditions [44], as well as neuronal CREB signaling, which is one of the major regulators of neurotrophin responses [45,46], and G-protein coupled receptor signaling. In contrast, the transcription of genes involved in several immune response pathways was upregulated. These included pathways involved in NFAT-regulated dendritic cell maturation, phagosome formation, and communication between innate and adaptive immune cells. Upregulated gene transcription was also observed in the GP6 signaling pathway, which functions in platelet activation and thrombus formation [47]. The Tec kinase signaling pathway, which regulates lymphocyte development, activation, and differentiation [48], as well as in the opioid signaling pathway, which is critically involved in many physiological processes including neuroprotection and immune response [49,50,51]. The QIAGEN IPA analysis also permitted the categorization of differentially expressed genes according to known disease associations and function as listed below. Ophthalmic Disease, Organismal Injury, and Visual System Function. Many differentially expressed genes were noted in sections of this category. As shown in Table 3, differential expression of genes implicated in retinal degeneration, including degeneration of the photoreceptor, rod, and outer segments of the cone cell, was detected with z scores greater than 2. Seven differentially expressed genes were noted with a z score of 1.568. In addition, differential expression of genes required for maintenance of retinal cell and photoreceptor function and quantity were noted with z scores less than −2. 2. Cell Death and Survival. As shown in Table 4, many differentially expressed genes implicated in the death of retinal cells and epithelial tissues, either by apoptosis or necrosis, were detected. TUNEL assays and Western blots were used to confirm this observation. As shown in Figure 3A, many TUNEL-positive cells were observed in the choroid (indicated by white arrows), RPE layer (indicated by arrow heads), and outer nuclear layer (indicated by red arrows) in virus-infected eyes, while in contrast, only a few TUNEL-positive cells were observed in the outer nuclear layer of eyes from age-matched uninfected mice. Eyes of MCMV neonatally infected mice, as well as the eyes of the age-matched, uninfected controls, were also analyzed by Western blotting. As shown in Figure 3B, MCMV infection was associated with increased production of cleaved caspase 3, RIP3, MLKL, and decreased production of rhodopsin, indicating that both apoptosis and necroptosis may contribute to cell death. 3. Cellular Movement and Immune Cell Trafficking. As shown in Table 5, significant changes in the transcription of genes involved in migration, infiltration, and activation of multiple immune cell types were observed in infected eyes. Immunostaining was used to confirm these observations. As shown in Figure 4, accumulation of Iba1 positive macrophage/microglia and GFAP positive Müller cells/glia was observed in the subretinal space and outer nuclear layer of the eyes of aged, infected mice, but not in age-matched uninfected controls. Pathway analysis identified 36 upstream regulators of these differentially expressed genes with activation Z scores of greater than 1.5 and p-values of overlap less than 0.05 (Table 6). The majority of these upstream regulators are cytokines (IL1, IFN, IL17A, TNF, etc.) or transcription factors (STAT1, STAT3) that are involved in innate immunity/inflammation. Several growth factors (VEGF, TGF) were also identified as activated upstream regulators. As shown in the summary graph (Figure 5), upstream regulators, such as IL1, OSM, IL17A, and STAT1, stimulate activation and migration of immune cells, tissue degeneration, and antigen presentation. Compared to infected eyes without severe retinal degeneration, we identified 48 DEGs in three infected eyes with severe retinal degeneration (Supplementary Table S2). As shown in Table 7, the majority of differentially regulated genes were involved in pathways related to neuroretinal signaling, cell death, and retinal degeneration. In contrast, no significant differences were detected in immune response pathways, such as those involved in migration, infiltration, or activation of immune cells, between infected eyes with and without severe retinal degeneration. The data presented here complements our earlier studies on lifelong MCMV latency in the retina. These studies demonstrated that systemic MCMV infection of newborn mice resulted in ocular pathology later in life which exhibited some similarities to AMD [8]. Although no infectious virus could be isolated from the retina during latent ocular infection, many gross pathological changes in the retinal architecture were observed. The transcriptional data presented here provides a theoretical framework for understanding retinal and choroidal pathologies in latently infected eyes and demonstrates widespread alterations in the expression of genes critical for maintaining retinal form and function. Thus, systemic virus infection of neonates results in disruption of normal ocular gene expression patterns even in advanced age. The studies presented herein support the idea that cytomegalovirus ocular latency could be associated with in situ inflammation. Transcription of many inflammatory molecules was elevated, and pathway analysis indicated that several inflammatory pathways, including IL1, STAT1, IL17A, and OSM, were activated with activation Z scores greater than 2 and p-values of overlap less than 0.05. These inflammatory molecules may activate immune cells including macrophages/microglia and could also induce degeneration of photoreceptor, RPE, or choriocapillaris, either directly or indirectly, via activation of immune cells. Our results also demonstrate that multiple cell death pathways, including caspase-3 dependent apoptosis, necrosis, and necroptosis are activated and contribute to degeneration of photoreceptors, RPE, and choroidal capillaries, although future studies are still needed to determine which pathways contribute to which aspects of retinal degeneration. Our previous studies have shown that during systemic neonatal MCMV infection of 129S1/SvImJ (129S1) mice [7], MCMV spreads to the eye with subsequent establishment of latency in the choroid/RPE. Unlike the neonatal infection of BALB/c mice, only a few MCMV genes are expressed, and no remarkable retinal or choroidal pathology, such as deposits, degeneration of the choriocapillaris, RPE, or photoreceptors, was observed [7]. BALB/c mice, which are susceptible to light damage due to a lack of melanin [52,53], exhibited photoreceptor degeneration (infiltrating cells, loss of outer segments, and decreased retinal thickness) in aged, uninfected control mice in both our own previous studies [8], as well as those of Bell and colleagues [54]. Thus, melanin in the RPE may prevent light-induced retinal damage by absorbing most of the light passing through the pupil and helping to scavenge free radicals [53,55,56], thereby protecting photoreceptors from oxidative stress [57,58]. Melanin might also prevent toxicity through its anti-oxidative function [59] and could play a role in protecting choroidal blood vessels from light damage [60]. Therefore, the susceptibility of BALB/c mice to light damage [61,62,63,64] may be due to an accumulation of reactive oxygen species (ROS) and subsequent oxidative stress and inflammation [65,66]. Previous studies have suggested that oxidative stress mediates the initial activation of viral gene expression during cytomegalovirus latency [67,68]. Following light damage, oxidative stress in MCMV latently infected BALB/c mice could activate expression of ocular virus genes, which in turn might promote production of inflammatory/angiogenic factors, thereby facilitating development of retinal and choroidal pathologies. The choroid/RPE may be a site of HCMV latency since we have shown that HCMV DNA is present in some human choroid/RPE samples [7]. However, whether HCMV ocular latency contributes to human AMD remains to be determined. AMD is a complex multifactorial disease, and the majority of risk factors, including genetics, environmental insults, and age-related issues, are linked to the induction of oxidative stress, which could activate expression of ocular HCMV genes, resulting in the production of inflammatory/angiogenic factors and thereby facilitating development of an AMD-like pathology. MCMV strain K181 was originally provided by Dr. Edward Morcarski, Emory University, Atlanta, GA. The virus was prepared from the salivary glands of MCMV-infected immunosuppressed BALB/c mice, and virus stocks were titered on monolayers of mouse embryo fibroblast (MEF) cells, as described previously [69]. A fresh aliquot of virus stocks was thawed and diluted to the appropriate concentration for each experiment. We purchased breeding pairs of BALB/c mice from Jackson Laboratory (Bar Harbor, ME). All mice were given unrestricted access to food and water and were maintained on a 12 h light cycle alternating with a 12 h dark cycle. The breeding and treatment of animals in this study adhered to the Association for Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmic and Vision Research and was approved by the Institutional Animal Care and Use Committee of Augusta University. The rd8 mutation was excluded by genotyping. Anti-RPE65 (ab231782), anti-RIP3 (phosphor s232, ab195117), and anti-Rodopsin (ab5417) were purchased from abcam (Boston, MA, USA). Other antibodies used in this study were obtained from the following sources: anti-cleaved caspase 3 (#9664), anti-RIP1 (#3493), and anti-mouse β-actin (#3700) were purchased from Cell Signaling Technology, Inc. (Danvers, MA, USA). Mouse anti-GFAP (glial fibrillary acidic protein, specific for glia/Müller cells) was obtained from BD Biosciences (San Jose, CA, USA). Anti-mouse Iba1 was purchased from FUJIFILM WAKO Chemicals, U.S.A. Corporation (019-19741, Richmond, VA, USA). Goat anti-rabbit IgG HRP (sc2004) and rabbit anti-goat IgG HRP (sc2768) were sourced from Santa Cruz (Santa Cruz Biotechnology, CA, USA). Anti-Mouse IgG HRP Conjugate (W402B) was obtained from Promega (Madison WI, USA). Anti-mouse Alexa 488 and anti-rabbit Alexa 594 were obtained from Vector Laboratories, Inc. (Burlingame, CA, USA). A total amount of 50 pfu of MCMV or culture medium as control were injected into BALB/c mice within 3 days after birth via the intraperitoneal (i.p.) route. At 18 months post infection (p.i.), the mice were anesthetized, and spectral-domain (SD) optical coherence tomography (SD-OCT) was performed using the Bioptigen Spectral-Domain Ophthalmic Imaging System (En-visu R2200; Bioptigen, Morrisville, NC, USA). The OCT imaging protocol included averaged single B scan and volume intensity scans with images centered on the optic nerve head (1.4 mm × 1.4 mm, @0.0, 1000X100X4X1). Total retinal thickness was measured by manual assessment of retinal layers using InVivoVue™ Diver 2.4 software (Bioptigen) following the software introduction, as described previously [8]. The mice were euthanized, and the eyes were collected and prepared for RNA-Seq, immunofluorescence staining, Western blot, and real time RT-PCR (qRT-PCR) as described below. Six eyes from infected mice at eighteen months p.i., and three eyes from age-matched, uninfected controls were used for RNA sequencing. Following removal of the lens, total RNA was extracted from whole eyes using Trizol (Invitrogen, CA, USA) according to the manufacturer’s instructions, and all nine RNA samples were then treated with DNase to exclude genomic DNA contamination. RNA concentrations were obtained using a Nanodrop 2000c spectrophotometer (Thermo Scientific Inc., Waltham, MA, USA), while RNA integrity was assessed using an Agilent 2200 Tape station instrument (Agilent Technologies, Santa Clara, CA, USA. RNA Integrity Number (RIN) scores for the nine samples were 7.6, 7.7, 7.7, 7.8, 8.0, 8.2, 8.2, 8.8, and 8.9, respectively. One microgram of total RNA from each sample was used to prepare Ribo-Zero RNA-Seq libraries. RNA-Seq libraries were prepared using the Illumina TruSeq Stranded Total RNA kit (Illumina, Inc., San Diego, CA, USA) according to the manufacturer’s protocol. Briefly, ribosomal RNA (rRNA) was removed using biotinylated, target-specific oligos combined with Ribo-Zero rRNA removal beads according to the Illumina Reference Guide (Illumina, San Diego, CA, USA). Following purification, the RNA was fragmented into small pieces using divalent cations at an elevated temperature. First strand cDNA synthesis was performed at 25 °C for 10 min, 42 °C for 15 min, and 70 °C for 15 min, using random hexamers and ProtoScript II Reverse Transcriptase (NEW ENGLAND BioLabs Inc.). For second strand cDNA synthesis, RNA templates were removed, and a second replacement strand was generated through the incorporation of dUTP (in place of dTTP, to maintain strand identity) and double-strand cDNA was generated. Blunt-ended cDNA was isolated from the second strand reaction mix using beads. The three ends of the cDNA were then adenylated, and the cDNA was ligated to indexing adaptors. PCR (15 cycles of 98 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s) was used to selectively enrich for DNA fragments with adapter molecules on both ends, and to amplify the amount of DNA in the library. Libraries were quantified and qualified using the D1000 Screen Tape on an Agilent 2200 Tape Station instrument and were normalized, pooled, and subjected to cluster and pair read sequencing for 150 cycles on a HiSeqX10 instrument (Illumina, Inc. San Diego, CA, USA), according to the manufacturer’s instructions. Coding RNA data were analyzed by Rosalind (https://rosalind.onramp.bio/) (accessed on 21 September 2020), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. (San Diego, CA, USA) [70]. Reads were trimmed using cutadapt [71]. Quality scores were assessed using FastQC [72], and reads were aligned to the Mus musculus genome build mm10 using STAR [73]. Individual sample reads were quantified using HTseq [74] and normalized via Relative Log Expression (RLE) using the DESeq2 R library [75]. Read distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC [76]. DEseq2 was also used to calculate fold changes (FDs) and p-values. The significant gene set was selected with a q-value (p-value that has been adjusted for False Discovery Rate, FDR) of <0.05 threshold. Using QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA) (Ingenuity® Systems, www.ingenuity.com) (accessed on 23 March 2021), we illustrated clustering of genes for the final heatmap of differentially expressed genes and performed functional analyses to identify relevant gene pathways and networks. RNA-seq transcriptome data were validated by qRT-PCR analysis. All primer sequences used for qRT-PCR are shown in Supplementary Table S3. Genes were amplified in 20 µL reaction consisting of 10 µL 2×SYBR Mix (Bio-Rad), 0.2 µL of 20 pmol/µL primer mixture, and 1 µL cDNA, using CFX96TM Real Time PCR System (Bio-Rad). PCR conditions were as follows: 3 min at 94°C, followed by 40 cycles of 94 °C for 10 s, 60 °C for 20 s, and 72 °C for 30 s. All CT values were analyzed and normalized to β-actin using the method of 2–ΔΔCT. Eyes (four eyes in each group) were embedded in OCT compound, frozen, and sectioned in a cryostat. Sections were then fixed with 4% paraformaldehyde for 15 min and stained by TUNEL assay (in Situ Cell Death Detection Kit, Fluorescein; Roche Diagnostics, Indianapolis, IN, USA) and/or for RPE-65, GFAP, Iba-1 as described previously [77,78]. Lenses were removed from harvested eyes and the remaining eye tissues homogenized in a lysis buffer containing protease inhibitors (Complete™ Lysis-M, Roche, Germany). Proteins were extracted as previously described [79,80], and equal amounts of protein were separated by 10% or 12% SDS-PAGE, followed by electroblotting onto polyvinylidene difluoride membranes (Amersham Biosciences, Amersham, UK). Following blocking with 5% nonfat dry milk for 1 h at room temperature, membranes were incubated overnight at 4 °C with primary antibody. Binding of HRP–conjugated secondary antibody was performed for 1 h at room temperature, and bands were visualized using chemiluminescence (ECL; GE Healthcare, Chicago, IL, USA).

PMC10001590

Vasiliki Tsigkou,Evangelos Oikonomou,Artemis Anastasiou,Stamatios Lampsas,George E. Zakynthinos,Konstantinos Kalogeras,Maria Katsioupa,Maria Kapsali,Islam Kourampi,Theodoros Pesiridis,Georgios Marinos,Michael-Andrew Vavuranakis,Dimitris Tousoulis,Manolis Vavuranakis,Gerasimos Siasos

Molecular Mechanisms and Therapeutic Implications of Endothelial Dysfunction in Patients with Heart Failure

21-02-2023

heart failure,endothelial dysfunction,pathophysiology,molecular mechanisms

Heart failure is a complex medical syndrome that is attributed to a number of risk factors; nevertheless, its clinical presentation is quite similar among the different etiologies. Heart failure displays a rapidly increasing prevalence due to the aging of the population and the success of medical treatment and devices. The pathophysiology of heart failure comprises several mechanisms, such as activation of neurohormonal systems, oxidative stress, dysfunctional calcium handling, impaired energy utilization, mitochondrial dysfunction, and inflammation, which are also implicated in the development of endothelial dysfunction. Heart failure with reduced ejection fraction is usually the result of myocardial loss, which progressively ends in myocardial remodeling. On the other hand, heart failure with preserved ejection fraction is common in patients with comorbidities such as diabetes mellitus, obesity, and hypertension, which trigger the creation of a micro-environment of chronic, ongoing inflammation. Interestingly, endothelial dysfunction of both peripheral vessels and coronary epicardial vessels and microcirculation is a common characteristic of both categories of heart failure and has been associated with worse cardiovascular outcomes. Indeed, exercise training and several heart failure drug categories display favorable effects against endothelial dysfunction apart from their established direct myocardial benefit.

Molecular Mechanisms and Therapeutic Implications of Endothelial Dysfunction in Patients with Heart Failure Heart failure is a complex medical syndrome that is attributed to a number of risk factors; nevertheless, its clinical presentation is quite similar among the different etiologies. Heart failure displays a rapidly increasing prevalence due to the aging of the population and the success of medical treatment and devices. The pathophysiology of heart failure comprises several mechanisms, such as activation of neurohormonal systems, oxidative stress, dysfunctional calcium handling, impaired energy utilization, mitochondrial dysfunction, and inflammation, which are also implicated in the development of endothelial dysfunction. Heart failure with reduced ejection fraction is usually the result of myocardial loss, which progressively ends in myocardial remodeling. On the other hand, heart failure with preserved ejection fraction is common in patients with comorbidities such as diabetes mellitus, obesity, and hypertension, which trigger the creation of a micro-environment of chronic, ongoing inflammation. Interestingly, endothelial dysfunction of both peripheral vessels and coronary epicardial vessels and microcirculation is a common characteristic of both categories of heart failure and has been associated with worse cardiovascular outcomes. Indeed, exercise training and several heart failure drug categories display favorable effects against endothelial dysfunction apart from their established direct myocardial benefit. Heart failure (HF) is a heterogenous clinical syndrome with a broad range of symptoms and signs, which are attributed to divergent underlying etiologies that induce structural or functional abnormality of the heart [1]. HF affects more than 60 million adults globally and is characterized by severe morbidity, mortality, and poor quality of life [1]. Despite the decreased age and sex-adjusted incidence for both heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF), the prevalence remains high and is projected to increase worldwide due to the aging process, improved therapeutic options for ischemic heart disease, and the availability of effective evidence-based therapies [2]. Although the prognosis of HF has, in general, been improved, it should be emphasized that mortality remains high according to recent studies, irrespectively of left ventricular ejection fraction (LVEF) and type of HF (acute or chronic) [2]. HFrEF is mainly attributed to the loss of cardiomyocytes due to ischemia, myocarditis, or genetic mutations, which trigger the mechanisms of cardiovascular remodeling [3]. Patients with HFrEF are predominantly men, usually post-myocardial infarction (MI), and develop more frequently a profile of eccentric hypertrophy of the left ventricle (LV) due to pressure overload [3]. On the other hand, HFpEF, which is now considered to be the most common category of HF, is associated with the presence of comorbidities, such as arterial hypertension, diabetes mellitus, renal dysfunction, obesity, and increased age [4]. Endothelial dysfunction (ED) has an important role in the pathophysiology of HF since repeated episodes of microvascular dysfunction might precipitate myocardial stunning and ventricular remodeling and might be associated with HF hospitalizations [5]. Moreover, ED has been linked to the presence of a procoagulant state, the expression of adhesion molecules, and a pro-inflammatory status [6,7]. Additionally, the pathophysiology of HFpEF is quite different from HFrEF according to the literature since it is largely attributed to the existence of coronary microvascular ED due to the presence of cardiovascular risk factors, which trigger a state of systemic inflammation [8]. However, HFpEF exhibits a profile of concentric hypertrophy [3], and these differences in risk factors, as well as in the phenotypic presentation of different HF categories, may be caused by differences in molecellular mechanisms and expression [9]. Therefore, in this article, we review the molecular mechanisms underlying the association of ED with the development and progression of HF as well as the therapeutic implications of ED improvement in patients with HF. Activation of neurohormonal systems is a key component of HF [10]. The sympathetic nervous system (SNS) and the renin–angiotensin–aldosterone system (RAAS) are the most important neurohormonal systems in the pathophysiology of HF and an important target of several therapeutic regimens [11]. Persistent overstimulation of SNS has been linked to the development of cardiomyocyte hypertrophy, interstitial fibrosis, inflammation, and oxidative stress, which progressively result in loss of myocardial contractility and LVEF deterioration [12]. Moreover, there is an interrelation between the RAAS system and SNS, which exacerbates cardiovascular damage [13]. It should be highlighted that aldosterone is associated with the development of ED, inflammation, and production of reactive oxygen species (ROS), which further deteriorate cardiovascular function [14,15]. All these effects are mainly driven by angiotensin (AT) receptors AT-1 since AT-2 receptors have displayed antifibrotic, anti-inflammatory, and anti-apoptotic actions due to activation of bradykinin and nitric oxide (NO) synthesis [16]. Oxidative stress results as an imbalance between ROS production and their reversal by the antioxidant systems of the body; normally, a small amount of ROS is formed during mitochondrial respiration, which is detoxified by cells’ antioxidant enzymes [17]. Nevertheless, excessive ROS production at mitochondria and the presence of dysfunctional antioxidant systems are the key contributors to oxidative stress in HF [17]. ROS overproduction induces ED due to NOS uncoupling and concomitant superoxide anion and peroxynitrite release, which decreases nitric oxide (NO) availability and further causes vasoconstriction [18]. Ischemia or hypoxia further exacerbates ROS production by mitochondria which accelerates myocardial damage, both at the stages of ischemia–reperfusion injury as well as in chronic ischemic conditions [19]. ROS induces post-translational modification of cellular compartments, reversibly or irreversibly, in a process that ends in cardiac hypertrophy [20]. Indeed, protein kinase C and mitogen-activated protein kinases (MAPK) induce myocardial remodeling and hypertrophy [21]. Moreover, excessive ROS production might damage mitochondrial DNA, which has a low capacity for repair; therefore, a vicious cycle of ROS overproduction and subsequent myocardial damage occurs [22]. Indeed, oxidative stress upon neurohormonal activation deteriorates the proper mitochondrial function of cardiomyocytes; mitochondrial dysfunction causes further deficiency of mitochondrial energetics [23]. Contrarily, cardiovascular risk factors in HFpEF create an environment of oxidative stress, inflammation, and microvascular ED; nonetheless, less is understood so far about the implication of mitochondrial dysfunction and cardiac energetics and should be further investigated [23,24,25]. Importantly, oxidative stress and chronic, low-grade inflammation are important contributors to the poor regenerative properties of cardiomyocytes [26]. High cytosolic calcium input in HF is particularly attributed to the dysfunction of the L-type calcium channel (LTCC); indeed, increased phosphorylation of LTCC is evident in HF and results in a compensatory leak of sarcoplasmic reticulum (SR) calcium through ryanodine receptor 2 (RyR2), which is in the uncoupled form due to chronic SNS stimulation and oxidative stress [27,28]. On the other hand, dysfunction of the sodium–calcium exchanger (NCX), which controls cytosolic calcium outflux due to the diminished transmembrane sodium gradient, results in sarcolemmal depolarization [29,30,31]. It should be mentioned that myocardial hypertrophy per se is associated with prolonged duration of isometric contraction and relaxation and molecular changes due to fibrosis and dysregulated creatine kinase system [32]. Furthermore, the transverse tubular system and proteins of the excitation–contraction coupling system display dysfunction in the aged cardiomyocytes, especially in the HF condition [33]. On the other hand, improper function and diminuted expression of cardiac SR calcium ATPase (SERCA2 a) end in reduced calcium transfer to SR [34]. Additionally, dephosphorylazation of phospholamban from protein phosphatase-1 has also been identified in HF [35]. SERCAa is controlled by hormones, microRNAs (miR), and endogenous protein inhibitors and undergoes post-translational modifications [36]. Lastly, recent data indicate that inflammation and, specifically, damage-associated molecular patterns (DAMPs, i.e., destroyed or stressed cardiomyocytes) release mediators that trigger the immune response; as a result, dysfunctional cardiac contraction and electromechanical uncoupling occur due to the unfavorable calcium homeostasis [36]. Myocardial contraction and relaxation depend on the proper energy production by cardiomyocytes, which is orchestrated by cardiac mitochondria during oxidative metabolism in the mitochondrial matrix in a process known as mechano-energetic coupling [37]. Normally, cardiac mitochondria, which are the main source of energy, ROS production, and calcium ion control, utilize free fatty acids in order to produce energy in the form of adenosine triphosphate (ATP) [37]. As a matter of fact, utilization of free fatty acids results in more efficient energy production than utilization of glucose in terms of ATP production at the expense of more oxygen consumption, though [38]. According to the literature, HF is characterized by dysfunctional energy metabolism [37]. HF is perceived to be a state of ‘energy deprivation’ due to mitochondrial dysfunction, which is attributed to the altered mitochondrial structure and function [39]. Indeed, evidence from animal studies of HF has revealed that mitochondria display hyperplasia, decreased size or fragmentation, and disruption of their inner and outer membranes [39]. Several mitochondrial proteins in HF undergo post-translational modification (such as phosphorylation, acetylation, ubiquitination, conjugation of small ubiquitin-like modifier proteins, O-linked-N-acetyl-glucosamine glycosylation, proteolysis), which results in fission and fusion of mitochondria [40]. Furthermore, HF is associated with impaired mitochondrial biogenesis, dysfunctional mitochondrial DNA replication as well as mitochondrial DNA depletion [41]. Lastly, mitochondria-related genes (such as IFIT3, XAF1, RSAD2, and MX1) have displayed associations with HF and the biological processes of oxidative stress, amino-acid metabolism, and aging [42]. Inflammation and HF display a strong interrelationship; as a matter of fact, inflammation is associated with the development of molecular, cellular, and functional changes in the heart [43,44,45]. Inflammation in HFrEF is most commonly the result of cardiomyocyte injury or loss; on the other hand, cardiovascular risk factors in HFpEF create an environment of chronic, low-grade systemic, and local ongoing myocardial inflammation, which trigger myocardial damage [46]. Moreover, activation of neurohormonal mechanisms in HF, such as the RAAS system, strengthens the inflammatory response and induces immune system activation [47]. Additionally, the presence of a pro-inflammatory milieu in HF has been associated with the tendency for thromboses [48]. Furthermore, acute HF with peripheral hypoperfusion and systemic congestion favor damage to endothelial glycocalyx and cause ED [49]. Interestingly, inflammation has been linked to worse cardiovascular prognosis in patients with HF, cardiac cachexia due to muscle wasting, higher vascular resistance, and decreased functional capacity [50]. Indeed, patients with HFpEF display higher levels of inflammatory markers such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α); furthermore, CRP levels are proportional to the number of associated comorbidities [51]. Other circulating biomarkers, such as miRs, have been involved in the pathophysiology of inflammation in HF; specifically, miR-21 is highly expressed during the course of chronic HF [52]. Indeed, miR-21 has been implicated in the development of ischemia/reperfusion injury and LV remodeling [53]. As a matter of fact, several miRs are implicated in the pathophysiology of coronary artery disease, acute coronary syndromes, post-MI myocardial remodeling, and fibrosis, and they could serve as possible biomarkers for diagnosis, prognosis, and treatment [53]. Most importantly, some mechanisms of their action involve detrimental effects on endothelial function, inflammation, and oxidative stress [53]. Last but not least, other non-coding RNAs have been studied intensively as possible pathophysiologic mediators not only for HF but also for several cardiovascular diseases [45]. On the other hand, in circumstances of myocardial damage, DAMPs activate the pathogen recognition receptor (RPR) pathway, which is responsible for the release of intracellular cytokines such as the receptor for advanced glycation end-products (RAGE) and cluster of differentiation-36 (CD-36) [54]. Dying cells release pro-oxidant mediators, ROS, IL-1β, myeloperoxidase (MPO), and matrix metalloproteinases (MMPs), which break down extracellular matrix (ECM) [54,55,56]. ROS provoke irreversible damage to cellular compartments and death of cardiomyocytes; also, oxidative stress is highly linked to the inflammatory response [57]. B-and T-cells participate in the inflammatory processes and cardiac remodeling; although T-lymphocytes have a protective role in cardiac remodeling post-MI, the balance between destroyed cardiomyocytes and the adaptive immune response is insufficient to prevent cardiac damage in HF [58,59,60]. Infections (i.e., viral infections) might result in the development of myocarditis and HF due to non-sterile inflammatory damage; as a matter of fact, in the acute phase, there is a loss of cardiomyocytes and myocardial infiltration with mononuclear cells [61]. Afterward, innate immune cells such as natural killer (NK) cells and macrophages hinder viral propagation until the adaptive immunity response begins; finally, antigen-specific T cells are activated, and in the subacute phase, the specific CD8+ cytotoxic T cells destroy the affected cells [61]. Last but not least, in the chronic phase, low-grade inflammation (due to the presence of infection or an inflammatory disease in general), which might last for years, triggers cardiac remodeling and augments the risk for dilated cardiomyopathy [62,63,64]. The endothelium is a monolayer of cells that cover the inner surface of the vascular wall; apart from an anatomic barrier that separates blood flow from the vascular wall, endothelium serves as an important endocrine organ, which controls vascular tone, inflammation, and oxidative stress, cellular proliferation and coagulation [65]. Healthy endothelium produces NO from the enzyme endothelial nitric oxide synthase (eNOS); production of NO by eNOS is one of the most important mediators of endothelial-dependent relaxation as a response to mechanical stimuli (i.e., shear stress) or chemical substances (i.e., acetylcholine, arachidonic acid) [66]. Interestingly, eNOS is calcium-calmodulin dependent, and its activation depends on the intracellular concentration of calcium [66]. Sufficient NO production is responsible for anti-inflammatory, antioxidant, anti-coagulant, and vasodilative effects [67]. Moreover, normal endothelial function affects almost every vascular bed, such as myocardial and coronary circulation and renal, systemic, and pulmonary circulation [68]. Reduced NO production due to ED might contribute to hemodynamic compromise in the settings of acute HF [69]. According to the literature, decreased production of NO by eNOS has been linked to augmented vascular tone, cellular proliferation, and myocardial remodeling [69]. Furthermore, it has not been determined if venous congestion and fluid accumulation are the initial trigger or the result of ED in patients with acute HF [70]. Moreover, ED is also implicated in the pathophysiology of cardiorenal syndrome, which is characterized by the bidirectional dysfunction of the heart and kidney due to acute or chronic dysfunction of either organ or due to another systemic disease [48]. On the other hand, acute endotheliitis might present in acute HF and has been associated with ED and impaired NO production due to excessive oxidative stress, inflammation, and vasospasm upon an initial myocardial insult [68]. As a matter of fact, acute endothelial injury is a key characteristic of Takotsubo cardiomyopathy and is related to the presence of an excessive inflammatory response, oxidative stress, and SNS activation [71]. Experimental data in hypertensive rats demonstrate that apart from the pressure-dependent uncoupling of eNOS during pulmonary edema, disrupted endothelial cell integrity and impaired endothelial mechanotransduction favor eNOS uncoupling and excessive ROS production during episodes of acute HF [72]. Chronic HF is characterized by disturbance of normal endothelial function, which is associated with poor cardiovascular prognosis irrespectively of the cause or severity of HF [69,73,74]. Patients with chronic HF display vasoconstriction and deteriorated peripheral tissue perfusion due to ED; as a result, myocardial injury occurs, whereas poor tissue perfusion exacerbates the pre-existing vasoconstriction of both renal and coronary vascular beds [69]. Excessive oxidative stress in chronic HF is attributed to the dysfunctional antioxidant cellular defense systems due to eNOS uncoupling and impaired NO production [69]. Additionally, oxidative stress has been implicated in the dysfunctional regulation of calcium ions during systole and impaired cardiac relaxation [75]. Furthermore, oxidative modification and damage of cellular phospholipids result in high endothelial permeability and loss of endothelial integrity [76]. Decreased NO/ endothelin-1 (ET-1) ratio in chronic HF is linked to the continuous impairment of cardiac function, which is reflected by echocardiographic indices such as LVEF and LV short-axis shortening rate [77]. Lastly, increased expression of serum soluble angiotensin converting enzyme-2 (ACE) is related to poor exercise tolerance and raised asymmetric dimethyl-arginine (ADMA) levels, implying its association with the development of oxidative stress-induced ED [78]. Regarding myocardial fibrosis, the transition of endothelial cells into fibroblast-like cells in chronic HF, which is termed endothelial-to-mesenchymal transition, has been intensively investigated recently [79]. There is evidence that epigenetic control of gene transcription and translation, including DNA methylation, histone modifications, and the actions of non-coding RNAs (miR, long non-coding RNAs, and circular RNAs), are implicated in these cellular processes in chronic HF [79]. As for miR, their role in the mechanisms of vascular repair has already been established for HF [77]. Moreover, RhoA/Rho kinase overexpression is another pathway of myocardial fibrosis and LV remodeling in chronic HF, associated with impaired NO bioavailability [80]. Lastly, according to a recent study in patients with chronic HF, apoptotic endothelial cell-derived micro-vesicles such as cluster of differentiation 31 (CD31)+/annexin V+ might discriminate different HF categories along with the measurement of classic biomarkers of fibrosis such as galectin-3 [81]. On the other hand, another important pathophysiologic aspect of chronic HF is the dysfunction of endothelial circulating progenitor cells (EPCs); as a matter of fact, the pathway of AMP-activated protein kinase is involved in the regulation of EPCs number and expression and could be a possible therapeutic target for HF [73,82]. Additionally, deteriorated NO production in chronic HF exhibits detrimental effects in the expression of Vascular endothelial growth factor (VEGF), which normally mediates angiogenesis; as a result, diminuted capillary density occurs, which along with the dysfunctional energy metabolism of cardiomyocytes progress to the development of cardiomyopathy [83,84]. Moreover, high expression of von Willebrand factor (vWF), which is a biomarker of endothelial damage, results in worse endothelial function reflected by deteriorated flow-mediated dilatation (FMD) [85,86]. Coronary microvascular ED has been linked to asymptomatic LV dysfunction and could be an early step in the pathophysiology of systolic HF [87]. On the other hand, peripheral ED is a risk factor for the development of stage B HF, which is defined as the asymptomatic stage of HF with impaired systolic function [88]. Interestingly, even low-risk individuals of both sexes but with ED have a greater risk for the development of stage B HF, although the exact pathophysiologic mechanisms of cardiac remodeling have not been determined [88]. Possibly, the diminuted NO production could result in LV systolic dysfunction and the progression of HF syndrome [88]. The association between endothelial function and HF is thought to be more complex than the already known NO-mediated effects in the cardiovascular system; as a matter of fact, ED is a key characteristic of several circulatory beds in HF, irrespectively of LVEF [89]. Moreover, biomarkers of endothelial glycocalyx degradation, such as elevated heparin sulfate, have also been linked to HFrEF and worse prognosis [90,91]. What is more, ED occurs at a late stage in patients with HFrEF in contrast to HFpEF [4]. Lastly, according to the Multi-Ethnic Study of Atherosclerosis, impaired FMD of the brachial artery has been related to the risk of developing HF, and especially HFrEF, independently of the classic risk factors and natriuretic peptides levels [92]. Interestingly, polymorphisms of eNOS might be implicated in the risk of systolic HF in certain populations, whereas ethnic differences between microvascular and macrovascular ED have also been recorded [93,94,95]. Moreover, depletion of inducible nitric oxide synthase (iNOS) in an experimental study of wild-type mice with chronic transverse aortic constriction beneficially affect cardiac hypertrophy, dilation, fibrosis, and dysfunction, implying the detrimental effects of iNOS dysregulation in the maladaptive response to systolic overload of the heart [96]. On the other hand, oxidative stress plays an important role in the pathophysiology of ED in both systolic and diastolic HF; while low concentrations of ROS are normal during cellular function, ROS overexpression damages cellular gene expression and signaling pathways, which affects cardiac mechanics and energy utilization [97]. Additionally, ROS overproduction is implicated in the development of myocardial hypertrophy and dilation [98]. HFrEF is characterized by dysfunction of the NO-sCG-cGMP pathway, which implies the presence of ED; oxidative stress induces deleterious effects in the enzymes of this system, which end in deteriorated NO production and development of myocardial injury according to preclinical and clinical data [99,100]. As a matter of fact, treatment with agents with established antioxidant properties, such as allopurinol, has displayed beneficial effects in markers of systolic dysfunction of patients with HF, such as global longitudinal peak strain [98]. Moreover, activation of the nuclear factor kappa B (NF-κB) pathway and release of pro-inflammatory molecules such as intracellular adhesion molecule-1 (ICAM-1) and vascular adhesion molecule-1 (VCAM-1) is evident in systolic HF according to studies [101,102]. Additionally, other data indicate that decreased adiponectin levels are related to diminuted NO production in patients with systolic HF and the severity of HF [103]. Patients with chronic systolic HF display not only ED but also abnormal ventricular-arterial uncoupling, which is the pressure–volume interaction between LV and the vascular system [104]. Ventricular–arterial uncoupling has been associated with dysfunction of the mechanisms of cardiac energetics, pump efficiency, and poorer clinical outcomes [105]. Lastly, mineralocorticoid receptors of the endothelial cells have been related to the transition of cardiac hypertrophy to systolic dysfunction of the heart independently of pressure-induced overload damage and LV remodeling [106]. Finally, patients with systolic HF and pulmonary arterial hypertension display low numbers of EPCs and increased levels of osteoprotegerin, implying the deleterious effects of osteoprotegerin in the development of pulmonary ED and worsening the prognosis of systolic HF [107]. Similarly, according to another study, high levels of osteoprotegerin, diminuted EPCs, and increased mean pulmonary artery pressure have been associated with the damage induced by hypoxemia in patients with sleep-disordered breathing; as a result, it is speculated that ED and vascular remodeling of pulmonary vasculature orchestrate the deterioration of systolic function in HF [108]. ED induces LV diastolic dysfunction, which is a key characteristic of both HF (and, in particular, HFpEF) and coronary artery disease [109,110]. Decreased microvascular reactivity in patients with diastolic HF implies that microvascular ED is responsible for the progression of subclinical heart remodeling [111]. Furthermore, ED is an important contributor to the development of LV diastolic and right ventricular dysfunction in patients with end-stage renal disease, whose volume status is normal [112]. Interestingly, a study in mice revealed that the induction of endothelial permeability was associated with the provocation of diastolic dysfunction and deterioration of cardiac function due to the disruption of endothelial cell–cardiomyocyte interactions and decreased ECM protein synthesis [113]. Moreover, overexpression of the human β3 adrenergic receptor in a transgenic rat model was linked to diminuted NOS3 mRNA expression, diastolic dysfunction of the aging heart, and reduced aortic flow upon diastolic stress [114]. Additionally, deterioration of the NO-cGMP pathway is implicated in the pathophysiology of diastolic HF since a dysfunctional endothelium is related to repeated episodes of ischemia/reperfusion and the development of a chronically stunned myocardium with systolic dysfunction and increased diastolic stiffness [68]. Nonetheless, a study in knockout mice for nuclear factor (erythroid-derived 2)-like 2 demonstrated that the development of LV diastolic dysfunction is attributed to SERCA2 a downregulation and not to the changes in coronary vascular function or systemic hemodynamics, which were preserved by compensatory upregulation of eNOS expression in the aorta and the heart [115]. Another important pathophysiologic aspect to be mentioned is the impact of diabetic metabolic derangement on LV function; interestingly, according to an experimental study, the effects of metabolic syndrome (hyperglycemia, hypercholesterolemia, and hypertriglyceridemia) were related to eNOS uncoupling, excessive nitroso-redox balance, alteration in genes of glucose and fatty acid metabolism as well as mitochondrial dysfunction [116]. Similarly, in another study of prediabetes, poor coronary endothelial function was linked to increased protein kinase C activity, mitochondrial oxidative stress, as well as rho-kinase-impairment of myosin head extension to actin filaments [117]. Additionally, a decrease in soluble guanylyl cyclase/PKG activity and stiffness of myocardial titin was evident, too [117]. Interestingly, epicardial adipose tissue demonstrates a positive association with cardiac structural and protein alterations, ED, reduced insulin sensitivity, and inflammation; possibly, local mechanic or paracrinic effects of epicardial fat could justify these results [118]. Furthermore, impaired expression of endothelial sirtuin-6 in diabetes mellitus has been linked to the dysregulated fatty acid transportation across the endothelium, which might contribute to the pathophysiology of HFpEF [51]. Finally, according to recent data, miR-30 d/e has been associated with the presence of diastolic dysfunction, impaired free fatty acid metabolism, and microvascular dysfunction in diabetes [119]. As for the role of inflammation, it should be mentioned that several risk factors induce a chronic, pro-inflammatory environment, which is responsible for the stimulation of the immune response, the perpetuation of hypoxemia, and the activation of neurohormonal systems; as a result, coronary microvascular ED develops and, subsequently, diastolic dysfunction of the heart [112,120]. As a matter of fact, circulating pro-inflammatory biomarkers such as IL-6 and CRP have displayed an association with echocardiographic parameters of diastolic dysfunction in HF [121]. Interestingly, according to a recent study in female mice, a deficiency of endothelial sirtuin-3 is responsible for the induction of diastolic dysfunction in the aged heart as well as for the elevation of blood pressure [122]. Evidence from basic science and clinical studies has revealed that systemic and myocardial oxidative stress derived by the enzyme nicotinamide adenine dinucleotide phosphate (NADPH) oxidase is an important contributor to LV diastolic dysfunction [123]. Moreover, according to an experimental study of pulmonary hypertension, diastolic HF has been associated with the lack of normal endothelial function as well as the development of increased pulmonary vascular resistance, vascular thickness, and biventricular cardiac hypertrophy [124]. Finally, according to other data, angiotensin II has been linked to the dysfunctional mechanisms of angiogenesis through effects in the Akt pathway, which ultimately result in the development of diastolic HF [125]. Acute myocardial ischemia (i.e., post MI) may result in scar formation, triggering cardiac remodeling and the development of HF [126]. Ischemic and non-ischemic HF display differences regarding their relation to ED [127]. Specifically, patients with ischemic HF usually have systemic ED, which involves arteries and veins, microcirculation, as well as coronary, pulmonary, and peripheral vessels [128]. Interestingly, according to a study, peripheral endothelium-dependent and endothelium-independent function was more deteriorated in patients with ischemic HF than in patients with non-ischemic HF [129]. Furthermore, a study by our research team has revealed that patients with ischemic HF have impaired FMD, and there is a linear improvement of FMD according to LVEF, while impaired endothelial function was associated with a worse cardiovascular prognosis [130]. Regarding the pathophysiology, according to a protein network analysis from patients with ischemic and non-ischemic HF, upregulation of 18 proteins related to the pathways of inflammation, ED due to superoxide production, coagulation, and atherosclerosis was evident in ischemic HF [126]. Surprisingly, five key network proteins such as acid phosphatase 5, epidermal growth factor receptor, insulin-like growth factor binding protein-1, plasminogen activator urokinase receptor, and secreted phosphoprotein-1 could discriminate ischemic HF from non-ischemic HF [126]. The involvement of inflammation in the development of post-MI HF has already been established; according to an experimental study in mice, angiotensin II exerts myocardial damage post-MI through the attachment of the pro-inflammatory Nox2+ myelomonocytic cells, macrophages, and monocytes at the vessel wall along with stimulation of oxidative stress and the ED [131]. Other important pro-inflammatory mediators in the pathophysiology of ischemic HF are CRP, pentraxin-3, osteoprotegerin, BNP, neopterin, and soluble suppression of tumorigenesis-2 (sST2) [132]. As a matter of fact, sST2, which is a biomarker of fibrosis, has exhibited higher expression in patients with ischemic HF and has been linked to the functional capacity of the patients; moreover, this biomarker is inversely associated with FMD of the brachial artery [133]. Finally, according to another study, endothelial function in patients with ischemic HF is further impaired compared to patients with dilated cardiomyopathy, implying the involvement of underlying atherosclerosis in the pathophysiology; indeed, patients with ischemic HF displayed higher levels of IL-6 and TNF-α [134]. As for oxidative stress, increased ADMA levels, which antagonize NO production and are a key characteristic of ED, were associated with poor cardiovascular prognosis in patients with ischemic HF [135]. Moreover, increased thrombogenicity, ED, and oxidative stress have been implicated in the development of atrial fibrillation in ischemic HF; specifically, evidence from a study in mice exhibited decreased expression of atrial eNOS, SERCAa, thrombomodulin, tissue factor pathway inhibitor, and tissue plasminogen activator [135]. Additionally, at the molecular level, exosomes (which are vectors for intracellular communication) are associated with ischemic heart disease and its evolution to HF through the mechanisms of ED, lipid accumulation, atherosclerotic plaque development, and ischemia–reperfusion injury [136,137]. Post-MI remodeling is a detrimental consequence of MI; interestingly, in a study of patients with ischemic HF, increased mRNA levels of adrenomedullin exhibited a relation with post-ischemic myocardial remodeling [138]. Similarly, dysfunction of the T-regulatory cells has also been implicated in the development of chronic ischemic HF through immune system activation and LV remodeling [139]. What is more, mineralocorticoid receptors and RAAS activation are important determinants of post-MI LV dysfunction; specifically, according to a study in mice, deletion of mineralocorticoid receptors in Vascular smooth muscle cells (VSMCs) could ameliorate LV dysfunction post-MI through control of coronary flow reserve and improvement of endothelial function [140]. As for EPCs, increased circulating levels of EPCs along with FMD could predict LV remodeling post-MI and the occurrence of major adverse cardiovascular events [141]. Interestingly, insulin resistance in diabetic patients with ischemic HF has been linked to decreased circulating numbers of proangiogenic EPCs [141]. Additionally, the reduced amount of CD14(+)CD309(+)-and CD14(+)CD309(+)Tie2(+) circulating EPC was related to the severity of LV dysfunction in patients with ischemic HF, whereas CD45(+) CD34(+) and CD45(-) CD34(+) mononuclear cell counts were associated with the severity of coronary artery lesion [142]. Last but not least, increased endothelial-derived apoptotic microparticles in patients with ischemic HF are associated with ED and poor prognosis [143]. Non-ischemic HF is a primary disease of cardiomyocytes and interstitial space [144]. The use of multiple circulating biomarkers might be used to reveal possible pathophysiologic pathways that are linked to a certain phenotype of HF (ischemic vs. non-ischemic) [126]. As for ED, there is evidence that peripheral endothelial function is not impaired in patients with non-ischemic HF [145]. Indeed, the pattern of ED in patients with non-ischemic HF is more heterogenous and exhibits fewer systemic abnormalities, whereas ED of coronary circulation occurs more frequently [128]. Abnormal coronary microvascular flow has been associated with deteriorated myocardial perfusion and consequent metabolic changes in cardiomyocytes, which trigger local myocardial ischemia [146,147]. Other studies have also confirmed the heterogenous nature of microvascular ED in patients with LV dysfunction of unknown cause [146]. Coronary endothelial-independent microvascular dysfunction has been related to higher brain natriuretic peptide (BNP) levels and ventricular wall tension in patients with non-ischemic HF, especially in those with cardiac fibrosis [148]. As for the pathophysiology, diabetes mellitus is associated with the development of diabetic cardiomyopathy irrespectively of the ischemic damage through complex effects on vascular endothelial function; specifically, hyperglycemia increased free fatty oxidation, decreased NO production, oxidative stress, inflammation, and dysfunctional endothelial permeability contribute to the pathophysiology of diabetic cardiomyopathy [149]. Additionally, according to the literature, patients with non-ischemic HF display increased myocardial expression of vWF (which is a glycoprotein produced by endothelial cells that control platelet aggregation and thrombus formation at the sites of vascular injury), implying the impact of ED-derived vWF release in the development of non-ischemic HF [150]. On the other hand, in patients with non-ischemic HF, NO and a secondary endothelium-derived relaxing factor sensitive to high K+ have demonstrated vasodilative properties [151]. In general, ED impairs LV systolic function due to the increase in systemic vascular resistance; then, LV dysfunction further deteriorates endothelial function through decrease in shear stress and NO bioavailability [152]. Interestingly, in a study of patients with systolic, non-ischemic HF, LVEF was related to ED, implying the importance of the management of ED in these patients [152]. Lastly, NO inhibition of the peripheral vasculature in patients with non-ischemic HF resulted in higher basal vascular tone and the progression of the disease [153]. Therefore, the involvement of ED in the pathophysiology of non-ischemic HF is not consistent among the studies suggesting that further research should be performed in order to elucidate the exact impact of ED on non-ischemic HF. In pulmonary arterial hypertension (PAH), the increase in pulmonary vascular resistance is responsible for the rise in right ventricular afterload and the progression to right HF [154]. The abnormal hypertrophy of small pulmonary arteries ranges from hypertrophy and hyperplasia of the media layer to the excessive apoptosis and proliferation of pulmonary arterial smooth muscle cells, which end in the formation of plexogenic lesions that obstruct artery lumen and decrease pulmonary blood flow [155]. Importantly the progressive occlusive arterial remodeling of pulmonary arterioles is characterized by the presence of significant ED [156,157]. Interestingly, it has been proposed that ED of pulmonary circulation displays an association with ED of the systemic circulation, implying that PAH is a situation of global vasculopathy [158]. Pulmonary artery endothelial cells are vulnerable to several insults (such as toxins, hypoxia, pro-inflammatory cytokines, and shear stress), which, along with the presence of genetic susceptibility, are responsible for the pathophysiology of disease; increased shear stress, in particular, develops an environment of raised arterial pressure and fluid dynamics which end in endothelial cell injury [159,160]. As a result of pulmonary artery endothelial cell damage, excessive oxidative stress, hyperproliferation, and coagulation occur [159]. Moreover, at the later stages of endothelial cell damage, apoptosis-resistant endothelial cells develop along with excessive angiogenesis [161,162]. Interestingly, research has revealed that pulmonary microvascular endothelial cells have intrinsic deficits that hinder proper response to Vascular endothelial growth factor A stimulation [155]. Another pathophysiologic characteristic is the poor tolerance of pulmonary microvascular endothelial cells to hypoxic injury [155]. Moreover, evidence from experiments in mice has revealed that inhibition of Hypoxia-inducible factor 1α could decrease right ventricular systolic pressure and hypertrophy as well as the amount of fibrosis and obliterative pulmonary vascular remodeling [163]. Endothelial cell permeability is compromised in PAH and, under the expression of growth factors such as VEGF, activation of pro-inflammatory mediators and cytokines occurs [164]. As a result, inflammation perpetuates vascular damage and endothelial barrier permeability, which is responsible for the distorted gas exchange and coagulation between lung and blood tissue [165]. Moreover, crosstalk between endothelial cells and VSMCs, as well as with non-smooth muscle cells, results in the chemoattraction of immune cells at the sites of vascular damage such as myofibroblasts and pro-inflammatory leucocytes [166]. Another important pathophysiologic aspect is endothelial-to-mesenchymal transition in which endothelial cells transform into a profile that resembles myofibroblast or mesenchymal cells; as a matter of fact, endothelial cells lose the expression of their typical markers such as CD31 and cadherins and exhibit proliferation of α-smooth muscle actin and vimentin [167]. According to the studies, the transforming growth factor-β (TGF-β) signaling pathway mediates the expression of Smooth muscle alpha-actin (αSMA) and type I collagen (and not VE-cadherin) in pulmonary arterial endothelial cells [168,169]. Endothelial-to-mesenchymal transition is controlled epigenetically by several miR such as miR-21; for instance, TGF-β augments miR-21 expression in endothelial cells through AKT-dependent pathway [170]. Also, there is evidence that miR affect ion expression, mitochondrial function and are implicated in the angiogenic impairment in PAH [171]. A genetic involvement is present in 6–10% of patients with PAH and most commonly involves the heterozygous germline mutation of the Bone Morphogenetic Protein Receptor (BMPR) gene, which encodes type 2 bone morphogenetic protein receptor (BMPR-2) [172]. There is evidence that autophagy in the lysosomes of human pulmonary artery endothelial cells might contribute to BMPR-2 deletion in PAH [173]. Additionally, control of gene expression at the transcriptional, post-translational, and post-transcriptional level by long non-coding RNAs is implicated according to recent studies in the development of pulmonary vascular remodeling through effects in endothelial function, cell proliferation, angiogenesis, endothelial-to-mesenchymal transition and cellular metabolism [174]. Last but not least, improper NO release due to ED induces DNA damage and metabolic dysregulation [175]. Indeed, it has been described that pulmonary artery endothelial cells and adventitial fibroblasts display a shift from glucose oxidation towards uncoupled aerobic glycolysis, which hinders the contractility of cardiomyocytes [176]. Moreover, metabolic abnormalities in PAH include irregular polyamine and sphingosine metabolism, impaired insulin sensitivity, and poor iron handling [177]. Gene expression studies indicate that human pulmonary artery smooth muscle cells exhibit increased fatty acid metabolism, formation of unsaturated fatty acids as well as a phenotype of the energy-driven proliferative profile along with decreased expression of the genes of the tricarboxylic acid cycle [178,179]. For instance, the deletion of peroxisome proliferator-activated receptor γ (PPARγ) results in systolic dysfunction of both ventricles and lipid accumulation inside cardiomyocytes [180]. Finally, a disintegrin and metalloproteinase with thrombospondin motifs 8 (ADAMTS8) (disintegrin and metalloproteinase with thrombospondin motifs 8) is implicated, according to experimental evidence, in mitochondrial fragmentation under hypoxia as well as with the proliferation of pulmonary artery smooth muscle cells [181]. Impaired endothelial function in patients with HF is associated with poorer cardiovascular outcomes, implying proper therapeutic management of ED [182]. ED is more severe in HFpEF and appears earlier during the progression of HFpEF; contrarily, ED is evident at a later stage in HFrEF [4]. Moreover, ED of the peripheral vasculature is maximal during the early stages of HF in contrast to more severe stages of this syndrome [183]. Patients with acute HF are characterized by the presence of a procoagulant state due to ED, which is linked to poorer cardiovascular prognosis [184]. Deteriorated peripheral endothelial function may also predict long-term cardiovascular events in patients with end-stage HF and could facilitate risk stratification strategies in these patients [185]. Similarly, impaired peripheral endothelial function and decreased exhaled NO (as a marker of pulmonary circulation) during submaximal exercise in patients with chronic HF have been linked with higher mortality after adjustments for clinical factors [186]. Finally, according to another study, evaluation of ED in patients who receive treatment with cardiac resynchronization therapy (CRT) might also indicate the patients with better response [187]. On the other hand, there is evidence that not only peripheral but also coronary microvascular and epicardial dysfunction are associated with the clinical outcomes in HF; as a matter of fact, preservation of endothelial function might improve LVEF in patients with HF [188]. Coronary microvascular ED drives the development of HFpEF due to the existence of an environment of chronic, subclinical inflammation [188]. Interestingly, circulating inflammatory markers have been related to coronary microvascular ED (assessed by transthoracic Doppler echocardiography), as well as with markers of diastolic dysfunction, such as the increased E/e’ ratio [188]. Similarly, in a study of patients with HFpEF, oxidative stress reflected by the levels of increased MPO, uric acid, calprotectin, and symmetric dimethyl arginine, are associated with diastolic dysfunction; as a matter of fact, microvascular ED was linked to worse cardiovascular prognosis [189]. Patients with acute and chronic HF exhibit various degrees of ED and circulating biomarkers of endothelial activation [190]. Interestingly, a study in hypertensive patients revealed that impaired endothelial function and high CRP levels are associated with the development of new-onset HF [191]. Furthermore, according to recent data, high circulating levels of biomarkers of endothelial glycocalyx impairment are linked to increased mortality in patients with decompensated HFrHF, implying the role of dysfunctional endothelium for poor prognosis [90]. Moreover, ED could predict the incidence of adverse events in acute HF as well as HF progression [68]. Moreover, the identification of ED might reveal individuals at risk for developing HF and facilitate the therapeutic monitoring of those who already receive cardiotoxic agents [88]. Interestingly, according to another study by our research team, FMD could serve as a risk-stratification tool in order to evaluate anthracycline-induced cardiotoxicity in patients who receive chemotherapy [192] (Figure 1). Exercise and cardiac rehabilitation have demonstrated beneficial effects in patients with HF and especially in HFrEF; as a matter of fact, HFrEF is characterized by exercise intolerance, and several assessments could evaluate exercise capacity in these patients, such as a 6-minute walk test and peak oxygen uptake (VO2) [193]. It should be pointed out, though, that not all patients are capable of cardiac rehabilitation, and those who are appropriate candidates achieve an improved quality of life, better exercise capacity, and fewer cardiovascular events [193]. The benefits of structured exercise training (ET) have been well established for HF and have been given a class IA recommendation for patients with stable HF [194,195]. Three modalities of ET have been proposed: endurance-aerobic (continuous or interval training, with superior effects of endurance on LV function), strength/resistance training (individually tailored to each patient’s needs), and respiratory training (preferred in circumstances of inspiratory muscle weakness) [196]. According to the literature, ET improves FMD through effects on shear stress as well as arterial compliance [197]. Specifically, evidence from a meta-analysis of 16 studies indicated that ET enhances NO bioavailability by assisting eNOS function and antioxidant enzymes expression, mobilization of EPCs, and decrease in TNF-α, IL-10, and IL-6 expression [197]. Interestingly, in the Leipzig Exercise Intervention in Chronic heart failure and Aging (LEICA) study, 4-week ET in patients with stable congestive HF improved FMD regardless of age; a rise in EPCs number and function was also documented [198]. Moreover, maximal cardiopulmonary exercise testing (CPET) in patients with chronic HF of different severity has displayed enhanced mobilization and circulation of EPCs, although the effect was irrelevant to the disease severity [60]. ET, according to another study, demonstrated favorable effects in LVEF of elderly patients with chronic HF, which was mediated by mobilization of EPCs, stimulation of NO and VEGF expression, as well as of PI3 K/AKT pathway of angiogenesis [199]. Interestingly, 12 weeks of high-intensity interval exercise (HIIT) in patients with HFrEF resulted in decreased SNS activity and better peripheral vascular function reflected by brachial artery FMD in contrast to moderate-intensity continuous training [200]. As for HFpEF, ET has indicated favorable effects and is considered to be an important non-pharmaceutic option, which improves the quality of life and exercise capacity through mechanisms that involve endothelial function, such as the regulation of inflammation [201]. In elderly patients with HFpEF, 16 weeks of ET had a beneficial effect on VO2 and quality of life without effects on FMD or arterial stiffness, and the possible mechanism could be better skeletal muscle perfusion or oxygen utilization [202]. According to a systematic review of 9 studies of patients with HFpEF, ET resulted in higher VO2 uptake, 6-minute walking distance, and improved ventilation threshold, although there was no significant effect on endothelial function and arterial stiffness; interestingly, only in some of the studies echocardiographic parameters and quality of life displayed improvement [195]. In conclusion, ET exerts beneficial effects in the appropriate candidates with HF, and the possible mechanisms involve the improvement of endothelial function and SNS activity, which result in enhanced exercise tolerance, possibly due to better diffusion and utilization of oxygen in skeletal muscles and oxygen transport to tissues [193]. Statins, known as 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductases, display not only lipid-lowering actions against LDL levels but also pleiotropic actions such as anti-inflammatory, anti-atherosclerotic, and antioxidant actions [203]. In fact, these cardiovascular effects are independent of lipid-lowering actions and involve the mobilization of EPCs in circulation and enhancement of endothelial function [203]. According to the literature, statins improve LV remodeling and diastolic dysfunction of the heart as well as natriuretic peptide expression, possibly through the control of inflammation and restoration of endothelial function [204]. Patients with HFrEF statins displayed increased mobilization of EPCs, which further improved the exercise capacity and morbidity of the patients [205]. Additionally, statins decrease TNF-α levels and augment NO bioavailability through the stabilization of the mRNA of eNOs synthase [205]. Similarly, in another study, short-term administration of rosuvastatin, but not allopurinol, increased the number of circulating EPCs in patients with systolic HF, although EPCs did not display associations with biomarkers of oxidative stress and inflammation [206]. Furthermore, in a study of patients with congestive HF administration of statins activated circulating CD34+ EPCs and enhanced the neovascularization process, which was reflected by higher expression of VEGF, improved endothelial function, and LVEF [207]. In patients with congestive HF, atorvastatin restored peripheral endothelial function assessed by gauge-strain plethysmography and decreased the expression of TNF-α, IL-6, and VCAM-1 [208]. Higher doses of atorvastatin, in contrast to the lower doses, induced favorable effects on endothelial function in patients with ischemic HF and improved FMD, augmentation index, as well as MMP-9 and ICAM-1 levels [209]. Last but not least, the administration of rosuvastatin in patients with congestive HF raised FMD in contrast to the administration of ezetimibe, although there were no differences in lipid levels between study arms [210]. Beta-blockers (β blockers) belong to the antagonists of beta-adrenergic receptors, which are normally expressed in cardiomyocytes and mediate the actions of SNS [211]. Beta-blockers bind to β1, β2, and β3 receptors of the G-protein-coupled receptors family; the first generation of β blockers is non-selective for β1 receptors, the second-generation of beta blockers is more cardio-selective, and the third generation is highly selective for β1 receptors [211]. As a matter of fact, the third generation of β blockers exhibits vasodilatory properties along with antioxidant, anti-proliferative, anti-hypertrophic, angiogenic, and anti-apoptotic properties, which are under investigation [211]. Therefore, β blockers with NO-mediated vasodilatory properties could be a promising treatment for the restoration of ED [212]. Moreover, according to clinical trials, β blockers display a survival benefit for patients with chronic HF, too [213]. Interestingly, in the study of Chin BSP et al., a three-month scheme of β blockers in patients with chronic HF resulted in marked improvement of biomarkers of lipid peroxidation without changes in total antioxidant capacity and vWF levels [214]. In another study in patients with congestive HF and NYHA II-III, administration of bisoprolol for 20 ± 10 weeks could also restore ED [215]. Similarly, a 3-week regimen of carvedilol in patients with HF and NYHA II-III improved L-arginine and L-citrulline levels and decreased the expression of VCAM-1, implying a beneficial effect in endothelium-dependent dilatation, fibrinolysis and hemorheological profile of patients [216]. Furthermore, Poelzl G. et al. displayed that short-term administration of β blockers and ACE inhibitors in patients with chronic HF improved FMD and submaximal exercise capacity [217]. Additionally, treatment with carvedilol for six months could improve HF functional class, LVEF, 6-minute walk distance in patients with chronic HF (without effects in peak VO2 max), and plasma malondialdehyde levels [218]. What is more, in a study of patients with HF and NYHA II-IV, administration of carvedilol for 40 ± 14 months ameliorated HF symptoms and the expression of pro-inflammatory biomarkers irrespectively of LVEF; interestingly, patients who exhibited an improvement of LVEF had also decreased ADMA levels [219]. As for the effects of specific β blockers, the switch from carvedilol to either metoprolol tartrate or succinate in patients with mild HF did not affect endothelium-dependent vasodilation, blood pressure, or heart rate [220]. Last but not least, combination of sacubitril/valsartan and metoprolol in patients with congestive HF ameliorated LV end-systolic and end-diastolic dimensions, LVEF, biomarkers of oxidative stress and coagulation parameters, implying possible effects in cardiac remodeling [221]. In conclusion, the majority of studies underline that β blockers might restore ED in HF, and to our knowledge, only one study in patients with chronic HF has demonstrated neutral effects of β blockers on markers of endothelial, platelet, or hemorheological function [222]. Angiotensin-converting enzyme inhibitors (ACEi) are the mainstay drug category of patients with HF due to their antihypertensive and anti-atherosclerotic properties, including the ability to delay the progression of LV remodeling [223]. Concerning their effect on endothelial function, chronic administration of ACEi in patients with congestive HF has proven beneficial through the improvement of FMD, compliance, and distensibility of the brachial artery, possibly due to their blood-pressure-lowering actions [224]. According to another study, administration of ACEi but not beta-blockers in patients with chronic HF resulted in a more controlled hypercoagulable state, which was reflected by the decreased levels of soluble P-selectin, vWF, and fibrinogen, especially in females and in patients with more progressed NYHA status [222]. Interestingly, treatment of patients with chronic HF with ramipril and sildenafil (both solely and in combination) improved FMD, and this effect remained significant at 4-hour post intervention [225]. Lastly, a recent study that investigated patients with HFmrEF and HFpEF demonstrated that therapy with perindopril for 12 months ameliorated endothelial function of large blood vessels and microvessels (assessed by the method of photoplethysmography); interestingly, both categories of HF exhibited decreased expression of E-selectin, whereas ET-1 had the maximal improvement in patients with HFpEF [226]. Angiotensin Receptor Inhibitors (ARBIs) selectively block the AT-1 receptor pathway, which mediates their antihypertensive functions; as a result, angiotensin-II binds to the AT-2 receptor, which possesses atheroprotective functions [227]. ARBIs do not affect the bradykinin pathway, which is characteristic of ACEi effects on endothelial continuity [227]. Angiotensin-II induces deleterious effects on endothelial function, which involve the senescence of EPCs due to oxidative stress and telomerase inactivation [228]. ARBIs, in general, have beneficial actions against several atherosclerotic diseases, including HF [33]. Nevertheless, their exact effects on endothelial function are less well understood [227]. For instance, according to a study of patients with congestive HF, ACEi or AT-II antagonists improved flow-dependent vasodilatation, shear stress, as well as compliance and distensibility of the radial artery [229]. Aldosterone is a mineralocorticoid hormone with detrimental effects on endothelial cells and cardiomyocytes; specifically, aldosterone is implicated in cardiac hypertrophy and fibrosis in HF in addition to direct vascular injury [230]. Aldosterone also hinders the function, growth, and mobilization of EPCs in a concentration-dependent manner through VEGF-mediated phosphorylation of the Akt pathway [230,231]. MRAs, such as eplerenone and spironolactone, neutralize the harmful effects of aldosterone on the cardiovascular system [231]. Indeed, treatment with spironolactone restores endothelial function through augmentation of NO bioavailability and endothelium-dependent vasodilation in patients with NYHA class II-III chronic HF under standard diuretic/ACEI therapy [232]. Similarly, in another study, the administration of spironolactone in patients with congestive HF improves FMD at 4 weeks, and these effects remain at 8 weeks, possibly due to the attenuation of aldosterone actions in endothelial function [233]. Finally, in a recent study, Levi et al. displayed that 8-week treatment with eplerenone or spironolactone in patients with congestive HF raised VEGFR2+/CD34+ and VEGFR-2+/CD133+ levels of circulating EPCs, implying the beneficial effects of MRA antagonism for maintenance of endothelial function [230]. The novel drug category of sodium-glucose cotransporter 2 (SGLT2) inhibitors has cardioprotective effects in patients with HF through various mechanisms, including improvement of endothelial function, aside from their established effectiveness in the treatment of diabetes mellitus [234]. SGLT2 inhibitors act on the renal proximal tubule, reduce glucose reuptake, and promote sodium excretion leading to glycosuria, natriuresis, and diuresis [235]. According to the literature, the intracellular decrease in sodium levels is responsible for the cardioprotective actions of SGLT2 inhibitors in HF through amelioration of calcium ion handling in cardiomyocytes; as a result, there is an enhancement of the electromechanical function of the heart [235]. Other mechanisms involve control of oxidative stress, fibrosis, autophagy, and inflammation [235]. Evidence from experimental studies indicates that SGLT2 inhibitors counteract mitochondrial dysfunction (due to energy starvation in HF), activate the sirtuin-1 pathway and stimulate ketogenesis [236,237]. Apart from the direct effects of SGLT2 inhibitors in cardiomyocytes, many of their actions involve the regulation of ED, diastolic dysfunction, cardiac stiffness, and reduction of epicardial tissue [238,239]. Empagliflozin improves eNOS-dependent PKGIα oxidation and decreases the expression of ICAM-1, VCAM-1, IL-6, and TNF-α in myocardial tissues of patients with HFpEF [240]. Additionally, empagliflozin enhances the phosphorylation of myofilament proteins and the NO-cGMP pathway, which reflects its antioxidant effects [240]. Cardiomyocytes and macrophages, when treated with empagliflozin, present activation of the AMK kinase pathway as well as inhibition of iNOS function [241]. Data from clinical studies are yet scarce; nevertheless, according to an observational, nonrandomized study, the administration of empagliflozin in diabetic patients with chronic HF improves FMD [182]. Sacubitril-valsartan, an angiotensin-receptor/neprilysin inhibitor (ARNI), is a drug category for chronic symptomatic HFrEF and HFpEF according to the recent guidelines for HF [242]. Sacubitril is a pro-drug, and its activated metabolite inhibits neprilysin from breaking down natriuretic peptides; as a result, vasodilation, natriuresis, and diuresis occur, which are associated with enhanced endothelial function [242]. Blockage of neprilysin increases bradykinin, which possesses endothelium-dependent vasodilatory actions, too [242]. On the other hand, valsartan is an ARB that blocks the RAAS system and protects against vasoconstriction, hypertension, and cardiac remodeling in HF [242,243]. Nevertheless, neprilysin disintegrates angiotensin II; therefore, sacubitril should be used along with an ARB in order to counteract high levels of angiotensin II [242,243]. Considering the effects of sacubitril-valsartan on endothelial function, Amore et al. indicated that treatment with sacubitril-valsartan for six months in patients with dilated cardiomyopathy and reduced LVEF restore endothelial function, LVEF, diastolic dysfunction, and mitral regurgitation, without any significant effects in arterial stiffness [244]. Interestingly, a study in 80 patients with HFrEF proves that sacubitril-valsartan added on standard-of-care regimens for a period of 12 weeks augment FMD; furthermore, an increase in NO and NOS levels, LVEF and calcitonin gene-related peptide was found as well as decreased expression of ET-1 [245]. Lastly, sacubitril-valsartan administration for 12 weeks on top of conventional treatment improved FMD as well as arterial stiffness of patients with HF, too [246] (Table 1). ET-1 is a powerful vasoconstrictor involved in several cardiovascular diseases, including PAH and congestive HF, and participates in the processes of cardiac hypertrophy, inflammation, and atherosclerosis [259]. ET-1 exerts vasoconstrictive and pro-inflammatory actions upon binding to ETA receptors of smooth muscle cells and contradictory actions upon binding to ETB receptors of pulmonary artery endothelial cells, which trigger the clearance of ET-1 from circulation and the release of endogenous NO and prostacyclin [260,261]. Therefore, blockage of ETA receptors could be a promising therapeutic option against PAH, and currently three endothelin receptor antagonists (ERAs)—bosentan, ambrisentan, and macitentan—have been evaluated in clinical trials and have displayed favorable effects in PAH [259,262]. According to evidence from in vitro studies, bosentan improves endothelial function and decreases neointimal and smooth muscle cell proliferation in PAH [263]. Furthermore, evidence from in vivo studies in pigs have demonstrated that bosentan might partially improve hypoxia-related decrease in NO production [264]. Interestingly, administration of bosentan in humans with PAH for a period of six months resulted in ameliorated endothelial function of pulmonary microcirculation upon invasive assessment of endothelial function during right heart catheterization [263]. Similarly, in a study of patients with systemic sclerosis and no-PAH-related symptoms, bosentan reduced exercise-induced PAH; as a matter of fact, bosentan improved 6-minute walk indices, FMD of the brachial artery, and peripheral vasodilation [265]. Another study in patients with moderate to severe idiopathic PAH indicated that treatment with bosentan diminuted the expression of pro-inflammatory biomarkers such as ICAM-1, VCAM-1, IL-6 along with BNP and ameliorated the clinical status of the patients [256]. What is more, evidence from a study of patients with systemic sclerosis and PAH proved that a regimen of bosentan for 12 months could normalize the expression of ICAM-1, VCAM-1, P-selectin, and platelet/endothelial cell adhesion molecule (PECAM-1) and restore T-cell function [257]. Additionally, in patients with connective tissue diseases and PAH, a 3-month scheme of bosentan could ameliorate several biomarkers of endothelial function such as NO and sCD40 L and clinical status; interestingly, responders to the treatment demonstrated a decrease in P-selectin levels [266]. Last but not least, a study by Sfikakis PP et al. demonstrated that a 4-week scheme of bosentan in patients with systemic sclerosis could improve FMD of the brachial artery but not endothelium-independent vascular function; therefore, it has been speculated that this drug protects against systemic scleroderma-associated endothelial injury [258] (Table 2). HF is a clinical syndrome with a diverse etiopathology but common pathophysiologic background. Several mechanisms are implicated in the course of HF, progression, and prognosis, with neurohormonal activation considered the key determinant of the syndrome. Endothelial dysfunction is a significant characteristic that accompanies HF of either etiology or type. Moreover, HF per se may worsen endothelial health through decrease in shear stress, stimulation of inflammation, inactivation of eNOS, and increased oxidative stress. Indeed, impaired endothelial function has been linked to deteriorated functional status and ventricular function in patients with HF. Importantly, most of the therapeutic options with established benefits in patients with HF have a parallel beneficial effect in the restoration of endothelial function.

PMC10001612

Vanessa Delcroix,Olivier Mauduit,Menglu Yang,Amrita Srivastava,Takeshi Umazume,Cintia S. de Paiva,Valery I. Shestopalov,Darlene A. Dartt,Helen P. Makarenkova

Lacrimal Gland Epithelial Cells Shape Immune Responses through the Modulation of Inflammasomes and Lipid Metabolism

21-02-2023

lacrimal gland,inflammasome,lipid metabolism,Sjogren’s syndrome,dry eye,regeneration,acute inflammation,chronic inflammation

Lacrimal gland inflammation triggers dry eye disease through impaired tear secretion by the epithelium. As aberrant inflammasome activation occurs in autoimmune disorders including Sjögren’s syndrome, we analyzed the inflammasome pathway during acute and chronic inflammation and investigated its potential regulators. Bacterial infection was mimicked by the intraglandular injection of lipopolysaccharide (LPS) and nigericin, known to activate the NLRP3 inflammasome. Acute injury of the lacrimal gland was induced by interleukin (IL)-1α injection. Chronic inflammation was studied using two Sjögren’s syndrome models: diseased NOD.H2b compared to healthy BALBc mice and Thrombospondin-1-null (TSP-1-/-) compared to TSP-1WT C57BL/6J mice. Inflammasome activation was investigated by immunostaining using the R26ASC-citrine reporter mouse, by Western blotting, and by RNAseq. LPS/Nigericin, IL-1α and chronic inflammation induced inflammasomes in lacrimal gland epithelial cells. Acute and chronic inflammation of the lacrimal gland upregulated multiple inflammasome sensors, caspases 1/4, and interleukins Il1b and Il18. We also found increased IL-1β maturation in Sjögren’s syndrome models compared with healthy control lacrimal glands. Using RNA-seq data of regenerating lacrimal glands, we found that lipogenic genes were upregulated during the resolution of inflammation following acute injury. In chronically inflamed NOD.H2b lacrimal glands, an altered lipid metabolism was associated with disease progression: genes for cholesterol metabolism were upregulated, while genes involved in mitochondrial metabolism and fatty acid synthesis were downregulated, including peroxisome proliferator-activated receptor alpha (PPARα)/sterol regulatory element-binding 1 (SREBP-1)-dependent signaling. We conclude that epithelial cells can promote immune responses by forming inflammasomes, and that sustained inflammasome activation, together with an altered lipid metabolism, are key players of Sjögren’s syndrome-like pathogenesis in the NOD.H2b mouse lacrimal gland by promoting epithelial dysfunction and inflammation.

Lacrimal Gland Epithelial Cells Shape Immune Responses through the Modulation of Inflammasomes and Lipid Metabolism Lacrimal gland inflammation triggers dry eye disease through impaired tear secretion by the epithelium. As aberrant inflammasome activation occurs in autoimmune disorders including Sjögren’s syndrome, we analyzed the inflammasome pathway during acute and chronic inflammation and investigated its potential regulators. Bacterial infection was mimicked by the intraglandular injection of lipopolysaccharide (LPS) and nigericin, known to activate the NLRP3 inflammasome. Acute injury of the lacrimal gland was induced by interleukin (IL)-1α injection. Chronic inflammation was studied using two Sjögren’s syndrome models: diseased NOD.H2b compared to healthy BALBc mice and Thrombospondin-1-null (TSP-1-/-) compared to TSP-1WT C57BL/6J mice. Inflammasome activation was investigated by immunostaining using the R26ASC-citrine reporter mouse, by Western blotting, and by RNAseq. LPS/Nigericin, IL-1α and chronic inflammation induced inflammasomes in lacrimal gland epithelial cells. Acute and chronic inflammation of the lacrimal gland upregulated multiple inflammasome sensors, caspases 1/4, and interleukins Il1b and Il18. We also found increased IL-1β maturation in Sjögren’s syndrome models compared with healthy control lacrimal glands. Using RNA-seq data of regenerating lacrimal glands, we found that lipogenic genes were upregulated during the resolution of inflammation following acute injury. In chronically inflamed NOD.H2b lacrimal glands, an altered lipid metabolism was associated with disease progression: genes for cholesterol metabolism were upregulated, while genes involved in mitochondrial metabolism and fatty acid synthesis were downregulated, including peroxisome proliferator-activated receptor alpha (PPARα)/sterol regulatory element-binding 1 (SREBP-1)-dependent signaling. We conclude that epithelial cells can promote immune responses by forming inflammasomes, and that sustained inflammasome activation, together with an altered lipid metabolism, are key players of Sjögren’s syndrome-like pathogenesis in the NOD.H2b mouse lacrimal gland by promoting epithelial dysfunction and inflammation. Dry eye disease affects millions of adults worldwide and can be divided into two major types: evaporative dry eye and aqueous-deficient dry eye (ADDE) [1,2]. ADDE is characterized by a reduced secretion or an altered composition of fluid from the lacrimal gland (LG) [3,4], the exocrine tubuloacinar gland responsible for secreting the aqueous layer of the tear film [5,6,7]. ADDE induces eye irritation and pain, and may lead to severe ocular surface disorders [8,9]. The leading cause of ADDE is the chronic inflammation of the LG triggered by aging or auto-immune diseases such as Sjögren’s syndrome (SS). Dry eye disease involves inflammatory mechanisms and the production of several tear cytokines, including interleukins (IL)-1α and IL-1β, which correlate with clinical severity [10]. IL-1α/β are potent proinflammatory cytokines that function as key danger signals during infection or tissue damage. IL-1α/β binding to their receptor IL-1R1 promotes the transcription of genes involved in acute and chronic inflammation. In mice, reversible ADDE can be experimentally induced by a single injection of IL-1α into the LG [11,12]. Several studies demonstrated that IL-1α induces acute LG inflammation and destruction within the first two days after the injury [11,12,13,14]. The resolution of inflammation was noted on the third day after injury and was followed by cell proliferation and complete regeneration within 5–7 days [12,13]. Inflammasomes are large intracellular multiprotein complexes that play a central role in innate immunity [15,16]. The largest class of inflammasomes contain an apoptosis-associated speck-like protein containing a CARD (ASC, encoded by the gene Pycard) and pathogen/danger sensors, which recruit and activate the pro-caspase 1 (pro-CASP1). Each type of inflammasome is characterized by a particular sensor or receptor: PYRIN, the nucleotide-binding domain (NOD), leucine-rich repeat (LRR)-containing protein (NLR) family (e.g., NLRP1/3/6, NLRC4), or the pyrin and HIN domain-containing protein (PYHIN) family (e.g., AIM2 and IFI204, the murine homolog of human IFI16). Canonical inflammasomes cleave IL-1β and IL-18 precursors to generate the mature cytokines. Activated CASP1 and the non-canonical inflammasome formed by CASP4/11 can also cleave the pore-forming protein gasdermin D (GSDMD), which mediates interleukin secretion [17]. GSDMD is required for pyroptosis, an immunogenic form of cell death which enables the massive release of active IL-1α and IL-1β from dying cells [15,18]. Inflammasomes can be activated by a multitude of infectious and sterile stimuli, including microbiome-derived signals and host-derived signals, and were found in different cell types [19]. We previously showed that acute LG inflammation triggers the upregulation of inflammasome-related molecules: Casp4, Nlrp3, the purinergic receptors P2RX7 and P2RY2, the Pannexin-1 (Panx1) membrane channel glycoprotein—a key regulator of inflammasome assembling—, and numerous proinflammatory factors including IL-1β and IL-18 [14]. Whilst inflammasome signaling is critical for the initiation of a fast innate immune response to tissue damage or invading pathogens, aberrant inflammasome activation contributes to various pathologies, including autoimmune disorders, cardiometabolic diseases, cancer, and neurodegenerative diseases [20,21]. It has been shown that NLRP1, NLRP3, NLRC4, and AIM2 inflammasomes play a significant role in shaping immune responses and regulating the homeostasis of intestine and ocular surface immunity in several inflammatory diseases [22,23,24,25,26,27]. In human patients suffering from SS dry eye, conjunctival impression cytology demonstrated an upregulation of Nlrp3 and Casp1 [25]. Baldini and coauthors [28] showed that in the salivary glands of SS patients, the increased expression of NLRP3, CASP1, and P2RX7 was a marker of disease and correlated with the focus score evaluating the number of immune cell infiltrates in gland sections. Moreover, AIM2 inflammasomes are activated in the salivary epithelium of primary Sjögren’s syndrome (pSS) patients and correlate with the expression levels of type I interferon (IFN) signature genes [29]. IFNs are major regulators of the innate immune response and contribute to the activation of canonical and non-canonical inflammasomes [30], partly by inducing the guanylate-binding proteins (GBPs) that are dynamin-like GTPases [31,32]. Among them, GBP1 and GBP2 are upregulated in the biopsies of pSS salivary glands [33] and the latter was proposed as a biomarker for SS in saliva [34] and salivary glands [35]. Although the implication of inflammasome modulators in chronically inflamed LG remains unknown, their therapeutic potential is supported by our observation that the inhibition of Panx1 or Casp4 reduced the inflammation of LGs from thrombospondin-1-null (TSP-1-/-) mice, a model for SS, and improved the epithelium repair through the increased engraftment of progenitor cells [14]. In this study, we investigated inflammasome formation during acute and chronic LG inflammation using the R26ASC-citrine mouse. This reporter mouse forms fluorescent ASC specks when inflammasomes are activated [36]. We show that LG epithelial cells can form inflammasomes upon sensing danger signals and inflammation. To identify inflammasome types involved in acute and chronic inflammation, we analyzed RNA-sequencing (RNA-seq) data from a previously published study on acute injury [13] and performed RNA-seq of LGs from NOD.H2b mice [37]—a pSS mouse model. Our results indicate a strong activation of multiple inflammasome complexes during acute and chronic inflammation. Finally, we analyzed the RNA-seq data of the LGs during acute and chronic inflammation in terms of the biological pathways to identify candidate mechanisms that promote the resolution of inflammation. We demonstrated that lipid biosynthesis is activated during the resolution of inflammation/regeneration after acute injury, but that genes for fatty acid and cholesterol synthesis are, respectively, down- and upregulated during chronic inflammation. Moreover, chronic inflammation also downregulates the genes involved in the tricarboxylic acid (TCA) cycle and the β-oxidation of fatty acids in the mitochondria. Combined, our results show that during chronic inflammatory disease, LG epithelial cells have reduced lipid biosynthesis, accumulated cholesterol, and showed mitochondrial dysfunction. Altogether, these alterations likely induce cell damage, sustain inflammasome activation, and impair LG regeneration and function. For the in vivo detection of the activated inflammasome complex, we employed the transgenic mouse expressing a mouse ASC-citrine fusion protein in the Rosa26 (R26) locus (R26ASC-citrine, kind gift of Dr. Golenbock) [36]. R26ASC-citrine mice were bred and maintained on the C57BL/6J background. B6.129S2-Thbs1tm1Hyn/J mice (TSP-1-/-, RRID:IMSR_JAX:006141) were originally purchased from Jackson Laboratory (Sacramento, CA, USA) and were bred and maintained on the C57BL/6J background. For immunoblotting, TSP-1-/- mice were compared to age-matched wild-type (WT) C57BL/6J mice. For immunofluorescence, we crossed R26ASC-citrine x TSP-1-/- mice to obtain TSP-1-/-:R26ASC-citrine mice that were compared to age-matched R26ASC-citrine mice. The NOD.B10Sn-H2b/J mice (NOD.H2b, RRID:IMSR_JAX:002591) and their BALB/cJ controls (BALBc, RRID:IMSR_JAX:000651) mice were purchased from Jackson Laboratory (Sacramento, CA, USA). The mice were housed under standard conditions of temperature and humidity, with a 12 h light/dark cycle and free access to food and water. All experiments were performed in compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and the Guidelines for the Care and Use of Laboratory Animals, published by the US and National Institutes of Health (NIH Publication No. 85-23, revised 1996), and were pre-approved by TSRI Animal Care and Use Committee. R26ASC-citrine mice were primed by intraglandular injection of lipopolysaccharide (LPS, 1 µg/mL). After 3 h, inflammasome activation was induced by the injection of nigericin (10 µM). Six hours after the last injection, the mice were sacrificed and LGs were dissected out and processed for frozen section preparation. For acute injury experiments, the R26ASC-citrine and WT C57BL/6J females were used. LG inflammation in these mice was induced by the intraglandular injection of IL-1α, as previously described [11]. Briefly, 12 female mice (10 to 12 weeks old) were anesthetized, and the exorbital LG was injected with either saline (vehicle) or IL-1α (1 μg; PeproTech, Cranbury, NJ, USA) in a total volume of 2 μL using a Hamilton glass syringe (#300329, World Precision Instruments, Inc., Sarasota, FL, USA) and NanoFil 35G needle (#NF35BV-2, World Precision Instruments, Inc., Sarasota, FL, USA). The LGs from uninjected mice were used as additional controls. The LGs were harvested 6 and 12 h after injection and processed for immunohistochemistry and RNA extraction. The dissected LGs were fixed with 2% paraformaldehyde in PBS (pH 7.4) for 45 min and frozen in 2-methylbutane cooled by liquid nitrogen, and 10-μm cryosections were prepared using Hacker/Bright OTF5000-LS004 Cryostat. The sections were blocked with 1% bovine serum albumin in Tris-buffered saline containing 0.05% Tween 20. The following primary antibodies were used for immunostaining overnight at 4 °C: mouse monoclonal α-smooth muscle actin antibody (1/200, clone 1A4; #A2547, RRID:AB_476701, Millipore-Sigma, Rocksville, MD, USA) was used to label the myoepithelial cells (MECs) and pericytes (contractile cells around the endothelial cells), rat monoclonal CD31 antibody (1/100, #553370, RRID:AB_394816, BD Biosciences, Franklin Lakes, NJ, USA) was specific to blood vessels, mouse monoclonal E-Cadherin antibody (1/200, #610182, RRID:AB_397581, BD Biosciences) labeled the epithelial cells, and the rabbit polyclonal AIM2 antibody (1/100, #63660, RRID:AB_2890193, Cell Signaling Technology, Danvers, MA, USA) was used to detect AIM2-inflammasomes. Appropriate fluorochrome-conjugated secondary antibodies were obtained from Invitrogen (Waltham, MA, USA) and nuclei were counterstained with DAPI. The formation of inflammasome complexes following bacterial and sterile stimuli in WT mice and during chronic inflammation in TSP-1-/- mice was detected using the ASC-citrine fusion protein that is constitutively and ubiquitously expressed in R26ASC-citrine mice and forms fluorescent specks upon inflammation. Images were taken using a LSM 880 laser scanning confocal microscope (Zeiss, Oberkochen, Germany) at the microscopy core of the Scripps Research Institute (La Jolla, CA, USA). Three different fields were analyzed per animal. Inflammasomes were counted using Imaris Spots software, which allows for the detection and counting of small particles. The number of specks detected in the inflamed LGs was adjusted by subtracting the number of specks found in their respective controls. For protein extraction, the dissected LGs were rinsed in cold PBS and transferred into 2 mL tubes pre-filled with 2.8 mm ceramic beads (#19-628, Omni, Inc., Kennesaw, GA, USA) containing 500 µL of ice-cold RIPA buffer without detergents (50 mM Tris-HCl + 150 mM NaCl + 1 mM EGTA) and supplemented with protease/phosphatase inhibitor cocktail (#5872, Cell Signaling, Danvers, MA, USA). The tissue was homogenized with the Omni Bead Ruptor 4 (# 25-010, Omni, Inc., Kennesaw, GA, USA; 2 cycles: Speed 5, 40 s each) and lysate was kept on ice. An appropriate volume of complete RIPA buffer supplemented with each detergent five-times concentrated was added to the sample (final concentration: 1% Nonidet P-40 + 0.5% sodium deoxycholate + 0.1% SDS + 1 mM EDTA) before incubation on ice for at least 30 min. Then, lysate was centrifuged (15 min, 15,000× g, 4 °C) and the supernatant was collected for protein quantitation using Pierce BCA protein Assay kit (#23225, Thermo Fisher Scientific, Waltham, MA, USA). After denaturation (with NuPAGE LDS + β-mercaptoethanol at 70 °C for 10 min), 30 µg total protein from each sample was separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (NuPAGE Novex Bis Tris gels, ThermoFisher Scientific, Waltham, MA, USA) and transferred to polyvinylidene difluoride membranes using the iBlot2 system (ThermoFisher Scientific, Waltham, MA, USA). The transfer membranes were stained for total protein with the No-Stain Protein Labeling Reagent (#A44449, ThermoFisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. The membranes were then blocked with TBS + 0.1% Tween + 5% milk for 1 h at RT before incubation with the appropriate primary antibody at 4 °C overnight: anti-GSDMDC (1/1000, #sc-393656, RRID:AB_2728694, Santa Cruz Biotechnology, Dallas, TX, USA,), anti-CASP1 (1/1000, #22915-1-AP, RRID:AB_2876874, ProteinTech, San Diego, CA, USA), or anti-IL-1β (1/1000, #A16288, RRID:AB_2769945, Abclonal, Woburn, MA, USA). After 3 washes, the blots were incubated for 1 h with the appropriate horseradish peroxidase-linked secondary antibody and processed for chemiluminescence. The signal was detected using the iBright (ThermoFisher Scientific, Waltham, MA, USA) or ChemiDoc MP (Bio-Rad, Hercules, CA, USA) imaging systems. The quantification of the relative protein abundance was performed using Image Lab software (v6.1, Bio-Rad, Hercules, CA, USA). Background-adjusted band volumes were corrected with the normalization factor (calculated using total protein stain for BALBc/NOD.H2b samples and β-actin for TSP-1-/- samples) and normalized to a reference volume corresponding to the average of the biological replicates in the control condition. The total RNA was isolated using Trizol and converted into cDNA using RT2 First Strand Kit (#330404, Qiagen, Germantown, MD, USA). The gene expression was assessed using the SYBR Green kit, according to the manufacturer’s instructions (Applied Biosystems, Waltham, MA, USA) using a 7300 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). The sequences for qRT-PCR primers are listed in Table 1. qPCR data were analyzed using the comparative Ct (ΔΔCt) method. Actb and Gapdh were used as internal reference genes. For acute injury experiments, we used the previously published data available from the Gene Expression Omnibus (GEO) database (Accession number: GSE99093) [13]. To study chronic inflammation, we mined our RNA-seq data of BALBc and NOD.H2b male LGs processed at 2, 4, and 6 months of age, deposited under GSE210332. Data were analyzed using ROSALIND®® software (https://rosalind.bio/ accessed on 19 February 2023) developed by ROSALIND, Inc. (San Diego, CA, USA). As previously described, ROSALIND uses DESeq2 R library to normalize read counts and calculate fold-changes along with the corresponding p-values [37]. For all projects, differentially expressed genes (DEGs) were selected based on a fold-change (FC) cut-off equal to log2(FC) ≈ ±0.585 (corresponding to FC = 1.5) and p-adj < 0.05. On the figures, the statistical significance of log2(FC) compared to the respective control (uninjured LG for acute injury; age-matched BALBc for NOD.H2b LGs) is shown with: * p-value adj. < 0.05; ** p-value adj. < 0.01; *** p-value adj. < 0.001. For acute injury data, a pathway enrichment analysis was conducted using Metascape [38] using default parameters (min. overlap = 3, p-value cut-off = 0.01, min. enrichment = 1.5) and Gene Prioritization by Evidence Counting (GPEC), an algorithm identifying the subset of input genes that are more likely to be true hits, and by interrogating WikiPathways and Gene Ontology Biological Process databases. The dendrogram was created with the web-based tool Clustergrammer (https://maayanlab.cloud/clustergrammer/ accessed on 25 May 2022) using complete-linkage clustering with Euclidian distances. Prism9 v9.1.2 (GraphPad software Inc, La Jolla, CA, USA) was used to plot the results and test their statistical significance. First, a Shapiro-Wilk normality test was performed to evaluate if the data follow a normal distribution. If data passed the normality test, the statistical significance between the two conditions was assessed with an unpaired t-test and the results were represented as mean ± standard deviation (SD). To compare more than two groups, a one-way ANOVA was used. Otherwise, non-parametric tests were used (Mann-Whitney test for two groups, and Kruskal-Wallis otherwise) and the plots showed the median ± interquartile range (IQR), as specified in the legends. The significant differences are represented as * if p value p < 0.05, ** if p < 0.01, and *** if p < 0.001. In contrast to non-canonical inflammasomes formed by CASP4, most types of canonical inflammasomes require the assembly of the adaptor protein ASC for the activation of the inflammasome cascade leading to IL-1β/IL-18 maturation, and eventually release through the GSDMD pores, also leading to cell death (Figure 1A). Thus, for the in vivo detection of the canonical inflammasome complexes, we used a transgenic mouse constitutively expressing the ASC-citrine fusion protein (R26ASC-citrine) (see enlarged micrograph in Figure 1A) [36]. To test whether inflammasomes could be formed in the LG, we first mimicked a bacterial infection in the R26ASC-citrine reporter mouse, as previously described [36]. In brief, LGs were injected with lipopolysaccharide (LPS, typical pathogen-associated molecular pattern, or PAMP), which constitutes the priming signal inducing Il1b transcription, and with nigericin, a pore-forming bacterial toxin which activates the NLRP3 inflammasome (Figure 1A) [39]. An anti-α-SMA antibody was used to identify MECs that surround the secretory units formed by acinar cells and the pericytes wrapped around blood vessels (blood vessels were labeled by antibody to CD31). Therefore, the tubular structures negative for CD31 and α-SMA staining were identified as ducts. In control LGs, we observed just a few ASC specks (Figure 1B and Figure S1A). By contrast, numerous ASC specks were formed 6 h after LPS-nigericin stimulation, compared to the vehicle-injected LGs (Figure S1C). Many of the ASC specks were found in the LG epithelium (Figure 1C and Figure S1B). To test whether LG cells could also form inflammasomes upon sterile stimuli, we induced acute injury by injecting IL-1α in the LG on one side of the R26ASC-citrine mouse and injected the other LG with a saline (vehicle control) (Figure 1D,E). Similar to LPS-nigericin stimulation, ASC specks were detected 6 h after IL-1α-injection (Figure 1E). They were primarily formed in acinar cells and MECs (Figure 1E,F and Figure S1D). At 12 h after injury, ASC complexes were also detected within ducts and infiltrating immune cells (Figure 1G). The proportion of cells forming inflammasomes and the diameter of ASC specks significantly increased over time in injured LGs (Figure 1H,I). Following inflammasome formation (Figure 1J), we also observed higher cell death rates with characteristic nuclear fragmentation in IL-1α-injected LGs (Figure 1K,L). Altogether, these results show that LG epithelial cells form inflammasomes in response to microbial or sterile pro-inflammatory stimuli and may eventually undergo pyroptosis prior to immune infiltration. We then tested whether inflammasomes are activated in the LG epithelial cells during chronic inflammation. First, we analyzed NOD.H2b males, which develop robust lymphocytic infiltrates in the LG at 4–6 months of age [37] but do not have autoimmune diabetes [40,41]. To determine whether inflammasomes are active, we performed Western blotting to study the proteolytic maturation of downstream targets in the LGs of 6-month-old (6M) NOD.H2b and control BALBc males (Figure 2A,B and Figure S1E). Although there was no significant difference in the abundance of the full length CASP1 (Pro-CASP1, p46) between the LGs of NOD.H2b and BALBc mice, it was detected at a slightly higher molecular weight in NOD.H2b mice (Figure 2A). This suggests a post-translational modification (ubiquitination, phosphorylation) that could modulate the inflammasome activity [42]. Moreover, in NOD.H2b LGs, we detected an increased amount of the cleaved form of CASP1 p33, which forms the active species on the inflammasome hub with the p10 subunit of CASP1 (Figure 2A). This complex is able to process many different substrates [43]. Consistent with inflammasome/CASP1 activation, we found that both the precursor (31 kDa) and the mature form of IL-1β (17 kDa) were significantly increased in the diseased LGs, and that the cleaved form of GSDMD was approximately five times more abundant in NOD.H2b as compared to BALBc LGs (Figure 2B). To determine if the activation of inflammasome signaling is not specific to NOD.H2b mice, we also analyzed the TSP-1-null (TSP-1-/-) mouse—another model of pSS [44,45]. We generated the TSP-1-/-:R26ASC-citrine mice and used the R26ASC-citrine mice as controls. While the control mice had only a few ASC specks in the LG, we found significantly more ASC specks in epithelial cells of the TSP-1-/-:R26ASC-citrine mice at 2M and 6M, respectively (Figure 2C–F). The number of ASC specks increased with disease progression in the LGs of TSP-1-/-:R26ASC-citrine mice (Figure 2G) and correlated with the increased abundance of pro-IL-1β and IL-1β (Figure 2H). In summation, these results show that inflammasome complexes are constitutively formed in the LG of two murine pSS models of different background strains. Inflammasome activation gradually increases with the progression of the disease, suggesting that it could play a significant role in the pathogenesis through the secretion of inflammatory cytokines. Key components of the inflammasome machinery are highly expressed during inflammation in different tissues [24,46,47,48]. To identify the signaling pathways promoting their transcription, we analyzed a previously published RNA-seq data (GSE99093) obtained for LG acute injury/regeneration in BALBc mice [13]. Similar to our experiments, the LGs of these mice were injured by the intraglandular injection of IL-1α and the bulk RNA-seq was performed at days 0 (uninjected control), 1, 2, 3, 4, 5, 7, and 14 after injury. Consistent with our previous study [14], we found a strong induction of Il1b and Il18 transcription on days 1 and 2 (Figure 3A, Table S1). Therefore, we investigated the expression of all genes listed in the Gene Ontology Biological Process entitled “Interleukin-1 production” (GO:0032612) that were significantly enriched on days 1 and 2 after injury (Figure 3B, Table S1). Thus, the gene expression heatmap demonstrates that LG samples from day 1 and 2 after injury cluster together, showing an upregulation of many genes of this pathway, while data from day 3 after injury display a significant decrease in the set of genes involved in inflammasome activation and interleukin processing (Figure 3B). In agreement with the original study [13], uninjected (day 0) and saline-injected controls (day “14S”) were highly similar (Figure 3B) and, thus, we retained the uninjected LGs as controls to study the gene expression changes. The main cluster of genes upregulated during the inflammatory phase (lower part of the heatmap) included the genes of the toll-like receptor (TLR)-myeloid differentiation primary response 88 (MyD88)-nuclear factor kappa B (NFκB) axis that promotes the transcription of Il1b and inflammasome components [49,50], and its partners such as F2rl1 that acts synergistically with TLR2 and TLR4 [51,52], both upregulated in this dataset (see selected genes shown in frame on Figure 3B, Table S1). The inflammasome sensors Ifi204 (murine ortholog of human IFI16 [49]), Nlrp3, Aim2, Mefv (encoding PYRIN), and Naip5 (that forms hetero-oligomeric inflammasomes with NLRC4 upon recognition of bacterial fragments [53]) were significantly upregulated on days 1 and/or 2 after acute injury, and decreased to basal levels on day 3 (Figure 3B,C, Table S1). By contrast, there was no significant change in the expression of Nlrp6, Nlrc4, and Nlrp1b (Figure 3C). Nlrp1a was not expressed at any time (Figure 3C), which was expected, as the BALBc strain lacks Nlrp1a expression [54]. Casp1 and Casp4 were significantly increased during the first two days after injury, while the expression of both caspases returned to basal levels on day 3 after injury (Figure 3B and Figure S2A, Table S1). There was a modest increase in the Casp4 mRNA level at days 4 and 5 but, from day 7, its expression level was not different from the uninjected controls. In addition, we detected the upregulation of the NLRP3-activator Gbp5, and the mediators of pyroptosis Panx1 and Gsdmd (Figure 3B, Table S1). The results obtained by qRT-PCR performed in our lab after acute injury of LG in C57BL/6J mice were highly similar to the RNA-seq data from BALBc mice (Figure S2B). This demonstrates that the experimental model of LG acute injury provides mechanistically robust and reproducible results, even when different mouse strains are used. Therefore, these findings suggest that several types of canonical inflammasomes (NLRP3, AIM2, IFI204, PYRIN, NAIP5/NLRC4, and possibly non-canonical inflammasomes) are transiently activated by IL-1α-injection. We also analyzed the transcriptomic changes leading to inflammasome priming and activation during the development of chronic inflammation. To do this, we mined our previously published RNAseq data of LGs from 2M, 4M, and 6M NOD.H2b (diseased) and BALBc (control) males (GSE210332) [37]. In this study, we showed that, although there is a major shift in the gene expression between 2M and 4M/6M mice, 2M NOD.H2b LG already features many alterations at the transcriptomic level compared to BALBc controls. We also found that B and T cell infiltrates appeared as early as 2M (early stage of the disease) in NOD.H2b males—although not to the same extent as 6M males (clinical stage). Consistent with our previous observations, the expression of Il1b and Il18 was significantly increased with the disease progression (Figure 4A) and genes for the “Interleukin-1 production” (GO:0032612) pathway were significantly enriched in the list of differentially expressed genes (DEGs) between all diseased and control animals. Our data show a set of genes that are upregulated in NOD.H2b mice (Figure 4B, Table S2). Among these genes, we found several pro-inflammatory factors, including TLRs, Tnf, and Nod2, as well as Ifng, (coding for IFN-γ) that participate in NF-κB activation (Figure 4B and Figure S3A, Table S2). IFN-γ not only promotes the transcription of inflammasome components, but also their assembly through the activation of GBPs [55,56]. In NOD.H2b mice, the expression of genes for inflammasome sensors Nlrp3, Nlrc4, Naip5, Ifi204, Aim2, and Mefv were significantly increased from 2 months of age, while no significant changes were detected for Nlrp1b and Nlrp6 mRNAs (Figure 4C, Table S2). Although Nlrp1a is not expressed in BALBc mice, we noticed that its mRNA expression significantly increased with age in NOD.H2b LGs. Importantly, the upregulation of inflammasome sensors was associated with the increased transcription of Casp1 and Casp4 (Figure 4B and Figure S3B), along with Gsdmd and Panx1 (Figure 4B, Table S2). While most of inflammasome-related genes were significantly upregulated at 2M compared to the healthy controls, the expression level of some of them (including Aim2, Nlrc4, Nlrp1a, Casp1, Panx1, and Gsdmd) increased further in NOD.H2b LGs at 4M (Figure S3C). By contrast, none of the genes from the “Interleukin-1 production” pathway passed our thresholds when comparing 4M and 6M NOD.H2b LGs. No DEGs were found for the 2M/4M and 4M/6M comparisons in the control BALBc LGs. This shows that, in NOD.H2b LGs, the activation of the inflammasome pathway amplifies up to 4M, the age at which the chronic inflammation is established [37]. Therefore, during chronic inflammation, several transcriptional activators of inflammasome components are upregulated from the early stages of the disease. Compared to acute injury, which leads to the upregulation of Nlrp3, Aim2, Ifi204, Mefv, and Naip5, chronic inflammation also increased Nlcr4 expression, suggesting that most of the identified sensors can be involved in both acute and chronic inflammation. By immunostaining, we confirmed an increased signal for AIM2 expression in NOD.H2b compared to BALBc LGs (Figure S3D,E). In NOD.H2b LGs, AIM2 complexes were found in the perinuclear areas of epithelial cells, and eventually in other compartments, such as blood vessels (Figure S3F). The constitutive activation of inflammasomes in NOD.H2b LGs could be also facilitated by the upregulation of the gene encoding SYK (spleen-associated tyrosine kinase) (Figure S3G), which induces ASC phosphorylation and oligomerization that is essential for the assembly of NLRP3 inflammasome and CASP1 activation [57]. Finally, the upregulation of Casp4 and members of IFN-γ signaling suggests the activation of the non-canonical inflammasomes. Taken together, these results demonstrate that the expression/activation of multiple inflammasome complexes in the LG is transient during acute inflammation but is sustained during chronic inflammation. Both acute and chronic inflammasome activities likely lead to epithelial cell damage, the induction of adaptive immune response, and the formation of lymphocytic infiltrates. Therefore, we investigated whether molecular pathways promoting the resolution of inflammation and inflammasome inhibition after acute injury are altered during chronic inflammation. An analysis of the transcriptome of regenerating LGs determined day 3 as the critical time point for the switch between inflammatory and regenerative processes [13]. This time point is characterized by a significant decrease in the CD45+ (immune) cells in the LG, particularly neutrophils and monocytes as previously reported [13], and a decrease in the inflammasome components and a reduction in Il1b and Il18 expression down to basal levels. Therefore, we hypothesized that the genes involved in the resolution of inflammation and inhibiting the inflammasome signaling are upregulated on day 3 after LG injury. We identified 337 differentially expressed genes (DEGs) on day 3 after the injury (relative to the uninjured control, log2(FC) cutoff = ±log2(1.5) and p-adj < 0.05), including 219 upregulated genes. An analysis of these 219 upregulated genes with Metascape demonstrated that most of the top 30 significant biological pathways were related to the activity of immune cells and inflammatory responses (Figure 5A). The protein-protein interaction enrichment analysis carried out with these 219 genes (Figure S4) identified 6 densely connected network components related to: (1) phagocytosis and tumor necrosis factor (TNF) production, (2) mitosis, (3) sterol biosynthesis, (4) leukocyte/myeloid activation, (5) inflammatory response, and (6) memory. The heatmap comparing the enrichment p-values between the first three days after injury revealed that the inflammatory pathways belong to the primary response highly activated on days 1 and 2. Indeed, these pathways become much less significant on day 3, meaning that many of these genes were not upregulated anymore (Figure 5A). Hierarchical clustering showed a group of pathways related to the metabolism and localization of lipids that were significantly enriched on day 3, but not significant on days 1 and 2. Nonetheless, some of the genes that were involved in sterol biosynthesis (Acat2), inert fat breakdown (Lipa), and lipid transport/signaling (Abcg1, Apobec1, Apoe, Lrp1, Ldlr, Pltp, Ptafr) were maximally expressed on day 2—except for Ldlr, whose expression level reached a peak on day 3 (Figure S5). To exclude the genes belonging to the primary inflammatory response initiated on day 1 and to identify the mechanism(s) specifically promoting the switch to LG repair on day 3 after the injury, we selected genes specifically upregulated on day 3 after the LG injury (53 genes) and genes upregulated on day 2 with the expression level maintained or increased from day 2 to day 3 (13 genes) (Figure S6). Based on these criteria, we generated a list of 66 genes (Table S3) and performed gene set enrichment analysis with Metascape to interrogate several ontology sources (Gene Ontology, KEGG Pathway, Reactome, WikiPathways) (Figure 5B). The enriched ontology terms were grouped by similarity into clusters and named after the most significant pathway. Most of the resulting biological processes were interconnected and related to the biosynthesis and metabolism of lipids (Figure 5C). The most significant cluster with 18 differentially activated genes was “Cholesterol metabolism with Bloch and Kandutsch-Russell pathways” (Figure 5B). Among these genes, we identified Srebf1 reaching its highest level on day 3 after IL-1α injury (Figure 6A), and its inhibitors Insig1 and Prkaa2 (coding for a subunit of AMPK) (Figure S7A) [58,59]. Srebf1 encodes the transcription factor sterol regulatory element-binding protein 1 (SREBP-1), a master regulator of lipid homeostasis (Figure 6B). The translocation of SREBP-1 into the nucleus activates the transcription of genes regulating the biosynthesis and uptake of fatty acids and cholesterol [59] (Figure 6B). Consistent with SREBP-1 activation, many enzymes of the pathways processing acetyl-CoA for the biosynthesis of fatty acids and cholesterol reached their highest expression level on day 3 after injury (Figure S7B,C), including the rate-limiting enzymes acetyl-CoA carboxylase beta (ACACB) and squalene epoxidase (SQLE) (Figure 6A,B). Similarly, the gene encoding Acyl-CoA synthetase short-chain family member 2 (ACSS2) that catalyzes the formation of acetyl-CoA and the activation of fatty acids into fatty acyl-CoA (Figure 6B) was upregulated only on day 3 (Figure 6A). We also found an upregulation of the predicted lipase gene Gm8978 and other lipid-related genes: Aldh3b2, coding for the aldehyde dehydrogenase 3 family member B2 (ALDH3B2) protein that removes toxic aldehydes from lipid droplets, and Dgkg, encoding diacylglycerol kinase gamma (DGKγ), which produces phosphatidic acid (PA) by phosphorylating the second messenger diacylglycerol (DAG) (Figure S7D). Taken together, these results confirmed that the inflammatory pathways significantly enriched among the 219 upregulated DEGs outlined the primary immune response that occurred within days 1 and 2. Furthermore, in contrast to cholesterol conversion and transport pathways that increased quickly after injury, most of the genes involved in lipid biosynthesis were upregulated only on day 3, suggesting that this transcriptional program may specifically control the resolution of inflammation and gland regeneration. The activation of lipid metabolism most likely promotes the synthesis of cell membranes (that is composed of triglycerides, phospholipids, cholesterol), adenosine triphosphate (ATP) production (through β-oxidation and the use of acetyl-CoA by the TCA cycle), and the generation of second messengers (i.e., PA and DAG) regulating various cell processes (Figure 6A), and may also repress the inflammasome pathway. We hypothesized that the mechanism(s) promoting the resolution of inflammation after acute injury might be altered in NOD.H2b mice. We have recently reported that the downregulation of genes involved in fatty acid biosynthesis, TCA cycle, and fatty acid β-oxidation was associated with disease progression in NOD.H2b LGs [37]. Since these pathways are interconnected and participate in lipid homeostasis (Figure 6B), we analyzed the genes of relevant pathways that were significantly enriched in the meta-analysis of BALBc/NOD.H2b comparisons, according to Gene Ontology (p-elim < 0.05) and WikiPathways (p-adj < 0.05) (Table S4). Among these genes, we found key regulators of lipid metabolism downregulated in the LG of NOD.H2b mice as early as 2 months of age (Table S4). Most altered were the genes coding for peroxisome proliferator-activated receptor alpha (PPARα, encoded by Ppara, Figure 7A) and its partner retinoid X receptor alpha (RXRα, Rxra) that regulate fatty acid transport, oxidation ketogenesis, and promote SREBP-1 activity [60,61]. There was also a downregulation of Agt coding for the angiotensin precursor ANGT that promotes the transcription of lipogenic genes, such as Srebf1. Srebf1 itself was reduced (Figure 7A) along with its synergic partner ChREBP (encoded by Mlxipl, Table S4) that, together with SREBP-1, activates the transcription of genes involved in fatty acid biosynthesis. Consistent with the downregulation of the PPARα-dependent transcriptional program, the expression levels of enzymes involved in mitochondrial fatty acid β-oxidation were also reduced in the LGs of the NOD.H2b mice (Table S4, Figure 7B). Similarly, we noted the downregulation of enzymes mediating ketone catabolism such as Oxct1 (Table S4). Altogether, this suggests a reduced mitochondrial pool of acetyl-CoA available for the TCA cycle. The decreased TCA cycle activity most likely reduces ATP production and also the amount of citrate that can be used for acetyl-CoA synthesis in the cytosol (Figure 6A). We also found the downregulation of enzymes catalyzing the synthesis of acetyl-CoA from citrate, acetate, or pyruvate (Table S4), including ACSS2 (Figure 7C), which also activates fatty acids by the reaction with acetyl-CoA to form fatty acyl-CoA (Figure 6A). Since acetyl-CoA is the primary substrate for de novo lipid biogenesis, we hypothesized that this process is altered in chronically inflamed LGs. In addition to reduced Srebf1 levels, many enzymes for fatty acid biosynthesis such as Acacb were indeed downregulated in diseased glands, thus suggesting a reduced generation of free fatty acids and activated acyl-CoA (Figure 7C,D and Figure 8, Table S4). The number of significant DEGs in NOD.H2b LGs and the extent of the alterations in their expression level compared to their respective BALBc controls increased with age, especially between 2M and 4M (Figure 7C,D, Table S4). By contrast, the enzymes catalyzing the elongation of long-chain fatty acids (LCFAs) into very long-chain fatty acids (VLCFAs) (Elovl5, Elovl6, Hacd4) were upregulated in diseased LGs (Figure 7D, Table S4). VLCFAs serve as precursors for eicosanoids such as inflammatory prostaglandins, thus suggesting that eicosanoid metabolism was also altered in NOD.H2b mice. In addition, many enzymes of the pathways leading to cholesterol synthesis were upregulated during chronic inflammation, including the rate-limiting enzymes 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and SQLE (Figure 7E,F, Table S4). We also noticed the downregulation of Cyp27a1 and Cyp46a, which convert free cholesterol into secreted metabolites. Finally, there was a significant increase in the expression level of Soat1 and Soat2 catalyzing the formation of cholesterol esters (Figure 7E,F, Table S4). Of note, the expression level of Srebf2, which preferentially promotes cholesterol biosynthesis, was not altered by chronic inflammation (Figure S8A). This supports our observation that fatty acids, but not cholesterol synthesis, are reduced during chronic inflammation. Some of the genes altered during chronic inflammation were in the list of lipogenic genes upregulated on day 3 after injury so we analyzed the expression of the entire gene set activated at the beginning of the regenerative phase after acute injury (Table S4, Figure S8B). As expected, we found a significant downregulation of enzymes responsible for acetyl-CoA and fatty acid biosynthesis contrasting with the upregulation of genes related to cholesterol biosynthesis, transport, and lipases. All these observations indicate an alteration of mitochondrial metabolism—with decreased acetyl-CoA and ATP production—and of de novo lipid biosynthesis using these metabolites, due to the downregulation of PPARα/SREBP-1-dependent transcriptional programs (Figure 8). It is possible that enzymes involved in eicosanoid metabolism might promote the synthesis of pro-inflammatory lipids over anti-inflammatory metabolites (Table S4, Figure 8). Considering the overall upregulation of enzymes of the mevalonate and lanosterol pathways (Figure 8), the chronically inflamed glands may accumulate cholesterol, as previously reported in NOD mice [62]. Together with the impaired mitochondrial and fatty acid metabolism, this may induce epithelial cell stress and promote inflammasome activation in the LGs of NOD.H2b mice. Few of these alterations were significantly aggravated between 2M and 4M/6M NOD.H2b mice (Table S4). Only Acacb was further downregulated at each time-point, while it was not affected in BALBc mice. Altogether, this suggests that changes in lipid metabolism are one of the earliest mechanisms of disease development. In this study, we showed that several types of inflammasomes could be activated in LG epithelial cells during acute and chronic inflammation. This suggests that, similar to corneal epithelial cells [63], LG epithelial cells function as sentinel cells [64]. We also found that inflammasome activation precedes epithelial cell death after IL-1α-induced acute injury and discovered increased GSDMD cleavage in chronically inflamed LGs of NOD.H2b mice. Studies in the salivary gland suggest that inflammasome activation and pyroptosis exacerbate inflammation through immune cell infiltrations in SS patients and promote salivary gland dysfunction [28,29]. To our knowledge, there is no such report about pyroptotic events in the lacrimal gland. However, topical administration of anakinra (IL-1 receptor antagonist) to the cornea improved the ocular surface integrity and tear secretion in Aire-deficient mice with SS-like disease [65], suggesting that secretion of IL-1β by epithelial LG cells during chronic inflammation could participate in corneal damage. During acute and chronic inflammation, various types of inflammasome sensors are upregulated in the LG, thereby illustrating the complexity of the innate inflammatory response in this organ. Surprisingly, both acute and chronic inflammation activated similar inflammasome sensors: (1) AIM2 and IFI204 that are activated by DNA released from damaged mitochondria or dead cells; (2) NLRP3 that is activated by a wide variety of stimuli, including oxidative stress and stress-induced lipid signaling; (3) PYRIN, whose loss-of-function mutation causes the monogenic autoinflammatory disease familial Mediterranean fever [66]; and (4) NLRC4 that, if mutated, causes constitutive CASP1 cleavage in cells leading to severe autoinflammatory syndromes in humans [67]. In the LG, Nlrc4 was upregulated only by chronic inflammation but the transcription of its partner NAIP5 was increased during both acute and chronic inflammation. Our data also show that several signaling pathways essential for NF-κB/inflammasome activation, including TLR/MyD88, were upregulated by acute and chronic inflammation. MyD88-deficiency was shown to significantly dampen disease development in NOD.H2b mice [68], thus supporting a critical role in SS pathogenesis for this signaling pathway. In our study, we also noted the robust upregulation of GBPs and SYK that were recently described as modulators of inflammasome signaling [69]. SYK controls the activation of AIM2 and NLRP3 inflammasomes by phosphorylating ASC [70], thereby promoting its oligomerization and the recruitment of pro-CASP1 [71]. GBPs are part of the interferon signature that is involved in the pathogenesis of SS [72] and may play a role in SS and other autoimmune diseases by regulating inflammasome activation. Taken together, we propose that the sustained activation of inflammasome pathways observed in NOD.H2b and TSP-1-/- mice contribute to LG chronic inflammation. In the search for molecular suppressors of inflammasome signaling following IL-1α injection, we discovered that Srebf1 and the genes involved in lipid biosynthesis/transport were upregulated at the resolution of inflammation. As for now, the link between lipid metabolism and inflammasome activity was mostly studied in immune cells. During the acute phase of inflammation, macrophages accumulate cholesterol, which activates inflammasomes through TLR signaling [73]; for example, by forming TLR4-inflammarafts that upregulate Il1b as shown in microglia [74]. Oishi and co-authors elegantly showed that in macrophages, SREBP-1 is first inhibited and then induced by TLR4/MyD88 at the later stages of the inflammatory response to promote the synthesis of anti-inflammatory fatty acids that promote the resolution of inflammation [75]. Similarly, the SREBP-dependent lipogenic program is induced by the activation of CASP1 by NLRP3 and NLRC4 inflammasomes in vitro [76]. Whether similar crosstalk between SREBP-1 signaling and inflammasomes occurs in LG epithelial cells remains to be determined. By contrast, we showed that reduced mitochondrial metabolism (fatty β-oxidation, TCA cycle) and fatty acid metabolism were associated with disease progression in NOD.H2b mice and mainly affected LG epithelium [37]. NOD.H2b LGs might display similar mitochondrial alterations as diabetic NOD mice [77] and the mitochondrial damage itself could enhance inflammasome signaling [78]. A decrease in acetyl-CoA pools may also have a profound effect on gene expression through protein acetylation [79,80] and impair de novo lipid biosynthesis. Our data indeed showed the downregulation of PPARα/SREBP-1 signaling and of downstream genes involved in fatty acid metabolism, while genes promoting the generation and transport of cholesterol were upregulated at the early stages of the disease. Thus, the activation of cholesterol biosynthesis could be SREBP-1-independent and/or higher free cholesterol levels would exert negative feedback on Srebf1 expression. We also found the downregulation of Cyp27a1 and Cyp47a1 catalyzing the conversion of cholesterol into the secreted form 25-Hydroxycholesterol that reduces Il1b transcription and CASP1 activation [81]. In agreement with our study, Wu et al. (2009) showed altered lipid homeostasis in the LGs of diabetic NOD males [62]. In this model, cholesterol ester accumulation preceded lymphocytic infiltration and was not a consequence of dacryoadenitis. Altered lipid homeostasis is not restricted to mouse models of SS, since fat deposition in the LG is a feature of SS patients [82]. We previously reported large cytoplasmic vacuoles in LG acinar cells of NOD.H2b mice, suggesting they accumulate lipid droplets [37]. The activation of the lanosterol/mevalonate pathway could be a compensatory mechanism aimed at increasing other non-steroid products, for example, to rescue mitochondrial function [83]. The resulting cholesterol accumulation in epithelial cells might in turn downregulate SREBP-1-dependent lipid biosynthesis, promote lipotoxic damage, and activate inflammasome signaling. Consistent with our findings, PPARα is also downregulated in experimental mouse models of LG inflammation induced by high-fat diets [84] or obstructive sleep apnea [85] that lead to lipid accumulation and dry eye symptoms. The latter can be alleviated by fenofibrate [84,85], an FDA-approved PPARα activator. Fenofibrate is a hypolipidemic drug that is used to treat the symptoms of high cholesterol and triglycerides in human. Recently, Guo and co-authors showed that fenofibrate improved tear production and corneal surface state and reduced lymphocytic infiltrates in the LGs of NOD/ShiLtJ mice through the modulation of Th17/Treg cell differentiation [86]. Thus, promoting fatty acid β-oxidation and biosynthesis through the activation of PPARα/SREBP-1 signaling could inhibit pro-inflammatory pathways in the LGs of NOD.H2b mice and promote regenerative processes. The crosstalk between epithelial cells and lymphocytes plays a key role in SS development [87,88]. In fact, anti-inflammatory drugs such as Rituximab (B-cell depleting agent) and Anakinra (anti-IL-1) had only transient or no effects on SS patients [87,89]. We thus believe that anti-inflammatory molecules, combined with drugs that restore epithelial cell homeostasis, may lead to better outcomes. Therefore, one possible avenue of research is the combination of fenofibrate with iguratimod (anti-rheumatic drug inhibiting TNF-α, IL-1, IL-6, BAFF-R, CD38 signaling) or necrosulfonamide (inhibitor of GSDMD-pore formation) to decrease inflammation, reduce deleterious effects of inflammasome activation, and durably improve the epithelial function in SS. In summary, our work shows that, in addition to secreting antimicrobial and immunoregulatory factors into the tear fluid, the lacrimal gland epithelium plays a pivotal role in the innate immune response by activating inflammasome signaling in response to exogenous or endogenous stimuli. The dysregulation of this protective defense mechanism during chronic inflammation is associated with an imbalance between the metabolic pathways producing fatty acids and cholesterol. These alterations contribute to pSS pathogenesis and thus represent a new avenue for therapeutics development.

PMC10001614

Ealia Khosh Kish,Yaser Gamallat,Muhammad Choudhry,Sunita Ghosh,Sima Seyedi,Tarek A. Bismar

Glycyl-tRNA Synthetase (GARS) Expression Is Associated with Prostate Cancer Progression and Its Inhibition Decreases Migration, and Invasion In Vitro

21-02-2023

prostate cancer,GARS,PTEN,proliferation,cell cycle regulation

Glycyl-tRNA synthetase (GARS) is a potential oncogene associated with poor overall survival in various cancers. However, its role in prostate cancer (PCa) has not been investigated. Protein expression of GARS was investigated in benign, incidental, advanced, and castrate-resistant PCa (CRPC) patient samples. We also investigated the role of GARS in vitro and validated GARS clinical outcomes and its underlying mechanism, utilizing The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) database. Our data revealed a significant association between GARS protein expression and Gleason groups. Knockdown of GARS in PC3 cell lines attenuated cell migration and invasion and resulted in early apoptosis signs and cellular arrest in S phase. Bioinformatically, higher GARS expression was observed in TCGA PRAD cohort, and there was significant association with higher Gleason groups, pathological stage, and lymph nodes metastasis. High GARS expression was also significantly correlated with high-risk genomic aberrations such as PTEN, TP53, FXA1, IDH1, SPOP mutations, and ERG, ETV1, and ETV4 gene fusions. Gene Set Enrichment Analysis (GSEA) of GARS through the TCGA PRAD database provided evidence for upregulation of biological processes such as cellular proliferation. Our findings support the oncogenic role of GARS involved in cellular proliferation and poor clinical outcome and provide further evidence for its use as a potential biomarker in PCa.

Glycyl-tRNA Synthetase (GARS) Expression Is Associated with Prostate Cancer Progression and Its Inhibition Decreases Migration, and Invasion In Vitro Glycyl-tRNA synthetase (GARS) is a potential oncogene associated with poor overall survival in various cancers. However, its role in prostate cancer (PCa) has not been investigated. Protein expression of GARS was investigated in benign, incidental, advanced, and castrate-resistant PCa (CRPC) patient samples. We also investigated the role of GARS in vitro and validated GARS clinical outcomes and its underlying mechanism, utilizing The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) database. Our data revealed a significant association between GARS protein expression and Gleason groups. Knockdown of GARS in PC3 cell lines attenuated cell migration and invasion and resulted in early apoptosis signs and cellular arrest in S phase. Bioinformatically, higher GARS expression was observed in TCGA PRAD cohort, and there was significant association with higher Gleason groups, pathological stage, and lymph nodes metastasis. High GARS expression was also significantly correlated with high-risk genomic aberrations such as PTEN, TP53, FXA1, IDH1, SPOP mutations, and ERG, ETV1, and ETV4 gene fusions. Gene Set Enrichment Analysis (GSEA) of GARS through the TCGA PRAD database provided evidence for upregulation of biological processes such as cellular proliferation. Our findings support the oncogenic role of GARS involved in cellular proliferation and poor clinical outcome and provide further evidence for its use as a potential biomarker in PCa. Prostate cancer (PCa) is the second leading cause of cancer-related deaths and the most common type of cancer in men [1]. With the advances in sequencing and diagnostic technologies, PCa trails other cancers in the field of gene-targeted therapy. Since the prostate-specific antigen test lacks specificity, biomarkers have become an essential tool in diagnosing and assessing prognosis in PCa [2]. However, the use of potential biomarkers such as ERG, PTEN, and TP53 help in predicting the outcomes of lethal PCa [2,3,4]. However, there is an urgent need to identify biomarkers that could potentially assess early diagnosis or therapeutic benefits. Glycyl-tRNA synthetase (GARS) is an aminoacyl-tRNA synthetase which is involved with charging amino acids onto their respective tRNA molecules in the primary steps of protein synthesis [5,6]. Wang et al. demonstrated that the increased expression of GARS in hepatocellular carcinoma (HCC) tissue was significantly associated with both poor overall survival and disease-free survival [5]. In vitro, GARS knockdown in HCC cells promoted apoptosis, inhibiting HCC cells proliferation and cell cycle [5]. GARS was also documented to have increased expression in lung adenocarcinoma and was associated with an unfavorable prognosis [7]. In urothelial carcinoma, Chen et al. displayed the role of GARS as a urine biomarker aiding the diagnosis of urothelial cancer [8]. Furthermore, GARS appears to play an oncogenic role in breast cancer. Li et al. exhibited the increased expression of GARS in breast cancer tissue compared to healthy tissue and demonstrated that reduced cellular proliferation, colony formation, and migration abilities were in line with GARS inhibition [9]. In addition, GARS appeared to be significantly overexpressed in early-stage breast cancer compared to benign breast disease and normal healthy control samples [10]. Furthermore, a previous study on the role of Aminoacyl TRNA Synthetases showcased the direct interaction between the Androgen Receptor and GARS promoter in PCa [11]. The current data point towards the role of GARS as a possible oncogene in various cancers. In this study, we explored the prognostic role of GARS as a potential biomarker in PCa by assessing its protein expression using IHC and validating its mRNA expression in public cohorts. Additionally, we characterized the role of GARS in vitro cellular models and correlated this to changes in cellular invasion, proliferation, and cell cycle. GARS IHC showed moderate and high intensity of GARS protein expression in 82/134 (61.2%) and 31/134 (23.1%) of cases, respectively. The mean expression was 1.73 ± 0.59 in benign, 1.98 ± 0.55 in incidental, 2.07 ± 0.68 in advanced, and 2.09 ± 0.68 in castrate-resistant cases (p = 0.626) (Figure 1). High GARS protein expression was seen in 28.6% of GG 5 cases vs. 8.5% of GG 1. Comparatively, low GARS expression was seen in 91.5% of GG 1 cases vs. 71.4% of GG 5 cases (p = 0.023). Overall, our data revealed that GARS expression increased dramatically with higher Gleason Groups in this cohort (Table 1). We explored the relationship between GARS mutations in relation to overall (OS) and cancer-specific survival (CSS), using 5015 samples in 25 studies in a public database for prostatic adenocarcinomas. Figure 2A,B confirm the prognostic significance of GARS genomic alteration and its relation to poor OS and CSS for patients diagnosed with PCa. The Pan-Cancer data analysis revealed that GARS mRNA expression was significantly upregulated in all 22 cancer types (Figure 3A). Specifically, in PCa, there was a significant increase in GARS expression in tumor tissue compared to normal tissue. This difference was more significant when the tumor was compared to non-cancerous normal samples rather than to adjacent normal tissue of the prostate. Furthermore, the GARS gene expression was upregulated significantly in metastatic PCa (Figure 3B–D). Additionally, we found that GARS overexpression significantly correlated with lethal disease genomic aberration, such as PTEN-loss, TP53 mutant tumors, ERG, ETV1, ETV4 gene fusion, FXA1-mutation, IDH1-mutation, and SPOP-mutation (Figure 3E–G). Bioinformatic analysis using TCGA PRAD database revealed that elevated GARS expression in PCa is significantly associated with lymph node involvement and higher pathological staging (Figure 4A,B). Furthermore, GARS gene expression is significantly associated with the number of lymph nodes involved in metastatic PCa (Figure 4C). Higher GARS expression appears to be significantly related to increased residual tumor levels (Figure 4D) and higher Gleason scores in PCa (Figure 4E). Differentially expressed genes were analyzed for GARS. Our data uncovered an interesting and distinctive profile of GARS associated genes (Figure 5A). Heatmaps depict the top 50 positively (Figure 5B) and negatively correlated genes (Figure 5C). GARS GSEA conveys an interesting pattern of upregulated genes involved in biological processes, cellular components, and molecular functions (Figure 5D). Positively correlated genes for molecular function were mostly involved in protein binding (6492 genes), nucleic acid binding (2363 genes), and nucleotide binding (1308 genes). Cellular components in GARS upregulated group revealed enrichment of genes localized to membrane (4767 genes) and the nucleus (4154 genes), which appears to contain the most altered gene in cellular components. Lastly, our data demonstrated the upregulation in biological processes, such as genes involved in biological regulation (6513 genes), metabolic processes (6252 genes), cellular component organization (3649 genes), cellular proliferation (1184 genes), and reproduction (784 genes) (Figure 5D). Two separate enrichment methods, GSEA and ORA, were performed for this analysis. When overrepresentation genes in association to GARS were analyzed using ORA, many biological processes such as translation initiation, mitochondrial gene expression, and DNA strand elongation were identified (Supplementary Figure S1A). Furthermore, genes involved in cellular components, such as in condensed chromosome, mitochondrial protein complex, and replication fork, were seen (Supplementary Figure S1B). Lastly, genes involved in molecular functions such as unfolded protein binding, cyclin-dependent protein kinase activity, ligase activity, and catalytic activity acting on DNA were further overrepresented (Supplementary Figure S1C). Our GSEA GARS analysis provides evidence for the overrepresentation of genes in biological processes such as RNA processing, cellular response to stress, negative regulation of gene expression, cell development, and cell cycle (Supplementary Figure S1D). Genes involved in molecular functions such as structural constituent of ribosome, RNA binding, and kinase binding were also seen (Supplementary Figure S1E). GARS protein expression levels were estimated in PC3, PC3-ERG, and DU-145 cell lines using Western Blot. Our data showed high GARS protein expressions in PC3, PC3-ERG, and DU-145 (Figure 6A). GARS knockdown was successfully performed on PC3 and PC3-ERG cell lines. The optimal duration of the knockdown was observed to be nearer 36 h than to to 48 h. Knockdown efficiency was further tested using Western Blot analysis (Figure 6B). Furthermore, we observed a significant downregulation of Cyclin B1, P-PDK1, and AURKA and AKT after GARS knockdown in PC3 cell lines, but there was no significant difference observed in PC3-ERG cells. (Figure 6C). These data suggested a potential role of GARS in cell cycle and proliferation. To further elucidate the role of GARS as an oncogene in PCa, we examined the cells migration and invasion using transwell assays. GARS knockdown significantly reduced the ability of PC3 and PC3-ERG cells to invade and migrate in vitro (Figure 7). We explored the potential role of GARS as a cell cycle regulator and its involvement in the proliferation of PCa cell lines. Our data revealed significant dysregulation of cell cycle after knockdown of GARS using flowcytometry. We found that after the knockdown, the cells are arrested in the S phase and are unable to enter the mitotic phase. Furthermore, the cells appear to undergo early apoptosis after GARS knockdown when compared to the negative control (Figure 8). PCa is a heterogenous disease with a high overall survival for localized disease. However, the percent survival at 5 years decreases to 26–30% for advanced and metastatic castrate-resistant PCa [12,13]. To overcome this issue, the search for other biomarkers with easily accessible and reliable outcomes is urgently needed. In the current study, we reported that GARS overexpressed significantly in PCa and 21 other cancers. In our cohort, we also found that GARS was significantly upregulated in localized, incidental, advanced, and metastatic PCa tumors when compared to normal tissue. From the TCGA PRAD database, GARS overexpression was correlated with the presence of commonly occurring oncogenic mutations. These included PTEN loss P53 mutant tumors, ERG, ETV1, ETV4 gene fusion, FXA1-mutation, IDH1-mutation, and SPOP-mutation. Most of these mutations are currently used as a clinical biomarker to determine the disease prognosis and outcomes. Interestingly, their association with GARS could suggest that it is an integral part of producing a more lethal phenotype in tandem with these mutations [14,15]. Previous studies in HCC indicated that GARS overexpression was significantly associated with poor overall and disease-free survival [5,9]. Similarly, looking at TCGA PRAD database, we found that GARS overexpression is significantly related to higher PCa pathological stages, Gleason grade groups, and lymph nodes metastasis. Furthermore, our clinical data showed a significant association between high GARS expression and higher Gleason grade grouping. Our IHC results demonstrated trends of increased protein GARS expression from benign, to incidental, advanced, and castrate-resistant PCa samples. It is important to note that clinicians use Gleason grade grouping, lymph node involvement, and residual tumor as means of assessing PCa patients’ progression risk and prognosis [16,17,18,19]. Furthermore, it appears that PTEN loss has been associated with poor outcome in localized and castrate-resistant PCa [20,21]. TP53 has been shown to predict Abiraterone/Enzalutamide outcomes in metastatic castrate-resistant PCa [22]. Herein, we found that GARS gene overexpression is significantly associated with PTEN loss, ERG gene fusion and TP53 mutational status among additional genomic aberrations. This suggests that GARS exhibit oncogenic effects and may be of potential use as a prognostic biomarker in lethal PCa. Together, our results provide clinical and molecular support for the role of GARS as an oncogene and possible biomarker in PCa. Through our investigation of the mechanism underlying the oncogenic role of GARS we performed gene set enrichment analysis of GARS overexpressed/overrepresented cases using GSEA and ORA analysis on the TCGA PRAD database, we concluded that many tumor-associated process are upregulated when GARS is overexpressed. For example, we found that genes involved in biological processes such as cellular proliferation, reproduction, biological regulation, translation initiation, DNA strand elongation, and metabolic processes were enriched in GARS-overexpressed cases. Additionally, genes involved in molecular functions such as cyclin-dependent kinase activity and catalytic activity acting on DNA were also upregulated. Previous studies performed on the gene ontology analysis of GARS indicated that many genes involved in cell division, cell proliferation, and cell cycle were enriched [5]. Our data indicate that GARS might be involved in PCa cell cycle regulation and proliferation. Furthermore, using cellular in vitro models, we documented that GARS knockdown inhibits the migration and invasion abilities of PCa cells. Previously, GARS overexpression has been shown to accelerate cell cycle, migration, and invasion of breast cancer cells [5,9]. Mechanistic studies also indicated that GARS may act as an oncogene in breast cancer through controlling the mTOR pathway and regulating cellular proliferation [9]. Our data also suggest that GARS knockdown results in S phase arrest and promotes early apoptosis which attenuates PCa cellular proliferation. This is further supported by significant downregulation of Cyclin B1 and AURKA cell cycle regulators after GARS knockdown [23]. Our data also suggest a downregulation in the levels of Pyruvate Dehydrogenase Kinase 1 (PDK1) in association with GARS knockdown. PDK1 overexpression has been shown to induce proliferation and metastasis through the Warburg effect in non-small cell lung cancer [24,25]. Furthermore, PDK1 knockdown in vitro has been shown to reduce PCa cellular proliferation, migration and invasion in vitro in PCa [26]. These data point towards the oncogenic ability of GARS in connection with PDK1 in regulating PCa proliferation in vitro. The limitations of this study include its lack of in vivo support for the down regulations of GARS in animal models, which would further elucidate the oncogenic ability of GARS in tumors. Further research is required to elucidate the mechanism of GARS in proliferation of PCa. A tissue microarray (TMA) was constructed from a cohort of 264 patients diagnosed with adenocarcinoma of the prostate. GARS expression in association to Gleason grade groups was assessed. Histological diagnoses of individual cores on the TMA were confirmed by the study pathologist (TAB). GARS intensity expression was assessed using a four-tiered system (0, negative; 1, weak; 2, moderate; 3, high intensity). Gleason grade grouping were assessed according to the 2018 WHO and ISUP grade group by the study pathologist (TAB). GARS protein expression was assessed using IHC on the Dako Omnis auto Stainer. Briefly, 4 µm formalin-fixed paraffin-embedded (FFPE) sections were first treated with citrate epitope retrieval buffer (pH 6.0). Following that, incubation with rabbit monoclonal GARS antibody (1:500) (Cat#HPA019097, Sigma-Aldrich, St. Louis, MO, USA) was used. After the secondary antibody incubation, the FLEX DAB+ Substrate Chromogen system was used as a detection reagent. Human PCa cell lines used in this study include LnCaP, PC3, and DU-145. All cell lines were purchased from the American Type Culture Collection (ATCC; Manassas, CA, USA). Stable PC3-ERG was obtained from Felix Feng, University of Michigan [27]. DU-145 cells were cultured in DMEM media (GIBCO life technology, Grand Island, NY, USA). PC3 and PC3-ERG cells were cultured in DMEM/F12 (GIBCO life technology, Grand Island, NY, USA). LnCaP cells were cultured in RPMI 1640 medium (GIBCO life technology, Grand Island, NY, USA). All the above were supplemented with 10% FBS (GIBCO life technology, Grand Island, NY, USA) and grown at 37 °C in 5% CO2 environment. GARS knockdown was performed using pre-designed silencer siRNA GARS, and scramble siRNA was used as a negative control (Cat# AM16708, Ambion, Grand Island, NY, USA). PC3 and PC3-ERG cells were plated in six well plates until 75–80% confluency was reached. Furthermore, the siRNA transfection mix, including Opti-MEM (Cat#31985-070, GIBCO life technology, Grand Island, NY, USA) and Lipofectamine RNAiMAX (Cat# 13778-075, Invitrogen, Carlsbad, CA, USA), were used according to the manufacturer’s instructions. The transfection efficiency was checked by western blotting. Total protein was extracted using RIPA buffer (Sigma-Aldrich, St. Louis, MO, USA) pre-mixed with protease inhibitors and PMSF (Cat# 5872S, Cell signaling, Danvers, MA, USA). When loading on the polyacrylamide SDS gel, equal quantities of proteins were loaded in each separate lane. PVDF membrane (Cat# ISEQ85R, Millipore Sigma, Burlington, MA, USA) was used for the transfer of the proteins. The membrane was placed in blocking buffer prepared with 5% skim milk in PBS for 1 h at room temperature. After blocking, the membrane was incubated with primary antibodies (Supplementary Table S1) overnight at 4 °C with shaking. After primary antibody incubation and washing, the membrane was incubated with either anti-mouse IgG or anti-rabbit IgG secondary antibody conjugated to HRP horseradish peroxidase (Cell signaling, Danvers, MA, USA) in TBS for 1 h. at 37 °C. After final washings, the signal was detected using ChemiDoc imaging system (Bio-Rad Laboratories, Hercules, CA, USA). PC3 and PC3-ERG cells were seeded in six well plates. They were transfected with GARS siRNA#1, siRNA#2, or scramble siRNA (negative control). After 24 h. post transfection, cells were trypsinated and counted with automatic cell counter (Olympus, PA, USA). Approximately 25,000 cells were placed on either 0.8 µ insert Corning Biocoat control inserts for migration assay (Ref# 354578, Corning, Bedford, MA, USA) or Corning Matrigel invasion chamber (Ref# 354480, Corning, Bedford, MA, USA). After 48 h., all cells were fixed and stained with Diff Quick (Siemens Healthcare diagnostics, Tarrytown, NY, USA). All cells were captured on brightfield 10× and 40× magnification using an inverted EVOS FL life microscope. The number of cells for multiple frames were counted for each treatment and average from the 40× magnification. The knockdown was compared to the negative control. For cell cycle, GARS knockdown using siRNA#1, siRNA#2, and a scrambled siRNA used as negative control with appropriate replicates were prepared as previously stated. The cells were harvested after the knockdown, washed with cold PBS, and fixed in 70% ethanol for at least 2 h. Further, they were stained with 100 µg/mL of RNase A in PBS and 50 µg/mL propidium iodide (Cat#F10797, Invitrogen, Carlsbad, CA, USA). The DNA content of cells were analyzed using BD LSR II Flow Cytometer. For apoptosis or Annexin V/PI assay, cells were grown and transfected as described in MM 2.3 above. The cells were further trypsinated and treated with Annexin V apoptosis kit (Cat# V13241, Invitrogen, Carlsbad, CA, USA) per the manufacturer’s instructions. All data were analyzed using FlowJo™ v10 Software-BD Biosciences. Genomic signature data were obtained from TCGA PRAD transcriptomics database [28]. Pan-cancer analysis was used across TCGA, GTex, and TARGET databases to analyze GARS expression in 22 types of tissues, and in tumor vs normal. This tool functioned based on the RNA-seq-rapid analysis servers. GARS gene expression for tumor vs. adjacent normal tissue and tumor vs non-adjacent normal tissue was further analyzed (R0 = no residual tumor, R1 = microscopic residual tumor, R2 = macroscopic residual tumor [29]. Results were blotted and gene expression at tumor was compared to normal at each of the quantile cut-off values (minimum, 1st quartile, median, 3rd quartile, maximum) [28]. Furthermore, we used the data available in TCGA PRAD database to compare mutations of PTEN, TP53, FXA1, IDH1, SPOP, and gene fusions such as ERG, ETV1, and ETV4 with GARS expression. Furthermore, we compared GARS expression to PCa pathological stage, lymph node involvement, number of lymph nodes involved, and residual tumor in this database. We used LinkedOmics (http://www.linkedomics.org (accessed on 12 December 2022)) to explore GSEA and ORA functions of GARS utilizing the TCGA PRAD database [30]. GARS overrepresented and gene set enrichment were analysis and ranked based on highest FDR score. Furthermore, they were grouped into molecular functions, biological processes, and cellular components using WEB-based Gene Set Analysis Toolkit and Explorer [31]. Data collected from TCGA were analyzed through UALCAN (http://ualcan.path.uab.edu/index.html (accessed on 12 December 2022)) in order to explore the relationship between GARS transcript to pathological features, such as Gleason score and association to various gene mutations [32,33]. GARS expression was compared to Gleason scores 6–10 and normal. Furthermore, GARS expression was compared in normal samples, TP53 mutant tumors, and TP53 wild type tumors. Box–whisker plots contain the minimum, 1st quartile, median, 3rd quartile, maximum, and interquartile range. Welch’s T-test was used to analyze the difference in expression levels between normal and tumors. Descriptive statistics were used to describe the current study data. For categorical data, frequency and proportions were reported. For continuous data, mean and standard deviations were reported. A two-tailed t-test was used to compare two continuous measures (p-value < 0.05). Box–whisker plots contain the minimum, 1st quartile, median, 3rd quartile, maximum, and interquartile range. Overall survival (OS) was defined as the time from diagnosis to death. Analysis was performed using Graph pad version 7. p-value < 0.05 was used for statistical significance and two-sided t tests were conducted. In conclusion, our study provides evidence for the oncogenic role of GARS in PCa. We documented that GARS is overexpressed in various cancers, including PCa, and is associated with a higher pathological stage and number of lymph nodes involved. Furthermore, GARS overexpression is further associated with higher Gleason grade groups, as well as patients’ clinical prognosis. Furthermore, GARS knockdown reduces the ability of PCa cells to invade and migrate while inducing S phase arrest. Our data suggest that GARS functions as a cell cycle and proliferative regulator in association with PDK1 in PCa. Further research is needed to demonstrate the mechanisms underlying GARS in the proliferation pathways in PCa and other types of cancer.

PMC10001634

Aleksandra E. Mrozikiewicz,Grażyna Kurzawińska,Marcin Ożarowski,Michał Walczak,Katarzyna Ożegowska,Piotr Jędrzejczak

Polymorphic Variants of Genes Encoding Angiogenesis-Related Factors in Infertile Women with Recurrent Implantation Failure

21-02-2023

RIF,ART,IVF,SNP,VEGFA,FGF2,FLT1,KDR

Recurrent implantation failure (RIF) is a global health issue affecting a significant number of infertile women who undergo in vitro fertilization (IVF) cycles. Extensive vasculogenesis and angiogenesis occur in both maternal and fetal placental tissues, and vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) family molecules and their receptors are potent angiogenic mediators in the placenta. Five single nucleotide polymorphisms (SNPs) in the genes encoding angiogenesis-related factors were selected and genotyped in 247 women who had undergone the ART procedure and 120 healthy controls. Genotyping was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). A variant of the kinase insertion domain receptor (KDR) gene (rs2071559) was associated with an increased risk of infertility after adjusting for age and BMI (OR = 0.64; 95% CI: 0.45–0.91, p = 0.013 in a log-additive model). Vascular endothelial growth factor A (VEGFA) rs699947 was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; 95% CI: 1.11–4.94, padj. = 0.022) and a log-additive model (OR = 0.65; 95% CI 0.43–0.99, padj. = 0.038). Variants of the KDR gene (rs1870377, rs2071559) in the whole group were in linkage equilibrium (D’ = 0.25, r2 = 0.025). Gene–gene interaction analysis showed the strongest interactions between the KDR gene SNPs rs2071559–rs1870377 (p = 0.004) and KDR rs1870377–VEGFA rs699947 (p = 0.030). Our study revealed that the KDR gene rs2071559 variant may be associated with infertility and rs699947 VEGFA with an increased risk of recurrent implantation failures in infertile ART treated Polish women.

Polymorphic Variants of Genes Encoding Angiogenesis-Related Factors in Infertile Women with Recurrent Implantation Failure Recurrent implantation failure (RIF) is a global health issue affecting a significant number of infertile women who undergo in vitro fertilization (IVF) cycles. Extensive vasculogenesis and angiogenesis occur in both maternal and fetal placental tissues, and vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) family molecules and their receptors are potent angiogenic mediators in the placenta. Five single nucleotide polymorphisms (SNPs) in the genes encoding angiogenesis-related factors were selected and genotyped in 247 women who had undergone the ART procedure and 120 healthy controls. Genotyping was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). A variant of the kinase insertion domain receptor (KDR) gene (rs2071559) was associated with an increased risk of infertility after adjusting for age and BMI (OR = 0.64; 95% CI: 0.45–0.91, p = 0.013 in a log-additive model). Vascular endothelial growth factor A (VEGFA) rs699947 was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; 95% CI: 1.11–4.94, padj. = 0.022) and a log-additive model (OR = 0.65; 95% CI 0.43–0.99, padj. = 0.038). Variants of the KDR gene (rs1870377, rs2071559) in the whole group were in linkage equilibrium (D’ = 0.25, r2 = 0.025). Gene–gene interaction analysis showed the strongest interactions between the KDR gene SNPs rs2071559–rs1870377 (p = 0.004) and KDR rs1870377–VEGFA rs699947 (p = 0.030). Our study revealed that the KDR gene rs2071559 variant may be associated with infertility and rs699947 VEGFA with an increased risk of recurrent implantation failures in infertile ART treated Polish women. Recurrent implantation failure (RIF) is the condition in which the embryo fails to implant after at least three transfers in three consecutive in vitro fertilization (IVF) cycles. Currently, RIF is considered one of the main challenges of reproductive medicine and concerns about 15% of women treated for infertility [1]. Moreover, it was estimated that 5% of women suffer from recurrent pregnancy loss, 75% of cases of which were observed to be due to RIF [1]. The described risk factors of RIF include advanced maternal age, BMI, tobacco, alcohol intake, and endometriosis. The reasons for RIF could also be divided into embryo factors (genetic abnormalities) and uterine factors (anatomical abnormalities; immunological factors; biomolecular factors; glycodelin-A; infection). However, the influence of the male factor (sperm quality) and female factors (low quality of gametes; thrombophilia; inherited and acquired; other genetic polymorphisms such as miRNA, HLA–G, p53, VEGF; vitamin D deficiency; alterations in vaginal microbiota) on the occurrence of RIF has also been observed [2,3,4]. Unfortunately, the complex details of the processes that occur in women with RIF remain unclear to date. Moreover, recurrent pregnancy loss (RPL) is multifactorial and many cases remain unexplained. Recurrent implantation failures and recurrent miscarriages has partially overlapping causes, and the association of genetic variants with RPL is more frequently studied [5,6,7]. In both RIF and RPL, the problem is pregnancy loss, but women with RIF also have difficulty getting pregnant. Therefore, despite the similarities, the causes of RIF and RPL may differ. Implantation is essential for embryo survival and successful reproduction. This process requires the competent blastocyst, receptive endometrium, and the synchronized dialogue between maternal and embryonic tissues. The delicate balance between these factors is very important for the embryo adhesion and attachment to the endometrium and the formation of fetal–mother contact [8,9,10]. In the next phase, the embryo invades the endometrium and blood cells arise from the mesoderm. A normal pregnancy requires the development of the complex vascular network of both the mother and the fetus to meet the increasing oxygen and metabolic demands of the developing embryo. The placenta is a unique vascular organ that receives blood supplies from both the maternal and the fetal systems and thus has two separate blood circulatory systems [11]. Blood vessels form in two ways: vasculogenesis, whereby vessels arise from blood islands, and angiogenesis (branching and nonbranching), which entails sprouting from existing vessels [12]. Extensive angiogenesis occurs in both the maternal and fetal placental tissues [13]. The embryonic vasculature is formed by the segregation, migration and assembly of mesodermal angioblasts, a process called vasculogenesis. In the complex process of angiogenesis, the activity of many growth factors and their receptors on various pathways plays a key role. The most potent angiogenic factors to promote vasculogenesis and angiogenesis in the placenta include vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs), FGF family molecules, the angiopoietin system, and many others [11]. Angiogenesis is a multi-stage process, during which significant changes occur in the environment surrounding the cells. Growth factors increase vascular permeability, stimulate specific proteases (collagenases and plasminogen activators) to proteolytic degradation of the extracellular matrix (ECM) and cause proliferation of endothelial cells. The final stage is followed by chemotactic migration of endothelial cells and invasion of the ECM, formation of the lumen and functional maturation of the endothelium [14]. In humans, the VEGF family is composed of multiple isoforms encoded by five genes (VEGFA, VEGFB, VEGFC, VEGFD, and placental growth factor—PIGF). These ligands bind to VEGFRs belong to the type IV receptor tyrosine kinase (RTK) family and include VEGFR1 (FLT1 gene), VEGFR2 (KDR gene) and VEGFR3 (FLT4 gene). VEGFR1 and VEGFR2 regulate angiogenesis and vascular permeability, and VEGFR3 mainly regulates lymphangiogenesis [15]. VEGF also interacts with heparan sulfate proteoglycans (HSP), and neuropilin 1 and 2 co-receptors (NRP1 and NRP2). Moreover, growth factors are dimers and can form both homo and heterodimers [16]. VEGFA (usually called VEGF), first described by Senger et al. [17], is one of the most studied growth factors. It is a highly specific vascular endothelial cell mitogen and also the strongest pro-angiogenic factor in the VEGF family. VEGFA binds with high affinity to two VEGF receptor tyrosine kinases (VEGFR1, VEGFR2) and with lower affinity to co-receptors NRP1 and NRP2 [16,18,19]. There is a correlation between altered VEGF expression and reproductive failure, including recurrent implantation failure and recurrent miscarriage (RM) [20]. The VEGFA gene is highly polymorphic, especially in the promoter, 5′-untranslated and 3′-untranslated regions. Some of these variants—rs699947 (−2578C > A), rs1570360 (−1154G > A), rs2010963 (−634C > G) and rs3025039 (+936C > T)—have been associated with variable VEGF protein expression and serum VEGFA levels [21]. Several publications have reported an association of VEGFA gene variants rs833061 (−460T > C), rs25648 (−7C > T) and mainly rs1570360 (−1154G > A) with recurrent implantation failures [22,23,24,25]. As well as the expression of angiogenic factors during embryonic implantation, also the expression of their receptors has been demonstrated. In the placenta, the activity of VEGFR1 and VFGFR2 receptors was observed [26,27]. Tyrosine kinase 1 (FLT1) is the VEGFA and placental growth factor receptor and is expressed in the trophoblasts of the placenta throughout gestation. A soluble form of VEGFR1 called sFlt-1 is markedly increased during the last two months of preeclamptic pregnancy compared with normotensive pregnant controls [28]. The rs722503 polymorphism is located in intron 10 of the FLT1 gene and can alter the regulatory motif for binding of nuclear factor-κB (NF-κB). NF-κB is a transcription factor that can participate in both activation and repression of transcription and is associated with angiogenesis and cell proliferation [29,30,31]. In addition, multiple-SNP analysis by Wujcicka et al. [32] showed that the TT variants for CSF2 (rs25881) and FLT1 (rs722503) polymorphisms were associated with an approximately two-fold increase in the prelabor rupture of membranes (PROM) risk when corrected for APTT and PLT parameters and pregnancy. The kinase insertion domain receptor (KDR), also known as vascular endothelial growth factor receptor 2 (VEGFR2), plays an important role in embryonic development. VEGF-activated receptor stimulates endothelial cell proliferation and is crucial for the development of the embryonic vascular system and hematopoietic system [33,34]. Studies show that the minor allele G of the rs2071559 polymorphism, located in the promoter region, may lead to a decrease in VEGFR2 transcriptional activity, while the minor allele T of the rs1870377 (Gln472His) polymorphism has been associated with reduced VEGFR2 binding affinity [35,36]. Basic fibroblast growth factor 2 (FGF2) is the prototype member of a family of structurally related fibroblast growth factors (FGFs). Growing evidence suggests that fibroblast growth factor/FGF receptor (FGF/FGFR) signaling has crucial roles in a multitude of processes during embryonic development and adult homeostasis by regulating cellular lineage commitment, differentiation, proliferation, and apoptosis of various types of cells. Fibroblast growth factor 2 (FGF2) has a particular role in the formation of endothelial precursors, angioblasts, and their assembly into the initial pattern of the vasculature early during embryonic development [37,38,39]. The rs308395 polymorphism within the FGF2 gene promoter may influence transcription factor binding, and thus FGF2 expression [40]. Considering the above-mentioned interesting insights, we tested the hypothesis that single nucleotide polymorphisms (SNPs) in genes encoding the angiogenesis pathway predispose to infertility and recurrent implantation failure. We evaluated the association of five polymorphic variants in VEFGA (rs699947), FLT1 (rs722503), KDR (rs2071559, rs1870377) and FGF2 (rs308395) genes with infertility and recurrent implantation failure among Polish women. There was no significant difference in maternal age between cases and controls (33.11 ± 3.51 vs. 32.50 ± 3.60 years, p = 0.123). Body mass index was significantly higher in the cases than in the control group (23.36 ± 4.17 vs. 20.71 ± 1.79, p < 0.001). Over a quarter (25.9%) of the women in the study group had a BMI above 25. In the cases, the median AMH before ART treatment level was 21.00 pmol/L (IQR 11.17–30.79). Of the total 247 infertile women who underwent an ART treatment cycle, 70.9% had a maximum of two prior failed embryo transfers and 29.1% had at least three prior failed embryo transfers (RIF patients). In 89 cases, the indication for the ART procedure was the male factor, in 119 cases idiopathic infertility and in 39 cases the female factor (oviduct + ovulatory). In the study group, 95 (38.5%) women did not become pregnant. One hundred and twenty women (48.6%) achieved one, 11.3% two, and four women (1.6%) achieved three pregnancies. From the whole number of 188 pregnancies obtained after in vitro fertilization, in 55 cases (29.3%) fresh embryo transfer was performed and in 133 cases (70.7%) frozen embryo transfer was performed. Detailed patient characteristics are summarized in Table 1. As a first step the frequencies of genotypes and alleles of selected VEGFA, FLT1, KDR and FGF2 polymorphisms were analyzed. The genotype distribution of these SNPs in controls were in accordance with the Hardy–Weinberg equilibrium (p > 0.05). Differences in SNP allele frequency distribution between the cases and the healthy controls were analyzed using the chi2 test and odds ratios (ORs). A statistically significant difference was observed only for KDR rs2071559. Compared with the A allele, the G allele of rs2071559 was more frequent in infertile women (0.55% vs. 0.47% in controls, OR = 1.378, 95% CI 1.011–1.877, p = 0.042 (Table 2). Multiple logistic regression analysis with adjustment for age and BMI was performed in codominant, dominant, recessive, over-dominant and log-additive models. The genotype distribution of these SNPs is shown in Table 3. Based on the data, KDR rs2071559 was associated with an increased risk of infertility in crude analysis under a log-additive model (major allele homozygotes vs. heterozygotes vs. minor allele homozygotes); p = 0.034. After adjusting for age, BMI was significantly associated under a codominant model (p = 0.04190), a recessive model (AA + AG vs. GG: OR = 1.89; 95% CI 1.07–3.34, p = 0.025) and a log-additive model (OR = 0.64; 95% CI 0.45–0.91, p = 0.013). The results indicated that rs2071559 might have a significant association with infertility in our population. For other analyzed polymorphisms, no statistically significant difference was observed (all p > 0.05) (Table 3). In order to investigate the possible impact of the analyzed SNPs on the occurrence of recurrent implantation failures, we divided 247 infertile cases into two subgroups: women with RIF (n = 72) and those with less than 3 previous failed embryo transfers (n = 175). Clinical characteristics are shown in Table 4. Comparing the groups separated in this way, we observed that the patients with RIF were statistically significantly older (mean ± SD: 34.1 ± 3.7 vs. 32.7 ± 3.4 years, p = 0.005). However, we did not observe any differences in BMI means and serum AMH level medians between groups. In both groups, the indications for the ART procedure and the type of embryos used were similar (p = 0.763 and p = 0.6985, respectively). As many as 58.3% of women with recurrent implantation failures never became pregnant. In the RIF group, 41 pregnancies were achieved, whereas in the group without RIF there were 147 (Table 4). A comparison of pregnancy outcomes was also made between fresh embryos (n = 55) and frozen embryos (n = 133) after IVF treatment. Pregnancy ended with childbirth in 85.5% of mothers from the fresh and in 88.7% from the frozen embryo transfer. Women who underwent frozen blastocyst transfer more often gave birth by caesarean section (65.3% vs. 44.7%, p = 0.015). The average birth weight of infants was slightly lower in the fresh embryo transfer group and was 3315.1 ± 512.6 g compared to 3458.1 ± 412.8 g in the frozen group (p = 0.0940). There were no statistically significant differences between the groups in gestational age, placenta weight and Apgar score (Table 5). Next, we evaluated the possible associations between studied polymorphic variants and recurrent implantation failures. Our data indicated no significant difference in the genotype frequencies of studied FLT1, KDR and FGF2 gene polymorphisms between RIF and NO-RIF women. However, comparing subgroups, we observed a statistically significant difference between them for the VEGFA rs699947 variant. In the codominant model, the genotype frequency was: CC–27.4% and 13.9%, CA–50.9% and 58.3%, AA–21.7% and 27.8% in women without and with RIF, respectively (p = 0.070, padj. = 0.052). This SNP was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; 95% CI 1.11–4.94, p = 0.023, padj. = 0.022) and a log-additive model (OR = 0.65; 95% CI 0.43–0.99, p = 0.040, padj. = 0.038) (Table 6). To generate a linkage disequilibrium (LD) map, polymorphisms of the KDR gene (rs1870377, rs2071559) and FGF2 rs308395 located on the same chromosome 4 were selected. An LD plot was constructed using combined genotype data from both groups of cases and controls (plot 1A), only cases (plot 1B) and only for controls (plot 1C) using the program HaploView, version 4.1. The LD analysis showed that rs1870377 and rs2071559 (distance between 19392 bp) in the whole group (cases and controls) were in linkage equilibrium (D’ = 0.25, r2 = 0.025, LOD = 1.74); thus, haplotype analysis was not conducted. We only observed weak LD between examined KDR gene polymorphisms in infertile cases (D’ = 0.395, r2 = 0.053, LOD = 2.31). The results are shown in Figure 1. To search for gene–gene interactions, we used multifactor dimensionality reduction (MDR 3.0.2). Analysis of the dataset of infertile cases and controls revealed synergistic interactions between KDR rs2071559 and KDR rs1870377 (IG = 1.86%) and KDR rs1870377 and VEGFA rs699947 (IG = 1.13%) (Figure 2). These gene relationships were confirmed in the SNPassoc package. The analysis showed the strongest interaction between the KDR gene rs2071559–rs1870377 (p = 0.004) and rs1870377–rs699947 (p = 0.030). The interaction between the polymorphism of the KDR rs2071559 gene and VEGFA rs699947 was not statistically significant (p = 0.372). Statistical power for infertility susceptibility analysis was calculated by a Genetic Association Study (GAS) Power Calculator [41] using the following parameters. Numbers of cases and controls and allele frequencies are presented in Table 4 and Table 5. Infertility prevalence is 10–15% on average in the European populations [42]. Under an additive model, the power of our study to detect an association at a significance level of 0.05 was 10% (average for all tested SNPs) for a genotype relative risk (GRR) equal to 1.1 and 0.69% for a GRR 1.5. The proper development and function of the placenta are crucial not only for the survival and development of the fetus in utero. The placenta, being the first fetal organ to develop and to function normally, must be highly vascularized [13,43]. An appropriate course of angiogenesis is necessary for a successful pregnancy, and the correct uteroplacental circulation is crucial in the process of implantation and embryo development. Disruption of these processes can lead to various undesirable consequences in pregnancy, such as recurrent pregnancy loss, including recurrent miscarriage and recurrent implantation failure. Some of the most important genes involved in angiogenesis are from the vascular endothelial growth factor family. The best characterized family member is VEGFA, an important factor that regulates angiogenesis, with several isoforms, and that participates in multiple physiological pathways. Several polymorphisms have been reported in the promoter region of the VEGFA gene, including −2578C > A (rs699947) and −1154G > A (rs1570360), which are associated with altered VEGF secretion (Peach et al., 2018; Almawi et al., 2013). Several studies have been conducted in different populations to investigate the association between VEGFA gene polymorphisms and RIF, with conflicting results [22,23,24,25,44]. Most research between recurrent implantation failure and VEGFA gene polymorphisms has paid attention to the −1154G > A (rs1570360) variant. Although studies have been conducted in different populations, there is a noticeable relationship between RIF and the frequency of the minor −1154A allele. Turienzo et al. [22] reported that the rs1570360 polymorphism in the dominant model (GG vs. GA/AA) is associated with an increased risk of implantation failure (OR = 1.842, CI 95% 1.002–3.422). Goodman et al. [25] found that homozygosity of the VEGFA −1154AA gene was significantly higher among women experiencing recurrent implantation failure compared with fertile control women (19% vs. 5%, p = 0.02) and may serve as a susceptibility factor affecting the chances of recurrent implantation failure [25]. In addition, Vagnini et al. [23] found an association between this variant and RIF in Brazilian women (OR = 2.12 95% CI: 1.16–3.87, p = 0.01 in the dominant model). In a meta-analysis of three case–control studies comprising 305 RIF cases and 378 controls, Zeng et al. [45] confirmed the association of (−1154G > A) polymorphism and RIF under the allele (OR 1.39, 95% CI 1.08–1.78, p = 0.01) and dominant genetic model (OR 1.56, 95% CI 1.10–2.20, p = 0.01). Other polymorphic variants of the VEGF gene may also be associated with the occurrence of recurrent implantation failure. In 119 Korean women with RIF and 236 controls, the VEGF rs833061 (−460T > C), rs25648 (−7C > T) and rs3025020 (−583C > T) genetic polymorphisms were analyzed. The rs833061 C and rs25648 T VEGF alleles were associated with a higher risk of RIF (OR = 1.813, p = 0.009 and OR = 2.213, p = 0.005, respectively) [24]. Another study found that the VEGF rs2010963 (+405G > C in the 5′-untranslated region) CC genotype may predispose to recurrent implantation failure after intracytoplasmic sperm injection—embryo transfer (ICSI-ET) [46]. In this study, we observed a statistically significant difference for VEGFA −2578C > A polymorphism between women without and with RIF. This variant was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; 95% CI: 1.11–4.94, p = 0.023, padj. = 0.022) and a log-additive model (OR = 0.65; 95% CI: 0.43–0.99, p = 0.040, padj. = 0.038). Although the polymorphism rs699947 selected in our work is very often studied in connection with various diseases, we found only one study that investigated the occurrence of RIF in Korean females. In the 116 women with RIF and 218 controls, the VEGF −2578C > A, −1154G > A, −634C > G and 936C > T genetic variants were determined. The VEGF -2578AA genotype was associated with an increased prevalence (≥4) of RIF (AOR = 2.77; 95% CI: 1.10–7.02; p = 0.031). The results of this research indicated that the VEGFA -2578AA genotype, −634G allele and −2578A/−1154A/−634G/936C haplotype could be a genetic marker of RIF. Interestingly, in this study, no statistically significant difference was observed between the RIF and the control women for the −1154G > A polymorphism [44]. The influence of FLT1 gene polymorphisms is often studied in preeclampsia [29,30,47]. Soluble FLT1 (sFLT1), which is encoded by an alternatively spliced transcript of FLT1, is an antagonist of VEGF and PIGF. Levels of sFLT1 in maternal blood have been found to be elevated in PE patients. In white women, FLT1 rs722503, FLT4 rs307826, and VEGFC rs7664413 were significantly associated with preeclampsia [47]. Several studies have found circulating levels of sFLT1 to be raised in women with threatened abortion and RM [48,49]. However, little is known about the role of FLT1 and its polymorphic variants in RIF. In a study by Bansal et al. [50], serum levels of VEGFA and its receptor FLT1 were compared with levels of NK cells, activated NK cells, and NK cytotoxicity in 62 women with re-implantation failure (RIF) and 72 healthy controls. VEGFA levels were found to be significantly elevated in women with RIF compared to healthy controls, but there was no difference in FLT1 levels between the groups. In our study, the FLT1 gene rs722503 polymorphism was not associated with infertility or RIF in the population of Polish women. Genetic variants of the second VEGF receptor, encoded by the KDR gene, are a frequent subject of association studies with recurrent miscarriages. Rah et al. [51] reported that the kinase insert domain-containing receptor gene (−604T > C) rs2071559 polymorphism was associated with recurrent pregnancy loss in Korean women. In the present study, this variant was associated with an increased risk of infertility (after adjusting for age and BMI, rs2071559 was significantly associated under a codominant [p = 0.042], a recessive [p = 0.0245] and a log-additive model [p = 0.013]). For the second analyzed KDR polymorphism (rs1870377), no statistically significant difference was observed. However, in gene–gene interaction analysis, this variant was in strong interaction with VEGFA rs699947 (p = 0.030). Fibroblast growth factor 2 (FGF2) belongs to the FGF superfamily, comprising at least 22 members in humans. It is a pleiotropic signaling molecule involved in many biological processes including angiogenesis, embryonic development and wound healing. FGF2 is widely used in stem cell research as an agent of self-renewal (proliferation) and differentiation in vitro [52]. Several polymorphisms in the FGF2 gene have been identified, of which rs2922979 (intron), rs308395 (promoter) rs1476217 (3′-UTR), rs308397 (promoter), and rs3747676 (3′-UTR) are the most investigated. The rs308395 variant selected for this study was previously studied in connection with the development of high myopia, diabetic retinopathy, multiple myeloma, risk of cleft lip or in the process of restenosis in patients with stable coronary artery disease treated with a metal stent [53,54,55,56,57]. We did not observe an association of this SNP with infertility or recurrent implantation failures in the studied population of Polish women. Our results show that the maternal body mass index was significantly higher in the infertile women than in the control group (23.36 ± 4.17 vs. 20.71 ± 1.79, p < 0.001). More than a quarter (25.9%) of women undergoing ART therapy were obese, which may indicate the importance of BMI in infertility. However, we did not observe differences in BMI means between the RIF groups and women with less than three previous failed embryo transfers. Recently, two interesting studies on this topic have been published. In the first, Nogales et al. (2021), in a multicenter study with 2832 patients undergoing pre-implantation genetic testing for aneuploidies (PGT-A), investigated which factors, excluding embryo aneuploidies, are associated with miscarriage in patients who have undergone a single euploid blastocyst transfer. One of the main findings was a significant relationship between body mass index (BMI) and miscarriage rates (13.4% in underweight women, 12.1% in normal weight, 14.5% in overweight, and 19.2% in obese women, odds ratio (OD) 1.04; 95% CI, 1.01–1.07, p = 0.006). However, in the second, Canadian study, gestational carriers (healthy women with proven fertility and a good obstetric history, who chose to carry a baby not genetically related to them for intended parents) were matched by BMI to infertile patients treated during the same years provided they had undergone a cycle completed to a transfer. The results of this study showed that BMI was not statistically or clinically predictive of ART outcomes or of pregnancy outcomes, among gestational carriers. It is possible that BMI alone may not be a major factor in determining the outcome of infertility treatment; other metabolic and endocrine factors may be at play [58]. The studies of the Forkhead transcription factors family (FOX) conducted in recent years are also interesting. They play an important role in regulating the expression of genes involved in cell growth, proliferation and differentiation. Studies of human endothelial cells and gene knockout mouse models have revealed the role of FOXO proteins in regulating endothelial cell angiogenic activity and blood vessel formation [59,60]. Study in loss-of-function mouse models revealed that FOXO1 significantly downregulated arterial gene expression in the mouse yolk sac prior to the onset of blood flow in early embryonic development and downregulated Kdr transcripts without affecting the overall identity, survival, or proliferation of endothelial cells [61]. Another member of the FOX family, FOXP3, has been reported to inhibit breast cancer angiogenesis by downregulating VEGF expression [62]. FOXP3 gene variants and haplotypes are associated with altered incidence of RPL [5,6]. Normal angiogenesis enables the development of the placenta and a successful pregnancy. It is tightly regulated by a balance of pro- and anti-angiogenic factors that are the subject of much research. There are suggestion that infertile women with RIF could benefit from the use of platelet-rich plasma (PRP) containing growth factors (PDGF, EGF, TGFβ, VEGF, HGF, FGF2) [63]. Moreover, miRNAs are abundantly expressed in the human placenta, and miRNA dysregulation is associated with recurrent pregnancy loss and the pathogenesis of repeated implantation failures. Recently published studies indicate that miR-16 regulates angiogenesis and placental development by targeting VEGF expression and is involved in the pathogenesis of RSA [64]. In a study, Wang et al. [65], differentially analyzed the raw data deposited in microarray datasets, to screen DE-mRNAs, DE-miRNAs, and DE-circRNAs, respectively. The kinase insertion domain receptor (KDR) gene was identified by the protein–protein interaction network as one of six hub genes and was downregulated in RIF endometrial tissue samples compared to fertile control samples. In addition, three miRNAs (hsa-miR-424-5p, hsa-miR-195-5p and hsamiR-29b-3p) targeting KDR mRNA were differentially expressed in RIFs [65]. The improvement of conditions for successful implantation in patients with RIF includes the variety of strategies. It is well known that one of the important causes of RIF is the poor oocytes quality. Some interesting studies shown that the oocytes quality could be improved by myo-inositol supplementation, a compound known for its multiple role in the induction of ovulation [66]. In the case of chronic anovulation, the other form of this compound, d-chiro-inositol, was shown to modulate the activity of aromatase by reducing gene expression, inducing in this way the ovulation [67]. Some considerations focus on enhancing the implantation rate by using the embryo culture supernatant to endometrial cavity before embryo transfer [68]. Another reason of fertilization failure caused by the male factor is the cryptic sperm defects in apparently normal spermatozoa. Some studies focused on these problems indicate the necessity to conduct routine tests to detect sperm defects [69]. It is also very important to determine the role of genetic causes connected with infertility, which is suspected in at least about half of all cases. The genes involved in meiosis, DNA repair, ovarian development, steroidogenesis, folliculogenesis, and spermatogenesis could play pivotal role in fertilization failure mechanisms. On the other hand, the presence of autoimmune antibodies remains to play the role in infertile processes. Thus, cell and gene therapies could be very helpful for infertile couples to improve their autoimmune conditions and, in this way also, the oocyte maturation and embryo development [70]. Interesting also is the use of artificial intelligence algorithms for enhancing diagnosis of the RIF and ART outcome (pregnancy rate, live birth rate). The computerised analysis systems include ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, prediction of post implantation embryo development, and oocyte and semen analysis. Through the implementation of different computer algorithms, it is possible to analyse the biological and clinical predispositions in infertile couples [71]. Relatively new are the insights of psychological variables involved in the risk condition of medically-assisted reproduction. The studies focus on depression and anxiety levels according to the number of ART attempts and, on the other hand, they assess the impact of ART on the quality of life and family interactions in couples undergoing ART procedures. These considerations could enhance mental wellbeing in infertile couples [72]. Our study population included 247 infertile women who underwent an ART treatment cycle and were recruited into the study. All women were enrolled in the Department of Infertility and Reproductive Endocrinology of Poznan University of Medical Sciences, Poznan, Poland between January 2017 and December 2022. Recurrent implantation failure was defined as the absence of pregnancy after three cycles of IVF using good quality embryos. All women included in the study had their own good quality embryos available for transfer. Each patient in the study group had a regular menstrual cycle and an optimal basal serum follicle stimulating hormone (FSH) level measured on the third day of the last cycle. None of the patients had been taking hormone therapy within the last three months. The exclusion criteria for the study group were as follows: an abnormal karyotype of parents and any identified fetal genetic abnormalities, systemic connective tissue disorder, antiphospholipid antibody syndrome, hereditary thrombophilia, positive antinuclear antibodies, endocrine dysfunction (luteal insufficiency, hyperprolactinemia, thyroid diseases), and alternative reason for subfertility such as infectious and anatomical causes. All women in the study group received luteal phase support and underwent ICSI to increase the chance of conception. Fresh or frozen embryo transfer was always performed on the fifth day, by two people (minimum 15 years of experience in the same clinic). Preimplantation Genetic Screening and Diagnosis (PGS/PGD) methods were not performed due to lack of medical indications. One hundred and twenty age-matched, healthy women with at least two uncomplicated pregnancies ending in the live birth of a healthy full-term newborn were selected for the control group. All women from the control group without evidence of reproductive difficulty had naturally conceived pregnancies. All subjects from the control group had regular menstrual cycles, no evidence of autoimmunity and no past history of pregnancy loss or immunological and endocrinological diseases. Patients and controls were of Polish origin, from the same geographical area. All patients were informed about the purpose of the study and gave their written consent to participation. The study was approved by the Ethics Committee of the Poznan University of Medical Sciences (no. 1159/19, date: 5 December 2019). All procedures performed in this study were in accordance with the ethical standards of our university and with the Helsinki Declaration. The genomic DNA sample was stored in S-Monovette EDTA-coated tubes (Sarstedt, Nümbrecht, Germany) and extracted from peripheral blood leukocytes using the QIAamp DNA Mini Kit according to the manufacturer’s instructions (Qiagen GmbH, Hilden, Germany). DNA concentration and quality were determined spectrophotometrically using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Isolated DNA was stored at −80 °C until analysis. All participants signed informed consent for genetic testing, in which the study management was described. Five SNPs, localized in the genes encoding angiogenesis-related factors, were selected according to the SNP database (dbSNP) of the National Center for Biotechnology Information (NCBI) [41] (http://www.ncbi.nlm.nih.gov/projects/SNP, accessed on 22 February 2022) and the 1000 Genomes Project data (http://www.internationalgenome.org/, accessed on 22 February 2022), based on minor allele frequency (MAF) of at least 5% in European populations. Basic information about the tested variants is presented in Table 7. Genotyping was performed in the Molecular Biology Laboratory of Poznan University of Medical Science by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). The primers and restriction enzymes used for the RFLP reactions were from previously published research and are presented in Table 2 [29,40,73,74]. Products were analyzed by electrophoresis on 2% agarose gel with Midori Green Advanced DNA Stain (Nippon Genetics, Düren, Germany). Positive and negative controls were included in each reaction and for quality control, 10% of the samples were randomly genotyped twice by different individuals, and the reproducibility was 100%. SNP characteristics, primer sequences, and details of the PCR-RFLP assays are presented in Table 8. Blood samples were drawn from an antecubital vein between 8 a.m. and 10 a.m. after an 8 h fast into serum vacuum tubes (Becton, Dickinson and Company Franklin Lakes, NJ, USA). After the blood had clotted at room temperature for 15–30 min, the samples were centrifuged at 1000–2000× g for 10 min and stored at −80 °C until analyses were conducted. The serum anti-Müllerian hormone (AMH) levels for the infertile cases were measured on the cobas Modular E170 immunoanalyzer (Roche Diagnostics International Ltd., Rotkreuz, Switzerland) using the Elecsys AMH Plus (measuring range: 0.07–164 pmol/L). All statistical analyses were conducted in the R statistical software version 4.1.2 [75]. For continuous variables, normality was checked by the Shapiro–Wilk test. Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and in the absence of normal distribution as median and interquartile range (IQR). Bivariate analyses were conducted with the t-test or the Mann–Whitney test for ordinal scales, and the chi-square test or Fisher’s exact test for nominal scales. Genotype frequency distributions and the Hardy–Weinberg equilibrium (HWE) were evaluated using the SNPassoc package [76]. Genotype distributions are shown as numbers and percentages (%).The associations between infertility and the SNP variants were evaluated by odds ratios (ORs), adjusted odds ratios (AORs), and 95% confidence intervals (95% CIs) from logistic regression. Linkage disequilibrium (LD) among the selected SNPs was calculated using Haploview v.4.2 software [77]. Interaction analyses were performed using the open source MDR software [78]. GAS (Genetic Association Study Power Calculator) was used to perform power calculations [79]. A p value less than 0.05 was considered significant. We conducted a case–control study to investigate the relationship between genetic variation in four genes of the angiogenesis pathway with infertility and RIF in Polish females. The genetic variants selected by us have been the subject of many studies before, but not in connection with RIF. We found only one article regarding the importance of rs699947 of the VEGFA gene in RIF Korean women. The strength of our study is that the study population consisted of a homogenous population, which minimized other possible confounding genetic variables. Another one of the strengths of our study is the careful selection of the control group. In order to test the influence of genetic variants not only on RIF but also on infertility, we selected as controls the mothers of at least two children who became pregnant without assisted reproduction methods and did not have any miscarriages. Since maternal age and BMI are some of the major factors contributing to implantation failure, patients in the infertile and control groups were age-matched. Unfortunately, the body mass index was significantly higher in the subjects than in the control group, but we did not observe differences in mean BMI between the RIF groups and women with less than three previous failed embryo transfers. After dividing the study group, we showed that patients with RIF were statistically significantly older than infertile NO-RIF women. Because confusion is a major issue and accounts for many discrepancies between published studies, we adjusted for maternal age and BMI in the statistical analysis of the results. This study has several potential limitations which should be acknowledged. Embryo implantation is a very complex process dependent on many factors; therefore, it is unlikely that only single nucleotide polymorphism explains the entire susceptibility to infertility and RIF. Therefore, we performed a gene–gene interaction analysis. A combination of polymorphisms of several genes is more effective in predicting disease susceptibility. For complex analysis, it could also consider the environmental data influenced to infertility and RIF. Our study focused only on maternal genetic variants, although angiogenesis occurs in both maternal and fetal placental tissues and its genetic polymorphisms may have influenced RIF. Furthermore, the sample size of the current study was relatively small, thus, the present findings need to be confirmed in future studies with a large sample size. Due to the biological complexity and multifactorial nature of many common diseases, single genetic variants still show poor discriminatory power for diagnosis. However, understanding the molecular mechanisms of infertility and RIF by identifying new genetic variants may be the key to developing new therapeutic strategies in the future. Molecular pharmacology is the basis of new drug development and, currently, VEGFR inhibitors have been widely used in the treatment of various tumors. However, current VEGFR inhibitors are limited to a certain extent due to limited clinical efficacy and potential toxicity, which hinder their clinical application [80]. Understanding the molecular mechanisms and function genes encoding angiogenesis-related factors in health and disease is fundamental to the development of new ways to target VEGFs and their receptors.

PMC10001635

Jose M. Romero-Márquez,Tamara Y. Forbes-Hernández,María D. Navarro-Hortal,Rosa Quirantes-Piné,Giuseppe Grosso,Francesca Giampieri,Vivian Lipari,Cristina Sánchez-González,Maurizio Battino,José L. Quiles

Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease

22-02-2023

olive leaves,bioactive compounds,Alzheimer’s Disease,oleuropein,hydroxytyrosol

Alzheimer’s Disease (AD) is the cause of around 60–70% of global cases of dementia and approximately 50 million people have been reported to suffer this disease worldwide. The leaves of olive trees (Olea europaea) are the most abundant by-products of the olive grove industry. These by-products have been highlighted due to the wide variety of bioactive compounds such as oleuropein (OLE) and hydroxytyrosol (HT) with demonstrated medicinal properties to fight AD. In particular, the olive leaf (OL), OLE, and HT reduced not only amyloid-β formation but also neurofibrillary tangles formation through amyloid protein precursor processing modulation. Although the isolated olive phytochemicals exerted lower cholinesterase inhibitory activity, OL demonstrated high inhibitory activity in the cholinergic tests evaluated. The mechanisms underlying these protective effects may be associated with decreased neuroinflammation and oxidative stress via NF-κB and Nrf2 modulation, respectively. Despite the limited research, evidence indicates that OL consumption promotes autophagy and restores loss of proteostasis, which was reflected in lower toxic protein aggregation in AD models. Therefore, olive phytochemicals may be a promising tool as an adjuvant in the treatment of AD.

Molecular Mechanisms of the Protective Effects of Olive Leaf Polyphenols against Alzheimer’s Disease Alzheimer’s Disease (AD) is the cause of around 60–70% of global cases of dementia and approximately 50 million people have been reported to suffer this disease worldwide. The leaves of olive trees (Olea europaea) are the most abundant by-products of the olive grove industry. These by-products have been highlighted due to the wide variety of bioactive compounds such as oleuropein (OLE) and hydroxytyrosol (HT) with demonstrated medicinal properties to fight AD. In particular, the olive leaf (OL), OLE, and HT reduced not only amyloid-β formation but also neurofibrillary tangles formation through amyloid protein precursor processing modulation. Although the isolated olive phytochemicals exerted lower cholinesterase inhibitory activity, OL demonstrated high inhibitory activity in the cholinergic tests evaluated. The mechanisms underlying these protective effects may be associated with decreased neuroinflammation and oxidative stress via NF-κB and Nrf2 modulation, respectively. Despite the limited research, evidence indicates that OL consumption promotes autophagy and restores loss of proteostasis, which was reflected in lower toxic protein aggregation in AD models. Therefore, olive phytochemicals may be a promising tool as an adjuvant in the treatment of AD. Alzheimer’s Disease (AD) is the cause of around 60–70% of global cases of dementia [1] and approximately 50 million people have been reported to suffer from this disease worldwide [2]. In fact, AD incidence rates double every 5 years from 60 years of age [3] and it is estimated that dementia will affect 81.1 million people worldwide in 2040 [2,4]. The etiopathogenesis of AD is characterized by two histopathological events: the senile plaque aggregation formed by amyloid-β peptides (Aβ) in the central nervous system and the formation of neurofibrillary tangles (NFTs) associated with the accumulation of Tau protein in the hippocampus, neocortical area, and amygdala [4]. These events are associated with an increase in mitochondrial dysfunction, oxidative stress, glucose homeostasis alteration in the brain, neuroinflammation, and disturbances in the proteostatic network, which favor the appearance of senile plaques and NFTs, generating atrophy and neuron death characteristic of AD [5]. AD is a multifactorial disease whose appearance and development are marked by the interaction between genetic predisposition and external factors throughout life [4]. Among the risk factors, aging, gender (higher incidence in women), alcohol and tobacco consumption, obesity, and metabolic disorders such as diabetes mellitus, as well as a low cultural level and family history, have been highlighted [1]. Some of these risk factors, including physical activity, diet, smoking, and alcoholism, could be modified in order to reduce the onset of the disease [4]. Although there is still no pharmacological therapy for its treatment, the preventive and/or therapeutic nutritional interventions against AD have been gaining prominence in recent years [1]. It is known that 35% of dementias could be caused by modifiable risk factors associated with lifestyle, including the type of diet [1]. In particular, the Mediterranean Diet (MD), characterized by a high consumption of legumes, vegetables, fruits, vitamins, and virgin olive oil, and a low consumption of red meat, has been shown to reduce the incidence of AD [1]. MD presents a high contribution of bioactive substances such as phenolic compounds, which have been shown to exert a protective effect in AD [6]. In particular, the intake of phytochemical compounds naturally present in foods, such as oleuropein (OLE), hydroxytyrosol (HT), luteolin (LU), catechin, and curcumin, are related to neuroprotective effects in AD through the modulation of mechanisms such as oxidative stress and neuroinflammation, besides reducing the deposition and toxicity of the misfolded proteins involved [7,8,9,10,11,12]. The leaves of the olive tree (Olea europaea) are the most abundant by-product in the olive grove industry. In Spain, between 1 and 5 tons of waste are generated per hectare in the form of branches and leaves. OLs are long, hard, and lanceolate, and their edges curl up due to desiccation [13]. It is possible to obtain olive leaf extracts enriched in certain compounds after grinding and processing them with an extraction solvent such as methanol, ethanol, or water, or a mixture thereof. In addition, separation or concentration procedures can be applied to enrich extracts in particular molecules [14]. OLs have excellent medicinal properties thanks to the wide variety of bioactive compounds present in them. In this review, the preventive and therapeutic effect of olive compounds against AD were reviewed from the point of view of the molecular mechanisms involved. OLs are consumed worldwide as a nutraceutical product due to their numerous and demonstrated health properties. In fact, considerable attention has been given to OLs because of their remarkable content of polyphenols [15]. The most representative compounds of OLs are those illustrated in Figure 1. Table 1 shows most representative compounds present in olive leaves. To date, the best-known phenolic compounds in OLs are secoiridoid derivatives, of which OLE is the most abundant. Additionally, the presence of phenolic alcohols (e.g., HT, tyrosol, and oleoside) and flavones (e.g., LU and luteolin-7-o-glucoside) should be highlighted together with others. In the same way, other phenolic compounds, such as phenolic acids (e.g., verbascoside), flavanols (e.g., Epicatechin gallate), and flavonols (e.g., kaempferol-7-O-glucoside and rutin), have also been described in OLs (Figure 1). The phytochemical profile of the OL can be affected by several factors, such as the olive tree’s geographical location, cultivars, harvest season, drying temperature of leaves, and the solvents used for extraction. In this regard, Kabbash et al. (2021) demonstrated that olive leaves from Spain presented higher total flavonoid, phenolic, and OLE content compared with olive leaves from Italy and Egypt [16]. Similar results were obtained by Zhang et al. (2022), which showed a higher total flavonoid, OLE, and HT content, among other phenolic compounds, in Spanish olive leaves compared with those harvested in China or Italy [17]. These differences could also be attributed to the use of different cultivars. In this context, Nicoli et al. (2019) found significant differences among 15 different olive tree cultivars from Italy regarding OLE, HT, verbascoside (VB), and flavones (LU and luteolin-7-O-glucoside) content [18]. In the same way, Pasković et al. (2022) reported several differences in OLE, VB, rutin, catechin, phenolic alcohol (HT and tyrosol), and flavone (LU and apigenin derivatives) content in four different olive tree cultivars from Croatia [19]. Furthermore, the harvesting season has been demonstrated to influence the content of most of the phenolic compounds of OLs, even those of the same cultivar, with increases in olives harvested between March and April [16,19]. Additionally, it has been reported that the temperature of leaf drying after pruning can also affect the phenolic content. In this context, olive leaves dried using a freezing protocol (−80 °C) presented higher amounts of phytochemicals compared with those dried using a hot air protocol (105 °C), with the exception of OLE, the content of which increased in hot-air-dried olive leaves [17]. Authors attributed these results to the fact that some molecules, such as flavonoids, easily break down into smaller compounds during dehydration, while the stability of secoiroids, such as OLE, is relatively high. Finally, the method and solvent used for the extraction of olive leaf polyphenols has been reported to dramatically influence the final phenolic content. Most of the evaluated studies in this work used different proportions of methanol [19,20,21,22,23], ethanol [16,17,18], or water [23,24] as solvent to obtain the olive leaf extract. However, only Kontogianni et al. (2013) evaluated the direct influence of the solvent used in OL polyphenol extraction. Independently of the detection method (high performance liquid chromatography or nuclear magnetic resonance spectroscopy), the olive leaves extracted with methanol presented a higher content of OLE, HT, and LU and its derivatives compared with the aqueous OL extract. In fact, LU was not detected in the aqueous extract [23]. For further studies, the phytochemical characterization of OL needs to be standardized to generate a homogeneous phenolic profile through the regulation of the previously mentioned factors. A crucial point of drug administration is the capacity of the active principle to be absorbed and passed to the systemic circulation, and to exert its action on the specific sites. In the case of a multicomplex food matrix such as the OL, it is necessary to evaluate the absorption and metabolism of numerous compounds present in it, and to evaluate the role of these compounds in the observed healthy effects. Some studies have investigated the pharmacokinetics of olive leaf phenolics by administering isolated compounds (not explored in this review). However, in this review, the bioaccessibility and bioavailability of individual compounds were examined, but after OL administration. According with in vitro studies, gastric, intestinal, and colonic digestion significantly reduced the bioaccessibility of numerous compounds naturally present in OLs, such as phenolic acids (e.g., VB, chlorogenic, gallic, and caffeic acid), phenolic alcohols (e.g., HT and tyrosol), secoiridoid derivatives (e.g., OLE), flavones (e.g., luteolin 7-o-glucoside, apigenin 7-o-glucoside), flavanols (e.g., epicatechin), and flavonols (i.e., quercetin-3-o-rutinoside, quercetin-3-o-galactoside, kaempferol, and rutin) [25,26]. However, in vitro digestion also promoted the bioaccessibility and the potential bioavailability of some secoiridoid derivatives related to OLE hydrolysis, such as oleoside and oleoside 11-methyl ester [26]. To date, only three investigations have explored the bioavailability of phenolic compounds from OLs in humans. As can be observed in Table 2, numerous compounds such as secoiridoid derivatives, phenolic alcohols, and flavonoids can be found in plasma or urine samples after OL ingestion. No significant influence of gender on the absorption of OL phenolic compounds was observed in middle-aged people after OL consumption (270 or 400 mg/d). Interestingly, the administration of OLs through liquid glycerol preparation increased the plasma bioavailability of OLE and reduced the time to peak of HT derivatives compared with softgel capsule administration [27]. According to the pharmacokinetics, the biological half-life of plasma OLE metabolites (1.33–2.01 h) was shorter than that of HT derivatives (1.73–6.53 h), whereas the excretion peak rate in urine was 8-24 h for both metabolite classes [28,29]. The most abundant compounds found in urine were secoiridoids and HT and its derivatives, probably due to the rapid hydrolysis of OLE in the upper gastrointestinal tract [28,29]. It should be noted that the hydrolysis of OLE is not complete and numerous glucuronidated and sulfated derivatives from OLE can be found in plasma and urine, indicating that OLE is also conjugated by Phase II enzymes [29]. Curiously, a study of pre- and post-menopausal women fed with 250 mg of OL revealed that OL-related metabolites, such as HT glucuronide and sulfate, OLE aglycone glucuronide, and aglycon derivative I, were present in higher concentrations in the plasma from post-menopausal women. The authors attributed these results to potential age-related changes such as alterations in hormonal status and a decrease in gastric emptying [29]. These results are extremely interesting due the existence of a potential increase of bioavailability of phenolic compounds from OL, related, at least in part, to women’s age, opening the door to their potential use in aging and age-related diseases. Olive leaves have been widely used as therapeutic tools throughout history [30]. In contrast to the classical belief that botanic-related products are completely safe and lack toxicity, these products could cause several side effects due to the fact that most of their chemical content remains uncharacterized. In addition, due to the easy access and low cost of these by-products, as well as the possibility of self-medication without medical advice for many people around the world, the study of OL-related toxicity is necessary. Therefore, in this section, the evidence regarding toxicity related to the intake of olive leaves is discussed. According to in vitro studies, the co-incubation of different concentrations (51.2, 128, 320, 800, 2000, and 5000 µg/mL) of OL did not reveal pro-mutagenic effect in different Salmonella typhimurium (TA98, TA100, TA1535, and TA1537) and Escherichia coli (WP2 uvrA) strains in the Bacterial Reverse Mutation Test [31]. In the same way, coincubation with rising concentrations of OL (250, 500, 750, 1000, and 1250 µg/mL) did not affect the number of aberrant cells, polyploidy rates, or endoreduplication metaphases in V79 male Chinese hamster lung cells in the Chromosomal Aberration Test [31]. Similarly, treatment with lower OL dosages (20, 40, 60, 70, or 80 µg/mL) was demonstrated not to reduce or even increase viability in different cell lines [32,33]. Acute toxicity of OLs has also been evaluated in in vivo models. In this context, no adverse reactions, toxicity clinical signs, or lethality were observed after 24–48 h of OL administration in Caenorhabditis elegans (0.1, 1, 10, 100, 1000 µg/mL, [C. elegans]), NMRI BR mice (500, 1000, and 2000 mg/kg of body weight [bw]), or Swiss albino mice (2000 mg/kg bw) [31,34,35]. In fact, no genotoxic activity of OL was observed in bone marrow from these NMRI BR mice consuming 500, 1000, or 2000 mg/mL for 48 h [31] or Drosophila melanogaster acutely exposed to 3.75 or 30 µg/mL of OL [36]. Additionally, no embryolethality or embryotoxicity were found after an acute exposure to 100 µg/mL of OL for 24 h in C. elegans Wild-type strain [35]. Regarding sub-chronic toxicity of OL, the intake of 100, 200, 400, or 2000 mg/kg bw of OL daily via gavage for 14 or 28 days did not produce mortality, signs of toxicity, or behavioral and physical alterations in Wistar male and female rats. In addition, necropsy showed no abnormalities in the liver and kidney of treated rats [37]. Similar results were obtained in Wistar rats orally supplemented with 360, 600, or 1000 mg/kg bw of OL daily for three months [31]. Additionally, these authors found no alterations in organ weight (liver, adrenals, kidneys, thyroid/parathyroid, thymus, spleen, brain, heart, epididymides, testes, ovaries, fallopian tubes, and uterus) or pathological lesions in the most representative organs from locomotor, digestive, lymphatic, integumentary, respiratory, cardiovascular, endocrine, excretory, reproductive, and central and peripheral nervous systems [31]. Similarly, the consumption of 250 mg/day of OL in a double-blind, randomized controlled trial for 12 months revealed an absence of side effects in aged women [38]. In accordance with chronic toxicity, only one study evaluated the long-life effect of OL. In this context, lifelong administration of 100 µg/mL did not modify the survival rates in the C. elegans Wild-type strain [35]. Among the in vivo endpoint studies, some research has evaluated the influence of OL treatment in biochemical and hematological parameters. Interestingly, after an acute administration of 2000 mg/kg bw of OL, some hematological (hematocrit, hemoglobin, mean corpuscular volume, red blood cells, and platelets) and biochemical parameters (cholesterol and creatinine levels) were reduced without producing abnormalities in liver or kidneys [37]. It should be noted that the solvent used in this work was a solution made with 51% of ethanol, which could also interfere in hematological and biochemical studies, meaning results may not be attributed to the treatment itself. In fact, when the same solvent was administered for 28 days, the effect on hematological and biochemical parameters disappeared, probably due to an adaptation to alcohol consumption [37]. Similarly, the intake of 360, 600, or 1000 mg/kg bw of OL diluted in distilled water daily for three months did not alter most of the hematological parameters, electrolytes, or renal and hepatic markers studied in rats [31]. According to hepatic markers, no adverse effects were noted related to aspartate and alanine aminotransferase, gamma glutamyl transferase, and alkaline phosphatase levels in middle-aged people who consumed 270 or 400 mg of OL [27]. Similarly, no clinical changes were observed in classical biochemistry, hematological, or electrolytes parameters, or renal- and liver-function-related parameters, in a randomized controlled trial that administered 1000 mg/day of OL for 8 weeks [39]. In the following section, the scientific evidence regarding the effects of OL bioactive compounds on the main mechanisms involved in the pathogenesis of AD will be discussed. A summary can be found in Table 3. As mentioned before, an abnormal extracellular accumulation and clearance of the Aβ in the brain is one of the main features of AD, which leads to neuron death and the typical symptoms of dementia [40]. In this regard, several studies have evidenced the protective role of OL and its bioactive molecules. In vitro experiments indicated that individual compounds from OL were able to reduce both the aggregate size and occurrence of Aβ42 fibrils [40]. In the human neuroblastoma SH-SY5Y cell line, treatment with an OL fraction enriched in triterpenoid compounds (OLE, HT, VB, LU, and quercetin [QU]) reversed the loss of viability induced by the neurotoxic agent Aβ1–42. The lipid profile analysis performed using bioinformatic tools revealed that a significant number of phosphatidylcholines and phosphatidylethanolamines significantly increased, whereas several triacylglycerols decreased in the treated neuroblastoma cells [32]. In the same model, treatment with the aforementioned compounds and commercial preparation of OL exerted a stronger protection against Aβ42-, Cu-Aβ42-, or L-DOPA-Aβ42-induced neurotoxicity, manifesting an increase in cell viability [40]. In accordance with a computational binding affinity test, the neurotoxicity reduction mentioned above may be attributed to the ability of OLE, HT, LU, VB, and QUE, as well as their derivatives, to strongly bind to the hairpin-turn of the Aβ1–40 and Aβ1–42 monomers and the subsequent reduction in Aβ fibrillization [41]. On the other hand, β-secretase site-1 (BACE-1) is involved in the generation of the Aβ aggregation since it participates in the amyloidogenic processing of the amyloid precursor protein (APP). In vitro assays have demonstrated that bioactive compounds present in olive-tree-derived products, both non-flavonoids (e.g., HT, VB) and flavonoids (e.g., RU, QU), have a remarkable inhibitory effect on the BACE-1 enzyme. The commercial olive biophenol extracts (olive leaf extract rich in OLE or HT) also exerted a strong inhibitory activity, the latter being the most powerful. Although the action mechanism of extraction of olive biophenols is not clearly understood, the results showed a synergistic effect of the combination of flavonoid or non-flavonoid compounds in the extracts which are rich in biophenols [42]. Regarding the in vivo evidence, a transgenic strain of C. elegans expressing the human Aβ1–42 peptide in muscle cells was used by Romero-Márquez et al. (2022) for evaluating the anti-Aβ aggregation effect of an olive leaf extract containing 40% of OLE. Results showed a delay in the amyloid-induced paralysis of worms and a reduction in the amount of Aβ deposits stained by Thioflavin T. The RNAi test showed the participation of DAF-16/FOXO, SKN-1/Nrf2, and HSP16.2 pathways in those effects. This extract has been authorized to be used as an ingredient for nutritional supplements in human nutrition, so it could be a very promising approach for AD therapy [35]. Similar results were found for a HT-rich extract from olive fruit [43]. Apart from cells and nematode models, the anti-amyloid effect of olive leaves has also been evaluated in rodents. The AD models 5xFAD and APPswe/PS1dE9 mice overproduce the Aβ peptide and develop progressive cerebral Aβ deposits and learning and memory impairment. Olive leaf extract enriched in OLE mixed with the powdered food was able to reduce the total Aβ deposits in the hippocampus and cortex in both 5xFAD [44] and APPswe/PS1dE9 [40] animals. The soluble Aβ40 levels [44] and the size of Aβ plaques [40], respectively, were also reduced. Furthermore, an increase in the expression of Aβ clearance proteins (P-gp and LRP1) was observed in the treated group. The induction of anti-amyloidogenic protein and enzyme expression (sAPPα and α-secretase) and the reduction in the amyloidogenic protein sAPPβ suggested that the olive leaf extract is able to modulate APP processing [44]. The effect of an olive leaf extract containing 40% OLE on Aβ production was also investigated in male white rabbits, although not in an AD model but in one of cervical myopathy. The increase in Aβ in spinal cord tissue neuron cells after receiving compression treatment was effectively reduced by the treatment [45]. These results could be attributed to the high content of OLE aglycone present in olive leaves, which was also demonstrated to reduce Aβ-induced neurotoxicity through the reduction in Aβ aggregates in rats cerebrally injected with Aβ42 [46]. These results could be explained by results found by Brogi et al. (2020) using molecular docking. In this research, the authors showed that OLE aglycone was able to move deeply within the Aβ fibrils targeting a key motif in Aβ peptide, promoting structural instability and Aβ fibril disaggregation [47]. Together with Aβ aggregation, AD is characterized by the intracellular accumulation of hyperphosphorylated NFTs. The Tau aggregation has been investigated using the experimental model C. elegans. In this case, a transgenic strain expressing the pro-aggregate human Tau protein in a constitutive pan-neuronal way was used. This strain manifests locomotion defects derived from Tau deposition. Treatment with an OL extract enriched in OLE (40%) improved several locomotive parameters related to Tau neurotoxicity through the modulation of the DAF-16/FOXO, SKN-1/Nrf2, and HSP16.2 pathways. Those transcription factors were also involved in the protective effect of the treatment against the Aβ proteotoxicity [35]. Likewise, in a rabbit model, extracts with the same percentage of OLE decreased the high levels of p-Tau in spinal cord tissue neuron cells after receiving compression treatment in the cervical myelopathy [45]. These results might be attributed to the high content of OLE, OLE aglycone, and HT, which have been shown to prevent Tau fibrillization in vitro [48]. The action of specific enzymes on neurotransmitters or certain proteins can lead to the development and progression of neurodegenerative diseases such as AD. The cholinesterases (acetylcholinesterase [AchE] and butyrylcholinesterase [BchE]), histone deacetylase, and tyrosinase are some of the enzymes associated with AD. AChE and BChE hydrolyze choline esters degrade the neurotransmitter acetylcholine. In fact, one of the hypotheses of AD is the cholinergic hypothesis. This system is severely affected in AD, and the over-activation of the mentioned enzymes appears to promote amyloid Aβ fibril formation. The deficit in cerebral cholinergic transmitters ultimately results in memory loss with other cognitive symptoms that are characteristic of AD. In that sense, one enzyme involved is histone deacetylase, which is required for memory formation. Studies have shown the relation between defects in this enzyme and the development of neuropathology and Tau neurofibrillary tangles formation. Likewise, tyrosinase activity is related to processes and sequences involved in the progression of AD [42]. On the other hand, AChE and BChE were effectively inhibited by the aforementioned commercial leaf extracts rich in HT or OLE [42], and by an ethanolic extract of olive leaves from different geographic origins [49]. In addition, different types of olive leaf extracts obtained by supercritical fluid extraction also exerted that activity [50]. The best adsorbent was sea sand, which yielded extracts rich in triterpenes with moderate inhibitory activity of the enzyme [50]. In contrast, HT [42], OLE [42], or maslinic acid [51] alone were not able to inhibit the enzymes, whereas oleanolic acid [52,53,54] and pinoresinol [55] had a very slight inhibitory activity. In the same way, some representative compounds present in OL, such as tyrosol, luteolin 7-O glucoside, and ligstroside, showed a null correlation with both AChE and BchE inhibitory activity [56]. These results suggested that the enzyme inhibitory effect of OL might be caused, once again, by a synergism between different compounds present in the leaves. Inflammation is a common condition present in neurodegenerative diseases and is considered another pathological feature of AD. In vitro assays based on the ability of inhibiting the lipoxidase (LOX) demonstrated a modest anti-inflammatory potential of different types of OL extracts obtained by supercritical fluid extraction [50]. Positive results in that sense were also found in cell cultures. Treatment with green olive leaves containing OLE 20% in N1 murine microglia cell culture decreased BSA-AGE-induced NO production [33]. The LPS-induced increase of inflammatory markers was ameliorated by treatment with olive leaf extracts in human THP-1 monocytes [32] and in activated murine macrophages RAW 264.7 [57]. Concentrations of 20 and 40 µg/mL of an olive leaf fraction enriched in triterpenoid compounds reduced IL-6 and IL-1β secretion levels. Furthermore, the highest dose was able to reduce TNF-α levels in the monocyte cell line [32]. RAW 264.7 macrophages were treated with OLE-rich leaf extract in acute (50 µM extract with LPS for 24 h) or chronic exposure (50 µM extract pre-treatment for 24 h followed by LPS). Both acute and chronic treatment decreased NO production and strongly reduced the levels of iNOS and COX-2. In addition, both types also decreased the mRNA expression of IL-1β and IL-6, whereas the acute treatment was able to reduce IL-1βR protein expression and mRNA expression for TGF-β [57]. In the same line, the potential anti-inflammatory activity of olive leaves was also demonstrated in rodents. The 5xFAD mice model accumulated high levels of Aβ along with astrogliosis and microgliosis. Animals treated with an OL extract spiked in refined olive oil and mixed with powdered food showed less astrocyte activation and GFAP levels, together with an ameliorated astrocyte shape, compared with the control group fed only with the vehicle. Microglial activation in both the hippocampus and cortex was reduced, as were the IL-1β levels. In addition, the treatment also decreased NLRP3 in the brain, a finding which is associated with the significant reduction in pro-caspase-1 and pro-caspase-8. Components of the NF-κB, a classical pathway involved in inflammation, were also modulated by the extract. OL consumption significantly reduced the expression of p-IKKβ, an effect that was associated with increased levels of total IκBα and reduced p-IκBα [44]. In addition, the authors studied the receptor for advanced glycated end products (RAGE), which is considered a major source of Aβ entry to the brain and is related with the increase in pro-inflammatory cytokines. The interaction of RAGE with high mobility group box protein 1 (HMGB1) and the upregulation of this last protein in AD are well known. Protein levels of RAGE and HMGB1 were downregulated by the treatment. Overall, the mechanisms involved in the anti-inflammatory activity of olive leaves were the reduction in the NF-κB pathway, which regulates NLRP3 and RAGE/HGMB1 [44]. Likewise, the increase in TNF-α, IL-1β and prostaglandin E2 levels caused by lead (Pb) neurotoxicity were reduced with olive leaf extract in the hippocampus of male Wistar rats. Pb is a well-known neurotoxic agent considered to be a key mediator of inflammation and oxidative-stress-induced neuropathological effects. The oral administration of the extract was able to reduce tissue Pb deposition and prevent the negative effects [58]. However, oral treatment with olive leaf extract to kainic-acid-induced epilepsy Wistar rats did not exert a statistically significant decrease in the pro-inflammatory cytokine TNF-α, although other parameters related to oxidative stress were improved [59]. Additionally, treatment with OL extract containing 40% OLE resulted in a significant decrease in the inflammatory marker CD-68 (biomarker of activated microglia-macrophage) in a model of cervical myopathy in male white rabbits [45]. Inflammatory markers were also reduced in Peripheral Blood Mononuclear Cells (PBMCs) from male human patients treated for 8 weeks with 20 mL of liquid olive leaf extract, which provided 121.8 mg of OLE and 6.4 mg of HT daily. A downregulation of COX-2 and IL-8 gene expression was observed in PBMCs. Furthermore, the authors found a downregulation of the transcription factor jun-B, which is related to macrophage activation, and the Heparin binding EGF-like growth factor, both related to the NF-κB, in the treated group [60]. Oxidative stress (OS) is involved in the occurrence and progression of AD. Aβ plaques and NFTs elevation are associated with increased levels of oxidation products from proteins, lipids, and nucleic acids in the hippocampus and cortex [61]. In this context, OL supplementation was found to reduce DNA damage and protein carbonyls in human PBMCs [62,63,64]. At the brain level, several in vivo studies demonstrated that OL consumption reduced DNA fragmentation, protein carbonyls, lipid peroxidation, and peroxynitrite levels in different murine models of neurodegenerative diseases [58,59,65,66,67,68] or aging [69,70,71]. However, OL supplementation did not alter the urinary markers of oxidative status of healthy young adults, indicating a possible protective role of OL only in redox-homeostasis-impaired conditions such as aging or AD [72]. The antioxidant effects of OL may be explained by the capacity to reduce ROS or NOS content, which leads to the reduction in oxidizing molecules such as DNA, protein, and lipids from different tissues. In this context, some authors have demonstrated the role of OL as a ROS scavenger in vitro and in vivo. Among them, De Cicco et al. (2020) observed that the preincubation with OL was able to reduce the ROS content increase induced by sodium palmitate, a free radical generator, in RAW 264.7 cells [73]. More recently, Romero-Marquez et al. (2022) demonstrated that N2 Wild-type C. elegans strain supplemented with OL presented lower ROS content after an acute exposition to the prooxidant 2,2′-Azobis (2-methylpropionamidine) dihydrochloride [35]. Additionally, OL supplementation has demonstrated protective effects at the neuron level. In this context, a combination of OL with Hibiscus sabdariffa leaves (13:2, w/w) was able to reduce ROS content in human SH-SY5Y neuroblastoma cells damaged by H2O2 [74]. Beyond its free radical scavenger activity, OL supplementation has shown a modulatory activity over some inducible enzymes related to antioxidant response element (ARE). According to the literature, plasma glutathione levels and antioxidant enzyme activity, such as that of glutathione peroxidase (GSH-Px), catalase (CAT), and superoxide dismutase (SOD), which contribute to the progression of the disease, significantly decreased in early AD [75]. Among the neurodegenerative-like murine model studies, OL supplementation has been found to increase the brain activity of numerous AREs, such as SOD [65,66,67,68,69], CAT [66,67,68,69], GSH-Px [66,69], and glutathione S-transferase (GST) [58], as well as increase glutathione [67,76] brain levels. Notably, the common factor of these AREs is that they are regulated, totally or partially, by the nuclear factor erythroid 2-related factor 2 (Nrf2) [77]. Nrf2 is a transcription factor involved in the protection against OS. Under OS conditions, Nrf2 translocates to the nucleus and promotes the genetic expression of a significant number of AREs [78]. Clinically, the hippocampus from AD patients presents less nuclear Nrf2 compared to healthy controls, although OS markers are higher [79]. This feature indicates that AREs cannot be activated as Nrf2 does not translocate from the cytoplasm into the nucleus in hippocampal neurons in patients with AD [80]. Therefore, Nrf2 activation has been proposed as a novel target in the treatment of AD. Interestingly, some treatments, such as methylene blue, have demonstrated that the reduction in tauopathy, OS, and locomotive and memory impairment induced by NFT formation was mediated by Nrf2 activation in a mouse model of tauopathy [81]. In this context, some authors also described the capacity of OL to modulate Nrf2 in vitro [73] and in vivo [35,82]. Nonetheless, only one work researched the role of OL-induced Nrf2 nuclear translocation to fight AD in vivo. In this context, the authors focused on the role of OL in two different experimental C. elegans AD models. As mentioned in the previous sections, the authors demonstrated that OL treatment was able to reduce the cytotoxic effect of Aβ through the reduction in Aβ plaque aggregation. In the same way, the authors described a reduction in the neurotoxic effect caused by Tau aggregation in OL-treated animals. The authors partially described the mechanism of action of OL using RNAi technology, indicating a key role of SKN-1, a C. elegans ortholog of the human Nrf2, in the progression of Aβ and Tau protein cytotoxicity in C. elegans [35]. Therefore, the increase in Nrf2 translocation, and the subsequent activation of ARE, might be a possible mechanism of action underlying the protective anti-AD effect by olive leaf supplementation. Scientific evidence indicates that some AD hallmarks such as senile plaque formation are closely related to an alteration in the autophagic pathway and the incapacity to eliminate Aβ1–42 aggregates [83]. Indeed, alterations in autophagic-lysosomal degradation of proteins has been associated with AD, which were correlated with AD progression in both animal models and humans [83]. Recently, it has been demonstrated that the expression of MAPK/p38α protein is upregulated in the brain of APP-PS1 transgenic AD mouse, whereas the knockdown of MAPK in the APP-PS1 mouse stimulates macroautophagy/autophagy, reducing amyloid pathology by increasing autophagic-lysosomal degradation of BACE1 [84]. In the same way, high levels of phosphorylated AKT were associated clinically with the progression of NFT aggregation in AD patients [85]. These features seem to be consistent in a neurodegenerative rodent model induced by Pb. The exposure to Pb induced MAPK/p38 and AKT phosphorylation in the hippocampus of rats. Interestingly, OL supplementation was able to reduce both autophagy markers, which were associated with a reduction in Pb-induced neurotoxicity and an improvement of behavioral and locomotive tests [58]. Although there is limited data available about the modulatory effect of OL on autophagy markers during AD, Leri et al. (2021) investigated the role of an equal mix of OLE aglycone and HT in a cellular model of AD [86]. In this context, the Aβ1−42 oligomers’ exposure to human SH-SY5Y cells increased Beclin1, p62, and S6 expression, as well as the LC3II/I ratio. It seems that Aβ1–42 promotes the expression and activation of autophagy regulation markers, indicating an accumulation of autophagosomes with disrupted degradative activity [87]. In addition, p62 is remarkably involved in AD due to autophagic degradation through the binding of the autophagy marker LC3 [88]. With this background, HT-mix-treated cells presented a significant time-dependent reduction in p62 levels, which was reflected in lower Aβ1–42 oligomers on the surface, suggesting that these aggregates were digested by autophagolysosomes. In the same way, the phosphorylation level of the ribosomal protein S6, a key downstream substrate of TOR, was reduced in cells treated with the HT mix, indicating an involvement of the AMPK pathway in autophagy activation mediated by olive leaf phenols [86]. Similar to autophagy modulation, the role of proteostasis network modulation has been proposed as an intervention for AD management. There is extremely limited information about this topic. As far as it is known, the only work that evaluated the direct role of OL on the proteostasis network component during AD was evaluated in C. elegans. In this research, OL treatment was able to reduce both Aβ and Tau-protein-induced cytotoxicity, whereas an overexpression of HSP-16.2 was reported in a GFP-reporter strain. HSP-16.2 is an important element of protein homeostasis, which involves highly conserved stress responses that prevent protein mismanagement. In C. elegans, HSP-16.2 encodes HSP-16, which directly interacts with Aβ peptide and interferes with oligomerization pathways, leading to reduced formation of toxic species. To confirm the role of this protein in the protective effect shown by OL against AD, the authors used RNAi technology to knock down HSP-16.2 in two different AD-like strains of C. elegans. Interestingly, the authors demonstrated that the protective effect of OL to fight both Aβ and Tau-protein-induced cytotoxicity was mediated by HSP-16.2 overexpression. These results were confirmed by Thioflavin-T staining, which showed lower Aβ accumulation on OL-treated worms, probably due to an increase in Aβ clearance mediated by HSP-16.2 [35]. In conclusion, knowledge about the role of olive leaves in combating AD via autophagy/proteostasis modulation, in order to use it as AD prevention or therapy, is far from being complete. Nonetheless, the limited research available seems to indicate that the protective role of OL might be mediated by an enhancement of autophagy and proteostasis, although more research is needed. Despite all the efforts, AD continues to remain a challenge, with no effective treatment to combat it, increasing the dependency and, subsequently, the death, of patients. Natural products such as olive leaves and their compounds contribute to the discovery of new anti-AD interventions to combat AD progression. The effectiveness of OL, and of the molecules present in this olive tree by-product, in reducing or even preventing the different processes related to AD, including Aβ and Tau protein production, Aβ fibrogenesis and NFT deposition, inhibition of AD-related enzymes, and oxidative stress and neuroinflammation, have been described (Figure 2). In particular, OL, OLE, and HT reduced both amyloid-β formation and neurofibrillary tangles formation through amyloid protein precursor processing modulation. Although the isolated olive phytochemicals exerted lower cholinesterase inhibitory activity, OL demonstrated high inhibitory activity in the cholinergic tests evaluated. The mechanisms underlying these protective effects might be associated with decreased neuroinflammation and oxidative stress via NF-κB and Nrf2 modulation, respectively. Furthermore, memory impairment is associated with early AD stage. However, with AD progression, patients tend to develop symptoms of cognitive and behavioral alterations, such as depression, anxiety, disorientation, and paranoia, which affect daily living activities [91]. Some authors have explored the potential therapeutic effect of OL consumption to combat the deleterious effect of neurodegenerative disease induced by chemicals on memory and cognitive function. OL consumption was able to restore the locomotive impairment caused by Pb-induced neurological damage in rats subjected to an open field test [58]. Similarly, in a rat model of Parkinson’s disease induced by rotenone, OL consumption was able to partially restore balance, motor coordination, and muscle strength in a dose-dependent manner [66]. The limited research available that addresses the therapeutic effect of OL in AD-like symptoms failed to find a significant test to measure the behavioral alterations related to AD progression. In this context, Omar et al. (2019) tried to evaluate some behavioral analysis related to anxiety, locomotive function, and orientation in APPswe/PS1dE9 and Wild type mice supplemented or not with OL. As mentioned previously, APPswe/PS1dE9 mice overproduce human Aβ, causing learning and memory impairment. After 5 months of housing, the applied tests failed to show impairments in the studied behavioral parameters in AD or Wild type mice, although Aβ plaque senile deposits were found in AD mice. These results indicate that the test, the age of the mice, or the AD phenotype used were not appropriate for the parameters studied. As mentioned above, behavioral parameters such as anxiety, orientation, and locomotive function are linked to late stages of AD, and it is necessary to produce a relatable late AD stage model with measurable results. In contrast to behavioral tests, Abdallah et al. (2022) found an improvement in memory function using the Morris water maze in OL-treated AD-like 5xFAD mice. Despite the promising results obtained in the Morris water maze test, the authors also failed to measure locomotive impairment related to AD, finding no differences between Wild type and AD mice, treated or not [40]. Although these results suggest that memory impairment related to AD progression might be restored by OL supplementation, more research is necessary to find a correct test to analyze AD behavioral impairment. Summarizing, despite the fact that direct evidence is still limited, many investigations corroborate the potential use of OL as an adjuvant in AD treatment. Olive leaves have been proven to reduce the toxicity of protein aggregation through the reduction in Aβ formation and aggregation, as well as the reduction in Tau fibrogenesis and deposition. In the same way, the possible mechanism of action underlying the protective effect might be attributed to oxidative stress and neuroinflammation modulation, as well as an increase in toxic protein clearance through the modulation of autophagy and the proteostasis network. Nonetheless, most of the reviewed evidence comes from in vitro studies, indicating that more preclinical and clinical research is needed for a deeper understanding of the molecular mechanisms associated with the observed effects.

PMC10001661

Shiyou Wang,Qichun Yao,Fan Zhao,Wenfei Cui,Christopher A. Price,Yifan Wang,Jing Lv,Hong Tang,Zhongliang Jiang

1α,25(OH)2D3 Promotes the Autophagy of Porcine Ovarian Granulosa Cells as a Protective Mechanism against ROS through the BNIP3/PINK1 Pathway

22-02-2023

1α,25(OH)2D3,porcine granulosa cells,autophagy,ROS,signaling pathway

Vitamin D (VD) is one of the important nutrients required by livestock; however, VD deficiency is reported to be widespread. Earlier studies have suggested a potential role for VD in reproduction. Studies on the correlation between VD and sow reproduction are limited. The aim of the current study was aimed to determine the role of 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3) on porcine ovarian granulosa cells (PGCs) in vitro to provide a theoretical basis for improving the reproductive efficiency of sows. We used chloroquine (autophagy inhibitor) and reactive oxygen species (ROS) scavenger N-acetylcysteine in conjunction with 1α,25(OH)2D3 to explore the effect on PGCs. The results showed that 10 nM of 1α,25(OH)2D3 increased PGC viability and ROS content. In addition, 1α,25(OH)2D3 induces PGC autophagy according to the gene transcription and protein expression levels of LC3, ATG7, BECN1, and SQSTM1 and promotes the generation of autophagosomes. 1α,25(OH)2D3-induced autophagy affects the synthesis of E2 and P4 in PGCs. We investigated the relationship between ROS and autophagy, and the results showed that 1α,25(OH)2D3-induced ROS promoted PGC autophagy. The ROS-BNIP3-PINK1 pathway was involved in PGC autophagy induced by 1α,25(OH)2D3. In conclusion, this study suggests that 1α,25(OH)2D3 promotes PGC autophagy as a protective mechanism against ROS via the BNIP3/PINK1 pathway.

1α,25(OH)2D3 Promotes the Autophagy of Porcine Ovarian Granulosa Cells as a Protective Mechanism against ROS through the BNIP3/PINK1 Pathway Vitamin D (VD) is one of the important nutrients required by livestock; however, VD deficiency is reported to be widespread. Earlier studies have suggested a potential role for VD in reproduction. Studies on the correlation between VD and sow reproduction are limited. The aim of the current study was aimed to determine the role of 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3) on porcine ovarian granulosa cells (PGCs) in vitro to provide a theoretical basis for improving the reproductive efficiency of sows. We used chloroquine (autophagy inhibitor) and reactive oxygen species (ROS) scavenger N-acetylcysteine in conjunction with 1α,25(OH)2D3 to explore the effect on PGCs. The results showed that 10 nM of 1α,25(OH)2D3 increased PGC viability and ROS content. In addition, 1α,25(OH)2D3 induces PGC autophagy according to the gene transcription and protein expression levels of LC3, ATG7, BECN1, and SQSTM1 and promotes the generation of autophagosomes. 1α,25(OH)2D3-induced autophagy affects the synthesis of E2 and P4 in PGCs. We investigated the relationship between ROS and autophagy, and the results showed that 1α,25(OH)2D3-induced ROS promoted PGC autophagy. The ROS-BNIP3-PINK1 pathway was involved in PGC autophagy induced by 1α,25(OH)2D3. In conclusion, this study suggests that 1α,25(OH)2D3 promotes PGC autophagy as a protective mechanism against ROS via the BNIP3/PINK1 pathway. Ovarian granulosa cells (GCs) play a pivotal role in follicle growth and atresia. Autophagy is a process of self-phagocytosis widely present in almost all eukaryotes and is one of the degradation pathways of redundant or abnormal cellular components. The molecular pathways that regulate autophagy are highly conserved. As an important autophagy-related protein, ATG7 is associated with autophagosome formation. BECN1 (Beclin1) forms a complex with the class III phosphoinositol 3-kinase molecule Vps34, which initiates and promotes autophagy. Microtubule-associated protein 1 light-chain 3 (LC3) is essential for the formation and maturation of autophagosomes. SQSTM1(P62) protein functions as a selective autophagy receptor for the degradation of substrates. In the ovary, GC autophagy affects follicle development. Autophagy occurs in GCs of porcine follicles [1]. Previous studies showed that autophagy is the leading cause of follicular atresia in neonatal mice [2], and autophagy-related genes and proteins are continuously expressed during cytogenesis. ATG7 is expressed in oocytes, and LC3 exists in GCs [3]. Autophagy is induced specifically in GCs during folliculogenesis. The LC3 protein is expressed mainly in GCs during all developmental stages [4]. Autophagy is closely related to the growth, proliferation, and apoptosis of GCs [5]. Inadequate autophagy of GCs leads to reduced progesterone synthesis [6] and disruption of GC differentiation [7]. Our previous studies showed that mir-21-3p regulates autophagy in bovine granulosa cells through the PI3K/AKT signaling pathway [8]. Both FSH [9] and ERβ [10] can induce autophagy in bovine ovarian granulosa cells via AKT/mTOR pathway. The underlying mechanism of GC autophagy remains to be determined, and the relationship between GC autophagy and follicle development remains unclear. Vitamin D (VD) is a steroid derivative with a wide range of biological properties, and its main active form is 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3). Studies conducted over the past 20 years have found that VD plays a vital role in maintaining the regular female reproductive system. Vitamin D and its clinical implications regarding the developmental competence and fertilization of oocytes has prompted researchers’ attention. A recent study revealed that in the case of VD deficiency and hypocalcemia at the same time, a significant reduction in oocyte retrieval after ovarian stimulation was observed, and the generated oocytes showed a poor maturation ability [11]. Moreover, VD signaling leads to an increased production of steroid hormones in granulosa cells, which are crucial for oocyte maturation and pregnancy, too. Vitamin D is essential for female gametes and their micro-environment [12]. Vitamin D might improve follicular development and subsequently oocyte quality. The biological effects of VD are usually achieved through the vitamin D receptor (VDR), which is found in the female reproductive system, including the uterus, endometrium, ovary, and placenta [13]. Vitamin D stimulates the production of estrogen, progesterone, and IGF-binding protein 1 in human ovarian cells [14]. To date, most of the studies on VD in follicle development have focused on humans. The effect of VD on porcine follicle development has been less reported. The classic function of VD is to maintain musculoskeletal health by maintaining calcium homeostasis. Meanwhile, it has been shown that VD can affect the equilibrium state of oxidation/reduction in C2C12 cells [12], resulting in a balance of ROS production and elimination. Vitamin D affects the oxidative capacity of cells by regulating the activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX) [15]. Vitamin D’s effects on the redox status of porcine ovarian granulosa cells has not been reported. In addition, VD plays an essential role in the autophagy induced by primary monocytes and macrophages [16]. Vitamin D was reported to regulate autophagy via calcium ions [17], the PI3K/AKT/mTOR pathway [18], inflammatory factors [19], antimicrobial peptide [16], etc. In recent years, increasing evidence has demonstrated the critical role of GC autophagy in follicle development and atresia [20]; however, the effect of VD on the autophagy of GCs remains unclear. In the current study, we assumed that 1α,25(OH)2D3 regulates the PGC autophagy and affects the redox status and function of PGCs. To test this hypothesis, we examined the effects of 1α,25(OH)2D3 on intracellular ROS content and autophagy in PGCs and evaluated the effects of 1α,25(OH)2D3 on 17β-estradiol (E2) and progesterone (P4) secretion in PGCs. We first investigated the effects of doses and times of 1α,25(OH)2D3 treatment on PGC viability and the expression of cell-cycle-related genes in PGCs (Figure 1). The proliferation of PGCs (treatment with 10 nM 1α,25(OH)2D3) was observed under a light microscope (Figure 1A). The results indicated that the viability was significantly increased in the groups of 10, 100, and 200 nM of 1α,25(OH)2D3 treatments for 12 h, 24 h, and 36 h in comparison to the control group, respectively (Figure 1B). The PGCs with the treatment of 10 nM of 1α,25(OH)2D3 for 24 h were selected for further studies. Then, quantitative real-time PCR was used to detect the expression of cell-cycle-related genes. The results showed that the gene expression of VDR, CDK1 and CCNB1 was increased, while the expression of P21 was decreased in 1α,25(OH)2D3 treatment compared with the control (Figure 1C–F). These results suggest that 1α,25(OH)2D3 promotes the viability of PGCs. To study the role of 1α,25(OH)2D3 on intracellular ROS in PGCs, PGCs were treated with 1α,25(OH)2D3, and the intracellular ROS was determined by DCFH-DA staining and observed under a fluorescence microscope (Figure 2A). The results indicated that 1α,25(OH)2D3 significantly increased the ROS content (Figure 2B). Similar results were confirmed by flow cytometry (Figure 2C,D). The results of relative gene expression in PGCs showed that 1α,25(OH)2D3 significantly down-regulated the expression of SOD1 and GSH-PX-1 genes in PGCs (Figure 2E,F); however, 1α,25(OH)2D3 did not change the CAT gene relative expression gene in PGCs (Figure 2G). Our results showed that VD3 significantly decreased enzyme activities of SOD (Figure 2H) and GPX (Figure 2I) in PGCs. Together, these results indicated that 1α,25(OH)2D3 increased intracellular ROS content in PGCs. Mitochondria are the primary source of ROS in cells. To study the relationship between 1α,25(OH)2D3 and ROS, Mito-Tracker was used to label the PGC mitochondria in this experiment (Figure 3A). The results showed that 1α,25(OH)2D3 significantly increased the abundance of mitochondria in PGCs (Figure 3B). Moreover, 1α,25(OH)2D3 increased considerably the relative expression of the ND1 gene, a mitochondrial DNA (mtDNA, Figure 3C). In contrast, the current results showed that 1α,25(OH)2D3 did not change the mitochondrial membrane potential of PGCs in this experiment (Figure 3D). These results suggest the mitochondria status in the 1α,25(OH)2D3-treated GCs. The cumulative data suggest that mitochondria play an important role in activating autophagy. To study the effects of 1α,25(OH)2D3 on PGC autophagy, MDC was used to label the autophagic vacuoles in PGCs treated by 1α,25(OH)2D3 and chloroquine (an inhibitor of autophagy) in this experiment, and the fluorescence of autophagic vacuoles in PGCs was detected by fluorescence microscope (Figure 4A). The current results showed that the treatments of 1α,25(OH)2D3, chloroquine significantly increased the number of autophagic vacuoles (Figure 4B). The relative expression of ATG7, Beclin1, and LC3 genes were significantly up-regulated in PGCs treated with 1α,25(OH)2D3, chloroquine, and 1α,25(OH)2D3 with chloroquine, respectively (Figure 4C–E). The treatment of 1α,25(OH)2D3 down-regulated the P62 mRNA expression, while chloroquine significantly up-regulated P62 mRNA expression in the PGCs compared with that of the control group; however, the treatment of 1α,25(OH)2D3 with chloroquine did not change the P62 mRNA expression in comparison to the control group (Figure 4F). Figure 4G showed the expression of autophagy proteins, and the results showed that the expression pattern of the P62 protein was similar to that of its gene in PGCs (Figure 4H). Moreover, 1α,25(OH)2D3 significantly increased the LC3II/LC3I level compared with that of the control group, while the treatments of chloroquine and 1α,25(OH)2D3 with chloroquine decreased the LC3II/LC3I level, respectively (Figure 4I). In addition, the LC3II/LC3I levels in the cells treated with chloroquine were lower than the treatment of 1α,25(OH)2D3 with chloroquine (Figure 4I). These results suggest that 1α,25(OH)2D3 induces PGC autophagy. To investigate the effects of 1α,25(OH)2D3 on steroid production, the PGCs were treated by 1α,25(OH)2D3, chloroquine and 1α,25(OH)2D3 with chloroquine together, and the contents of E2 and P4 were measured in this experiment. The results showed that compared with the control group, the concentration of E2 in PGC medium treated with 1α,25(OH)2D3 was significantly increased. In contrast, that in PGCs treated with chloroquine was significantly decreased (Figure 5A). Furthermore, the co-treatment of 1α,25(OH)2D3 and chloroquine did not change the E2 concentration in PGCs compared to the control group (Figure 5A). Although 1α,25(OH)2D3 increased the concentration of P4 in PGCs, both the treatments of chloroquine and 1α,25(OH)2D3 with chloroquine did not change P4 production in PGCs compared to the control group (Figure 5B). The treatments of 1α,25(OH)2D3 and 1α,25(OH)2D3 with chloroquine significantly up-regulated the relative expression of ESR1, CYP19A1, PGR, and STAR genes in PGCs in comparison to the control group; nevertheless, chloroquine did not change the expression of these genes (Figure 5C–F). The STAR protein level was identified by Western blotting (Figure 5G), and the results showed that 1α,25(OH)2D3 significantly increased the STAR level in PGCs in comparison to the control group; however, the treatments of chloroquine or 1α,25(OH)2D3 with chloroquine did not change the STAR level in PGCs (Figure 5H). These results suggest that the steroid production in PGCs was affected by 1α,25(OH)2D3-induced autophagy. To explore the effects of 1α,25(OH)2D3-induced ROS on PGC autophagy, ROS scavenger N-acetylcysteine (NAC) was used to treat the PGCs for 24 h, followed by 1α,25(OH)2D3 treatment. The results indicated that NAC significantly decreased the viability of PGCs with or without 1α,25(OH)2D3 treatment (Figure 6A). As Figure 6B shown, ROS content was significantly reduced in NAC-treated PGCs, and the cells co-treated with 1α,25(OH)2D3 and NAC (4, 8 mM). Based on the results above, 4 mM NAC was used for the following experiments. DCFH-DA staining was used to detect the ROS, and similar results as those above were observed in PGCs treated with 4 mM NAC (Figure 6C,D). Moreover, 4 mM of NAC significantly decreased the number of autophagic vacuoles induced by 1α,25(OH)2D3 treatment in PGCs (Figure 6E,F). To confirm the results of MDC staining, the expression of autophagy-related genes in PGCs was measured. NAC down-regulated the 1α,25(OH)2D3-stimulated expression of ATG7, Beclin1, and LC3 (Figure 6G–I) and up-regulated the P62 mRNA expression (Figure 6J). In PGCs treated with NAC in the presence/absence of 1α,25(OH)2D3, LC3, and P62 protein expression patterns were observed to be similar to their gene expression patterns (Figure 6K–M). Together, we demonstrated that 1α,25(OH)2D3-induced ROS promotes autophagy in PGCs. To study the orientation of 1α,25(OH)2D3-induced autophagy, PGCs were treated with 1α,25(OH)2D3, chloroquine, and NAC. RT-qPCR was used to measure gene expression, and Western blotting was used to determine the protein levels in this experiment. Both 1α,25(OH)2D3 and chloroquine up-regulated the relative expression of BNIP3 (the marker of mitophagy) and PINK1 gene in PGCs compared with that of the control (Figure 7A,B). Furthermore, the protein levels of BNIP3 and PINK1 were increased in the treatments of 1α,25(OH)2D3, chloroquine, and 1α,25(OH)2D3 with chloroquine, which was similar to the relative expression of genes (Figure 7C–E). Compared with the treatment of 1α,25(OH)2D3, the relative expression of BNIP3 and PINK1 genes were significantly decreased in treatments of PGCs of NAC with 1α,25(OH)2D3 and NAC alone (Figure 7F,G). Moreover, the protein levels of BNIP3 and PINK1 were similar to the expression patterns of their genes in the treatments of 1α,25(OH)2D3, 1α,25(OH)2D3 with NAC, and NAC in PGCs (Figure 7H–J). We found that 1α,25(OH)2D3 induces mitophagy in PGCs through the ROS-BNIP3-PINK1 signaling pathway. 1,25-dihydroxy vitamin D3 is a lipid-soluble secosteroid hormone established to play a wide range of biological functions [21]. More and more studies have shown that VD plays a vital role in life processes, including reproduction [22]. Breeding sows are the foundation of pig farm production, and their fecundity plays a crucial role in the benefit of the pig farms. Studies on the correlation between VD and sow reproduction are limited. Although studies have revealed the role of 1α,25(OH)2D3 on autophagy via the PI3K/AKT/mTOR pathway, the mechanism of 1α,25(OH)2D3 in the autophagy of ovarian granulosa cells remains unclear. Here, we demonstrate that (1) 1α,25(OH)2D3 increased porcine ovarian granulosa cell viability and the ROS content by increasing the number of mitochondria and decreasing the activities of superoxide dismutase and glutathione peroxidase; (2) porcine ovarian granulosa cell autophagy is regulated by 1α,25(OH)2D3 and affected the synthesis of E2 and P4; and (3) the ROS-BNIP3-PINK1 pathway was involved in porcine ovarian granulosa cell autophagy induced by 1α,25(OH)2D3. The present results showed that 1α,25(OH)2D3 increased the VDR mRNA expression and promoted the proliferation of PGCs. This finding suggests that VD may play an important role in follicle development, which was supported by studies of VDR expression in goats [23] and mice [13]. Cell proliferation is controlled by the balance between cyclin-dependent kinases (CDKs) and its inhibitor (CKI). The expression of CDK1 and CCNB1 promotes the increase of cell number [24], and CDK function is tightly regulated by CKIs such as P21, which is related to cell cycle proliferation [25]. Our results showed that 1α,25(OH)2D3 up-regulates the expression of CDK1 and CCNB1 genes, while the expression of P21 was down-regulated in PGCs. The mechanism by which 1α,25(OH)2D3 regulates GC proliferation remains incompletely understood. The mechanism by which 1α,25(OH)2D3 regulates the cell cycle process needs to be further studied. In this study, the results of fluorescence microscope and flow cytometry analysis in PGCs indicated that 1α,25(OH)2D3 increased the content of ROS with down-regulation of SOD1 and GSH-PX-1 genes and the reduction of SOD and GSH enzyme activities. Generally, VD has antioxidant abilities through the reduction of ROS production to decrease oxidative stress [15]; however, VD also induces ROS production as a byproduct in reproductive tissues accompanied by steroidogenesis [26]. Antioxidant enzymes can eliminate excessive ROS production and protect the redox homeostasis in cells. Although VD deficiency has been associated with increased SOD enzyme activity in patients with chronic low back pain [27], a study showed that SOD enzyme activity was lower in rats deficient in VD. The absence of vitamin D leads to decreased SOD activity in vivo and in vitro. Vitamin D deficiency led to an increase in activities of the glutathione-dependent enzymes and a decrease in SOD and catalase enzymes in rat muscle [28]. These studies suggest that vitamin D supplementation is associated with changes in antioxidant enzyme activity. The current data indicates that 1α,25(OH)2D3 treatment significantly decreased SOD and GPX enzyme activities in PGCs. Intracellular ROS mainly originate from mitochondria. Our results showed that 1α,25(OH)2D3 treatment increased the expression of the ND1 gene, a mitochondrial metabolism-related gene, and the abundance of mitochondria in PGCs. Previous studies have demonstrated that VD is related to mitochondrial density [29] and the direct role of VDR in regulating mitochondrial respiration in skeletal muscle in vitro [30]. Mitochondrial abundance and mitochondrial DNA (mtDNA) copy number determine the metabolic activity of mitochondria [31]. The integrity of mtDNA and the activation of transcription and translation processes are essential for the induction of mitochondrial activity [32]. The mitochondrial mRNA transcription (such as mt-ND1~mt-ND6, CoxI~CoxIII) and their translation processes are activated with the increase of mitochondrial activity in serum-stimulated HeLa cells [33]. Meanwhile, the present results indicated that 1α,25(OH)2D3 did not affect the PGC mitochondrial membrane potential (MMP), which reflects the mitochondria functional status and is thought to be correlated with the cell differentiation status, tumorigenicity, and malignancy [34]. Mitochondrial fusion requires an intact MMP. The dissipation of MMP results in the rapid fragmentation of mitochondrial filaments, reforming interconnected mitochondria upon the withdrawal of MMP inhibitors [35]. The present results showed that 1α,25(OH)2D3 increased mitochondrial activity without negatively affecting mitochondrial function. Our results demonstrate that 1α,25(OH)2D3 is responsible for autophagy in PGCs. Autophagy is influenced by various factors and environmental stimuli, including oxidative stress [36], starvation, and epigenetic regulation [37]. It has been reported that VD can regulate autophagy in different degrees, including induction, maturation, and degradation [38]. A particular concentration of 1α,25(OH)2D3 can induce autophagy in primary monocytes and macrophages [16]. Meanwhile, the association between vitamin D and autophagy has also been reported in immunity [39] and cancer [40]. 1α,25(OH)2D3 plays a protective role in acute myocardial infarction through autophagy induced by the PI3K/AKT/mTOR pathway [18]. Active vitamin D attenuates osteoarthritis by activating autophagy in chondrocytes through the AMPK-mTOR signaling pathway [41]. In addition, 1α,25(OH)2D3 can reduce cell dysfunction and intracellular oxidative stress by lowering excessive autophagy in cells [42]. These studies suggest that vitamin D can maintain cellular homeostasis by promoting or inhibiting autophagy. The current results showed that 1α,25(OH)2D3 promotes autophagy in PGCs, and the elevation of PGC autophagosome was confirmed by MDC staining. GCs are one of the primary cell types in the follicle, and steroidogenesis is an essential physiological process affecting follicle maturation and ovulation. Here, we found that 1α,25(OH)2D3 promoted the secretion of E2 and P4 in PGCs. The current results showed that 1α,25(OH)2D3 stimulates the expression of ESR1, PGR, CYP19A1, and STAR mRNA in PGCs and increases the concentration of E2 and P4 in PGCs. The bilateral role between hormone secretion and autophagy has been confirmed in GCs of bovine [9] and goose follicles [43], which showed that hormone secretion induces autophagy; on the contrary, autophagy promotes hormone secretion in granulosa cells. In the present study, the results of co-treatments of 1α,25(OH)2D3 and chloroquine on PGCs reveal the weak expression of ESR1, CYP19A1, PGR, and STAR mRNA and lower STAR protein level in PGCs compared with that of 1α,25(OH)2D3 alone. These results suggest that 1α,25(OH)2D3-induced autophagy in PGCs promotes steroid hormone synthesis by regulating steroid synthesis enzymes. No reports exist about a possible role of autophagy in steroid hormone synthesis in GCs, but autophagy is implicated in the development and regression of ovarian cells. Autophagy is involved in the death of rat luteum cells through apoptosis, which is most evident in corpus luteum regression [44]. The accumulation of autophagosomes induces apoptosis of granulosa cells [3]. In addition, a link between steroid hormones and autophagy has been reported in farm animals. E2 and P4 increased autophagy in bovine mammary epithelial cells in vitro [45]. E2 and P4 may regulate mammary gland development, proliferation, and apoptosis of mammary epithelial cells in dairy cows by inducing autophagy [46]. Oxidative stress is one of the impact factors for cellular autophagy, and mitochondria are the primary source of intracellular ROS. In this study, the role of ROS in the autophagy of PGCs was investigated, and the results revealed that 1α,25(OH)2D3-induced ROS promotes the PGC autophagy. Moreover, 1α,25(OH)2D3 increases autophagosomes and LC3 protein levels in PGCs, while the inhibitor of ROS reverses this effect. Recent studies have shown ROS can initiate the formation of autophagosomes and autophagic degradation [47], and autophagy, in contrast, serves to reduce oxidative damage and ROS levels by removing protein aggregates and organelles such as mitochondria [48]. In general, there is a relative balance of ROS produced by mitochondria in the cell. Once the balance is disrupted, the cell gets rid of the excess mitochondria. Our results showed that the account of mitochondria was increased, which is the primary reason for the ROS increase in PGCs treated with 1α,25(OH)2D3. Together, these results indicated that 1α,25(OH)2D3-induced ROS promotes autophagy in PGCs. Accumulating evidence suggests that ROS can induce autophagy through various mechanisms. Here, we detected the gene expression and protein level of BNIP3 and PINK1 in PGCs treated with 1α,25(OH)2D3; the results showed that 1α,25(OH)2D3-induced PGC autophagy is mainly mitophagy caused by ROS. It has been confirmed that various mechanisms were involved in ROS-induced autophagy, such as ROS–NRF2–P62 [49], ROS–HIF1–BNIP3/NIX [50], and ROS–TIGAR [51]. BNIP3 is a receptor for mitophagy, an autophagy process that eliminates excess or damaged mitochondria. Previous studies have shown that BNIP3 plays a vital role in PINK1 localization to the outer mitochondrial membrane and proteolysis [52]. Our results showed that 1α,25(OH)2D3 could induce the expression of BNIP3 and PINK1 mRNA and protein levels, and NAC could reverse the effect of 1α,25(OH)2D3 by reducing intracellular ROS. It has been reported that BNIP3 promotes autophagic cell death in response to hypoxia; however, Bellot et al. identified autophagy induced by BNIP3 in response to hypoxia as a mechanism to promote tumor cell survival [53]. Although autophagy activation is critical, autophagy is not always beneficial for cell survival or death. The results of this study indicate that 1α,25(OH)2D3 promotes cell survival and activates PGC autophagy, which is related to 1α,25(OH)2D3-induced ROS. These results suggest that 1α,25(OH)2D3-induced ROS induces PGC mitophagy via the BNIP3-PINK1 pathway. The reproductive performance of sows is an essential factor affecting the economic benefits of the pig industry. Improving the reproductive performance of sows is also one of the goals pursued by breeders and pork producers. The number of ovulations in sows depends on the number of follicles initially collected and the number of terminal atresias. Autophagy and apoptosis of GCs in follicles are closely related to follicular atresia [4]. Follicle growth and development is a complex biological process which is regulated by many factors, including various steroid hormones, metabolic enzymes, and local growth factors [54]. The close relationship between the changes in steroid hormones and synthetic enzymes and GC autophagy is rarely reported. Based on this, the effects of 1α,25(OH)2D3 on the synthesis of E2 and P4, proliferation, and autophagy were investigated in PGCs, and the specific mechanisms were explored to provide a theoretical basis for improving the reproductive efficiency of animals. These results provide new insights into the ability of 1α,25(OH)2D3 to regulate the biological function of GCs and follicular development, which may have reference significance for the study of the reproductive performance of pigs. Granulosa cells were isolated and cultured using the method described by Jiang et al. [55]. Porcine ovaries were collected from local slaughterhouses from commercial pigs aged about 1 year, independent of the stage of the estrus cycle. About 20–30 ovaries were collected and transported to the laboratory in saline with penicillin (100 IU/mL) (Gibco-BRL, Gaithersburg, MD, USA) and streptomycin (100 mg/mL) (Gibco-BRL) within 1 h. The ovaries were washed twice with 75% alcohol and then 2–3 times with saline buffer (0.9%, PH < 7) (37 °C). For each replicate, at least 20 ovaries were collected to obtain sufficient GCs from follicles. Medium-sized follicles (3–5 mm) were selected, and a 10 mL syringe was used to aspirate follicular fluid with GCs. Then, the cell suspension was filtered through a 40 μm cell filter, and the mixture was centrifuged at 800× g for 5 min to remove the follicular fluid. Granulosa cell pellets were resuspended in DMEM/F12 (Gibco-BRL) medium. Cell viability was determined by trypan blue exclusion (Solarbio Technology Co., LTD, Beijing, China). For cell culture, cells were diluted with DMEM/F12 and seeded in tissue culture plates at a specific density (5 × 103 cells/well for 96-Well, 1 × 106 cells/well for 24-well). DMEM/F12 of diluted cells contained the following substances: 4 ng/mL sodium selenite, 10 mM sodium bicarbonate, 0.1% bovine serum albumin (BSA), 100 U/mL of penicillin, 100 μg/mL streptomycin, 1 mmol/L non-essential amino acid mix, 2.5 μg/mL transferrin, 10 ng/mL bovine insulin, 10−7 M androstenedione, and 1 ng/mL bovine FSH (Bioniche Inc., Belleville, ON, Canada). The cells were cultured at 37 °C in 5% CO2 and 95% air. After 24 h, the medium was replaced, and then the treatment was carried out. At least three independent replicates were performed. The CCK-8 (Beyotime Biological Technology Co Ltd., Shanghai, China) proliferation assay was used to evaluate the cell viability of PGCs. The cells were seeded at a specific density (5 × 103 cells/well) into 96-well plates, and then the cells were treated with 1α,25(OH)2D3 (1 nM, 10 nM, 100 nM, 200 nM; 12 h, 24 h, 36 h, 48 h) (Solarbio), chloroquine (10 μM, 24 h) (Sigma-Aldrich, St. Louis, MO, USA), or N-acetylcysteine (2 mM, 4 mM, 8 mM; 24 h) (Beyotime) at different doses and times. After various treatments, 10 μL of CCK-8 solution was added to each well and placed at 37 °C for 3 h. The treated wells had cells, culture medium, CCK-8 solution, and drugs. The control (untreated) wells had cells, culture medium, and CCK-8 solution. The blank wells had culture medium and CCK-8 solution. The absorbance of each well was measured at a wavelength of 450 nm. For each group of 4–6 wells, the average of their optical density (OD) was calculated as follows: cell viability (%) = [treated wells OD − blank wells OD] / [control wells OD − blank wells OD] × 100%. At least three independent replicates were performed. Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. DNA was extracted using Universal Genomic DNA Purification Mini Spin Kit (Beyotime). RNA was extracted using SimplyP Total RNA Extraction Kit (Hangzhou Bioer Technology Co Ltd., Hangzhou, China) according to the manufacturer’s protocol. Briefly, cells were collected, and 300 μL RIPA was added. Then, the binding solution was added, the mixture was transferred to the purification column and centrifuged at 12,000× g for 30 s, and the liquid in the tube was discarded. After that, washing liquid was added and centrifuged at 12,000× g for 30 s. The RNA purification column was transferred to the RNA eluent tube, and 40 μL eluent was added and centrifuged at the highest speed (14,000–16,000 g) for 30 s. The purified RNA was obtained. The process of DNA extraction is similar. Related solvents were added successively and the purified DNA was extracted by centrifugation. The concentration of DNA and RNA was measured by microvolume spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The A260/A280 ratio ranges from 1.9 to 2.0. RNA (500 ng) was reverse transcribed into cDNA according to the FastKing cDNA First Strand Synthesis Kit instructions (Tiangen Biochemical Technology Co Ltd., Beijing, China). Then, the mRNA expression level of genes was measured using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) in a StepOnePlus real-time PCR System (Applied Biosystems, Foster, CA, USA). The reaction volume was 20 μL: 10 μL 2×ChamQ SYBR qPCR Master Mix, 0.4 μL F-primer (10 μM), 0.4 μL R-primer (10 μM), 5 μL template DNA/cDNA, 4.2 μL ddH2O. The primer sequences are shown in Table 1. The common thermal cycling parameters of RT-PCR are as follows: pre-denaturation: 95 °C for 3 min. Cycle reaction: 95 °C for 10 s; 60 °C for 30 s; 40 cycles. Melting-curve: 95 °C for 15 s; 60 °C for 1 min; 95 °C for 15 s. Melting-curve analyses were performed to verify product identity. Target gene expression was quantified relative to GADPH expression. The 2−∆∆Ct method was used to calculate the relative gene expression. All samples were run in triplicate. Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. Protein was extracted with RIPA buffer (Beyotime) following the manufacturer’s protocol and quantified using the bicinchoninic acid (BCA) protein assay kit (Beyotime). Briefly, after the PGCs were treated, the medium was removed and the cells were washed twice with PBS. RIPA buffer was treated with the cells for 30 min (on ice). The supernatant was collected by centrifugation at 12,000 r/min. The BCA working solution was prepared, the protein standard concentration was 0.5mg/mL, and the protein standard was added to the 96-well plate according to the following amounts: 0, 1, 2, 4, 8, 12, 16, 20 μL. Appropriate volume protein samples were added to standard wells (refill to 20 μL per well). BCA working solution (200 μL) was added to each well for 30 min at 37 °C. The wavelength between A562–595 nm was determined, and the protein concentration was calculated according to the standard curve. The proteins were adjusted to the same concentration by sample buffer. Cytosolic protein (20 μg) was subjected to 12% SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes (Beyotime) in a Bio-Rad wet Blot Transfer Cell apparatus (transfer buffer: 39 mM glycine, 48 mM Tris-base, 1% SDS, 20% methanol, pH 8.3). The obtained membranes were blocked in QuickBlockTm Western’s blocking buffer (Beyotime) for 1 h at room temperature. Membranes were washed in TBST (150 mM NaCl, 2 mM KCl, 25 mM Tris, 0.05% Tween20, pH 7.4) and incubated with primary antibodies: β-actin (ACTB) (Mouse polyclonal to ACTB, 1:5000 dilution) (Proteintech Group, Inc., Chicago, IL, USA), LC3 (Rabbit polyclonal to LC3I/II, 1:2000 dilution) (Abcam, Cambridge, UK), BNIP3 (Rabbit polyclonal to BNIP3, 1:1000 dilution) (Abcam), PINK1 (Rabbit polyclonal to PINK1, 1:1000 dilution) (Cell Signaling Technology, Danvers, MA, USA), STAR (Rabbit polyclonal to STAR, 1:500 dilution) (Cell Signaling Technology), P62 (Rabbit polyclonal to P62, 1:10,000 dilution) (Servicebio, Wuhan, China) in QuickBlock™ Primary Antibody Dilution Buffer (Beyotime) overnight at 4 ℃. Membranes were then washed and labeled for 2 h at room temperature with anti-rabbit HRP-conjugated IgG goat (1:4000 dilution) or anti-mouse HRP-conjugated IgG (1:4000 dilution) (Sungene Biotechnology, Tianjin, China) diluted in QuickBlock™ Secondary Antibody Dilution Buffer (Beyotime). Finally, membranes were washed in TBST, and the protein bands were visualized with a chemical luminous imaging system (Millipore, Billerica, MA, USA). Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of chloroquine (10 μM) for 24 h. E2 and P4 in the medium were measured by the specific ELISA kit (Ruixin Biological Technology Co., Ltd., Quanzhou, China) following the manufacturer’s protocol. Briefly, medium in the co-culture system was collected by centrifuging at 1000 g for 10 min at 4 °C, and the liquid supernatant was used for steroid assays. Each sample was measured 5 times and averaged. The inter- and intra-assay CVs of E2 were 5.6% and 3.4%, and that of P4 were 6.9% and 7.9%. The minimum detected concentrations of E2 and P4 were 4.8 pg/mL and 1.45 ng/mL, respectively. ROS generation was detected by DCFH-DA (Beyotime). Fluorescence intensity was measured by a fluorescence microplate reader, fluorescence microscope, or flow cytometry. Fluorescence microplate reader: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of N-acetylcysteine (2 mM, 4 mM, 8 mM) for 24 h. After treatments, the cells were co-cultured with 10 μM DCFH-DA for 30 min, the residual DCFH-DA was removed, and the cells were washed with PBS 3 times. The intracellular fluorescence was read by a fluorescence microplate reader at an excitation/emission wavelength of 488/525 nm. Each sample was measured 3 times and averaged. Fluorescence microscope: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of N-acetylcysteine (4 mM) for 24 h. Then the cells were stained with DCFH-DA as described above. Cells were observed under a fluorescence microscope, and fluorescence images were obtained. LED intensity, integration time and camera gain were fixed during taking pictures (Olympus Corporation, Tokyo, Japan). Image J software was used to process the images, and the mean fluorescence values of different groups were calculated. Flow cytometry: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 for 24 h. The cells were stained with DCFH-DA as described above. After staining, cells in each group were collected (about 1 × 106 cells/mL), and ROS was detected by flow cytometry (BD FACSAria™ III) (Becton Dickinson, Franklin Lakes, NJ, USA) within 30 min. The excitation light was 488 nm, and the emission light was 525 nm. Fluorescence was detected by the FL1 channel. Samples were acquired on a flow cytometer using a stop condition of 10,000 events on the gate of interest. Using the flow cytometry software, dot plots of FSC (on the X-axis) and SSC (on the Y-axis) were opened, and a gate was drawn around the cells of interest. In the experiment, untreated normal cells were set as the control group and the gate position was developed according to the two-parameter scatter plot of the control group. Data were analyzed using the FlowJO software. Approximately 1 × 106 cells/well were seeded into 24-well plates. After the cells were attached, they were treated with 10 nM 1α,25(OH)2D3 for 24 h. Cells were stained with Mito-Tracker Green (Beyotime) according to the manufacturer’s instructions. After removing the cell culture medium, the cells were incubated with prepared Mito-Tracker Green working solution for 30 min at 37 °C. Then the cells were washed with PBS 3 times. The cells were treated with an anti-fluorescence quenched sealing solution and then observed under a fluorescence microscope (Olympus Corporation). LED intensity, integration time, and camera gain were fixed during picture-taking. Image J software was used to process the images, and the mean fluorescence values of different groups were calculated. Approximately 1 × 106 cells/well were seeded into 24-well plates. After the cells were attached, they were treated with 10 nM 1α,25(OH)2D3 for 24 h. Then, the cells were treated with a mitochondrial membrane potential detection kit (JC-1) (Solarbio) following the manufacturer’s protocol. Briefly, JC-1 staining working solution was added to each well, thoroughly mixed, and then the cells were incubated for 20 min at 37 °C in a cell incubator. The cells were washed 2–3 times with PBS. After staining, cells in each group were collected (about 1 × 106 cells/mL), and the JC-1 signal was visualized by flow cytometry (excitation: 488 nm; emission: 530 nm) (BD FACSAria™ III) within 30 min. Green fluorescence was detected through FL1 channel, and red fluorescence was detected through FL2 channel. Samples were acquired on a flow cytometer using a stop condition of 10,000 events on the gate of interest. Using the flow cytometry software, dot plots of FSC (on the X-axis) and SSC (on the Y-axis) were opened, and a gate was drawn around the cells of interest. In the experiment, untreated normal cells were set as the control group, and the gate position was developed according to the two-parameter scatter plot of the control group. Data were analyzed using the FlowJO software. Approximately 1 × 106 cells/well were seeded in 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. MDC (monodansylcadaverine) was used as a tracer of autophagic vesicles. The autophagosomes are marked as clear green dots under the fluorescence microscope. After treatment, the cells were treated with MDC (0.05 mM) (Kaiji Biotechnology Co., Ltd., Nanjing, China) and DAPI (1 μg/mL) (4′,6-diamidino-2-phenylindole) (Solarbio) following the manufacturer’s protocol. Briefly, the cells were grown with MDC and DAPI at 37 °C for 15 min and fixed immediately with paraformaldehyde (4%) in PBS for 20 min, then observed under a fluorescence microscope (Olympus Corporation). LED intensity, integration time, and camera gain were fixed while taking pictures. Image J software was used to process the images, a total of 200 cells in each sample were analyzed, and the percentage of cells with green spots indicates the percentage of autophagy. Approximately 1 × 106 cells/well were seeded in 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 for 24 h. The activity of intracellular SOD and GPX were measured in PGCs using kits (Nanjing Jiancheng Bioengineering Research Institute Co., Ltd., Nanjing, China) for scientific research following the manufacturer’s protocol. Briefly, after the cells were washed twice with PBS, the cells were carefully scraped off with a cell scraper, and the cell mixture was centrifuged at 1000× g for 10 min, and then the supernatant was discarded. Protein concentration was determined with the BCA assay kit (Beyotime). The results were detected by a visible spectrophotometer (550 nm wavelength) (Thermo Fisher Scientific). Each sample was measured 5 times, and the average of the results was taken. The inter- and intra- assay CVs averaged 7.8% and 6.5%, respectively. Independent t-tests were used to evaluate the significance of the results between groups. Statistical significance was determined by ANOVA followed by post hoc tests. The Tukey–Kramer HSD test was used to analyze the differences between the means (GraphPad Prism version 9.0, GraphPad Software Inc., San Diego, CA, USA). p-values < 0.05 were considered statistically significant. All data are presented as the mean ± SM of 3 or more repeated observations from at least 3 independent experiments. In summary, these results demonstrate for the first time that 1α,25(OH)2D3 induces mitophagy through the ROS-BNIP3-PINK1 signaling pathway, which promotes the proliferation and maintains the function of PGCs. Our results provide important information for determining the role of 1α,25(OH)2D3 during ovarian follicular development.

PMC10001664

Jennilee M. Davidson,Stephanie L. Rayner,Sidong Liu,Flora Cheng,Antonio Di Ieva,Roger S. Chung,Albert Lee

Inter-Regional Proteomic Profiling of the Human Brain Using an Optimized Protein Extraction Method from Formalin-Fixed Tissue to Identify Signaling Pathways

21-02-2023

human brain,tissue,neuroanatomic region,formalin-fixed,proteomics,method,signaling,protein,pathways,mass spectrometry

Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain.

Inter-Regional Proteomic Profiling of the Human Brain Using an Optimized Protein Extraction Method from Formalin-Fixed Tissue to Identify Signaling Pathways Proteomics offers vast potential for studying the molecular regulation of the human brain. Formalin fixation is a common method for preserving human tissue; however, it presents challenges for proteomic analysis. In this study, we compared the efficiency of two different protein-extraction buffers on three post-mortem, formalin-fixed human brains. Equal amounts of extracted proteins were subjected to in-gel tryptic digestion and LC-MS/MS. Protein, peptide sequence, and peptide group identifications; protein abundance; and gene ontology pathways were analyzed. Protein extraction was superior using lysis buffer containing tris(hydroxymethyl)aminomethane hydrochloride, sodium dodecyl sulfate, sodium deoxycholate, and Triton X-100 (TrisHCl, SDS, SDC, Triton X-100), which was then used for inter-regional analysis. Pre-frontal, motor, temporal, and occipital cortex tissues were analyzed by label free quantification (LFQ) proteomics, Ingenuity Pathway Analysis and PANTHERdb. Inter-regional analysis revealed differential enrichment of proteins. We found similarly activated cellular signaling pathways in different brain regions, suggesting commonalities in the molecular regulation of neuroanatomically-linked brain functions. Overall, we developed an optimized, robust, and efficient method for protein extraction from formalin-fixed human brain tissue for in-depth LFQ proteomics. We also demonstrate herein that this method is suitable for rapid and routine analysis to uncover molecular signaling pathways in the human brain. The human brain is a highly complex organ with various functions, including higher cognitive functions (e.g., thought, memory, and emotion) that control speech, motor skills, vision, and other processes that are fundamental to life. The human brain’s surface is historically divided into four main lobes, i.e., the frontal, temporal, parietal, and occipital lobes, although the modern atlas includes a much more sophisticated structural and functional parcellation map [1]. The historical (but reductionistic) view is that each of these lobes is mainly related to some specific functions. It is important to note that different areas of the same lobe are specialized for different functions, and that many functions are distributed among several brain regions. Recent research into the brain’s connectome has revealed higher complexity of regulation than the simplified historical functional areas [2]. Omics studies have increased our understanding of the human brain’s functions [3,4]. Genetic variants have been reported to influence the total surface area and thickness of the brain. There are specific genetic influences on inter-regional cortical areas [5]. In a similar fashion to how the genetic architecture influences the human cortex and shapes our understanding of human brain functions, proteomics approaches are increasingly being applied to understand the complex networks of proteins that are co-regulated within the brain. A preliminary investigation of Broadmann areas in one hemisphere of one human brain revealed proteomic similarity within common functional areas (i.e., speech). This suggests that the proteomic map reflects the functional parcellations of the human cerebral cortex [3]. Hence, proteomics offers vast potential for studying the underlying proteome and signaling pathways in different areas of the brain [3,4,6,7]. Proteomic and bioinformatic analyses can be used to identify differentially regulated proteins in different brain regions, which can help elucidate the molecular bases underlying brain functions. Understanding molecular mechanisms in different brain regions can provide insights into brain function in both healthy and diseased states. Several proteomic techniques can be used to investigate the human brain’s proteome, including multiplexed quantitative method tandem mass tag (TMT) isobaric labelling [8,9], two-dimensional liquid chromatography coupled with tandem mass spectrometry (2DLC-MS/MS) combined with isobaric tags for relative and absolute quantitation (iTRAQ) [3], matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) [10], parallel reaction monitoring (PRM) [6,11] and liquid chromatography–tandem mass spectrometry (LC-MS/MS) [4,6,12]. Due to the inherent technical limitations incurred while obtaining and storing human brain tissue samples, the appropriate proteomic method must be selected. Samples may be affected by the storage conditions and the age of the sample. Unfortunately, most of these approaches can be expensive and are highly dependent upon quality of tissue, hence the preferred use of fresh frozen tissue. However, with clinical samples, formalin fixation is a common method for preserving human tissue. It crosslinks the proteins in the tissue for long term storage. LC-MS/MS proteomics has been carried out with formalin-fixed, paraffin-embedded (FFPE) prostate, bile duct, colorectal, renal and other human tissues [13,14,15,16]. LC-MS/MS also offers a relatively cost-effective, unbiased label-free quantitative method to investigate the global proteome. Moreover, FFPE archival tissue has proven its utility and comparability to fresh frozen tissue for LC-MS/MS analysis [17]. In this study, we developed an efficient and robust protein extraction method for LC-MS/MS analysis of formalin-fixed human brain cortex tissue. We present an optimized method to efficiently extract proteins using lysis buffer containing a high concentration of tris (hydroxymethyl) aminomethane hydrochloride (TrisHCl, denoted as Tris), sodium dodecyl sulfate (SDS), sodium deoxycholate (SDC) and Triton X-100 combined with heating. This was followed by in-gel digestion and tryptic cleavage for proteomic analysis. This method resulted in high proteomic reproducibility, enabling the comparison of inter-regional brain cortex tissue by bioinformatic analyses. Ingenuity Pathway Analysis and PANTHERdb were utilized to reveal differential regulation of signaling pathways between brain regions (Figure 1). These data provide a rich source of information for researchers and clinicians investigating specific areas of the human brain. Notably, there were similarly activated or inhibited signaling pathways in different brain regions. This indicates commonalities in the underlying molecular signaling pathways in various functional areas of the brain. The mapping of these brain region proteomes may help elucidate neurological processes and identify potential targets for therapeutics. In this study, we used a high Tris concentration extraction buffer, together with detergents and heating, to efficiently extract proteins and remove formalin crosslinks. For this protein extraction method, two buffers were compared: (i) Tris/SDS/SDC/Triton X-100 buffer and (ii) Tris/SDS buffer. The protein extraction methods were each applied to three biological replicate motor cortex samples (from separate donors). Following extraction, equal amounts of protein were prepared for in-gel digestion for mass spectrometry (MS) analysis as previously described [18]. Similar distributions of protein sample abundances were observed in the MS analysis (Figure 2A), and an FDR of 0.05 was set for confidence in the assessment of the dataset. Two-dimensional PCA analysis showed a high level of consistency amongst the samples analyzed. Biological replicates were grouped together by protein-extraction buffer (Figure 2B). Although a similar number of protein identifications and a large overlap were observed at the protein level between the two extraction methods (Tris/SDS/SDC/Triton X-100: 1446 vs. Tris/SDS: 1083) (Figure 3A), the Tris/SDS/SDC/Triton X-100 buffer showed distinctly more abundant protein patterns, which were sufficiently robust to cluster together like samples while distinguishing them from the Tris/SDS lysed samples (Figure 3B). This indicated that the catalogues of proteins extracted by each lysis buffer were similar among replicates, but distinguishable between lysis buffers, and that there was higher abundance of the commonly identified proteins in the Tris/SDS/SDC/Triton X-100 lysis buffer group. Despite the similar identification of number of proteins, the Tris/SDS/SDC/Triton X-100 extraction buffer resulted in a 65% higher number of identified peptide sequences compared to the Tris/SDS extraction buffer (Tris/SDS/SDC/Triton X-100: 5976 vs. Tris/SDS: 3616) (Figure 3C) and a 69% higher number of identified peptide groups compared to the Tris/SDS extraction buffer (Tris/SDS/SDC/Triton X-100: 6596 vs. Tris/SDS: 3913) (Figure 3D). Similarly to protein abundance patterns, peptide groups were also distinctly more abundant and robustly clustered together in the Tris/SDS/SDC/Triton X-100 buffer compared to the Tris/SDS buffer (Figure 3E). To further interrogate the obtained proteomes with the two protein extraction methods, the datasets were subjected to Gene Ontology (GO) annotation using PANTHERdb. We observed similar distributions of protein classes and molecular functions for the two extraction methods (Figure 4A,B). Taken together, these results indicate that both extraction buffers perform sufficiently well in extracting proteins from formalin-fixed human brain tissue for MS analysis. Although both buffers extracted similar numbers of proteins, there were greater protein abundance, greater peptide group abundance and higher percentages of identified peptide sequences and peptide groups using the Tris/SDS/SDC/Triton X-100 lysis method compared to the Tris/SDS lysis method. Therefore, the Tris/SDS/SDC/Triton X-100 lysis buffer method was selected for further experiments. To assess the quantitative reproducibility among experimental replicates with the selected method, three technical replicates were prepared from one motor cortex tissue sample in Tris/SDS/SDC/Triton X-100 protein-extraction buffer. The Pearson correlation of protein abundance was evaluated for the commonly identified proteins. All replicates had a high level of correlation (R = 0.868–0.929) (Figure 5). This demonstrates high experimental reproducibility with the Tris/SDS/SDC/Triton X-100 lysis buffer. There were similar numbers of identified proteins and peptides groups in the technical replicates (Figure S1). Between 65 and 81% of proteins and 68 and 81% peptide groups were consistently identified in at least two of the replicates. An average of 2450 ± 338 proteins and 9277 ± 882 peptide groups were identified from 20 µg of tissue lysate per replicate. These results indicate that a robust number of proteins can be extracted from formalin-fixed brain tissue. The developed protocol using Tris/SDS/SDC/Triton X-100 protein-extraction buffer and heating together with in-gel tryptic digestion and MS analysis was applied to the analysis of 12 human brain tissue samples from four distinct neuroanatomical brain regions (pre-frontal cortex, motor cortex, temporal cortex, and occipital cortex). Label-free protein quantifications were obtained from each of the four distinct brain regions of the three donors. Label-free quantification was used to maximize the number of protein identifications per sample [19]. The majority of proteins identified had low region specificity (Figure 6A). The frontal (including motor cortex) and temporal lobe cortex are implicated in neurodegenerative diseases, whereas the occipital lobe cortex undergoes minor pathological changes in later stages [20]. Consistent with gene-expression-level studies, we compared protein levels and signaling pathway analyses in the pre-frontal/occipital lobe, temporal/occipital lobe, and motor/occipital lobe in the brain tissue samples for relative LFQ analysis. In general, there were greater degrees of significantly enriched proteins (abundance ratio p-value < 0.05) in the pre-frontal and motor cortexes (69.7% and 65.9%, respectively) than in the temporal cortex (57.5%). Using region normalized protein expression data, we identified the 20 most significantly enriched proteins in each brain region compared to the occipital cortex (Table S1). ‘Metabolite interconversion enzymes’ and ‘Translational proteins’ were generally the most represented protein classes according to GO annotation by PANTHERdb (Figure 6B), excluding proteins that could not be classified into PANTHERdb protein classes. The ‘Ubiquitin proteasome pathway’ and ‘Heterotrimeric G-protein signaling pathway-Gq alpha and Go alpha mediated pathway’ were the second and third most enriched protein pathways among all three brain regions compared to the occipital cortex region, respectively (Figure 6C). Ingenuity Pathway Analysis (QIAGEN) software was used to identify the significantly enriched canonical Pathways (p-value) and corresponding predicted activation states (z-score) of the total proteins identified (see Section 4.6). The top three most significant canonical pathways identified when comparing the motor cortex to the occipital cortex were ‘Synaptogenesis Signaling Pathway’ (p-value = 1.32 × 10−10, z-score: 0.784, 27 molecules), ‘Protein Ubiquitination Pathway’ (p-value = 2.28 × 10−8, z-score = N/A, 22 molecules), and ‘Ephrin Receptor Signaling’ (p-value = 9.11 × 10−8, z-score: 1.069, 18 molecules). ‘Synaptogenesis Signaling Pathway’ and ‘Ephrin Receptor Signaling’ were assigned positive z-scores, suggesting predicted activation of these canonical pathways when comparing the motor cortex to the occipital cortex (Figure 7A). The three most significant canonical pathways identified when comparing the pre-frontal cortex to the occipital cortex were ‘Synaptogenesis Signaling Pathway’ (p-value = 1.43 × 10−18, z-score: 1.826, 34 molecules), ‘Opioid Signaling Pathway’ (p-value = 1.43 × 10−18, z-score: 1.826, 23 molecules), and ‘G Beta Gamma Signaling’ (p-value = 2.00 × 10−8, z-score: 2.496, 17 molecules). All three pathways were assigned positive z-scores, suggesting activation of these canonical pathways when comparing the pre-frontal cortex to the occipital cortex (Figure 7B). The three most significant canonical pathways identified when comparing the temporal cortex to the occipital cortex were the ‘Synaptogenesis Signaling Pathway’ (p-value = 7.49 × 10−11, z-score: 2.711, 24 molecules), ‘Huntington’s Disease Signaling (p-value = 1.34 × 10−6, z-score: 1.342, 17 molecules), and ‘Estrogen Receptor Signaling’ (p-value = 3.82 × 10−6, z-score: 1, 20 molecules). All three pathways were assigned positive z-scores suggesting activation of these canonical pathways when comparing the temporal cortex to the occipital cortex (Figure 7C). IPA was also used to assign canonical pathways in the form of a bubble chart in order to identify pathway categories that were significantly similar or different between the brain regions and clusters of proteins associated with these categories. The most significant pathway categories identified in all three comparisons were ‘Neurotransmitter and Other Nervous System Signaling’, and ‘Organismal Growth and Development’. Notably, large clusters of proteins (27 for motor cortex vs. occipital cortex, p-value = 1.43 × 10−10; 34 molecules for pre-frontal cortex vs. occipital cortex, p-value = 1.43 × 10−18; 24 molecules for temporal vs. occipital cortex, p-value = 7.49 × 10−11) were involved in the ‘Synaptogenesis Signaling Pathway’. Compared to the occipital cortex, these pathways were consistently assigned a positive z-score (motor vs. occipital cortex, z-score = 0.784; pre-frontal cortex vs. occipital cortex, z-score = 1.826, temporal vs. occipital cortex, z-score = 2.711), suggesting predicted activation of this pathways in the motor cortex, temporal cortex and pre-frontal cortex when compared to the occipital cortex (Figures S2–S4). Comparative analysis of significant molecules that had at least an absolute 1.5 fold-change difference in activation values was carried out to reveal differential regulation of canonical pathways between brain regions compared to the occipital cortex (Figure 8A). Although several canonical pathways were similarly activated or inhibited among the brain regions compared to the occipital cortex, pathways with differential regulation between brain regions were further interrogated. ‘RHOGD1 signaling’ was predicted to be activated in the motor cortex (p-value = 3.97 × 10−3 and z-score = 1.6333), inhibited in the pre-frontal cortex (p-value = 2.29 × 10−4 and z-score = −2.449), and not significantly different in the temporal cortex. Energy production pathways were also differentially regulated. ‘Glycolysis I’ was predicted to be activated in the motor cortex (p-value = 1.72 × 10−3, z-score = 2) and pre-frontal cortex (p-value = 8.32 × 10−4, z-score = 2), but not significantly different in the temporal cortex. ‘Gluconeogenesis I’ was predicted to be activated in the motor cortex (p-value = 1.72 × 10−3, z-score = 2) but not significantly different in the pre-frontal and temporal cortexes. ‘Oxidative phosphorylation’ was predicted to be activated in the motor cortex (p-value = 5.96 × 10−3, z-score = 1.89) and not significantly different in the pre-frontal and temporal cortexes. Upon further interrogation of the oxidative phosphorylation pathway in the motor cortex compared to occipital cortex, complexes I, III, and V of the inner mitochondrial membrane were predicted to be activated (Figure 8B). We then searched this dataset further for molecules related to free radicals, given their relation to mitochondrial oxidative phosphorylation. We found 44 differentially regulated molecules in the motor cortex related to the inhibition or activation of free radical species (Figure 8C). This proof-of-concept study provides an optimized lysis buffer for protein extraction from formalin-fixed human brain tissue to investigate proteins of interest and inter-regional expression patterns by proteomic analysis. Notably, we demonstrate that this method is suitable to identify the underlying cellular signaling pathways by LFQ proteomics. We propose that 600 mM Tris/SDS/SDC/Triton X-100 protein-extraction buffer combined with heating at 90 °C, agitation, and in-gel tryptic digestion enables in-depth proteomics analysis of formalin-fixed human brain cortex tissue. This optimized method enabled us to use a low quantity of protein as starting material for MS analysis. Overall, we identified an average of 2450 ± 338 proteins and 9277 ± 882 peptide groups per sample and obtained reliable technical replication of these proteins and peptide groups. The ’Ubiquitin Proteasome Pathway’ and ‘Translational Proteins‘ and ‘Metabolite Interconversion Enzymes’ were the most clearly enriched protein pathway and classes of the 20 most enriched proteins identified in the pre-frontal, motor and temporal cortices compared to the occipital cortex according to gene ontology analysis. Further, synaptogenesis signaling was consistently predicted to be activated in each of the studied brain regions relative to the occipital cortex. Comparative analysis revealed differential proteomic regulation of energy metabolism pathways among different brain regions, and we further provide evidence of metabolic molecular regulation within the human brain cortex. Previous studies have indicated that fresh frozen tissue is preferred to formalin-fixed tissue due to concerns about formaldehyde-induced crosslinking and degradation during processing. However, the availability of fresh frozen tissue is often limited. Formalin crosslinking also inactivates pathological and biochemical processes and may be required for the investigation of organ specimens affected by pathogens [21]. As such, formalin fixation may be an essential step. Studies have investigated the potential of formalin-fixed tissue for proteomic analyses, increasingly demonstrating comparability in proteomics datasets [22,23,24,25,26,27,28]. High quality of sample preparation and protein extraction is essential for reliable proteomic analysis [27]. Several studies have reported successful results using heat or a barocycler [29] to reverse formaldehyde crosslinking. A recommended minimum 300 mM Tris hydrochloride (TrisHCl) concentration was suggested to achieve optimized FFPE proteomics analysis in mouse tissue [21]. Kawashima et al. proposed that at least two mechanisms may be involved in the Tris-catalyzed enhancement of protein extraction from FFPE tissue. Firstly, the Tris molecule may act as a scavenger to remove released formaldehyde, and secondly, Tris may act as a transamination catalyst directly breaking down the crosslinks [21]. We found that heating the samples at 90 °C in 600 mM TrisHCl and SDS protein-extraction buffer with agitation effectively reversed protein crosslinks in the formalin-fixed human brain tissue investigated in this study. Additional detergents included in the lysis buffer, SDC and Triton X-100, assisted in extracting increased protein yields to achieve in-depth proteomics datasets in a quick and suitable manner. A major limitation encountered in clinical proteomics has been obtaining sufficient protein quantity for in-depth proteomic analysis. LFQ proteomic analysis of 200 µg of powdered, frozen brain tissue has been reported to detect an average of 3612 proteins per sample [4]. In the current study, only 20 µg of extracted protein from the formalin-fixed brain tissue was analyzed per sample. The average number of regional proteins identified in this study was 2199 proteins per sample. Hence, the protein extraction method achieved relatively in-depth proteomic analysis for the quantity of protein analyzed by MS. Proteomic analysis of matched formalin-fixed, paraffin embedded (FFPE) and fresh frozen meningioma tissue also resulted in a similar number of proteins and similar quality of mass spectra, but there were differences in chemical and post-translational modifications, which was not the focus of this study [30]. Most proteins identified in the brain regions from this study had low regional specificity, which is consistent with gene expression in the human brain reported in the human protein atlas (v22.proteinatlas.org) [31]. This indicates that protein extraction using Tris/SDS/SDC/Triton X-100 lysis buffer with heating and in-gel tryptic digestion is a robust method for LFQ proteomic analysis of formalin-fixed human brain tissue. This also demonstrates that successful MS and bioinformatic analyses can be performed on protein extracted from formalin-fixed human brain tissue. With formalin-fixed human tissue becoming more readily available, this method has great potential for future global proteomic analyses. Importantly, this method demonstrated the feasibility of using formalin-fixed human brain tissue to identify underlying cellular pathways by LC-MS/MS and label-free proteomics. Previous studies focused on the integration of proteomics and omics data or neuroanatomical-specific proteins to investigate the human brain [3,4,6,7,32]. This research expands our understanding of the human brain by identifying inter-regional specific signaling pathways. Evaluating brain region specific changes, we found ‘Neurotransmitters’, ‘Other Nervous System Signaling’, and ‘Synaptogenesis’ were the most abundantly represented canonical pathways. This was expected for brain tissue and confirms the robustness of the protein extraction employed in this study. Moreover, the enriched signaling pathways that were predicted to be activated in our study are consistent with the current literature and offer several potential proteins of interest for future investigations. For example, in the motor cortex, differential sirtuin expression patterns have been identified in post-mortem tissue of patients with motor neuron degeneration from amyotrophic lateral sclerosis (ALS) [33]. The enriched sirtuin signaling pathway identified in this study can be further scrutinized to identify proteins of interest relative to the motor cortex. It was unsurprising to find that sirtuin signaling predominated the motor cortex, given its broad role in glucose and lipid metabolism, and that the motor cortex controls physical movement, whereas the occipital cortex is generally responsible for vision [34]. Furthermore, fine movement is triggered by the neurons in the motor cortex through the corticospinal tract, which requires ephrins and their Eph receptors for topographical mapping of the corticospinal tract [35]. Ephrins and Eph receptors function within the ephrin receptor signaling pathway, which in this study, was enriched and predicted to be activated in the motor cortex relative to the occipital cortex. Additionally, strategies to inhibit RHOA signaling improve axonal regeneration of injured motor axons [36] and delay onset and extend survival [37] in neurodegenerative disease models of ALS. This is consistent with our predicted inhibition of RHOA signaling in healthy brain tissue in the motor cortex relative to the occipital cortex. The occipital cortex may be a suitable comparative control tissue for future investigations of RHOA signaling. Thus, this dataset provides a rich resource with which to investigate signaling pathways specific to movement in the motor cortex. Our results corresponded well to positron emission tomography imaging regarding elevated aerobic glycolysis in the pre-frontal cortex of the normal human brain, which differs among brain regions [38]. In contrast, this study predicted that glycolysis signaling was most activated in the motor cortex, which may not be related to the level of energy metabolism in the brain [38]. General regional differences in glucose metabolism in the brain were consistent with the literature [39]. The main energy source for the brain is oxidative phosphorylation, which is also a source of free radicals [40]. An insufficient energy supply can increase mitochondrial free radical production [41]. The motor cortex had the highest predicted activation of oxidative phosphorylation and of energy production pathways (i.e., glycolysis and gluconeogenesis). It is possible that there is a cooperative balance between these energy pathways to compensate for the increased metabolic demand, as evidenced by the apparent balanced molecular regulation of free radical species. The pre-frontal lobes are involved in regulating affect, personality, mood, and social and moral reasoning, amongst others [42]. The broad functions of the pre-frontal lobes likely contribute to the lack of predicted inhibition of the top pathways in the pre-frontal cortex relative to the occipital cortex. Dysregulated motivation is related to pre-frontal cortical opioids [43]. Opioid signaling was the second most enriched canonical pathway in the pre-frontal cortex tissue in this study and was predicted to be activated in the pre-frontal and temporal cortexes but inhibited in the motor cortex (vs. occipital cortex). Interestingly, our findings were consistent with RNA sequencing of post-mortem, pre-frontal cortex tissue of opioid use disorder (OUD) patients, which found that transcripts were related to synaptic remodeling [44]. ‘Synaptogenesis’ and ‘Opioid Signaling’ were the most enriched canonical pathways in the pre-frontal cortex in this study, confirming the robustness of the proteomic approach and offering neuroanatomically or functionally related pathways that may be explored further. These findings also suggest that opioid regulation within the temporal lobe might be of interest. The molecular mechanisms of prostanoid signaling involve the heterotrimeric G-protein signaling pathways regulating the downstream Wnt signaling pathway [45,46]. These pathways were enriched in each of the regions analyzed compared to the occipital cortex, suggesting inter-regional commonalities in molecular functions. Targeting these pathways may ultimately affect function in several brain regions. Moreover, this molecular signaling affects neurite extension length and calcium levels in neuronal growth cones [47], both of which are important for neuronal function. Prostaglandin E2 G-protein-coupled receptor signaling is also involved in the dendritic cell’s life cycle [48]. Interestingly, the cell cycle pathway was also enriched in each of the brain regions studied compared to the occipital cortex. Similarly activated or inhibited pathways in this study could be investigated further to potentially identify master regulator proteins of dysregulated signaling pathways in disease-relevant brain regions. During the human brain’s development into and throughout adulthood, protein levels increase or decrease over time [4]. Improvements to this study could be made with more samples, or by comparing different ages or comparing by sex. Future dataset comparisons of the human brain’s proteome should account for sub-specific regional proteomes (i.e., the frontal cortex proteome for the medial frontal gyrus or dorsolateral pre-frontal cortex compared to the superior frontal gyrus, which have proteomic dysregulation in Alzheimer’s disease [8,49] and the alcoholic brain [50], respectively). Nonetheless, this study demonstrated that it is possible to perform a high-throughput proteomic study without the need for expensive mechanical equipment to maintain consistency in sample preparation and increased output. Despite the inherent issues of working with post-mortem human brain tissue, during which membrane breakdown and protein translocation may occur during the post-mortem-to-fixation interval [12], the simple and optimized protein extraction from formalin-fixed human brain tissue presented in this study provided high quality proteins for MS analysis. Our proteomics findings were consistent with other omics studies on fresh frozen human brain tissue, highlighting the robustness of this approach. The proteomic data generated in this study ultimately present a rich source of information for neuroscientists and clinical proteomics, and knowledge of the different cellular pathways that are being driven within the examined brain regions. As a proof-of-concept, three formalin-fixed, post-mortem brain donors were included for sample collection. Four distinct neuroanatomical regions were collected from each of the donor brains: the frontal pole cortex (pre-frontal cortex), motor cortex, temporal cortex, and occipital cortex. A total of 12 brain tissue samples were collected (n = 12). Brain tissue was obtained from the Macquarie Medical School at Macquarie University under the approval of the Macquarie University Human Research Ethics Committee (5201300835) in accordance with the Declaration of Helsinki and relevant local guidelines. No formal criterion of post-mortem interval time from death to formalin fixation was used. The donors had no known clinicopathological diagnosis. Each brain was dissected and collected by a neuroanatomist and neurosurgeon with about 20 years of experience in the field, and stored in 70% ethanol at 4 °C. Samples were obtained from four neuroanatomical regions of three donors. A 1:10 ratio of tissue (mg) per extraction buffer (µL) was used with a minimum of 10 mg of brain-tissue starting material per sample. Each piece of tissue was chopped with a scalpel into smaller pieces prior to Dounce homogenization in cold protein-extraction buffer. Buffer was supplemented with protease and phosphatase inhibitors to a final concentration of 1X. Homogenate was transferred to a 1.5 mL microcentrifuge tube and kept on ice. Intermittent vortexing was repeated for 10 min. Homogenates were passed 5 times through a 25-gauge needle, followed by 3 times through a 30-gauge needle. Samples were incubated at 90 °C for 120 min with agitation at 750 rpm to remove the crosslinking which occurs with formalin fixation. Then samples were bath sonicated for 20 min. Lysates were clarified by centrifugation for 20 min at 16,000× g at 4 °C. The supernatant containing extracted proteins was collected and further processed. Total protein concentration was determined using the bicinchonic acid (BCA) method with bovine serum albumin as a standard, and samples were frozen as aliquots at −80 °C until further processing. Aliquots of lysate were diluted to be compatible with the protein quantitation assay, and the dilution factor was accounted for in determining the final protein concentration. High concentration tris (hydroxymethyl) aminomethane hydrochloride (TrisHCl; referred to as Tris in manuscript) and sodium dodecyl sulfate (SDS) protein-extraction buffers with or without sodium deoxycholate (SDC), Triton-X-100, sodium chloride (NaCl), and ethylenediaminetetraacetic acid (EDTA) were compared to identify the buffer that extracted the most proteins. Equal weights of motor cortex tissue from each of the donors (n = 6) were prepared in parallel, as previously described. Buffers contained (i) 600 mM TrisHCl, 2% SDS, 150 mM NaCl, 0.5% SDC, 1 mM EDTA, 1% Triton X-100, phosphatase and protease inhibitor, pH 8.0, (referred to as Tris/SDS/SDC/Triton X-100 buffer) or (ii) 600 mM TrisHCl pH 8.0, 2% SDS, supplemented with protease and phosphatase inhibitor (referred to as Tris/SDS buffer). Three biological replicates for each buffer were prepared for protein analysis by mass spectrometry. The buffer that extracted the most proteins, as determined by MS analysis, was selected for further analysis. For multiregional brain proteome analysis, tissue from the motor, pre-frontal, temporal, and occipital cortexes (each approximately 15 mg of tissue in 150 µL buffer) from each of the three donors were prepared, as previously described, totaling 12 samples. Following extraction, equal amounts of protein lysates (20 µg) were mixed with Laemmli sample buffer and DTT (1X) and incubated for 5 min at 95 °C. The protein was loaded into Mini Protean TGX (4–15%) gels (BioRad) and electrophoresed for approximately 10 min at 200 Volts. Gels were then stained with Coomassie Blue for protein visualization. Proteomic procedures were carried out as recently described [18] with slight modifications. LC-MS/MS analysis of extracted proteins was performed. Samples were separated by SDS-PAGE gel electrophoresis (BioRad) for in-gel reduction with 10 mM DTT, alkylation with 55 mM IAA, and trypsin digestion (1:50 enzyme:protein) overnight at 37 °C (V5111, Promega). Extracted peptides were lyophilized and then resuspended in 0.1% formic acid (FA) for desalting using C18 OMIX tips (Agilent). Samples were lyophilized again and resuspended in 0.1% FA, bath-sonicated for 20 min, and then centrifuged at 14,000× g for 15 min to remove insoluble debris, and analyzed by LC-MS/MS. The Ultimate 3000 nanoLC (Thermo Fisher Scientific) fitted with the Acclaim PepMap RSLC column particle size of 2 μm, diameter of 0.075 mm, and length of 150 mm (Thermo Fisher Scientific) was used, employing a 120 min gradient (2–80% v/v ACN, 0.1% v/v FA) running at a flow rate of 300 nL/min to separate peptides. Subsequently, eluted peptides were ionized into the Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) that had an electrospray source fitted with an emitter tip 10 μm in diameter (New Objective) and maintained at 1.6 kV electrospray voltage. The capillary temperature was set to 250 °C. A data-dependent “Top 10” method operating in FT acquisition mode with HCD fragmentation was used for MS/MS fragmentation to select precursor ions. On the Q Exactive Plus, FT-MS analysis was carried out at 70,000-times resolution with an AGC target of 1 × 106 ions in full MS and a maximum injection time of 30 milliseconds. Additionally, MS/MS scans were carried out at 17,500-times resolution with an AGC target of 2 × 104 ions with the maximum injection time set to 50 milliseconds. The ion selection threshold was set to 25,000 counts to trigger MS/MS fragmentation. HCD fragmentation was performed using an isolation width of 2.0 Da with a normalized collision energy of 27. Raw files were searched with Proteome Discoverer 2.4 (Thermo Fisher Scientific) against Uniprot FASTA database incorporating the Sequest search algorithm. Search parameters accounted for 20 ppm precursor ion tolerance and 0.1 Da MS/MS fragment ion tolerance. The search allowed for static modifications of cysteine carbamidomethylation and variable modifications of methionine oxidation, asparginine, and glutamine deamidation on acetylated N-terminal residues. Two missed cleavages were allowed for. The data were processed through Percolator for estimation of false discovery rates. Protein identifications were validated employing a q-value of 0.05. Protein identification required at least one unique peptide per protein. Statistically significant differences in protein, peptide sequence, and peptide groups identified were calculated using Proteome Discoverer 2.4. Common proteins identified in the LFQ of technical replicates were evaluated for Pearson correlation; the coefficients were calculated using the Scipy (v1.9.1) library in Python based on the logarithm values of proteins’ abundance ratios with variance stabilization. The histogram and scatter plots were generated using the Matplotlib (v3.6.0) library in Python. The frontal (Iding motor cortex) and temporal lobe cortexes are implicated in neurodegenerative diseases, whereas the occipital lobe cortex undergoes minor pathological changes in later stages [20]. Consistently with gene expression level studies, we compared protein levels and signaling pathway analyses in the pre-frontal/occipital lobe, temporal/occipital lobe, and motor/occipital lobe in the brain tissue samples for relative label-free quantification (LFQ). High-confidence proteins with abundance ratios having p < 0.05 were selected and included for pathway analysis. An absolute expression fold-change cutoff of 1.5 was used to identify analysis-ready molecules in Ingenuity Pathway Analysis (IPA). Data were analyzed with the use of QIAGEN IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA, accessed September–December 2022) [51]. Gene Ontology (GO) analysis of the proteomic results was conducted using online open-source software PANTHERdb (Protein ANalysis THrough Evolutionary Relationships) classification system 17.0, which annotated proteins to biological processes [52].

PMC10001679

Alba Ordoñez-Rodriguez,Pablo Roman,Lola Rueda-Ruzafa,Ana Campos-Rios,Diana Cardona

Changes in Gut Microbiota and Multiple Sclerosis: A Systematic Review

06-03-2023

multiple sclerosis,gut microbiota,microbiome: short-chain fatty acid

Introduction: Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease mediated by autoimmune reactions against myelin proteins and gangliosides in the grey and white matter of the brain and spinal cord. It is considered one of the most common neurological diseases of non-traumatic origin in young people, especially in women. Recent studies point to a possible association between MS and gut microbiota. Intestinal dysbiosis has been observed, as well as an alteration of short-chain fatty acid-producing bacteria, although clinical data remain scarce and inconclusive. Objective: To conduct a systematic review on the relationship between gut microbiota and multiple sclerosis. Method: The systematic review was conducted in the first quarter of 2022. The articles included were selected and compiled from different electronic databases: PubMed, Scopus, ScienceDirect, Proquest, Cochrane, and CINAHL. The keywords used in the search were: “multiple sclerosis”, “gut microbiota”, and “microbiome”. Results: 12 articles were selected for the systematic review. Among the studies that analysed alpha and beta diversity, only three found significant differences with respect to the control. In terms of taxonomy, the data are contradictory, but confirm an alteration of the microbiota marked by a decrease in Firmicutes, Lachnospiraceae, Bifidobacterium, Roseburia, Coprococcus, Butyricicoccus, Lachnospira, Dorea, Faecalibacterium, and Prevotella and an increase in Bacteroidetes, Akkermansia, Blautia, and Ruminocococcus. As for short-chain fatty acids, in general, a decrease in short-chain fatty acids, in particular butyrate, was observed. Conclusions: Gut microbiota dysbiosis was found in multiple sclerosis patients compared to controls. Most of the altered bacteria are short-chain fatty acid (SCFA)-producing, which could explain the chronic inflammation that characterises this disease. Therefore, future studies should consider the characterisation and manipulation of the multiple sclerosis-associated microbiome as a focus of both diagnostic and therapeutic strategies.

Changes in Gut Microbiota and Multiple Sclerosis: A Systematic Review Introduction: Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease mediated by autoimmune reactions against myelin proteins and gangliosides in the grey and white matter of the brain and spinal cord. It is considered one of the most common neurological diseases of non-traumatic origin in young people, especially in women. Recent studies point to a possible association between MS and gut microbiota. Intestinal dysbiosis has been observed, as well as an alteration of short-chain fatty acid-producing bacteria, although clinical data remain scarce and inconclusive. Objective: To conduct a systematic review on the relationship between gut microbiota and multiple sclerosis. Method: The systematic review was conducted in the first quarter of 2022. The articles included were selected and compiled from different electronic databases: PubMed, Scopus, ScienceDirect, Proquest, Cochrane, and CINAHL. The keywords used in the search were: “multiple sclerosis”, “gut microbiota”, and “microbiome”. Results: 12 articles were selected for the systematic review. Among the studies that analysed alpha and beta diversity, only three found significant differences with respect to the control. In terms of taxonomy, the data are contradictory, but confirm an alteration of the microbiota marked by a decrease in Firmicutes, Lachnospiraceae, Bifidobacterium, Roseburia, Coprococcus, Butyricicoccus, Lachnospira, Dorea, Faecalibacterium, and Prevotella and an increase in Bacteroidetes, Akkermansia, Blautia, and Ruminocococcus. As for short-chain fatty acids, in general, a decrease in short-chain fatty acids, in particular butyrate, was observed. Conclusions: Gut microbiota dysbiosis was found in multiple sclerosis patients compared to controls. Most of the altered bacteria are short-chain fatty acid (SCFA)-producing, which could explain the chronic inflammation that characterises this disease. Therefore, future studies should consider the characterisation and manipulation of the multiple sclerosis-associated microbiome as a focus of both diagnostic and therapeutic strategies. Multiple sclerosis (MS) is a chronic, inflammatory, neurodegenerative condition caused by autoimmune reactions which progressively demyelinate the central nervous system (CNS) and the spinal cord [1,2]. It seems to begin when autoreactive T cells cross the blood–brain barrier (BBB) and provoke specific cascades in the CNS, leading to inflammation and axonal degeneration [3,4], although it is not clear what causes T cells activation [5]. MS affects an estimated 2.3 million people worldwide and its incidence is increasing from 50 to 300 per 100,000 inhabitants, affecting women 3-fold times [1,6,7]. It is the most common non-traumatic neurological disabling disorder in young people. It causes disability, including intestinal disfunction in more than 70% of cases [8], cognitive impairment, and a severe decrease in quality of life in young adults between 20 and 40 years old [7,9]. MS aetiology remains unclear; interactions between environmental and genetic factors appear to promote the disease [2,6,9,10,11]. In addition to genetics, environmental factors such as obesity, tobacco use, microbiota alterations, Epstein–Barr virus (EBV) infection or vitamin B deficiency play an important role in progression of the disease [10,12]. Regarding the evolution of the disease, MS has been classified into subgroups such as relapsing–remitting (RRMS), secondary progressive (SPMS), primary progressive (PPMS), progressive–relapsing (PRMS), and benign (BMS) [13]. In RRMS (83–90% of cases), the flare-ups of neurological symptoms are practically reversible, which recur unpredictably and may disappear completely or leave some sequelae, and, between relapses there seems to be no progression of MS. SPMS is described as a disease of continuous progression, with or without flare-ups, irrelevant remissions, and phases of stability. Only 10% of patients present with PPMS, which starts with disabling flare-ups with no response to treatment and has a slow onset and progressive deterioration. RPMS is characterized by occasional exacerbations in a progressive course of the disease. Finally, in BMS, after the diagnosis of the disease, the patient retains functional capacity for 10–15 years [14]. Research is currently focusing on the influence of the gut microbiota (GM) on the onset and development of MS [15,16,17]. GM is the combination of bacteria, fungi, archaea, eukaryotes, and viruses that reside in the intestinal mucosa, and Actinobacterium, Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, and Verrucomicrobia are the main phyla that compose it [18]. GM microorganisms contribute to food digestion and fermentation, nutrient absorption, vitamin synthesis, epithelial cell maturation, intestinal barrier integrity, protection against inflammation and pathogens, and metabolic regulation [19,20]. GM may impact on the CNS and participate in its regulation through neurochemical changes, while the CNS is a crucial element in the regulation of gut function and homeostasis. This complex interaction is well-known as the gut–brain axis (GBA) [21]. Bidirectional interactions between gut and brain have an important role in gastrointestinal function modulation such as motility, secretion, blood flux regulation, intestinal permeability, immunity activity, and visceral sensations, including pain, where evidence suggests that GM has a vital role. GM can interact with the brain through activation of immune, endocrine, and neural pathways, including vagal afferents and through microbial metabolites which act directly or indirectly in the brain [22,23,24]. Some molecules derived from microorganisms, such as short-chain fatty acids (SCFAs), may have a relevant role in the gut-brain axis. SCFAs such as butyric acid (BA), acetic acid (AA), and propionic acid (PA) are produced in the colon by non-digestible carbohydrates undergoing bacterial fermentation [21,25]. SCFAs have important immunomodulatory functions mediated by increasing the number of T regulatory cells and suppressing the collaborative T cells (Th) 17 and 1, which lead to an anti-inflammatory response state [26]. Likewise, SCFAs can cross the BBB by using transporters located in the endothelial cells and influence CNS neuroinflammation [27,28]. Specifically, BA, compared to PA and AA, has strong immunomodulatory properties and regulates inflammatory processes by maintaining the balance of Th 17 cells and the levels pro and anti-inflammatory cytokines [25,29]. A disruption in GM composition, so-called gut dysbiosis, plays a fundamental role in several autoimmune conditions, including intestinal inflammatory disease, rheumatoid arthritis, and type 1 diabetes [30]. MS has also been associated with dysbiosis, including depletion and enrichment of certain bacteria in patients compared to healthy people [31,32,33]. However, a cause–effect relationship between MS and intestinal dysbiosis has not been clearly established. Considering all of the above, there seems to be an association between MS and GM. Thus, the objective of the present work is to perform a systemic review about the relation between intestinal microbiota and MS. The systematic review was conducted in the first trimester of 2022 using studies published between January 2018 and March 2022. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) recommendations were utilized [34]. The PICO (Patient, Intervention, Comparation, Outcome) method was used to design a search strategy. Accordingly, the objective of review was reflected in the question: “does it exist a relation between intestinal microbiota and MS?” Articles were selected and collected from 6 electronic databases: PubMed, Cochrane Library, ProQuest, The Cumulative Index of Nursing and Allied Literature Complete (CINAHL), ScienceDirect, and Scopus. The terms used to access to the articles of interest in the mentioned databases were a combination of natural language and structured language using the Medical Subject Heading (MeSH) thesaurus: “multiple sclerosis”, “gut microbiota”, and “microbiome”, and using “AND” between terms and “OR” between synonyms. Research strategies are shown in Table 1. The inclusion criteria used for this review were (i) Cohort studies, transversal studies, patient and control comparative studies, and comparative cohort studies (analytic observational studies) in MS patients, (ii) articles analysing GM in MS patients, (iii) articles analysing SCFAs in intestinal metabolome, (iv) articles including a population of study composed of MS diagnosed individuals between 18 and 70 years old (including all the MS subtypes) and (v) studies published in both English and Spanish. In addition, the exclusion criteria included (i) systematic reviews, metanalysis, book chapters, doctoral dissertations, end-of-study projects, congress publications, clinical protocols, and letters to the editor, (ii) other study designs, such as interventional studies and studies without a control group, and (iii) articles analysing intestinal metabolome, but no SCFAs. Restrictions in relation to geographical location, setting (community or hospital), or the course of the clinical study were not applied. Study eligibility was performed in three phases. The first phase consisted of reading the title of identified articles in the research database. Once selected, all abstracts were reviewed in a second phase, and, finally, a full reading was used to clarify the suitability of the article for analysis. The eligibility process was conducted by the first two authors (AOR, PR) independently and in duplicate; if consensus could not be achieved, a third author (DC) was consulted. In relation with the included studies, a bibliometric analysis was performed on the following variables: (i) author and year, (ii) number of participants and controls, (iii) intestinal microbiota changes compared to control group, and (iv) changes in SCFAs compared to control groups. Regarding the quality of the studies, the Newcastle Ottawa scale (NOS) [35], which evaluates bias in observational studies, was applied. The NOS records 8 items with 3 subscales and is scored up to 9 points. A study is considered to be of high quality when its score is ≥7. It uses predefined criteria and assigns up to 9 stars, with a maximum of 4 stars for the quality of patients selected, 2 for the comparability between cases and controls, and 3 starts for exposure or outcomes. After the database research, 1004 results were obtained (27 in PubMed, 229 in Scopus, 196 in ScienceDirect, 509 in Proquest, 12 in Cochrane, and 31 in CINAHL). Articles related to the objective, which fulfilled the inclusion criteria, were selected, and duplicated articles were discarded. Preliminary title selection facilitated the exclusion of duplicated articles (21 articles excluded after reading title and abstract). After selection, 100 results were obtained. Subsequently, articles that were not relevant to the topic (oral microbiota), interventional articles, animal studies, or studies on other demyelinating pathologies were excluded, resulting in 16 articles. The next step consisted of a second reading of the full text based on the exhaustive analysis of the study, excluding all the articles which did not fit because of inadequate participants. Finally, 12 articles were obtained for final revision, as shown in the flowchart (Figure 1). Twelve articles were included in this research [17,36,37,38,39,40,41,42,43,44,45,46]. The characteristics of the studies reviewed, as well as the main variables analysed, are listed in Table 2. All studies involved a total of 570 MS cases and 478 controls, i.e., healthy subjects without a diagnosis of the disease. A total of 54% of the study population was diagnosed with MS. The majority (301/570, 53%) of the cases presented a remittent–recurrent course, while 9.3% (53/570) were diagnosed with PPMS, and 3.5% (20/570) were diagnosed with BMS. A total of 34.4% (196/570) were diagnosed with MS without subtype specification. Seven studies used McDonald 2010 criteria for MS diagnosis, one used Poser criteria [44], and four did not specify the diagnosis method [37,39,41,45]. All the studies provided demographic data, with the women/men ratio being 388/182 (68%/32%) for MS and 394/184 (61.5%/38.5%) for controls. Only in one study was the percentage of men higher, 60% of MS cases and 53% of controls [40]. Furthermore, two studies reported on the ethnicity of patient, one of them distinguishing between Caucasian, Hispanics, and Afroamericans [38], and another only identified Caucasians, where 80% of the controls were Caucasian compared 95% of the MS cases [17]. Three studies recruited the participants in the USA [17,38,39], two in Spain [36,45], one in Italy [37], one in Belgium [46], and one in China [42], Brasil [44], Israel [41], Egypt [43] and Russia [40]. As shown in Table 3, all the revised articles were low risk regarding NOS scale [35]. Ten of the twelve selected articles evaluated GM, and eighty percent analysed alpha and beta diversity. Alpha diversity was evaluated in eight studies. On the one hand, a decrease in alpha diversity was observed in RRMS cases [39], while an increase in alpha diversity was shown in PPMS [40]. In the remaining studies, no statistically significant differences were found, affirming that there are no apparent discrepancies in the diversities between MS cases and controls. Analysing the specific taxonomic differences in the assessed articles, we found no uniform observations among the studies as shown in Table 4 [36,38], whereas it diminished in 40% of the studies [17,37,42,46]. At the phylum level, Firmicutes was observed to increase in 20% of the studies [36,38], while, conversely, it decreased in 40% of them [17,37,42,46]. Bacteroidetes increased in 30% [39,43,44] and diminished in 10% of the cases [17]. Actinobacteria increased [36] and decreased in 10% of the studies [44]. Proteobacteria and Lentispharaea decreased in 10% of the studies [36]. Regarding the class, Clostridia increased in one study [38] and decreased in another one [40]. With respect to families, the family Lachnospiraeae significantly decreased in controls, and Ruminococcaceae increased [40] and decreased in controls [44]. Furthermore, two articles found a decrease in bacteria of the genus Bifidobacterium [17,44]. Similarly, two other articles found a decrease in Coprococcus [37,39], Butyricoccus [42,46], and Lachnospira [37,38]. In contrast, Akkermansia was significantly increased compared to controls [37,38,40]. Blautia was also found increased in three articles [36,42,44] but decreased in two other works [37,38]. There was also controversy regarding Parabacteroides, which was increased in two studies [44,46], but decreased in two others [17,37]. This same divergence was observed in other genera, such as Dorea, which was both augmented [38] and decreased in MS depending on the research [37,42]. Likewise, Ruminococcus, Faecalibacterium, Prevotella, Methanobrevibacter, and Dialister also increased and diminished depending on the article [17,36,37,38,42,46]. Two studies evaluated GM at different phases of MS. Stratifying MS patients according to disease severity showed significantly less diversity in SPMS compared to RRMS and controls [37]. Other studies compared intestinal microbiota in different MS subtypes, considering the use of interferon. Microbiota richness was lower in RRMS patients treated with interferon and patients with non-treated RRMS during the relapse compared to BMS and PPMS. Controls and non-treated active RRMS showed an intermediate microbial richness [46]. In this regard, the 10 revised articles agree that MS patients have a different intestinal microbiota than controls, with different abundancies depending on the microbiota [17,36,37,38,39,40,42,43,44,46]. Four of the twelve studies analysed SCFA levels in intestinal metabolome, and those levels were compared between patients and controls [17,37,41,45]. These four selected articles analysed serum SCFA levels, finding decreases in BA [37,41] and increases in AA [45]. Consistent with this, there was a trend towards a decrease in the SCFAs in the faeces of MS patients compared to controls [17]. The causes of multiple sclerosis are unknown, but there is evidence to indicate that GM may influence the immune system and, consequently, impact on the disease. Therefore, our aim was to analyse recent literature with the objective of investigating the relation between intestinal microbiota and MS. In the present systematic review, 12 case-control studies with intestinal dysbiosis were included, as well as SCFA alterations in patients with MS. As for studies examining alpha diversity and beta diversity, only three studies found significant differences in MS compared to the controls. A decrease in alpha diversity was found in RRMS associated with cases of chronic low-grade inflammation [39,47]. This diversity was observed in other autoimmune diseases, such as inflammatory bowel disease [48,49], preclinic type 1 diabetes [50,51], and psoriatic arthritis [52], as well as inflammatory diseases such as obesity [53]. Previous studies also indicate that alpha diversity tends to decrease in patients with normalized active RRMS during remission [54]. An increase in alpha diversity [40] related to PPMS was also found. This MS subtype is quite strange [14], so the information about the structure and composition of intestinal microbiota is scarce. These changes in alpha diversity can be explained depending on whether the disease is active or not. In addition, it is necessary to elucidate whether these changes are the product of an immune response or whether they promote autoimmunity. In this regard, recent research proposes that EBV infection contributes to the production of B cells that stimulate the activation of these CNS inflammatory responses [55]. On the other hand, changes in beta diversity were observed, with no changes in alpha diversity between Hispanic American subjects with MS and controls [38]. Furthermore, a difference in beta diversity was also found in other previous studies [32,54,56,57]. According to our results, other reviews found no significant differences between alpha and beta diversity in MS [58,59]. Taxonomic differences reflected in the revised studies are quite diverse, making it difficult to draw firm conclusions. For this reason, and to simplify our results, we will focus on highlighting differences in gut microbial communities between MS cases and matched controls in two or more studies. Thus, we observed a decrease in Firmicutes phylum [17,42,46] and an increase in phylum Bacteroidetes [39,43]. These phyla are SCFA producers with immunoregulatory functions and, therefore, their alterations affect MS [60]. These alterations have also been detected in Chron’s disease [61]. Regarding intestinal bacteria families, Lachnospiraceae was found diminished [17]. Previous studies have shown a decrease in this family in MS patients [54], a decrease that was also observed in Alzheimer patients [62]. Intestinal bacteria genres Bifidobacterium [17,44], Roseburia [37,42], Coprococcus [37,39], Butyricicoccus [42,46], Lachnospira [17,37], Dorea [37,42], Faecalibacterium [17,42], and Prevotella [17,38] were also found to be decreased. Bifidobacterium has a fundamental role in immune response regulation as well as SCFA production, specifically AA [63]. Previous data corroborate the findings of this review, as low levels of this bacterium have been linked to MS [64]. In fact, probiotic administration might produce an anti-inflammatory effects in MS patients [65]. Prevotella, which is associated with a fibre-rich diet and has regulatory functions through the generation of butyrate [54], also decreased. This decrement has been observed in previous studies [32,57,66,67], which support a possible link between this bacteria and MS, as is the case with other conditions such as diabetes mellitus type 2 [68] or non-alcoholic fatty liver [69]. The Faecalibacterium low levels are consistent with the levels observed in other studies [57,70,71,72], as well as in other diseases such as inflammatory intestinal conditions and irritable bowel syndrome [61,73,74]. Faecalibacterium can convert acetate and lactate into butyrate [32], so these bacteria are considered butyrate producers. Butyrate is thus reduced in inflammatory conditions such as MS [75]. Among its properties, its capacity to attenuate inflammation has been shown in preclinical studies of colitis in mice by modulating mucosa T cells [76]. Similarly, Coprococcus, Butyricicoccus, and Lachnospira, butyrate-producing bacteria, have been observed diminished in previous studies and in other pathologies [56,67,72,77,78]. Roseburia, also decreased in MS patients, is a SCFA producer and essentially a butyrate producer [79]. In addition, Roseburia reduction has been observed in other pathologies such as juvenile idiopathic arthritis [80,81], Behcet syndrome [66,82], irritable bowel syndrome, obesity, type 2 diabetes, nervous system affections, and allergies [79,83,84,85]. Although some of the revised studies in the present work have found a decrease in Dorea, other research has shown an increase in MS patients [86] and also in other pathologies such as Chron’s disease [87]. Therefore, Dorea appears to have either proinflammatory or anti-inflammatory functions depending on the surrounding intestinal bacteria and/or the available nutrients [86]. In contrast, an increase in Akkermansia [37,38,40], Blautia [36,42,44], and Ruminococcus [36,46] has been demonstrated. Akkermansia has immunoregulatory effects by converting mucin to SCFAs [26]. However, as it degrades intestinal mucosa, it can cause intestinal inflammation [88]. Its increase has also been observed in previous studies [32,54,56,57,72] supporting a possible link between its increase and MS, as occurs in other conditions such as Parkinson’s [89,90] and in children with autism spectrum disorders (ASD) [91]. Regarding Blautia, it is an acetate producer [92], which can impulse insulin release and promote metabolic syndromes such as hyperglyceridemia, fatty liver disease, and insulin resistance [93]. Ruminococcus plays an important role in SCFA production and in decreasing inflammation. Furthermore, it is part of the healthy GM, although some species degrade mucosa and, consequently, increase inflammatory conditions such as in MS [94]. It is worth noting that GM composition is subjected to many complex interactions and there are many confounding factors that might influence its healthy levels, making a comprehensive comparison difficult. The disparity in GM among revised studies might be related to patient and control characteristics: MS types, age, disease duration, sample ubication, ethnicity, and the intake of disease-modifying drugs [95,96,97,98]. How these factors contribute to variation in GM is complex, context-dependent, and not completely understood [97]. Accordingly, differences in study methodology could explain, at least in part, the variability observed among studies. Thus, revised studies have used different protocols regarding stool collection, as some were collected by the participants themselves at home [37,38,39,43,44,45,46], while others were collected at the hospital [17,36,40,42]. Differences were also observed in the amplification of the V region of the 16S rRNA target gene, with some studies using the V3-V4 region [37,40,42,44], and others only amplifying the V4 region [36,38,39,46]. Regarding SFCAs, revised articles indicated a decrease in BA serum levels in MS patients, in agreement with a decrease in SCFA-producing bacteria [37,41]. These results are consistent with previous studies showing a decrease in many butyrate-producing bacteria in MS patients [32,37,72], as well as in other autoimmune diseases [50,52]. Similarly, an increase in AA was found in MS patients [45]. This is the most abundant SCFA produced by intestinal bacteria, although it may also be converted to acetyl-CoA by glycolysis. Furthermore, some colonic bacterial strains can convert butyrate through cross-feeding mechanisms [27]. Under conditions of intestinal dysbiosis, SCFA production is often reduced, contributing to an inflammatory environment [99]. In animal models, some results suggest that SCFAs influence the pathogenesis of experimental autoimmune encephalomyelitis and, consequently, the same context is likely to be found in MS [100]. SCFAs produced by GM may alter cellular activity, contribute to modulated immune cells [101], and may also have inhibitory effects on EBV reactivation in MS [55]. Therefore, future research should evaluate the role of GM and EBV reactivity in MS. In fact, in recent years, accumulated evidence on the protective effect of SCFAs has been updated in preclinical data and, recently, in MS patients. Studies support the possibility that SCFAs are potential bidirectional regulators [102]. Several studies have confirmed that SCFAs can promote T cell differentiation directly into proinflammatory cytokine-producing T cells depending on the cytokine context. Thus, SCFAs and their receptors may have the potential to regulate CNS autoimmune inflammation both positively and negatively [103,104]. In particular, SCFAs can cross the BBB via endothelium-localised transporters [27,28]. Therefore, in a dysbiotic situation, the production of SCFAs decreases, which would contribute to an inflammatory state favouring neuroinflammation [99]. It is worth mentioning that if a high fibre diet is ingested, SCFA levels might be drastically altered [105], and it has been suggested that such diets are related to increased levels of butyrate production [106]. Of the four revised studies, only one indicates a dietary control, finding a negative correlation between meat intake and levels of SCFA-producing bacteria [17]. In addition to diet, other factors that alter SCFA levels, such as body mass index, smoking, drug treatment, and probiotic intake [107,108,109,110,111], could be taken into account in future research. There are only few studies evaluating the effect of disease-specific drugs on GM, although some studies suggest that these therapies may restore the intestinal ecosystem to a state of eubiosis [112]. Our results seem to point in this line, as interferon beta-treated patients have similar bacterial abundancy to heathy subjects in different taxa, which are altered in untreated MS patients [36]. In terms of GM modulation, probiotic intake has been found to improve mental health in MS patients, possibly by reducing levels of inflammatory and oxidative biomarkers and decreasing insulin resistance [113,114]. In fact, preclinical studies suggest that probiotic intake may have beneficial effects in reducing the incidence and severity of MS, delaying its progression, and ameliorating motor function impairment. These effects might be mediated by the modulation of immune and inflammatory markers and the GM composition [115]. Ultimately, the studies reviewed in this article highlight the relationship between GM and MS, although a cause–effect relationship between MS and dysbiosis has not yet been established [94,116]. However, new research suggests that disturbed GM may lead to deficient SCFA production by intestinal bacteria, which may deplete the beneficial anti-inflammatory effects on the CNS [117]. Therefore, future work might consider the characterisation and modulation of the MS-associated microbiota as a strategic diagnostic and therapeutic target. In the present review, several limitations have to be determined. The main restriction is that methodological differences between the revised studies are not considered. In addition, all the included studies used a relatively moderate sample size, with a total of 570 MS cases and 478 controls from different regions of the world. Finally, we have reported the results in the taxon observed similarly in two or three studies; some associations might be overlooked, especially in the least abundant taxon. Despite a modest cohort size, diversity in geographical location of participants, and sample processing, the present systemic review brings to light a dysbiosis of the GM in MS patients compared to healthy controls. More specifically, and despite variability among different studies, consistent patterns have been found, as many taxa were identified as over- or under-represented in MS compared to controls. Most of the altered bacteria are SCFA producers, which might explain the chronic inflammation which characterises this disease. Therefore, future research should consider the characterisation and modulation of the MS-associated microbiota as a target for diagnosis and therapy.

PMC10001700

João D. Magalhães,Ana Raquel Esteves,Emanuel Candeias,Diana F. Silva,Nuno Empadinhas,Sandra Morais Cardoso

The Role of Bacteria–Mitochondria Communication in the Activation of Neuronal Innate Immunity: Implications to Parkinson’s Disease

22-02-2023

mitochondria,alphaproteobacteria,innate immunity,antimicrobial peptides,α-Synuclein

Mitochondria play a key role in regulating host metabolism, immunity and cellular homeostasis. Remarkably, these organelles are proposed to have evolved from an endosymbiotic association between an alphaproteobacterium and a primitive eukaryotic host cell or an archaeon. This crucial event determined that human cell mitochondria share some features with bacteria, namely cardiolipin, N-formyl peptides, mtDNA and transcription factor A, that can act as mitochondrial-derived damage-associated molecular patterns (DAMPs). The impact of extracellular bacteria on the host act largely through the modulation of mitochondrial activities, and often mitochondria are themselves immunogenic organelles that can trigger protective mechanisms through DAMPs mobilization. In this work, we demonstrate that mesencephalic neurons exposed to an environmental alphaproteobacterium activate innate immunity through toll-like receptor 4 and Nod-like receptor 3. Moreover, we show that mesencephalic neurons increase the expression and aggregation of alpha-synuclein that interacts with mitochondria, leading to their dysfunction. Mitochondrial dynamic alterations also affect mitophagy which favors a positive feedback loop on innate immunity signaling. Our results help to elucidate how bacteria and neuronal mitochondria interact and trigger neuronal damage and neuroinflammation and allow us to discuss the role of bacterial-derived pathogen-associated molecular patterns (PAMPs) in Parkinson’s disease etiology.

The Role of Bacteria–Mitochondria Communication in the Activation of Neuronal Innate Immunity: Implications to Parkinson’s Disease Mitochondria play a key role in regulating host metabolism, immunity and cellular homeostasis. Remarkably, these organelles are proposed to have evolved from an endosymbiotic association between an alphaproteobacterium and a primitive eukaryotic host cell or an archaeon. This crucial event determined that human cell mitochondria share some features with bacteria, namely cardiolipin, N-formyl peptides, mtDNA and transcription factor A, that can act as mitochondrial-derived damage-associated molecular patterns (DAMPs). The impact of extracellular bacteria on the host act largely through the modulation of mitochondrial activities, and often mitochondria are themselves immunogenic organelles that can trigger protective mechanisms through DAMPs mobilization. In this work, we demonstrate that mesencephalic neurons exposed to an environmental alphaproteobacterium activate innate immunity through toll-like receptor 4 and Nod-like receptor 3. Moreover, we show that mesencephalic neurons increase the expression and aggregation of alpha-synuclein that interacts with mitochondria, leading to their dysfunction. Mitochondrial dynamic alterations also affect mitophagy which favors a positive feedback loop on innate immunity signaling. Our results help to elucidate how bacteria and neuronal mitochondria interact and trigger neuronal damage and neuroinflammation and allow us to discuss the role of bacterial-derived pathogen-associated molecular patterns (PAMPs) in Parkinson’s disease etiology. Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized by the progressive loss of movement control, gaiting and bradykinesia, in consequence of substantia nigra pars compacta (SNpc) neuronal degeneration [1]. The pathophysiological traits of the disease are the presence of Lewy bodies (LBs) in the brain, mainly composed by aggregated alpha-synuclein (α-Syn) [2], microgliosis [3,4] and mitochondrial dysfunction [5]. The neuroinflammatory hypothesis for PD is based on pathological findings showing an increased expression of pro-inflammatory mediators in affected brain areas, DNA polymorphisms of different pro-inflammatory cytokine genes that modify the risk of PD, and finally, epidemiological studies demonstrating that nonsteroidal anti-inflammatory drugs users have a lower risk of developing PD [6]. Additionally, mitochondria dysfunction also plays a key role in the etiology of sporadic PD [5], acting as a hub that connects neuroinflammation and α-Syn expression and aggregation [7]. Indeed, LBs often harbor mitochondrial components [8], which corroborates the fact that neuronal mitochondria are severely affected in PD patients’ brains. Post-mortem studies in PD patients identified alterations in the mitochondrial electron transport chain (ETC), highlighted by a decrease in complexes I and III activities [9,10]. Moreover, several PD mouse models induced with mitochondrial toxins recapitulate some PD features [11,12,13]. Mitochondrial dysfunction induced by rotenone is sufficient to promote the accumulation of p-S129 α-Syn, the pathological form of α-Syn, in mice brains [14]. Studies in post-mortem brains of PD patients revealed that neuronal mitochondria appear to be smaller and swelled [15], suggesting a more reticulated mitochondrial network than age-matched controls. In fact, it was observed that treatment with MPTP, a toxin that targets mitochondria, induced phosphorylation of DRP1, resulting in mitochondrial fragmentation [16]. Moreover, the proteins responsible for mitochondrial elongation, mitofusins 1 and 2, were found to be recruited and sent for degradation by the PINK/Parkin axis in homeostatic conditions [17,18] to allow mitophagy. Mitochondria are distinctive organelles that possess their own DNA (mtDNA) and replication processes independent of their host cellular division. Furthermore, mitochondria harbor unique ribosomes and contain cardiolipin (named after its initial identification in animal hearts) in the inner membrane, a diphosphatidylglycerol lipid also found in the membrane of most bacteria [19,20,21]. This may be explained in light of the endosymbiotic theory, where mitochondria are proposed to have evolved from ancestor Proteobacteria that were engulfed by an archaeal or a different proto-eukaryotic host [22,23] but conserved some of their characteristics during evolution. Since their components are distinct from the rest of the mammalian cellular elements, they are recognized as damage-associated molecular patterns (DAMPs) by toll-like receptors (TLRs) and Nod-like receptors (NLRs) when they are released to the surrounding cytosol [24,25]. In fact, several TLRs are crucial players in disease modulation in PD. A study revealed that neuronal TLR2 is specifically upregulated in the anterior cingulate cortex and substantia nigra in PD [26]. It was also observed that TLR4 expression is essential for the pathogenesis of PD [27,28]. In a mouse model of PD, it was found that neuronal TLR4 ablation was protective [29], halting inflammasome formation and consequent dopaminergic degeneration, thus corroborating the role of TLR4 in PD. In PD neurons, the mitochondrial network is fragmented, which leads to prolonged exposure to mitochondrial DAMPs, triggering a chronic inflammatory response denominated as “sterile inflammation” [30]. Indeed, mitochondrial DAMPs may be released from injured cells and signal other cells [31]. For instance, in our previous studies, we verified that a bacterial metabolite induced neuronal immune activation by targeting mitochondria [11,32]. An increased release of cytochrome c in a cellular model of PD using rotenone was also observed [33]. Cytochrome c can also be recognized by TLR4 and elicit an inflammatory response [34]. Innate immunity activation generates a multitude of cellular responses. TLR and NLR activation not only trigger the inflammatory cascade, but also induce the expression of antimicrobial peptides, a crucial response against bacterial infections. For instance, it was observed that TLR2 is essential to drive the expression of antimicrobial peptide human β-defensin 2 [35]. Likewise, the activation of the heterodimer TLR2/1 is essential in monocytes to activate the production of antimicrobial peptide β-defensin 4 in response to Mycobacterium tuberculosis infection [36]. Notably, the ablation of TLR4 prevents the deleterious effects of rotenone, a complex I inhibitor [37]. Interestingly, α-Syn may also be recognized as a DAMP by astrocytes due to its interaction with TLR4 [38]. Since the innate immune response is extremely well conserved, it is reasonable to believe that neuronal α-Syn- and mitochondrial-released DAMPs can induce autocrine or paracrine signaling pathways [39,40]. Herein we show that an environmental proteobacterium strain can activate innate immunity in mesencephalic neurons, partly through the activation of TLR4 and NLRP3 signaling pathways. α-Syn is expressed upon exposure of neurons to the bacteria and aggregates inside mitochondria, whose dysfunction leads to the fragmentation of their network to promote their degradation by mitophagy. Although we observed activation of the autophagic pathway, we also detected a decrease in the autophagic flow, which potentiated the accumulation of defective mitochondria, thus contributing to the exposure of additional DAMPs and further activation of innate immunity in a self-amplified cycle with obvious deleterious effects. Bacteria invading the gut mucosa or the brain parenchyma can infect multiple neural cell types, leading to inflammation with dysfunction of neural networks and excitotoxicity, regional damage and cell death [41,42]. Indeed, Proteobacteria are gram-negative bacteria that expose lipopolysaccharides (LPS) in their outer membrane and activate innate immunity through TLR4 [43]. To guarantee that we are tackling neuronal contribution to innate immunity activation, we treated primary neuronal cultures with FDU [44] to keep a low level of glial cell contamination in primary mesencephalic neuronal cultures (less than 1% of Iba1, CD11b+ or Trem2-positive cells and less than 20% of GFAP+ cells) [11]. Our data show that neurons exposed to the bacteria increased TLR4 levels (Figure 1a,b; n = 7). TLR4 can activate NF-κB signaling pathway and regulate pro-inflammatory cytokine expression. We observed that bacteria led to the activation of NF-kB, although at the time point selected for the study, we do not see statistical significance (Figure 1c; n = 3). However, we observed caspase-1 activation (Figure 1d; n = 3) associated with NLRP3 inflammasome, which promotes pro-IL-1β cleavage into its mature form (Figure 1e; n = 5–6) to be released (Figure 1f; n = 4–5). Indeed, NF-κB is required for the induction of a large number of inflammatory genes [45], including those encoding IL-1β, TNF-α and IL-6. Additionally, we observed that these neurons also produce and release other inflammatory cytokines, such as TNF-α (Figure 1g,h; n = 3) and IL-6 (Figure 1i,j; n = 4), which may mediate innate immunity in the absence of glial cells. Recently, it has been hypothesized that α-Syn expression in neurons could be part of innate immune response [11,46]. We observed that neurons exposed to the proteobacterium induced α-Syn expression and aggregation (Figure 2a,b; n = 3 and c; n = 4). Previous reports indicate that α- Syn also localizes to mitochondria and contributes to the disruption of key mitochondrial processes [47,48]. We show that α- Syn oligomers also accumulate in the mitochondria (Figure 2d; n = 7) after bacterial exposure. Aberrant α- Syn mitochondrial interaction has been associated with mitochondrial dysfunction, increased mitochondrial reactive oxygen species (ROS) production and mitochondrial fragmentation [49]. We observed an increase in mitochondrial ROS production in neurons exposed to the bacteria (Figure 3a; n = 4) and a decrease in mitochondrial membrane potential (Figure 3b; n = 5). Upon dysfunction, the mitochondria network fragments to allow the removal of damaged components by mitophagy [50]. We detected an increase in the number of mitochondrial individuals (Figure 3c,d; 16 images from n = 4) and a decrease in mitochondrial network branches (Figure 3c,e; 16 images from n = 4), which associates with an increase in mitochondrial levels of the phosphorylated form of the fission protein Drp1 (Figure 3f,g; n = 4–3). Excessive mitochondrial fission can expose DAMPs that will contribute to further activation of innate immunity, creating a positive feedback loop augmenting inflammation [7] unless they are removed by mitophagy. To determine autophagy, we used NH4Cl plus leupeptin (NL) to inhibit lysosomal hydrolases and accurately determine autophagic flux. We observed that LC3II levels increased upon bacteria exposure (Figure 4a,b; n = 4), but its levels did not increase after intralysossomal protein degradation inhibition, which indicates a decrease in autophagic flux (Figure 4c; n = 4). Despite the marginal increase in the formation of mitophagosomes (Figure 4d,e; 6 images from n = 3) and autolysosomes (Figure 4g,h; 6–8 images from n = 3), we clearly see deficient signaling either in the formation of mitophagosomes (Figure 4f; n = 3) or their fusion within the lysosome (Figure 4i; n = 3), which indicates a decreased turnover of dysfunctional mitochondria. The goal of this study was to investigate the potential influence of bacteria–mitochondria communication on PD-related neuronal degeneration. This study reveals that an extracellular proteobacterium is capable of activating neuronal innate immunity, namely cytokine production and α-Syn expression that ultimately target the mitochondria. Alterations of neuronal mitochondria dynamics are crucial to PD neurodegenerative process, which contributes to creating a positive feedback loop to further activate innate immunity. Proteobacteria that represent one of the most diverse bacterial phyla are gram-negative and LPS -producing bacteria [51] that have been proposed to be at the origin of mitochondria [52,53]. Plasma membrane TLR4, also expressed in neurons [54], are activated by LPS (endotoxin) to induce pro-inflammatory responses to invade pathogens [55]. This signaling pathway culminates in the activation of NF-kB that will target inflammatory genes, such as TNFα, Il-1β and IL-6, to trigger neuroinflammation and eliminate the bacterial aggressor [56]. We have exposed mesencephalic neurons to an environmental proteobacterium and observed an increase in TLR4 expression, which upon activation, induced the release of pro-inflammatory cytokines, namely IL-1β, TNFα and IL-6. Our group has previously shown, in pure cortical neurons and mesencephalic neurons, that a bacterial toxin (BMAA) activated innate immunity through TLR4 signaling [11,32]. Moreover, we observed that innate immunity activation in cortical neurons also led to an increased expression and aggregation of Abeta peptide [32] and that mesencephalic neurons innate immunity activation was correlated with an increased expression and aggregation of α-Syn [11]. Using a proteobacterium strain as a challenger, our data clearly show an increase in α-Syn aggregation, which corroborates the potential key role of α-Syn in neuronal innate immunity responses. The activation of TLRs in non-immune cells such as neurons has a pivotal role in recognizing exogenous and endogenous stimuli to trigger inflammatory responses that, in the short run, might have protective effects, namely to clear protein oligomers such as α-Syn in PD, delaying disease progression [56]. Indeed, an increase of pro-inflammatory markers in the blood [57,58], brain parenchyma [59,60] and cerebrospinal fluid [61] in PD patients and patients with other Synucleinopathies was observed, which indicates a chronic activation of TLRs and neuroinflammation that may lead to neurodegeneration. Additionally, it is believed that α-Syn misfolding and mitochondrial dysfunction may trigger neuroinflammation associated with PD [62]. The role of α-Syn is not yet completely understood, but upon expression, it may oligomerize and translocate into the mitochondria, where it interferes with mitochondrial respiration [63]. Indeed, mitochondrial dysfunction induced by α-Syn has been demonstrated [47]. Moreover, in PD patients’ substantia nigra, accumulation of α-Syn oligomers was correlated with mitochondrial complex I deficiency [64]. Further corroborating this data, we show that α-Syn enters the mitochondria after neuronal innate immunity activation by a bacterial strain and induces its dysfunction. Relevant data also revealed that other stressors, namely the bacteria toxin BMAA, also potentiate the accumulation of α-Syn in the mitochondria and are associated with its dysfunction and network fragmentation [11]. One prominent consequence of mitochondrial dysfunction is the induction of its fragmentation in order to eliminate the defective parts of the network by mitophagy and keep cellular homeostasis [65]. Mitochondrial dysfunction and consequent fragmentation will expose DAMPs that initiate multiple inflammatory pathways [62]. Cells developed efficient mechanisms to prevent auto-inflammatory or auto-immune responses towards mitochondrial DAMPs, establishing a state of immune tolerance towards mitochondria [53]. Indeed, an efficient mitophagy allows the removal of defective mitochondria [50] but also plays a role in NLRP3 inflammasome pathway [52]. The activation of NLRP3 inflammasome requires two signals, the first related to TLR activation and production of pro-IL1β, and the second signal is based on the detection of mitochondrial DAMPs that leads to the activation of caspase-1 that cleaves the pro-IL1β, thus allowing the release of these inflammatory cytokines in the extracellular milieu [52]. Our data show that despite the fragmentation of the mitochondrial network after bacteria-induced innate immunity activation, mitophagy is not functioning properly to avoid further activation of NLRP3 inflammasome. Previous data in PD models clearly show that mitochondrial dysfunction impairs mitophagy due to altered microtubule-dependent traffic [13,42,66]. Chung and coworkers showed that neuronal activation of TLR4 by activated microglia led to neuronal autophagy impairment and α-Syn aggregate accumulation [67]. Nevertheless, it was previously demonstrated that neuronal α-Syn may be released to activate microglia. Activated microglia will then degrade α-Syn by selective autophagy via TLR4 activation, which induces transcriptional upregulation of p62/SQSTM1 through the NF-κB signaling pathway [68]. Interestingly, recent data show that mitochondria and α-Syn may be transferred between microglia cells [69], lowering dysfunctional mitochondria and α-Syn burden, thus attenuating the inflammatory profile. Although we do not test this hypothesis, we postulate that neurons may be initially involved in the neuroinflammation signaling pathway by releasing dysfunctional mitochondria and α-Syn aggregates, eventually through extracellular vesicles, to activate microglia cells. This initial activation of microglia might have protective effects regarding the clearance of α-Syn, thus delaying disease progression while chronic activation will lead to neurodegeneration. Neuronal responses after exposure to proteobacteria clearly show a close interconnection between innate immunity activation and mitochondrial dysfunction [70], which allows us to consider the key role of microbes in PD development. Recently, the existence of a BrainBiota that may play a role in brain development and immunity was proposed [71]. It was postulated that low-level bacteria would travel through the gut–brain axis and colonize the brain during fetal development. Later in life, and upon BBB leakage, a characteristic mark of PD, bacteria would reach the brain, change the BrainBiota and contribute to chronic inflammation [71]. Our findings tend to support the hypothesis that translocation of PAMPs (bacteria metabolites, bacterial vesicles or even bacteria) resulting from a dysbiotic leaky gut in “gut-first” PD cases and their access to neurons of the central nervous system may affect neuronal function through mitochondrial signaling and eventually trigger cellular processes characteristic of PD neuropathology. Materials are depicted in Table S1 and Experimental Flowchart in Figure S1 in Supplementary Material. Primary mesencephalic neuronal cultures were performed by harvesting the mesencephalon of C57Bl/6 mice embryos brains at gestation day 14/15 and cultured as described previously [72]. Embryos were collected in Hanks’ balanced salt solution (HBSS) [5.36 mM KCl, 0.44 mM KH2PO4, 137 mM NaCl, 4.16 mM NaHCO3, 0.34 mM NaH2PO4.H2O, 5 mM glucose, 5.36 mM sodium pyruvate, 5.36 mM Hepes, 0.001% Fenol Red, (pH 7.2)] under aseptic conditions. Brains were dissected and the mesencephala were carefully harvested and submerged in HBSS. Collected mesencephala were trypsinized (0.5 g/L) for 10 min at 37 °C. Trypsin action was halted with the addition of trypsin inhibitor (type II-S; 0.75 g/L) in HBSS containing DNase I (0.04 g/L), followed by mechanical dissociation. Cells were then centrifugated at 1000 rpm for 5 min at 4 °C. The pellet was resuspended and washed in HBSS and centrifugated again at 1000 rpm for 5 min at 4 °C. The pellet was then suspended in fresh Neurobasal medium supplemented with 2 mM L-glutamine, 2% B-27 supplement, penicillin (100,000 U/L) and streptomycin (100 mg/L) and 1% heat-inactivated FBS and seeded on poly-L-lysine (0.1 g/L)-coated dishes at a density of 1.3 × 106 cells/mL. For mitochondrial membrane potential and mitochondrial ROS production experiments, neurons were seeded on poly-L lysine (0.1 mg/mL)-coated 24-well plates at a density of 1.3 × 106 cells/mL. Cultures were grown at 37 °C in a fully humidified air atmosphere containing 5% CO2. Half of the medium was changed every other day to serum-free and antibiotic-free medium. At DIV3, cultures were treated with 1:2000 5-Fluoro-2′-deoxyuridine (FDU) to inhibit glial cell proliferation. For autophagy experiments, 20 mM NH4Cl and/or 20 μM Leupeptin (Sigma, St. Louis, MO, USA) were added to the culture medium 4h prior to protein extracts preparation. NH4Cl in combination with Leupeptin allows for the blockage of several types of autophagy by increasing lysosomal lumen pH. This increase halts the activity of lysosomal proteases activity, maintaining the activity of the intracellular proteolysis systems [73]. The alphaproteobacterium strain used in this study belongs to the species Labrys neptuniae, as confirmed from the 16s rRNA gene sequence (Figure S2 in Supplementary Material) amplified with primers 27f and 1492R (Table S1) and sequenced (Eurofins). The strain was streaked on a Tryptic Soy agar (TSA) plate and grown overnight at 35 °C. Bacterial biomass (a loopful of cells) was then suspended in sterile PBS to a final OD600nm = 0.1 and administered to the neuronal cultures at a MOI = 10 for 48 h at 37 °C in a humidified chamber with a 95% air/5% CO2 atmosphere. For all experimental procedures, controls were performed in the absence of bacteria. Protein extracts were prepared for western blotting and assessment of innate immunity pathway markers by ELISA as described in [11]. Mesencephalic neurons were washed with PBS 1× and lysed in 1% Triton X-100 containing hypotonic lysis buffer (25 mM HEPES, 2 mM MgCl2, 1 mM EDTA and 1 mM EGTA, pH 7.5 supplemented with 2 mM sodium orthovanadate, 50 mM of sodium fluoride, 2 mM DTT, 0.1 mM PMSF and a 1:1000 dilution of a protease inhibitor cocktail from Sigma (St. Louis, MO, USA). Scrapped cellular suspensions were frozen three times in liquid nitrogen and centrifuged at 20,000× g for 10 min. The supernatants were collected and stored at −80 °C until further use. Mitochondrial fractions were prepared for α-Syn determination using an ELISA kit. To this end, cell cultures were washed in PBS 1× and scraped in a buffer containing 250 mM sucrose, 20 mM Hepes, 1 mM EDTA, 1 mM EGTA, supplemented with 2 mM sodium orthovanadate, 50 mM of sodium fluoride, 0.1 mM PMSF, 2 mM DTT and 1:1000 dilution of a protease inhibitor cocktail followed by manual homogenization. Cell suspensions were then centrifuged at 492× g for 12 min at 4 °C and the resulting supernatant was centrifuged again at 11,431× g for 20 min at 4 °C. The resulting pellet corresponding to the mitochondrial fraction was resuspended in buffer solution and frozen three times in liquid nitrogen. To analyze innate immunity markers with Elisa kits, cytosolic fractions were prepared by washing neuronal cultures with PBS 1× and lysing cells with lysis buffer (10 mM HEPES; 3 mM MgCl2; 1 mM EGTA; 10 mM NaCl, pH 7.5, supplemented with 2 mM DTT, 0.1 mM PMSF and a 1:1000 dilution of a protease inhibitor cocktail) supplemented with 0.1% Triton X-100. After scraping neurons, the suspensions were incubated on ice for 40 min and then centrifuged at 2300× g for 10 min at 4 °C. The supernatant corresponding to the cytosolic fraction was stored at −80 °C until further use. Protein content was assessed by using Pierce™ BCA Protein Assay Kit (Thermo Scientific, Rockford, IL, USA) according to the manufacturer’s instructions. Western blotting was performed as previously described in [13]. Samples were diluted in 6× sample buffer (4× Tris-Cl/SDS, pH 6.8, 30% glycerol, 10% SDS, 0.6 M DTT, 0.012% bromophenol blue) and boiled at 95 °C for 5 min. For α-Syn oligomers determination, samples were suspended in 2× PAGE sample buffer (40% glycerol, 2% SDS, 0.2 M Tris-HCl pH 6.8, 0.005% Coomassie Blue) and loaded under non-denaturing conditions. Ran gels were transferred onto PVDF membranes (Millipore, Billerica, MA, USA) and blocked for 1 h with 3% BSA, 0.1% Tween in Tris-buffered solution (TBS) at RT. Primary antibodies were incubated overnight at 4 °C with gentle shaking: 1:100 anti-TLR4 from Santa Cruz Biotechnology (Santa Cruz, CA, USA), 1:100 monoclonal anti-α- Syn LB509 from Zymed Laboratories Inc. (South San Francisco, CA, USA); 1:1000 anti-phospho-Drp1 from Cell Signaling (Danvers, MA, USA); 1:1000 polyclonal anti-Tom20 from Santa Cruz Biotechnology (Santa Cruz, CA, USA) and 1:1000 polyclonal anti-LC3B from Cell Signaling (Danvers, MA, USA). 1:10,000 monoclonal anti-α-tubulin from Sigma (St. Louis, MO, USA), 1:5000 β-tubulin from Sigma (St. Louis, MO, USA) or 1:5000 β-actin from Sigma (St. Louis, MO, USA) were used to normalize band intensities. After primary incubation, membranes were washed with TBS-T three times for 5 min each and then incubated with the appropriate secondary antibody for 2 h at RT. After three washes with TBS-T, the chemical fluorescence of bands was enhanced with chemical fluorescence reagent (ECF from GE Healthcare, Piscataway, NJ, USA). Membranes were revealed using a Bio-Rad Chemidoc System. Western blot band densities were assessed using Quantity One Software (Bio-Rad). Band intensities were normalized to housekeeping genes (β-actin and α-tubulin were used as cytosolic samples loading control and TOM20 for mitochondrial samples) and relative densities were calculated against control conditions for each membrane. For immunocytochemistry experiments, mesencephalic neurons were grown in Ibidi 8-well µ-Slides as described in [11]. Following bacteria exposure, cultures were washed twice with PBS 1× and fixed with 4% paraformaldehyde for 20 min at RT. After fixation, cells were washed twice in PBS 1× for 5 min each and permeabilized with 0.2% Triton X-100 in PBS for 20 min at RT. After three washes with PBS 1× for 5 min each, unspecific binding was blocked with a 10% goat serum solution for 1 h at 37 °C. Primary antibodies were then incubated overnight at 4 °C in a 1% goat serum solution: 1:100 polyclonal anti-Tom20 from Santa Cruz Biotechnology (Santa Cruz, CA, USA); 1:400 rabbit monoclonal anti-LC3 XP® from Cell Signaling (Danvers, MA, USA); 1:200 anti-SDHA from Abcam (Cambridge, UK) and 1:100 anti-Lamp1 (clone H4A3) from the Developmental Studies Hybridoma Bank (University of Iowa, Iowa City, IA, USA). After, cells were incubated with the respective secondary antibodies: 1:250 Alexa Fluor 488 and 1:250 Alexa Fluor 594 from Molecular Probes (Eugene, OR, USA). After three washes with PBS 1× for 5 min each, cells were incubated with Hoechst 15 μg/μL for 15 min at RT. After two washes with PBS 1× for 5 min each, 4-88 Mowiol (Sigma; St. Louis, MO, USA) mounting medium was applied to the wells. Images were obtained on a Zeiss LSM 710 confocal workstation (Zeiss Microscopy, Germany) using a Plan-Apochromat/1.4NA 63 lens. Tom20/Lamp1 and LC3/SDHA co-localizations were evaluated using the JACoP plug-in of the ImageJ software as previously described [11]. First, threshold of images was obtained to improve image quality, and mitochondrial footprint was calculated. Then, mitochondrial networks were skeletonized to calculate the remaining parameters. For each condition, a minimum of 20 cells were examined. Mitochondrial membrane potential (ΔΨmit) was evaluated by using the tetramethylrhodamine methyl ester dye (TMRM) (Molecular Probes, Eugene, OR, USA) [74]. This dye enters cells through diffusion and accumulates essentially in the mitochondria due to its negatively charged lumen. As TMRM is positively charged, functional mitochondria are able to retain this probe. Thus, a decrease in TMRM retention associates with a decreased Δψm. After treatments, neuronal cultures with PBS 1× and loaded with 300 nM TMRM in Krebs buffer (132 mM NaCl, 4 mM KCl, 1.4 mM MgCl2, 6 mM glucose, 10 mM HEPES, 10 mM NaHCO3 and 1 mM CaCl2, pH = 7.4) for 2 hr at 37 °C in a humidified chamber and protected from light. Basal readings were recorded for the first 5 min at 37 °C (λex = 540 nm and λem = 590 nm). 1 μM FCCP (protonophore) and 2 μg/mL oligomycin (inhibitor of H+ transporting ATP synthase and an inhibitor of Na+/K+ transporting ATPase) were then added to each well to allow for maximal mitochondrial depolarization and to prevent ATP synthase reversal, respectively. Readings were performed for another 3 min at 37 °C. TMRM retention ability determined as the difference between the total fluorescence (after depolarization) and the basal value of fluorescence. Results were expressed as a percentage of the dye retained within the untreated WT neurons. Measurements were performed using a Spectramax Plus 384 spectrofluorometer (Molecular Devices, Sunnyvale, CA, USA). MitoPy1 is a fluorescent probe that measures the concentration of hydrogen peroxide (H2O2) in mitochondria. After treatments, neuronal cultures were incubated with 300 nM of MitoPY1 dye for 1h in Krebs medium at 37 °C as described in [32]. Basal fluorescence readings were performed for 5 min (λexc = 503 nm; λem = 540 nm). Neurons were then incubated with 5 µM of rotenone (complex I inhibitor) to determine mitochondrial vulnerability, and measurements were taken for the following 30 min (λexc = 503 nm; λem = 540 nm). Amplitudes were obtained by subtracting basal readings from peak values under rotenone challenge and were expressed in relative values to untreated neurons. Caspase.1 activation was assessed as described in [32]. Briefly, 40 μg of protein extracts were incubated in a reaction buffer (25 mM HEPES pH 7.5, 0.1% (w/v) 3[(3-cholamidopropyl)dimethylammonio]-propanesulfonic acid (CHAPS), 10% (w/v) sucrose, 2 mM DTT) with 100 μM of the colorimetric substrate for caspase-1 from Sigma Chemical Co. (St. Louis, MO, USA) for 2 h at 37 °C. Reaction extent was measured at 405 nm using a Spectramax Plus 384 spectrophotometer (Molecular Devices, Sunnyvale, CA, USA). To determine the cytokine levels in neuronal extracts, 25 μg of protein were used for each ELISA kit. NFκB p65, IL-1β, TNF-α, IL-6 and α-Syn oligomers ELISA kits were used per the manufacturer’s instructions as described in [11]. Absorbance was assessed at 450 nm in a SpectraMax Plus 384 multiplate reader. Results were expressed as pg/mL for all markers except results for NFκB, which were expressed as μg/mL protein. All the results were obtained from at least 3 independent experiments done in duplicates. All data are represented as the mean ± SEM. Normality distribution analysis (Shapiro-Wilk’s test) was applied to choose the subsequent parametric or non-parametric tests. Unpaired Student’s t-test was used, and significant values are shown as: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

PMC10001721

Asan M. S. H. Mohideen,Steinar D. Johansen,Igor Babiak

mtR_find: A Parallel Processing Tool to Identify and Annotate RNAs Derived from the Mitochondrial Genome

22-02-2023

mitochondria,mitochondrial long non-coding RNAs,mitochondrial small RNAs,mitochondrial tRFs,multiprocessing,read count algorithm,small RNA tool

RNAs originating from mitochondrial genomes are abundant in transcriptomic datasets produced by high-throughput sequencing technologies, primarily in short-read outputs. Specific features of mitochondrial small RNAs (mt-sRNAs), such as non-templated additions, presence of length variants, sequence variants, and other modifications, necessitate the need for the development of an appropriate tool for their effective identification and annotation. We have developed mtR_find, a tool to detect and annotate mitochondrial RNAs, including mt-sRNAs and mitochondria-derived long non-coding RNAs (mt-lncRNA). mtR_find uses a novel method to compute the count of RNA sequences from adapter-trimmed reads. When analyzing the published datasets with mtR_find, we identified mt-sRNAs significantly associated with the health conditions, such as hepatocellular carcinoma and obesity, and we discovered novel mt-sRNAs. Furthermore, we identified mt-lncRNAs in early development in mice. These examples show the immediate impact of miR_find in extracting a novel biological information from the existing sequencing datasets. For benchmarking, the tool has been tested on a simulated dataset and the results were concordant. For accurate annotation of mitochondria-derived RNA, particularly mt-sRNA, we developed an appropriate nomenclature. mtR_find encompasses the mt-ncRNA transcriptomes in unpreceded resolution and simplicity, allowing re-analysis of the existing transcriptomic databases and the use of mt-ncRNAs as diagnostic or prognostic markers in the field of medicine.

mtR_find: A Parallel Processing Tool to Identify and Annotate RNAs Derived from the Mitochondrial Genome RNAs originating from mitochondrial genomes are abundant in transcriptomic datasets produced by high-throughput sequencing technologies, primarily in short-read outputs. Specific features of mitochondrial small RNAs (mt-sRNAs), such as non-templated additions, presence of length variants, sequence variants, and other modifications, necessitate the need for the development of an appropriate tool for their effective identification and annotation. We have developed mtR_find, a tool to detect and annotate mitochondrial RNAs, including mt-sRNAs and mitochondria-derived long non-coding RNAs (mt-lncRNA). mtR_find uses a novel method to compute the count of RNA sequences from adapter-trimmed reads. When analyzing the published datasets with mtR_find, we identified mt-sRNAs significantly associated with the health conditions, such as hepatocellular carcinoma and obesity, and we discovered novel mt-sRNAs. Furthermore, we identified mt-lncRNAs in early development in mice. These examples show the immediate impact of miR_find in extracting a novel biological information from the existing sequencing datasets. For benchmarking, the tool has been tested on a simulated dataset and the results were concordant. For accurate annotation of mitochondria-derived RNA, particularly mt-sRNA, we developed an appropriate nomenclature. mtR_find encompasses the mt-ncRNA transcriptomes in unpreceded resolution and simplicity, allowing re-analysis of the existing transcriptomic databases and the use of mt-ncRNAs as diagnostic or prognostic markers in the field of medicine. Mitochondria are organelles present within all eukaryotic cells, performing oxidative phosphorylation [1] and apoptosis processes [2], among others. Metazoan mitochondria possess their own genomes, which are relatively small (usually 15–20 kb) and contain 14 to about 40 genes, typically 37 in vertebrates [3]. Owing to the multiple cellular copies of mitochondrial DNA, the abundance of mitochondrial transcripts can range from 5 to 30% (depending on the cell type) of the total cellular RNA [4,5]. Mitochondrial non-coding RNAs (mt-ncRNAs) are referred as those encoded in the mitochondrial genome, although nuclear genome-encoded non-coding RNAs (ncRNAs) can be present in mitochondria [6]. Both mitochondrial small non-coding RNA (mt-sRNA) and long non-coding RNA (mt-lncRNA) have been identified both inside mitochondria and in other cellular compartments, and some of their implicated gene regulatory functions have been proposed [4,5,7,8,9]. Despite the growing evidence of regulatory functions of mt-ncRNAs, no appropriate bioinformatic tools to identify them are available up to date. There are tools such as MITOS [10] or DOGMA [11] to annotate mitochondrial genome, but these tools cannot identify and quantify mt-ncRNAs. Although DOGMA can annotate nucleotide sequences to the mitochondrial genome, the tool requires the entire mitochondrial genome sequence as input and does not work with mt-ncRNAs, which are much shorter. The current analysis of the high-throughput sequencing data relies on the use of tools designed for the nuclear genomic RNA. These tools, as well as DOGMA, cannot identify mt-sRNAs effectively for mt-sRNAs, and frequently have non-templated additions, as well as sequence and length variants [12]. Tools such as tDRmapper [13], SPORTS [14], or MINTmap [15] can be used to analyze mitochondrial tRNA derived fragments (mt-tRFs). However, there is no tool to simultaneously analyze all small RNAs (sRNA) mapping to the mitochondrial genome. Most tools designed for small RNA data analysis deploy a three-step procedure with some minor modifications [16]. This includes: (1) read count generation, (2) mapping the unique set of sequences to a reference FASTA, and (3) parsing the mapped output files. Read count generation is the most time-consuming step, but it can be significantly reduced by parallelizing the processes on all the available CPU cores. We have developed mtR_find, a bioinformatic tool for identification, annotation and analysis of mtRNA in new or existing transcriptomic datasets produced in any type of sequencing technology. mtR_find uses PYTHON’s multiprocessing functionality that helps to parallelize the analysis of multiple sequencing files for read count generation, thereby massively reducing the data processing time. Along with the tool, we propose a nomenclature to encompass the mt-RNA specificity. The tool allows retrieving the important biological information from the existing datasets in a high-throughput mode in an unpreceded efficiency. The total read counts for the three datasets were: 332.3 million (dataset-1, sRNA-seq of liver samples from malignant tumor tissue of HCC patients and non-malignant tissue from uninfected individuals), 318.2 million (dataset-2, sRNA-seq of semen samples from lean versus obese men), and 93.4 million (dataset-3 (RNA-seq of mouse oocytes); Supplementary File S1). The sRNA datasets were analyzed through parallel processing by mtR_find, and the total execution time for datasets 1 and 2 was 3 min 44 s and 2 min 38 s, respectively. For comparison, the total execution time using MINTmap for dataset-1 and dataset-2 was 48 min 2 s and 34 min 7 s, respectively. The mt-lncRNA analysis was not performed using the parallel processing due to pickling limitations in PYTHON multiprocessing module [17]), and the total execution time was 11 min 29 s. The duration of serial execution of datasets 1 and 2 was 9 min 9 s and 11 min 40 s, respectively. Consequently, the serial execution took ~2.75 times longer than the parallel execution, indicating the efficiency of parallel execution. Besides parallel execution, there are other differences in the way the tool handles mt-sRNAs and mt-lncRNAs. The tool does not consider sequences longer than 50 nt for mt-sRNA computation and shorter than 50 nt for mt-lncRNA. For mt-sRNA, every single sequence is considered unique by the tool. For mt-lncRNA, the tool outputs the unique sequence count and, in addition, the counts of lncRNA sequences with same 5′ end but variable 3′ end are summed together. In addition to mt-lncRNAs that are longer than 200 nt, mt-lncRNA option of mtR_find also identifies ncRNAs that are 50–200 nt long, which are categorized as mid-size or intermediate RNAs. In order to study only lncRNAs that are longer than 200 nt, users can use the “—filter 200” argument as a command line option while running mtR_find. Datasets-1, -2, and -3 had, respectively, 36,136, 93,128, and 9222 unique sequences with a total read count greater than 200 (Supplementary Files S2–S4). The numbers of sequences that mapped to the mitochondrial genome were 2120 (constituting 1.2% of total reads), 8899 (4.4%), and 178 (1.4%), respectively (Supplementary Files S5–S7). Out of these, reads mapping to heavy strand composed 71.5%, 67.4%, and 43.5% of the total mitochondria-derived sequences respectively, while the remaining reads mapped to the light strand (Supplementary File S8, Figures S1–S3). We found a diverse size range (Supplementary File S8, Figure S4) and gene origins (Supplementary File S8, Figure S5) of mitochondrial non-coding RNAs in the datasets examined. Datasets-1 and -2 were enriched in mt-sRNAs in the size range of 31–32 nt and 27 nt, respectively, while the mt-lncRNAs in the dataset-3 were in the size range of 87 to 141 nt. Most of mt-lncRNAs in the dataset-3 had length variants (Supplementary File S7). The majority of them belonged to three genes, namely, ATP6, ATP8, and CytB (Supplementary File S8, Figure S5C). There were differences in number of reads mapping to mitochondrial genes between the subject and control groups in both the dataset-1 and dataset-2 (Supplementary File S8, Figure S5). PCA for mt-sRNAs (Supplementary File S8, Figures S6 and S7) and the heatmap of top 50 highly variable read sequences (Figure 1) showed clustering of two different groups consistent with the subject and controls, although there was a small variability within groups resulting from biological replicates. Differential expression (DE) analysis of mt-ncRNAs was performed on the data from dataset-1 (chronic hepatitis C-associated cancer vs. non-cancer liver samples; chronic hepatitis B-associated cancer vs. non-cancer liver samples; chronic hepatitis C-associated cancer vs. uninfected cancer liver tissue samples; and chronic hepatitis B-associated cancer vs. uninfected cancer liver tissue samples, Table 1) and dataset-2 (semen from obese vs. lean subjects). In the dataset-1, there was a significant reduction (p < 0.005) in the relative abundance of tRNA half (tRH) mapping to tRNA genes of nuclear genome origin, namely, tRFs from tRNAGly and tRNAVal in cancer tissue when compared to non-cancer liver tissue [18]. We observed a similar trend for DE mitochondrial tRHs. For example, when looking to chronic hepatitis C-associated cancer vs. non-cancer liver tissue samples comparison, 13 out of 354 DE tRFs were tRHs and 10 of them were significantly downregulated in the cancer cells (Supplementary File S9). Five of these ten mitochondrial tRHs originated from tRNAVal. In the dataset-2, 75 DE mt-sRNAs (39 up- and 36 down-regulated in semen samples from obese vs. lean individuals) were identified, all of them originating from the mitochondrial large subunit rRNA (Supplementary File S10). The majority of them existed as length variants and all of them clustered at a region with sequence start site between 2690 and 2706 in the mitochondrial large subunit (mtLSU) rRNA gene, with 2704 and 2705 being the two most common sequence start sites. The DE mt-tRFs (783 unique mt-tRFs) from the dataset-1 were compared with tRFs downloaded from MINTbase, an extensive database of 28,824 nuclear and mitochondrial tRFs obtained from 12,023 cancer datasets using MINTmap tool [19]. There were 365 (46.6%) tRFs not found in MINTbase, including 214 tRFs-5, 42 tRFs-3, 43 i-tRFs-3, 56 i-tRFs-5, 8 tRNA-half-5, and 2 tRNA-half-3 (Supplementary File S11). All these novel tRFs had normalized reads per million (RPM) value greater than one (Supplementary File S11), a cut-off value in MINTbase. There were 16 simulated sequences of mt-lncRNA, including 7 from the heavy strand, 5 from the light strand, and 4 antisense to heavy strand genes with substitutions and grouped as light strand transcripts. The simulation gave results concordant with the mtR_find (Supplementary File S8, Table S1). The CSV files from both the simulation and mtR_find analyses were loaded as data frames using PYTHON pandas module, element-wise comparison was performed between the two data frames, and the results were similar (Supplementary File S12). mtR_find is the first small RNA tool to incorporate parallel processing by reading multiple input files simultaneously and processing them at the same time. The mtR_find tool performs much better when compared to published small RNA tools such as MINTmap [15]. Results from testing mtR_find on the simulated dataset shows that the sensitivity of mtR_find is high. The read count algorithm of mtR_find can be used for developing tools for the analysis of other sRNA types by replacing the reference and modifying the annotation criteria. Even though the parallel processing significantly reduces the execution time, it has to be noted that the execution time is CPU-dependent. Furthermore, if the number of CPU is not commensurate with the available RAM, the script might run into memory errors. In such a case, a user has to lower the CPU count manually by using the command line parameters to circumvent the issue. The execution time of mtR_find is much lower than MINTmap and also includes the time to download both the GTF file and the mitochondrial genome. If these files are provided manually as input files, then the execution time will be further reduced. Moreover, mtR_find identified 365 tRFs that are not present in MINTbase v2.0. Due to the presence of overlapping reading frames in several mitochondrial genes, mt-sRNA sequence start and end sites of ±3 were used for annotating the mt-sRNAs in our tool; indeed, 266 out of the 365 sequences had sequence start site or end site at ±3 nt from the gene start or end boundary, respectively (Supplementary File S11). And, 42 out of these 266 mt-sRNAs, had sequence start or end site either before or after the 5′ and 3′ end of tRNA gene boundary, respectively. Hence, mtR_find is highly sensitive in capturing all mtsRNAs from the mitochondrial genome. mtR_find identified features in the test datasets that had not been identified before. mtR_find identified reads mapping to the light strand in the range of 28.5–56.5%. This result is discrepant with the previous studies on mt-sRNAs, where it has been shown that the number of reads from the light strand constituted approximately 3–5% of all the mitochondrial reads [4,12]. Notably, we found a considerable number of reads mapping to the light strand in an anti-sense orientation to the heavy strand genes. Small RNAs derived from a nuclear genome are classified based on their biogenesis pathways, and the length of small RNAs acts as a proxy indicator for biogenesis. For example, tRNA half (tRH), miRNAs, and piRNAs are typically 32–34 nt, 21–22 nt, and 26–31 nt in length, respectively, in most studied species [20]. A quick review of the findings from the original studies (dastasets-1 and -2; [18,21]) revealed that these datasets were enriched in tRHs and piRNAs of nuclear genome origin, respectively. Interestingly, we found that a majority of mt-sRNAs in the dataset-1 were tRH of 31–32 nt length, and this frequency of mitochondrial tRH was strikingly similar to that of nuclear tRH [18], suggesting a similar biogenesis pathway. In the case of dataset-2, majority of mt-sRNAs of 27 nt size mapped to mt-rRNA. Although the size range is indicative of piRNA biogenesis, there is only a single study showing the localization of PIWI proteins as well as piRNAs mapping uniquely to the mitochondrial genome [22]. We found the sequence start sites of these putative 29 mitochondrial piRNAs [22] either exactly overlapped or were in the proximity of ±3 nt of sequence start sites of 27-nt mt-sRNAs from the dataset-2. However, it is not known whether these mt-sRNAs are processed through a particular biogenesis pathway with a defined biological function. Except for tRFs, no curated database exists for mitochondria-derived sRNAs or ncRNAs. Therefore, all the remaining differentially expressed mt-RNAs from datasets 1 and 2, have been not catalogued before. In case of mt-lncRNAs in dataset-3, the majority of sRNAs were derived from ATP6, ATP8, and CytB. lncCytB is among the most abundant mitochondrial lncRNAs in HeLa cells [23] and its abnormal trafficking has been demonstrated in human hepatocellular carcinoma cells [24]. To our knowledge, other mt-lncRNAs found in mouse oocytes and 1-cell embryos (dataset-3) have no functional annotations yet. mtsRNAs identified in datset-1 and daaset-2 might have biological implications. The abundance of tRH of nuclear genome origin is positively correlated (Spearman’s rho = 0.67–0.87) with angiogenin mRNA/protein abundance in non-cancer liver tissue [18]. Differences in the expression of nuclear genome-derived tRFs produced through enzymatic cleavage of angiogenin have been observed [25]. These nuclear genome-derived tRFs bind to cytochrome C (a protein complex partially encoded by the mitochondrial genome) to prevent cells from undergoing apoptosis [25] and it has also been showed that these tRFs improve cell survival by acting in response to stress [26,27]. Although it is unknown whether tRFs of mitochondrial origin act in a similar way, differences in the expression of mitochondrial non-coding RNAs have been associated with cancer [8,28,29]. Moreover, it has been shown that the processing of the mitochondrial tRNAs at both the 5′ and 3′ ends has a substantial effect on mitochondrial gene expression [30,31]. Since mitochondrial tRFs are generated from both the 5′ and 3′ end of the mitochondrial tRNAs, and aberrant expression of mitochondrial genes leads to many disease conditions including cancer, DE mitochondrial tRFs in dataset-1 could potentially be implicated to disease condition. In dataset-2, the authors have indicated that differences in expression of piRNAs between spermatozoa from lean and obese men may increase the chances of offspring to develop obesity. No studies investigating the expression of mt-sRNAs in obesity are available; however, it has been shown that mitochondrial peptides are involved in regulating metabolism [32]. The expression of mitochondrial peptides is hypothesized to be controlled by mt-sRNAs [4]. Hence, altered expression of mt-sRNAs may result in an impaired metabolic pathway, which, in turn, might result in obesity. Interestingly, no single mt-sRNA mapped to the termination association sequence (TAS) in the mitochondrial DNA control region, neither in the dataset-1 nor in the dataset-2. Small RNAs originating from the TAS region (co-ordinates 16,161 to 16,188 in the mouse mtDNA sequence) within the mitochondrial control region were expressed in mice [33]. Studies on tRFs have shown that a disproportionately high number of unique tRFs was derived from mitochondrial tRNA genes (n = 22) when compared to nuclear tRNA genes (n = 625) in humans [34,35]. For example, a study on samples from prostate cancer patients demonstrated that 62.0% tRFs originated from nuclear tRNA genes, while the remaining 38% originated from the mitochondrial tRNA genes [35]. This indicates the diversity of mitochondrial tRFs. Many of these mt-sRNAs map uniquely to the mitochondrial genome and not to the mitochondrial DNA-like sequences (NUMTs) in the nuclear genome [36]. Moreover, it has been shown that expression of mt-sRNAs is not associated with levels of NUMT but varies across different tissues depending on the mitochondrial DNA content [36]. This indicates mt-sRNAs have biological roles and, hence, mt-sRNAs were found to be differentially expressed in dataset-1 and 2 could be implicated in disease condition. The code for mtR_find is written in PYTHON 3.6.8 (also compatible with PYTHON 2.7.5) and requires dependencies that include PYTHON modules: pandas (version 0.21.0 and above) [37], multiprocessing, matplotlib [38] (optional) and other tools such as bowtie (version 1.1.2 and above) [39] and samtools (version 1.9 and above) [40]. Depending on the species of interest (input parameter), mitochondrial genomes of Homo sapiens, Danio rerio, Gallus gallus, Mus musculus, and Rattus norvegicus have been downloaded from Ensembl [41]. In the case of Xenopus laevis and Xenopus tropicalis, the mitochondrial genomes have been downloaded from Xenbase [42]. A bowtie index corresponding to the particular genome was created using default parameters. The gene annotations were obtained by downloading the gene transfer format (GTF) annotation file for the species of interest from Ensembl/Xenbase and extracting the information pertinent to the mitochondrial genes. For any other species not listed above, the FASTA and GTF files have to be downloaded and provided manually by the user. The script mt_annotaion.py is useful to pre-process the GTF file (https://github.com/asan-nasa/mtR_find/blob/master/add-on/mt_annotation.py, accessed on 26 August 2022). In the ncRNA-count generation step, a dictionary of unique sequences was created from the list of all input FASTQ files. Using this as a reference, the count number for each unique sequence was determined for individual FASTQ files. The default cut-off threshold value for sequences is <200, because the counting accuracy of low ncRNA-count sequences can be erratic [5,43]. However, users can specify their own cut-off value tailored for the specific needs of their analyses. The output read count file is in comma separated value (CSV) format, in which the row names are unique sequences and column names are file names. Individual rows display the count number of a particular sequence in the corresponding library. In the case of SOLiD sequencing data, reads have to be mapped to the corresponding genome and converted from color-space to FASTQ files using adapt_find script [44], available at https://github.com/asan-nasa/adapt_find/blob/master/adapt_find.py (accessed on 26 August 2022) prior to the read-count generation step. Unique sequences from the read count file were extracted, converted to FASTA format, and mapped against the mitochondrial bowtie index using the following parameters: bowtie --best –v 1 –p 20. The mapped and unmapped sequences from the resulting SAM file were filtered out using samtools. Unmapped sequences carrying a non-templated CCA motif at their 3′ ends were retrieved, the CCA motif was trimmed, and the sequences were again mapped to the mitochondrial genome, this time under zero-mismatch stringent criterion to avoid false positive findings. The sequences mapping to the 3′ end of mitochondrial tRNA genes in the sense direction or to the 5′ end in the anti-sense direction were annotated as having a non-templated CCA additions at their 3′ ends (Figure 2). Genomic locations of mapped sequences were determined (Figure 3). Then, the gene annotation was performed using individual mitochondrial genes (Supplementary File S8, Table S2). The final sequence annotation was based on the position of a mapped sequence and its length within a gene using the MINTbase criteria [19] with some modifications (Supplementary File S8, Table S3). For both mt-sRNA and mt-lncRNA, if the sequence start site is in one gene and the end site is in another gene (Figure 3D), the gene that has the sequence start site is taken for annotation. The only exception to this rule is tRF-1. MINTbase classification of mt-sRNAs includes tRH-5′ and tRH-3′, and tRNA derived fragments (tRFs) include tRF-5′, tRF-3′, tRF-1, and i-tRF. Two levels of ID were produced. The specificID provides a unique annotation for every possible isoform of a sequence. The general ID provides the annotation of the family the given sequence belongs, in the terms of typical starting nucleotide, and skipping information on the sequence length and modifications from the main form. The nomenclature format for mt-sRNA is: “species_name”|”mt-sRNA”|”gene”|”sequence subtype”|”Strand”|”Orientation”|”Sequence start position”|”Sequence length”|Substitutions. For mt-lncRNA, the format is “species name”|”mt-lncRNA”|“gene”|”strand”|”sequence start position”|”sequence length”. The species abbreviation is a three- or four-letter organism code as proposed in Kyoto Encyclopedia of Genes and Genomes (www.genome.jp/kegg/catalog/org_list.html (accessed on 19 February 2023)). The species abbreviations used in the present study are given in Supplementary File S8, Table S4. Gene name refers to one of the mitochondrial genes (Supplementary File S8, Table S2). If the sequence falls in a non-coding region, then it is denoted as “non-coding (“nc”) (Figure 3). The sequence subtype refers to the specific location in a gene transcript (applicable only for mt-sRNAs), as defined in Supplementary File S8, Table S3. Sequence start position refers to the genomic position of the 5′ nucleotide of the sequence. Strand refers to either heavy or light strand. Antisense orientation indicates anti-sense mapping of the sequence to a particular gene. Substitutions refer to any mismatches in the sequence as compared to the reference genome; if they occur, nucleotide position (from the start of the sequence) is given, along with the base letter to which the main form has been altered. The example nomenclature is given in Table 2. We tested the tool on two small RNA (sRNA) datasets [18,21] downloaded from NCBI, and one long non-coding RNA dataset (unpublished study [45]) downloaded from European Nucleotide Archive (ENA). MINTmap was also tested on the two sRNA datasets to compare the performance of mtR_find with that of MINTmap. The two sRNA datasets were generated in studies where mt-ncRNAs were not analyzed. The dataset-1 contained information from sRNA-seq of hepatocellular carcinoma (HCC) versus non-malignant liver samples from subjects with chronic hepatitis B or C (n = 4 for each group), as well as uninfected subjects undergoing resection of metastatic tumors control group (n = 4, Supplementary File S13). In the dataset-2, the information was obtained from sRNA-seq of semen samples from 23 human subjects, classified as either lean (n = 13) or obese (n = 10; Supplementary File S13). The dataset-3 has been generated from RNA-seq of mouse oocytes (n = 2) and 1-cell embryos (Supplementary File S13). In the case of sRNA datasets, the SRA files were downloaded using prefetch SRA utility tool. The SRA file format was converted to FASTQ files using fastq-dump tool [46]. Adapter sequences were removed from the raw FASTQ files, bases with quality score less than 20 were trimmed from the 3′ end. Sequences shorter than 15 nt were removed. The read count of mt-sRNA sequences was extracted by running mtR_find and differential expression analysis was performed using DESeq2 R package [47]. mt-sRNA sequences with a Benjamini–Hochberg adjusted p-value of <0.1 were considered differentially expressed (subject versus control). For mt-lncRNAs, paired-end FASTQ files obtained from ENA were converted to single-read FASTQ files using FLASH [48] and then run on the mtR_find tool. Due to the lack of biological replicates in the dataset-3, only the relative abundance of read counts was reported in our analysis. mtR_find was tested on simulated datasets for both mt-sRNA and mt-lncRNA using separate scripts with the following command line parameters: (1) FASTA file (in this case, zebrafish mitochondrial genome); (2) GTF file (zebrafish mitochondrial gene annotation information); (3) desired number of unique sequences in each stimulated file; and (4) total number of stimulated files to be created. The GTF file was read and separated into two lists. The first list was based on the strand specificity: heavy strand or light strand, while the second one was based on genes. The simulation script picked a random sequence start position from a random gene or from the non-coding region, in either the heavy or the light strands. Then, a random length was selected and added to the sequence start position to compute the sequence end position. Using the sequence start- and end-positions as co-ordinates, the sequence was extracted from the input mitochondrial genome. For the light strand sequences, the reverse compliment of the forward strand sequence was extracted, and a random count number for this particular sequence was assigned for each simulated file. This information was then used to create a simulated FASTQ file using the sequence and count information for each sequence. Random simulation of sequences and the corresponding read counts was performed using PYTHON module “random”. The simulation script outputs a simulated read count CSV file with sequence and annotation information, which should match the output of the mtR_find when the simulated FASTQ files are being analyzed. Simulation scripts used different strategies to distribute reads among different sequences as described in Supplementary File S8, and Tables S2, S4 and S5. However, in both methods the total number of reads was split in such a way that 80–95% were simulated from the heavy strand and the remaining 5–20% were from the light strand. The simulated dataset has been tested using mtR_find tool, and the results were compared with the results from the simulation. The four different parameters were calculated to check the concordance: (1) number of unique sequences; (2) sequences mapping to the mitochondrial genome and the distribution of sequences between the two strands; (3) total read count and count of individual sequences in each file; and (4) annotation information and read count distribution among four bio-types. The bio-types included rRNA, tRNA, non–coding region, and protein-coding genes. Simulation and testing of the tool were performed on a Linux server (Red Hat 4.8.5–28) with Python 3.6.8 (64 CPU cores, 504 GB RAM). tRFs were downloaded from MINTbase [19] as a tab delimited file, while the mitochondrial tRFs (test sequences), obtained from mtR_find, were in CSV format. Both files were loaded as separate pandas data frames and the sequence column was extracted into two separate lists. Then, the sequences from the two lists were compared (Supplementary File S14). Only exact sequence matches were allowed. Existing tools can identify only a sub-group of mtsRNAs. mtR_find is the first publicly available tool to comprehensively analyze and annotate all mitochondrial non-coding RNAs. The novel read count algorithm significantly reduces the execution time, making a high-throughput analysis of multiple datasets possible. mtR_find does not create any intermediate files and, hence, saves disk space. Moreover, mtR_find generates a single script for pre-processing data, mapping reads, and then generating count data with annotation information for files. mtR_find identifies novel mt-sRNAs, such as tRFs or mt-lncRNAs, in the existing datasets. It opens a new analytical possibility to re-examine thematic RNA-seq clusters of datasets in search for novel diagnostics markers.

PMC10001739

Vanessa Ribeiro,Susana G. Martins,Ana Sofia Lopes,Sólveig Thorsteinsdóttir,Rita Zilhão,Ana Rita Carlos

NFIXing Cancer: The Role of NFIX in Oxidative Stress Response and Cell Fate

21-02-2023

NFIX,oxidative stress,cell fate,cancer,development

NFIX, a member of the nuclear factor I (NFI) family of transcription factors, is known to be involved in muscle and central nervous system embryonic development. However, its expression in adults is limited. Similar to other developmental transcription factors, NFIX has been found to be altered in tumors, often promoting pro-tumorigenic functions, such as leading to proliferation, differentiation, and migration. However, some studies suggest that NFIX can also have a tumor suppressor role, indicating a complex and cancer-type dependent role of NFIX. This complexity may be linked to the multiple processes at play in regulating NFIX, which include transcriptional, post-transcriptional, and post-translational processes. Moreover, other features of NFIX, including its ability to interact with different NFI members to form homodimers or heterodimers, therefore allowing the transcription of different target genes, and its ability to sense oxidative stress, can also modulate its function. In this review, we examine different aspects of NFIX regulation, first in development and then in cancer, highlighting the important role of NFIX in oxidative stress and cell fate regulation in tumors. Moreover, we propose different mechanisms through which oxidative stress regulates NFIX transcription and function, underlining NFIX as a key factor for tumorigenesis.

NFIXing Cancer: The Role of NFIX in Oxidative Stress Response and Cell Fate NFIX, a member of the nuclear factor I (NFI) family of transcription factors, is known to be involved in muscle and central nervous system embryonic development. However, its expression in adults is limited. Similar to other developmental transcription factors, NFIX has been found to be altered in tumors, often promoting pro-tumorigenic functions, such as leading to proliferation, differentiation, and migration. However, some studies suggest that NFIX can also have a tumor suppressor role, indicating a complex and cancer-type dependent role of NFIX. This complexity may be linked to the multiple processes at play in regulating NFIX, which include transcriptional, post-transcriptional, and post-translational processes. Moreover, other features of NFIX, including its ability to interact with different NFI members to form homodimers or heterodimers, therefore allowing the transcription of different target genes, and its ability to sense oxidative stress, can also modulate its function. In this review, we examine different aspects of NFIX regulation, first in development and then in cancer, highlighting the important role of NFIX in oxidative stress and cell fate regulation in tumors. Moreover, we propose different mechanisms through which oxidative stress regulates NFIX transcription and function, underlining NFIX as a key factor for tumorigenesis. The nuclear factor I (NFI) family of transcription factors controls the expression of several genes that play a role in various cellular processes (e.g., proliferation, migration, and differentiation) during normal development, as well as in the context of disease, including cancer [1,2]. This family includes four closely related genes, NFIA, NFIB, NFIC, and NFIX, present in human chromosomes 1p31.2-p31.3 (NFIA), 9p24.1 (NFIB) and 19p13.3 (NFIX, NFIC), whose proteins share a highly conserved N-terminal DNA-binding and a dimerization domain [3]. The binding of NFI proteins to DNA occurs in the form of homodimers or heterodimers, which increases the diversity of targets for these transcriptional regulators [4]. Additionally, a recent study on the interaction between a large universe of transcription factors showed that 118 out of the 202 interactions analyzed involved members of the NFI family [5]. This suggests that NFI family members may play a variety of roles in the regulation of transcription, either acting directly as activators or repressors or by interacting with other proteins, namely transcription factors, to modulate their function [1]. While NFI family members share several common features, allowing for compensatory roles [6,7], they also have specific regulatory functions [8,9]. Important information about these specific roles comes from the analysis of knockout mice for different NFI family members [10]. For example, NFIA, NFIB, and NFIX play an important role in glial and neuronal differentiation in the central nervous system [10], while NFIC plays a specific role in tooth development [8], and only NFIX plays a role in muscle development [9]. Not surprisingly, alterations in the expression of these genes can lead to several pathologies, including developmental defects and cancer [2,10]. This multi-faceted family of transcription factors has also been implicated in the regulation of epigenetic modifications in various ways, possibly due to their transactivation domain that interacts with histones H1 and H3 or through the binding and modulation of the activity of different chromatin modifiers [11,12,13]. The global effect of NFI-chromatin interactions seems to be the increase in chromatin accessibility and gene expression [14,15]. Likewise, NFI proteins were shown to positively regulate transcription by recruiting histone acetylases and nucleosome remodeling enzymes (e.g., NURF) and to drive an increase in active chromatin modifications, such as H3K4me3 and H3K36me3 [16]. Apart from the role that NFI transcription factors play in regulating gene expression, they are themselves regulated at different levels. This regulation may occur at (i) the transcriptional level, (ii) the post-transcriptional level via alternative splicing, (iii) the mRNA stability and translational level, regulated by different non-coding RNAs, and (iv) the post-translational level [1,10,17,18,19]. Transcriptional regulation may occur, for example, through the action of paired box gene 6 (PAX6) or empty spiracles homolog 2 (EMX2), two transcription factors that allow the transcription of NFI family members [1,17,18]. NFI family regulation by non-coding RNAs has been addressed particularly in the context of cancer and includes: (i) microRNAs (miRNAs), which are 18–25 nucleotide long abundant non-coding RNAs that inhibit translation or promote degradation of messenger RNAs (mRNAs) and can stimulate proliferation and migration [20,21,22,23,24,25]; (ii) circular RNA (circRNA), which are non-coding RNAs, most of them originated from protein-coding exons that sponge and regulate the activity of miRNAs or serve as protein decoys to recruit and modulate the transcription and translation of downstream target genes [26,27,28]; and (iii) long non-coding RNAs (lncRNAs), which are functional 200 nucleotide transcripts that mainly modulate transcription through a variety of epigenetic mechanisms, post-transcriptional processing via cross-talk with other RNA species, or modulate gene expression through lncRNA-protein interactions [29,30]. Finally, an example of a post-translational modification of the NFI family transcription factors is the conserved cysteine residue that has been shown to undergo oxidative inactivation [19]. This oxidative inactivation of NFI transcription factors, proposed as important for cellular responses to oxidative stress, can be reverted by the glutathione antioxidant pathway [31]. Although more and more examples of this multilevel regulation of NFI transcription factors are being discovered, the choice of downstream target genes and pathways, which are decisive for developmental and disease processes, still needs to be clarified. Cancer is a complex disease characterized by multiple events known as hallmarks [32,33]. These hallmarks are associated with a profound change in the cell’s expression profile, allowing cancer cells to acquire the ability to proliferate, migrate and regain certain characteristics of stem cells. The increased plasticity of cancer cells often correlates with a blockage in differentiation, which, in its turn, depends on alterations in the expression of transcription factors that play key roles during development, such as the HOX, SOX, and PAX families [32]. The co-option of developmental pathways during cancer onset and progression has been described [32,34,35,36], raising the possibility that NFI proteins may play a relevant role in cancer [2,7,10,11,17]. In this review, we build on the existing knowledge about the role of NFIX during development to examine its role in cancer. Thereafter, in the context of cancer, we will focus on how NFIX relates to oxidative stress and alters cell fate and how that impacts tumor progression. To understand the role being played by NFIX in cancer, it is important to characterize its function during development. During mouse fetal myogenesis, NFIX activates specific fetal muscle genes, such as those encoding enolase-β (Eno3) and muscle creatine kinase (Ckm), both known downstream targets of the NFIX pathway [9] (Figure 1A). The transcription factor PAX7 (a key muscle stem cell marker) binds to the Nfix promotor to activate its expression. Nfix transcription also occurs through the action of JUNB (a member of the AP-1 family of transcription factors), which binds to the Nfix promotor via an unknown mechanism [37]. JUNB is activated downstream of ERK kinase signaling, which is low during embryonic myogenesis but increases at the beginning of fetal myogenesis due to a decrease in the RhoA/ROCK axis [37]. The activation of NFIX is, thus, downstream of a switch between RhoA/ROCK and ERK signaling, which occurs precisely at the onset of fetal myogenesis (Figure 1A) [37]. NFIX, in its turn, activates the expression of the fetal muscle-specific genes and inhibits the transcription of embryonic muscle-specific genes, such as slow myosin heavy chain (slow MHC, encoded by Myh7), marking the transition between embryonic and fetal muscle development [9]. NFIX is normally not expressed in adult muscle stem cells (satellite cells), but its abnormal activation is associated with disease progression, namely in the context of muscular dystrophies [38]. Muscular dystrophies are a group of diseases characterized by loss of muscle mass, fibrosis, and chronic inflammation, which are frequently associated with an increase in oxidative stress [39,40,41,42]. The deleterious role played by NFIX in the context of muscular dystrophies has been associated with consecutive cycles of regeneration and degeneration [38]. Additionally, NFIX is thought to contribute to increased oxidative stress both by driving regeneration in dystrophic muscles and by countering the switch of myofibers towards oxidative slow-twitch fibers, which are thought to reduce oxidative stress [9,38,43]. Consequently, this supports the idea that NFIX has a role in regulating oxidative stress levels in the muscle. During muscle regeneration, there is a close interaction between myogenic cells and macrophages, where NFIX regulates macrophage differentiation [44]. Under this scenario, injury-activated satellite cells attract blood monocytes that infiltrate into the damaged muscle and differentiate into pro-inflammatory macrophages, which, in their turn, stimulate myoblast proliferation [44]. Macrophages then switch to an anti-inflammatory phenotype, which sustains myogenic differentiation [45]. This phenotypic change is controlled by NFIX, which becomes activated in response to the inhibition of the RhoA/ROCK axis and by the induction of phagocytosis, a necessary feature for the acquisition of anti-inflammatory phenotype [44]. The anti-inflammatory phenotype enhances tissue repair and promotes fibroblast proliferation, which may lead to fibrosis and is highly detrimental to dystrophic muscles [44,46,47,48,49,50]. In addition to the crucial role in skeletal muscle development, Nfix is also expressed within the nervous system throughout embryogenesis [51]. In mouse cortical development, NFIX promotes the timely generation of intermediate progenitor cells that will originate cortical neurons through the transcriptional activation of the Insc (encoding inscuteable protein) [52]. Moreover, during mouse spinal cord development, the transition from producing neurons to producing glial cells (gliogenic switch) occurs via sequential action of NFI transcription factors [53]. In particular, during this gliogenic switch, NFIX has been shown to act downstream of NFIA and NFIB [53] (Figure 1B). NFIX has also been described to promote the differentiation of neural progenitor cells within the developing neocortex and hippocampus, triggering cell cycle exit via the transcriptional repression of SOX9 [54], a transcription factor required for the self-renewal of cortical neural progenitors [55]. Additionally, NFIX is an important regulator of proliferation and migration in the subventricular zone of the neurogenic niche during mouse embryonic development, a region that continuously generates neurons throughout adult life [54]. NFIX also plays a key role postnatally, by maintaining proliferative progenitor cells in this region, such as those expressing Pax6, Sox2, Hes1 and Hes5, and Mash1, markers for progenitor or transit-amplifying cells [54]. The regulation of proliferation and migration, mediated by NFIX, may be associated with the neuroblast chemoattractant GDNF (glial cell-derived neurotrophic factor), whose gene has been shown to be a potential target for transcriptional activation by NFIX [56]. In addition, NFIX has been implicated in the regulation of post-mitotic cell migration within the hippocampus [54] and rostral migratory stream [56]. NFIX is also involved in the proliferation and repopulation activity of hematopoietic stem and progenitor cells [2]. This transcription factor has been implicated as a modulator of hematopoietic cell fate since its expression prevents early B cell development and favors myeloid differentiation [57]. The transcription factor PU.1, known to control myeloid and early B and T-cell development, and the transcription factors E2A (encoded by Tcf3), EBF (encoded by Ebf1), and PAX5 [58], necessary for B cell lineage commitment and development into mature B cells, are altered in the presence of NFIX [57] (Figure 1C). Moreover, during a stressful event, such as a hematopoietic stem and progenitor cells transplant, NFIX regulates c-MPL (thrombopoietin receptor or myeloproliferative leukemia protein) signaling pathway, promoting the survival of the hematopoietic stem cells [59]. It does so by directly activating the c-MPL promoter, which regulates the maintenance of hematopoietic stem cells in the bone marrow niche, promotes their survival via JAK/STAT and MAPK/ERK signaling cascades, and prevents apoptosis (Figure 1C) [59]. NFIX has also been shown to play a key role in meiosis during spermatogenesis, with NFIX deficiency leading to a blockage in prophase 1 (diplotene), possibly associated with a defect in the synaptonemal complex and accumulation of DNA damage in mouse spermatocytes [60]. The possible role of NFIX as a cell cycle checkpoint regulator during human spermatogenesis was further suggested by another study where the regulation of NFIX expression during spermatogenesis was shown to be controlled by the microRNA miR-663 [23]. Silencing NFIX stimulated proliferation, possibly by increasing the expression of Cyclin A2, Cyclin B1, and Cyclin E1, and DNA synthesis, and inhibited apoptosis of human spermatogonia stem cells [23]. NFIX may also play a role in heart development, even though the data available is still scarce [61]. Interestingly, circRNAs have been indicated in several studies as playing a key role in physiological processes in various diseases, including the initiation and progression of cardiovascular diseases [62,63]. One such example is circNFIX (a circRNA derived from NFIX), which has been suggested to play a role in cardiac development and disease [64,65,66]. Moreover, the downregulation of circNFIX has been shown to lead to increased cardiomyocyte proliferation and angiogenesis [65], supporting the idea that circNFIX downregulation could be important for cardiac regeneration after injury. In addition, circNFIX counters heart hypertrophy by indirectly targeting activating transcription factor 3 (ATF3) in cardiomyocytes through binding to the microRNA miR-145-5p [64]. ATF3, a member of the cAMP response element-binding protein/ATF family, has been linked to heart hypertrophy [67,68]. Research has shown that, by regulating the miR-145-5p/ATF3 axis, circNFIX can attenuate pressure overload-induced cardiac hypertrophy [64]. Additionally, circNFIX expression is altered in response to increased levels of oxidative stress [28]. For example, in the fetal cardiomyocyte-derived H9c2 cell line, circNFIX was found to be downregulated after treatment with the pro-oxidant agent hydrogen peroxide, which correlated with reduced apoptosis [28]. Furthermore, overexpression of circNFIX promoted apoptosis in this model, possibly by reducing the cellular response to oxidative stress [28]. These results suggest that regulation of circNFIX or NFIX may impact heart development and disease through a mechanism that is linked to the oxidative stress response. The crosstalk between NFIX and oxidative stress extends beyond the heart. For example, its expression contributes to increased oxidative stress in response to optic nerve crush in the retina [69]. Taken altogether, these studies allow us to conclude that NFIX plays multiple roles during the development of a variety of tissues, influencing cell proliferation, cell fate, and differentiation. It also affects cell migration, apoptosis, and oxidative stress. These cellular processes are either well-documented hallmarks or emerging hallmarks of cancer [32,33], opening the possibility of an important role of NFIX in cancer. Tumorigenesis is characterized by the gain of malignant properties, including sustained proliferative signaling, phenotypic plasticity, and epigenetic reprogramming, all features also observed during embryonic development [32]. Not surprisingly, several pathways that play central roles during development are also altered during tumorigenesis. This is the case of RhoA/ROCK and JUNB signaling pathways that regulate NFIX expression during myogenesis and are involved in cancer cell proliferation and invasion [70,71]. In prostate cancer cells, SOX4, a transcription factor involved in the development of various tissues and which is commonly overexpressed in tumors [72], is overexpressed and activates NFIX [73]. Moreover, the overexpression of acyl-CoA synthetase 4 in the MCF-7 breast cancer cell line leads to changes in various developmental pathways, including the overactivation of NFIX and its target gene ENO3 [74]. Apart from its positive and negative transcriptional regulation, genomic analysis of the NFIX gene in various tumors has revealed several mutations, including gene fusions [75]. Gene fusions are chromosomal rearrangements, usually involving insertions, deletions, inversions, or translocations, where two independent genes fuse together to form a hybrid gene [76]. These fusions have been studied primarily in the context of hematological and mesenchymal malignancies, but they also contribute to epithelial tumors [76]. Even though the role of NFIX in gene fusions is still not fully understood, it is likely that most of the gene fusions involving this gene have oncogenic properties (Table 1). This is the case of NFIX-MAST1 [77] fusions in breast cancer and may also include the NFIX–PKN1 translocation, described in carcinoma of the skin [78], the BSG-NFIX fusion identified in breast cancer [79] and the NFIX–STAT6 gene fusion, which was identified in a tumor lesion with histological features of a solitary fibrous tumor [75]. To understand the role of NFIX in cancer, it is essential to know how the gene fusions, epigenetic changes, non-coding RNAs targets, and mutations in NFIX and in its regulatory elements contribute to specific pathways that drive tumor progression. This can reveal when NFIX acts as an oncogene and when it acts as a tumor suppressor (Table 1). Tumors are characterized by increased levels of oxidative stress, which impact tumorigenesis in different ways, including by (i) triggering DNA damage; (ii) altering signaling pathways involved in cell proliferation and tumor growth; (iii) leading to chronic inflammation in the tumor environment; and (iv) changing the composition of the extracellular matrix, which impacts cell survival, proliferation, migration, and adhesion [41,83,84,85,86]. Members of the NFI family are thought to be pro-oxidants, and their inactivation is crucial for proper oxidative stress response [19]. NFIX may act as an oxidative stress producer, for example, by activating the transcription of CYP1A1 (encoding cytochrome P450 1A1), which has an NFI binding site in the promoter region [87]. CYP1A1 is known to be pro-carcinogenic [88] and, similarly to other monooxygenases, leads to the generation of reactive oxygen species (ROS) as part of its catalytic activity [89,90]. Under normal conditions, the expression of CYP1A1 is suppressed, possibly due to an autoregulatory loop that controls the expression of CYP1A1 via CYP1A1-based hydrogen peroxide production and the NFI family [89,91]. However, when deregulated, the increased production of ROS and the production of pro-oncogenic metabolites may contribute to tumor progression [88]. Studies have shown that CYP1A1 is upregulated in breast [91], bladder [92], and colon cancers [92]. Accordingly, the knockdown of CYP1A1 has been found to downregulate ERK and PI3K/AKT pathways and to induce the AMPK pathway, leading to a reduction in tumor progression and cancer cell survival [91]. Supporting the idea that oxidative stress impacts the function of NFI family members, hepatoma cell lines treated with the pro-oxidant hydrogen peroxide or L-buthionine- (S,R)-sulfoximine showed impaired NFI binding to its DNA binding site due to increased oxidative stress, resulting in the inhibition of its function as a transcription factor [87]. Analysis of oxidative stress-related differentially expressed genes using data from 594 lung adenocarcinoma patients revealed that NFIX is downregulated in this type of cancer and has a direct correlation with poor prognosis [93]. This study proposed that NFIX downregulation serves as a mechanism for cancer cells to reduce ROS production, thus, increasing their fitness [93]. Similarly, another study found that NFIX upregulation is associated with poor prognosis in breast cancer because of its role in ROS status [94]. This indicates that NFIX may be used as a key gene in a ROS scoring system to predict prognosis and therapeutic efficiency. NFIX has also been identified as part of the common mitochondrial defect signature genes in hepatocellular carcinoma, which are genes activated in response to mitochondrial dysfunction, a major source of ROS in organisms [95], and associated with poor prognosis and reduced overall survival [96]. Besides NFIX protein being associated with oxidative stress in different contexts, circNFIX has also been shown to have an impact on both tumor progression and oxidative stress [28,97,98]. For example, circNFIX was found to promote cancer progression by upregulating glycolysis, as well as glucose uptake in glioma [99] and in non-small cell lung cancer [100], which can lead to overproduction of ROS in the context of diabetes [101]. In glioma, tumor progression was associated with the suppression of miR-378e and consequent expression of ribophorin-II (RPN2) [99], a target of miR-378e that promotes increased ROS and glycolysis [99,102]. Similarly, in non-small cell lung cancer, tumor progression was associated with the suppression of miR-212-3p and upregulation of ADAM10 [100], a protein that has been shown to be involved in oxidative stress-related conditions, such as cancer, Alzheimer, neurodegeneration, and inflammation [103]. Further research is needed in order to understand whether NFIX’s role as a pro-oxidant contributes to ROS accumulation in tumors and therefore promotes genomic instability, increased proliferation, and differentiation. In support of this notion, studies are recognizing NFIX and its target genes/proteins that are involved in oxidative stress as potential therapeutic targets for cancer therapy [91,93,94,99]. Given the pleiotropic role of NFIX during development, it is not surprising that changes in NFIX expression can significantly influence proliferation and differentiation. Apart from NFIX’s indirect role in proliferation through its involvement in oxidative stress, NFIX has also been shown to be involved in cell cycle regulation and cell fate decisions, which are closely linked to proliferation. For example, NFIX downregulation has been shown to reduce proliferation and cell viability in lung cancer [80] but to lead to increased proliferation in the context of endometrial carcinoma [21] and colorectal cancer [20]. On the other hand, overexpression of NFIX in esophageal squamous cell carcinoma has been shown to reduce cell proliferation and induce cell cycle arrest in G1/G0 phase [25]. The role of NFIX in cancer proliferation, migration, and invasion has been linked to the expression of non-coding RNAs, namely miRNA and lncRNA (Table 1). One example is the regulation of NFIX mediated by miR-1290, which has a target site on the NFIX 3′-UTR [25] (Figure 2A). An inverse correlation between the levels of miR-1290 and NFIX protein and mRNA was observed in esophageal squamous cell carcinoma tissue samples, suggesting that miR-1290 is an oncogene that downregulates NFIX and promotes proliferation, migration, and invasion in this type of tumor [25]. Moreover, analysis of the genetic profile of colorectal cancer tissue through screening of genes that were upregulated or downregulated identified increased expression of two miRNAs, miR-1914 and miR-647, in colorectal cancer specimens and cell lines [20]. These miRNAs were shown to promote the proliferation and migration of colorectal cancer cells, functioning as oncogenes, possibly by directly targeting and downregulating NFIX (Figure 2B). The impact of NFIX on proliferation has also been associated with lncRNAs that play diverse roles in regulating gene expression [104]. Numerous lncRNAs can act as competing endogenous RNAs (ceRNAs) to regulate the expression of coding genes that have common miRNA response elements [105], with pancreatic cancer being one example. In normal pancreatic tissue, miRNA-3196 is expressed, leading to a downregulation of NFIX [30]. However, in pancreatic cancer tissue, the lncRNA MAFG-AS1 acts as a ceRNA and binds to the miR-3196, resulting in the neutralization of miR-3196 and the upregulation of NFIX [30]. Functional assays have shown that MAFG-AS1 knockdown suppresses cell proliferation and migration while promoting cell apoptosis in pancreatic cancer [30]. Additionally, when miR-3196 is up-regulated, the proliferative and migratory capacities of pancreatic cancer cells are inhibited (Figure 2C). In addition to cell cycle regulation and cell proliferation, NFIX may also play a role in other cell fates. Apoptosis is a central pathway that is rendered inactive in cancer cells [22,59,106]. It was recently shown that NFIX overactivation has an anti-apoptotic effect via the STAT5 signaling pathway leading to a reduction in apoptosis levels in hematopoietic stem and progenitor cells [59]. This is supported by the observation that the overactivation of NFIX leads to increased expression of the anti-apoptotic factor Bcl2l1 (encoding BCL-XL) in these cells [59]. Additionally, NFIX downregulation through overexpression of miR-744-5p in ovarian cancer has been shown to decrease the expression of BCL2, an anti-apoptotic factor, leading to an increase in apoptosis levels [22]. Moreover, hematopoietic stem and progenitor cells that lack NFIX cannot survive in the bone marrow after transplantation due to an increase in apoptosis [107]. Nevertheless, NFIX silencing in the context of human spermatogonia stem cells seems to suppress early apoptosis [23], suggesting that its role in apoptosis may be tissue and/or cell-type-dependent. Considering the important role of the NFI family in neuronal development, several studies have analyzed NFIX’s role in glioblastomas as a potential tumor-promoter [81,107,108]. One such study found that NFIX promotes glioblastoma cell migration by directly upregulating the expression of EZR (encoding ezrin), which is involved in linking the actin cytoskeleton and the plasma membrane and plays a role in cell migration [81] (Table 1). In accordance with the role of NFIX promoting cell migration, NFIX has been identified as a potential oncogene that plays a role in the development of metastasis. NFIX was recently described as a master regulator activating the expression of 17 genes that are involved in migration and invasion in lung cancer [80]. Using two different cell lines for lung cancer, it was shown that NFIX regulates interleukin-6 receptor subunit β (IL6ST), metalloproteinase inhibitor 1 (TIMP1), and integrin β-1 (ITGB1) genes, all of which are involved in cell proliferation, migration, and invasion [80] (Table 1). Altogether these studies suggest that NFIX may be a key player during cancer onset and progression, modulating several pathways implicated in tumorigenesis. NFIX has been established as a central transcription factor during development, for example, by promoting the switch between embryonic and fetal myogenesis [9] and in adulthood, being required for muscle regeneration [38,43]. The role of NFIX in mediating the switch between different cellular differentiation stages is not unique to muscle. For example, it occurs during the production of glial cells [53] or during hematopoietic cell fate [57]. This raises the possibility that NFIX is a critical factor for cell differentiation, which may be critical during tumor progression and metastasis. While some studies have suggested that NFIX may have a putative role as a tumor suppressor, most studies have identified NFIX as an oncogene (Table 1). However, the exact mechanisms that contribute to the alterations of NFIX mRNA or protein expression in cancer have not yet been fully described. One possible mechanism explaining the importance of NFIX in cancer might be dedifferentiation, which has recently been proposed as an emerging cancer hallmark [32]. In this scenario, it is possible that the control of NFIX expression leads to changes in the differentiation status and promotes a stem cell-like phenotype in cancer cells. This is in line with the role of NFIX in development, where it has been shown to control the differentiation stage of various cell types, including muscle [9,38], nervous system [54], and hematopoietic lineages [57,106,109]. The mechanisms controlling NFIX are diverse, and it is possible that several of these mechanisms may be regulated, or be regulated by, the production of ROS. Apart from the direct regulation of NFIX by ROS through the oxidative inactivation of its cysteine residues [19,31] (Figure 3i), other pathways that control NFIX activation may also be modulated by oxidative stress. For example, RhoA/ROCK and JUNB signaling pathways [70,71] or SOX4 overexpression [73], which control NFIX expression both during embryonic development and in the context of cancer, have been linked to oxidative stress. RhoA/ROCK pathway has been shown to have a bi-directional inhibitory effect of the RAC GTPase RAC1, which leads to ROS generation via NADPH oxidase [110,111,112] (Figure 3ii). This can occur during the phagocytosis of pathogens and apoptotic cells by macrophages, where ROS production may control NFIX expression [110,111,112]. Another RAC GTPase and catalytic subunit of NADPH oxidase, RAC2, has also been described to promote transcriptional activation of JUNB in lung cancer [71] (Figure 3iii), which could be yet another mechanism that leads to NFIX activation. In the scenario of high levels of ROS produced via NADPH oxidase, RhoA becomes activated and subsequently leads to the activation of the downstream targets, such as ROCK. This then allows ROCK to phosphorylate LIM kinase, leading to F-actin stabilization. With LIM kinase upregulation, MAL (megakaryocytic acute leukemia) can no longer be sequestered by actin monomers and translocates to the nucleus, where it activates SRF (serum response factor), a factor that responds to morphological changes in the actin cytoskeleton. RAC GTPases are well known for their role in cancer progression [110,113]. It is possible that NFIX is one of the downstream targets promoting tumorigenesis through increased proliferation, migration, and metastasis. SOX4 was shown to be activated via TGF-β and ROS, promoting cell senescence [114] (Figure 3iv). Another piece of evidence supporting the important role of NFIX in oxidative stress regulation is the fact that NFI I/CCAAT box transcription factor (NFI/CTF1) domain, present in the NFIX family members, was shown to interact with pirin [115] (Figure 3v). Pirin is an iron-binding protein, which is involved in iron metabolism, one of the sources of oxidative stress in organisms, and is regulated by NRF2 (nuclear factor erythroid 2-related factor 2), a major regulator of antioxidant response [116,117]. The role of pirin in cancer has been widely studied in the last decades [118]. Pirin is overexpressed in various types of cancer, such as colorectal cancer [116] and melanoma [119]. Therefore, it is possible that oxidative stress-dependent pirin activation allows its interaction with NFIX, modulating the activation of its downstream target genes. In addition, the important role that miRNA and lncRNA play in regulating NFIX expression may also be linked to oxidative stress (Figure 3vi). There is a growing body of evidence showing that miRNA and lncRNA lead to either a pro-oxidant or antioxidant response [120,121,122,123,124]. This is the case of miR-212-3p, which contributes to oxidative stress [125,126]. Finally, another link between NFIX and oxidative stress comes from circNFIX [64,65,66] (Figure 3vii). The expression of circNFIX leads to glioma progression through the increase in ROS. RPN2, which is part of an N-oligosaccharyl transferase complex, is considered oncogenic and a ROS inducer. In gliomas, miR-378e targets RPN2, suppressing its oncogenic functions, for example, by inhibiting ROS production. circNFIX can sponge the miR-378e action, allowing RPN2 activity (as part of the ceRNA network). By doing so, circNFIX alters glucose metabolism, reduces proliferation, and consequently contributes to glioma progression. [99,102]. These data, therefore, suggest a putative oncogenic role for NFIX. Together, these mechanisms contribute to the regulation of NFIX and ROS levels, which may influence the outcome of cell fate decisions. This is supported by the important role that oxidative stress plays, for example, in proliferation, cell migration and metastasis, and apoptosis [84,86,127,128]. In addition, there is an important link between oxidative stress and glucose metabolism, which has been described not only in cancer [84,86] but also in the context of several other pathologies [129,130,131,132]. In keeping with this notion, studies have suggested that NFIX and circNFIX play an important role in regulating glucose metabolism [99,133]. One example is the above-mentioned activation of RPN2 by circNFIX [99], which has been suggested to promote glycolysis [102]. A recent study has also shown that Nfix was downregulated in a mouse model of obesity in response to glucokinase deficiency, a glycolytic enzyme possibly associated with a reduction in oxidative stress levels [133]. The work being reviewed highlights the role of NFIX in cancer, particularly by showing its strict association with increased oxidative stress. Previous studies suggest that NFIX may be a promising prognostic marker [80,94,134], and even though more research is needed to fully understand the relationship between NFIX and ROS in the context of cancer, it is possible that targeting NFIX offers a means of modulating ROS levels in cancer. When designing new therapeutic strategies, it is crucial to consider that targeting NFIX can impact vital cell mechanisms in various cell types, such as hematopoietic, neuronal, or germ cells. Strategies, such as using adeno-associated virus (AAV)-based gene therapy for efficient and tissue/cell-specific delivery of NFIX silencing molecules (including miRNA or lncRNA), could provide a successful approach. Additionally, a growing number of therapeutic approaches are currently being established to increase ROS levels in cancer cells to a point that surpasses the cells’ redox tolerance, triggering an overt oxidative stress response that ultimately may lead to cancer cell death [135]. A better understanding of how NFIX crosstalks with the oxidative stress response may provide yet another application for the modulation of NFIX levels in cancer treatment. Over the past few decades, NFIX has primarily been studied in the context of skeletal muscle development and muscle dystrophies, as well as in relation to neuronal and hematopoietic cell differentiation and fate. In this review, we explored the role of NFIX in cancer and its crosstalk with oxidative stress pathways. Given the crucial function of NFIX in cell differentiation during embryonic development, it is possible that a potential link between NFIX, oxidative stress, and cancer cell dedifferentiation might be a pivotal factor in tumor progression. Collectively, this review increases our understanding of the involvement of NFIX in both development and cancer, which is essential for the establishment of targeted cancer therapies.

PMC10001743

Stana Tokić,Maja Jirouš,Vera Plužarić,Martina Mihalj,Marija Šola,Maja Tolušić Levak,Kristina Glavaš,Peter Balogh,Mario Štefanić

The miR-20a/miR-92b Profile Is Associated with Circulating γδ T-Cell Perturbations in Mild Psoriasis

21-02-2023

hsa-mir-20a,hsa-mir-92b,hsa-mir-29a,hsa-let-7c,psoriasis vulgaris,γδ T cells

Psoriasis vulgaris (PV) is an autoinflammatory dermatosis of unknown etiology. Current evidence suggests a pathogenic role of γδT cells, but the growing complexity of this population has made the offending subset difficult to pinpoint. The work on γδTCRint and γδTCRhi subsets, which express intermediate and high levels of γδTCR at their surface, respectively, is particularly scarce, leaving their inner workings in PV essentially unresolved. We have shown here that the γδTCRint/γδTCRhi cell composition and their transcriptom are related to the differential miRNA expression by performing a targeted miRNA and mRNA quantification (RT-qPCR) in multiplexed, flow-sorted γδ blood T cells from healthy controls (n = 14) and patients with PV (n = 13). A significant loss of miR-20a in bulk γδT cells (~fourfold decrease, PV vs. controls) largely mirrored increasing Vδ1-Vδ2- and γδintVδ1-Vδ2- cell densities in the bloodstream, culminating in a relative excess of γδintVδ1-Vδ2- cells for PV. Transcripts encoding DNA-binding factors (ZBTB16), cytokine receptors (IL18R1), and cell adhesion molecules (SELPLG) were depleted in the process, closely tracking miR-20a availability in bulk γδ T-cell RNA. Compared to controls, PV was also associated with enhanced miR-92b expression (~13-fold) in bulk γδT cells that lacked association with the γδT cell composition. The miR-29a and let-7c expressions remained unaltered in case–control comparisons. Overall, our data expand the current landscape of the peripheral γδT cell composition, underlining changes in its mRNA/miRNA transcriptional circuits that may inform PV pathogenesis.

The miR-20a/miR-92b Profile Is Associated with Circulating γδ T-Cell Perturbations in Mild Psoriasis Psoriasis vulgaris (PV) is an autoinflammatory dermatosis of unknown etiology. Current evidence suggests a pathogenic role of γδT cells, but the growing complexity of this population has made the offending subset difficult to pinpoint. The work on γδTCRint and γδTCRhi subsets, which express intermediate and high levels of γδTCR at their surface, respectively, is particularly scarce, leaving their inner workings in PV essentially unresolved. We have shown here that the γδTCRint/γδTCRhi cell composition and their transcriptom are related to the differential miRNA expression by performing a targeted miRNA and mRNA quantification (RT-qPCR) in multiplexed, flow-sorted γδ blood T cells from healthy controls (n = 14) and patients with PV (n = 13). A significant loss of miR-20a in bulk γδT cells (~fourfold decrease, PV vs. controls) largely mirrored increasing Vδ1-Vδ2- and γδintVδ1-Vδ2- cell densities in the bloodstream, culminating in a relative excess of γδintVδ1-Vδ2- cells for PV. Transcripts encoding DNA-binding factors (ZBTB16), cytokine receptors (IL18R1), and cell adhesion molecules (SELPLG) were depleted in the process, closely tracking miR-20a availability in bulk γδ T-cell RNA. Compared to controls, PV was also associated with enhanced miR-92b expression (~13-fold) in bulk γδT cells that lacked association with the γδT cell composition. The miR-29a and let-7c expressions remained unaltered in case–control comparisons. Overall, our data expand the current landscape of the peripheral γδT cell composition, underlining changes in its mRNA/miRNA transcriptional circuits that may inform PV pathogenesis. Psoriasis vulgaris (PV) is a debilitating autoimmune dermatosis with a complex etiology and lifelong duration. Psoriatic arthritis, diabetes, and cardiovascular disorders often accompany the skin manifestations, making PV a systemic and highly polymorphic condition [1,2,3,4,5]. Currently, PV is considered a T-cell-driven disease, and conventional αβ T cells have been assigned a major role in epithelial, stromal, and vascular skin remodeling. Emerging evidence, however, demonstrates that innate-like lymphocytes, particularly γδ T cells, also participate in this process, in both human [6,7,8] and animal models of PV [9,10,11]. Mature, human γδ T cells predominantly segregate into Vδ1 and Vδ2 subsets [12] that preferentially populate epithelial barriers and blood, respectively [6,13,14,15,16,17,18]. Both the Vδ2 and nonVδ2 subsets can be further divided into type 1- (cytotoxic, TBX21+EOMES+IFNG+) [19,20,21], type 3- (RORC+IL17A+IL18R1+) [22,23], and type 2-like (ZBTB16+) [24] effector cells. More recently, another classification scheme has been put forward based on the bimodal distribution of γδTCR surface expression in fluorescence-activated flow cytometry [23]. Two distinct classes of blood and tissue γδ T cells have thus been proposed: γδhi cells, which largely align with TRDV1 usage, and γδint cells, which adopt a more nuanced TRDV profile (at least in the bloodstream). The exact fractions, however, vary widely between different individuals and populations [23,25]. In addition, high-throughput RNASeq studies have identified many more distinct subsets of γδ T cells [19,22,26], but their biological significance for PV is virtually unknown. For example, numeric aberrations of γδTCRint (blood), Vδ2+ γδTCRint (blood), and (Vγ9)Vδ2+ T cells (lesional skin and blood) have been reported [6,25], but the exact mechanism that underpins those alterations is unknown. A challenge for future research will be to account for all the processes contributing to γδ T-cell granularity in human PV. In this study, we examined the microRNA (miRNA) expression and its relation to the γδhi:γδint dichotomy in the circulating γδ T cells of PV donors. Endogenously expressed miRNAs are well-established epigenetic regulators of T-cell development and function, with growing evidence demonstrating their critical role in various autoimmune diseases [27]. As such, PV has also been linked to the aberrant expression of >400 miRNAs [28], most of which have been identified in full-depth biopsies of involved and non-involved psoriatic skin [29,30,31,32,33,34,35]. Several whole blood [36] and exosome-derived [37] miRNAs have also been studied, showing potential as biomarkers and instruments for a PV diagnosis [38], prognostication [39], and developing epigenetic therapy [40]. Nonetheless, very few of those associations have so far been confirmed in PV, and even fewer have been examined in circulating γδ T cells [41]. That includes the members of the miR-17~92, miR-29, miR-25~92, and let-7 families, which make up a part of the characteristic miRNA signature in psoriatic plaques [28,29,30,31,32,33,34,35] and play roles in TCR-mediated signaling, cytokine production [42,43], keratinocyte biology [44], type I [45] and innate [46] immunity, and T-cell survival [47]. This paper focuses on four of these miRNAs (miR-20a, miR-29a, miR-92, and let-7c), which mediate translational repression by pairing with the 3′-untranslated region of target mRNAs [48]. First, we tested for their differential expression in sorted γδ blood T cells from healthy controls and PV, then matched these findings to the γδhi:γδint and Vδ2:Vδ1 composition by using cytometric data from our recent and updated study [25]. In Section 2.3, we further related the differentially expressed miRNAs to the bulk expression of their putative mRNA targets (as indicated by TargetScan, miRDB, and TarBase tools, Supplementary Table S1) in γδ T cells, namely, ZBTB16, RORC, RUNX3, TBX21, EOMES, IL18R1, and SELPLG. The associated methodology and data reduction methods are described in Section 4.7. The final section discusses the implications of our results and future challenges. The detailed structure of our sample is described in [25], but we have summarized the relevant points here. The baseline characteristics of the subjects are shown in Table 1. No difference in the studied properties was observed between the two groups (PV vs. controls, Table 1). Most participants were mildly affected, young and middle-aged white males with a history of prior CMV infection, low systemic inflammatory burden, and normal-to-overweight score on the BMI scale. Both groups were well-balanced on the CMV status, a factor that strongly imprints on the γδ T-cell composition [49,50,51]. Neither the CMV viral load (DNAemia) nor the CMV glycoprotein-specific IgG binding were examined. As previously shown [25], the γδ T-cell composition was heavily influenced by the CMV infection history (Figure 1A) and the case–control status (Figure 1B). To illustrate this behavior, we divided the participants by the median CMV IgG level, irrespectively of case–control data. The total number of γδ T cells was similar in both CMV IgG groups, but their composition changed (Figure 1A, Source Data). As expected, the PB Vδ1+ γδ cells were numerically expanded in the highly CMV-experienced environment [49], replacing the γδint Vδ2+ populations (Figure 1C). At the transcriptional level, EOMES, TBX21, and RUNX3 expressions were much stronger at the higher end of the IgG CMV range (Figure 1D), culminating in the highest Vδ1 cell densities (Figure 1D,E, Supplementary Figure S2A,B,D–F), consistent with their transition to the cytotoxic/effector program [19,26,52] (Supplementary Figure S2B,D,F, Supplementary Figure S3). This agrees with the observational results from the independent bulk (Figure 1F) and two scRNASeq studies [26,53]. As expected for a middle-income country [54,55,56], a high prevalence of CMV seropositivity was observed, which could help explain a higher proportion of TRDV1 usage in γδhi cells from our cohort [25] compared to some other populations [23]. This suggests that even more complex effects of the CMV on γδ T-cell biology may be expected [50,51,57]. Still, a small, subdominant channel of γδ cell remodeling would be challenging to detect with the current sample size. In addition, a loosely constrained proxy parameter such as CMV IgG may not possess the desirable properties for this task [58]. As a result, we chose to rigorously account for any potential bias stemming from CMV exposure in our downstream analysis. In contrast to the CMV, Figure 1B shows that in PV γδint Vδ1-Vδ2- cells, a poorly characterized subset of clonally divergent γδ T cells was found in relative excess. Again, no change in total γδ T-cell frequencies was found (Source Data). The reason for this excess of Vδ1-Vδ2- cells in the PV γδint fraction remains to be explained. No change was observed in other cell subsets (Source Data). Next, we investigated the effect of PV on selected peripheral blood γδ T-cell miRNAs. For this purpose, we considered several poorly studied miRNA candidates (miR-20a, miR-29, miR-92b, and let-7c), which potentially target RORC, RUNX3, TBX21, EOMES, IL18R1, ZBTB16, and SELPLG transcripts encoding proteins instrumental in γδ T-cell commitment and differentiation (Supplementary Table S1). The results showed that the bulk miR-20a values were lower in PV compared to control γδ T cells (Figure 2A). Apparently, this loss of miR-20a expression was largely (but not completely) dependent on increasing Vδ1-Vδ2- (Figure 2B), and particularly, γδint Vδ1-Vδ2- cell densities (Figure 2C,D), indicating different timescales for their production (Vδ1-Vδ2- cells vs. miR-20a). This is potentially explained by the suppressed miR-20a formation in certain Vδ1-Vδ2- lineages, which may lead to less efficient miR-20a enrichment. The inclusion of age, sex, and CMV status in the model did not materially affect these results (Figure 2D,E). Notably, even though PV and miR-20a were both associated with Vδ1-Vδ2- γδ T-cell numbers, these relations need not be mediated by the same Vδ1-Vδ2- subset. The miR-20a levels were also decoupled from any other studied cell types and mRNA levels of their predicted targets (RUNX3 and RORC), but coincided well with the ZBTB16, and to a lesser degree with the IL18R1 and SELPLG expressions (Figure 3A), which, in turn, were predominantly associated with TRDV2 usage (but not vice versa; ZBTB16, IL18R1, Supplementary Figure S2G,H), innate-like differentiation (ZBTB16, Supplementary Figure S2D,E), and cell trafficking (SELPLG, Supplementary Figure S3). No statistical evidence of inverse miRNA–mRNA association was found in the pooled analysis, although it has been shown that miRNAs can decrease their target availability. This, however, is highly model-dependent, as the relationship relies heavily on the strength of miRNA–mRNA coupling. In complex cellular mixtures, m(i)RNA composition is primarily determined by cell lineage, the proportion of each cell type, and cell-type-specific programs. miRNAs, by contrast, impart complex [59], mostly weak negative effects on their targets. Under such conditions, modest relations (if any) may be missed, so their absence in leading-order approximations may not be surprising. Nevertheless, a weak reciprocal relation between the miR-20a and the TBX21 expression was observed in the control samples (Figure 3B). This, however, neither confirms a mechanistic relationship nor precludes effects at the protein level. We also detected a difference in miR-92b abundance between the two groups (13.2-fold enrichment, PV vs. controls, bulk γδ T cells, Figure 2A), but it lacked association with either cell numbers, levels of putative target mRNAs (ZBTB16 and SELPLG), or any other biological covariate. For miR-29a and let-7c, no evidence of differential expression by case–control status was observed nor association with the expression of predicted target genes (TBX21, EOMES, and ZBTB16, respectively). The miR-29a expression was higher in men than in women (Source Data); however, this statement must be toned down due to the small number of female participants. Altogether, these results point to the role of the miR-20a in regulating the compositional and transcriptional features of the γδ T-cell pool in PV. The results, however, rest largely on mild illness, raising the question of their applicability to more evolved disease settings. Therefore, much remains unknown about miRNA distribution under realistic conditions. The γδ T cells play an essential role in animal models of the disease [7,9,10]. However, drawing a direct connection between the properties of human γδ T cells and their murine counterparts is not possible. As a result, a mechanistically relevant population has yet to be identified among many different γδ T-cell populations. Here, the subsets at hand are the γδhi/int cells, a largely neglected category that owes its name to a distinct pattern of γδTCR surface expression in flow cytometry. Current evidence suggests that γδhi cells differ from γδint cells by the effector cytokines they produce, TRDV usage, and key transcription factors [23,25]. This notion is further underscored by preclinical data, suggesting that γδhi cells are selectively associated with synovial inflammation in patients diagnosed with spondyloarthritis, a common companion to psoriasis [60]. We improved upon the existing literature in several ways. First, we demonstrated that PV can be sufficient to increase the relative size of the nonVδ1nonVδ2γδint compartment in the bloodstream, although no evidence exists that this is enough to change the composition of skin T cells. This updates the result from Plužarić [25]. Second, a significant downregulation of the miR-20a was observed in PV patients. We found that bulk γδ T cells, depleted from the miR-20a host’s larger Vδ1-Vδ2-γδint population in the blood, lose reciprocally larger amounts of transcripts commonly associated with Vδ2 cells [22,25], such as those encoding DNA-binding factors (ZBTB16), cytokine receptors (IL18R1), and cell adhesion molecules (SELPLG) [22,61], while the levels of miR-20a-predicted targets, related to cytotoxic effector (RUNX3) and Th17-like (RORC) γδ T-cell subsets, were apparently not affected [22]. The total number of γδ T cells did not change in the process, indicating a change in composition, rather than in the size of the circulating γδ T-cell pool. Although we cannot definitively assign this result to a single biological process, these findings are broadly consistent with the suggested blood-to-skin trafficking of Vγ9Vδ2 cells in PV [6], and with reports of a diminished miR-20a expression in joint-infiltrating Vγ9Vδ2 T cells from rheumatoid arthritis [62]. Correspondingly, an increased miR-20a-5p expression has been repeatedly observed in normal-looking and affected human psoriatic skin [28,31]. In terms of functioning, a lower miR-20a expression was previously associated with stronger TCR-mediated signaling and cytokine secretion in CD4+ T cells [42], as well as with improved NK cell-killing capacity [63]. Similarly, mice lacking miR-17~92 in mature CD8+T cells exhibit enhanced memory differentiation and lymphoid homing of T-betloCD8+T cells upon the LCMV challenge [64]. Additional miRNAs that might underlie changes in peripheral γδT cell composition in PV remain to be addressed, as case–control differences in miR-29a and let-7c expression were imperceptible, at least at the level of bulk γδ T-cell transcriptome. This calls for a deeper analysis of miRNAs and their interplay with distinct γδ T-cell populations in PV. Meanwhile, the mechanisms leading to Vδ1-Vδ2-γδint cell accumulation in the blood are still unknown. The case of miR-92b overexpression in bulk γδ T cells is more enigmatic, given the absence of any cellular context or mRNA relationship. Higher levels of miR-92b-5p and its antisense pair, miR-92b-3p, have been observed in non-lesional skin [28,31] and psoriatic keratinocytes [32], respectively, supporting the role of this miRNA family in PV. Elevated miR-92b levels were also reported in activated T cells, and implicated in the negative feedback regulation of the calcineurin/NFAT signaling pathway [65]. Unfortunately, very few studies specifically analyzed the impact of miRNAs on the γδ T-cell properties. There are also a few limitations in our study that should not be overlooked. First, the γδhi/int cells have not been examined to the same depth and extent like the other T cells. This triggers a series of important questions: How are the γδhi/int cells related to the γδ T-cell clusters from high-throughput studies? How does this dichotomy translate into biological differences? Does the clonotypic composition of Vδ1-Vδ2-γδint cells change in PV? This, however, is beyond the scope of the present study and an attempt at a unified description must be left to future studies. Second, the γδhi/int cells have been dichotomized according to the fluorescence intensity of the cells stained with the pan-γδTCR antibody. This dichotomy, however, is far from perfect. A more diffuse pattern of staining can be observed in some individuals [25,66]. In others, the γδhi and γδint populations of blood T cells may be split into multiple clusters [25]. Thus, methodological and technical variations between the studies may result in significant inconsistencies. In addition, genetic diversity, infection history, and environmental effects may conspire to obscure the results in human studies. To provide confidence in these new results, we performed a rigorous check against confounding by common covariates. Third, we used RNA from bulk γδ T cells, thus precluding an efficient probe into target mRNA silencing by miRNAs. This further emphasizes the importance of perturbative studies in highly resolved and carefully purified cell populations, avoiding cell mixtures. The complementary approach is to perform comprehensive (genome-wide) miRNA profiling, which is particularly important when considering spillover effects arising from tightly co-expressed miRNAs. Fourth, direct measurements of miRNA abundance suffer from sensitivity limits in low-expressing cell populations; therefore, they are likely biased towards targets and samples where such a measurement is possible, but are not representative of the population-level trend. As a result, some degree of incompleteness is generally expected at the lower end of the miRNA expression, which effectively puts the obtained estimates closer to their upper boundaries. Improved measurements will be necessary to resolve the existing uncertainties. Finally, mechanistic insight is central to the validity of these findings, posing an unmet need for a deeper, orthogonal characterization of miRNA biology in γδhi/int cells. This would help identify not only the most promising candidates, but also potential targets in PV that could be exploited for a prognostic or therapeutic effect. Despite these imitations, our new analysis provides an updated insight into the γδhi/int partition of blood T cells and its association with the miRNA expression in PV. Elucidating the biological mechanism is essential for interpreting the data from our and future observations. We used archival RNA samples extracted from flow-sorted CD3+γδTCR+ lymphocytes of 13 clinically active, therapeutically naïve psoriatic patients (PV) [(M/F ratio: 10/3; median years of age (IQR): 35(28–43)], and 14 sex- and age-matched, unrelated healthy controls [(M/F ratio: 9/5; 32(28–41) years of age); Table 1.]. Study participants were originally recruited at the Department of Dermatology and Venereology, University Hospital Center Osijek, following physical examination and pathohistological confirmation of psoriasis vulgaris. Disease severity and the impact on the quality of life were assessed using the PASI (Psoriasis Area and Severity Index) and the DLQI (Dermatological Life Quality Index) questionnaires, respectively. The serological markers of past bacterial (QuantiFERON-TB Gold test) and viral exposure (anti-CMV IgG, anti-CMV IgM, anti-HBsAg, anti-HCV) were tested at the time of recruitment, together with a complete blood count (CBC), C-reactive protein (CRP) serum levels, erythrocyte sedimentation rate (ESR), and body mass index (BMI). Patients on either systemic immunomodulation or cytostatic therapy, with malignant, autoimmune, and infectious diseases or allergic reactions within 6 weeks before diagnostic processing were excluded from the study. Written informed consent was collected from all participants prior to sample collection. The study protocol was reviewed and approved by the Ethics Committee of the Faculty of Medicine in Osijek (number: 2158-61-07-18-135). In order to select the miRNAs targeting previously tested mRNAs (RUNX3, IL18R, ZBTB16, RORC, TBX21, EOMES, and SELPLG) [25], we relied on reports of previously validated targets [40,41,42,43,44,45,60,61,62,63] and three target prediction algorithms, namely, TargetScanHuman (Release 8.0) [67], miRDB [68], and TarBase (v.8) [69]. These algorithms incorporate computational methods and experimental validation to predict miRNA–mRNA interactions and they have been widely used in the field of miRNA research [65,70,71,72,73]. In line with that, high target score predictions in at least one target algorithm, or simultaneous targeting of at least two tested mRNAs were used as the miRNA selection criteria (Supplementary Table S1). Some candidates were targeted by multiple miRNAs. Peripheral blood mononuclear cells (PBMCs) were isolated from 10 mL of freshly collected, heparinized blood samples and fractionated by density gradient centrifugation on the Lymphoprep medium (Stemcell Technologies, Vancouver, Canada), as advised in the manufacturer’s leaflet. In short, 10 mL of whole blood was diluted with saline (0.9% (w/v) NaCl) in the 1:1 ratio, carefully layered onto 15 mL of the Lymphoprep medium, and sedimented into leukocyte fractions during a 25 min centrifugation at 800× g, with break off. The harvested mononuclear cells were carefully washed twice in phosphate-buffered saline (PBS), followed by resuspension and 10 min centrifugation at 550× g. The PBMC numbers were determined with the use of the Countess II automated cell counter (Thermo Fisher Scientific, USA) and aliquoted for flow cytometry (min 1 × 106 cells) and fluorescence-activated cell sorting or FACS (min. 6× 106 cells). The flow cytometry of peripheral blood γδT cells was accomplished by monoclonal antibody staining of CD3ϵ (FITC, 1:250, clone UCHT1 gamma, produced at the Department of Immunology and Biotechnology, University of Pecs), TCRγδ (PE-Cy7, 1:100, clone B1, BioLegend), TCRVδ1 (APC, 1:100, clone TS8.2, eBiosciences), and TCRVδ2 (PerCP/Cy5.5, 1:200, clone B6, BioLegend)] surface markers. Dead cells were excluded using LIVE /DEAD Fixable Near IR Dead fluorescent viability dye (ThermoFisher Scientific, Rockford, IL, USA) and unspecific antibody binding was prevented by a 10 min pre-staining incubation with the 5% FcR blocking reagent (TruStain FcX, Biolegend). The γδT cell-count acquisition was performed with the use of the BD FACS Canto II cytometer (FACS Canto II, Becton Dickinson, San Jose, CA, USA) and the collected data were analyzed with FlowLogic v7.2.1. software (Inivai Technologies, Mentone, VIC, Australia). The gating strategy for peripheral γδT cell populations (Supplementary Figure S1) was set according to compensation parameters selected by fluorescence-minus-one (FMO) and single-stained control processing, as described in more detail previously [25]. The second, larger aliquot of paired PBMC samples was used for cell sorting of CD3+γδTCR+ expressing cells on a 4-color S3e cell sorter (Bio-Rad Laboratories, Hercules, CA, USA). As previously reported, a minimum of 15,000 sorted γδT cells were collected from each freshly collected PBMC aliquot, directly into the miRVana™ miRNA Lysis/Binding buffer (Thermo Fisher Scientific, Rockford, IL, USA) and used for subsequent RNA extraction, which was done according to the manufacturer’s instructions. The purity of the sorted γδT cells was estimated using RNASeq analysis of the α-, β-, γ-, and δ-chain TCR repertoire (Archer Immunoverse High Sensitivity TCR Kit, Illumina MiniSeq sequencer, manuscript in preparation). The results of the TRA/TRB/TRG and TRD CDR3 clonotype analysis (Archer Analysis Software) for one representative sample are given in Supplementary Table S2. Before being processed into cDNA, cryopreserved total RNA samples were thawed and the available quantities were measured using the DeNovix QFX Fluorometer (DeNovix Inc., Wilmington, USA). Reverse transcription (RT) of four candidate (hsa-miR-20a-5p, hsa-miR-29a-3p, hsa-miR-92b-5p, hsa-let-7c-5p) and three control miRNAs (hsa-miR-192-5p, hsa-miR-345-5p, hsa-miR-423-3p) was carried out in four sequential steps using the TaqMan Advanced miRNA cDNA Synthesis kit (Thermo Fisher Scientific, Rockford, IL, USA). In short, for initial 3′ poly-A tailing, 5 ng of total RNA was incubated in 5 µL of Poly(A) reaction mixture (45 min at 37 °C) and the 5′ ligation of an adaptor sequence in a 15 µL ligation blend (60 min at 16 °C) was performed to extend all mature miRNAs present, prior to cDNA synthesis. Next, the extended miRNAs were reverse transcribed (15 min at 42 °C) in a 15 µL RT reaction mix composed of 6 µL of 5X RT buffer, 1.2 µL of dNTP mix, 1.5 µL of 20Xuniversal RT primers, 3 µL of 10X RT enzyme mix, and 3.3 µL of RNase-free water. In order to improve the detection of low-expressing miRNA targets while maintaining their relative differential expression levels, 5 µL of each cDNA sample was pre-amplified with 2.5 µL of Universal miR-Amp Primers and 25 µL of miR-Amp Master Mix. The pre-amplified cDNA products were diluted fivefold and the transcript levels of target miRNAs were measured using the QuantStudio 5 Real-Time instrument (Thermo Fisher Scientific, Rockford, IL, USA) in triplicate 15 µL quantitative real-time PCR (qRT-PCR) reactions containing 6.75 µL of the cDNA template, 7.5 µL of TaqMan Fast Advanced Master Mix, and 0.75 µL of TaqMan Advanced miRNA Assay (Applied Biosystems Foster City, CA, USA). The cycling conditions were set according to the guidelines in the manufacturer’s leaflet and the list of assays is given in Table 2. The threshold cycle (Ct) values were collected using QuantStudio Design&Analysis software, v1.5.2. Amplification efficiency and pipetting precision, as assessed by the linear regression coefficient (R2), were measured by five-point, fourfold serial dilutions of the arbitrary standards that were run next to the samples in each experiment, providing an insight into the final achieved ranges of efficiency (80–100%) and R2 (0.980–0.998). Intra-assay variability was less than 1.96%, and a variation of less than 2.34% was achieved between different PCR experiments. Among the three tested control miRNAs, only the hsa-miR-423-3p was successfully amplified in our sample set and thus used for the NormFinder stability evaluation (M = 2.098) and normalization of target miRNAs expression levels. Finally, the fold difference in the relative miRNA quantity was determined with respect to the control group levels, using the efficiency corrected model of the 2−ΔΔCt method as described by Pfaffl [74]. The observed differences in miRNA expression were analyzed relative to the previously collected data on the peripheral γδT cell phenotype, frequency, and transcriptional reprogramming, as well as changes in cytokine and chemokine serum levels of PV patients. The quantification of mRNA for EOMES, RUNX3, TBX21, RORC, CCR6, ZBTB16, SELPLG, and IL18R was performed as reported earlier [25]. The processed single-cell (sc)RNASeq data from Tan et al. [22] were used for creating example figures. The dataset, including cell-type annotations, was downloaded from the Gene Expression Omnibus (GEO), accession number GSE149356 (FACS-sorted human γδ T cells from 2 cord blood donors and 2 adult blood donors, 10X Genomics). The analysis was carried out through Seurat v3.2.3 [52] and Nebulosa v1.6 [75] pipelines. For bulk RNAseq, 337 whole blood samples from the GTEx project [76] were processed using the Gene Expression Profiling Interactive Analysis interface [77] (http://gepia2.cancer-pku.cn, accessed on 20 August 2022). For this analysis, we restricted ourselves to gene–gene cross-correlations by adopting the harmonized TPM (transcript-per-million) data from UCSC Xena [78]. Gaussianity was assessed by the Shapiro–Wilk test, and the homogeneity of variances by Levene’s test. Generally, a nonparametric approach was adopted. Where possible, an equal allocation design was used to maximize statistical power. Continuous data is presented as median with the interquartile range (IQR), except for stacked barplots, where arithmetic means were utilized, because the sum of group-level medians does not readily converge on the grand median. This choice did not significantly affect our results. For downstream analysis, serum CMV IgG levels were winsorized at the upper limit of quantification (250 IU/mL). As most subjects were CMV-experienced, we also explored the effect of past CMV exposure by dividing the sample into two equal subgroups using median CMV IgG quantity. The Mann–Whitney U-test was used for independent group comparisons and the Fisher’s exact test was applied to contingency tables. Pairwise correlations were assessed by the Spearman’s rank test. Shapley’s additive explanations [79], representing the Shapley value decomposition of a multivariate model, were used to determine feature importance, i.e., their marginal contributions to target variables [80]. The computed Shapley values perform reasonably well in sparse models, when predictors are moderately correlated (shapviz v0.2.0 package). Baseline covariates (age, sex, CMV IgG) and the case–control status were used as predictors affecting miRNA expression. The SHAP values were then obtained by fitting the model with and without the cell composition included as a predictor. This allowed us to identify which covariates are likely to play a more vs. less important role in shaping miRNA expression. We also modeled a relationship between putative mediators (case–control status, cell composition) and the miRNA expression by adjusting for a set of baseline features (age, sex) and covariates (CMV IgG, BMI). To this end, we adopted a recently developed framework that can accurately handle interactions and nonlinearity, while minimizing problems due to overfitting [81]. The overall result did not differ qualitatively between the two approaches. For ternary diagrams, color maps were interpolated by fitting 2nd- and 3rd-order polynomials in Cartesian space under the general linear model. Each contour fit (isovalue line of a quantity) was checked for accuracy and consistency. Where appropriate, log-transformed data were used. If not otherwise stated, a two-tailed p < 0.05 was considered significant. No adjustment for multiple testing was applied. All statistical analyses were performed in R v4.0.3 (R Core Team, www.r-project.org). The boxplots, barplots, scatterplots, and ternary maps were generated using R-package cowplot v1.1.0, ggplot v3.3.5, ggpubr v0.4.0, ggtern v3.3.5, patchwork v1.1.1, RColorBrewer v1.1.2, reshape2 v1.4.4, rstatix v0.7.0, scales v1.1.1, Ternary v2.1.0, tidyverse v1.3.0, viridis v0.5.1, viridisLite v0.3.0, and xgboost v1.3.2.1.

PMC10001763

Matti Hoch,Jannik Rauthe,Konstantin Cesnulevicius,Myron Schultz,David Lescheid,Olaf Wolkenhauer,Valerio Chiurchiù,Shailendra Gupta

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System

22-02-2023

inflammation resolution,network modeling,lipid mediators,RNA-seq,machine learning

Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis. Inflammation is a complex and tightly regulated process that protects the body from any form of damage, insult, or infection [1,2,3]. In addition to secreted proteins (cytokines), lipid mediators (LMs) generated from polyunsaturated fatty acids (PUFAs) in the cell membrane play a key role in regulating all the phases of inflammation, from the initial acute response to its fine-tuning of inflammation transition and even termination [4]. During acute inflammation, arachidonic acid (AA) is the main PUFA that is used for the biosynthesis of over 150 different pro-inflammatory lipid mediators (PIMs) (i.e., various classes of prostaglandins, leukotrienes, and thromboxanes) that altogether act as the “fire-starters” of the inflammatory response by controlling vascular and cellular responses and by determining the cardinal signs of inflammation (redness, heat, swelling, pain, and loss of function) [5,6,7,8,9]. In the last two decades, various LMs involved in the termination of inflammation, so-called “specialized pro-resolving mediators (SPMs), have been identified and are composed of over 30 lipids derived from ω-3 PUFA such as docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) [10,11,12,13]. Unlike PIMs, SPMs promote the resolution of inflammation and tissue repair by activating the cardinal signs of resolution (removal, restoration, regeneration, remission, and relief). Their tightly regulated synthesis during the inflammatory response is a crucial step in extinguishing the fire of inflammation, thus favoring the return to homeostasis, as well as in the prevention of excessive inflammatory responses and the development of chronic inflammation [9,13,14,15]. Although acute inflammation involves a large number of cells and molecules, its initiation triggers relatively straightforward and ubiquitous cascades of various strengths depending on the type and amount of stimulus (e.g., production of PIMSs, vasodilation, chemotaxis of various immune cells) that ensure a rapid response in any tissue [1,3]. In contrast, the resolution of inflammation mechanisms (e.g., type and levels of SPM production and their downstream signaling cascades) strongly depends on the tissue microenvironment [9,12]. Although the SPM biosynthetic pathways, including regulatory enzymes, are now largely identified and are the very same as those involved in PIM production, the actual regulatory processes underlying cell-type-specific mediator profiles remain elusive. In 2018, Norris and Serhan performed a metabolipidomics analysis of human whole blood and identified functional and cell type-specific LM profiles [16]. Their results showed that haematopoietically and functionally distant cell types have similar LM profiles and, vice versa, closely related cells can synthesize substantially different LMs, indicating individual cell type-specific regulations. LMs are secreted to neighboring cells in an auto- and paracrine fashion [17,18]. Such a highly localized response would require cell-type-specific transcriptional programs and thus a cell-type-specific expression of transcriptional regulatory networks. Usually, cell types are defined by cell-type-specific markers, morphological features, and functional properties or by their distinct (multi-)omics profiles [19]. With the advancement of single-cell RNA sequencing (scRNA-Seq), new subsets of existing cell types are constantly being defined, and the established boundaries between cell types seem to disappear [20]. Thus, modern experiments focus on single-cell data rather than bulk samples of apparently related cells. However, the idea of subsets of a defined cell type also adds new complexity to understanding cell-type-specific signal transduction that distinguishes them from others. To address the challenge of analyzing physiological or functional relationships in single cells, unsupervised machine-learning approaches proved to be extremely useful for identifying patterns in single-cell expression profiles [21,22]. In addition to clustering cells based on their omics profiles, generating topological features from cell-type-specific molecular interaction networks enable the study of functional relationships between molecules and genes [23]. However, the analysis capabilities rely on the causal interactions in the network, making network construction and curation an essential step. Recently, we published the Atlas of Inflammation Resolution (AIR) as a publicly available, web-based knowledge platform of molecular interactions and biological processes involved in acute inflammation and its resolution [24]. We have identified key processes at each stage of inflammation and developed a standardized representation of the associated molecular interactions in so-called standardized molecular interaction maps (MIMs). The manually curated causal interactions enable the use of systems biology approaches to infer regulatory circuits, predict signal transduction pathways, or perform perturbation experiments [25]. Among others, the AIR provides a detailed description of the biosynthetic pathways of PIMs and SPMs from their precursors AA, DHA, and EPA. In this study, we investigated cell-type-specific transcriptional networks associated with LM synthesis pathways. We mapped scRNA-Seq data to gene regulatory networks extracted from the AIR and examined how the networks are affected by differences in the expression of transcription factors. We investigated how cellular LM profiles are modulated by changes in the cell-type-specific network topology of gene regulatory networks (GRNs). By applying unsupervised machine learning approaches to network topological features extracted from the GRNs, we clustered single cells according to their regulatory mechanisms and identified their key gene regulators. We have shown how the application of network-based approaches can improve the analysis of functional molecular pathways and their regulatory networks using scRNA-Seq data. Our results shed light on the gene regulation of LM synthesizing enzymes across various immune cell types. We clustered the cells based on the expression profile of genes included in the AIR database, i.e., being directly related to immunological processes (Figure 1A and Figure 2A). The dimensionality reduction largely restored the cell type clusters as they are defined in the metadata of both datasets. We investigated the expression of LM enzymes in the cells, and whether clusters of enzyme expression correspond to Uniform Manifold Approximation and Projection (UMAP) clustering. Additionally, for each cell, we analyzed whether the substrates of LM biosynthesis, AA, DHA, or EPA, are linked to the final products through the expression of catalyzing enzymes. The GSE122108 dataset consists of mononuclear phagocytes, mainly macrophages, of different tissues, with various pro- and anti-inflammatory stimuli. The cell types with fewer samples, such as monocytes, dendritic cells, and microglia cells, were partially restored (Figure 1A). Macrophage samples are widely scattered and partially mixed with the clusters of the other cell types because they originate from a wide variety of tissues. One macrophage cluster separates from all other cells and consists mainly of peritoneal cells. These peritoneal macrophages also show a distinct LM enzyme profile, with an expression of many genes and the only cells with consistently high expression of Alox15 and Ptgis and, thus, are the only cell types expressing the required enzymes for all LM classes (Figure 1C). From the analysis, it emerged that while almost all cell types are fully capable to synthesize prostaglandins, leukotrienes, and thromboxanes, very few cell types can only synthesize SPMs. Indeed, lipoxins (that are generated by AA but still belong to the super-family of SPMs), protectins, and D-resolvins are produced only by macrophages, maresins only by macrophages and microglia, while E-resolvins are produced by all immune cells, including dendritic cells and monocytes (Figure 1B). Interestingly, lipoxins, protectins, and D-resolvins show a similar pattern due to the expression of the enzyme Alox15. In contrast, a group of dendritic cells expresses only those enzymes required for synthesizing E-resolvins and leukotrienes. Microglia also show a consistent expression profile, particularly of Alox5, Cbr1, Gpx4, and Ptgs1. The GSE109125 dataset consists of many different cell types spanning the hematopoietic lineage and includes stem cells, epithelial cells, and both compartments of innate and adaptive immune cell populations, with monocytes being the only missing cell subsets. The UMAP of immune-filtered gene expression was able to restore the cell type groups to a high degree (Figure 2A). The two-dimensional projection of the UMAP graph shows the cell branching in two directions starting from the hematopoietic cell group. Except for B cells, which are placed closer to the myeloid cells, these two groups coincide with the lymphoid and myeloid lineages, respectively. The analysis revealed that the overall ability to synthesize LMs, based on the expression of required enzymes, is much lower in lymphoid than in myeloid cells (Figure 2B). In particular, cells belonging to the myeloid lineage and hematopoietic stem cells are the ones most capable to biosynthesize both PIMs and SPMs, with macrophages and granulocytes (neutrophils, basophils, and eosinophils) being the most efficient due to the high expression of LM enzymes (Figure 2C). Mast cells and ILCs show a similar biosynthetic pathway in producing PIMs and only one class of SPMs, i.e., E-resolvins. As expected, NK cells and NKT cells also share a similar ability to synthesize the same class of LMs, which are limited only to prostaglandins (except for I-prostaglandins) and thromboxanes; however, only NKT cells can produce maresins. Interestingly, epithelial cells display a biosynthetic pathway identical to NKT cells. Of note, it seems that neither T cells nor B cells are capable produce any LMs. Despite apparently similar expression profiles of LM enzymes, cells may differ in transcriptional circuits that tightly regulate LM synthesis. Moreover, a similar expression profile may be regulated by substantially different transcription factor networks, which would be required for cell-type-specific responses to stimuli in different tissues. Thus, we analyzed the connectivity between transcription factors and enzymes of each LM class in the cell-type-specific GRNs. After dimensionality reduction for all classes, the embeddings were combined and projected into single UMAPs for each dataset (Figure 3A,B). For each cluster, we identified the genes with the most significant differences compared with all other cells (adj. p-value < 0.05, see methods). Detailed information on all clusters, their predicted genes, and included samples are available in the Supplementary Material. In the GSE122108 dataset, we observed many separate clusters and good restoration of the main cell types, i.e., dendritic cells, macrophages, microglia, and monocytes (Figure 3A, Supplementary File S1). Of note, macrophages appeared as smaller clusters that were partially composed of tissue-specific cells, e.g., from the aorta, heart, or liver. We identified the significant (adj. p-value < 0.05 for any LM class) genes of the microglia cells, which build the most defined cluster in the UMAP plot (Figure 3C). For the two highest-ranked genes, Mef2a and Xrcc5, we additionally showed their regulatory score in relation to their expression in all samples. The plots show how the score is significantly increased in the microglia cells and, especially for Mef2a, is independent of its expression. In the literature, information on tissue-specific transcriptional regulation of LM biosynthesis is very sparse. Hence, to compare our results with experimental data, we searched the literature for any evidence supporting the immune modulatory function of the genes related to microglia. Of the thirteen genes, we found clear evidence in the literature for eight genes on their relevance in microglial function and neuronal inflammation (Mef2a [26], Hdac11 [27,28], Smad3 [29], Mef2c [30], Arid1a [31,32], Zfhx3 [33,34], Ets1 [35], and Jun [36]). Four genes were mentioned in experiments on microglial inflammation (Xrcc5 [37,38], Zfp191 [39], Prdm1 [40], and Usf2 [41]), whereas no information was found in the literature for only two genes (Znf383 and Nfrkb). The mode of action of the predicted genes in modulating microglia function has been attributed to their influence on cytokine expression. Our results suggest that they modulate the immune response by also regulating the expression of enzymes involved in the biosynthesis of LMs. Smad3, Jun, Usf2, and Xrcc5 have already been described in their regulation of prostaglandins, while little to no research is available on the other LM classes [42,43,44,45]. Mef2a and Mef2c have been identified as downstream effectors of PGE2, which could indicate a feedback loop on prostaglandin e synthesis [46,47]. In contrast, in the GSE109125 dataset, the original cell types are more heterogeneously distributed between clusters (Figure 3B, Supplementary File S2). The differences in the expression of immune-related genes between the major immune cell types are not reflected in the TFs associated with the LMs. However, two clusters consisting of hematopoietic stem cells and mast cells, respectively, are strongly separated. While no significant TFs were identified for the latter, the former shows a division into three subclusters, from each of which several significant TFs were identified. Interestingly, based on cell metadata, the three subclusters appear to represent stages of lymphoid hematopoiesis, namely (i) bone marrow-derived stem cells (BMSCs) followed by (ii) early (DN1 and DN2a lymphocytes) and (iii) late lymphoid progenitor cells. While BMSCs express many LM enzymes, they are downregulated in lymphoid progenitors. When comparing the regulatory scores of stem cells and early lymphoid progenitor cells, Hlf had the greatest difference in its score for all LM classes (not shown). Hlf is an important regulator of lymphoid development in the hematopoietic lineage [48]. Our results suggest that modulation of LM synthesis by gene regulation of LM enzymes may play a role in shaping the fate of lymphoid cells by Hlf. Several samples in the GSE122108 data were treated with pro- or anti-inflammatory stimuli at several time points, including lipopolysaccharide stimulation (LPS), C. albicans infection, induction of injury, paracetamol, and thioglycolate. We compared the cells at successive time points for each stimulus and identified the TFs with the strongest changes in their gene regulatory activity for each LM class (Figure 4A). For the selected genes, we additionally show violin plots comparing their expression values (read counts) and topology scores, showing that the estimated change in connectivity is independent of their expression (Figure 4B). In general, the predicted that TFs show a strong variability between cells and the different stimuli, suggesting that gene regulation of LMs in the immune response is highly cell-type and environment specific. Additionally, especially at early time points, the identified TFs also differ substantially between PIMs (e.g., the prostaglandin classes) and SPMs (e.g., the resolvin classes) due to the distinct enzyme profile, arguing for fine-tuned gene regulation. At later time points, the difference between PIM and SPM classes becomes smaller, and the number of overlapping TFs increases. Many predicted genes are well-known regulators of the immune response to respective stimuli. For example, in liver macrophages stimulated with APAP, Hes1 appears to be a key regulatory TF of most SPM classes. In vivo experiments showed that blocking the Notch signaling pathway in mice reduced Hes1 levels and increased susceptibility to APAP-induced liver injury [49]. In thioglycolate-stimulated monocytes/ macrophages, our model predicted several genes related to both PIMs and SPMs synthesis, which have also been described in the literature, such as Epas1 (prostaglandins), Egr2 (prostaglandins), Cebpb (all LM classes), and Srebp1 (SPMs). Epas1, coding for HIF-2α, is an important mediator of cellular processes and macrophage recruitment in response to hypoxia [50]. In an experimental thioglycolate periodontitis model, Egr2 and Cebpb were required for macrophage activation [51]. In Srebp1 knockdown mice, thioglycolate-elicited macrophages showed increased levels of pro-inflammatory cytokines and reduced levels of DHA and EPA during the resolution phase after Tlr4 activation [52]. Although being related cell types, the five subtypes of LPS-stimulated lung macrophages also differ in the predicted TFs. Two subtypes of lung macrophages originate from broncho-alveolar lavage (BAL) and show a similar gene regulation of prostaglandins through Klf10 and Vhl. Both genes have already been associated with inflammatory responses in BAL macrophages [53,54]. For the other LMs, both BAL subtypes do not overlap in the predicted TFs. The remaining lung macrophage subtypes are defined by cell sorting markers. Their samples for which data are available on days zero and three after LPS stimulation overlap at Stat1, Stat2, and Pias1. The results become more diverse at later time points (day six vs. day three). We observed that the three MHC-II- macrophage and monocyte subtypes partially overlap in Foxk2, Rora, and Ing4 genes that are associated with cytokine production in response to LPS [55,56], while for the MHC-II+ subtype, we predicted autophagy-related genes Rb1cc1, Rb1, and Hdac2 [57,58,59]. Whether or not this difference is caused by MHC-II is yet to be determined, as only limited evidence connects MHC-II with the predicted genes. Since the transcriptional regulation of LMs appears to be tightly regulated and cell-type-specific, we investigated the extent to which closely related cell types may differ in the transcriptional interaction networks of PIM and SPM synthesis. We identified the cell pairs with the smallest distance in expression-based UMAP but the largest distance in transcriptional network-based UMAP. The top-ranked sample pair consists of a macrophage from the aorta and a macrophage from the lung stimulated with LPS (Figure 5A). Both tissue-specific subtypes of macrophages appear to have a nearly identical transcriptomic profile but substantially differ in LM gene regulation. Thus, we extracted the core regulatory networks (CRNs) to gain further insight into the genes contributing to the observed differences (Figure 5B) and we additionally generated a CRN of an unstimulated sample of the same lung macrophage subtype but without LPS stimulation to ensure that the difference is not caused by the response to LPS. Interestingly, the CRN shows that the expression of most LM enzymes is similar except for Ptgs2, which is not expressed in aorta macrophages. In contrast, Ptgs2 is highly expressed in aorta macrophages with high expression levels of the TFs Jun, Egr1, and Fos. All these three genes are highly associated with atherosclerotic inflammation [60,61,62]. Egr1 is involved in the response to mechanical or oxidative stress and, thus, the development of atherosclerosis from plaques and hypertonia [60,63,64]. The immune response is a tightly regulated system involving a large number of different cell types with specific spatiotemporal functions. Over the years, experimental research has attempted to identify and describe the molecular and functional processes involved. However, although more and more knowledge is being gained and regulatory processes are being elucidated, increasing complexity is blurring the boundaries between cell types. At the same time, it is challenging to study the role of specific cells in immunological processes and cell-type-specific immune responses. One reason for this is the enormous cost and effort required to study the effects of a single transcription factor, e.g., using gene knockout or targeted inhibition of transcripts with miRNAs. Consequently, experimental identification of novel transcription factors regulating a particular process is not feasible and, therefore, tends to be targeted based on hypotheses from other experiments. Moreover, experimental data are mostly generated by measurable changes, such as changes in their expression using RNA-Seq, but TFs do not necessarily have altered expression themselves, and cell-type-specific changes could be mediated by changes in the topology of gene regulatory networks. As a result, very little information on cell-type-specific gene regulation can be found in the literature, especially for the relatively young field of LM biosynthesis. While the effects of LMs in cells and tissues have been extensively studied, particularly for PIMs but recently also for SPMs, the regulatory mechanisms underlying their biosynthesis in a cell-type-specific manner is still not very well investigated, which may be important to understand how various cells communicate to resolve inflammation. This complexity of the LM response is also shown by the ability of myeloid cells (i.e., macrophages and granulocytes) to synthesize both PIMs and SPMs, while lymphoid cells seem incapable to produce any LM. These results are also supported by the vast literature where both classes of pro-inflammatory and pro-resolving LMs have been detected in a low or high picomolar range in most cell populations belonging to the myeloid and innate compartment of immunity. In contrast, evidence that cells of the lymphoid and adaptive immune system can produce such LMs is very scarce (extensively reviewed in [8,9,65,66]). Here, we investigated LM synthesis at the transcriptional level using in silico analyses of cell-type-specific gene regulatory networks from scRNA-Seq data. Our results highlight that, although cell types have similar expression profiles, they might exhibit distinct transcriptional regulations of LM synthesis and, thus, respond with different LM productions to experimental conditions. For instance, the higher expression of the stress- and inflammation-related genes Egr1, Jun, and Fos in aorta macrophages than in lung macrophages and their association with LM gene regulation, despite their similar RNA-Seq profiles, might account for a physiological advantage in the aorta by enabling a sufficient LM response to stress stimuli, such as hypertonia. Thus, our study showed that systems biology approaches could identify cell- and tissue-specific patterns of gene expression–phenotype relationships. Correlating the measured gene expression with underlying gene regulation can improve the analysis and interpretation of scRNA-Seq data. While large numbers of gene regulatory interactions are available in public databases, identified using in silico predictions of binding motifs, information on the type and strength of these interactions is rather scarce. Even if available, including such information also introduces new challenges, such as integrating competitive TF interactions. As our study aims to compare cell-type-specific GRNs, we built the networks using qualitative data (considering whether there is an interaction between a TF and a gene) to avoid false negative information and, consequently, disruptions in the network. By integrating expression data and topology algorithms, the qualitative information is converted into quantitative regulation scores for machine learning algorithms, providing a valuable estimation of a TF’s relevance in the GRN. Similar in silico studies on gene interaction networks showed the use of network topology information to predict key regulators and motifs [67,68,69]. The resulting bias towards highly connected nodes was encountered by normalizing the regulation score by the node degree. In our approach, we include information on multiple genes per LM class in the calculation of regulatory scores as well as combining the predictions from machine learning for multiple LM classes. The approach can be translated equally to other immune mediators, such as cytokines. The interpretability of the molecular results of this study is further limited to mice, although the methodology can be easily translated into human data. We specifically chose murine RNA-Seq data as much more murine than human in vivo studies are available that provide experimental evidence on gene-to-phenotype associations. With our study, we provided examples of how network-based scRNA-Seq data analyses could provide insights into cellular mechanisms of LM regulation and generate new hypotheses for follow-up investigations using human data. Thus, our results account for integrating systems biology approaches to stratify cellular responses more accurately in experimental settings and to discriminate or predict pathological states based on the ability of specific disease-associated cells to engage in pro-inflammatory or pro-resolving pathways. We extracted molecular interactions from the “lipid mediator biosynthesis from arachidonic acid” (Figure S1), “lipid mediator biosynthesis from DHA” (Figure S2), and “lipid mediator biosynthesis from EPA” (Figure S3) submaps of the AIR using its Xplore tool. The maps were then extended with transcription factor (TF) and gene target interactions from the AIR MIM to create a gene regulatory network (GRN). Catalytic reactions were transformed into the activity flow format by integrating enzymes in between the source and target element with positive interactions each (Figure 6A). The resulting network can be considered as the graph of a set of elements (vertices ) and connecting interactions (edges ). The edges encode whether two elements are linked by (de)activation, up-, or downregulation and are defined as a collection of triples consisting of a source element , a relation , and a target element . Two murine single-cell RNA-seq profiles (GSE122108 and GSE109125) with preprocessed and library-size normalized read counts (q) by the Immunological Genome (ImmGen) Project were downloaded from their website (http://rstats.immgen.org/DataPage/, accessed on 10 November 2022). They include many different immune cell types from various tissues with extensive descriptions of the samples’ origins and sorting markers. Both datasets have been described in detail in their respective published studies [70,71]. While the GSE122108 dataset consists only of phagocytotic mononuclear cells, mainly macrophages and monocytes, the GSE109125 data includes cells from all major cell types of the lymphoid and myeloid lineage. We mapped the murine genes from the data with genes in the AIR using human–mouse gene identifier associations from the Ensemble database (https://www.ensembl.org/, accessed on 23 August 2020). We defined a read count of 10 as a threshold to mark a gene as expressed or unexpressed which is slightly higher than the threshold of 5 used by the ImmGen project to exclude more genes with non-functional expression levels [71,72]. Genes with read count values below the threshold in a cell type c were removed from G resulting in cell-type-specific subgraphs with and (Figure 6B). Proteins from the manually curated submaps, i.e., enzymes directly involved in the LM biosynthesis, as well as elements with no expression, such as metabolites or phenotypes, were not removed from Gc. For cellular normalization, we divided the read count value of each gene by its highest absolute value across all cell types, resulting in the cell type normalized read count . A path in the MIM of the length can be written as the sequence with . The relation between the first and final element of any is defined as for all interactions along . The shortest path between (u, v) is defined as an existing path between and where is minimized. In each subgraph , the shortest paths between precursors and the final products in the LM biosynthesis were identified using the Breadth-First-Search. In addition, pathing algorithms were applied to identify core regulatory networks (CRNs), which are combined pathways from genes to LM enzymes with the maximum score of genes passed. The identification of CRNs becomes a widest path problem and was solved with an adaptation of Dijkstra’s algorithm. The edge weights are based on the edge’s target node and were set to either for CRNs of a single cell or when comparing two sets of cells. For each LM class , we calculated a weighting factor for all elements in the submaps representing their topological inclusion in the paths connected to . We recently described this weighting approach [25]. In summary, the weighting of an element is calculated based on the percentage of elements and paths connected to . is the number of all paths to p and are paths that go through . is the number of elements connected to and the number of elements on the path from to : We generated a regulatory score for each gene in , representing its association to LM synthesis. We performed a stepwise signal propagation based on the approach presented by Lee and Cho [73], starting from the LM enzymes and continuing in the reverse direction through the transcription network (Figure 7A). The transcription factors’ scores were updated at each step based on degree centralities (=number of interactions) in the original GRN, their targets’ scores in the previous step, and their normalized read count (Figure 7B). The simulation was performed for each cell type and initiated separately for each LM class by setting the starting scores for each enzyme in the LM class . The final regulatory score for each node in the network is then defined as the area under the curve (AUC) of scores over 100 signaling steps: (Figure 7C). We performed a Uniform Manifold Approximation and Projection (UMAP) analysis for both datasets using both the filtered expression data and, for each LM class, using the regulatory scores generated from (Figure 6C). UMAP reduces the high dimensionality of the input data into a two-dimensional graphical representation where each point corresponds to a cell in the data. In this way, cells with similar values are positioned close to each other, while separated cells indicate larger differences. Cell clusters were identified using manually adjusted k-means clustering on the generated embeddings. To visualize distributions across all LM classes, their embeddings were combined into a single dataset and a new UMAP was performed. Clustering in the enzyme expression heatmaps was performed using the Euclid-based hierarchical clustering method of the Python package seaborn version 0.12.1 [74]. The goal of the statistical analysis is to identify features that differ in a group of samples, i.e., clusters. Since the calculation of regulatory scores is based on the expression of the feature in the cell, the final scores are biased towards . Therefore, instead of calculating the highest scores, the features should be analyzed in relation to . In an LM class, the and values of a gene in all cells, which are not in the cluster, were fitted to linear regression, and a half-normal distribution was created from the absolute distances of each cell from the line (Figure 7D). The p-value of the feature in the cluster is then calculated from the z-score of the average distance of the cluster’s cells in the distribution. In conclusion, this study demonstrates how the application of network-based approaches enables the identification of cell-type-specific regulatory networks from scRNA-Seq data. We showed that gene regulation of the lipid mediator biosynthesis is highly dependent on the cell type and stimuli. Our results further argue for a fine-tuned transcriptional modulation of immune cell types and emphasize the necessity of systems biology approaches in understanding the underlying mechanisms.

PMC10001788

Zhenming Lü,Yantao Liu,Shijie Zhao,Jiaqi Fang,Kehua Zhu,Jing Liu,Li Gong,Liqin Liu,Bingjian Liu

Amblyopinae Mitogenomes Provide Novel Insights into the Paraphyletic Origin of Their Adaptation to Mudflat Habitats

22-02-2023

Amblyopinae mitogenome,paraphyletic origin,mudflat habitat adaptation

The water-to-land transition is one of the most important events in evolutionary history of vertebrates. However, the genetic basis underlying many of the adaptations during this transition remains unclear. Mud-dwelling gobies in the subfamily Amblyopinae are one of the teleosts lineages that show terrestriality and provide a useful system for clarifying the genetic changes underlying adaptations to terrestrial life. Here, we sequenced the mitogenome of six species in the subfamily Amblyopinae. Our results revealed a paraphyletic origin of Amblyopinae with respect to Oxudercinae, which are the most terrestrial fishes and lead an amphibious life in mudflats. This partly explains the terrestriality of Amblyopinae. We also detected unique tandemly repeated sequences in the mitochondrial control region in Amblyopinae, as well as in Oxudercinae, which mitigate oxidative DNA damage stemming from terrestrial environmental stress. Several genes, such as ND2, ND4, ND6 and COIII, have experienced positive selection, suggesting their important roles in enhancing the efficiency of ATP production to cope with the increased energy requirements for life in terrestrial environments. These results strongly suggest that the adaptive evolution of mitochondrial genes has played a key role in terrestrial adaptions in Amblyopinae, as well as in Oxudercinae, and provide new insights into the molecular mechanisms underlying the water-to-land transition in vertebrates.

Amblyopinae Mitogenomes Provide Novel Insights into the Paraphyletic Origin of Their Adaptation to Mudflat Habitats The water-to-land transition is one of the most important events in evolutionary history of vertebrates. However, the genetic basis underlying many of the adaptations during this transition remains unclear. Mud-dwelling gobies in the subfamily Amblyopinae are one of the teleosts lineages that show terrestriality and provide a useful system for clarifying the genetic changes underlying adaptations to terrestrial life. Here, we sequenced the mitogenome of six species in the subfamily Amblyopinae. Our results revealed a paraphyletic origin of Amblyopinae with respect to Oxudercinae, which are the most terrestrial fishes and lead an amphibious life in mudflats. This partly explains the terrestriality of Amblyopinae. We also detected unique tandemly repeated sequences in the mitochondrial control region in Amblyopinae, as well as in Oxudercinae, which mitigate oxidative DNA damage stemming from terrestrial environmental stress. Several genes, such as ND2, ND4, ND6 and COIII, have experienced positive selection, suggesting their important roles in enhancing the efficiency of ATP production to cope with the increased energy requirements for life in terrestrial environments. These results strongly suggest that the adaptive evolution of mitochondrial genes has played a key role in terrestrial adaptions in Amblyopinae, as well as in Oxudercinae, and provide new insights into the molecular mechanisms underlying the water-to-land transition in vertebrates. The water-to-land transition is one of the most important events in evolutionary history, as it led to an explosive radiation of tetrapods, which is the most successful group of vertebrates on land [1]. Previous studies have provided insights into how tetrapods successfully moved onto land after the ancestor of bony fishes first colonized land during the Paleozoic era [2,3]. Interestingly, several groups of bony fishes that emerged much later also independently evolved terrestrial adaptations that enabled them to spend a considerable part of their life on land [4,5]. These adaptations often include morphological, behavioral, and physiological changes that allow organisms to cope with the challenges of terrestrial environments, including hypoxia, temperature variation, and low, often fluctuating humidity [1,4,5]. However, little is known about the genetic basis of these adaptations. Eel gobies (family Gobionellidae; subfamily Amblyopinae) are the largest group of marine fishes showing unique adaptations to life in terrestrial habitats [6,7,8,9]. Typically, they live in burrows in tidal mudflats and the muddy bottoms of estuaries, but they can also be found in trawls of muddy substrates from the sea up to approximately 100 m in depth [9]. Some species (e.g., the genus Odontamblyopus and Taenioides) are so well adapted to terrestrial environments that they possess unique abilities to breathe air via their richly vascularized inner epithelia in the buccal-opercular cavity to cope with the hypoxia conditions in their tidal mudflat habitat [6,8]. Therefore, eel gobies may represent one of the lineages of bony fishes that shows the primitive terrestrial adaptation and are thus a useful group for clarifying the genetic changes underlying terrestrial adaptations during the early water-to-land transition. Despite substantial work on the morphological, behavioral, and physiological characteristics of eel gobies [6,10,11], few studies have examined the molecular basis underlying adaptations to terrestrial mudflat habitats in these species, or in other non-Amblyopinae teleost fishes. It is, therefore, interesting to investigate the genetic mechanisms underlying their adaptations to mudflat habitats. Mitochondrial energy metabolism plays an important role in mediating adaptation to the terrestrial environment by aquatic animals through aerobic respiration [12]. The adenosine triphosphate (ATP) produced by mitochondria can supply energy for life activities and help maintain physiological homeostasis in unstable environments [13]. The mitochondrial genome contains 13 protein-coding genes (PCGs) that encode proteins involved in aerobic respiration [12]. To adapt to the unstable terrestrial environments, aerobic respiration in aquatic animals needs to undergo natural selection to improve their ATP production efficiency. Therefore, the evolution of mitochondrial genes might be affected by the terrestrial environment. Indeed, several mitochondrial DNA (mtDNA) analyses have detected signatures of adaptive evolution in the mitochondrial genes of terrestrial panpulmonate gastropods [12], crayfishes [14], and chitons [15]. However, evolutionary changes underlying the adaptations of Amblyopinae to terrestrial mudflat habitats have never been investigated. Here, we sequenced the complete mitogenome sequences of six Amblyopinae species and compared them with those of their closest terrestrial and non-terrestrial relatives to clarify the molecular basis underlying adaptation to terrestrial habitats in bony fishes during the early water-to-land transition. Given that signatures of adaptive evolution have been observed in the mitochondrial genes of other terrestrial marine animals [12,15], we hypothesized that positive selection will also be observed in the oxidative phosphorylation (OXPHOS) related mitochondrial genes in mud-dwelling Amblyopinae. We sequenced six new Amblyopinae mitogenomes. The general characteristics of the mitogenomes are summarized in Table 1 and Supplementary Tables S3–S8. The length of the mitogenomes generally ranges from 16,552 bp to 17,133 bp. All the mitogenomes encode 13 PCGs, 2 rRNAs, 22 tRNAs, and a putative control region, as has been reported for most other animal mitogenomes. Most genes are encoded on the heavy strand, only the gene encoding the NADH dehydrogenase subunit 6 (ND6) and eight tRNA (Ala, Asn, Cys, Gln, Glu, Pro, Ser, and Tyr) genes are encoded on the light strand. The arrangement of genes is similar to that observed in the mitogenomes of other goby species. We characterized the main features of the mitogenomes in Amblyopinae using these six new mitogenomes, as well as six other mitogenomes deposited in Genbank. A noteworthy feature of the Amblyopinae mitogenomes is the 50–53 bp putative origin of L-strand replication (OL) located between tRNA-Asn and tRNA-Cys in a cluster of five tRNA genes (Ala, Asp, Cys, Trp, and Tyr), which is known as the WANCY region [16] (Figure 1). It forms a stem-loop secondary structure (Figure 2A), which is a general characteristic of the origin of light strand replication. The highly conserved sequence motif 5′-GCCGG-3′, which is involved in the transition from RNA to DNA synthesis, was observed at the base of the stem within tRNA-Cys (Figure 2B). Another feature of Amblyopinae mitogenomes is the non-coding control region located between tRNA-Pro and tRNA-Phe, which contains tandemly repeated sequences (TRS). Both perfect and imperfect repeats were observed in the TRS (Table 2), as in other animal mitogenomes. Both the number (2–6 times) and the length (120–163 bp) of the tandem repeats varied greatly among species, which might imply a rapid evolution of the structure in the control region. However, the starting sequences (-AAACAGGA) of the tandem repeats in the control region were generally conserved among species, with the exception of species in the genus Odontamblyopus, in which the sequences start more than 140 bp behind in the mitogenome (started with -AAAGATTT) (Table 2), possibly indicating their different evolutionary origins. We constructed the phylogeny of Amblyopinae using concatenated sequences of 13 coding sequences (CDSs) using our six newly sequenced mitogenomes and 57 published mitogenomes sequences from Gobioidei. The mitogenomic phylogenetic analyses yielded trees with consistent topologies and with high levels of support based on both ML and BI inference methods (Figure 3). Both the ML and BI trees indicated that species in the genera Amblyotrypauchen, Paratrypauchen, Ctenotrypauchen, Trypauchen, and Taenioides were more closely related to non-Amblyopinae species in the subfamily Oxudercinae than to species in the genus Odontamblyopus comprising their sister clade. The observation that, in both trees, species in genera Amblyotrypauchen, Paratrypauchen, Ctenotrypauchen, Trypauchen, and Taenioides are clustered with non-Amblyopinae species rather than Amblyopinae species in the genus Odontamblyopus provides strong support for the paraphyletic origins of Amblyopinae. This suggests that these two subfamilies should be merged and reveals an expansion of phenotypic variation within the “terrestrial goby” clade, as has been suggested by Steppan [22]. Using fossil calibration, we estimated that this paraphyletic clade emerged approximately 34.5 million years ago (My) in the late Paleogene (Figure 4). However, within the clade, species in the genus Periophthalmus appear to have evolved slightly earlier (34.5 My vs. 29.1 My) than the rest of the species, which indicates that they comprise a basal lineage in terrestrial gobies. The selection pressure on mtDNA genomes was evaluated using CODEML in PAML v4.8a software, and the results of the analysis are shown in Table 3. The average ω ratio for each of the 13 PCGs calculated from M0 in the branch-specific model was significantly less than 1, suggesting that all the mitochondrial genes in the sampled Amblyopinae species and their terrestrial relatives in Oxudercinae have evolved under strong functional constraints, which is consistent with the known functional significance of mitochondria as respiration chains necessary for OXPHOS and electron transport. However, based on the two-ratio (M2) model, where the paraphyletic clade of terrestrial gobies (Amblyopinae + Oxudercinae) was set as the foreground, and the other non-terrestrial gobies within the same family of Gobionellidae were set as the background, we found that seven PCGs (COI, COIII, ND1, ND2, ND4, ND5, and ND6) of the clade had significantly (p < 0.05) higher ω values than other Gobioidei species, which indicates that these genes have been positively selected in terrestrial gobies (Table 3). The branch-site model was further used to detect positive selection in individual codons. Our analyses suggested that there was significant evidence of positive selection along the branch leading to the paraphyletic clade of terrestrial gobies. Eleven residues in the four PCGs, COIII (G162S), ND2 (Q87T, D123T, S213A, F220N, C296S, M303I, S312T), ND4 (N44S, T384V), and ND6 (Y78F) were inferred as positively selected sites in the terrestrial goby branch with posterior probabilities greater than 95% (Table 4). A high proportion (54.55%) of changes in amino acids resulting in changes in the property of proteins, including their polarity or hydrophilicity, were detected in branches leading to species that inhabit mudflats (Supplementary Table S9). The three-dimensional structural model of the proteins encoded by these four mtDNA genes showed that selection has affected the structure of ND2 and ND4 in complex I of the mitochondrial respiratory chain (Figure 5). The observed evolutionary changes in both amino acid properties and protein structure might imply functional alterations of mitochondria in terrestrial gobies that enhance aerobic respiration in mudflat habitat. The mitogenomes in animals contain 13 PCGs, and their products are necessary for oxygen usage and energy metabolism. An increasing number of cases of adaptive evolution in mitochondrial genes have been reported in aquatic animals [12,14,15]. In the present study, we analyzed the whole mitogenomes of six species in the subfamily Amblyopinae, along with the 57 mitogenomes of Gobioidei species deposited in Genbank, to detect signs of adaptive evolution on OXPHOS genes. We did not detect signs of gene re-arrangements of mitogenomes in Amblyopinae. The mitogenomes of Amblyopinae were similar in size and gene arrangement to those of other Gobioidei species. No obvious expansion, contraction, or new gene arrangements were observed in Amblyopinae mitogenomes, yet such changes have often been observed in animals that inhabit harsh environments [23,24,25]. TRS were common in the control regions of the Amblyopinae mitogenomes. TRS were also frequently observed in Oxudercinae species (Table 2), as well as in other animals inhabiting abiotic stress environment [24,25,26]. The occurrence of TRS in the control region was inferred to provide advantages in mitochondrial DNA replication because they provide an “attractive” conformation that permits the more efficient binding of DNA polymerase [27]. The high efficiency of mtDNA replication is necessary for compensating for the oxidative damage to DNA associated with environmental stress [27]. Therefore, TRS are more common in species inhabiting harsh environments [24,25,26]. Our results support this expectation, given that Amblyopinae and Oxudercinae species might require high mtDNA replication efficiency to compensate for the potential oxidative damage to DNA induced by the unstable oxygen, temperature, and low, often fluctuating moisture in mudflat habitats. To detect the signs of adaptive evolution acting on OXPHOS genes, we first constructed the phylogenetic trees of Amblyopinae based on the ML and BI inference methods. The result yielded trees with consistent topologies, and all branches were highly supported. In contrast to the monophyly inferred from traditional taxonomy of subfamily Amblyopinae, both the ML and BI trees strongly supported the paraphyletic origins of Amblyopinae with respect to Oxudercinae, which is consistent with the results obtained from several recent studies using both nuclear and mitochondrial DNA [22,28,29]. Additional analyses of the non-coding control regions revealed that the genera Amblyotrypauchen, Paratrypauchen, Ctenotrypauchen, Trypauchen, and Taenioides in Amblyopinae generally shared the same starting sequences (-AAACAGGA) of TRS with some Oxudercinae species, rather than with the genus Odontamblyopus, which again supports a paraphyletic origin of Amblyopinae (Table 2). These findings indicate that these two subfamilies should be merged, as has been previously suggested by Steppan [22]. However, the paraphyly of Amblyopinae with respect to Oxudercinae reveals an interesting evolutionary scenario among members of Gobioidei because, despite their substantial differentiation in phenotype and physiology, the more aquatic “eel-like” mud-dwelling Amblyopinae and the more terrestrial mudskipper Oxudercinae may have evolved terrestrial behavior from their common ancestors. The fact that species in the genus Odontamblyopus are more closely related to non-Amblyopinae species in Oxudercinae, and species in the genus Periophthalmus form a sister clade suggests that two independent water-to-terrestrial transitions have occurred in this lineage, or alternatively, the more aquatic Amblyopinae may have evolved from Oxudercinae through a “secondary loss” of their terrestrial traits. Although the slightly earlier emergence of Periophthalmus (34.5 My vs. 29.1 My) estimated for the lineage appears to support the latter, more detailed analyses are needed to clarify the evolutionary scenarios in these paraphyletic clades. Our results from both the branch and branch-site models revealed strong evidence of positive selection on the ancestral branch leading to the paraphyletic clade of terrestrial gobies (Table 3 and Table 4). The results from the branch model showed that seven PCGs (COI, COIII, ND1, ND2, ND4, ND5, and ND6) in these paraphyletic clades had significantly higher ω values (p < 0.05) than other Gobioidei species, suggesting a signal for positive selection (Table 3). Furthermore, our results from the branch-site model revealed 11 positively selected residues in four PCGs (COIII, ND2, ND4, and ND6) with posterior probabilities greater than 95%, further suggesting that the positive selection has acted on these genes in this paraphyletic clade (Table 4). These results are not surprising given that both cytochrome oxidases and NADH dehydrogenases play important roles in aerobic metabolism and an ever-increasing number of studies has shown that these mitochondrial genes could be targets of positive selection [12,15]. Intriguingly, our analyses have revealed a high concentration of evolutionary changes caused by positive selection pressure acting on the mitochondrial lineages of terrestrial gobies which might reflect mitochondrial adaptation to physiological stress in mudflat environments. Therefore, positive selection might be one of the major forces driving the evolution of mitochondrial OXPHOS genes to cope with environmental change in teleosts, as has been observed in other animals [12,15]. NADH dehydrogenase is the largest enzyme complex (OXPHOS complex I) in the mitochondrial electron transport chain [30,31], and it serves as a proton pump that mediates the transport of H+ ions from the matrix to the inner membrane space, which drives ATP production. Mutations in this complex likely affect the efficiency of proton pumping and thus metabolic efficiency. Our results indicated that the majority of the sites under positive selection were in this complex, especially the three genes ND2, ND4, and ND6, which indicates that ND genes have experienced strong selection. The gene that has experienced the strongest selection is ND2, which is a common target of positive selection in diverse taxa inhabiting harsh environments [15,32,33]. We identified seven positively selected sites in this gene, and six were present in or near the loop regions instead of in the transmembrane domain (Figure 5). High concentrations of positively selected sites in the loop regions of the mitogenome-encoded proteins have previously been observed in mammals [31] and birds [34,35] and this is interpreted as a result of relaxation of functional constraints on these regions compared with transmembrane domains [31,34]. However, Fiedorczuk [36] and Zhu [37] revealed that residues in loop regions may be critical for interactions with the supernumerary subunits of OXPHOS complex I, therefore, changes in these regions might have major effects on proton translocation pathways or protein–protein interactions mediated by these subunits [35]. We also identified positively selected sites in ND4, which is another gene that encodes elements of proton pumps in the hydrophobic region of complex I. This is also consistent with the results of a previous study examining chiton inhabiting the intertidal zone [15], which showed signals of positive selection in this proton pumping gene. Some studies have revealed mutations in other ND genes that are linked to adaptations in harsh environments, such as ND6 in deep-sea shrimps [38] and high-latitude loaches [39]. Therefore, positive selection of genes in this complex may have a large impact on proton pump efficiency, which affects metabolic performance. This might be related to metabolic adaptation to the stresses of mudflat habitats such as unstable oxygen, temperature, and low, often fluctuating moisture levels in terrestrial gobies. We also detected significant signals of positive selection in COIII, a component of OXPHOS complex IV that is encoded by a mitochondrial gene. OXPHOS complex IV appears to play a more vital role in energy supply compared with other complexes. Physiological studies have found that the free energy supply of complex IV is twice as high compared with that of complex I and complex III [40]. Several lines of evidence suggest that adaptive changes in the structure and activity of cytochrome c oxidase IV might enhance resistance to physiological stress in vertebrates that inhabit adverse environments [39,41]. This can, to some extent, explain why we detected positive selection in COIII, and suggests that the enhanced energy metabolism capacity of terrestrial gobies might help them cope with the stresses associated with the mudflat environment. In general, selection acting on OXPHOS genes might have affected the physicochemical properties of amino acids and the tertiary structure of proteins. For example, our analyses revealed obvious changes in the physicochemical properties of amino acids, including polarity and hydrophilicity, in branches leading to terrestrial gobies (Supplementary Table S9). These changes have affected the structure of ND2 and ND4 in complex I of the mitochondrial respiratory chain (Figure 5). Significant changes in the same direction have also been observed in other animals inhabiting harsh environments such as high-altitude [33] or deep seas environments [42]. Such changes might allow organisms to better cope with stressed conditions in extreme habitats [12]. In conclusion, our results provide new insights into the paraphyletic origin of Amblyopinae with respect to Oxudercinae, and suggest that these two subfamilies should be merged. New elements of TRS and episodes of positive selection in the mitogenome have occurred in the branches of the paraphyletic clades (Amblyopinae and Oxudercinae), indicating that they might play a role in adaptation to terrestrial habitats. The increased demand for energy and the need to cope with stresses associated with mudflat habitats might have induced physiological changes in these paraphyletic clades and triggered functional adaptations at the mitochondrial level. More in-depth analyses can take into account how these observed TRS and adaptively selected codons affect protein function and enhance oxygen utilization during the OXPHOS process. New genomic information including more mitochondrial and nuclear sequences is necessary for this clade in the future, which will likely reveal even more genes involved in the metabolic and structural processes essential for the colonization of the terrestrial realm. We sequenced the mitochondrial genomes of six species in four genera of the subfamily Amblyopinae: Amblyotrypauchen arctocephalus [17] (Alcock, 1890), Ctenotrypauchen chinensis [18], Paratrypauchen microcephalus [19], Taenioides gracilis [20], T. anguillaris [21], and T. sp. Thailand [10]. Specimens for these species were collected from various locations in coastal waters of China (Table 1). Muscle tissues were removed from each sample immediately after capture and stored in 100% ethanol. Genomic DNA was isolated from these samples using standard phenol-chloroform method [43] and 13 overlapping fragments of the mitochondrial genome were amplified by polymerase chain reaction (PCR) using sets of primers designed specifically for eel gobies (Supplementary Table S1). The PCR analysis was conducted in 50 uL volume containing 50 ng template DNA, 1× reaction buffer, 2.0 mM MgCl2, 0.2 mM dNTPs, 0.2 mM of each primer, and 4.0 U Taq DNA polymerase (Promega, Madison, WI, USA) using a PTC-200 (Bio-Rad, Hercules, CA, USA) PCR machine. The standard PCR conditions were as follows: 5 min initial denaturation at 94 °C, 40 cycles of 1 min at 94 °C for denaturation, 1 min at 45.7–54.1 °C for annealing, 1 min at 72 °C for extension, and a final extension at 72 °C for 5 min. All sets of PCR included a negative control reaction in which all reagents were included except for the template DNA. PCR products were sequenced using Sanger sequencing at Invitrogen Ltd., Shanghai, China. The complete mitogenomes were assembled using the gene fragments sequenced for each species with CodonCode Aligner 5.1.5 (CodonCode Corporation, Dedham, MA, USA). The complete mitogenome was annotated using Sequin software (version 15.10), which is a mitogenome toolkit [44]. The boundaries of PCGs and ribosomal RNA genes were delimited using NCBI-BLAST. Transfer RNA genes and their clover leaf structures were identified using tRNAscan-SE 1.21 [45], with cut-off value set to 1 when necessary. The putative L-strand replication origin (OL) and control region were identified by sequence homology and proposed secondary structures. Fifty-seven Gobioidei mitogenomes were downloaded from GenBank (https://www.ncbi.nlm.nih.gov/genbank/, accessed on 22 June 2022) for phylogenetic analysis (Supplementary Table S2). The nucleotide sequences of 13 PCGs from each mitogenome were concatenated and aligned using the CLUSTAL X program [46] for the phylogenetic analyses. The phylogenetic trees were constructed using the maximum likelihood (ML) and Bayesian inference (BI) methods in PhyML3.0 [47] and MrBayes 3.2.6 [48], respectively. For the ML tree, ModelTest 3.7 was used to identify the best-fitting model of sequence evolution with the Akaike information criterion. The GTR + I + G model was used for the concatenated nucleotide sequence alignment, and PhyML was used to infer the ML tree. The reliability of the tree topologies was evaluated using 1000 bootstrap replicates. For BI, the parameters estimated by ModelTest were used as priors in the analysis. Four Metropolis-coupled Markov chain Monte Carlo analyses were run for 2 × 106 generations, and trees were sampled every 1000 generations. The first 25% of the runs were discarded as burn-in. In both analyses, two species in the same superorder Percomorpha, Perciformes (Scalicus amiscus), and Clupeiformes (Alosa sapidissima) were used as the outgroups. The nonsynonymous to synonymous ratio ω (dn/ds) indicates changes in selection pressure at the protein level. A dn/ds ratio of 1, <1, or >1 in PCGs typically indicates neutral mutations, negative (purifying) selection, and positive selection, respectively [32]. The CODEML program from PAML v4.8a [49] was used to analyze the ω (dn/ds) ratio using ML to detect positive selection on each mitochondrial gene. To determine whether selection pressures differed between terrestrial lineages and their non-terrestrial relatives, the “two-ratios” (M2) model was used, which assumes that the branches of interest (foreground) have different ω ratios from the background in the branch-specific models. We constructed likelihood ratio tests to compare the “two-ratios” with “one-ratio” (M0) models, which assumes identical ω values for all branches. χ2 tests were used to determine whether the M2 model was a significantly better fit than the M0 model with the threshold p-value < 0.05, which hinted at the selective pressure between the two branches. Since our phylogenetic analysis finally unveiled a paraphyletic origin of Amblyopinae with respect to Oxudercinae, we here set the paraphyletic clade of terrestrial gobies (Amblyopinae + Oxudercinae) as foreground, and the other non-terrestrial gobies within the same family of Gobionellidae as the background. Given that positive selection may act in very short episodes during the evolution of a protein and affect only a few sites along a few lineages. We also used a branch-site model to identify sites under positive selection along lineages of interest. We compared model A (model = 2, NSsites = 2, fix_omega = 0, omega = 5) against the null model (model = 2, NSsites = 2, fix_omega = 1, omega = 1). Positive selected sites with a posterior ratio greater than 95% were determined using Bayes Empirical Bayes analysis in CODEML [49]. To gain insight into the functional significance of the putatively selected sites, genes identified as positively selected in the branch-site test were analyzed in Expasy (http://web.expasy.org/protparam/, accessed on 24 July 2022) to identify significant physicochemical changes induced by the putative selection. We also constructed the crystal structure of positively selected genes and mapped selected sites onto this structure. The automated mode program (http://swissmodel.expasy.org/workspace/index, accessed on 24 July 2022) in the SWISS-MODEL server was used to predict and visualize the 3D structures of the proteins using the optimal templates.

PMC10001800

Andrey V. Markov,Innokenty A. Savin,Marina A. Zenkova,Aleksandra V. Sen’kova

Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models

21-02-2023

colitis,colitis-associated cancer,inflammatory bowel disease,colorectal cancer,colon adenocarcinoma,ulcerative colitis,Crohn’s disease,cDNA microarray,transciptomics analysis,microarray

Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn’s disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia.

Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn’s disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia. Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide [1,2]. Colon inflammation, along with the particular host and environmental factors, plays a crucial role in the initiation and progression of CRC [3]. Colitis-associated cancer (CAC) is a type of CRC, which is preceded by clinically detectable inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), two highly heterogeneous, incurable, persistent, relapsing/worsening, and immune-arbitrated inflammatory pathologies of the digestive system [4,5]. Epidemiologic studies have showed that patients with IBD have a predisposition to CRC, and cancer risk is highly correlated with the duration and severity of colon inflammation [6,7]. In IBD, chronic long-term colon inflammation accompanied by oxidative stress can alter the expression patterns of key carcinogenesis-associated genes [8]. Moreover, persistent stimulation of epithelial proliferation in the colon by the pro-inflammatory stimuli and excessive cell damage with increased epithelial cell turnover result in detrimental genetic and immunological alterations, making patients with IBD prone to developing CRC [9]. Despite the proven involvement of “inflammation-dysplasia-carcinoma” axis in the malignant transformation of cells in IBD-related CRC [10], the molecular mechanism underlying this process is not yet fully understood. In particular, it remains rather unclear which core genes are involved in the regulation of acute colitis and how markedly their profiles change during colitis-associated malignant transformation of the colon tissue. In addition, the proven complexity of the colitis/CAC-related regulome underlies the low efficacy of conventional IBD/CRC therapy, making it inevitable that surgery is recommended for treating these pathologies [5,11]. Given the known adverse impact of the surgical management of colonic diseases on the quality of life, mental health, and work productivity of patients [5,11], the search for novel key genes involved in the inflammation-related tumor transformation, which can be used as potential molecular targets for IBD therapy, is urgently needed. Moreover, such regulatory genes can be considered as biomarkers of inflammation-driven tumorigenesis and serve as predictors for surveillance strategies and chemoprevention of colitis-related dysplasia and CRC in IBD patients. To date, extensive exploration of colitis- and CAC-associated regulomes has been performed using transcriptomics-based approaches [12,13,14,15,16,17,18,19,20,21]. Reported bioinformatics studies have revealed some candidate biomarker genes and key signaling pathways susceptible to the development of the mentioned disorders [14,15,16,17,18,19,20,21,22,23], colitis-induced changes in the landscape of immune infiltration of colon tissue [14,16], and a range of hub genes probably involved in the development of CAC [15,20,23]. Despite a plethora of published studies, obtained results are still uncertain and are not well correlated with each other, probably due to insufficient usage of a multiple microarray analysis algorithm (the exploration of three or more independent microarray datasets in the same study, which gives more valid results [18,20,21]), ineffective manual searching of the published literature on the topic of study [14,15,16,17,18,19,20,21], and, in some cases, the absence of proper experimental validation [18]. Since the obtained data still remain insufficient for a thorough understanding of colitis/CAC-associated gene signature, further comprehensive bioinformatics analysis of colitis/CAC-related core genes is required. In this study, deep re-analysis of multiple microarray datasets related to murine acute colitis (GSE42768, GSE35609, GSE64658, GSE71920, GSE35609) and CAC (GSE31106, GSE5605, GSE64658, GSE42768) was performed. Firstly, the differentially expressed genes (DEGs) were computed between injured and healthy colon tissues, followed by their functional annotation and Venn diagram analysis to identify acute colitis- and CAC-associated core genes. Next, the changes in the sets of core genes associated with the transition from colon inflammation to CRC were identified. Further reconstruction and analysis of gene association networks revealed a range of hub regulators among core genes, subsequent exploration of which by the text mining approach identified a list of candidate genes, which can be used as novel promising biomarkers and therapeutic targets for colitis and CAC. The obtained results were finally validated using an in vivo model of dextran sulfate sodium (DSS)-induced acute colitis and azoxymethane (AOM)/DSS-induced CAC. Furthermore, the role of identified core genes in the colonic carcinogenesis in the backstage of chronic long-term inflammation was analyzed with respect to IBD and CRC in humans. To reveal key genes involved in the regulation of acute colitis and its transformation to CAC in mice, a range of independent expression profiles of murine colon tissue were retrieved from the GEO database, including samples of mice of both sexes and different strains with acute colitis stimulated by DSS (GSE42768, GSE35609, GSE64658, GSE71920) or dinitrobenzene sulfonic acid (DNBS) (GSE35609), or chronic colitis driven by azoxymethane (AOM)/DSS accompanied by the development of colorectal cancer (GSE31106, GSE5605, GSE64658, GSE42768). The analysis of selected transcriptomic datasets using the GEO2R tool revealed the sets of differentially expressed genes (DEGs) (colitis vs. control and CAC vs. control) susceptible to the mentioned pathologies, further overlapping of which identified 54 and 109 common DEGs specific to colitis and CAC, respectively (hereafter referred to as core genes) (Figure 1A). Hierarchical clustering of the expression profiles of identified colitis-associated core genes revealed two main clades separating up- and down-regulated DEGs from each other (Figure 1B). The sub-clade of the most overexpressed DEGs included genes related to immune response (Ccl3, S100a9, S100a8, Cxcl2) and heme metabolism (Hp), whereas the most suppressed core genes in the colitis group were Hao2 and Slc26a3, associated with fatty acid metabolism and chloride ion transport, respectively (Figure 1B). Further functional analysis of colitis-specific core genes revealed high enrichment of inflammatory-related terms, including the production of pro-inflammatory cytokines IL-1 and TNF-α, IL-17, IGF1-Akt and Tyrobp signaling pathways, antiviral response, matrix metalloproteinases (MMPs), lung fibrosis, and rheumatoid arthritis (Figure 1C, upper panel). Hierarchical clustering of CAC-specific core genes (Figure 1D) revealed two main clades, grouping activated and suppressed DEGs separately, and one outgroup consisted of the most overexpressed CAC-associated DEGs, notably, regulators of host-microbiota interplay (Reg3b, Reg3g), immune response (S100a9), and extracellular matrix (ECM) remodeling (Mmp7). In turn, the most suppressed core genes in the CAC group were involved in the regulation of cell adhesion (Zan), pH homeostasis (Car4), and ion transport (Slc26a3, Slc37a2, Aqp8) (Figure 1D). Performed gene set enrichment analysis revealed that CAC-specific core genes are tightly associated with cell invasiveness (wound healing involved in inflammatory response and MMPs), immune response (acute inflammatory response, antimicrobial peptides, etc.), redox imbalance, ion transport, bile secretion, and numerous metabolic processes (Figure 1C, lower panel). Interestingly, the retrieved functional annotation map specific for CAC was significantly less interconnected compared with the acute colitis-associated GO term/pathways network (Figure 1C, upper panel), which can be explained by the more discrete disposition of identified core genes in the CAC-related regulome. To explore how strongly identified core genes are interconnected in acute and chronic (CAC) phases of colitis, their Venn diagram analysis and the reconstruction of the gene association network were performed. Overlapping of acute colitis- and CAC-related genes demonstrated that 22 of the core genes, playing a regulatory role in acute inflammation, were involved in CAC pathogenesis (Figure 1E), including immune genes (Ifitm1, Ifitm3, Il1a, Lcn2, S100a9, Saa3, Tnf), genes encoding protease inhibitors (Serpina3n, Slpi, Wfdc18), ion transporters (Slc26a2, Slc26a3, Trpm6), ECM remodeling proteins (Mmp10, Timp1, Mep1a), signal transduction components (Igfbp4, Lrg1) and regulators of cell motility (Capg), fatty acid homeostasis (Hao2), host-microbiota interplay (Sult1a1), and heme metabolism (Hp). Analysis of the gene association network generated from acute colitis- and CAC-associated core genes using the STRING database [24] demonstrated their relatively high interconnection: 72 of 141 uploaded core genes (51%) formed interactions with each other within the network (Figure 1F). Interestingly, only 34 of 87 CAC-specific genes (39%) were involved in the network, whereas the shares of acute colitis-specific and common genes in the reconstructed interactome were 66% (21 of 32 genes) and 73% (16 of 22 genes), respectively. Considering that highly interconnected genes can be involved in the same or similar biological processes [25], revealed low enrichment of the analyzed network by CAC-specific genes (Figure 1F) was in line with the discrete structure of the CAC-related functional annotation map shown above (Figure 1C). Further computing of degree centrality scores of explored core genes revealed a range of genes occupying hub positions in the analyzed network (Figure 1F). It was found that the most interconnected nodes were acute colitis-specific or common genes involved in immune response (C3, Cxcl2, Il1b, Tnf) and ECM remodeling (Mmp9, Timp1). Among CAC-specific genes, the highest degree was identified for stabilizer of endoplasmic reticulum structure Ckap4, regulator of cell–cell interaction Cd44, and gene Lyz1 encoding lysozyme (Figure 1F). Given the hub position of Mmp9 and Timp1 and the formation of a highly connected cluster of MMPs in the core gene-retrieved network (Figure 1F), the changes in the MMPs profile can be involved in the regulation of malignant transformation of colon tissue during chronic colitis. This pattern needs further clarification. To identify novel candidate genes for acute colitis and CAC, which can be used as both diagnostic markers and promising therapeutic targets, next we questioned how strongly evaluated core genes can be involved in the regulation of the mentioned pathologies and how well these genes have been studied in the field of inflammatory and neoplastic disorders of the colon. To address the first issue, the degree centrality scores of the core genes in gene association networks created for each analyzed transcriptomic dataset were computed. Given that hub genes can exert key regulatory functions in reconstructed gene networks [26], the top 20 acute colitis-specific hub genes were identified and are shown in Figure 2A. The obtained results demonstrated that the most interconnected genes associated with acute colitis included genes encoding cytokines (Tnf, Il1a, Il1b), chemokines and its receptors (Ccl2, Cxcl2, Ccl3, Ccr5), growth factors and signal transduction components (Igf1, Tyrobp, Arrb2), ECM remodeling regulators (Mmp3, Mmp9, Timp1), and immune (C3, Clec7a, H2-Aa, Sell, Selp) and protective (Hp, Ugt2b35) proteins. Next, to select genes poorly characterized for their role in colitis and colitis-associated disorders, a text mining approach was performed. Analysis of the mention of acute colitis-related core genes (Figure 1B) alongside the keywords “Colitis”, “Crohn’s”, “Dysplasia”, and “Colon cancer” in scientific texts deposited in the MEDLINE database revealed the most studied genes in the field of colitis (Tnf, Il1b, Mmp9, Igf1, Ccl2, Slc26a2, Timp1, Lcn2, Il1a, and Sell); the majority of them occupied hub positions in retrieved colitis-associated gene networks (key nodes) (Figure 2A). The rest of the genes were found to be less explored as colitis-related ones (Figure 2B), and, therefore, could be used as a source of novel promising markers/regulators of colitis. To experimentally verify the obtained data, Mmp3, C3, and Tyrobp, displaying, on the one hand, little connection with colitis in the published reports (Figure 2B), and, on the other hand, high degree centrality scores in colitis-associated gene networks (Figure 2A), were selected for further qRT-PCR analysis. Since the profile of MMPs was identified as hypothetically susceptible to transforming acute colitis into CAC (Figure 1F), expressions of Mmp9 and Timp1 (known inhibitor of MMPs) were also further validated. The ranking of CAC-specific core genes according to their degree centrality scores in CAC-related gene networks identified the top 20 genes occupying hub positions, including genes encoding cyto- and chemokines (Tnf, Il1a, Cxcl16), regulators of ECM remodeling (Timp1, Mmp7, Mmp13, Gusb), immune (Ctla4, Cyba) and protective (Gstt1, Hp, Clu, Cyp2s1) response, lipid homeostasis (Acss2, Chpt1), ROS production (Maoa), cell–cell interaction (Cd44), membrane fusion (Snap25), and signal transduction (Plce1, Lgr5) (Figure 2C). Further text mining study, combined with the computing of the association of CAC-related core genes with the overall survival of patients with colon (COAD) and rectal (READ) adenocarcinomas, clearly confirmed the credibility of our bioinformatics analysis: the most reported CAC-related genes (Tnf, Cd44, Timp1, Mmp7, Ctla4, Clu, Il1a, Hp) were not only associated with poor prognosis in COAD and READ patients but also occupied the hub positions in the networks retrieved from CAC-associated DEGs (Figure 2D). These results indicate a probable important regulatory function of the listed core genes in colitis-associated neoplastic transformation of colon tissue. To identify novel candidate genes for CAC, our attention was centered on the core genes that are, on the one hand, poorly characterized in the field of CAC, and, on the other hand, associated with ECM remodeling susceptible to “inflammation-dysplasia-carcinoma” axis (Figure 1C,F), notably, Mmp13 (key node) and Adam8 (extracellular metalloprotease-disintegrin involved in ECM digestion and markedly associated with pathogenesis of gastrointestinal malignancies [27]) (Figure 2D). In addition, the key nodes Timp1 and Mmp7 previously reported as probable regulators of CRC were also selected for qRT-PCR analysis. Acute colitis was induced in mice by administration of 2.5% DSS solution in drinking water for 7 days, followed by a 3 day recovery (Figure 3A). CAC was induced in mice by single intraperitoneal (i.p.) injection of AOM 1 week before DSS administration. Furthermore, mice were exposed to 3 consecutive cycles of 1.5% DSS instillations for 7 days, followed by 2 weeks of recovery (Figure 3A). After the experiment termination, the colons were separated from the proximal rectum, mechanically cleaned with saline buffer, and collected for subsequent histological analysis and qRT-PCR. Gross morphological analysis of healthy colons revealed the normal thickness of the colonic wall and mucosa structure (Figure 3B). Administration of 2.5% DSS for one week led to acute inflammatory changes in the colonic tissues, clearly demonstrating the development of acute colitis and represented by thickening of the colonic wall, hyperemia, hemorrhages, and scattered ulcers (Figure 3B). Long-term cyclic administration of 1.5% DSS with prior injections of carcinogen AOM caused the development of multiple adenomas in the distal part of mice colons with a significant decrease in the intensity of acute inflammatory changes in the colonic tissues (Figure 3B). Histologically, the colon tissue of healthy mice demonstrated intact colon architecture, non-disrupted crypts, and goblet cells with active mucus vacuoles (Figure 3C). Acute administration of DSS caused severe colon tissue damage, represented by massive epithelium disruption with erosions and ulcerations, diffuse destruction of crypts, and loss of mucosal architecture (Figure 3C). Pronounced inflammatory infiltration through the whole colonic wall, due to neutrophils and lymphocytes as well as mucosa edema, was revealed (Figure 3C). In the case of CAC, chronic administration of DSS after AOM injection caused adenomatous transformation of the colon mucosa, represented by multiple adenomas in the colonic tissue with epithelial hyperproliferation and hyperplastic crypts (Figure 3C). Residual inflammatory infiltration located in the mucosa and submucosa of colon tissue with adenomas and represented by lymphocytes and macrophages was detected (Figure 3C). In the colon tissue adjacent to adenomas (colitis in CAC), signs of chronic colonic inflammation with moderate destruction of the mucosal architecture and crypt damage were found (Figure 3C). Thus, we reproduced the process of colon carcinogenesis, starting with acute inflammation in the colon tissue, transitioning to chronic inflammation, and eventually ending up with the colonic tumor formation. Finally, the expression of the revealed hub genes related to acute colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) was validated by qRT-PCR in the colon tissue of mice with acute colitis and colitis-driven adenomas (Figure 3D). As expected, the expression of colitis-related genes C3, Tyrobp, Mmp3, Mmp9, and Timp1 was significantly up-regulated in inflamed colon tissue compared with healthy controls; among them, Mmp3 and Timp1 were found to be the most susceptible to acute colitis induction, demonstrating 306.3- and 110.6-fold increases in the expression, respectively, in DSS-treated mice compared with healthy controls (Figure 3D). The chronification of colonic inflammation led to significant reduction in the expression of C3, Tyrobp, Mmp3, and Timp1 in the adjacent to adenomas colonic tissue by 17.5, 6.6, 2.8 and 46.1 times compared with the samples from acute colitis group, and, moreover, the expression of C3 and Tyrobp in this compartment decreased to the healthy level (Figure 3D). Interestingly, chronification of colitis had no obvious effect on the expression of Mmp9: comparable induction of this gene in both DSS- and AOM/DSS-inflamed colon tissues was observed (Figure 3D), which could indicate the important role of Mmp9 in both acute and chronic colon inflammation, agreeing with [28]. The analysis of colonic adenomatous nodes revealed low expression of all the explored acute colitis-associated key genes: the expression levels of C3, Tyrobp, Mmp3, Mmp9, and Timp1 in adenoma tissue were 26.3, 3.4, 20.8, 11.9, and 27.7 times lower than those in the samples with acute colitis (Figure 3D). Note that adenomatous and adjacent tissues in mice with CAC mainly differed in the expression of the following genes: Tyrobp and Timp1 were found to be 1.9 and 1.7 times overexpressed in adenomas compared with the adjacent counterparts, respectively, whereas Mmp3 and Mmp9 were 7.6 and 13.9 times suppressed in tumor tissue, respectively (Figure 3D). Taken together, the obtained results clearly demonstrated that selected key genes associated with acute colitis indeed reached the maximum expression in the acute phase of colon inflammation, whereas chronification of the latter led to a marked decline in this parameter. As expected, all CAC-associated hub genes (Adam8, Mmp7, Mmp13, and Timp1 (mentioned above)) were characterized by significant overexpression in tumor nodes compared with healthy tissue, which confirms the expediency of their further exploration as CAC-related marker genes (Figure 3D). Interestingly, only Mmp7 and Mmp13 displayed a significantly higher level of activation in colon adenomas compared with both the adjacent tissue (13.8- and 13.4-fold increase, respectively) and colon tissue with acute colitis (186.4- and 19.6-fold increase, respectively). Adam8 and Timp1 mentioned above were also up-regulated in adenomas by 8.3 and 1.7 times compared with the adjacent tissue; however, the maximum of their expression was revealed in the acute colitis samples (86.6- and 110.6-fold increase compared with the healthy group, respectively) (Figure 3D). Thus, the performed qRT-PCR analysis successfully confirmed the expression of the acute colitis- and CAC-related hub genes identified by the in silico analysis in corresponding murine tissues and clearly demonstrated that colitis-driven colonic adenomatous transformation is accompanied by significant changes in the expression profiles of matrix metalloproteinases, which can be used as a novel prognostic signature for colorectal neoplasia in IBD. Despite the large collection of transcriptomics data from IBD and CAC studies, molecular regulators of the transition of colonic inflammatory lesions to cancer have not yet been clearly defined. The current study aimed to reveal core genes involved in the regulation of acute colitis and CAC development in mice and to explore how far their expression profiles changed during the chronification of colon inflammation. Performed bioinformatics analysis of multiple cDNA microarray datasets of acute colitis and CAC identified a range of core genes associated with the explored pathologies, further functional annotation of which clearly confirmed the reliability of the obtained data. Indeed, high enrichment of acute colitis-related functional terms with pro-inflammatory cytokines and IGF1-Akt signaling pathway (Figure 1C, upper network) agrees well with the proven regulatory role of the latter in colon inflammation and inflammation-induced mucosal injury [29,30]. Along with this, CAC-related core genes were associated with the processes which markedly changed during colitis-driven tumorigenesis (Figure 1C, lower network): it is known that dysplastic and malignant lesions of colon tissue markedly dysregulate sodium transport [31], bile acid secretion [32], and metabolic [33] and oxidative [34] homeostasis. Interestingly, the analysis of core genes common for both acute colitis and CAC (Figure 1E) also demonstrated the credibility of the performed in silico study. According to the published reports, the acute-phase genes Hp, Lcn2, Lrg1, and Serpina3n included in this list are not only activated in response to inflammatory stimuli, but their aberrant expression is also strongly implicated in tumorigenesis: high levels of Hp and Lcn2 resulted in glucose metabolic dysfunction, angiogenesis, and metastasis in different tumor types [35,36], and Lrg1 and Serpina3n were associated with epithelial–mesenchymal transition in colorectal cancer [37,38]. In addition, the interferon-responsive gene Ifitm3 is critical to early colon cancer development [12,39], along with S100a9 and Slpi, which, when highly expressed in inflamed colon tissues in mice and patients with colitis and IBD, respectively, can be considered as potent amplifiers of tumor invasion [40,41]. Analysis of gene association networks with subsequent processing of obtained results using the text mining approach revealed a range of core genes occupied hub positions in the acute colitis- and CAC-associated regulomes, which had not yet been extensively studied in relation to the explored diseases (acute colitis: C3, Tyrobp, Mmp3; CAC: Adam8, Mmp13) (Figure 2). Further qRT-PCR analysis clearly confirmed the overexpression of the mentioned hub genes in the colon tissue of mice with acute colitis and CAC (Figure 3D) that indicated the expediency of further exploration of these genes as promising novel biomarkers of colon inflammation and colon tumorigenesis. To independently examine how tightly revealed hub genes were associated with inflammation and colorectal cancer, their sub-networks with first gene neighbors from rodent inflammatome [42] and the gene network related to malignant tumors of the colon (DisGeNET ID: C00071202) were reconstructed and analyzed. As depicted in Figure 4, all explored hub genes, except for Adam8, indeed form tight modules with gene partners within the evaluated regulomes, and are related to diverse processes and signaling pathways important for the pathogenesis of colitis and CAC. For instance, the detection of the functional group “Interleukin-4 and 13 signaling” is in accordance with [43]: a marked IL-13 response from CD4+ natural killer T cells was previously detected in mice with oxazolone-induced colitis and its blockage was found to ameliorate intestinal inflammation and injury. The members of the integrin family (Figure 4A, Timp1-, C3- and Mmp3-centered sub-networks) play a crucial role in the intestinal homing of immune cells and in supporting the inflammatory mechanisms in the gut [44]. uPA-mediated signaling (Figure 4, Timp1-, Mmp3-, Mmp9-centered sub-networks) controls macrophage phagocytosis in intestinal inflammation, and uPA receptor deficiency leads to marked aggravation of experimental colitis in mice [45]. Moreover, uPA-/- mice demonstrated more severe colorectal neoplasia compared with their wild-type littermates [46]. In addition, remodeling of the extracellular matrix is a hallmark of both colitis/IBD [47] and CAC [48], and prostaglandin signaling is involved in the malignant transformation of inflamed intestinal tissue [49]. The detailed comparison of obtained results revealed a group of MMPs as key participants of acute colon inflammation and its transition to malignancy: functional term “Matrix Metalloproteinases” was identified as statistically significant in both acute colitis- and CAC-associated functional annotation maps (Figure 1C), the highly interconnected cluster of MMPs related to different phases of colitis was revealed in the gene network retrieved from computed core genes (Figure 1F), and MMPs occupied hub positions in all analyzed regulomes related to both acute colitis (Figure 2A: Mmp3, Mmp9) and CAC (Figure 2B: Mmp7, Mmp13). Interestingly, the tissue inhibitor of matrix metalloproteinase-1 (Timp1) was also detected as a hub gene specific to both acute colitis and CAC (Figure 1E and Figure 2A,B) and tightly interconnected with MMPs module (Figure 1F), which clearly indicated the importance of Timp1/MMPs balance in colitis-induced tumorigenesis. Indeed, Timp1 is a known regulator of colitis, knockout of which markedly attenuated fibrosis in DSS-inflamed colon tissue [50], and, according to the recent report of Niu et al. [51], a hub gene in colorectal cancer regulome. High expression of MMP3 and MMP9 in mucosa-resident macrophages/neutrophils and IgG plasma cells was detected in patients with IBD [52,53]. According to Pedersen et al. [54], MMP3 and MMP9 are two key enzymes involved in the degradation of intestinal tissue during IBD. Interestingly, the silencing of Mmp3 by siRNA markedly ameliorated DSS-induced colitis in mice [55], whereas knockout of Mmp9 or its pharmacological inhibition surprisingly had no obvious effect on the progression of DSS- and TNBS-stimulated colitis in the murine model [56]. Thus, the master regulatory functions of MMPs in colitis pathogenesis require further clarification: in some cases, their overexpression can be considered as a consequence rather than a cause of intestinal inflammation [56]. In the case of CAC-associated MMPs (Mmp7, Mmp13) revealed in this study, focal high expression of Mmp7 was previously observed in CAC-related dysplastic lesions [48] and its overexpression was associated with tumor growth, metastasis, and worse overall survival in patients with colon cancer [57]. According to Wernicke et al. [58], the up-regulation of MMP-13 was considered as an early predictive cancer biomarker in patients with colon adenoma, which agrees well with the results of our qRT-PCR analysis (Figure 3D). Despite the extensive studies of MMPs as candidate marker genes of colitis and CAC, to the best of our knowledge, the complex evaluation of the expression of Mmp3, Mmp7, Mmp9, and Mmp13 in acutely inflamed, adenomatous, and adjacent colon tissues has not yet been reported. Revealed marked changes in their expression profiles during chronification of colitis (Figure 3D) can be considered as a novel gene signature for predicting CAC. Besides MMPs, another ECM remodeling player, Adam8, a member of a disintegrin and metalloproteinase family (ADAMs), was identified as a core gene associated with CAC development (Figure 1F, Figure 2D and Figure 3D). Surprisingly, high expression of Adam8 was detected not only in CAC but also in DSS-inflamed colon tissue (Figure 3D). Along with the reorganization of ECM, ADAMs are engaged in the processing of various substrates, including cytokines, growth factors, cell adhesion molecules, and receptors, that determines their important role in a range of pathological processes [59]. The most studied ADAMs in IBD was Adam17, associated with EGFR and STAT3 signaling pathways crucial for the pathogenesis of colitis [60], high epithelial expression of which positively correlated with cell proliferation and goblet cell number in UC patients [61]. To the best of our knowledge, the involvement of Adam8 in the regulation of acute colitis and colitis-induced adenomatous transformation of colon tissue had not yet been reported. Only Christophi et al. and Guo et al. have discussed the overexpression of Adam8 in IBD patients [62] and AOM/DSS-induced colitis in mice [63]. Given the recently demonstrated ability of Adam8 to control neutrophil transmigration [64] and NLRP3 inflammasome activation [65], the processes tightly associated with colon inflammation [66,67], Adam8 can be considered as a novel promising master regulator of colitis and CAC; this requires further clarification. Interestingly, despite the revealed low interconnection of Adam8 with the colon cancer-associated gene network retrieved from DisGeNET (Figure 4), this gene seems to play an important role in the pathogenesis of CAC: Adam8 is involved in the activation of integrin, FAK, ERK1/2, and Akt/PKB signaling pathways related to cancer progression [68], its overexpression was identified in colorectal cancer compared with adjacent normal tissues [69], and the suppression of the expression of Adam8 by knockout or siRNA approaches resulted in reduced proliferation and invasiveness of colon cancer cells [69,70]. Finally, C3 and Tyrobp were also revealed as colitis-specific hub genes (Figure 2A and Figure 3D), which is in line with published reports. Previously, a high level of C3 in the serum and jejunal secretion of IBD patients was identified [71,72]. Moreover, C3 was found to be up-regulated in intestinal epithelial cells in the DSS-induced colitis model [73], and its ablation promoted inflammatory responses in the mid colon [74] and significantly reinforced DSS-induced colitis in C3 knockout mice compared with wild-type littermates [72]. Tyrobp is a known regulator of the production of pro-inflammatory mediators in macrophages and neutrophils [75], and, thus, is implicated in pathogenesis of various inflammation-associated diseases [75,76,77]. According to recent studies, Tyrobp was identified as a probable upstream regulator of UC [78], and its knockout robustly attenuated the severity of DSS-induced colitis in mice, whereas its overexpression resulted in a striking exacerbation of colon damage caused by DSS [79]. The published works discussed above demonstrated the involvement of the revealed core genes in the regulation of inflammation and malignant lesion of the colon, not only in murine models but also in patients. To independently confirm the translational bridge between our findings and the pathogenesis of colitis/CAC in humans, expression of core genes (acute colitis: C3, Tyrobp, Mmp3, Mmp9, Timp1; CAC: Timp1, Mmp7, Mmp13, Adam8) was further evaluated in the transcriptomics profiles of colon tissue from patients with UC and CD collected from GEO (Figure 5A) and colorectal cancer retrieved from The Cancer Genome Atlas (TCGA) (Figure 5B). As depicted in Figure 5A, the majority of the explored key genes were overexpressed in IBD and demonstrated more pronounced susceptibility to the induction of UC compared with CD, except for TYROBP, expression of which was more up-regulated in CD patients. Interestingly, despite the proven association with CAC (Figure 2B,D), TIMP1, MMP7, and ADAM8 were activated in IBD-affected colon tissues (Figure 5A), which is fully in line with our data: the high expression of these genes was demonstrated in DSS-inflamed and adjacent to adenomas colon tissues in mice (Figure 3D). In addition, similar to our results (Figure 3D), CAC-specific MMP13 was found to be slightly associated with IBD: its low activation in two of the four analyzed UC transcriptomics datasets and unchanged levels in CD samples were observed (Figure 5A). Presumably, Mmp13 plays a minor role in ECM remodeling in colitis, whereas CAC was associated with significant up-regulation of its expression, which makes Mmp13 a promising gene candidate for the predicting of colitis-associated tumorigenesis; this requires further detailed study. TCGA analysis of the identified CAC-related core genes revealed a significant association between high expression of TIMP1 and ADAM8 with low overall survival of patients with both colon (COAD) and rectal (READ) adenocarcinomas (Figure 5B). Despite the finding that Timp1 and Adam8 can play important regulatory functions in CAC, this supposition requires further detailed confirmation, since TCGA analysis was performed without consideration of the ratio of UC- and CD-associated CAC patients in COAD and READ cohorts. In addition, given recently reported sex disparities in the association of Timp1 expression with cancer progression [80], further exploration of its regulatory role in CAC in mice of both sexes is needed. The obtained results were finally summarized in the scheme depicted in Figure 5C. According to our findings, (a) revealed core genes not only occupy hub positions within explored acute colitis- and CAC-specific regulomes, but also are interconnected with each other, (b) Timp1 is identified as a hub node in gene association networks retrieved for both acute colitis and CAC, which can indicate its crucial role in colitis-associated tumorigenesis, (c) chronification of colonic inflammation is accompanied by a switch in MMPs profile (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13), which can serve as a gene signature panel for prognosis of malignant transformation of inflamed colon tissue; and (d) identified core genes are overexpressed in the colon tissue of patients with IBD (all explored genes) and highly aggressive colorectal cancer (TIMP1, ADAM8), confirming the interest in studying these genes within the framework of intestinal pathologies in humans (Figure 5C). The limitations of the study are as follows: First, given the relatively low number of mice used for experimental validation of the obtained data (n = 6), and their belonging to only one sex (female) and one strain (C57Bl6), further study is required to validate the results using a larger sample size obtained from mice of both sexes and different strains. Second, considering that our findings are predominantly animal-based, to more clearly elucidate how closely (if at all) the identified core genes are involved in the regulation of intestinal pathologies in humans, revealed translational bridge needs further large-scale verification study, using clinical samples of patients with UC, CD, and UC/CD-associated colorectal cancer. Third, despite the identification of high degree centrality scores of the explored key genes and their tight association with crucial colitis/CAC-related signaling pathways, the master regulatory functions of these genes in colitis and CAC should be further verified experimentally (for instance, using knockout models). The gene expression profiles associated with murine acute colitis and CAC, as well as ulcerative colitis and Crohn’s disease, in patients were acquired from the Gene Expression Omnibus database [81] (Table 1). The fold changes between the mean expression values of the genes in the experimental (pathology) versus control groups were computed using the GEO2R tool [82]. The Benjamini–Hochberg false discovery rate method was selected for adjusting p-values. The genes with a p-value < 0.05 and |fold change| > 1.5 were identified as differentially expressed genes (DEGs) and were collected for further analysis. Overlapping of the DEGs from different datasets was performed using the InteractiVenn tool [83]. Hierarchical clustering of DEGs according to their expression profiles was carried out using the Euclidean distance metric, using the Morpheus tool (https://software.broadinstitute.org/morpheus, accessed on 12 December 2022). Functional annotation of acute colitis- and CAC-associated DEGs was performed using the ClueGO 2.5.7 plugin in Cytoscape 3.7.2, using the latest updates of Gene Ontology (Biological Processes), Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathways, and REACTOME databases. The GO Tree interval was ranged from 3 to 8 and the minimum number of genes per cluster was set to 3. Enrichment of functional terms was tested using the two-sided hypergeometric test corrected using the Bonferroni method, followed by selecting significantly enriched terms with a p-value < 0.05. To cluster similar functional groups retrieved from different databases in the common pathway-specific modules, the GO Term Fusion was used. Functional grouping of finally selected functional terms was performed using kappa statistics (kappa score ≥ 0.4). Functional annotation of gene modules, consisting of core genes and their first gene partners extracted from murine inflammatome and colon cancer-related regulome, was performed using the ToppFun tool (databases: KEGG, REACTOME, MSigDB C2 BIOCARTA, BioSystems: Pathway Interaction Database, Pathway Ontology; Bonferroni adjustment) [84]. Gene association networks were reconstructed from the genes of interest using the Search Tool for the Retrieval of Interaction Genes (STRING) database, using the stringApp 1.5.1 tool [85], and were visualized using Cytoscape 3.7.2. The cutoff criterion of the confidence score was set as >0.7 to eliminate inconsistent “gene–gene” pairs from the dataset. The number of neighbors of a gene of interest within reconstructed networks was calculated using the NetworkAnalyzer plugin [86] and visualized using the Morpheus platform [87]. The search for the co-occurrence of the names of core genes with various colitis- and CAC-related terms in the same sentences in abstracts of published reports deposited in the MEDLINE database was performed using the GenCLiP3 tool [88], with the following settings: impact factor of 0–50 and year of publication of 1992–2022. The results were visualized using Circos [89]. Eight-week-old female C57Bl6 mice with an average weight of 22–24 g were obtained from the Vivarium of the Institute of Chemical Biology and Fundamental Medicine SB RAS (Novosibirsk, Russia). Mice were housed in plastic cages (7 animals per cage) under normal daylight conditions. Water and food were provided ad libitum. Experiments were carried out in accordance with the European Communities Council Directive 86/609/CEE. The experimental protocols were approved by the Committee on the Ethics of Animal Experiments at the Institute of Cytology and Genetics SB RAS (Novosibirsk, Russia) (protocol No. 56 from 10 August 2019). Acute colitis was induced in mice (n = 10) by administration of 2.5% DSS solution in drinking water for 7 days, followed by 3 days of recovery. Mice were sacrificed on day 10 after colitis initiation. CAC was induced in mice (n = 10) by a single intraperitoneal (i.p.) injection of carcinogen AOM (10 mg/kg) 1 week before DSS administration, as described in [90]. Furthermore, mice were exposed to 3 consecutive cycles of 1.5% DSS instillations with drinking water for 7 days, followed by 2 weeks of recovery. The mice were sacrificed 10 weeks after the start of the experiment. At the end of the study, the colons were separated from the proximal rectum, mechanically cleaned with saline buffer, and were then collected. Only 8 of 10 samples had well-formed adenomas in the colon, which were selected for the subsequent gross examination, histological analysis, and qRT–PCR. For the histological study, colon specimens were fixed in 10% neutral-buffered formalin (BioVitrum, Moscow, Russia), dehydrated in ascending ethanol and xylols, and embedded in HISTOMIX paraffin (BioVitrum, Moscow, Russia). The paraffin sections (5 μm) were sliced on a Microm HM 355S microtome (Thermo Fisher Scientific, Waltham, MA, USA) and stained with haematoxylin and eosin. The images were examined and scanned using an Axiostar Plus microscope equipped with an Axiocam MRc5 digital camera (Zeiss, Oberkochen, Germany) at magnifications of ×100. Total RNA was isolated from the colons of experimental animals using TRIzol reagent (Ambion, Austin, TX, USA) according to the manufacturer’s instructions. Briefly, colon tissue was collected in 1.5 mL capped tubes, filled with 1 g of lysing matrix D (MP Biomedicals, Irvine, CA, USA) and 1 mL of TRIzol reagent, then homogenized using a FastPrep-24 TM 5G homogenizer (MP Biomedicals, Irvine, CA, USA) with QuickPrep 24 adapter. The homogenization was performed at 6.0 m/s for 40 s. After homogenization, the content of the tubes was transferred to the new 1.5 mL tubes without lysing matrix. Total RNA extraction was performed according to the TRIzol reagent protocol. Due to the known ability of DSS to linger in the RNA extracted from the colon tissue, and, thus, interfere with both reverse transcription and PCR reactions, the extracted total RNA was diluted to a volume of 250 μL and purified using Microcon Centrifugal Filter Devices (MilliPore, Burlington, MA, USA) by centrifuging for 1 h at 14,000× g. The first strand of cDNA was synthesized from total RNA (n = 6 per group, the samples with the highest RNA purity and integrity) in 100 μL of reaction mixture containing 2.5 μg of total RNA, 20 μL of 5× RT buffer (Biolabmix, Novosibirsk, Russia), 250 U of M-MuLV-RH revertase (Biolabmix, Novosibirsk, Russia), and 100 μM of dT(15) diluted to a volume of 100 μL. Reverse transcription was performed at 25 °C for 10 min followed by the incubation at 42 °C for 60 min with subsequent termination at 70 °C for 10 min. Amplification of cDNA was performed in a 25 μL PCR reaction mixture containing 5 μL of cDNA, 12.5 μL of HS-qPCR (2×) master mix (Biolabmix, Novosibirsk, Russia), 0.25 μM each of the forward and reverse primers to Hprt and Hprt specific ROX-labeled probe, 0.25 μM each of the forward and reverse gene-specific primers, and FAM-labeled probe (Table 2). Amplification was performed as follows: (1) 94 °C, 2 min; (2) 94 °C, 10 s; (3) 60 °C, 30 s (steps 2–3: 50 cycles). The relative level of gene expression was normalized to the level of Hprt expression according to the ΔΔCt method. Amplification was performed using a C1000 Touch with CFX96 module Real-Time system (BioRad, Hercules, CA, USA), and the relative level of gene expression was calculated using BioRad CFX manager software (BioRad, Hercules, CA, USA). Three to five samples from each experimental group were analyzed in triplicate. The sequences of the primers used in the study are listed in Table 2. To explore the association of revealed core genes with the progression of colon (COAD) and rectal (READ) adenocarcinomas, analysis of the survival rates and their correlation with the expression of studied genes was performed using The Cancer Genome Atlas (TCGA) clinical data for patients with COAD and READ. Kaplan–Meier survival curves for COAD and READ patients depending on the mRNA expression level of core genes were constructed using the OncoLnc tool [91]. The statistical analysis was performed using Benjamini–Hochberg false discovery rate method (identification of DEGs; GEO2R tool), two-sided hypergeometric test with Bonferroni correction (functional analysis of DEGs; ClueGO plugin and ToppFunn tool), and two-tailed unpaired Student’s t-test (qRT-PCR analysis; Microsoft Excel). p-values of less than 0.05 were considered statistically significant. In summary, this animal-based research revealed a range of core genes associated with acute colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Mmp7, Mmp13) in mice. The observed high rate of interconnection of these genes with gene networks retrieved for intestinal inflammation and malignancy, their significant association with key colitis/CAC-related signaling pathways, and probable involvement in the pathogenesis of IBD and colorectal cancer in patients demonstrated the expediency of further detailed studies of identified core genes as novel master regulators and promising therapeutic targets for colitis and CAC.

PMC10001833

Hilda Martínez-Coria,Isabel Arrieta-Cruz,Roger Gutiérrez-Juárez,Héctor Eduardo López-Valdés

Anti-Inflammatory Effects of Flavonoids in Common Neurological Disorders Associated with Aging

21-02-2023

flavonoids,ischemic stroke,neurodegenerative diseases,Alzheimer’s disease,Parkinson’s disease,neuroinflammation

Aging reduces homeostasis and contributes to increasing the risk of brain diseases and death. Some of the principal characteristics are chronic and low-grade inflammation, a general increase in the secretion of proinflammatory cytokines, and inflammatory markers. Aging-related diseases include focal ischemic stroke and neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Flavonoids are the most common class of polyphenols and are abundantly found in plant-based foods and beverages. A small group of individual flavonoid molecules (e.g., quercetin, epigallocatechin-3-gallate, and myricetin) has been used to explore the anti-inflammatory effect in vitro studies and in animal models of focal ischemic stroke and AD and PD, and the results show that these molecules reduce the activated neuroglia and several proinflammatory cytokines, and also, inactivate inflammation and inflammasome-related transcription factors. However, the evidence from human studies has been limited. In this review article, we highlight the evidence that individual natural molecules can modulate neuroinflammation in diverse studies from in vitro to animal models to clinical studies of focal ischemic stroke and AD and PD, and we discuss future areas of research that can help researchers to develop new therapeutic agents.

Anti-Inflammatory Effects of Flavonoids in Common Neurological Disorders Associated with Aging Aging reduces homeostasis and contributes to increasing the risk of brain diseases and death. Some of the principal characteristics are chronic and low-grade inflammation, a general increase in the secretion of proinflammatory cytokines, and inflammatory markers. Aging-related diseases include focal ischemic stroke and neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Flavonoids are the most common class of polyphenols and are abundantly found in plant-based foods and beverages. A small group of individual flavonoid molecules (e.g., quercetin, epigallocatechin-3-gallate, and myricetin) has been used to explore the anti-inflammatory effect in vitro studies and in animal models of focal ischemic stroke and AD and PD, and the results show that these molecules reduce the activated neuroglia and several proinflammatory cytokines, and also, inactivate inflammation and inflammasome-related transcription factors. However, the evidence from human studies has been limited. In this review article, we highlight the evidence that individual natural molecules can modulate neuroinflammation in diverse studies from in vitro to animal models to clinical studies of focal ischemic stroke and AD and PD, and we discuss future areas of research that can help researchers to develop new therapeutic agents. The most common aging-related diseases include focal ischemic stroke and neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Aging is a progressive, irreversible, and inevitable process that involves a distinctive decline in physiological functions and physical appearance, resulting from tissue degeneration and the dysfunction of vital organs [1]. A focal ischemic stroke is caused by the occlusion of a cerebral artery and is the most frequent type of cerebrovascular disease, and aging is its most nonmodifiable risk factor. Neurodegenerative diseases have no cure and are caused by progressive degeneration and/or neuron death to produce debilitating conditions. Neurodegenerative diseases also include Lewy body dementia, vascular dementia, amyotrophic lateral sclerosis, and frontotemporal dementia [2,3]. Meanwhile, the components of the human diet, such as vegetables, cereals, tea, wine, and fruits, contain different compounds including flavonoids, which are a class of polyphenols [4]. Many recent studies have shown that these compounds have a beneficial effect on the primary cell culture of glial and neurons and pre-clinical animal models of human focal ischemic stroke and neurodegenerative diseases. The active compounds and their mechanism of action for most flavonoids in the human diet and herbal medicine are still not well defined because they contain multiple bioactive molecules that can modulate multiple pharmacologic targets. To obtain more significant information, the scientific research has concentrated mainly on known biological activities of purified single compounds to offer an evidence base for the rationale of traditional practice but also to support their integration into modern medical practice [5]. In the sections below, we will describe the basic knowledge of natural flavonoids, neuroinflammation, aging, focal ischemic stroke, AD, and PD, and we will summarize the evidence relating to the anti-inflammatory effects of single flavonoids in different model systems of those diseases and clinical studies, and finally, we will suggest future areas of research to improve our understanding of single flavonoids molecules to help to establish more solid bases to facilitate their future use as a therapeutic alternative. Flavonoids are present in all vascular plants and are the most common class of polyphenols. In the plant, flavonoids are secondary metabolites that have a wide range of biochemical, physiological, and ecological functions, such as the coloration of petals and flowers, protection against ultraviolet light, and cell growth. Moreover, a single plant often contains many different flavonoids [6]. In nature, flavonoids are broadly distributed in the plant kingdom and are the main phytochemicals found in more than 6000 species of plants, and they also are abundantly found in plant-based foods and beverages, including vegetables, tea, fruits, grains roots, cocoa, and wine [7]. Flavonoids are low-molecular-weight compounds that can be divided into subclasses such as: anthocyanins, chalcones, flavanols (or catechins), flavones, flavanones, flavonols, flavanonols, and isoflavonoids (Figure 1) [7]. Except for catechins, all flavonoids contained in foods and beverages are in the form of glycosides, which must be removed to be absorbed from the small intestine upon ingestion. Flavonoid aglycones undergo conjugation reactions before passing into the bloodstream, and these reactions form sulphates, glucuronides, and/or methylates metabolites (conjugates), which can be subjected to additional phase II metabolism in the liver, and then returned to the circulatory system. Moreover, some conjugates may be exported into the bile duct or excreted by the kidney. Flavonoids not absorbed from the small intestine will be degraded in the colon [8]. Except from catechins, the rest of the flavonoids present in plasma and urine are primarily conjugated forms. Consequently, cells in the body are usually exposed to apparently less active flavonoid metabolites and conjugates, rather than aglycones [9]. However, there is evidence that deconjugation may occur in situ. An in vivo study has shown that glucuronidated quercetin metabolites were deconjugated in their aglycone form in the mesenteric vasculature of rats by the action of β-glucuronidase, and this effect was inhibited when an inhibitor of that enzyme was present [10], which suggests that deconjugation may occur in situ to produce a more effective aglycone form. The absorption and distribution of flavonoids around the body, as well as their excretion in urine, are carried out by members of ATP-binding cassette (ABC) transport systems, which translocate solutes across cell membranes [11]. The physicochemical properties (such as molecular size and lipophilicity, solubility, configuration, and pKa value) of each flavonoid determine the degree of absorption [12]. Flavonoids show great variability in the velocity and magnitude of absorption, plasma half-life, bioavailability, and plasma kinetics, but in general, they show rapid urinary and biliary excretion and low bioavailability, and it has been suggested that after the consumption of 10–100 mg of a single phenolic compound, their plasma concentration rarely exceeds 1 μM [13]. Moreover, Manach et al. [14] analyzed 97 bioavailability studies in humans and found that plasma concentrations of total metabolites ranged from 0 to 4 µmol/L with an intake of 50 mg aglycone equivalents, and the relative urinary excretion ranged from 0.3% to 43% of the ingested dose depending on the specific polyphenol. They also found that among all of the compounds analyzed, isoflavones and gallic acid had the best rate of absorption, followed by catechins, flavanones, and quercetin glycosides, and that proanthocyanidins and anthocyanins had very low bioavailability. Some evidence suggests that some flavonoids and/or their metabolites such as anthocyanins and (−)-epicatechin and some of their metabolites can cross the blood–brain barrier [15,16,17]. Flavonoids have pleiotropic effects that can produce health benefits, as shown in several diseases including cardiovascular diseases, neurological disorders, and cancer, and these benefits are seen in antiviral, antioxidant, and anti-inflammatory mechanisms, among others [6,8]. An inflammatory reaction in the central nervous system (CNS) or neuroinflammation are generally induced when innate immune cells detect tissue damage or an infection. This response is critical to isolate damaged tissue from uninjured areas and to repair and clean the extracellular matrix. Acute inflammation is beneficial and promotes regeneration, but chronic and excessive, as well as stable, low-grade, inflammation can produce the onset or exacerbation of cell injury [3]. It is generally accepted that low-grade neuroinflammation is the main factor in the onset and development of several neurological diseases. In aging, the major source for low-grade, chronic inflammation is the accumulation of endogenous host-derived cell debris due to both increased production and impaired elimination, and also immunosenescence, but these alterations are not commonly the initiating factor of neurodegenerative diseases. However, they contribute to amplifying the disease state, which would suggest that neuroinflammation plays an important role in neuronal dysfunction and death [18]. Glial cells in the CNS carry out different activities during neuroinflammation and can promote protection or damage, depending on the particular environments of inflammation and time. All glial cells play a role in the immune response, but the most important ones are the microglia and astrocytes. Microglia is the main immune cell in the CNS. These cells continuously scan the microenvironment of the parenchyma and are the first cells to respond to the occurrence of any damage [18]. In addition to being the most numerous cell type in the CNS, astrocytes have varied homeostatic functions, for example, they control the extracellular pH, antioxidant functions, neurotransmitter uptake, and the recycling of glutamate and GABA, regulate the cerebral blood flow and the blood–brain barrier (BBB), promote synaptogenesis, supply energy metabolites to the neurons, and they form part of the innate immune system of the CNS [19]. In neurological disease, glial cells can recognize endogenous molecules released by damaged or dead cells (damage-associated molecular patterns: DAMPs) and molecules present in pathogens (pathogen-associated molecular patterns: PAMPs) through pattern recognition receptors (PRR), which are formed by several subfamilies including the Toll-like receptors (TLRs) [18,20]. TLRs are type 1 transmembrane glycoproteins that are extensively expressed in microglia and astrocytes, with specific subtypes expressed in neurons and oligodendrocytes [21]. Each TLR subtype recognizes different PAMPs or DAMPs, for example, TLR4 recognizes lipopolysaccharide (LPS), and also, accumulated, misfolded proteins, including Aβ and α-synuclein present in AD and PD, respectively [18]. DAMPs comprise an extensive range of molecules such as uric acid, cytokine IL-1α, ATP, and nuclear and cytoplasmic proteins released during necrosis, and it has also been suggested that some members of the extended IL-1 cytokine family including IL-1β, IL-18, IL-33, IL-36α, IL-36β, and IL-36γ also act as DAMPs and stimulate the sterile inflammation induced by necrosis [22]. The interaction of ligands with the TLR of the host cell activates an intracellular signaling cascade that causes the release of inflammatory cytokines and other immune modulators as a protective mechanism and to repair the damaged tissue. However, excessive TLR activation disrupts immune homeostasis, causing constant proinflammatory cytokine and chemokine production, which contributes to the development and progression of many diseases [21]. After the initial interaction of the ligand and TLR, the latter one activates one of the several signal transduction pathways such as phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), mammalian target of rapamycin (mTOR), or mitogen-activated protein kinase (MAPK), which leads to the activation of different transcription factors such as activator protein 1 (AP-1), nuclear factor kappa-B (NF-κB), nitric oxide synthase (iNOS), interferon regulatory factor 3 (IRF3), and cyclooxygenase-2 (COX-2), which mediate the production of proinflammatory cytokines, chemokines, and inducible enzymes, all of which result in neuroinflammation [23,24]. The activation of the PI3K/AKT/mTOR pathway can activate the transcription factor NF-κB and induce the expression of proinflammatory molecules (e.g., IL-6 and TNF-α), iNOS, and COX-2, while other TLRs can activate the MAPK (p38 MAPK or SAPK/JNK) pathway, which then activates the transcription factor AP-1 and promotes the expression of proinflammatory molecules, including cytokines (e.g., IL-6, iNOS, and COX-2) [24]. The NOD subfamily is another well-known PRR member, which is part of the inflammasome NLRP3, together with the adaptor protein apoptosis-associated speck-like protein comprising a caspase recruitment domain (ASC) and procaspase 1, which together form an oligomer when the stimuli activate the receptor, and thus, induce the active precursors of proinflammatory cytokines, such as IL-1β and IL-18 [25]. Cytokines are the main communication mechanism used by the immune system and consist of polypeptides and glycoproteins synthetized by immune cells such as chemokines, lymphokines, interleukins (IL), tumor necrosis factor (TNF), and interferons (IFN), which can act as pro- and anti-inflammatory molecules [26]. Cytokines can have both anti- and proinflammatory effects. Anti-inflammatory cytokines include: IL-4, IL-6, IL-10, IL-11, IL-13, IL-1 receptor antagonist (IL-1RA), and TGF-β, while the proinflammatory cytokines include IL-1β, IL-6, IL-8, IL-12, TNF-α, and interferons, among others [26]. After secretion to the extracellular space, these molecules interact with cytokine receptors to initiate cytokine intracellular signaling, which modulates a diverse range of biological functions, and most cytokine receptors activate the JAK-STAT pathway [27]. The primary proinflammatory cytokines, such as TNF-α, IL-1α, and IL-6, contribute significantly to inflammaging in healthy elderly people and play a major role in several age-related diseases, including neurodegenerative diseases [22]. Furthermore, TNF-α also induces apoptosis by activating receptors such as tumor necrosis factor receptor 1 (TNFR1), 2 (p75), and CD95 (APO-1/Fas), which contain a homologous cytoplasmic sequence identifying an intracellular death domain [24]. Microglia and astrocytes can release anti- and proinflammatory cytokines, but the type of secretion seems to be related to the specific phenotypes of the glial cells, their interaction with each other, and the specific context. Glial cells show different phenotypes that are the result of transcriptional and functional changes that are generally known as activation or reaction ones. Several different phenotypes have been observed, but M1 for harmful and neurotoxic inflammation functions and M2 for pro-reparative and anti-inflammation functions are considered to be opposite states of reactive microglia, but the microglia can switch between these two phenotypes according to different environments [28]. Similarly, for astrocytes, the A1 phenotype is toxic to neurons and oligodendrocytes, while the A2 phenotype is protective [29,30]. In human neurodegenerative illnesses such as PD and AD, M1 microglia, as well as astrocyte A1, are highly present, and it has been proposed that M1 microglia induces the A1 astrocyte phenotype in these illnesses [31]. M1 microglia and A1 astrocytes secrete proinflammatory cytokines, which can induce apoptosis through the activation of extrinsic pathways and also induce the overproduction of ROS, mitochondrial dysfunction, DNA damage, and the production of more inflammatory mediators that contribute to cell aging, and also, induce the permeabilization of the BBB [32,33]. Most types of CNS diseases have a neuroinflammatory component, and activated microglia and astrocytes have been found [18]. Aging is a complex and inevitable process that results from the interaction of environmental epigenetic and genetic factors that produce a gradual reduction of homeostasis with age. This change is characterized by the progressive degeneration of tissue and functions in several organs, which contributes to increasing the risk of disease and death [34,35]. In the brain, aging affects different cell types and regions differently, and the individual variability is broad, but in general terms, there is a reduction of white and grey matter density, volume loss, cortical thinning, and the atrophy of specific brain regions, including the hippocampus [36]. Age-related diseases or aging-related diseases group different illnesses together, including arthritis, cancer, hypertension, type 2 diabetes, focal ischemic stroke, and many neurodegenerative diseases such as AD and PD. In normal aging, alterations occur in the nervous system to produce functional and structural changes. Functional changes include a decrease in blood flow and a reduction of synapses and neurotransmitter release and several areas, but the area that is most commonly affected is the hippocampus. On the other hand, structural changes include the enlargement of the ventricles, cerebral atrophy, and neuronal loss [37]. It is important to note that endothelial dysfunction is a significant contributor to cerebrovascular aging, which promotes oxidative stress and neuroinflammation in age-related disorders such as dementias and focal ischemic stroke [38]. There is a noticeable change in altered intercellular communication known as ‘inflammaging’, which is characterized by a process of chronic and low-grade inflammation, and a general increase in the secretion of proinflammatory cytokines and inflammatory markers [39]. Additionally, another feature is the presence of cellular senescence in tissues and organs, including the immune system (immunosenescence), producing reduced humoral and cellular responses for both the innate and adaptative immune systems. Senescent cells show increased activity of senescence-associated β-galactosidase (SA-β-GAL), a failure to re-enter the cell cycle in response to mitogenic stimuli, resistance to cell death, and a proinflammatory secretome called senescence-associated secretory phenotype (SASP) [32]. Some alterations such as neuroinflammation, DNA damage, and oxidative stress that are present in neurodegenerative diseases and focal ischemic stroke can induce cellular senescence, and it has been suggested that cellular senescence contributes to the pathophysiology of these disorders [40,41]. Many of the inflammaging characteristics can be found in the blood, for example high levels of proinflammatory cytokines (e.g., IL-1β, IL-6, IL-8, and IL-10) in sera or plasma [26], monocytes and macrophages with impaired phagocytosis and ability to heal injuries, the presence of senescent T cells, impaired macrophage polarization, and antibody production by the activated B cells [42]. In age-related diseases, systemic inflammation may contribute to neuroinflammation because circulating proinflammatory molecules can interact with endothelial cells from cerebral vasculature and induce the release of more cytokines to cause BBB impairment [43]. Oxidative stress is another factor present in neurodegenerative age-related diseases. This alteration is due to the excessive production of unstable molecules that contain oxygen called reactive oxygen species (ROS), and also, because the antioxidant system in the cells is not able to neutralize them. This mitochondrial dysfunction produces DAMPs in the cell, which initiate several inflammatory cascades, causing the activation of the innate immune system and NLRP3 inflammasome, which results in chronic inflammation [18]. Ischemic strokes or cerebrovascular accidents are the most frequent types of cerebrovascular diseases and are one of the main causes of death and disability worldwide [44]. The main type of strokes is the focal ischemic, which is due to an obstruction of the arterial blood flow to a specific brain region [45]. Aging is considered to be the most important nonmodifiable risk factor, and the risk of a stroke occurring doubles every 10 years after the person reaches the age of 55, and approximately three quarters of all strokes occur in persons aged ≥65 years [46,47]. It is important to highlight that modifiable risk factors such as diabetes and high blood pressure steadily increase with age, and it has been shown that these chronic conditions are very highly prevalent in persons that have suffered strokes [47]. In most countries, the approved treatments for an acute focal ischemic stroke are the endovascular thrombectomy and intravenous recombinant tissue plasminogen activator (rtPA), which focus on removing the occlusion in the artery and saving the penumbra cells to reduce the infarct core enlargement [45], but several conditions must be met to obtain the therapeutic benefits. Endovascular thrombectomy shows benefits when it is applied within the first six hours and rtPA shows benefits when it is applied within the first four and a half hours of stroke onset [48]. The cessation of or decrease in cerebral blood flow may produce different levels of damage depending on several factors such as the time elapsed, cell resistance, and the magnitude of the ischemia, which results in the activation of very complex cascades of cellular and molecular events with a temporal overlapping profile that evolves over minutes, hours, or days, inducing transient-to-irreversible injuries (e.g., cell death) in all cell types and damage. The pathologic cascades produce damage in two different areas: the ischemic core and the penumbra. In the ischemic core, the abrupt decrease in cerebral blood flow leads to permanent damage in the cell and rapid cell death by necrosis. The size of the necrotic area will depend mainly on the location of the stroke, its duration, and its magnitude. The penumbra surrounding the core area is perfused by collateral blood vessels, which help to keep the cell structures intact, but functionally weakened [45]. After a few minutes of cerebral blood vessel occlusion, the first pathological event is activated due to the reduction of oxygen and glucose, which leads to a failure to produce high-energy molecules to maintain cellular homeostasis. This event sets off several mechanisms, which include ionic imbalance, cytotoxic and vasogenic edemas, excitotoxicity, calcium overload, excitotoxicity, oxidative and nitrosative stress, peri-infarct depolarization, blood–brain barrier (BBB) disruption, apoptosis, and inflammation [45,49]. Inflammation responses occur after the damaged cells release DAMPs, which interact with Toll-like receptors in the microglia and astrocytes. After this interaction, the microglia become reactive and accumulate at the lesion core and penumbra. During the first hours after the damage, these reactive microglia have an anti-inflammatory profile, but after this period, they switch to a proinflammatory profile, and several of the proinflammatory mediators (e.g., reactive oxygen species, cytokines, and tumor necrosis factor-α) released can induce astrogliosis from 4 to 24 h after stimulation [50,51,52]. M2 microglia found around the lesion site migrate towards the lesion core and in the penumbra, and following cell death, they begin the phagocytic removal of cell debris [53,54]. Proinflammatory cytokines (e.g., IL-1, IL-6, and TNF-α) released by M1 microglia promote a gradual alteration in the BBB and allow the infiltration of circulating leucocytes, which eventually release proinflammatory cytokines (e.g., interleukin-1β and interferon-γ) to damage the cell structures directly or indirectly and contribute to the enlargement of the lesion [55,56,57]. Cytokines released by M1 microglia and other glial cells and neurons promote astrogliosis, and these reactive astrocytes participate in many protective mechanisms after a stroke (e.g., neurotransmitter uptake, pH regulation, the anti-inflammatory release of cytokines, and glial scar formation), but they also have detrimental effects including the release of several detrimental factors such as proinflammatory cytokines (e.g., TNF-α and IL-1), matrix metalloproteinases (e.g., the degradation of the matrix protein), and proteoglycans (e.g., these cause inhibition of axon regeneration and myelination), which can contribute to expanding the lesion and/or decreased recovery [58,59]. Around 6 days after the injury, glial scar formation starts, and involves a subset of reactive astrocytes and other cells (e.g., reactive microglia and NG2 cells) and is completed between 2 and 4 weeks after the stroke [60,61]. Even after the glial scar, which serves as a protective barrier, has been formed, the cells in the penumbra are still exposed to several deleterious mechanisms, such as vasogenic edema, apoptosis, astrogliosis, and inflammation, which can last many months or even years. However, neurons and other cell types are viable and have a long-term potential for remodeling the tissue and forming new circuits to sustain a functional recovery [45,62], and this also the main target for any therapeutic interventions. AD is a progressive disease that is generally manifested by a loss of memory and difficulties with communicating, behaving correctly, and using problem-solving skills. AD is a neurodegenerative disorder that results in neuronal loss and brain atrophy in extensive areas of the hippocampus and cerebral cortex, synapse loss, and ultimately, death [63]. AD is generally divided into familial AD (FAD) and idiopathic or sporadic AD (SAD). FAD is caused by dominant genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), and amyloid-beta A4 precursor protein (APP), and it accounts for 3% of the reported cases of AD. SAD has no single genetic cause, and it represents over 95% of all cases. Since age is considered to be the main risk factor for AD, there is another classification that takes into account the age at which the disease began, and this is divided into early-onset and late-onset types. Early onset occurs before the age of 65, and most cases are FAD ones. Most late-onset cases are SAD one, and it has a mean onset age of 80 years [64,65]. In addition to APP, PSEN1, and PSEN2 genes, the ε4 allele of apolipoprotein E (APOE) is another genetic risk factor for AD. APOE is a protein related to lipid metabolism and is immunochemically colocalized to vascular amyloid deposits, neurofibrillary tangles (NFT), and senile plaques in AD [66]. AD is related to the accumulation of insoluble forms of amyloid-β (Aβ) in plaques and the intraneuronal deposition of neurofibrillary tangles (NFT), which are composed of hyperphosphorylated tau protein. The amyloid hypothesis of AD suggests that alterations to APP metabolism and Aβ accumulation are the main events in AD [67]. On the other hand, the tau hypothesis of AD suggests that aggregates of misfolded and fibrillar hyperphosphorylated NFT accumulate inside the neurons and propagate through cells in a prion-like way, eventually disseminating into the brains of AD patients. The neuropathologic hallmarks of AD are Aβ plaques and NFTs, but generally, they are accompanied by neuronal and synaptic loss, reactive astrocytes, microglial activation, the blood–brain barrier alterations, and brain atrophy [68]. Moreover, it is noteworthy that a high percentage of patients with AD also have cerebral amyloid angiopathy, a condition characterized by an accumulation of amyloids in the cerebral vasculature, which can lead to intracerebral hemorrhages and microbleeds, and these events accelerate AD [64]. In AD, the microglia and astrocytes play an important role in the neuroinflammatory response, and also, in the development of the disease. Aβ can interact with the microglia through the NLRP3 inflammatory complex and CD36-TLR4-TLR6 receptor complex, causing immune responses, cell damage, and the release of inflammation-inducing factors, such as IL-1β and TNF-α. Moreover, high levels of proinflammatory cytokines IL-1β and Il-6 are elevated in the peripheral blood of AD patients [69]. Activated microglia release the proinflammatory cytokines IL-1α, C1q, and TNF-α that can induce the A1 proinflammatory astrocytes, which can produce a secondary inflammatory response [31]. The evidence from postmortem analyses of AD patients shows alterations in microglia morphology such as reduced branching and arborized areas and immunoreactivity to ionized calcium-binding adaptor molecule 1 (Iba 1), which is a microglia/macrophage-specific protein that is upregulated in activated microglia [70]. Activated microglia were observed in the entorhinal, temporoparietal, and cingulate cortices in positron emission tomography (PET) studies in humans. These PET studies use the selective marker 11PK11195, which labels the target translocator protein (TSPO) on the external mitochondrial membrane that increases its density in active microglia and the regions [71]. Additionally, the occurrence of microglia activation was confirmed using different markers alone (e.g., 11PK11195 and 11 PBR28) or in combination with tau (e.g., 18 F-AV145) or Aβ plaques markers (e.g., 18 F-flutemetamol). One study found that AD patients have increased microglial activation and amyloid in the frontal, temporal, parietal, occipital, and cingulate cortices [72], while another study found activated microglia in the occipital lobe in AD patients [73]. Additionally, it was reported that microglial activation, tau aggregation, and amyloid deposition were found in similar areas of the association cortex [74]. A longitudinal PET study (14 months) evaluated the change in microglia activation in AD patients with mild cognitive impairment (MCI), and the results show that patients with MCI demonstrated reduced activated microglia levels, which is in contrast to AD patients with more activated microglia [75]. Another study used PET (11PK11195) in combination with structural magnetic resonance imaging (structural MRI) to investigate brain atrophy, and they found activated microglia in the anterior temporal region, tau in the temporoparietal area, and grey matter atrophy [76]. A similar study found that activated microglia in temporal cortices are related to parieto-occipital atrophy and cortical thinning [77]. All of these studies confirm the presence of activated microglia in patients with AD. PD is an age-related neurodegenerative disease with a multifactorial etiology that causes tremors at rest, bradykinesia symptoms, and rigidity, and it shares these characteristics with other clinical syndromes referred to as “Parkinsonism” disorders [78]. The other main characteristics of PD are the degeneration of neurons in the substantia nigra pars compacta (one of the basal ganglia), intraneuronal protein aggregates called Lewy bodies, and Lewy neurites [79]. In addition to those mentioned, PD patients may also present other alterations such as sleep disorders, depression, and dementia [80]. A few cases of PD seem to have a genetic origin, and the majority of them correspond to the idiopathic form, for which the risk factors include aging and behavioral and environmental factors such as a history of melanoma, traumatic brain injury, and exposure to pesticides [79]. The monogenic mutation in several genes that encode different proteins constitute the genetic forms of PD, including the α-synuclein, in which gene duplications and triplications, as well as mutations, have been found, and this comprises the main component of Lewy bodies [79]. In physiological conditions, the neuronal protein α-synuclein participates in several activities, such as dopamine synthesis and vesicle trafficking, and it has two conformations, a soluble unfolded monomer and a multimeric membrane-bound helical α-synuclein. Meanwhile, in pathological conditions, the soluble unfolded monomer forms β-sheet-like oligomers named protofibrils, which transform into amyloid fibrils and ultimately deposit into Lewy bodies. Moreover, the protofibrils and fibrils may propagate by a transcellular mechanism from neuron to neuron [79]. Lewy body inclusions, in addition to being present in neurons of the substantia nigra, also are present in structures such as raphe nuclei, the basal nucleus of Meynert, the neocortex, and the amygdala, and also, in oligodendrocytes from the midbrain and the basal ganglia [81]. The contribution of neuroinflammation to cellular damage in PD has been confirmed in several studies. A positron emission tomography (PET) study in PD patients using a marker for active microglia showed that the rate of the activation of the microglia was increased in the substantia nigra, the caudate nucleus, the pre- and postcentral gyrus, the frontal lobe, and the putamen, which agrees well with the known distribution of neuropathological changes [82]. Additionally, increased proinflammatory mediators (e.g., TNF-β) and active astrocytes and microglia have been found in the substantia nigra in post-mortem studies on PD patient brains [83,84,85]. Moreover, the animal model of PD MPTP (1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine), as well as human brains, exhibited an increase in the proinflammatory molecules (e.g., COX-2) in dopaminergic neurons from the substantia nigra [86]. Furthermore, some evidence suggests that the adaptive immune system also participates in the pathology, for example, the presence of CD4+ and CD8+ T cells in the substantia nigra from postmortem studies of PD patients and animal models [87]. Neuronal cell culture studies also show that IL-1 increases the α-synuclein [88] and that α-synuclein induces microglial activation and increases the production of TNF and IL1β [89]. When it is analyzed together, this information shows that inflammatory responses are involved in the pathophysiology of PD. Figure 2 show the main characteristic of inflammatory responses that share focal ischemic stroke, AD and PD. The general neuroprotective and neuroplastic effects of flavonoids are related to anti-inflammatory activity, which can increase the protection of neurons and glial cells against neurotoxins-induced injury and improve the endogenous mechanism of neuroplasticity to gain CNS functions. The effects of flavonoids in the modulation of molecular pathways involved in neuroinflammation have been mainly described in cell cultures using primary cells exposed to oxygen-glucose deprivation as the main model of ischemic stroke and lipopolysaccharides as a model of neuroinflammation in neurodegenerative disease, but animal models and clinical studies have also provided strong evidence of the beneficial effects of flavonoids. In the next sections, you will find a narrative description of the anti-inflammatory effects of the most used flavonoids on each disease in cellular, animal models, and clinical studies. Oxygen glucose deprivation (OGD) is an in vitro model used for the study of the cellular and molecular pathway associated with stroke, and usually, the cells are incubated in a glucose-free medium in a deoxygenated atmosphere for different durations of exposure to these conditions depending on the preparation. Sometimes, after the OGD, the cells are returned to the pre-deprivation conditions to model the focal ischemic reperfusion injury that occurs after the blood supply is restored, and this variant is called OGD/R [90,91]. Myricetin showed a significant protective effect against inflammation in human brain microvessel endothelial cells (HBMECs) in OGD/R conditions by decreasing the number of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6 [92]. Quercetin inhibits inflammation that is mediated by TLR4/MyD88/NF-κB, signaling BV2 microglial cells in mice [93]. Isoquercetin, a glucoside derivative of quercetin, has neuroprotective effects in rat cortical neurons in OGD/R conditions by inhibiting the protein expression of TLR4 and nuclear NF-κB and the mRNA expression of TNF-α and IL-6 [94]. Moreover, in rat hippocampal neurons in OGD/R conditions, isoquercetin inhibits the activation of Toll-like receptor 4 (TLR4), nuclear factor-kappa B (NF-κB), and caspase-1; the phosphorylation of ERK1/2, JNK1/2, and p38 mitogen-activated protein kinase (MAPK); the secretion of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6 [95]. Furthermore, cortical neurons in similar conditions inhibit the protein expression of TLR4 and NF-κB and the mRNA expression of TNF-α and IL-6 [94]. Baicalin could effectively downregulate the expression of the NOD2 receptor (protein associated with inflammatory reactions) and TNFα at both the mRNA and protein levels in BV2 microglial cells in OGD conditions [96], inhibit the NLRP3 inflammasome IL-1β, and IL-18 expression in cortical neurons in OGD/R conditions [97], and also, decrease the secretion of TNF-α, IL-1β, IL-6 and inhibit the NF-κB signaling pathway in the brain microvascular endothelial cells (BMECs) in OGD conditions [98] and the BV2 microglia cell line in OGD/R conditions [99]. Moreover, baicalin inhibits the proinflammatory microglial polarization through the inhibition of the TLR4/NF-κB pathway and the downregulation of phosphorylated STAT1 in a microglia-neuron co-culture system in OGD conditions [100] and in human brain microvascular endothelial cells (HBMEC) in OGD conditions, it inhibits the expression of TLR4, MYD88, and p-NF-κB and decreases the release of inflammatory factors IL-6, IL-1α, IL-1β, IL8, and TNF-α [101,102]. Baicalin also decreases the release of TNF-α, IL-1β, IL-6, and IL-8, and Tlr4 mRNA expression in microglia in OGD conditions [102]. Icariin applied before the OGD/R-reduced protein level expression of IL-1β, IL-6, and TNF-α in OGD/R conditions injured the microglia [103]. Casticin reduced the expression of TLR4, NF-κB p65, and NF-κB p50 in PC12 cells in OGD/R conditions [104]. Pratensein in HT22 cells in OGD/R conditions suppresses NLRP3 inflammasome activation through Nrf2 activation, resulting in reduced inflammatory responses [105]. Tectorigenin inhibited ROS inflammatory cytokines IL-1β, IL-6, and TNF-α production in OGD/R-induced HT-22 cells [106]. Astilbin inhibits NLRP3 inflammasome and decreases the release of IL-1β and IL-18 in PC12 cells in OGD conditions [107]. Anthocyanin significantly reduced the secretion of TNF-α, IL-1β, and IL-6 in SH-SY5Y cells exposed to OGD [108]. In the mouse neuroblastoma cells N2a, tricin, an O-methylated flavone, decreases the expression of TNF-α, IL-6, and IL-1β [109]. Diosmetin inhibits the NLRP3 inflammasome pathway and inflammatory cytokines IL-1β and IL-18 in PC12 cells in OGD/R conditions [110]. Schaftoside inhibits the expression of TLR4, IL-1β, IL-6, and TNF-α in OGD-simulated BV2 microglia [111]. The most commonly used animals in preclinic stroke research are rats and mice. Animal models for ischemic stroke can be divided into global, focal, and multifocal ones. There are several global ischemic stroke models, and the most common ones are cardiac arrest, the four-vessel occlusion model, and systemic hypotension and hypoxia. Animal models for focal ischemic strokes have been developed to induce damage within the territory irrigated by the middle cerebral artery (MCA) region to mimic a common clinical situation. There are different approaches to achieve this, but the most common ones are the intraluminal suture middle cerebral artery occlusion model without reperfusion (MCAO) and with reperfusion (MCAO/R), the photothrombotic model, and the endothelin-1 induced stroke model [112]. In this section, we will analyze only the studies that applied flavonoids after vessel occlusion with or without reperfusion because these models are more similar to clinical strokes. In a rat MCAO/R model, kaempferol-3-O-rutinoside (KRS) and kaempferol-3-O-glucoside (KGS) reduced the neurological deficits and infarct volume and inhibited the proinflammatory mediators (STAT3 and NF-κB) and interleukin 1β [113]. In the same model, kaempferol administered for 7 days after stroke also decreased the NF-κB and pro-inflammatory cytokines such as IL-5, TNF-α, IL-1β, and IL-6, but the reduction of last three cytokines only occurred with high doses [114]. Fisetin applied 3 h after the onset of ischemia to the MCAO/R mice model significantly reduced the infarct size and decreased TNF-α production in the microglia and the infiltration of leukocytes and macrophages [115]. A morin treatment for 7 days after MCAO in rats reduced the neurological deficits and inhibited the proinflammatory cytokine mRNA expression of TNF-α and IL-6 [116], and in the MCAO/R rat model, it decreased the rates of pNF-κB, TNF-α, IL-1β, and TLR4 expression and improved the tight junctions of the BBB by significantly increasing occludin and claudin expression [117]. (−)-Epigallocatechin-3-gallate (EGCG) decreased the infarct volume, TNF-α, IL-1β, and IL-6, and also, inhibited NF-κB/p65 in a MCAO/R rat model when it was applied immediately after reperfusion [118]. Luteolin administered intraperitoneally after MCAO/R in rats suppressed hippocampus inflammation, reduced the infarct volume, and decreased the astrocyte and microglia activation [119]. Luteoloside decreased the infarct volume, TNF-α, and IL-1β when this molecule was intraperitoneally injected immediately and 12 h after MCAO surgery in rats [120]. MCOA/R rats treated with nobiletin after reperfusion resulted in improved neurological deficits and decreased brain swelling and infarct volume [121]. Eriodictyol applied after stroke improves neurological deficits in the MCAO mice model and also decreases infarct volume TNF-α and GFAP expression [122]. Tricin applied by oral administration 2 h, 4 h, and 6 h after MCA/R resulted in decreased serum levels of TNF-α, IL-6, and IL-1β [109]. Eupatilin administered to mice 5 h after MCAO/R showed a reduction of the activated microglia in the peri-ischemic tissue and inhibited the NF-κB pathway [123]. The simultaneous treatment of EGCG and rt-PA 4 h after MCAO significantly reversed the neurobehavioral deficit, brain infarction, cerebral edema, and blood–brain barrier disruption [124]. Rats treated with naringenin once daily for 21 days, and then subjected to MCOA/R, resulted in a significant decrease in the infarct volume, the expression of NF-κB, TNF-α, IL-1β, and GFAP in the astrocytes, and Iba1 in the microglia, and improved neurologic deficits [125]. Moreover, rats treated with the same molecule for 4 days before MCAO also showed a decrease in NF-κB, the infarct volume, and improvements to the neurologic deficits [126]. Moreover, the same molecule applied 7 days before MCAO/R in rats decreased the expression of TNF-α and IL-6 in the brain tissue [126]. Rats treated with hesperidin for 15 days followed by MCAO showed an improvement of the neurological deficits, infarct volume, and decreased levels of IL-1β [127]. In a rat model of global stroke, a pinocembrin treatment administered daily for 7 days before a stroke decreased the infarct size and NF-κB, TNF-α, and IL-6 in the hippocampal tissue [128]. In an MCAO mice model, a fourteen-day-long genistein treatment before a stroke reduced the infarct volume, improved the neurological deficit, and inhibited NF-κB activation [129]. Sanggenon administered intragastrically in rats seven days before MCAO/R surgery resulted in a decrease in the levels of TNF-α, IL-1β, and IL-6 [130]. Astilbin applied 3 days before the MCAO in rats inhibited NLRP3 inflammasome and decreased the serum concentration of IL-1β and IL-18 [107]. Chrysin applied for 7 days before the MCAO in rats decreased the release of inflammatory cytokines IL-6, IL-1β, and TNF-α [131]. Baicalin applied 4 days before the MCAO in rats decreased the expression level of the NLRP3 inflammasome, IL-1β, and IL-18 [97]. Eupafolin applied for 7 days before the MCAO/R in rats decreased TLR-4, TNF-α, IL-1β, and IL-6 expression [132]. Rats were treated with Biochanin A for 14 days before the MCAO in rats decreased the protein and gene expression of TNF-α and IL-1β [133]. Though no clinical studies using flavonoids as a post-stroke treatment could be found, some human studies suggest that flavonoids can be useful in the treatment of this condition. A study using the flavonol fisetin combined with rt-PA in stroke patients shows that the addition of this flavonoid extends the therapeutic window of rt-PA treatment and dramatically improves the neurological deficits evaluated by the National Institutes of Health Stroke Scale (NIHSS) and decreases the plasma levels of C-reactive protein (CRP) and matrix metalloproteinases (MMP)-2 and -9 [134]. In a similar study, EGCG also extends the therapeutic window of the rt-PA treatment and improves the NIHSS scale, while decreasing plasma levels of matrix metalloproteinases (MMP)-2 and -9 [135]. Numerous in vitro studies have evaluated the effects of different flavonoids in the Aβ oligomer and its assembly into aggregates. EGCG inhibits the fibrillogenesis of Aβ [136], modifies Aβ fibrils into smaller protein aggregates that are nontoxic to mammalian cells [137], and in cultured hippocampal neuronal cells, it has protective effects against Aβ-induced neuronal apoptosis through scavenging reactive oxygen species [138]. Quercetin used as a pretreatment with primary hippocampal cultures significantly decreases Aβ (1–42)-induced cytotoxicity, lipid peroxidation, protein oxidation, and apoptosis [139]. Luteolin and diosmetin decrease Aβ (1–40 and 1–42) in primary neuronal cells and SweAPP N2a cells [140]. Myricetin prevents the fibrillogenesis of Aβ [141]. Cyanidin 3-O-β-glucopyranoside in neuroblastoma SH-SY5Y cells reduces the cytotoxicity of Aβ (25–35) and its aggregation [142]. In the same cells, wogonin reduces Aβ aggregation and phosphorylated Tau [143]. Through different biochemical techniques, it has been found that Baicalein prevents the aggregation of the human tau protein [144,145]. Quercetin and rutin prevent the formation of Aβ fibrils and disaggregate Aβ fibrils in a cell system overexpressing APP Swedish mutation (APPswe) [146]. Animal models for AD include chemically induced ones (e.g., amyloid infusion and streptozotocin), spontaneous ones (e.g., senescence-accelerated mouse), and several transgenic mice and a few transgenic rats that express mutant human genes related to the production of amyloid plaques and neurofibrillary tangles (e.g., 3XTg and 5XFAD, TG2576, and APP/PS1) [147]. These transgenic animals model familial AD and partly recapitulate the idiopathic forms and they express amyloid plaques and neurofibrillary tangles and all of the manifested deficits in memory, but the majority the animals do not present with neurodegeneration, and this is one of the aspects that limits their use in neuroinflammation research [147,148]. Since these animal models do not present with neurodegeneration, the main interest of flavonoids in the research in these animal models has been focused on the effects of the Aβ aggregation and the impairment of the cognition functions. In the chemical mouse model of memory deficits induced by scopolamine, isorhamnetin inhibits learning and memory deficits, and also, induces an increase in brain-derived neurotrophic factor (BDNF) levels in the prefrontal cortex and hippocampus [149], while naringin and rutin improve memory [150]. In another chemical model (streptozotocin), kaempferol increases the density of intact neurons in the CA1 area of the hippocampus and improves memory [151]. Luteolin improves memory [152] and a hesperidin pretreatment decreases inflammatory markers, such as NF-κB, iNOS, COX-2, and astrogliosis, and improves memory [153]. Nobiletin in APP-SL 7-5 transgenic mice reduces the quantity of soluble Aβ (1–40 and 1–42) and Aβ plaques in the hippocampus [154]. In 3XTg mice, diosmin and its bioactive metabolites decrease tau hyperphosphorylation and Aβ generation [155]. In transgenic APP/PS1 mice, hesperidin reduces Aβ plaque in the cortex and the hippocampus, decreases astrogliosis and microglial activation, and restores the ability to perform social interaction [156]. In transgenic h-APPswe, h-Tau P301L, and h-PS1 M146V mice, wogonin improves memory [143]. In the Tg2576 transgenic mouse model, diosmin and luteolin reduce Aβ 1–40 and 1–42 [140]. In APPsw transgenic mice, EGCG reduces the amount of soluble Aβ (1–40 and 1–42) and Aβ deposits in different cortical brain regions and the hippocampus [157], and in the Aβ infusion model, it prevents memory dysfunction and reduces the Aβ (1–42) and alpha-secretase levels and increases beta- and gamma-secretase in both the cortex and hippocampus, and similar results were obtained in the presenilin 2 (PS2) mutant mice [158], while in the APPsw transgenic mouse model, diosmin shows improved memory [159]. Nobiletin reduces Aβ plaques in the hippocampus and improves memory deficits in APP-SL 7-5 transgenic mice [154], and in 3XTg mice, it reversed the damage to memory and decreased the levels of Aβ 1–40 [160]. Nobiletin reverses memory impairment in the hippocampus in senescence-accelerated mice SAMP8 [161]. In 3XTg mice, quercetin reduces the plaques of Aβ and hyperphosphorylated tau in the CA1 area of the hippocampus and improves memory [162]. Cyanidin 3-O-glucoside decreases tau phosphorylation in the hippocampus and reverses memory impairment in Aβ infusion rats [163] and in the APP(swe)/PS1(ΔE9) mouse model, it improved memory and learning [142]. Fisetin in APPswe/PS1dE9 double transgenic mice inhibits the development of memory and learning problems through the modulation of cyclin-dependent kinase 5 (Cdk5), where hyperactivity induces neuroinflammation and neurodegeneration [164]. In two different transgenic mouse models (TG2576 and TG-SwDI), a dihydromyricetin treatment improves exploratory and locomotor activities, decreases anxiety, improves memory, and reverses Aβ accumulation [165]. As you may note, the majority of the studies mentioned focused on the effects of flavonoids on amyloidopathy and cognitive deficits, while studies on tauopathy are scarce. There are no clinical studies with a single flavonoid molecule, but we can illustrate the potential of these molecules using the results obtained with cocoa flavanol, which is a mixture of flavanols, mainly catechin and epicatechin. Cocoa flavanol consumption for 8 weeks improved cognitive functions in patients with mild cognitive impairment [166], and a double-blind study showed improved cognitive functions in aging subjects [167]. Moreover, the consumption of these flavanols by healthy 50–69-year-old subjects over 3 months improves the dentate gyrus functions evaluated by a high-resolution variant of functional magnetic resonance imaging (fMRI) and cognitive testing [168]. Some in vitro studies have evaluated the effects of different flavonoids on the formation of α-synuclein oligomers and their assembly into aggregates and found that flavonoids inhibit oligomer formation and aggregation. These flavonoids include: apigenin, baicalein myricetin, genistein, morin, quercetin, EGCG, and scutellarein [169,170,171]. In activated microglia induced by the LPS model, nobiletin prevents the release of the proinflammatory cytokines TNF-α and IL-1β [172], and apigenin and luteolin decrease TNF-α and IL-6 [173]. Naringenin inhibits NF-κB, iNOS, and COX-2, and induces the expression of the suppressor of cytokine signaling 3 (SOCS-2), a negative regulator of cytokines in activated microglia [174,175]. Diadzein downregulates the activation of NF-κB and the production of IL-6 and [176], and its metabolite Equol (7-hydroxy-3-(4′-hydroxyphenyl)-chroman) prevents the secretion of TNF-α, IL-6, and NF-κB activation [177]. In PC12 cells exposed to the neurotoxic MPP+, morin reduces cell apoptosis and mortality [178] and decreases the rate of astrogliosis and the nuclear translocation of NF-κB in primary cultured astrocytes exposed to the same neurotoxicity [179]. Butein, butin, fisetin, fustin, and sulfuretin protect the murine hippocampal HT22 cells against glutamate-induced neurotoxicity and reduce the induced nitric oxide (NO) production in BV2 cells microglial cell lines, and also, butein suppresses the expression of iNOS and COX-2 [180]. Genkwanin suppressed the MPP+-induced activation of the TLR4/MyD88/NLRP3 inflammasome pathway in SH-SY5Y cells [181]. Chemically induced animal models of PD are the most widely used ones. Rat or mice models of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 6-hydroxydopamine (6-OHDA) are two of the most common types. MPTP is the precursor to the neurotoxic 1-methyl-4-phenyl-2,3-dihydropyridinium (MPDP+), which is converted into glial cells. Both MPDP+ and 6-OHDA are neurotoxic to dopaminergic neurons and elicit a motor phenotype [182,183]. Additionally, the rotenone model of PD also shows the main pathological hallmarks of the disease [184]. In this last model, baicalein reduced the formation and accumulation of α-synuclein oligomers and protected dopaminergic neurons [185]. In the MPP+ rat model, this flavonoid attenuates α-synuclein aggregation, inhibits inflammasome activation [186], improves the motor ability, decreases the number of activated microglia and astrocytes, and increases dopamine and serotonin neurotransmitters in the striatum [187,188,189]. Apigenin in the rotenone rat model decreases the expression of NF-κB, increases the expression of dopamine D2 receptor (D2R), and decreases α-synuclein aggregation in the rat rotenone model [190]. Nobiletin in the MPP+ rat model preserved the expression of the glial-cell-line-derived neurotrophic factor (GDNF), inhibited microglial activation [191] and increased the dopamine contents in the striatum and hippocampal CA1 region, and improved the motor deficits [192]. In the MPP+ rat model, naringin increased the GDNF level in the substantia nigra, reduced TNF-α expression [193], and protected the nigrostriatal DA projection [194]. In the MPDP+ mice model, EGCG reduced the dopamine neuronal loss in the substantia nigra [195], and the same flavonoid in the rotenone rat model inhibited TNF-α, IL-1β, and IL-6 in the striatum [196]. Quercetin increased the striatal dopamine level and reduced dopaminergic neuronal loss in the 6-OHDA rat model [197]. The same flavonoid in the MitoPark transgenic mouse models of PD reversed dopaminergic neuronal loss, striatal dopamine depletion, and the behavioral deficits [198]. Quercetin and kaempferol in an MTPT mouse model improved striatal dopamine secretion and motor coordination [199,200]. In the same model, hesperidin reduced the expression of IL-1β, TNF-α, and IL-6 and improved motor coordination [201,202]. In the 6-OHDA rat model, tangeretin protected the striatal dopaminergic neurons [203], rutin protected dopaminergic neurons and improved motor coordination [204], troxerutin reduced neuronal loss, astrogliosis, and striatal lipoperoxidation [205], and myricitrin inhibited the expression of TNF-α and protected the dopaminergic neurons from the substantia nigra [206]. In the MPTP mouse model, a pretreatment with morin reduced dopaminergic neuronal death, behavioral deficits, and striatal dopamine depletion [178]; it reduced the dopaminergic neuronal losses, astrogliosis, and improved motor dysfunction [179]. In the MPTP mouse model, icariin inhibited NLRP3 inflammasome and decreased the IL-1β and TNF-α serum levels [207]. Europinidin in the rotenone rat model decreased the IL-6, IL-1β, and TNF-α levels in the brain tissue [208]. Diosmin in a rotenone rat model decreased the expression of NF-κB and the TNF-α levels [209]. We did not find any clinical study using a single flavonoid molecule, but research using flavonoid-rich pure cocoa in patients with PD could illustrate the possible benefits for this disease. A randomized (1:1), double-blind, placebo-controlled feasibility study with 30 patients with PD showed a reduction of fatigue and fatigability [210]. Table 1, Table 2 and Table 3 show the main anti-inflammatory effects in models of focal ischemic stroke, AD, and PD. Flavonoids have pleiotropic effects, which are demonstrated mainly through in vitro and animal models of diverse human diseases. The best-known mechanism is antioxidants, but in the last two decades, our knowledge of their effects on neuroinflammation has grown. Many in vitro and animal model studies highlight the anti-inflammatory effect of flavonoids by decreasing the activated microglia and astrocytes, and also, by decreasing the proinflammatory cytokines such as TNF-α, IL-1β, IL-6, COX-2, and iNOS, either by the indirect or direct inactivation of transcription factors such as NF-κB and AP-1, and also, through the inactivation of the inflammasome (Figure 3). On the other hand, several studies have found that flavonoids can downregulate other pathological pathways, and some human studies show that after consuming flavonoid-rich foods and beverages, there is a significant reduction of the proinflammatory molecules, including C-reactive protein and IL-6 [211,212,213]. Searching the clinical trial data set from ClinicalTrails.gov revealed the interest of the scientific community in the beneficial effects of flavonoids for human health, as several ongoing studies are focused on topics related to ischemic strokes and neurodegenerative diseases, such as cognition performance, cognitive aging, the risk of dementia, and endothelial dysfunction, suggesting a growing interest in translating the preclinical knowledge into clinical trials. Although there have been substantial achievements in the bioavailability of flavonoids, for example, the cutaneous delivery system and application of the nanoencapsulation of bioactive compounds [214,215], we still need to improve our knowledge about aspects such as metabolic transformation, the identification of active molecules (parent molecule and/or their metabolites), the mechanism to cross the blood-brain barrier, and toxicology to find single flavonoids for the future therapy for neurological disorders associated with aging.

PMC10001880

Jieun Kim,Eun Hee Kim,Hanbee Lee,Ji Hee Sung,Oh Young Bang

Clinical-Scale Mesenchymal Stem Cell-Derived Extracellular Vesicle Therapy for Wound Healing

21-02-2023

mesenchymal stem cells,extracellular vesicles,exosomes,wound healing,functional recovery

We developed an extracellular vesicle (EV) bioprocessing platform for the scalable production of human Wharton’s jelly mesenchymal stem cell (MSC)-derived EVs. The effects of clinical-scale MSC-EV products on wound healing were tested in two different wound models: subcutaneous injection of EVs in a conventional full-thickness rat model and topical application of EVs using a sterile re-absorbable gelatin sponge in the chamber mouse model that was developed to prevent the contraction of wound areas. In vivo efficacy tests showed that treatment with MSC-EVs improved the recovery following wound injury, regardless of the type of wound model or mode of treatment. In vitro mechanistic studies using multiple cell lines involved in wound healing showed that EV therapy contributed to all stages of wound healing, such as anti-inflammation and proliferation/migration of keratinocytes, fibroblasts, and endothelial cells, to enhance wound re-epithelialization, extracellular matrix remodeling, and angiogenesis.

Clinical-Scale Mesenchymal Stem Cell-Derived Extracellular Vesicle Therapy for Wound Healing We developed an extracellular vesicle (EV) bioprocessing platform for the scalable production of human Wharton’s jelly mesenchymal stem cell (MSC)-derived EVs. The effects of clinical-scale MSC-EV products on wound healing were tested in two different wound models: subcutaneous injection of EVs in a conventional full-thickness rat model and topical application of EVs using a sterile re-absorbable gelatin sponge in the chamber mouse model that was developed to prevent the contraction of wound areas. In vivo efficacy tests showed that treatment with MSC-EVs improved the recovery following wound injury, regardless of the type of wound model or mode of treatment. In vitro mechanistic studies using multiple cell lines involved in wound healing showed that EV therapy contributed to all stages of wound healing, such as anti-inflammation and proliferation/migration of keratinocytes, fibroblasts, and endothelial cells, to enhance wound re-epithelialization, extracellular matrix remodeling, and angiogenesis. Cutaneous wounds are common injuries caused by trauma, burns, ulcers, or surgery. Non-healing cutaneous wounds can impose severe clinical burdens on patients without effective treatment strategies. The beneficial effects of exogenous mesenchymal stem cells (MSCs) on wound healing have been observed in various animal models and clinical cases [1,2] Clinical test results using MSCs to enhance wound healing have been promising [3,4]. Notwithstanding the promising results obtained in clinical trials, MSC-based therapies are not considered a standard of care in clinical settings due to various limitations to their applicability [5,6]. A cell-free treatment paradigm using MSC-derived extracellular vesicles (EVs) can avoid the cell-related problems associated with stem cell therapy and exert the paracrine actions of MSCs. In addition, the “off-the-shelf” use of allogeneic MSC-derived EVs from healthy and young stem cells, such as MSCs derived from the umbilical cord, has the advantage of scalable production and storage with standardized procedures with high restorative capacity. However, critical hurdles remain in the translation of MSC-EVs into clinical therapeutics. Previous studies have used EV preparations obtained from the conventional 2D culture of MSCs; however, to date, no preclinical or clinical studies have examined the effects of MSC-EVs via scale-up production with customized therapeutic properties. We have previously reported that MSCs 3D-cultured as size-controlled cellular aggregates on a large scale better preserved the innate phenotype and properties of MSCs compared to 2D monolayer cultures, which resulted in the significantly augmented secretion of therapeutic MSC-derived EVs and their therapeutic contents (miRNAs and cytokines) from MSCs compared to conventional 2D cultures [7]. In the present study, we hypothesized that a clinical-scale EV product using a 3D micropatterned well system would enhance the wound healing process. To verify this, we developed an EV-bioprocessing platform designed using a cell non-adhesive microwell-patterned array for the scalable production of human Wharton’s jelly (WJ)-MSC-derived EVs in serum-free media. The effects of clinical-scale EV products on wound healing were tested in two different wound models: subcutaneous injection of EVs in a conventional full-thickness rat model and topical application of EVs using a sterile re-absorbable gelatin sponge in a chamber mouse model that was developed to prevent the contraction of wound areas. In addition, we performed in vitro and in vivo mechanistic studies using multiple cell lines involved in the wound healing process. The amount of EVs obtained from 3D culture system was estimated to be approximately 8155.28 EVs per cell. The EVs had a typical round shape as seen on electron microscopy (TEM and Cryo-EM) (Figure 1A), and the mean particle diameter was 146.0 nm (Figure 1B). We investigated the expression of CD9, CD63 and CD81 using the Exoview Tetraspanin kit. EVs were primarily captured by antibodies against each tetraspanin, and then fluorescently labeled by detection antibodies for the three tetraspanins. It was demonstrated that the subpopulation of CD63+ was higher than CD9+ or CD81+ (Figure 1C). The presence of EV-specific positive markers (CD63, CD81 and Syntenin-1) further confirmed the identity as EVs (Figure 1D). The particle/protein ratio was 6.5 × 108 particles/μg. Specific contaminating proteins, including histone H2A.Z, GM130, and antibiotics, were identified by Western blot or ELISA. Antibiotics GM130 and histone H2A.Z were not detected (Figure 1E). The characteristics of EVs and their cargo contents did not change at room temperature after 1 week (Figure 1F). To investigate the efficacy and mechanism of MSC-EVs in a full-thickness rat wound model, rats were induced into a full-thickness wound model, and 2 × 108 EVs/rat were injected subcutaneously for 3 d (Figure 2A). Wound closure in the MSC-EVs group was higher than that in the PBS-treated group (Figure 2B,C). In addition, 14 d after wound induction, the contractility and repair ability of the wound center were measured, and the percentage of re-epithelialization was analyzed for wound repair capacity (Figure 2D,E). MSC-EV treatment significantly increased re-epithelization (Figure 2D) and reduced the size of the wound area compared with the controls (Figure 2E). In addition, we tested the effects of EVs in a mouse chamber wound model because, unlike in humans, rodent skin wounds contract soon after wound formation (Figure 2F). In the chamber model, topical application of EVs using a sterile re-absorbable gelatin sponge (Cutanplast) in the chamber mouse model induced wound closure (Figure 2G,H) and improved re-epithelialization and granulation tissue in the chamber (Figure 2I,J). MSC-EVs induced a significant reduction in the size of the wound areas (%) in the chamber, strengthened the newly formed epidermal layer, and promoted the production of granulation tissue in the chamber. MSC-EVs stimulated epithelial regeneration in both wound models. MSC-EVs promoted hypertrophy of the epithelial cell layer after 3 d of treatment with EVs (Figure 3A,B). Immunohistochemical examination 7 d after wounding showed that the number of keratinocytes was increased in the epithelial cell layer, suggesting that MSC-EVs promote the proliferation of keratinocytes for re-epithelization (Figure 3E,F). Interestingly, the epithelial cell layers returned to normal thickness after 2 weeks of MSC-EV treatment (Figure 3C,D), suggesting that EV-mediated regeneration of the epidermis occurs mainly during the initial phase of wound healing and the remodeling of the scar tissue maturation phase. In a study of wound tissue treated with MSC-EV, it was observed that the skin tissue underwent stabilization and thinning during the maturation stage. Immunohistochemistry for keratin 14 (a marker of keratinocyte cells) and Ki-67 (a marker of proliferating cells) showed that MSC-EVs stimulated the proliferation and migration of keratinocytes (Figure 3E,F). MSC-EV therapy stimulated the proliferation of fibroblasts to promote the maturation of granulation tissue in both the full-thickness and chamber wound models (Figure 4). Treatment with MSC-EVs increased the number of proliferating fibroblasts that were positive for both Ki67 and vimentin (a marker of fibroblast cells) in immunological staining (Figure 4B,E). In addition, the migration of proliferating fibroblasts to the granulation tissue was increased after treatment with MSC-EVs, from subcutaneous areas in the chamber model and from the non-injured regions in the full-thickness model (Figure 4A,D). Immunohistochemical staining for CD31 (a marker of vascular structure) showed that MSC-EVs enhanced the vascular structure in both the epithelial cell layer and the wound center region during the wound healing process (Figure 5A). Similarly, immunohistochemical staining for a vascular endothelial growth factor (VEGF, a blood vessel marker) showed that MSC-EVs promoted angiogenesis (Figure 5C). We also measured the tissue levels of pro-angiogenic growth factors and found that VEGF, angiopoietin (Anpt)-1, and Anpt-2 levels were significantly increased in tissue lysates obtained from the dorsal wound area in the EV group compared to those in the control group (Figure 5E–G). We performed in vitro studies to investigate the mechanisms of MSC-EVs using multiple cell lines involved in the wound healing process: keratinocytes (HaCaT), fibroblasts (NIH-3T3), endothelial cells (HUVECs), and inflammatory cells (RAW264.7). For both NIH-3T3 and HaCaT cells, cell motility was assessed using a scratch wound model. Various MSC-EVs (2, 5, and 10 × 108 EVs) were administered for 24 h (Figure 6A,B). MSC-EVs promoted the proliferation of both keratinocytes and fibroblasts, although the maximal effective dose was lower in fibroblasts than in keratinocytes. The tube formation assay using HUVECs showed a dose-dependent increase in angiogenesis (Figure 6C). Lastly, inflammation-induced macrophage RAW264.7 cells were tested using the Griess reagent for NO production (Figure 6D). Treatment with MSC-EVs promoted the polarization of M2-type macrophages (Figure 6E). In addition, compared to the control group, the levels of inflammatory cytokines were significantly decreased, but the levels of anti-inflammatory cytokines (IL-10) were increased in the EV group (Figure 6F). This study is the first to show that clinical-scale EV therapeutics are feasible using a micro-patterned well system and can improve the wound healing process. In this study, the effects of EV treatment were tested in different wound injury models under different treatment modes, which showed consistent findings. The mechanisms of action of MSC-EVs were assessed using both in vivo and in vitro models. The therapeutic potential of EVs can contribute to multiple stages of wound healing, such as cell proliferation and differentiation, inflammation, angiogenesis, and extracellular matrix remodeling. Specifically, our clinical-scale EV therapeutics could effectively induce the proliferation and migration of endothelial cells, keratinocytes, and fibroblasts to improve angiogenesis and re-epithelialization and regulate inflammatory cells in rodent wound models. To date, multiple studies have investigated the effects of stem cell-derived EVs in wound models [8,9,10,11,12,13,14,15,16,17,18]. MSC-EV therapies obtained from various MSC sources, such as bone marrow, adipose tissue, and umbilical cord, have been used to improve recovery in various wound models. However, the development of MSC-EV therapeutics faces several hurdles, including establishing a consistent, scalable cell source and developing robust GMP-compliant upstream and downstream manufacturing processes [19]. MSCs undergo senescence, and their intrinsic ability to secrete EVs significantly declines in conventional 2D cultures; therefore, MSC-EV preparations may differ in their therapeutic potential. In addition, according to the US FDA conversion guideline documents for industry estimating the maximum safe starting dose in adult healthy volunteers (July 2005), one patient in clinical testing requires more than 100 times higher doses than those of one mouse or rat. Low output limits of EV preparations obtained from the conventional 2D culture of MSCs limit the clinical application of EVs. EVs obtained under 3D cultures, such as micro-patterned well systems, as shown in the present study, hollow fiber bioreactor-based 3D culture systems, and 3D scaffolds cultures, exhibited enhanced EV yield and a heightened damage-repair ability [20,21]. Therefore, for effective clinical-scale production of therapeutic EVs, large batches of MSCs are needed, which significantly affects the labor, time, and cost of production. In this study, we established a cell bank, used the 3D culture method, and the combination of filter and TFF system, as it allowed the large-scale production of EVs (the yield of EVs is more than 10–20 fold that of conventional 2D culture) without the use of serum. Compared to conventional stem cell-based therapeutics, our EV therapy has potential benefits in terms of cost-effectiveness when WJ-MSCs are cultured in a 3D micropatterned well system and isolated using a TFF system (Supplementary Figure S2). More importantly, our scalable 3D-bioprocessing EV production method reduced the donor/batch variation. Lastly, our small RNA sequencing data revealed that MSC-EVs containing miRNAs played important roles in angiogenesis, cytoprotection, immune modulation, and rejuvenation, and miRNAs, such as miR-21-3p, miR-125a, and miR-126-3p, were involved in the wound healing process after treatment with MSC-EVs (Supplementary Figure S3) [8,9,10,14,22,23]. MSC-EVs treatment has been found to promote wound healing by increasing the expression of VEGF-A, Wnt, and PI3K/AKT in fibroblast and keratinocyte cells. These findings suggest that EV-contained miRNA and cargo play a key role in wound healing by regulating specific signaling pathways, but more research is needed to fully understand the mechanism and potential therapeutic applications of MSC-EVs in wound healing (Supplementary Figures S3 and S4) [8]. In this study, the effects of MSC-EV treatment were tested in different species (mouse and rat) and wound models (mild [traditional full-thickness model] and severe [chamber model]), which showed consistent therapeutic benefits. The chamber model prevents the migration of keratinocytes into the wound and the closure of the wound via contraction [24]. It facilitates the de novo generation of epithelial tissues from the surface of the skin ulcers. Our results suggest that the application of EVs stimulates wound-resident stem cells to promote the wound-healing process; however, further studies are required to evaluate the de novo generation of epithelial tissues from wounded tissues [24]. Wound healing is classically divided into four stages: hemostasis, inflammation, proliferation, and remodeling. Each stage is characterized by key molecular and cellular events and is coordinated by a host of secreted factors that are recognized and released by the cells of the wounding response [25]. As various cellular components are involved at different stages of the wound healing process, we performed an in vitro assay to determine EV effects on four major cell types: fibroblasts, keratocytes, endothelial cells, and inflammatory cells. Depending on the severity and chronology (time interval from the onset of wound injury) of the wound and the presence of any comorbidities, such as infection and diabetes mellitus, in patients, one stage may be more prominent than others, and the target of treatment could be different among patients. For example, therapies with anti-inflammatory effects are needed in the inflammatory phase, the first phase after the cutaneous wound, while enhancing angiogenesis can be an important strategy in patients with diabetes mellitus. Proliferation and remodeling are important targets for the treatment of chronic deep wounds. The in vitro assay can aid in assessing the targets for different wound healing treatments. The results of this study showed that MSC-EV therapeutics exert their effects in most phases of wound healing. This study has several limitations. First, the molecular action mechanisms of MSC-EVs could not be investigated. Of the cargo in exosomes, miRNAs are of prime importance in mediating the therapeutic effects on wound healing [8,9,10,11]. Molecular pathways of EV-miRNAs involved in wound healing are under investigation. In addition, we studied the effects of MSC-EVs in healthy young mice and rats. Cutaneous wounds are difficult to heal in older patients and those with comorbidities, especially diabetes mellitus. We are currently investigating the effects of MSC-EVs in diabetic wound animal models. Lastly, further in vivo studies are needed to determine the dose-responsiveness and optimal dose of EVs based on the specific phase of wound healing, as the optimal doses for angiogenesis and proliferation of keratinocytes and fibroblasts were different in our in vitro studies. In conclusion, the present study demonstrated that our scalable 3D-bioprocessing production method is feasible for clinical-scale MSC-EV therapy. Moreover, our results showed that MSC-EVs promote wound healing in both mild and severe injuries via the regulation of various wound-healing phases. All studies involving human subjects were approved by the Institutional Review Board of Samsung Medical Center. WJ was provided to the healthy volunteers. All volunteers or their guardians provided written informed consent to participate in the study. All experimental animal procedures were approved by the Institutional Animal Care and Use Committee (Laboratory Animal Research Center, AAALAC International approved facility) of Samsung Medical Center. MSCs derived from human WJ of the umbilical cord (WJ-MSCs) were culture expanded at passage five with growth medium in a 5% CO2 incubator at 37 °C. WJ-MSCs were used at passage six to generate 3D spheroid cultures. WJ-MSCs were seeded into a micro-patterned well system (EZSPHERE; ReproCELL Inc., Tokyo, Japan), washed with phosphate-buffered saline (PBS), and trypsinized using TrypLE Express (GIBCO, NY, USA). After the WJ-MSCs were centrifuged, a fresh serum-free medium without heterologous proteins was added, and the cells were counted using a hemocytometer. After cell counting, 60 mL of the cell suspension was placed in a microarray containing approximately 69,000 microwells, each with a diameter and depth of 500 μm × 200 μm coated with 2-methacryloyloxyethyl phosphorylcholine polymer at a density of 400 cells/well. For the 3D spheroid culture of WJ-MSC, serum-free medium (α-minimal essential medium) was used, without any antibiotic. A 3D spheroidal cell aggregate was prepared by inducing spontaneous spheroidal cell aggregate formation while maintaining a static state by dispensing uniformly and culturing in a CO2 incubator at 37 °C for 4 d. EV isolation was performed in a biological safety cabinet. The culture medium was collected via gentle pipetting at the top of each well. To remove the cell debris and apoptotic bodies, 1800 mL of culture medium was centrifuged at 2500× g for 10 min, followed by filtration through a 0.22-μm membrane. The filtered medium was separated using a 300-kDa MWCO mPES hollow fiber MiniKros filter module (Spectrum Laboratories, Rancho Dominguez, CA, USA) on a commercially available KrosFlo KR2I tangential flow filtration (TFF) system (Spectrum Laboratories, Rancho Dominguez, CA, USA), which facilitates the large-scale processing of samples. EV-containing samples were recirculated into a filtration bottle. Small molecules, including free proteins, were passed through the membrane pores, eluted as a permeate, and collected. The collected solution was used as the secretome. EVs were maintained in circulation as retentate and concentrated in the bag. We conducted five volume exchanges of EVs with PBS, and EVs were subsequently concentrated to a final volume of 300 mL of recovery solution (PBS). The recovered solution was filtered through a 0.22-μm membrane. After harvesting the conditioned media, the EV isolation process was started immediately using the TFF procedure. All processes were performed according to the guidelines on quality, non-clinical, and clinical assessment of EV therapy products of the Korean Food and Drug Administration (FDA, released December 2018) using good manufacturing practice (GMP)-compliant methods. Schematics of the processes of EV production, isolation, and quality control are shown in Supplementary Figure S1. Following the guidelines recommended by the International Society for Extracellular Vesicles (Minimal Information for Studies of Extracellular Vesicles 2018) and the Korean FDA, EVs isolated from the WJ-MSC culture medium were characterized in terms of their morphology, size distribution, surface markers, purity, potency markers, efficacy, stability, and safety [26]. See the Supplementary detailed methods for nanoparticle tracking analysis, Western blotting, transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), Exoview analysis, quantitative reverse transcription-polymerase chain reaction, and small RNA sequencing. All animal experiments were approved by the Institutional Animal Care and Use Committee of Samsung Biomedical Research Institute and performed in accordance with the Institute of Laboratory Animal Resources guidelines. All animals were maintained in compliance with the relevant laws and institutional guidelines of the Laboratory Animal Research Center (AAALAC International-approved facility) at Samsung Medical Center. A conventional full-thickness cutaneous wound model was used in this study. Briefly, excisional wounds were created using an 8 mm diameter punch (Acuderm, Inc., Ft. Lauderdale, FL, USA) on the shaved dorsal skin under ketamine (100 mg/kg) and xylazine hydrochloride (5 mg/kg) anesthesia. Silicone splints were fixed around the excised wound. EVs were injected subcutaneously at four different points around the wounds, while an equal volume of PBS was injected subcutaneously in the same position in the control group rats. Based on the results of our preliminary experiments, a dose of 2 × 108 EVs/rat was selected for further experiments using the rat model. Unlike human skin, rodent skin has panniculus carnosus, a thin layer of muscle attached to the subcutaneous tissue that acts as a contractile force for wound closure. Therefore, in the full-thickness rat model, it was difficult to measure the regeneration and recovery mechanisms of skin epithelial cells because of rapid wound healing by contraction. Therefore, we tested the effects of EVs in a mouse chamber model [24,27]. We surgically removed the skin from the back of the mice to generate an ulcer and isolated the resulting wound from the surrounding skin using a skin chamber sutured to the deep fascia. A chamber-made EP tube was placed inside the skin layer and fixed to the skin layer by a simple suture. Since mice are half as small as rats based on their body surface area, a dose of 1 × 108 EVs/mouse was selected for the mouse model and applied for 3 d after a full-thickness excision wound. Cutanplast was moistened with EVs and placed inside the chamber. To prevent inflammation in the chamber, antibiotics (Baytril) were injected for 2 weeks after surgery. Measurements of wound contraction and wound closure were performed using surgical calipers, and the wound areas were quantified using Aperio Image Scope V 12 software. Wounds were photographed on days 0, 1, 3, 5, 7, 10, 14, and 21 post-wounding, and wound size was determined using the ImageJ software (National Institutes of Health, Bethesda, MD, USA) to measure the wound area. The percentage of wound closure was calculated using the following equation: Using histological samples, the general linear model for the determination of time versus wound closure (re-epithelialization) and granulation tissue formation for each treatment was evaluated. Wound contraction was calculated as a percentage of the original wound size, taken as 100% of each animal in the group using the equation given above. The percentage of wound area was calculated using the following formula: Skin tissue samples were fixed in 4% paraformaldehyde for 24 h and underwent dehydration with graded ethanol. The samples were then embedded in an optimal cutting temperature compound and cut into 10–30-μm thick sections. Hematoxylin and eosin (H&E) staining was performed using commercial staining kits (H&E Staining Kit (ab245880), Abcam, Cambridge, UK)), according to the manufacturer’s instructions. Images were captured using a microscope (ScanScope image, USA). After 15 d of induction of wound models, the effect of MSC-EVs was compared with that of the control (basal medium) by immunostaining with Ki-67 (a cell proliferation marker) and vimentin (a fibroblast marker), according to the manufacturer’s instructions. Dorsal skin tissues were fixed in 4% paraformaldehyde and blocked with 10% normal goat serum. Dorsal skin was incubated overnight at 4 °C with rabbit anti-Ki-67 (1:50; Abcam, UK) and goat anti-vimentin (1:500; Abcam, UK) antibodies. The cells were then washed with PBS and incubated with secondary DyLight-labeled anti-goat IgG (1:200, 594 nm; Abcam, UK) and DyLight-labeled anti-rabbit IgG (1:200, 488 nm; Vector Laboratories, Burlingame, CA, USA) antibodies. Samples were imaged using a fluorescence microscope (EVOS; Advanced Microscopy Group, Bothell, WA, USA), and positively stained cells were quantified using ImageJ software. ELISA was performed using commercial kits according to the manufacturer’s instructions. The following ELISA kits were used: tumor necrosis factor-α (MBS140025, MyBioSource, San Diego, CA, USA), Ang-1 (MBS2601637, MyBioSource, San Diego, CA, USA), Ang-2 (MBS8420366, MyBioSource, San Diego, CA, USA), interleukin (IL)-10 (MBS140013, MyBioSource, San Diego, CA, USA), IL-6 (MBS 824703, MyBioSource, San Diego, CA, USA) and IL-beta (MBS 175967, MyBioSource, San Diego, CA, USA). All kits included standard proteins; therefore, the amount of protein and EV counts were determined based on the standard curve from each kit. The level of NO was determined by measuring the quantity of nitrite in the supernatant using the Griess reaction. Macrophage RAW264.7 cells (1.0 × 105) were seeded into a 24-well plate and treated with lipopolysaccharide (LPS; 100 ng/mL) for 24 h. To measure the amount of NO produced, 50 μL of conditioned medium was mixed with an equal volume of Griess reagent (Sigma, Saint Louis, MO, USA) and incubated for 15 min at room temperature. Absorbance was measured at 540 nm using a microplate reader, and the absorbance versus sodium nitrite concentration plot was constructed. NIH-3T3 cells were seeded at 1.8 × 105/well into a 12-well plate. The wells were then scratched longitudinally using a yellow tip. After washing twice with high glucose media, cultures were treated with the same medium containing 5 μg/mL mitomycin C (Sigma, Saint Louis, MO) with or without MSC-EVs (2, 5, and 10 × 108 /mL). Cell migration was assayed 24 h after MSC-EV treatment using optical microscopy. Wound areas were measured using the ImageJ software, and the percentage of cell motility was calculated using the following equation: ([Area at 0 h − Area at 12 h]/Area at 0 h) × 100. HaCaT cells were seeded at 2.2 × 105/well into a 12-well plate. The experimental procedure was the same way as the one used in the NIH-3T3 fibroblast wound-healing assay. In vitro capillary network formation was determined using a tube formation assay on Matrigel (354248; Corning, Glendale, AZ, USA). Human umbilical vein endothelial cells (HUVECs) (1.5 × 104 cells/mL) were seeded onto Matrigel-coated wells of a 96-well plate and cultured in 1% fetal bovine serum-supplemented Dulbecco’s Modified Eagle’s medium (10567014; Gibco, Waltham, MA USA) in the presence of 5 × 108/mL MSC-EVs or PBS. Tube formation was observed using an inverted microscope (Leica DMi8, Wetzlar, Germany). The number of network structures was quantified by randomly selecting five fields per well using ImageJ software. Statistical analyses were conducted using the SPSS program (SPSS Statistics Version 24.0, IBM Corp, Armonk, NY, USA) and GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA). The normality of the data was evaluated using the D’Agostino–Pearson test. One- and two-way analyses of variance with Tukey’s multiple comparison tests were used to analyze the three groups. Student’s t-test and Wilcoxon–Mann–Whitney test were used for paired and unpaired analyses of the two groups. Statistical analysis results are indicated in the figure legends. Results are expressed as the mean ± standard error. Statistical significance was defined as p < 0.05.

PMC10001906

Yakun Li,Lihong Ding,Mei Zhou,Zhixiang Chen,Yanfei Ding,Cheng Zhu

Transcriptional Regulatory Network of Plant Cadmium Stress Response

22-02-2023

cadmium stress,transporter,transcription factors,regulatory network,plants

Cadmium (Cd) is a non-essential heavy metal with high toxicity to plants. Plants have acquired specialized mechanisms to sense, transport, and detoxify Cd. Recent studies have identified many transporters involved in Cd uptake, transport, and detoxification. However, the complex transcriptional regulatory networks involved in Cd response remain to be elucidated. Here, we provide an overview of current knowledge regarding transcriptional regulatory networks and post-translational regulation of the transcription factors involved in Cd response. An increasing number of reports indicate that epigenetic regulation and long non-coding and small RNAs are important in Cd-induced transcriptional responses. Several kinases play important roles in Cd signaling that activate transcriptional cascades. We also discuss the perspectives to reduce grain Cd content and improve crop tolerance to Cd stress, which provides a theoretical reference for food safety and the future research of plant varieties with low Cd accumulation.

Transcriptional Regulatory Network of Plant Cadmium Stress Response Cadmium (Cd) is a non-essential heavy metal with high toxicity to plants. Plants have acquired specialized mechanisms to sense, transport, and detoxify Cd. Recent studies have identified many transporters involved in Cd uptake, transport, and detoxification. However, the complex transcriptional regulatory networks involved in Cd response remain to be elucidated. Here, we provide an overview of current knowledge regarding transcriptional regulatory networks and post-translational regulation of the transcription factors involved in Cd response. An increasing number of reports indicate that epigenetic regulation and long non-coding and small RNAs are important in Cd-induced transcriptional responses. Several kinases play important roles in Cd signaling that activate transcriptional cascades. We also discuss the perspectives to reduce grain Cd content and improve crop tolerance to Cd stress, which provides a theoretical reference for food safety and the future research of plant varieties with low Cd accumulation. Cadmium (Cd) is one of the naturally occurring heavy metals, which is extremely toxic to plants and humans [1]. In recent years, the increase in Cd content in soils has caused serious and widespread pollution to farmland. The accumulation of Cd in plants has toxic effects on the normal growth of plants. For example, Cd affects enzyme activity and the absorption and consumption of essential elements, generates reactive oxygen species (ROS), and impairs photosynthesis, respiration, and membrane systems. All these effects ultimately result in plant tissue necrosis, chlorosis, and eventual death [2,3]. Cd is also a threat for human health. The bone itai-itai disease in Japan in the 1950s was caused by long-term consumption of rice (Oryza sativa L.) produced in Cd-contaminated soils [4]. Cd enters the human body through the food chain and mainly accumulates in the kidneys, causing a series of diseases, such as anaemia, cancer, heart failure, steoporosis, emphysema, and renal function diseases [5,6,7,8]. Therefore, it is necessary to limit Cd in the food chain from soils to reduce health risks to humans. Recent studies have made important progress in elucidating the physiological and molecular mechanisms of Cd transport and tolerance in plants. According to the relation between the metal content in the soil and metal in the plants, plants are divided into three groups: excluder, indicator, and hyperaccumulator plants [9]. So far, many transporters related to Cd uptake, transport, sequestration, and detoxification in plants have been identified [10,11,12,13] (Table 1, Figure 1). Metal transporters and ROS-scavenging enzymes are major functional proteins that are induced by Cd stress. Heavy metal accumulation and tolerance in plants are associated with a highly complex regulatory network system involving a large number of genes. Recent studies in rice, Arabidopsis thaliana, and other plants have revealed multi-layered transcriptional networks comprising many transcriptional factors (TFs), long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in responses to Cd stress [14,15,16] (Figure 2). An increasing number of reports indicate that epigenetic regulation, such as DNA methylation, is important in Cd-induced transcriptional responses. Many kinases play important roles in Cd signaling that activate transcriptional cascades [17,18]. In this review, we focus on recent findings regarding the transcriptional network and post-translational regulation of TFs that control the expression levels of metal-responsive genes. The review on the regulatory mechanisms of Cd uptake, transport and accumulation in plants is of great significance for reducing Cd content in food crops to ensure food safety. Cd transport and accumulation in plants have been most extensively investigated in rice and involve four steps: (1) uptake by roots, (2) xylem-loading-mediated translocation to shoots, (3) redistribution through stems and nodes, and (4) further translocation to grains through the phloem [4]. As shown in Table 1, many metal transporters related to Cd uptake, transport, and detoxification have been cloned in plants, including iron (Fe)-regulated transporter1 (OsIRT1) [20,54], OsIRT2 [54,55], natural resistance-associated macrophage protein 1 (OsNRAMP1) [33], AtNRAMP1, AtNRAMP3, AtNRAMP4 [28,29,30,31], zinc (Zn)-/iron-regulated transporter-like protein 1 (OsZIP1) [21,22], OsZIP3 [21,23], Cd accumulation in leaf 1 (CAL1) [51], OsNRAMP5 [35,56], HvNRAMP5 [32], cation/calcium (Ca) exchanger (OsCCX2) [38], heavy metal ATPase 2 (OsHMA2) [42,57], OsHMA3 [43,58], low-affinity cation transporter 1 (OsLCT1) [46], and oligopeptide transporter 3 (OPT3) [53]; excluders–ATP-binding cassette, subfamily C/G (OsABCG36) [49], pleiotropic drug resistance 8 (AtPDR8) [50], and plant cadmium resistance protein 2 (SaPCR2) [52]. The discovery of these genes provides an important theoretical and practical basis for molecular breeding of crops with low Cd accumulation. At present, there are no transporters in plant roots that specifically absorb Cd. Cd can be absorbed mainly through synergistic action by other essential mineral elements, such as Zn, Fe, and manganese (Mn) ions. Several metal transporters, like OsIRT1, OsNRAMP1, and OsNRAMP5 have been reported to be responsible for Cd entry into rice roots [33,54]. OsIRT1 and OsIRT2 are located on the plasma membrane. After 10 days of 100 μM CdSO4 treatment, the expression of OsIRT1 and OsIRT2 was highly increased in rice roots [59]. OsIRT1 and OsIRT2 both display Cd, Fe, and Zn influx activities in yeast, and overexpression of OsIRT1 increases these metals in different plant tissues [20]. These results indicate that both OsIRT1 and OsIRT2 play important roles in rice by uptaking Cd from soil to roots. OsCd1, a major facilitator superfamily (MFS) protein, has been demonstrated to be involved in Cd uptake in root cells. OsCd1 resides in the plasma membrane of roots and contributes to Cd accumulation in rice grains [48]. In addition, OsNRAMP5 is located in the plasma membrane and is mainly responsible for the transport of Cd, Fe, and Mn in the rice root system. The osnramp5 knockout mutants significantly reduced Cd concentration in roots and buds and increased Cd tolerance [11,60,61]. OsNRAMP1 is highly homologous to OsNRAMP5 and is also involved in Cd uptake and transport by root cells. Knockout of OsNRAMP1 resulted in a significant decrease in the uptake of Cd and Mn by rice roots [33]. These results have important implications for the application of OsNRAMP1 and OsNRAMP5 mutations in mitigating Cd toxicity and reducing the risk of Cd contamination in rice grains. Under 10 μM CdCl2 stress for three days, compared with the yeast transformed with an empty vector, the growth of yeast expressing AtNRAMP1, AtNRAMP3, and AtNRAMP4 was seriously impaired. In the meantime, after 3 μM CdCl2 treatment for 24 h, these three kinds of yeasts contained more Cd in yeast cells than the yeast transformed with empty vector [29]. Under 2 μM CdSO4 stress for 14 days, the growth of the nramp1 Arabidopsis mutant roots was little affected compared to the wild type (WT) [28]. Under both 1 and 10 μM CdCl2 stress for 10 days, the growth of AtNRAMP3 overexpression in Arabidopsis roots was significantly reduced compared to WT [29]. Under 500 nM CdCl2 treatment for 14 days, the AtNRAMP4 overexpression in Arabidopsis roots accumulated more Cd than WT [31]. These results indicate that these genes are related to Cd transport in Arabidopsis roots. HvNRAMP5, located in the plasma membrane, is also a major transporter for the uptake of Cd and Mn in barley [32]. OsZIP1 functioned as a metal-detoxified transporter through preventing excess Cd and Zn accumulation in rice [22]. OsZIP1 is located at the endoplasmic reticulum and plasma membrane [62]. OsZIP1 overexpression in rice grew better under 5 μM CdCl2 stress for six days, but accumulated less Cd in plants. By contrast, the oszip1 mutants and RNA interference (RNAi) lines accumulated more Cd in roots and displayed Cd-hypersensitive phenotypes. Tian et al. (2019) [23] found that both roots and shoots of OsZIP3-overexpressed transgenic rice plants were longer than those of WT plants under 10 μM CdSO4 for seven days. OsZIP3 overexpression also reduced the Cd content in the roots and shoots. In addition, OsABCG36 localized at the plasma membrane was also involved in Cd efflux in rice roots. Knockout of OsABCG36 increased Cd accumulation in root cell sap and enhanced Cd sensitivity [49]. SaPCR2 is localized at the plasma membrane and plays an important role in Cd detoxification. Under 15 and 30 μM CdCl2 stress for seven days, the Cd content in the roots of SaPCR2-overexpressed transgenic Arabidopsis plants were decreased compared to WT. Under 10 μM CdCl2 stress for seven days, the Cd content in the roots of SaPCR2-overexpressed transgenic Sedum alfredii plants were also decreased compared to WT plants. That means SaPCR2 provided a route for Cd efflux in both Arabidopsis and non-hyperaccumulating ecotype (NHE) S. alfredii [52]. AtPDR8 is localized at the plasma membrane and was expressed in Arabidopsis roots and leaves. Kim et al. [50] found that under 5, 10, 20, and 30 μM CdCl2 for two to three weeks, atpdr8 knockout plants and atpdr8 RNAi plants were more sensitive to Cd than WT, while AtPDR8-overexpressed plants were resistant to Cd. That means AtPDR8 acts as an efflux pump of Cd2+ in plants. Cd is transported to shoots by loading into the xylem vessel. Xylem-mediated root-to-shoot translocation is shown as a major determinant for shoot Cd accumulation in many plants including rice [63,64]. CAL1 was a major quantitative trait locus (QTL) for Cd accumulation in rice leaves. CAL1 protein reduced Cd accumulation in rice leaves by specifically chelating Cd in the cytosol and promoting Cd secretion to extracellular spaces. CAL1 also regulated Cd root-to-shoot translocation through the xylem, and cal1 knockout mutants significantly reduced Cd concentration in rice leaves after 10 μM CdCl2 treatment for seven days [51]. OsHMA2 is mainly expressed in rice roots and enriched in the vascular tissues, facilitating root-to-shoot Cd translocation. Knockout of OsHMA2 significantly reduced Cd accumulation in shoots and grains [57]. The expression of OsHMA2 was prominent in rice, which accumulated more Cd in its grains [65]. These results mean Cd can be transported from shoots to the xylem and, finally, to grains through OsHMA2. OsHMA3, a close homolog of OsHMA2, is a tonoplast-localized transporter for Cd in rice roots and is responsible for sequestering Cd in vacuoles [43]. Overexpression of OsHMA3 significantly reduced Cd transport from roots to shoots and Cd content in grains (≥90%) [58,66]. Even in seriously Cd-contaminated soils, overexpression of OsHMA3 alone produced rice grains with Cd concentration below the Chinese limit (Cd, 0.2 mg kg−1) [67], representing an ideal target for breeding low grain Cd rice. In Arabidopsis, the P1B-type ATPases, AtHMA2 and AtHMA4, both regulate root-to-shoot translocation of Cd and Zn and were mainly expressed in the vascular tissues of roots, stems, and leaves [39]. Overexpression of AtHMA4 led to an increased tolerance to Zn, Cd, and Co and accumulated more metals in stems than WT [41]. Another P-type ATPase family member, AtHMA3, located at the vacuolar membrane, also participates in the vacuolar storage of Cd. Under 30 μM CdCl2 stress for 11 days, the roots and shoots of AtHMA3-overexpressed transgenic Arabidopsis plants accumulated more Cd than WT [40]. These results suggest that AtHMA3 plays a role in the detoxification of Cd through the vacuolar sequestration. Cd transported from the xylem to the shoots in rice is stored in nodes, transferred to the phloem, and then transported to rice grains through leaves, especially flag leaf phloem [48,68]. Phloem mediates nearly 100% of Cd deposition into grains in rice [69]. Cd can also be transferred to the grains through phloem in other plants, such as peanut (Arachis hypogaea L.), linseed (Linum usitatissimum L.), and potato (Solanum tuberosum L.) [70,71]. Cd mediated by phloem in S. alfredii participated in Cd remobilization from the older to younger leaves [72]. This reallocation could avoid excessive accumulation of Cd in leaves and stems. OsLCT1 is the first identified transporter for phloem Cd transport in plants [46]. OsLCT1 is mainly expressed in leaf blades and nodes during the reproductive stage. The Oslct1 knockdown mutant significantly reduced Cd content in rice grains as well as in phloem sap [46]. The expression of OsLCT1 was significantly enhanced in rice, which over-accumulated Cd in grains, indicating possible translocation of Cd from shoots to grains [65]. These results suggest that OsLCT1 in leaf blades functions in Cd remobilization by the phloem. In addition, OsCCX2, a putative Ca exchanger, is a node-expressed transporter involved in Cd accumulation in the grains of rice. OsCCX2 is mainly expressed in the xylem region of vascular tissues at the nodes and plays a crucial role in mediating Cd translocation and distribution. Knockout of OsCCX2 resulted in a significant decrease in Cd accumulation in rice grains when planted in 3.89 mg kg−1 Cd-contaminated paddy soils [38]. More recently, Gu et al. (2023) [69] identified a defensin-like gene, DEFENSIN 8 (DEF8), as the phloem Cd unloading transporter. DEF8 is mainly expressed in rice grains. The DEF8 mutant significantly decreased Cd accumulation in rice grains, offering an effective strategy to reduce the risk of Cd contamination without affecting important agronomic traits or the concentration of essential micronutrients. OPT3 is located at the plasma membrane and preferentially expressed in the Arabidopsis phloem. After 50 μM CdCl2 stress for two weeks, the OPT3-overexpressed transgenic Arabidopsis plants reduced the accumulation of Cd in grains and the opt3 mutant Arabidopsis plants accumulated more Cd in grains and roots [53]. These results suggest that OPT3 plays an important role in the transport of Cd from phloem to grains. Recent studies have identified the complex transcriptional networks of plant Cd stress responses (Figure 2). TFs are major regulators of plant growth and development, as well as in abiotic and biotic stress responses. TFs belong to different families, such as WRKY, myeloblastosis protein (MYB), basic leucine zipper (bZIP), and heat shock transcription factor (HSF) [73,74,75]. They play important roles in signal transduction of Cd stress response by activating or repressing a series of genes involved in Cd uptake, transport, and tolerance in rice. The sensing of heavy metals by plants generates responses such as modulation of molecular and biochemical mechanisms of cells [58,76]. The ultimate plant Cd stress responses include altered synthesis of metal transporter proteins and metal binding proteins to counteract excessive metal stress in plants [74,77]. The WRKY family is a unique plant TF family and plays an important regulatory role in plant development and response to various environmental stresses [75]. Under Cd stress, 35 WRKY genes were differentially expressed in rice, of which 25 were up-regulated and 10 were down-regulated. Under Cd treatment, the expression of OsWRKY15 was induced in both leaves and roots, which may participate in Cd response via NO and ABA signaling pathways. The expression of WRKY104 increased more than 90-fold after 24 h of Cd treatment [78]. Under Cd stress, WRKY12 negatively regulated Cd tolerance via the glutathione (GSH)-dependent PC synthesis pathway in Arabidopsis. WRKY12 directly targeted GSH1 by binding to its promoter and indirectly inhibited the expression of other PC synthesis-related genes (GSH1, GSH2, PCS1, and PCS2), thereby negatively regulating Cd accumulation and tolerance in Arabidopsis [79]. The expression levels of TaWRKY74 were significantly induced by Cd stress in wheat. TaWRKY74 alleviated Cd toxicity in wheat by regulating the expression of Ascorbic Acid (ASA)-GSH synthesis genes [16]. In addition, Cd stress induced the expression of WRKY13. Overexpression of WRKY13 decreased Cd accumulation and enhanced Cd tolerance, while the loss of function of WRKY13 led to Cd accumulation and increased Cd sensitivity. WRKY13 can bind the promoter of the Cd extrusion pump gene PDR8 and activate its expression to positively regulate Cd tolerance in Arabidopsis [80]. The MYB TF family is a large and functionally important class of proteins involved in the regulation of diverse biological processes. MYB proteins are divided into four classes according to the number and position of MYB repeats: 1R-MYB/MYB-related, R2R3-MYB, R1R2R3-MYB, and 4R-MYB [81]. BnMYB2, encoding a 1R-MYB protein from Boehmeria nivea (ramie), was significantly up-regulated in roots and leaves under Cd stress. The overexpression of BnMYB2 in Arabidopsis resulted a significant increase in Cd tolerance and accumulation [82]. In addition, Tiwari et al. (2020) [83] identified another member of the rice 1R-MYB family involved in heavy metal tolerance. OsMYB-R1-overexpressed rice plants exhibited a higher auxin accumulation and a significant increase in lateral roots, which resulted in the increased tolerance under 150 μM and 300 μM Chromium (Cr) (VI) exposure for 21 days. RNA-seq analysis revealed over-representation of salicylic acid (SA)-regulated genes in OsMYB-R1-overexpressed rice plants [83]. These results imply that OsMYB-R1 is part of a complex network of TFs controlling the cross-talk of auxin and SA signaling, which regulates heavy metal response. The R2R3-MYB genes are more prevalent in plants and involved in regulating responses to environmental stresses [82,84]. Recent reports have established the role of OsMYB45 in rice tolerance to Cd stress (Figure 3). The expression of OsMYB45 was induced by Cd stress and highly expressed in the leaves, husks, stamens, pistils, and lateral roots of rice. Under 5 μM CdCl2 treatment for three days, the Osmyb45 mutant was hypersensitive to Cd, which is associated with increased accumulation of hydrogen peroxide (H2O2) and reduced expression of antioxidative enzymes compared with WT. Catalase (CAT) is the main antioxidant enzyme and is encoded by three genes in the rice genome (OsCATA, OsCATB, and OsCATC). OsCATA and OsCATC expression was inhibited in Osmyb45 mutations, which may be associated with Cd-sensitive phenotypes. The overexpression of OsMYB45 in the mutant complemented the mutant phenotype [85]. In addition, another R2R3-type MYB member, MYB49a, was reported to be involved in the regulation of Cd accumulation in plants by physically interacting with the central ABA signaling molecule ABI5 [14]. MYB49 was induced under Cd stress. Overexpression of MYB49 in Arabidopsis significantly increased Cd accumulation, whereas myb49 knockout plants reduced Cd accumulation. Further investigations revealed that MYB49 positively regulated the expression of basic helix-loop-helix (bHLH) TFs, bHLH38 and bHLH101, by directly binding to their promoters and indirectly up-regulating expression of the IRT1 transporter gene. MYB49 also binds to the promoter regions of the heavy metal-associated isoprenylated plant proteins, HIPP22 and HIPP44, leading to the activation of their expression and subsequent Cd uptake and accumulation [14]. The bZIP family is one of the largest TF families in plants with important regulatory roles in various biological processes, including plant defense and responses to environmental challenges [86,87,88]. RNA-Seq results indicated that three differentially expressed genes encoding bZIP6, bZIP19, and bZIP43 were involved in Cd stress in bentgrass [89]. After 400 μM CdCl2 treatment for four days in Sedum plumbizincicola, the expression levels of 32 SpbZIP genes changed and most of their expression levels peaked earlier in roots than in stems and leaves [88]. These results suggest that SpbZIP may play a major role in the initial response to Cd stress in the roots. In addition, TGACGTCA cis-element-binding protein (TGA) factors in Arabidopsis represent a subfamily of bZIP TFs. In Arabidopsis, TGA3 transcription was induced by Cd [90]. Compared with WT plants, the tga3 mutant accumulated higher amounts of Cd in the roots and lower amounts in the shoots [91]. Fusco et al. (2005) [92] found that under Cd treatment, BjCdR15, acting as orthologue of TGA3 in Arabidopsis, regulated the expression of several metal transporters in Brassica juncea, such as PDR8, HMA4, and NRAMP3, thus mediating long-distance root-to-shoot transport of Cd. Overexpression of BjCdR15 in Arabidopsis and Nicotiana tabacum (tobacco) enhanced Cd tolerance and accumulation in shoots [91]. These results indicate that bZIP TFs play crucial roles in the regulation of Cd accumulation, which provide useful candidates for potential biotechnological applications in the phytoextraction of Cd-contaminated soils. The HSF family is an important member in plant stress response to several abiotic stresses by regulating the expression of stress-responsive genes, such as heat shock proteins (Hsps). In Arabidopsis, the Hsfs family is systematically divided into three classes of HsfA, B, and C [93]. In the plant response network, HsfA1 specifically interacts with HsfA2 to mediate the expression of genes encoding molecular chaperone HSPs such as HSP70 and HSP90 [94]. HSFs have been reported to play crucial roles in Cd tolerance in plants. HsfA1a conferred Cd tolerance in Solanum lycopersicum (tomato) by partially up-regulating Hsps expression [95] (Figure 4). After 100 μM CdCl2 stress for 15 days, Hsfa1a-silenced plants exhibited reduced melatonin levels, while HsfA1a overexpression stimulated melatonin accumulation and the expression of the melatonin biosynthetic gene caffeic acid O-methyltransferase 1 (COMT1). Exogenous melatonin promotes the modulation of GSH and PC biosynthesis which can detoxify Cd under Cd stress [96]. In S. alfredii, SaHsfA4c also played an important role in Cd tolerance. Compared with WT, the accumulation of ROS in SaHsfA4c-overexpressed Arabidopsis was reduced, and the expression of ROS-scavenging enzyme genes and Hsps was increased [97]. It has been found that the TaHsfA4a gene confers strong Cd tolerance in yeast and rice. CUP1, which encodes metallothioneins (MTs), contributes to the TaHsfA4a-induced Cd tolerance by acting as a downstream target of HsfA4a. OsHsfA4a is a rice homolog of TaHsfA4a. In rice plants expressing TaHsfA4a, Cd tolerance was enhanced, but in oshsfa4a knockdown rice plants, Cd tolerance was decreased. In addition, TaHsfA4a mediated Cd resistance in yeast by regulating MTs. The expression levels of HsfA4a and the MT gene were increased in rice roots under Cd stress. Therefore, HsfA4a in rice induced Cd tolerance by up-regulating MT gene expression in plants [98,99] (Figure 4). The no apical meristem (NAM), Arabidopsis transcription activation factor (ATAF1/2), and cup-shaped cotyledon (CUC2) (NAC) family is a kind of pivotal TF in the response to various abiotic stresses [100]. They contain a conserved N-terminal DNA-binding NAC domain and a highly variable C-terminal domain. In Aegilops markgrafii, AemNAC2 was found to be associated with reducing accumulation of Cd. Overexpression of AemNAC2 could decrease accumulation of Cd in roots, shoots, and grains of transgenic wheat. In this type of transgenic wheat, AemNAC2 suppressed the expression of TaNRAMP5 and TaHMA2 [101]. The ethylene responsive factor (ERF) family belongs to APETALA2/ethylene responsive factor (AP2/ERF) superfamily, which is one of the largest group of TFs involved in abiotic stress response in plants [102]. In Glycyrrhiza uralensis, overexpression of lrERF061 led to maximum Cd uptake and enhanced antioxidant enzyme activities (SOD, CAT, and POD) under 10 mg L−1 Cd treatment [103]. This study contributes to the understanding of the role of LrERF061 in Cd resistance and offers a useful way to increase the phytoextraction efficiency of Cd-polluted soils. As discussed above, TFs are the core regulators of transcription under Cd stress. However, increasing evidence has revealed a complex regulatory system comprising not only TFs, but also DNA methylation, long RNAs, and small RNAs with crucial roles in Cd response (Figure 2). Heavy metal stress has an effect on DNA structure, DNA stability, DNA methylation, and the regulation of gene expression. When these effects occur in plants, changes in DNA methylation can make plants adapt to heavy metal stress, especially to Cd stress [18,104]. DNA methylation can regulate gene expression and induce corresponding phenotypic changes without altering DNA sequence [105]. Cd treatment can lead to an increase in DNA methylation levels in rice, Arabidopsis, Zostera marina, and barley, which endows plants with higher tolerance to Cd [106,107,108,109]. Feng et al. (2016) [110] used high-throughput single-base-resolution bisulfite sequencing (BS-Seq) and RNA-Seq to analyze DNA methylation patterns in Cd-treated rice seedlings. A group of genes encoding metal transporters, Cd-detoxified proteins, and metal-related TFs were found to be differentially methylated, implying their roles in regulating rice tolerance to Cd stress. After 80 μM CdSO4 for four days, both GSH2 and GSHU35 upstream regions were hypermethylated. The sequence downstream of the coding region for iron-related transcription factor 2 (OsIRO2, a bHLH TF gene) was hypermethylated, while the coding region of metal transporter OsZIP1 was hypomethylated. The expression level of OsIRO2 was repressed, while OsZIP1 was induced by Cd. These results suggest that DNA methylated modification was most likely involved in transcriptional regulation of metal transporter genes. Sun et al. (2022) [111] found that grafting significantly reduced the total sulfur and Cd accumulation in soybean, which was mediated by DNA methylation. The expression level of methyltransferase genes decreased, leading to the decreased expression of sulfur metabolism-related genes, especially S-adenosylmethionine (SAM). These results imply that DNA methylation was involved in a decrease in total sulfur and Cd content. In addition, Cd treatment can lead to a decrease in DNA methylation levels in Trifolium repens and Cannabis sativa, which reduces the tolerance of plants to Cd [112]. These results indicate that DNA methylation dynamics in response to Cd vary with species. LncRNAs are a class of non-protein coding RNAs with >200 nt, which act as ‘biological regulators’ to control transcriptional regulation and genome imprinting [106,113]. Many lncRNAs in plants were induced or inhibited by Cd stress, affecting plant morphology, physiology, and biochemistry, and thus producing response to stress. They were reported to play key roles in controlling the uptake of heavy metals by the plant system in order to minimize the uptake of heavy metals from soil to plants [15,114]. Chen et al. (2018) [115] used deep sequencing to study the differential expression of lncRNAs under Cd stress in rice. A total of 75 lncRNAs were down-regulated and 69 lncRNAs were up-regulated by Cd treatment. Analysis of the target gene related pathways revealed significant changes in genes associated with the cysteine (Cys) and methionine (Met) metabolic pathways, for example, Os03g0196600, which was involved in these pathways, was clearly up-regulated and might contribute to the production of Cys-rich peptides. XLOC_086307, the lncRNA targeted Os03g0196600 in cis, was also up-regulated significantly, which suggests that XLOC_086307 likely participated in Cd response in rice by regulating the Cys-rich peptide metabolism-related gene Os03g0196600. In addition, Feng et al. (2016) [106] identified 301 Cd-responsive lncRNAs in Brassica napus by RNA-seq analysis, of which 67 acted as competing endogenous target mimics (eTMs) for 36 Cd-responsive miRNAs. Four lncRNAs were identified to serve as precursors of miR824, miR167d, miR156d, and miR156e in response to Cd stress. Interestingly, TCONS_00035787 was shown to target miR167d in B. napus. The target gene of miR167d encodes a NRAMP1-type metal transporter, which plays an important role in Cd uptake in plants [106,116]. This is the first report of a lncRNA (TCONS_00035787)–miR167-Nramp1 pathway in plants, indicating that lncRNAs can serve as new transcripts involved in the regulation of Cd uptake and accumulation in plants. MiRNAs are a new class of small non-coding RNA molecules in plants, which negatively regulate specific target mRNAs at the post-transcriptional level. They are involved in plant growth and development, organ morphogenesis, and responses to heavy metal, drought, and chilling stress [117,118]. In our lab, Ding et al. (2011) [119] used miRNA microarray to analyze miRNA expression patterns in 60 μM Cd-treated and untreated rice seedlings. In addition to the up-regulation of miR528 under Cd stress, miR166, miR171, miR159, miR390, and miR192 were significantly inhibited [119,120]. Most of these miRNAs were reported to target TF genes, for example, miR166, miR171, and miR396 target homeodomain-leucine zipper TFs, scarecrow-like TFs, and growth regulating factor TFs, respectively. These results imply that miRNAs are key components of the transcriptional regulatory network of heavy metal stress responses in plants. The expression of miR166 was significantly repressed under 60 μM CdCl2 exposure in rice seedlings. Overexpression of miR166 reduced both Cd translocation from roots to shoots and Cd accumulation in the grains. In 35S: miR166 plants, the expression of OsHMA2 decreased. Thus, the reduced Cd translocation in plants overexpressing miR166 may be at least partly attributable to the effect on OsHMA2 expression [121]. In addition, miR390 was found to be significantly down-regulated under Cd stress. Overexpression of miR390 increased Cd accumulation and reduced tolerance to Cd toxicity in rice [122]. Meng et al. (2017) [123] found that miR167 could cleave BnNRAMP1b (one of the NRAMP genes), thus BnNRAMP1b was a target of miR167. Huang et al. (2010) [124] validated that miR395 targeted the sulfate assimilation related genes-sulfate transporter 2; 1 (SULTR2; 1) and ATP sulfurylases (APS) by using 5′-RACE assay in B. napus. After 40 μM CdCl2 treatment for seven days, miR395-overexpressed B. napus plants exhibited high Cd accumulation and fewer toxicity symptoms in comparison to WT, due to increased synthesis of sulfur-containing compounds used for heavy metal chelation [125]. These results demonstrate the role of miR395 in the detoxification of Cd in B. napus. MiR398 targets two closely related cuprums (Cu)/Zn-SODs (CSDs), CSD1 and CSD2, which promote defense against ROS accumulation in Arabidopsis. Transgenic Arabidopsis plants overexpressing a miR398-resistant form of CSD2 accumulated more CSD2 miRNA than plants overexpressing a regular CSD2 and were consequently much more tolerant to heavy metals and other oxidative stresses [126]. Wang et al. (2022) [127] used high-throughput sequencing to analyze miRNA expression patterns in Cd-tolerant/sensitive barley. MiR156g was identified to be Cd-induced and target nucleobase-ascorbic acid transporters 2 (HvNAT2). HvNAT2 was negatively regulated in the high-Cd-accumulating and Cd-tolerant genotype Zhenong8. Overexpression of HvNAT2 enhanced ROS enzyme activities and GSH content, thus enhancing Cd tolerance in barley. These results indicate that metal-regulated miRNAs and their target genes are involved in the diverse processes of Cd response, including metal uptake and transport, sulfate allocation, metal chelation, and ROS detoxification. How plants sense and transduce Cd signals to transcriptional regulators is one of the most important open questions. Recent studies revealed that heavy metal stress activates Ca2+ and ROS signaling that mediate signal transduction and enhance the expression of stress-responsive genes or TFs. ROS can also act downstream of the mitogen-activated protein kinase (MAPK) pathway [128]. MAPKs are among the most important and highly conserved signaling molecules that are activated by ROS production and induced upon metal stress. MAPK cascade consists of three tier components MAP kinase kinase (MAPKKKs/MEKKs), MAP kinase kinase kinase (MAPKKs/MEKs), and MAPKs/MPKs mediating phosphorylation reactions from the upstream receptor to the downstream target [129]. It has been shown that Cd stress activates different kinase enzymes belonging to the MAPK family. The phosphorylation cascade is therefore thought to be involved in Cd signaling to the nucleus. Research confirms that transcripts for OsMSRMK2 (OsMPK3 homolog), OsMSRMK3 (OsMPK7 homolog), OsBWMK1 (or OsMPK12), and OsWJUMK1 (OsMPK20-4 homolog) increased in response to Cd and Cu treatment in rice roots and leaves [130,131]. A connection between miRNA and MAPK signaling was deciphered by a study which showed regulation of miR398b/c by oxidative signal-inducible kinase 1 (OXI1) upon Cd and Cu treatment [132]. OXI1 can enhance MAPK3 and MAPK6 activities based on the finding that knockout mutant plants for OXI1 could not activate MAPK3 and MAPK6 under H2O2 treatment [133]. MEKK1 and ANP1 are both Arabidopsis MAPKKKs, which are regulated by H2O2 under Cd stress and can activate MAPK3 and MAPK6 through MKK4 or MKK5 [134]. Apart from this, several TFs, like bZIP-, MYB-, and myelocytomatosis (MYC)-related TFs, are known to act as downstream targets of MAPKs [135,136]. In addition, Opdenakker et al. (2012) [137] reported that downstream signal transduction targets of MAPK during Cd or Cu stress included WRKY22, WRKY25, and WRKY29. MAPK cascades regulate gene transcription by activating or inhibiting TFs such as WRKY and TGA (a subfamily of bZIP TFs), thus regulating a variety of cellular responses [138,139]. Cd accumulation and exposure in crops poses a serious threat to organisms and human health. Breeding of new cultivars with low Cd levels is the most cost-effective and eco-friendly strategy to reduce the risk of Cd contamination in plants. To achieve this goal, we need a comprehensive understanding of not only the mechanisms but also the regulation of Cd uptake, translocation, sequestration, and other processes important for plant Cd stress responses. Over the past decades, different families of Cd transporters have been identified in plants, and their functional analysis through molecular and genetic approaches has provided critical insights into Cd uptake and translocation mechanisms (Figure 1). More recently, a large number of regulatory proteins including those involved in protein phosphorylation have been identified. Regulatory RNAs and DNA modifications have also been identified with roles in plant Cd accumulation and tolerance likely by affecting their expression, synthesis, activities, stability, and other properties (Figure 2). TFs are the core regulators of transcription under Cd stress. Several TFs in the transcriptional network and their functions during Cd stress have been analyzed. In addition, there is emerging evidence that epigenetic regulation through DNA methylation, lncRNAs, miRNAs, and kinases are involved in Cd-induced transcriptional responses. These signaling and responding mechanisms at transcriptional and post-transcriptional levels will facilitate our understanding of regulatory pathways and serve as a basis for developing efficient strategies to reduce Cd in plants. Despite the important progress, our understanding of the signaling and complex transcriptional regulatory networks in Cd stress response remains to be very limited. First, it is unclear how plants sense Cd. Do plant cells sense Cd through specific recognition of Cd itself or through indirect recognition of certain Cd-associated molecules or induced effects? Given that all identified Cd transporters also transport other metal ions, it is possible that plants sense Cd simply as a heavy metal and there are overlapping mechanisms in signaling upon exposure to different types of heavy metals. Second, upon Cd stress perception, what are the earliest signaling events? Even though MAPK cascades are implicated in plant Cd signaling and responses, there are usually other regulatory proteins that act upstream of MAPK cascades. For example, in plant immune responses, plasma membrane-localized pattern-recognition receptors can recognize specific pathogen elicitors to trigger plant immune response through activation of the MAPK cascade. Given that Cd is transported into plant cells through plasma membrane-localized transporters, it is possible that the early signaling in Cd response starts at the plasma membrane as well and could directly involve Cd transporters through coordination with other proteins such as plasma membrane-localized receptor-like proteins. Third, even though a substantial number of TFs have been identified with a role in Cd accumulation and tolerance, many lack information about their regulation and action mechanisms. For example, it is unclear how some of the identified TFs are activated or induced in response to Cd exposure. For many TFs, this remains unclear regarding direct target genes under their regulation. More importantly, there is little knowledge about the cooperation and coordination among different TFs for the effective and tight control of the transcription programs of plant Cd responses. Fourth, more recent discoveries about the role of DNA methylation and regulatory RNAs in Cd responses will expand the complex transcriptional landscape of plant Cd stress responses. It will be critical to identify the target genes that are subjected to regulation by epigenetic mechanisms and regulatory RNAs and establish the processes and pathways by which these target genes influence plant Cd accumulation and responses. Finally, most of the research on plant Cd accumulation and responses has been carried out in rice and Arabidopsis. It is very likely that there are many unknown components and mechanisms that are present in different plants with important roles in plant Cd accumulation and tolerance. There are, for example, plants that hyperaccumulate Cd and can be highly valuable research materials for discovery of novel mechanisms by which plants accumulate, sequester, and detoxify high levels of Cd from heavily contaminated soils. In the hyperaccumulator S. alfredii, some genes related to Cd uptake and hyperaccumulation have been characterized, such as SpHMA3, SaNramp6, and SaHsfA4c [97,140,141]. Isolation of new genes including those TFs and interacting factors with regulatory roles in plant Cd accumulation and tolerance will help elucidate regulatory mechanisms in response to heavy metal stress. They can also be exploited as potential targets for genetic engineering through molecular breeding and clustered regularly inter-spaced short palindromic repeat (CRISPR)-Cas9 technology to reduce grain Cd accumulation and increase Cd tolerance in crop plants.

PMC10001913

Ruo-Lan Li,Ling-Yu Wang,Hu-Xinyue Duan,Die Qian,Qing Zhang,Li-Sha He,Xue-Ping Li

Natural flavonoids derived from herbal medicines are potential anti-atherogenic agents by inhibiting oxidative stress in endothelial cells 10.3389/fphar.2023.1141180

24-02-2023

atherosclerosis,endothelial dysfunction,oxidative stress,herbal medicines,flavonoids

As the common pathological basis of various cardiovascular diseases, the morbidity and mortality of atherosclerosis (AS) have increased in recent years. Unfortunately, there are still many problems in the treatment of AS, and the prevention and treatment of the disease is not ideal. Up to now, the occurrence and development of AS can roughly include endothelial cell dysfunction, vascular smooth muscle cell proliferation, inflammation, foam cell production, and neoangiogenesis. Among them, endothelial dysfunction, as an early event of AS, plays a particularly important role in promoting the development of AS. In addition, oxidative stress occurs throughout the causes of endothelial dysfunction. Some previous studies have shown that flavonoids derived from herbal medicines are typical secondary metabolites. Due to its structural presence of multiple active hydroxyl groups, it is able to exert antioxidant activity in diseases. Therefore, in this review, we will search PubMed, Web of Science, Elesvier, Wliey, Springer for relevant literature, focusing on flavonoids extracted from herbal medicines, and summarizing how they can prevent endothelial dysfunction by inhibiting oxidative stress. Meanwhile, in our study, we found that flavonoid represented by quercetin and naringenin showed superior protective effects both in vivo and in vitro, suggesting the potential of flavonoid compounds in the treatment of AS.

Natural flavonoids derived from herbal medicines are potential anti-atherogenic agents by inhibiting oxidative stress in endothelial cells 10.3389/fphar.2023.1141180 As the common pathological basis of various cardiovascular diseases, the morbidity and mortality of atherosclerosis (AS) have increased in recent years. Unfortunately, there are still many problems in the treatment of AS, and the prevention and treatment of the disease is not ideal. Up to now, the occurrence and development of AS can roughly include endothelial cell dysfunction, vascular smooth muscle cell proliferation, inflammation, foam cell production, and neoangiogenesis. Among them, endothelial dysfunction, as an early event of AS, plays a particularly important role in promoting the development of AS. In addition, oxidative stress occurs throughout the causes of endothelial dysfunction. Some previous studies have shown that flavonoids derived from herbal medicines are typical secondary metabolites. Due to its structural presence of multiple active hydroxyl groups, it is able to exert antioxidant activity in diseases. Therefore, in this review, we will search PubMed, Web of Science, Elesvier, Wliey, Springer for relevant literature, focusing on flavonoids extracted from herbal medicines, and summarizing how they can prevent endothelial dysfunction by inhibiting oxidative stress. Meanwhile, in our study, we found that flavonoid represented by quercetin and naringenin showed superior protective effects both in vivo and in vitro, suggesting the potential of flavonoid compounds in the treatment of AS. Cardiovascular disease (CVD) ranks alongside cancer, diabetes, and chronic respiratory diseases as the four diseases with the highest morbidity and mortality worldwide (Zhong et al., 2019). More than 17 million people die from CVD every year, accounting for more than 31% of global deaths (Townsend et al., 2016; Benjamin et al., 2017). Shockingly, with the acceleration of population aging, the incidence and mortality of CVD are still increasing, and there are large problems in the existing treatment methods need to be solved (Yusuf et al., 2004). Among them, atherosclerosis (AS), as the common pathological basis of CVD, has also received extensive attention in the prevention and treatment of CVD. AS is a chronic, progressive multifocal arterial disease, which mainly causes damage to large and medium-sized arteries. Unfortunately, although much effort has been invested in AS, the prevention and therapy of the disease are not particularly ideal (Falk, 2006; Crea and Libby, 2017). So far, the measures to alleviate AS have mainly been to reduce hyperlipidemia, slow the disease process, and mitigate the consequences of AS (Khan et al., 2021). Smoking, unhealthy diet, obesity, alcohol consumption and other factors may contribute to the development of AS. However, due to the complexity of AS, the AS pathogenesis is not well understood, which greatly reduced the therapeutic effect of AS (Steven et al., 2019). Based on the evidence from recent years, the occurrence and development of AS mainly involves a variety of cellular events such as endothelial cell dysfunction, vascular smooth muscle cell (VSMC) proliferation, inflammation, foam cell production, and neovascularization (Shemiakova et al., 2020). Pleasantly surprise, endothelial dysfunction appears to be reversible with therapeutic interventions aimed at correcting risk factors for endothelial dysfunction. At the same time, most of the initiation of AS development is located in the subendothelial space, and can be controlled by the endothelium and hormones. The treatment and improvement of endothelial dysfunction also play a particularly important role in AS. At present, there are many theories about the causes of endothelial cell dysfunction. Notably, inflammation, oxidative stress, autophagy and other events are inseparable from endothelial cell dysfunction, while oxidative stress is carried throughout (Li et al., 2022a). For thousands of years, herbal medicines have been widely used in the prevention and treatment of diseases. With the development of medical information technology, flavonoids derived from herbal medicines have received more and more attention due to their significant efficacy and high safety (Li et al., 2022b). Flavonoids are mainly found in vacuoles of plants and are a secondary metabolite with abundant content. The main function of flavonoids is to protect plants against pathogens and UV radiation, and to participate in pollination by being recognized by pollinators (Pandey and Rizvi, 2009). Previous studies have shown that flavonoids have unique antioxidant activity due to their ability to provide hydrogen atoms or electrons, which can directly remove reactive oxygen species, thereby limiting the effects of oxidative stress (Li et al., 2022a). In addition, a large number of literature studies have shown that flavonoids derived from herbal medicines also have a significant effect on AS. Notably, flavonoids derived from herbal medicines also have been shown to regulate endothelial cell dysfunction during AS development (Yamagata, 2019). Therefore, based on the above explanation, we can propose that flavonoids derived from herbal medicines can inhibit oxidative stress, thereby inhibiting the occurrence of endothelial dysfunction. Endothelial cells, as a unique type of epithelial cells, are distributed in a monolayer of blood vessels and constitute the vascular endothelium that maintains vascular homeostasis (Krüger-Genge et al., 2019). The vascular endothelium is a semipermeable barrier between plasma and vascular tissue that extends along the entire circulatory system. Due to its unique location, endothelial cells can not only undergo metabolic exchange with plasma and interstitial fluid, but also interact with cells in the blood vessel wall (Yamaoka-Tojo, 2017). In addition, changes in blood composition and blood flow also have a great influence on the function of endothelial cells, among which mechanical transduction due to shear stress is considered to be the most important factor (Mitra et al., 2017; Mensah et al., 2020). In a healthy state, shear stress can directly promote the activation of endothelial NO synthase (eNOS) in endothelial cells, and also can activate eNOS by inducing rapid influx of Ca2+ into cells. eNOS promotes nitric oxide (NO) production by converting L-arginine to L-citrulline and NO (Förstermann and Munzel, 2006; Xu et al., 2021). As all we known, NO is an important vasoactive substance (Figure 1). NO can diffuse into vascular smooth muscle cells (VSMC), promote vasodilation by stimulating soluble guanyl cyclase and increasing cyclic guanosine monophosphate (cGMP), and has an antiproliferative effect on VSMC (Jin and Loscalzo, 2010). In the circulatory system, NO can also inhibit the adhesion and aggregation of platelets and exert anti-inflammatory properties. In addition, molecules represented by hydrogen sulfide (H2S), carbon monoxide, and arachidonic acid metabolites can also mediate vasodilation by inducing endothelium-derived hyperpolarization (Shimokawa, 2014). Under physiological conditions, in addition to vasodilation, endothelial cells can also mediate vasoconstriction by releasing a variety of vasoconstrictor molecules such as thromboxane A2 (TXA2), angiotensin (Ang) II and endothelin (ET) (Ley et al., 2007; Rao et al., 2007). Besides this, endothelial cells also can regulate platelet activity, coagulation cascade and fibrinolysis system. However, these functions of endothelial cells can be disrupted to varying degrees by diseases, including hyperlipidemia, diabetes, and heart failure (Tuñón et al., 2007; Tonelli et al., 2016; Giannitsi et al., 2019). Apparently, aging and genetic changes can also induce endothelial cell dysfunction. Inflammation, oxidative stress and autophagy are considered as important cellular events that affect endothelial function. Previous studies have shown that lipids in endothelial cells can be transported to autophagic vesicles for lysosome-mediated degradation after ox-LDL stimulation. At the same time, ER stress is triggered in endothelial cells and further induces autophagy (Torisu et al., 2016). In addition, endothelial cells can also regulate autophagic flux through different transcription factors when shear stress is changed (Hua et al., 2022). Therefore, autophagy has also been proposed as an effective tool to alleviate endothelial dysfunction. Since inflammation is an important factor in inducing endothelial dysfunction, its role in AS cannot be ignored. When endothelial cells are activated, interleukin (IL) −8, chemokines, vascular adhesion molecule-1 (VCAM-1), growth factors and other inflammatory factors are secreted, attracting monocytes and neutrophils to adhere to endothelial cells and penetrate the arterial wall to cause inflammation (Chistiakov et al., 2018). There are many ways to induce endothelial inflammation. For example, lipopolysaccharide release from the blood promotes inflammation by increasing the expression of interferon-induced proteins and tetrapeptide repeats in endothelial cells (Wang et al., 2020). Insulin can increase Ang-II expression through the p38 MAPK-cFOS pathway and enhance inflammation in a paracrine manner (Chandel et al., 2020). In addition, excessive ROS can also induce endothelial dysfunction by enhancing inflammatory response (Zeng et al., 2020). When endothelial injury occurs in blood vessels, white blood cells will combine with fibrin tissue to form fibrin network, which plays a role in the repair of endothelial injury (Ley et al., 2007; Rao et al., 2007). Unfortunately, when the body suffers from a wide range of pathological damage, vascular endothelial cells are continuously damaged and stimulated, and the repair effect is ineffective. Under these conditions, the endothelial cells undergo a phenotypic shift, the balance between vasodilator and vasoconstrictor is disrupted, and the arterial structure is destroyed (Incalza et al., 2018; Kim et al., 2019). As an early event of AS, endothelial cell dysfunction plays a role in the development of AS (Figure 2). After the occurrence of endothelial cell dysfunction, the vascular barrier function is weakened, and the low-density lipoprotein cholesterol (LDL-C) in the blood is more likely to accumulate in the intima and undergo oxidation reaction, and then produce oxidized low-density lipoprotein (ox-LDL) (Gao et al., 2017). The injured endothelial cells will release monocyte chemoattractant protein-1 (MCP-1), intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and so on to induce monocyte and macrophages to adhere to the vessel wall (Clapp et al., 2004; Chi and Melendez, 2007). Subsequently, macrophage colony stimulating factor (M-CSF) and granulocyte macrophage colony stimulating factor (GM-CSF) stimulate mononuclear macrophages to differentiate into macrophages, which will take up ox-LDL to generate foam cells and further aggravate AS (Trus et al., 2020; Zhi et al., 2023). As an important component of vascular composition, VSMC will switch from a contractile to a synthetic phenotype after endothelial cell injury. Similarly, VSMC also undergo abnormal proliferation and migration induced by chemokines and matrix metalloproteinases (MMP), which destroys the stability of plaques. In the intima, VSMC not only uptake ox-LDL to generate foam cells, but also secrete extracellular matrix components to form fibrous caps (Liang et al., 2018; Muqri et al., 2020). As mentioned above, factors such as hyperlipidemia, diabetes, heart failure, aging, and genetics may contribute to the development of endothelial dysfunction. Among these factors, we can find the presence of oxidative stress and ox-LDL. At present, many studies believe that excessive reactive oxygen species (ROS) can induce oxidative stress on the one hand, and aggravate the oxidative modification of LDL on the other hand. Subsequently, oxidative stress interacts with ox-LDL to jointly promote the occurrence of endothelial dysfunction. ROS is an endogenous and important mediator involved in various biological processes of the organism and can serve as a second messenger in cell signaling. Because ROS can easily acquire or loss electrons, it is widely involved in redox reactions. However, when the content of ROS exceeds limitation, it will disrupt the redox balance in the body, which in turn leads to the occurrence of oxidative stress, thereby affecting all aspects of physiological functions (Kattoor et al., 2017). Nowadays, the ROS family includes many small molecules and ions, such as superoxide, hydroxyl radicals, hydrogen peroxide and so on. It is well known that almost all cells in blood vessels can produce ROS, and its generation mechanism mainly includes NADPH oxidase (NOX), xanthine oxidase, mitochondrial respiratory chain and NOS (Figure 3) (Goszcz et al., 2015). As a membrane-binding enzyme complex, NOX is the only family of enzymes whose main function is to produce ROS. NOX is widely present in various vascular cells and is the main source of ROS by transferring electrons from NADPH to O2 and generate O2 −. When the body has hypertension, diabetes or high cholesterol, it is easy to increase the expression of NOX and thus increase the content of ROS in the body (Balaban et al., 2005). Existing studies have shown that the congeners of NOX are expressed in various types of vascular cells, but the difference in their content cannot be ignored. For example, NOX2, NOX4, and NOX5 are predominantly expressed in EC, whereas NOX1 and NOX4 are predominantly expressed in VSMC. Different NOXs produce different types of ROS, with NOX1 and NOX2 generating O2 −, NOX4 generating H2O2, and NOX5 generating O2 − and H2O2 (Dikalov et al., 2008; Lassègue et al., 2012). At the same time, different NOX have different effects on AS. For example, downregulation of NOX1, NOX2, and NOX5 can inhibit AS, while NOX-4 has a cardioprotective effect (possibly due to the fact that NOX4 mainly produces H2O2) (Guzik et al., 2008; Takac et al., 2011; Fulton and Barman, 2016). Xanthine oxidase is another important enzymatic source of ROS and is mainly present in EC. Xanthine oxidase can generate O2 − and H2O2 by oxidation of xanthine and hypoxanthine. In addition, xanthine oxidase can also elevate LOX-1 and CD-36 in macrophages and VSMCS, disrupt intracellular lipid metabolism, and increase the risk of AS (Kattoor et al., 2017). Mitochondria, as an important organelle within the cells, is an important source of energy required for cellular activities. Oxygen, which is required for cell survival, is converted to O2 − in the mitochondrial respiratory electron transport chain mainly by electron grant in complexes I, II, and III for energy production and oxidative phosphorylation (Peng et al., 2019; Peoples et al., 2019). This process is recognized as the main way to generate ROS. Normally, ROS generated by this process can be removed by various oxidoreductases to maintain homeostasis. Under pathological conditions, the disruption of this balance will lead to excessive accumulation of ROS and further induce ROS leakage (Peoples et al., 2019). NOS has three distinct isoforms, namely, neuronal NOS (nNOS), inducible NOS (iNOS) and endothelial NOS (eNOS). Among them, eNOS is most closely associated with AS. Notably, although eNOS could produce NO in the presence of tetrahydrobiopterin (BH4) to scour oxygen radicals and thus exert anti-atherosclerosis effect, it has been shown in previous studies that overexpression of eNOS may also promote the development of AS. The possible mechanism lies in the decoupling of eNOS caused by excessive BH4 depletion (Ozaki et al., 2002; Hossain et al., 2012). This hypothesis has been confirmed by a recent study. It was shown that when BH4 was scarce, eNOS uncouples to generate O2 − and combines with NO to generate peroxynitrite (ONOO−). ONOO− is a potent oxidant that induces the occurrence of oxidative stress damage (Li et al., 2015). nNOS can exert a synergistic effect with eNOS in anti-atherosclerosis by regulating vascular tone (Capettini et al., 2011). However, iNOS can not only induce excessive production of NO, but also compete with eNOS to bind BH4, promote the generation of ONOO−, and aggravate the occurrence and development of AS (Gunnett et al., 2005). It was shown that excessive ROS-induced oxidative stress can directly affect intracellular biomacromolecules to cause damage. ROS and its oxidation products can act as signal transduction molecules to activate related pathways, damage endothelial cells, and promote the development of AS. As one of the oxidation products, ox-LDL is thought to play a major role in lipid metabolic disorders. LDL-related modifications include oxidation, deacetylation, glycosylation and aggregation, among which the oxidation of LDL is closely related to AS (Nègre-Salvayre et al., 2017). ROS can oxidise a variety of polyunsaturated lipids in blood vessels, and the by-products formed can react with apolipoprotein B-100 and damage its function. Subsequently, modified ApoB-100 retards LDL removal and prolongs the exposure of lipids and apoB-100 to ROS attack, which further enhances LDL oxidation (Negre-Salvayre et al., 2008; Rabbani et al., 2010; Nègre-Salvayre et al., 2017). When endothelial cells are exposed to oxidative stress for a long time, their structure and function are continuously damaged, which also leads to the continuous oxidation of LDL to form ox-LDL (Stocker and Keaney, 2004). However, after numerous studies on the oxidation mechanism of LDL, it has been found that ox-LDL is heterogeneous, and different concentrations of ox-LDL also have a dual effect on vascular cells. For example, low concentrations of ox-LDL can induce cell migration and proliferation, and create a pro-inflammatory environment for AS, while high concentrations of ox-LDL can promote apoptosis (Dandapat et al., 2007; Cinq-Frais et al., 2013; Camaré et al., 2015). Excessive ROS can cause endothelial cell apoptosis through several major pathways. Firstly, ROS can not only activate nuclear factor kappa-B (NF-κB) through redox factor-1 (Ref-1), but also directly activate NF-κB. Subsequently, activated NF-κB translocates into the nucleus where it binds to the apoptosis-related gene c-Myc, promoting gene transcription and inducing apoptosis. The p38 pathway and c-Jun N-terminal kinase pathways are also strongly associated with ROS-induced apoptosis (Haghi Aminjan et al., 2019; Zhang L. et al., 2022). Notably, excessive ROS causes lipid peroxidation, damages the inner mitochondrial membrane, and ultimately induces both endogenous and exogenous endothelial cell apoptosis (Sinha et al., 2013). In addition, the generated ox-LDL disrupts the structure of actin filaments upon contact with endothelial cells, causing disruption of the cytoskeleton, which in turn alters endothelial cell permeability. The increased permeability of endothelial cells makes it easier for lipids to pass through the cells, further aggravating the development of AS (Chouinard et al., 2008; Zhang et al., 2022). Ox-LDL can enter endothelial cells through a variety of cell-surface expressed scavenger receptors, the most typical of which are LOX-1 and CD36 (Nègre-Salvayre et al., 2017). LOX-1 is the main receptor for ox-LDL uptake by endothelial cells. The combination of LOX-1 and ox-LDL can enhance the expression of NOX, promote the generation of O2 −, and aggravate the oxidative stress response in cells (Lu et al., 2011; Yoshimoto et al., 2011). At the same time, the oxLDL/LOX-1/ROS axis is activated, which promotes the production of various inflammatory cytokines, chemokines, adhesion molecules, and ultimately leads to the recruitment and adhesion of monocytes to the activated endothelium (Kamei and Carman, 2010; Lubrano and Balzan, 2016). As a multifunctional receptor, CD36 recognizes oxidized phospholipids and other ligands in addition to ox-LDL. When ox-LDL binds to CD36, MAPK, NF-κB and Toll-like receptors (TLR) are activated, which enhance the local response (Park et al., 2013). Flavonoids are a class of secondary metabolites widely found in plants and fungi. Their characteristic structure mainly contains 15 carbon atoms. Flavonoids can be subdivided according to their structure into anthocyanins, flavonoids, flavanones, flavonols, anthoxanthins, and isoflavonoids. Because flavonoids have hydroxyl groups in their structure, they can play an antioxidant role both in vivo and in vitro. In this review, we searched the relevant literature on flavonoids inhibiting oxidative stress to treat endothelial dysfunction in AS, and selected some important compounds to elaborate. Quercetin is a natural polyhydroxy flavonoid found in a variety of plants such as Bupleurum chinense DC, Bupleurum scorzonerifolium Willd (Apiaceae), mulberry leaves, Crataegus pinnatifida Bunge, and C. pinnatifida var. Major N. E. Br. It is a plant secondary metabolite with antioxidant activity (Zhi et al., 2023). In the past decades, quercetin has been widely used in clinical practice for various diseases due to its superior activity, including cancer, arthritis, neurodegenerative diseases and cardiovascular diseases (Wang et al., 2022). There are numerous studies on quercetin in the treatment of AS. In vivo and in vitro studies have shown that quercetin exerts multiple effects on various processes of AS development, including foam cell formation, lipid metabolism, monocyte migration, and endothelial cell dysfunction. Firstly, intragastric administration of quercetin ameliorated arterial lipid deposition in high-fat diet fed ApoE mice. In ox-LDL-induced human umbilical vein endothelial cells (HUVECs), quercetin reduced intracellular ROS and increased mitochondrial membrane potential. At the same time, apoptosis and senescence induced by ox-LDL were also alleviated, lipid droplet deposition was reduced, and cell morphology was improved. By exploring the underlying mechanism, p53 and mTOR signaling pathways were found to be involved in the pharmacological mechanism of quercetin (Jiang et al., 2020). Naringenin, a flavonoid extracted from the pericarp of Citrus reticulata Blanco, is a trihydroxy flavanone. It can be found in past reports that naringenin exerts antioxidant activity directly through free radical scavenging activity, and has the ability to induce endogenous antioxidant system (Hernández-Aquino and Muriel, 2018). The comparison of the antioxidant capacity of naringenin with that of quercetin has been controversial in some studies. It was reported that naringenin equivalent antioxidant activity was 1.53 mmol/L, a small value compared to the 4.7 mmol/L of quercetin (Rice-Evans et al., 1996). However, in the study of Cavia-Saiz et al., the antioxidant capacity of naringenin was worse than that of quercetin (Cavia-Saiz et al., 2010). Therefore, further studies are needed to compare the antioxidant capacity of naringenin with other flavonoids. However, it was no doubt about the role of naringenin in protecting endothelial dysfunction in AS. In previous experiments, naringenin was found to inhibit AS by ameliorating dyslipidemia, and subsequently it was found to protect mitochondrial membrane potential to ameliorate ischemic damage (Mulvihill et al., 2010; Testai et al., 2017). Therefore, in the study of Li et al., it was hypothesized that naringenin could ameliorate endothelial injury through a mitochondria-dependent pathway. After homocysteine-induced HUVECs injury, naringenin could inhibit the generation of ROS in mitochondria and cytoplasm, restore mitochondrial membrane potential, but there was no significant difference in Ca2+ concentration. RNA-seq transcriptome analysis and experimental validation showed that naringenin significantly restored the expression of Sirt1, AMPKα and eNOS. In addition, knockdown of Sirt1 and AMPKα by siRNA almost abolished this protective effect (Li et al., 2021). In vivo, endothelial injury was defined as plasma homocysteine levels higher than 15 μmol/L. Naringenin could significantly inhibit the damage of arterial wall and protect endothelial function after treatment, and its mechanism was consistent with the results in vitro (Li et al., 2021). Therefore, we can conclude that naringenin ameliorates homocysteine-induced endothelial injury through the AMPKα/Sirt1 pathway. Carthamus tinctorius L. has been used as a traditional medicinal plant for thousands years. According to Kaibao Materia Medica, the dried flowers of C. tinctorius L. can promote blood circulation and relieve pain. So far, C. tinctorius L. has been developed as Danhong injection, safflower injection and other preparations for the treatment of coronary heart disease and angina pectoris. Hydroxysafflor yellow A is an important active component of C. tinctorius L., and it is also the most abundant component of safflower yellow, an indicator component of C. tinctorius L (Xue et al., 2021). In recent years, hydroxysafflor yellow A has been shown to protect endothelial cells by inhibiting inflammation and apoptosis. First, Ji et al. found that hydroxysafflor yellow A could increase the ratio of Bcl-2/Bax at the mRNA and protein levels and reduce mitochondrial-dependent apoptosis in hypoxia-induced HUVECs (Ji et al., 2009). This phenomenon was further illustrated in the experiments of Xie et al., which showed that hydroxysafflor yellow A could regulate cell survival and proliferation by promoting AKT and inhibiting PTEN expression. Meanwhile, hydroxysafflor yellow A reduced ROS generation and restored intracellular redox balance by increasing intracellular superoxide dismutase (SOD) in H2O2-induced HUVECs (Xie et al., 2020). In addition, in ox-LDL-induced HUVECs, hydroxysafflor yellow A could upregulate VDAC2 or inhibit apoptosis through AMPK signaling, in which VDAC2 could exert an anti-apoptotic effect by interfering with Bak-mediated apoptosis (Ye et al., 2017; Zhang H. et al., 2022). Genistein is a natural isoflavone first obtained from Genista tinctoria L. It is mainly derived from Euchresta japonica Hook. f. ex Regel, Sophora japonica L. and so on. Currently, methanol, ethanol, acetonitrile and other organic solvents are used to extract genistein. Meanwhile, the chemical synthesis of genistein is simple and feasible (Spagnuolo et al., 2015). The structure of genistein is similar to that of endogenous estrogen, so it can bind to estrogen receptors and exert estrogen-like effects after being absorbed by the body. In addition, as a typical flavonoid, it is connected with multiple hydroxyl groups on the phenyl ring, which makes it have excellent antioxidant effects and can be applied to the treatment of cardiovascular diseases, diabetes, depression and other diseases (Borrás et al., 2006; Jafari et al., 2022). In endothelial dysfunction, genistein can effectively inhibit ROS and malondialdehyde (MDA) in cells, and restore the four oxidoreductases activities including superoxide dismutase (SOD), catalase (CAT), glutathione (GSH) and glutathione peroxidase (GPx). In this way, the redox balance of endothelial cells is maintained (Zhang et al., 2017). Further exploration revealed that the antioxidant activity of genistein was closely related to MR-34a/sirtuin-1/foxo3a. Genistein can downregulate the expression of MiR-34a in ox-LDL-induced HUVES, thereby promoting the expression of sirtuin-1. In addition, sirtuin-1 is known to exert antioxidant activity by activating fxo3a in previous studies. However, after genistein treatment, the expression of fxo3a was significantly increased (Zhang et al., 2017). Baicalein, also known as 5, 6, 7-trihydroxyflavone, is a well-recognized natural flavonoid with antioxidant and anti-inflammatory activities. Baicalein is the most abundant component in the root of Scutellaria baicalensis (S. baicalensis) Georgi, a traditional Chinese medicine (also known as Huangqin in Chinese) (Huang et al., 2005). In a previous study, it was shown that baicalein inhibited IL-1β-induced ICAM-1 expression in HUVECs, suggesting that baicalein could protect endothelial cell function (Kimura et al., 1997). In a recent study, ox-LDL was used to induce apoptosis in HUVECs and baicalein was preincubated before induction. It was showed that baicalein effectively inhibited the generation of intracellular ROS and the release of cytochrome C from mitochondria, and increased mitochondrial membrane potential. The expression of pro-apoptotic protein BAX was downregulated, while the expression of anti-apoptotic protein Bcl-2 was upregulated. In addition, the bioavailability of NO was also improved (Chan et al., 2016). Subsequently, it was also shown that baicalein pretreatment could inhibit the binding ability of ox-LDL by reducing the expression of LOX-1, thereby inhibiting the generation of ROS. In addition, baicalein inhibited the protein expression of NADPH oxidase and increased the phosphorylation level of AMPK, thereby inhibiting the activation of protein kinase C (PKC)-α and PKC-β (Tsai et al., 2016). Luteolin is a common flavonoid, which is usually found in the form of glycosylated in celery, green pepper, Perilla frutescens (L.) Britt., and Matricaria recutita L. Luteolin possesses the antioxidant properties, as well as anti-inflammatory ability. Therefore, it also has a good advantage in the treatment of AS (Prasher et al., 2022). Up to now, the antioxidant activity of luteolin has been fully confirmed. It can exert efficacy in all stages of AS, such as VSMC migration and proliferation, cell adhesion molecule secretion and endothelial cell dysfunction (Luo et al., 2017). When endothelial cells are dysfunctional, luteolin can inhibit the generation of intracellular ROS, while the phosphorylation of p38MAPK and nuclear translocation of NF-kB induced by ox-LDL are reversed. At the same time, the mRNA levels of ICAM-1, VCAM-1, selectin, MMP-1, MMP-2, and MMP-9 are also downregulated by luteolin (Yi et al., 2012). In another study, this conclusion was further developed. In other words, luteolin inhibited TNF-α-induced transcriptional activities of NF-κB and p38 as well as ERK1/2 phosphorylation, while it also exerted its inhibitory effect on Nox4 expression. Ultimately, luteolin restored the redox balance in endothelial cells, that is, the contents of GSH and SOD were restored and LDH was decreased (Xia et al., 2014). Erigeron breviscapus (Vant.) Hand.-Mazz is a traditional natural medicine used to treat heart and brain ischemic diseases. The modern pharmacological studies have shown that the main active substance is scutellarin. Scutellarin, also known as 4′, 5, 6-trihydroxyflavone-7-glucuronde, is a member of the natural flavonoid family. Previous studies have found that scutellarin not only prevents cerebral ischemia by inhibiting inflammatory response, but also improves liver damage by inhibiting oxidative stress (Yuan et al., 2016). In addition, scutellarin also plays a role in endothelial dysfunction through its antioxidant effect in AS. Scutellarin scavenged excess ROS and increased the bioavailability of NO in HAECs induced by either angiotensin II or H2O2. The contents of oxidoreductases, including SOD, GPx, CAT and Nox, could be restored to varying degrees after treatment with scutellarin. Subsequently, the mechanism of scutellarin against endothelial cell injury and apoptosis was further studied, and the results showed that the protective effect of scutellarin was closely related to Hippo-FOXO3A and PI3K/AKT signaling pathways. After treating with scutellarin, the mRNA levels of mammalian sterile-20-like kinases 1 (Mst1), Yes-associated protein (YAP) and FOXO3A were significantly downregulated, as well as the protein levels of p-Mst1, p-YAP and nuclear translocation of FOXO3A. At the same time, PI3K/AKT signaling pathway was activated, and its downstream apoptosis-related Bax and Bcl-2 proteins were also changed (Mo et al., 2018; Fu et al., 2019). We can draw the same conclusion in vivo that scutellarin can alleviate lipid metabolism disorder and maintain redox balance in AS rats through Hippo-FoxO3A and PI3K/AKT signaling pathways (Fu et al., 2019). The specific indicators are referred to Table 1. Acacetin, also known as 5, 7-dihydroxy-4′-methoxy flavone, is a monomethoxy flavonoid widely found in medicinal plants such as Robinia pseudoacacia L., Dendranthema morifolium (Ramat.)Tzvel., and Saussurea involucrata (Kar. et Kir.) Sch.-Bip. In nature, acacetin mostly exists in the form of free or glycosides, and has pharmacological activities on cancer, obesity, diabetes, etc (Wu et al., 2022). In recent years, acacetin has been found to have a protective effect on endothelial dysfunction in AS, which has attracted extensive attention from the scientific community. In vivo study believed that acacetin significantly accelerated lipid metabolism in AS mice and reduced the levels of inflammatory factors in plasma (Han et al., 2020). In vitro experiment confirmed that acacetin could protect mitochondrial function, reverse mitochondrial depolarization, and inhibit the excessive production of ROS and MDA in HUVECs induced by high glucose. On the other hand, the mitoBcl-2/mitoBax ratio in mitochondria was increased after acacetin administration. This protective effect was closely related to the SIRT1-mediated activation of Sirt3/AMPK signaling, and the protein expression of SOD, Bcl-2 and PGC-1α was increased during this process (Han et al., 2020). In addition, the study has shown that acacetin may restore the antioxidant function of endothelial cells by promoting the phosphorylation of Nrf2, the degradation of Keap1 and the expression of methionine sulfite reductase (Wu et al., 2021). Eupatilin is a flavonoid mainly found in Artemisia princeps Pampanini, and also known as 2- (3, 4-dimethoxyphenyl) −5, 7-dihydroxy-6-methoxy-ychromen-4-one. Artemisia princeps Pampanini has been widely used as a medicinal plant in Asia over the last thousands of years. In modern times, due to the rapid development of modern pharmacology, eupatilin has been found to have a wider range of pharmacological activities (Lim et al., 2021). For example, eupatilin has therapeutic potential in diseases such as oncology, allergy, and inflammation (Park, 2014; Jeong et al., 2015). In AS, eupatilin has been shown to inhibit the proliferation and migration of human aortic smooth muscle cells. The oxidative stress as well as inflammatory responses occurring in endothelial cells could also be inhibited by eupatilin. In addition, Yu et al. has been confirmed that eupatilin could effectively reduce the ROS content in TNF-α-induced HUVECs, inhibit the expression of VCAM-1 and ICAM-1, and thus reduce the adhesion ability of U937 cells to endothelial cells. The mechanism by which eupatilin exerted its therapeutic effect was closely related to MAPK-NF-ĸB. The phosphorylation of NF-kB p65 and MAPK was significantly inhibited by eupatilin. Taken together, it was suggested that eupatilin could protect endothelial cell function through ROS/MAPK-NF-ĸB (Yu et al., 2015). From the foregoing, it is known that the preceding flavonoid compounds can protect the cells from oxidative stress damage by restoring the antioxidant capacity of endothelial cells. However, glabridin extracted from the root of Glycyrrhiza glabra (licorice) could attenuate the oxidative stress injury to endothelial cells by inhibiting the oxidative sensitivity of LDL. Incubation of LDL with CuSO4 or 2,2’ -azobis (2-amidino-propane) dihydrochloride resulted in varying degrees of oxidation of LDL. However, the degree of LDL oxidation was significantly reduced after glabridin treatment, and glabridin inhibited the formation of lipid peroxides and cholesterol linoleic acid hydroperoxides (CLOOH) (Belinky et al., 1998). This protective effect of glabridin provides a novel form of protection for flavonoids. The protective effects of other flavonoid compounds on endothelial cells are shown in Table 1. In this review, we summarized the pathogenesis of endothelial dysfunction in AS, and then selected representative flavonoids with anti-oxidative stress effects for relevant elaboration. After summarizing, we have found that flavonoids from natural herbal medicines not only inhibit oxidative stress, but also have anti-inflammatory and anti-adhesion effects in the treatment of endothelial dysfunction. This result is consistent with the multi-level and multi-target advantages of traditional Chinese medicine. In modern clinical practice, it has been demonstrated that flavonoids can be used to reduce the incidence of AS. First of all, epidemiological investigations have shown that increasing the intake of flavonoids in daily diet can effectively reduce the risk of AS (Lagiou et al., 2006; Mursu et al., 2007). Subsequently, more and more evidence has shown that the intake anthocyanins, tea (the main components are flavan-3-ols), etc., can directly reduce the occurrence of AS (Jennings et al., 2012; Ivey et al., 2013). However, after in-depth understanding, flavonoids from natural herbal medicines also have certain limitations and problems that need to be solved urgently. Firstly, most of the models used in the existing studies are in vitro models. Flavonoids have been shown to exert protective effects on endothelial cells in experiments, but it is not clear whether this protective effect will change with the transformation of drug structure due to complex changes after drug entry into the body. Secondly, although some researchers have confirmed the protective effect of flavonoids on AS from in vivo and in vitro experiments, there is no relevant clinical data to support. At the same time, the toxicity and safety of drugs are also essential before the development of drugs. In the case of quercetin, after long-term addition of quercetin to the diet of F344/N rats, there was no obvious toxic damage in the rats at the beginning, but their weight gain was slow and they showed kidney carcinogenic activity in males after 2 years (Dunnick and Hailey, 1992). The oncogenic activity of quercetin remains controversial. However, it is generally believed that quercetin is safe when used under the intended conditions, and caution should be taken when taking quercetin in high doses or for a long time. Therefore, the safety and toxicity of flavonoids should be considered before they are used in clinical practice, and more work needs to be done. Finally, because the flavonoid compounds have more phenolic hydroxyl groups in their structure, it makes their structure unstable. Therefore, it is necessary to consider how to solve the problem of drug stability before developing flavonoid compounds into drugs. Looking at the existing flavonoid drug development, it can be found that the research on the treatment of endothelial dysfunction in AS is still relatively basic, and has not yet considered what kind of preparation the flavonoid is made into, or how it is administered. The development of flavonoids into modern formulations such as nanoparticles may change the instability of the compounds, which can also become the future development direction of flavonoids for the treatment of endothelial dysfunction. In summary, flavonoid compounds hold great promise in the treatment of endothelial dysfunction in AS, but further exploration is needed.

PMC10001914

Rui Huang,Hui Pan,Xing Zheng,Chao Fan,Wenyan Si,Dongguan Bao,Shanshan Gao,Jiayu Tian

Effect of Membrane Pore Size on Membrane Fouling of Corundum Ceramic Membrane in MBR

04-03-2023

ceramic membrane,membrane fouling,membrane pore size,membrane preparation,membrane bioreactor

Ceramic membrane has emerged as a promising material to address the membrane fouling issue in membrane bioreactors (MBR). In order to optimize the structural property of ceramic membrane, four corundum ceramic membranes with the mean pore size of 0.50, 0.63, 0.80, and 1.02 μm were prepared, which were designated as C5, C7, C13, and C20, respectively. Long-term MBR experiments showed that the C7 membrane with medium pore size experienced the lowest trans-membrane pressure development rate. Both the decrease and increase of membrane pore size would lead to more severe membrane fouling in the MBR. It was also interesting that with the increase of membrane pore size, the relative proportion of cake layer resistance in total fouling resistance was gradually increased. The content of dissolved organic foulants (i.e., protein, polysaccharide and DOC) on the surface of C7 was quantified as the lowest among the different ceramic membranes. Microbial community analysis also revealed the C7 had a lower relative abundance of membrane fouling associated bacteria in its cake layer. The results clearly demonstrated that ceramic membrane fouling in MBR could be effectively alleviated through optimizing the membrane pore size, which was a key structural factor for preparation of ceramic membrane.

Effect of Membrane Pore Size on Membrane Fouling of Corundum Ceramic Membrane in MBR Ceramic membrane has emerged as a promising material to address the membrane fouling issue in membrane bioreactors (MBR). In order to optimize the structural property of ceramic membrane, four corundum ceramic membranes with the mean pore size of 0.50, 0.63, 0.80, and 1.02 μm were prepared, which were designated as C5, C7, C13, and C20, respectively. Long-term MBR experiments showed that the C7 membrane with medium pore size experienced the lowest trans-membrane pressure development rate. Both the decrease and increase of membrane pore size would lead to more severe membrane fouling in the MBR. It was also interesting that with the increase of membrane pore size, the relative proportion of cake layer resistance in total fouling resistance was gradually increased. The content of dissolved organic foulants (i.e., protein, polysaccharide and DOC) on the surface of C7 was quantified as the lowest among the different ceramic membranes. Microbial community analysis also revealed the C7 had a lower relative abundance of membrane fouling associated bacteria in its cake layer. The results clearly demonstrated that ceramic membrane fouling in MBR could be effectively alleviated through optimizing the membrane pore size, which was a key structural factor for preparation of ceramic membrane. Membrane bioreactor (MBR) integrates membrane separation technology into the activated sludge process, having exhibited unique advantages such as excellent effluent quality, reduced footprint, lowered sludge production and robust process operation for wastewater treatment and recovery [1,2,3]. Currently, the membranes employed in MBR are mainly made of organic polymers. Polymeric membranes are susceptible to fouling by the activated sludge and can be easily aged by chemical cleaning, leading to low sustainability in MBR operation [4,5,6]. In recent years, ceramic membranes have gained ever-increasing attention all over the world and have been evaluated in both drinking water and wastewater treatment [7,8,9,10]. It is believed that ceramic membranes can provide longer service life, higher permeability, and robust physical and chemical stability in comparison with polymeric membranes [11,12]. For example, Kimura and Uchida [13] reported intensive physical cleaning (e.g., scrubbing by granular materials) and chemical cleaning (enhanced backwash with 1000 mg/L NaClO) can be applied to the ceramic membrane MBR (CMBR), which was shown able to operate at an elevated flux (30.1 LMH) for a prolonged period. However, intensive membrane cleaning would significantly increase the operation cost and maintenance difficulties of the CMBR system. It is highly expected to alleviate membrane fouling by optimizing the intrinsic properties of the ceramic membrane [14]. It has been recognized that membrane pore size can exert a significant influence on the membrane fouling behavior of polymeric membranes. However, the reported trends were found to be inconsistent. For example, Jin et al. [15] compared the membrane fouling behavior of four different pore sizes in MBR, finding the membrane with the smallest pore size experienced the lowest trans-membrane pressure (TMP) development rate, while on the contrary, Sano et al. [16] noticed a larger pore size (0.57 μm) was beneficial to suppressing the membrane fouling development in MBR. Chang et al. [17] also reported the membrane with smaller pore size tended to form a thicker cake layer during MBR operation, thus resulting in more severe membrane fouling. Considering the suitable membrane pore size of MBR strongly depends on the membrane material [18,19], it is meaningful to optimize the pore size of the ceramic membrane for alleviating the membrane fouling in MBR. Therefore, in this work, ceramic membranes with different pore sizes were prepared by using corundum powder with different grain sizes, and their membrane fouling behavior in MBR was systematically investigated and compared. The effect of membrane pore size on the accumulation of membrane foulants was also discussed. According to the results of this work, the optimum pore size of corundum ceramic membrane was obtained, which was shown able to significantly alleviate the membrane fouling in MBR. A ceramic membrane with a hollow flat-sheet configuration was prepared by extrusion molding followed by high-temperature sintering. The detailed procedure can be found in our previous study [20]. To investigate the effect of membrane pore size on membrane fouling in MBR, four corundum powders with different grain sizes (5, 7, 13, and 20 μm) were used to prepare the ceramic membranes, which were designated as C5, C7, C13, and C20, respectively. The particle size distribution of the corundum grains used for preparing the different ceramic membranes is shown in Figure 1. The MBR system is schematically illustrated in Figure 2. The working volume of the MBR was 8.0 L with a dimensional size of 40 cm × 10 cm × 20 cm in length, width and height, respectively. In order to avoid the influence of the difference in activated sludge characteristics on membrane fouling, the four membranes with an effective area of 0.0083 m2 were immersed in the same bioreactor in a vertical direction, which was operated in parallel by using four individual peristaltic pumps. The operation conditions of the MBR were similar to that reported in our previous study [20]. The mean pore size and pore size distribution of the ceramic membranes were measured by a membrane aperture tester (3H-2000PB, Beijing Bester Instrument Technology Co., Ltd., Beijing, China). The porosity was determined according to Archimedes’ principle with the detailed procedure shown in [20]. The surface morphology of original and fouled ceramic membranes was observed by a scanning electron microscope (Hitachi SU8020 SEM, Tokyo, Japan). The phase crystal structure of the ceramic membrane was assessed by X-ray diffraction (D8 Discover, Bruker AXS, Karlsruhe, Germany). The membrane fouling resistance was calculated according to the resistance-in-series model, as shown in the following equations [16,21]. where J is the permeation flux (L/(m2·h)); ∆P is the TMP (Pa); μ is the viscosity of water (Pa·s). Likewise, Rt is the total filtration resistance of the fouled membrane (m−1); Rm is the inherent membrane resistance (m−1); Rp is the resistance caused by membrane pore blockage (m−1); Rc is the fouling resistance of the cake layer formed on the membrane surface (m−1); Rr is the reversible fouling resistance (m−1); and Rir is the irreversible fouling resistance (m−1). The fouling layer formed on the membrane surface after MBR operation was wiped by a sponge and transferred to 50 mL of deionized water. The foulants were dispersed by ultrasonic and eddy oscillation for 5 min, respectively, and then centrifuged (10,000 r/min) for 5 min to obtain the supernatant [22]. After that, the supernatant was filtered by 0.45 μm membrane, and the concentration of protein, polysaccharide and DOC (TOC-L CPH, Shimadzu, Kyoto, Japan) were measured. Three-dimensional fluorescence excitation-emission matrix (EEM) spectroscopy (CARY Eclipse, Agilent Technologies, Santa Clara, CA, USA) was also used to characterize the foulants extracted from the membrane surface. The spectrum was collected by changing the emission wavelength from 220 to 450 nm and the excitation wavelength from 260 to 540 nm. The microbial community of sludge samples extracted from the cake layer of fouled ceramic membranes and that in the mixed liquid (ML) of MBR were analyzed by Illumina MiSeq sequencing (Sangon Biotech Co., Ltd., Shanghai, China). On the V3 and V4 regions of the 16S rRNA gene, the PCR was amplified and sequenced with primers 341F (5′-TACCGGGGGGCWGCAG-3′) and 805R (5′-GACACHVGGGTATCTAATCC-3′). Table 1 lists the characteristics of the ceramic membranes prepared with corundum grains of different sizes. It could be seen that the mean membrane pore size exhibited an increasing linear trend with the corundum grain size. For C5, C7, C13, and C20, the mean pore sizes were 0.50, 0.63, 0.80, and 1.02 μm, respectively. Synchronously, the porosity of the membrane also increased from 41.43% (C5) to 47.60% (C20). As a result, the pure water flux was remarkably enhanced from 2111.67 to 15,193.08 L/(m2·h·bar). From Figure 3, it can be seen the main crystal phase of the ceramic membrane was α-Al2O3 [23]. The position of X-ray diffraction peaks for the four membranes was essentially the same, indicating the grain size of the corundum would not influence its main crystal phase. However, it was interesting to note the microcrystal size calculated by Jade 6 was in the order of C5 < C7 < C13 < C20, which was consistent with the actual grain size of the corundum membranes. Figure 4(a1–d2) shows the morphology of the corundum ceramic membranes observed by SEM and AFM. It can be seen that C5 and C7 had more regular surfaces with fewer macropore defects, which may avoid the entrance of sludge flocs into the membrane pores. With the increase in corundum grain size, more macropore defects appeared on the membrane surface, especially for the C20 membrane. This trend was further witnessed by the pore size distribution, as displayed in Figure 4e. The C5 and C7, especially the C7, possessed a narrow pore size distribution, implying the ceramic membrane prepared with a smaller corundum grain size had a more uniform pore structure [24]. By contrast, the pore size distribution of C13 and C20 was remarkably enlarged with two wide shoulders, especially on the right side, demonstrating the uneven distribution of membrane pores and the appearance of macropore defects on the membrane. The root mean square (RMS) roughness values of the four membranes were also measured, which followed the order: C20 (0.475 μm) > C13 (0.385 μm) > C7 (0.370 μm) > C5 (0.303 μm). Figure 5a shows that the chemical oxygen demand (COD) removal in the CMBR by different membranes was approximately the same, with the removal rate higher than 83%. The high COD removal could be attributed to the high microbial activity of the activated sludge in the CMBR. In Figure 5b, it could be seen ~47% of total phosphorus (TP) removal was achieved by the CMBR, which was lower than the MBR systems reported in other studies [25,26]. The low phosphorus removal may be due to the lack of an anaerobic environment in the single-staged CMBR system, which was essential for efficient phosphorus uptake by phosphorus-accumulating organisms (PAOs) [27]. In contrast, ~88% ammonia nitrogen (NH4+-N) was obtained by the CMBR with different ceramic membranes (Figure 5c), which was shown to be higher than the 66.7~76.9% as reported by other researchers [28,29,30], further illustrating a high microbial activity was maintained in the CMBR. It was interesting that although a high DO of ~3 mg/L was maintained in the bioreactor, as high as 75% of total nitrogen (TN) removal efficiency was still achieved by the single-staged CMBR (Figure 5d), possibly due to the high MLSS in the bioreactor (~7500 mg/L). The high TN removal could be considered an additional advantage for the MBR system in comparison with the conventional activated sludge process [31]. Figure 6 shows the trend of TMP development for the four corundum membranes with different pore sizes. During the long-term operation of the CMBR, hydraulic backwashing was carried out once a day; when the maximum TMP exceeded 80 kPa, chemical cleaning would be conducted for the four ceramic membranes. During the whole operation period, the TMP development rate of the four membranes was 24.73, 10.84, 33.83, and 34.22 kPa/d, respectively. Obviously, the C13 and C20 with larger pore sizes experienced the most severe membrane fouling during the MBR operation. The lowest membrane fouling was observed for the C7 membrane with a medium pore size of 0.63 μm. With the further decrease of membrane pore size to 0.50 μm, a higher TMP development rate was once again experienced by the C5 membrane. From this result, it was clear the C7 membrane possessed the optimum membrane pore size for alleviating membrane fouling in MBR. Moreover, the narrow pore size distribution of C7 relative to C13 and C20 might also make some contribution to its excellent antifouling performance. Figure 7a shows the distribution of cake layer resistance (Rc) and pore-blocking resistance (Rp) for different membranes. It could be seen that for C5, the contribution of the cake layer and pore blockage to membrane fouling were essentially the same, while with the increase of membrane pore size, the proportion of cake layer resistance in total fouling resistance was significantly increased. The result manifested cake layer fouling played a dominant role in the membrane fouling of ceramic membranes with relatively larger pore sizes. The highest pore blockage fouling experienced by the C5 membrane might be attributed to its smallest pore size. Figure 7b displayed a detailed analysis of the reversible/irreversible fouling resistance (Rr and Rir). It could be seen that for all the ceramic membranes with different pore sizes, reversible fouling resistance was substantially higher than that of irreversible fouling, indicating the accumulation of foulants on the corundum membrane was highly reversible. In addition, both reversible and irreversible fouling of the C7 membrane was significantly lower than that of C5, C13, and C20, further demonstrating the C7 membrane possessed the optimum pore size that can effectively alleviate membrane fouling in MBR. Figure 8 displayed three-dimensional fluorescence spectra of the organic foulants extracted from the fouled membrane surface. Two distinct protein peaks were identified at 280/340 nm and 230/330 nm, which were associated with a tryptophan-like substance (peak A) and a tyrosine-like substance (peak B), respectively [32,33]. It could be seen that for both peak A and peak B, the fluorescence intensity followed the order of C20 > C13 > C5 > C7. This indicated that the concentration of organic foulants, especially the protein-like substances accumulated on the surface of C7, was the lowest. By contrast, the highest concentration of organic foulants was observed for the C20 with the largest pore size. This result was consistent with that in Section 3.3.1, i.e., the cake layer resistance of C5, C13, and C20 was much higher than that of C7. The protein, polysaccharide and TOC contents on the membrane surface were further quantified (Figure 9). Similar to the fluorescence peaks, the contents of protein, polysaccharide and DOC also followed the order of C20 > C13 > C5 > C7. Particularly, the protein content on the membrane surface of C7 was almost zero; the content of polysaccharides was also shown to be extremely low. Both protein and polysaccharide are the main constituent of EPS, which have been recognized as a critical factor for membrane fouling in MBR [34,35]. The result indicated that by optimizing membrane pore size, the accumulation of EPS on the ceramic membrane could be effectively inhibited, thus alleviating the membrane fouling during MBR operation. Figure 10 shows the SEM images of the fouled ceramic membranes in the CMBR. From Figure 10(a1–d1), it can be seen that the surface of all membranes was covered with a dense cake layer, which was in coincidence with the resistance analysis, as shown in Figure 7. By careful inspection, it was observed the number of microorganisms gradually increased with the increase of membrane pore size. From the high-resolution images (Figure 10(a2–d2)), it could be seen that many flat circular bacteria and rod-shaped bacteria were attached to C5, C13, and C20. By contrast, much fewer microorganisms were present on C7, and only a few rod-shaped bacteria could be noticed. SEM images revealed the difference in the amount and species of microorganisms among the four membranes, which might be an important factor accounting for their difference in membrane fouling. Therefore, microbial communities in the cake layer were analyzed for the different membranes in the CMBR. The result of Illumina HiSeq sequencing analysis is shown in Table 2. The coverage for the sludge samples taken from the fouled membranes and the ML in the CMBR was as high as 0.99, indicating that the measured sequences could truly and comprehensively reflect the microbial structure of the samples. The Chao1 index is usually used to estimate the variation in the number of OTUs, and the larger the number is, the richer the species source would be. The Shannon and Simpson indices are mainly used to compare microbial species diversity as well as homogeneity [36,37]. As can be seen, C7 exhibited relatively higher Shannon and Chao1 indices as well as a lower Simpson index in comparison with the other three ceramic membranes, implying that the species richness of C7 was slightly higher than other membranes. However, the total number of microorganisms on the membrane surface of C7 was smaller than the other three membranes, further verifying the cake layer on C7 possessed higher community diversity. The effect of membrane pore size on the microbial community composition of the cake layer was further analyzed. Figure 11a showed Proteobacteria, Bacteroidetes, Candidatus Saccharibacteria, and Firmicutes were the four dominant phyla on the membrane surface. Among them, Proteobacteria was a common phylum of bacteria for nitrification and denitrification; while Bacteroidetes was able to produce proteins, an important component of EPS [38,39]. The relative abundance of Bacteroidetes was 32.3%, 20.6%, 20.0%, and 16.4% for C5, C7, C13, and C20, respectively. This might be one reason for the more severe membrane fouling experienced by C5 as compared with C7. Moreover, it had been reported that Firmicutes was a bacterial phylum often found in the cake layer and was extremely easy to adhere to the membrane surface [40]. The relative abundance of Firmicutes for the four ceramic membranes were 3.6%, 3.7%,10.6%, and 25.5%, respectively: i.e., the C7 possessed a much lower abundance of Firmicutes in its cake layer. Therefore, it was reasonable to consider the comprehensive effect of Bacteroidetes and Firmicutes phyla resulted in the severe membrane fouling of C5, C13, and C20. Figure 11b shows the microbial community difference at the class level. Sixteen main bacterial classes were identified in the cake layer of the ceramic membranes. Among them, Alphaproteobacteria, Gammaproteobacteria, Betaproteobacteria, and Deltaproteobacteria were subordinated to Proteobacteria; while Sphingobacteriia, Cytophagia, and Flavobacteriia belonged to Bacteroidetes. C5 had the highest relative abundance in Flavobacteriia (29% vs. 17.1%, 17.3%, and 13.7% for C7, C13, and C20, respectively), which was reported to be involved in EPS secretion and was closely associated with membrane fouling [41]. This observation was in coincidence with that made at the phylum level. In addition, extensive research has shown that in the MBR system, Bacilli bacteria played a vital role in the cake layer fouling [42], which could produce proteins in the EPS and thus drove the formation and development of biofilm with strong adhesion properties [43]. It was interesting to observe that the relative abundance of Bacilli was significantly increased with the increase of membrane pore size. Its relative abundance on C13 (4.1%) and C20 (24.4%) was 1.6 and 9.4 times higher than that on C7 (2.6%). This could be an important reason for the more severe membrane fouling witnessed by C13 and C20. The result demonstrated once again that membrane pore size had an important influence on the microbial community structure in the cake layer, and by optimizing the membrane pore size, the membrane biofouling could be efficiently alleviated. In this work, hollow flat-sheet ceramic membranes with different pore sizes were prepared using corundum with different grain sizes. The obtained membranes were systematically characterized, and the membrane fouling behavior in MBR was studied. The following conclusions could be drawn. (1) With the increase of corundum grain size, the mean membrane pore size, porosity and pure water flux were shown to be increased. Correspondingly, the uniformity of pore size distribution was decreased, with the appearance of macropore defects. (2) C7, with a medium pore size (0.63 μm), exhibited the lowest TMP development rate. It was interesting that with the increase of membrane pore size, cake layer fouling became more dominant in the total membrane fouling of the ceramic membrane. (3) The content of protein, polysaccharide and DOC accumulated on the membrane surface of C7 was substantially lower than that on the other three membranes, further demonstrating the antifouling ability of the C7 membrane. (4) Compared with the other three membranes, C7 had a lower relative abundance of Bacteroidetes and Firmicutes at the phylum level and a lower relative abundance of Flavobacteria and Bacilli at the class level, which could slow down the formation and development of biofouling on the membrane surface.

PMC10001918

Jiashuo Liu,Xiaoxiao Duan,Guo Li,Zhenjie Cai,Sijie Wei,Qixuan Song,Zheng Zheng

Changes in Bacterial Communities and Their Effects on Soil Carbon Storage in Spartina alterniflora Invasion Areas, Coastal Wetland Bare Flats, and Sueada salsa Areas

28-02-2023

biological invasion,Spartina alterniflora,soil bacterial community,soil organic carbon

Spartina alterniflora is considered an invasive species that has affected the biogeochemical circle of carbon in coastal wetlands around the world. Nevertheless, it is still unclear how S. alternation invasion affects the carbon storage capacity of coastal wetlands as carbon pools through bacterial changes. Herein, bacterial communities and soil carbon content in coastal wetland native areas and S. alterniflora invasion areas were detected. It was found that an S. alterniflora invasion brought more organic carbon and resulted in the increase in Proteobacteria in bare flats and Sueada salsa areas. When decomposition capacity was not sufficient, large amounts of organic carbon may be stored in specific chemical forms, such as monosaccharides, carboxylic acids, alcohols, etc. The results have also shown that soil bacterial communities were highly similar between the bare flat and S. alterniflora invasion area, which is extremely conducive to the rapid growth of S. alterniflora. However, an S. alterniflora invasion would decrease total carbon contents and inorganic carbon contents in the Sueada salsa area. This is not conducive to the stability of the soil carbon pool and soil health. These findings may complement, to some extent, the shortcomings of the interaction between S. alterniflora and bacterial communities, and their joint effect on soil carbon storage.

Changes in Bacterial Communities and Their Effects on Soil Carbon Storage in Spartina alterniflora Invasion Areas, Coastal Wetland Bare Flats, and Sueada salsa Areas Spartina alterniflora is considered an invasive species that has affected the biogeochemical circle of carbon in coastal wetlands around the world. Nevertheless, it is still unclear how S. alternation invasion affects the carbon storage capacity of coastal wetlands as carbon pools through bacterial changes. Herein, bacterial communities and soil carbon content in coastal wetland native areas and S. alterniflora invasion areas were detected. It was found that an S. alterniflora invasion brought more organic carbon and resulted in the increase in Proteobacteria in bare flats and Sueada salsa areas. When decomposition capacity was not sufficient, large amounts of organic carbon may be stored in specific chemical forms, such as monosaccharides, carboxylic acids, alcohols, etc. The results have also shown that soil bacterial communities were highly similar between the bare flat and S. alterniflora invasion area, which is extremely conducive to the rapid growth of S. alterniflora. However, an S. alterniflora invasion would decrease total carbon contents and inorganic carbon contents in the Sueada salsa area. This is not conducive to the stability of the soil carbon pool and soil health. These findings may complement, to some extent, the shortcomings of the interaction between S. alterniflora and bacterial communities, and their joint effect on soil carbon storage. The coastal wetland flat is an ecological transition zone formed under the co-development of multi-water environments [1], which is located in the land-sea interaction zone. With unique hydrological conditions and physicochemical soil properties, it has been one of the most productive ecosystems on earth. Influenced by both ocean and land, the coastal wetland has an abundant environmental composition [2] that plays an important role in the global carbon cycle. Previous studies have indicated that the carbon storage of coastal wetland ecosystems, such as salt marshes, mangroves, and seagrass beds, accounts for more than 50% of all marine carbon storage [3,4]. However, coastal wetlands are also the zones most sensitive to climate change and most responsive to human activities. It is estimated that the carbon loss caused by the destruction of coastal wetlands per hectare is equivalent to 10–40 hectares of temperate forest [5]. Hence, the migration and transformation of soil carbon in coastal wetland carbon cycle research has attracted more attention. Serving as the representative plant of coastal wetlands, Spartina alterniflora (S. alterniflora) was originally introduced for the purpose of protecting tidal flats and promoting sediment deposition [6,7]. Unfortunately, owing to its high adaptability and reproductive ability, S. alterniflora occupies the growth space of local plants, affects the material cycle and soil ecosystem of coastal wetlands [8], and threatens local biodiversity [9,10]. Current evidence preliminarily suggests that S. alterniflora’s influence on coastal wetlands is considered to be two-sided. Scholars paid more attention to the positive effects, and there have been significantly higher numbers of studies on the positive effects of S. alterniflora than those on the negative effects [11,12]. Research on the negative effects of S. alterniflora has increased recently, whereas these limited negative effects studies have merely focused on the different performance of S. alterniflora and other plants in the ecosystem [13,14,15]. In particular, the detailed interaction mechanism of S. alterniflora and soil microorganisms has not yet been clearly determined. For instance, Levin et al. and Ma et al. have focused on S. alterniflora changing the landscape and microtopography of coastal intertidal mudflats, as well as the living conditions of native species and the nutrient structure of ecosystems [14,15]. As an important biological type of coastal wetland ecosystem, soil bacteria plays a crucial role in the process of how changes to coastal wetlands are caused by an S. alterniflora invasion. Previous studies showed that the invasion of S. alterniflora could increase the richness of denitrification bacteria and denitrification ability in coastal wetland soil [16]. The gradient investigation of S. alterniflora was also carried out in the wetlands with different invasion conditions, and it was found that the increase in the saprotroph-symbiotroph was conducive to the colonization and expansion of S. alterniflora [17]. However, it is still unclear how an S. alternation invasion affects the carbon storage capacity of coastal wetlands as carbon pools through bacterial changes, which is also one of the most important ecological functions of coastal wetlands. Thus, it is necessary to compare the changes brought by an S. alterniflora invasion at the bacterial level and reveal how they interact with each other and what their joint effects are in the process of an S. alterniflora invasion. In this paper, the intensity of soil carbon metabolism was substituted by the abundance of bacteria that control soil carbon metabolism. The bacterial community difference between S. alterniflora invasion areas and coastal wetland native ecology areas was defined by the difference in microflora phyla. At the same time, the changes of contents in the surface soil carbon storage, including soil organic and inorganic carbon, were detected. These changes in carbon contents were matched to changes in soil bacterial communities caused by an S. alterniflora invasion, in particular by combining changes in the bacteria that dominated carbon decomposition processes in coastal wetlands. Therefore, from the perspective of the interaction between plants and microorganisms, this paper reveals how S. alterniflora affects the soil bacterial community in coastal wetlands, and thus affects soil carbon storage in coastal wetlands. This contribution further provides a theoretical basis for the prevention and control of biological invasions and the protection of coastal wetlands’ soil health. The Tiaozini Wetland is a coastal wetland on the east coast of China, covering 600 km2 (Figure 1A). It is located in the transition area between the warm temperate zone and the subtropical zone (32°43′ N–32°52′ N, 120°53′ E–121°3′ E), with a mild climate and rich biodiversity. It is the central node of the migration route of East Asia-Australia migratory birds. In 2019, the Tiaozini Wetland was successfully selected as a protected World Natural Heritage Site. The texture of the wetland flat is mainly a silt flat, which has a strong ability to fix water and salt. Seventeen 5 m by 5 m quadrats were set up in three different ecological areas. Five different sample points were set up in each quadrat. Soil samples of 0–20 cm were collected from different sampling points in the same quadrat and mixed together to produce a sample. Finally, there were seventeen different soil samples collected at 0–20 cm in three different ecological areas. Eight soil samples (GT-1 to GT-8) were collected from a bare flat (GT). Four soil samples (JP-1 to JP-4) were collected from the Sueada salsa area (JP) that had been growing for more than ten years. These two areas were the native ecological areas of the coastal wetland. In addition, five samples were collected from an S. alterniflora invasion area (SA), including a perennial S. alterniflora growth area (SA-S-1,2,4), an S. alterniflora mature area (SA-S-P), and an S. alterniflora birth area (SA-2). All the sampling areas are located in the closed area of the World Natural Heritage protected area, with minimal human disturbance. Information on the samples and sampling locations are shown in Table 1. The carbon content in the sample was detected using a TOC-L SSM from the Shimadzu Company, which was able to directly detect the carbon content in soil solids and automatically acidified the soil to remove carbonates during the detection of inorganic carbon (IC). Each sample was freeze-dried, ground, and passed through 100 mesh screens. Each test was performed using 500 mg samples, and each sample was repeated three times. The data of soil total carbon content (TC) and inorganic carbon (IC) content was able to be obtained directly through instrument detection. The soil organic carbon content (SOC) of the sample is obtained from the difference between the TC and the IC of the sample. DNA was extracted from wetland flat soil samples using the E.Z.N.A soil kit (Omega Bio-tek, Norcross, GA, USA). Three copies of each sample were extracted, and the resulting DNA was mixed. DNA concentration and purity were measured by NanoDrop2000, and quality of extracted DNA was measured using 1% agarose gel electrophoresis. The V3-V4 region of bacterial DNA was amplified via a polymerase chain reaction (PCR) using 338FmodF (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806RmodR (5′-GGACTACHVGGGTWTCTAAT-3′) primers [18]. The two primers have increased the coverage of sequences available in the Ribosomal Database Project (RDP) and have been widely and successfully used in many previous studies for Illumina-based surveys of bacteria [19]. Detection results of PCR products from all samples were collected in a set for paired-end sequencing. Sequencing was performed on an Illumina Miseq instrument from the Shanghai Personalbio Technology Co., Ltd. (Shanghai, China). After obtaining the sequencing data, QIIME2 (2019.4) was firstly used for data optimization to remove the low-quality sequences with lengths shorter than 200 bp, average masses less than 25, and ambiguous bases. Using the dada2 method, only dereplication was applied to the high-quality sequences, which was equivalent to clustering with 100% similarities, and the OTU sequence with one and only one in all samples was removed. Each de-weighted sequence generated after quality control is called an ASV (corresponding to the OTU representing sequence), and the abundance table of these sequences in the sample is called the feature table (corresponding to the OTU table) [20]. Considering the differences in sequencing depth between different samples, the sequencing results were flattened according to the minimum sequencing volume in order to optimize the comparison between samples. Subsequently, species annotation and subsequent analysis of prokaryotic microorganisms and fungi were conducted based on the Green genes database [21]. In order to understand the changes of bacterial communities in coastal tidal wetlands after an S. alterniflora invasion, the bacterial composition and diversity were analyzed using classified sequences of all samples. Operational taxonomic units (OTUs) were denoised using QIIME2 DADA2 [22] and clustered using Vsearch [23] with a 97% similarity cut-off. The RDP FrameBot is used to correct insertion and deletion errors in nucleic acid sequences. A total of 1,890,167 raw sequences were generated from seventeen soil samples from three regions (eight collected from bare flats, four collected from areas of Sueada, five collected from areas invaded by S. alterniflora), and 1,541,492 amplicon sequence variants (ASVs) were screened. Finally, 1,175,770 high-quality prokaryotic 16S r RNA sequences were obtained from 17 soil samples. The length for all samples ranged from 366 to 435. The results of composition and diversity in the varied samples are shown in Figure 2, including the analysis of Chao1, Goods-coverage, Shannon, Simpson, and Observed_species. Community richness was represented by the Chao1 and Observed_species index. The Shannon and Simpson indexes were utilized to analyze the community diversity. In addition, community coverage analysis was acquired using Good’s coverage index to evaluate the bacterial diversity of each sample [24]. It was demonstrated that the samples covered all three areas well (Good-coverage > 0.985). A correlation of p = 0.059 (Kruskal-Wallis test) was observed between the OTUs and Chao1; thus, Chao1 can roughly indicate the total number of species in a sample. In general, there are significant differences among the three ecological areas. It showed that bacterial richness in Sueada salsa areas was the least, which was consistent with Observed_species (p = 0.042). The abundance and diversity of soil bacteria in S. alterniflora invasion areas were higher than that of the native species (Sueada salsa area), but lower than that of bare flat areas. In terms of the composition of the soil bacterial community, 42 bacteria phyla were detected in 17 soil samples (Figure 3). The abundant phyla varied across all the samples, but generally included Proteobacteria, Chloroflexi, Acidobacteria, Actinobacteria, Bacteroidetes, and Gemmatimonadetes. These bacteria were made up, respectively, of 57.8%, 10.9%, 6.9%, 6.7%, 5.8%, and 5.1% of the soil bacterial community on average, which in total made up more than 90% of the bacterial community. Therein, Proteobacteria accounted for the highest proportion. Especially in the area invaded by S. alterniflora, the proportion of Proteobacteria was nearly 70%, which was much higher than that of two native ecosystems in coastal wetlands. Nowadays, there is no comprehensive conclusion on the decomposition rate of organic carbon by microbiome [19,25]. However, previous studies on the assessment of bacterial carbon decomposition potential from the perspective of functional genes have shown that Proteobacteria, Actinobacteria, Green Bayobacteria, and Bacteroidetes are main bacterial groups involved in soil carbon metabolism in coastal wetland soil at the phylum level, among which Proteobacteria and Actinobacteria are the main contributors [26,27]. They had a strong carbon decomposition ability, which had the ability to degrade disaccharides, polysaccharides, and cellulose. In our study, it was clear that the Proteobacteria abundance in the S. alterniflora invasion area was close to the bare flat, and higher than that in the native Sueada salsa area (Figure 3). In addition, Proteobacteria occupied nearly 70% in the S. alterniflora invasion area (Figure 3). This meant that bacterial carbon metabolism activity was more active than that in the native areas, especially in the Sueada salsa area. As for Actinobacteria, the proportions of Actinobacteria in the three areas were all close and in the range of 5–8% (Figure 3). To sum up, when there are S. alterniflora invasions, the proportion of Proteobacteria in bare flat areas increased. Moreover, the total number of bacteria and the proportion of Proteobacteria in Sueada salsa areas were both increased according to Figure 2 and Figure 3. The changes in the bacterial community may enhance soil carbon decomposition. To further demonstrate the effects of an S. alterniflora invasion on a soil bacterial community in a coastal tidal flat, the distribution characteristics of samples in three areas were visualized using NMDS. The Anosim test was used to determine the inter-group differences among bare flats, Sueada salsa areas, and S. alterniflora invasion areas [28,29]. The distance algorithm was performed with Jaccard. According to Figure 4 and Table 2, the stress value of NMDS was 0.0772, much less than 0.2, which means the NMDS result is highly reliable [30]. The soil bacterial community in the Sueada salsa area was significantly different from that in the other two areas, in particular a very low similarity to S. alterniflora (RJP-SA = 1). Interestingly, the bare flats and Sueada salsa areas were both native ecological areas of the coastal wetland but showed a great difference in soil bacterial communities. The results revealed that the similarity of soil bacterial communities between bare flats and Sueada salsa areas (RGT-JP = 0.914) was lower than that between bare flats and S. alterniflora invasion areas (RGT-SA = 0.278). A more similar soil microbiome composition would be more conducive to plant growth. This may also be one of the reasons why S. alterniflora has been able to bio-invasively replace the ecological niche of proto-species such as Sueada salsa. Three groups (GT, JP, SA) of undrawn ASV data were divided into two groups and imported into zero-inflated log-normal models using the fitFeature Model function, respectively. The logarithm of genetic difference (−1 < log2FC < 1) was calculated, and the results were obtained (Figure 5). Among them, the round dot represented the abundance of the genus, which above the dotted line represented having significant differences in this genus. Through this analysis, the differences between the soil bacterial community in the S. alterniflora invasion area and the wetland native area, at the bacterial genus level, were found. Then, the differences were clustered to verify the results of the differences at the bacterial phylum level. Each of these points represents a different bacterial genus. The larger the point, the more significant the difference in the bacterial genus between the two groups. It can be clearly seen that, although the genus was different, the differences in Proteobacteria were the most significant in the whole bacterial community. The S. alterniflora invasion area had the highest abundance. The lowest abundance was found in the Sueada salsa area. There were very significant differences between the S. alterniflora invasion area and the Sueada salsa area. In addition, it can also be roughly seen that the difference between the S. alterniflora invasion area and the bare flat is smaller than that between the bare flat and the Sueada salsa area, which is also consistent with the previous conclusion—the S. alterniflora invasion area and the bare flat had high similarities. To further determine the similarity between soil communities in three areas, a hierarchical clustering analysis was performed for all samples. According to Figure 6, samples from the Sueada salsa area were significantly removed from the other two areas. It was also proved that although bare flat sand Sueada salsa areas were both the native ecological areas of the coastal wetland, the soil bacterial community composition of bare flats is more similar to that of S. alterniflora invasion areas. Especially, the intra-group sample distances between some bare flat samples were higher than the distances between the bare flat and S. alterniflora invasion area samples. This may be that bacterial communities in bare flats were significantly affected by factors such as tides, rivers, distance from the coast, and so on [31,32]. So, the soil bacterial community composition of some bare flats has been suitable for the long-term growth of S. alterniflora. To evaluate changes in soil carbon storage induced by an S. alterniflora invasion, the location information, pH, EC, and carbon content (TC, IC, and SOC) of the samples were detected and are shown in Table 1. Since all the areas were located near the same dike, the tidal cover, tidal water depth, and hydrological characteristics in all three ecological areas were very similar. The results revealed that the soil in the three areas was weak in alkaline. The pH in the area invaded by S. alterniflora was the lowest, which was consistent with the conclusion of previous studies that soil acidification was caused by S. alterniflora invasions [33]. Results of SOC suggested that the Sueada salsa area had the lowest concentration, and the bare flat was next. Relative to the Sueada salsa area and the bare flat, it showed the highest concentration invaded by S. alterniflora. These observations revealed that the soil of S. alterniflora invasion areas can store more organic carbon. Moreover, the soil bacterial abundance was lower in S. alterniflora invasion areas than in bare flats (Figure 2), suggesting that S. alterniflora invasion areas were more suitable for soil organic carbon storage than bare flats. Notably, according to Figure 3, Proteobacteria occupied a high proportion in the soil of the S. alterniflora invasion area. Proteobacteria had strong organic carbon decomposition ability, which usually reduces organic carbon content. Nevertheless, the high SOC content in the soil of S. alterniflora invasion areas may be related to the properties of S. alterniflora and the characteristics of the bacterial communities in coastal wetlands, which is not only associated with one factor. For one thing, previous studies have provided evidence that S. alterniflora had a stronger carbon fixation capacity than other wetland plants and was decomposed more slowly [34,35], which provided sufficient organic matter for bacteria. It partly explained the reason why soil in S. alterniflora invasion areas had higher SOC and was consistent with SOC contents in our study. For another, higher bacterial carbon metabolism activity did not lead to lower SOC in our study. This was possibly related to the decomposition ability of bacterial communities in coastal wetlands which had a lower ability to break down certain substances, such as monosaccharides, carboxylic acids, alcohols, and phenols [36,37,38]. Organic carbon is well stored in these forms. However, in terms of TC, the soil carbon content in S. alterniflora invasion areas was lower than that in Sueada salsa areas. This was because Sueada salsa areas’ soil contained higher IC. The S. alterniflora invasion could cause soil acidification, which is not conducive to the storage of inorganic carbon. That is to say, the soil in Sueada salsa areas can store more carbon, especially a large amount of inorganic carbon. Compared with S. alterniflora invasion areas, the bacterial richness in Sueada salsa areas was lower (Figure 2). These were both more conducive to soil carbon storage performance. To sum up, although an S. alterniflora invasion could bring some benefits to the bare flats, the carbon storage capacity of S. alterniflora soil was weaker than that of Sueada salsa area soil. When S. alterniflora flourished in coastal wetlands and threatened the survival of other species such as Sueada salsa, it reduced the carbon storage capacity of coastal wetlands. The changes of SOC caused by an S. alterniflora invasion is directly related to the changes of soil bacterial communities mentioned above. In coastal wetlands, Proteobacteria and Actinobacteria were the main bacterial groups involved in soil carbon decomposition, which could secrete a variety of organic carbon-degrading enzymes [39]. The S. alterniflora invasion led to the increase in SOC in coastal wetlands, which would lead to the increase in Proteobacteria richness and its proportion in the soil bacterial community. As for why S. alterniflora invasion areas had higher SOC when the bacterial community had higher carbon decomposition capacity, this may be related to the weak ability of soil bacteria to decompose monosaccharides, carboxylic acids, alcohols, phenols, and other substances in coastal wetlands [36,37,38]. A large amount of organic carbon may be stored in these forms. In this paper, one of the other reasons why S. alterniflora easily invades coastal wetlands and the changes in bacterial community affected in soil carbon storage were discussed. In detail, it has a high similarity between the naturally formed bare flat soil bacterial community, and in the soil environment where S. alterniflora grows, it does so for a long time. S. alterniflora is well adapted to the soil bacterial environment in bare flats. The invasion and expansion of S. alterniflora would lead to changes in soil bacterial communities and soil carbon storage in coastal wetlands. One of the most significant was an increase in Proteobacteria. At the same time, S. alterniflora invasions increased soil organic carbon content in coastal wetlands and also provided sufficient organic matter for bacteria. The combined effects of the two may further improve soil carbon decomposition rates in coastal wetlands. Due to lack of ability to break down some particular matters, a lot of organic carbon may be stored in the form of monosaccharides, carboxylic acids, alcohols, phenols, etc. In addition, the S. alterniflora invasion could also lead to soil acidification in coastal wetlands, thus reducing soil inorganic carbon content, which makes the carbon storage capacity of soil invaded by S. alterniflora lower. Thus, when S. alterniflora threatened the survival of other native species in coastal wetlands, it reduces the carbon storage capacity of coastal wetland.

PMC10001940

Takamasa Mizoguchi,Shohei Mikami,Mari Yatou,Yui Kondo,Shuhei Omaru,Shuhei Kuwabara,Wataru Okura,Syouta Noda,Takeshi Tenno,Hidekazu Hiroaki,Motoyuki Itoh

Small-Molecule-Mediated Suppression of BMP Signaling by Selective Inhibition of BMP1-Dependent Chordin Cleavage

21-02-2023

BMP1,BMP signaling,chordin,phenotype screening,small molecule,zebrafish

BMP signaling is critical for many biological processes. Therefore, small molecules that modulate BMP signaling are useful for elucidating the function of BMP signaling and treating BMP signaling-related diseases. Here, we performed a phenotypic screening in zebrafish to examine the in vivo effects of N-substituted-2-amino-benzoic acid analogs NPL1010 and NPL3008 and found that they affect BMP signaling-dependent dorsal–ventral (D–V) patterning and bone formation in zebrafish embryos. Furthermore, NPL1010 and NPL3008 suppressed BMP signaling upstream of BMP receptors. BMP1 cleaves Chordin, an antagonist of BMP, and negatively regulates BMP signaling. Docking simulations demonstrated that NPL1010 and NPL3008 bind BMP1. We found that NPL1010 and NPL3008 partially rescued the disruptions in the D–V phenotype caused by bmp1 overexpression and selectively inhibited BMP1-dependent Chordin cleavage. Therefore, NPL1010 and NPL3008 are potentially valuable inhibitors of BMP signaling that act through selective inhibition of Chordin cleavage.

Small-Molecule-Mediated Suppression of BMP Signaling by Selective Inhibition of BMP1-Dependent Chordin Cleavage BMP signaling is critical for many biological processes. Therefore, small molecules that modulate BMP signaling are useful for elucidating the function of BMP signaling and treating BMP signaling-related diseases. Here, we performed a phenotypic screening in zebrafish to examine the in vivo effects of N-substituted-2-amino-benzoic acid analogs NPL1010 and NPL3008 and found that they affect BMP signaling-dependent dorsal–ventral (D–V) patterning and bone formation in zebrafish embryos. Furthermore, NPL1010 and NPL3008 suppressed BMP signaling upstream of BMP receptors. BMP1 cleaves Chordin, an antagonist of BMP, and negatively regulates BMP signaling. Docking simulations demonstrated that NPL1010 and NPL3008 bind BMP1. We found that NPL1010 and NPL3008 partially rescued the disruptions in the D–V phenotype caused by bmp1 overexpression and selectively inhibited BMP1-dependent Chordin cleavage. Therefore, NPL1010 and NPL3008 are potentially valuable inhibitors of BMP signaling that act through selective inhibition of Chordin cleavage. BMP signaling is related to numerous biological phenomena. During development, the activity gradient of BMP signals is essential for dorsal–ventral (D–V) patterning and is tightly regulated by a multistep functional mechanism controlling the levels of ligands and antagonists in vertebrates [1]. BMP signaling also plays an essential role in bone formation and homeostasis [2,3]. Recently, the regulation of BMP signaling in the hippocampus was reported to be involved in the depression phenotype and aging-dependent decline in cognitive function [4,5,6]. Therefore, molecules that control BMP signaling are important for elucidating the regulatory mechanisms of BMP signaling and treating diseases caused by abnormal BMP signaling [7,8,9]. BMPs are secreted glycoproteins that belong to the transforming growth factor β (TGF-β) family. BMPs interact with two types of receptor serine/threonine protein kinases known as type I (BMPR1A, BMPR1B, and ACVR1) and type II (BMPR2, ActR2A, and ActR2B) receptors [7,8,9]. BMPs bind to the type II receptor, followed by phosphorylation of the type I receptor by the type II receptor. The phosphorylated type I receptor subsequently phosphorylates the receptor-regulated Smads (R-Smads), Smad1, Smad5, or Smad8. Phosphorylated R-Smads form a heterocomplex with a common Smad (co-Smad), Smad4, and translocate into the nucleus, where the Smad complex regulates the expression of downstream target genes of BMP signaling [7,8,9]. BMP signaling is negatively or positively modulated in several steps in its signal cascade. Among them, BMP ligand antagonists Chordin (Chrd), Noggin, Gremlin, etc., directly interact with BMP ligands and interfere with their binding to BMP receptors to negatively regulate BMP signaling [10]. Moreover, the function of Chrd is controlled by its cleavage, which is catalyzed by BMP1, a zinc-dependent metalloproteinase [11,12]. Several small molecules have been developed to artificially modulate BMP signaling and activity. Dorsomorphin, an inhibitor of BMP type I receptors, blocks the phosphorylation of Smads and inhibits BMP signaling [13,14]. UK383,367 is an inhibitor of BMP1 that suppresses BMP signaling by stabilizing the Chrd protein. However, dorsomorphin also inhibits the AMPK and Akt/mTOR pathways [15,16]. UK383,367 and their derivatives inhibit BMP1 procollagen C-proteinase activity required for collagen fibrogenesis and angiogenesis [12,17]. Thus, differences in the selectivity of inhibitors for the function of the BMP signaling pathway might lead to broader applications. In previous research, we screened Disheveled1(Dvl1)-PDZ domain inhibitors by performing virtual screening and identified N-substituted-2-amino-benzoic acid analogs [18]. We further explored the effect of N-substituted-2-amino-benzoic acid analogs in vivo by performing a phenotype screening in zebrafish. In the present study, we found that NPL1010 and NPL3008 are potential BMP signaling inhibitors that selectively suppress BMP1-mediated Chrd cleavage activity. We performed phenotype screening using zebrafish embryos to explore N-substituted-2-amino-benzoic acid analogs that had been identified from in silico screening for their capacity to bind to the PDZ domain of Dvl1. Among them, ten candidate compounds (Figure S1) were applied to zebrafish embryos at 3 h post-fertilization (hpf) using several concentrations for 8 or 21 h, and their effects on development were then examined. NPL1010 and NPL3008 induced animal–vegetal (A-V) axis elongated morphology at 11 hpf (Figure 1A,B: 25 μM NPL1010, 81% (n = 36) with an elongated A-V axis phenotype; 100 μM NPL1010, 82% (n = 34) with an elongated A-V axis phenotype; 2.5 μM NPL3008, 10% (n = 10) with an elongated A-V axis phenotype; and 5 μM NPL3008, 70% (n = 10) with an elongated A-V axis phenotype). At 24 hpf, NPL1010 or NPL3008 treated embryos exhibited reductions in the ventral tail fin and tail length at 24 hpf. These phenotypes are characteristic features of dorsalization [19]. We classified the dorsalized phenotypes into previously reported C1−C5 categories [19] (Figure S2). NPL1010 induced a severe phenotype at both 25 and 100 μM treatment. The severity of the phenotype induced by NPL3008 was dose-dependent (Figure 1C,D: 25 μM NPL1010, 86% (n = 28) with a dorsalized C1−C5 phenotype; 100 μM NPL1010, 76% (n = 25) with dorsalized C1−C5 phenotype; 2.5 μM NPL3008, 60% (n = 10) with dorsalized C1−C5 phenotype; and 5 μM NPL3008, 90% (n = 10) with dorsalized C1−C5 phenotype). Other compounds resulted in high lethality and/or did not induce specific morphological phenotypes (Figure 1B,D). NPL 1010 and NPL3008 share a common structure, 2-(3-benzamidobenzamido) benzoic acid, but the length and position of the ether groups differ: NPL1010 contains an ethoxy group in the para, whereas NPL3008 contains a propoxy group in the meta position (Figure 1E). On the other hand, NPL3005 and NPL3006 also contain 2-(3-benzamidobenzamido) benzoic acid, similar to NPL1010 and NPL3008, but they did not induce the elongated A-V axis phenotype or dorsalized phenotype. Based on these results, the 2-(3-benzamidobenzamido) benzoic acid structure is necessary but not sufficient to induce the elongated A-V axis phenotype and dorsalized phenotype; in addition, the ether group on benzene might play a role in inducing the morphological changes. Next, we further explored the effects of their analogs, which are commercially available, on zebrafish embryo morphogenesis. First, we modified the ether group; a change in the length of the carbon chain attached to the ethoxy group of NPL1010 to a propoxy group (NS00479226) did not enhance the effect of NPL1010. In addition, the change from an ether to an acetate group (NS02237411) did not improve the effect of NPL1010 (Figures S3 and S4). Second, we modified the benzoic acid groups of NPL1010 and NPL3008. However, any tested modification of the benzoic acid group of NPL1010 and NPL3008 did not enhance their effects on inducing the elongated A-V axis phenotype shape and dorsalized phenotype (Figures S3 and S4). Based on these data, the benzoic acid group and the position and length of the ether group are critical for the mechanisms of action of NPL1010 and NPL3008. NPL1010 and NPL3008 induced phenotypes of elongated A-V, reduction in the ventral tail fin, and short tail length, which are characteristic phenotypes of dorsalization [19,20]. To confirm the expansion of dorsal structure by changes in gene expression patterns, as previously reported [21,22], we performed double in situ hybridization with myoD and krox20 probes, which stain the presomitic mesoderm (Figure S5, black arrowheads) and rhombomeres 3 and 5, respectively (Figure S5, magenta arrowheads). Compared with DMSO-treated embryos, NPL1010- and NPL3008-treated embryos exhibited lateral expansion of the krox20-positive domain and myoD expression domain, indicating that the embryos have an enlarged dorsal structure at 11 hpf (Figure S5). These results indicate that NPL1010 and NPL3008 induce the dorsalization of zebrafish embryos. During vertebrate development, the proper regulation of dorsal and ventral gene expression is required [23]. Body axis dorsalization is induced by an increase in the expression of dorsal genes or a decrease in the expression of ventral genes [24]. We explored these possibilities by performing in situ hybridization of the dorsal-specific gene chrd and the ventral gene ved (Figure 2A–D). In dorsalized embryos, the dorsal expression domain of chrd was expanded [21,25,26]. Therefore, we measured the central angle of the chrd expression domain as previously reported [25]. chrd expression domain was significantly expanded by NPL1010 and NPL3008 treatments (Figure 2A,B and Figure S6A). In contrast, NPL1010- or NPL3008-treated embryos exhibited a reduction in ved expression (+ or ++ in Figure 2C,D) compared with that in DMSO-treated controls (normal in Figure 2C,D). In addition, expression of another ventral marker gene szl [27,28] was also reduced by NPL1010- or NPL3008 treatment (Figure S6B,C). Furthermore, the decrease in ventral ved and szl expression induced by these two compounds was dose-dependent (Figure 2D and Figure S6B,C). Previous reports indicated that ved and szl expression domain was reduced in dorsalized embryos [29,30]. Therefore, NPL1010 and NPL3008 induce dorsalization by inhibiting ventral gene function. NPL1010 and NPL3008 were originally obtained as Dvl1-PDZ domain inhibitors through virtual screening [18]. Therefore, we examined the effects of NPL1010 and NPL3008 on Wnt signaling. NPL1010 and NPL3008 did not affect TOP flash activity in HEK293T cells (Figure S7). These data indicate that NPL1010 and NPL3008 may not necessarily inhibit Wnt/βcatenin signaling. A reduction in ventral gene expression is known to be associated with reduced BMP signaling, as observed in BMP signaling mutants such as swirl (bmp2b mutant) [31]. Because BMP signaling is important for bone mineralization, the inhibition of BMP signaling affects the mineralization of bone [32]. We performed Alcian blue and Alizarin red double staining to assess the effects of NPL1010 and NPL3008 treatments on bone formation in vivo (Figure 3A). We investigated the effects of the two compounds on parasphenoid bone mineralization, in which shape and mineralization are readily observed (Figure 3B, white dotted lines). We classified bone mineralization into three categories: normal mineralization, slight reduction in mineralization/mild phenotype (+), and strong reduction in mineralization/severe phenotype (++) (Figure 3B). Treatment with the BMP inhibitor dorsomorphin, an inhibitor of BMP type I receptors, reduced bone mineralization (Figure 3C), consistent with previous reports [13,32]. Similarly, NPL1010 and NPL 3008 treatment decreased the percentage of embryos with mineralized bone (Figure 3C). These data suggest that NPL1010 and NPL 3008 are inhibitors of BMP signaling that modulate BMP signaling and activity in vivo. Next, we further explored the possibility that NPL1010 and NPL3008 inhibit BMP signaling by first performing a culture-cell-based BMP reporter dual-luciferase assay [33,34]. Overexpression of bmp2b, a BMP ligand, substantially increased the activity of the BMP signaling reporter in C2C12 cells (Figure 4A). The known BMP signaling inhibitor dorsomorphin suppressed this effect of bmp2b (Figure 4A). NPL1010 and NPL3008 also reduced bmp2b-induced BMP signaling activity in a dose-dependent manner (Figure 4A). We next examined the effects of the two compounds downstream of the BMP receptor. Alk6 is a BMP signaling receptor. Smad1 and Smad5 are BMP signaling mediators that function downstream of BMP receptors. Smad1 and Smad5 are phosphorylated by activated BMP receptors, form a complex with Smad4, translocate to the nucleus, and regulate downstream gene expression [7,8,9]. Overexpression of the constitutively active form of Alk6 (CA-Alk6), Smad1, and Smad5 activated BMP signaling, as previously reported [35] (Figure 4B). Dorsomorphin treatment significantly suppressed Smad1, Smad5, and CA-Alk6-dependent activation of BMP signaling (Figure 4B). On the other hand, NPL1010 and NPL3008 rarely inhibited BMP signaling activated by CA-ALK6 or the combined expression of Smad1 and Smad5 (Figure 4B). These results indicate that NPL1010 and NPL3008 act at a step upstream of the BMP receptors. We performed rescue experiments to further clarify the mechanisms of action of NPL1010 and NPL3008 in the BMP signaling pathway. mRNAs for bmp2b or genes encoding the BMP signaling modulators, bmp1a and tll1, which are metalloproteases that positively regulate BMP signaling by cleaving the BMP antagonist Chrd [11], were injected, and they induced the ventralized phenotype (Figure 5A–D), consistent with previous reports [11]. Dorsomorphin inhibited bmp2b-, bmp1a-, and tll1-dependent ventralization, as expected [13]. On the other hand, NPL1010 and NPL3008 partially suppressed bmp1a-induced ventralization (Figure 5A–D). In addition, NPL1010 and NPL3008 also weakly suppressed tll1-induced ventralization, although less efficiently than suppression of bmp1a-iuduced ventralization. NPL1010 and NPL3008 did not strongly inhibit bmp2b-induced ventralization in zebrafish embryos, unlike in C2C12 cells. This discrepancy might be due to the different ratios of exogenous bmp2b/endogenous bmp1a in embryos than in C2C12 cells, which express high levels of Chrd and BMP1 [25,36]. These results suggest that NPL1010 and NPL3008 are inhibitors of BMP1 that degrade the BMP antagonist Chrd [12,37,38]. In zebrafish, bmp1a mutants or morphants exhibit ruffled fins [39,40]. This might be due to the reduction in collagen maturation caused by Bmp1a-dependent procollagen cleavage. However, embryos treated with NPL1010 or NPL3008 did not show the ruffled phenotype (Figure S8A,B). These data suggest NPL1010 and NPL3008 might selectively inhibit BMP1-dependent Chrd cleavage. Therefore, we next examined the effect of these compounds on BMP1-mediated cleavage of Chrd. We transfected the C-term Myc-tagged Chrd expression vector into HEK 293T cells and prepared Chrd-Myc-containing conditioned medium. We confirmed that Myc-tagged Chrd was functional in zebrafish embryos because it induced the dorsalization phenotype (Figure S9). The addition of recombinant human BMP1 (rhBMP1) to Chrd-Myc-containing conditioned medium accelerated the cleavage of Chrd and increased the ratio of Chrd cleaved at the C-terminus to total Chrd (Figure 5E,F). NPL1010 or NPL 3008 treatment increased the abundance of full-length and intermediate part + C-term fragments of Chrd (Figure 5E). We found that NPL1010 or NPL3008 inhibit rhBMP1-dependent cleavage of Chrd in a dose-dependent manner (Figure 5E,F). Chrd is one of numerous substrates of BMP1, and we assessed whether NPL1010 and NPL3008 inhibit the cleavage of other BMP1 substrates. Collagen is also cleaved by BMP1. Although the fin phenotype suggests that NPL1010 and NPL3008 might not affect collagen cleavage (Figure S8A,B), we examined the effects of NPL1010 and NPL3008 on BMP1-dependent collagen type III alpha1 (COL3A1) cleavage. UK383,367, a BMP1 inhibitor, strongly interfered with BMP1-dependent COL3A1 cleavage (Figure 6A,B). On the other hand, NPL1010 and NPL3008 rarely inhibited BMP1-dependent COL3A1 cleavage (Figure 6A,B). Since the NPL compounds used in this study were initially identified as common molecular scaffolds for PDZ domain inhibitors based on their chemical structures, their potential to bind BMP1 was assessed by performing docking simulation experiments. After the visual assessment of the docking poses, we found that the docking pose of NPL1010 with the lowest energy and that of NPL3008 with the second-lowest energy were quite similar, wherein both bind to the shallow bottom of the ligand binding cleft of BMP1 near the Zn2+ atom in the catalytic center (Figure S10A–C). Although the configuration of ether groups differed between NPL 1010 and 3008, the dihedral angle of the carboxyl group of the ring was almost inverted in their docked poses. As a result, the positions of these ether groups were similar (Figure S10D). These simulation results show that NPL1010 and NPL3008 bind at a location near Asn128/Thr156/Phe157 of the BMP1 catalytic domain (Figure S10A–C). However, this putative interaction site for NPL1010 and NPL3008 with BMP1 is different from the interaction sites of known inhibitors Compound 1 (PDB: 6BTN), Compound 4 (PDB: 6BSM), and Compound 22 (PDB: 6BSL) reported in a previous study [41]. In addition, the simulated interaction between the BMP1 catalytic domain and the Chrd C-terminal region (241 aa) produced using AlphaFold2 [42,43] demonstrated that the Chrd C-terminal cleavage site (PMQADGPR) is closely located near Thr156/Phe157 of BMP1 and that its position overlaps with the putative NPL1010 and NPL3008 interaction sites (Figure S10E–G). These results suggest that NPL1010 and NPL3008 inhibit BMP1 activity through different mechanisms than those of previously reported inhibitors [17,41]. Our results show NPL1010 and NPL3008 can suppress BMP signaling activation via selectively inhibiting BMP1-dependent Chrd cleavage, unlike inhibitors of BMP type I receptors, such as dorsomorphin [13,14] (Figure 7). Several compounds have been reported as BMP1 inhibitors, such as UK383,367 and its derivatives. These compounds contain hydroxamic acid or reverse hydroxamic acid group structures. These structures inhibit the enzymatic activity of BMP1 by binding to zinc ions in the active center of BMP1 [17,41]. On the other hand, NPL1010 and NPL3008 do not contain these functional groups in their structures and therefore inhibit BMP1 function through a different mechanism from that of known BMP1 inhibitors. Both NPL1010 and NPL3008 contain the 2-(3-benzamidobenzamido) benzoic acid moiety, but the ether group length and position differ: NPL1010 contains an ethoxy group at the para position, whereas NPL3008 contains a propoxy group at the meta position. Our phenotype screening experiments indicated that modification of the 2-(3-benzamidobenzamido) benzoic acid structure or the length and position of the ether group did not enhance the effects of NPL1010 and NPL3008. Therefore, both 2-(3-benzamidobenzamido) benzoic acid and additional ether groups, namely para-ethoxy groups and meta-propoxy groups, are required for the strong inhibition of BMP1 function. The docking simulation shows that the ether group orientation is inverted between NPL1010 and NPL3008. The positions of these ether groups are similar. Thus, the three-dimensional angle and length of the ether group may be important for the interaction with BMP1, which may explain why other NPL1010 and NPL3008 derivatives that do not contain para- or meta-ether groups, such as NPL3005 and NPL3006, did not show inhibitory activity, in contrast to NPL1010 and NPL3008. UK383,367 and its derivatives also inhibit the cleavage of BMP1 substrates other than Chrd, such as procollagen [17]. In contrast, NPL1010 and NPL3008 did not inhibit the cleavage of COL3A1, which is a collagen family member and a BMP1 substrate other than Chrd [12,44]. Thus, NPL1010 and NPL3008 show high selectivity for the inhibition of Chrd cleavage by BMP1 (Figure 7), although the effects of NPLs on BMP1 substrates other than procollagen need to be investigated in future. NPL1010 and NPL3008 selectively inhibit BMP1-dependent Chrd cleavage and inhibit BMP signaling in a Chrd function-dependent manner. Known inhibitors (e.g., UK383,367) inhibit cleavage of both Chrd and collagen. Dorsomorphin blocks the phosphorylation of Smads and inhibits BMP signaling. The high substrate selectivity of NPL1010 and NPL3008 over UK383,367 may suggest that NPL1010 and NPL3008 do not inhibit the enzymatic activity of BMP1 itself but instead interfere with the interaction between BMP1 and Chrd. According to previous studies, the catalytic domain and CUB1 domain are required for Chrd cleavage but are not sufficient for procollagen cleavage; the CUB2 domain of BMP1 is required for procollagen cleavage [45]. Differences in the substrate recognition mechanism might contribute to determining the selectivity of NPL1010 and NPL3008 for Chrd. In addition, a previous structural analysis suggested that the CUB1CUB2 (C1C2) fragment of procollagen C-proteinase enhancer-1 (PCPE-1) binds to the C-propeptide trimer of procollagen III and pulls the procollagen chain toward C1C2. Then, the procollagen chain may enter the active site of the BMP1 catalytic domain, where the P1′ residue Asp interacts with Arg182 in the S1′ pocket in close proximity to the essential Glu94 and the catalytic water molecule bound to the active site zinc ion [46]. However, PCPE-1 does not enhance BMP1-dependent Chrd cleavage [45]. In addition, the putative interaction site for NPL1010 and NPL3008, comprised of BMP1 Asn128/Thr156/Phe157, seems to differ from that between BMP1 and procollagen [46]. Our interaction simulation demonstrated that the Chrd C-terminal cleavage site is located in close proximity to Thr156/Phe157 of BMP1. Therefore, Thr156/Phe157 may be required for the interaction between BMP1 and Chrd but not for the interaction between BMP1 and procollagen. These differences may contribute to the selectivity of NPL1010 and NPL3008, although future studies are needed to validate this model. Our simulation results suggest that NPL1010 and NPL3008 might interact with the putative Chrd-interacting site of the BMP1 catalytic domain. Therefore, NPL1010 and NPL3008 might function as reversible and competitive inhibitors. To address this possibility, further studies are needed. bmp1a mutants and morphants do not show the dorsalized phenotype [39,40]. However, bmp1a and tll1 double knockdowns show the dorsalized phenotype [40]. Our data indicate NPL1010 and NPL3008 might also affect Tll1 function. The catalytic domain is highly conserved between BMP1 and Tll1 [47]. In addition, the putative NPL1010 and NPL3008 interaction site comprising Asn128/Thr156/Phe157 is also conserved between BMP1 and Tll1. These indicate that NPL1010 and NPL3008 may inhibit BMP1 and Tll1 function by the same molecular mechanisms, and dorsalized phenotypes induced by NPL1010 or NPL3008 are due to suppression of BMP signaling by both BMP1 and Tll1 inhibition. Our data suggest that NPL1010 and NPL3008 can suppress BMP signaling activation by inhibiting BMP1-dependent Chrd cleavage. However, we cannot rule out the possibility that NPL1010 and NPL3008 might also inhibit the maturation or secretion of BMP ligands or induce a conformational change in BMP receptors. Further studies are needed to investigate the possible mechanisms by which NPL10101 and NPL3008 inhibit BMP signaling. We demonstrated that NPL1010 and NPL3008 can prevent human BMP1-dependent Chrd cleavage. Although the efficiency and pharmacokinetics of NPL1010 and NPL3008 in human body must be investigated, NPL1010 and NPL3008 may be applied in the treatment of abnormal BMP signaling causing human diseases, such as the depression phenotype and aging-dependent decline in cognitive function, by upregulating the functional Chrd protein level [4,5,6]. In conclusion, NPL1010 and NPL3008 provide additional options to regulate BMP signaling. The combination of NPL1010 and NPL3008 with existing inhibitors of BMP signaling may widen the therapeutic window for diseases caused by abnormal BMP signaling [4,5,6,7,8,9]. N-Substituted-2-amino-benzoic acid analogs were screened using a virtual screening method based on the structure of the Dvl1-PDZ domain [18]. Supplier and catalog numbers are shown in Supplementary Table S1 for the chemical compounds used in the structure–activity relationship experiment. The chemical compounds were dissolved in DMSO to 100 mM and stored at −30 °C. Dorsomorphin (LC Laboratories, Woburn, MA, USA, catalog no. D-3197) was dissolved in DMSO to 25 mM and stored at −30 °C. Recombinant human BMP1 (rhBMP1, 0.276 mg/mL, dissolved in 25 mM HEPES containing 400 mM ammonium sulfate, R&D Systems, Minneapolis, MN, USA, catalog no. 1927-ZN-010) and recombinant human COL3A1 (rhCOL3A1, 0.336 mg/mL, dissolved in 25 mM sodium acetate containing 1 M NaCl, R&D Systems, catalog no. 7294-CL-020) were divided into aliquots and stored at −80 °C. For measuring BMP signaling activity, C2C12 cells were seeded into 96-well plates at a density of 5 × 103 cells/well in DMEM (10% FBS, 1% penicillin streptomycin (PS)) and cultured for 24 h. pCS2+ full length-bmp2b or pCS2+ (100 ng), 50 ng of pGL4-BRE-fluc, and 4 ng of pGL4.74 [hRluc/TK] were transfected into the cells in each well using polyethyleneimine (PEI). In the CA-Alk6 and Smad overexpression experiment, 20 ng of pCDNA3-CA-Alk6, 15 ng of pcDNA3-FLAG-Smad1, and/or 15 ng of pcDNA3-FLAG-Smad5 were also transfected with PEI. For measuring Wnt signaling activity, HEK293 cells were seeded into 96-well plates at a density of 3 × 104 cells/well in DMEM (10% FBS, 1% penicillin streptomycin (PS)) and cultured for 24 h. Twenty nanograms of pCS2+ mouse Wnt1 or pCS2+, 70 ng of TOP Flash or FOP Flash reporter plasmid, and 4 ng of pGL4.74 [hRluc/TK] were transfected into the cells in each well using polyethyleneimine (PEI). After plasmid transfection, the cells were incubated at 37 °C with 5% CO2 for 3 h. NPL1010 and NPL3008 were added at 5, 12.5, 25, and 50 μM, and dorsomorphin was added at 1, 5, 12.5 μM. After 21–24 h of incubation at 37 °C with 5% CO2, the culture medium was removed, the cells were washed with 100 µL/well of 1× PBS, and 25 µL of 1× Passive Lysis Buffer (5× Passive Lysis Buffer (Promega, Madison, WI, USA) diluted to 1/5) was added to each well. The cells were lysed by shaking at room temperature for 20 min, and the lysates were transferred to 1.5 mL tubes. After centrifugation at 15,000× g for 2 min at 4 °C, 10 µL of the supernatant were transferred to a white 96-well plate (Thermo Fisher Scientific, Waltham, MA, USA). Fluc- and hRluc-dependent luminescence was measured by sequentially adding 40 µL/well of luciferin buffer (50 mM Tris-HCl, pH 7.8, 10 mM MgCl2, 500 µM coenzyme A, 300 µM ATP, and 200 µg/mL D-luciferin K) and 50 µL/well of Renlite buffer (0.04 mM PTC124, 6.7 μM coelenterazine h, 30 mM Na EDTA, 20 mM Na pyrophosphate, and 950 mM NaCl) and detected using the GloMax®-Multi Detection System (Promega). Each sample was measured in duplicate. The Fluc activity was normalized to the hRluc activity. HEK293T cells were seeded on collagen (Cellmatrix type I-C, Nitta Gelatin, Yao, Japan)-coated 10 cm dishes at a density of 3.0 × 106 cells/dish, simultaneously reverse-transfected with 20 µg of pCS2+ Chrd-Myc [48], and incubated at 37 °C with 5% CO2 for 24 h. After incubation, the medium was removed, cells were washed with 1× PBS, and the medium was replaced with Opti-MEM (Thermo Fisher Scientific). After 48 h of incubation under the same conditions, the culture supernatant was collected and stored at −80 °C. For this experiment, 18.4 µL of OptiMEM (Thermo Fisher Scientific) was added to a 1.5 mL tube, and 18 µL of conditioned medium containing Chrd-Myc was added to each tube. A stock solution of each compound was diluted with DMSO to 5, 10, 25, and 50 mM. The solution of each compound was further diluted 1:40 with Opti MEM, and 1.6 µL of the diluted solution was added to each sample. The 10 mg/mL rhBMP1 stock solution was diluted 1:10 with OptiMEM, and 2 µL of diluted rhBMP1 were added to each sample and mixed. After incubation at 37 °C for 1 h, an equal volume (40 µL) of 2× sample buffer (125 mM Tris-HCl, pH 6.8, 4% SDS, 20% glycerol, 10% 2-mercaptoethanol, and 0.001% bromophenol blue) was added to the samples, and they were then heated at 98 °C for 5 min. The prepared samples were separated on 10% sodium dodecyl sulfate (SDS)–polyacrylamide gel electrophoresis (PAGE) gels and transferred to PVDF membranes (Merck, Darmstadt, Hesse, Germany). The membranes were blocked with 0.3% skim milk (FUJIFILM Wako, Osaka, Japan) in Tris-buffered saline containing 0.1% Tween 20 (TBST) for 1 h and incubated with an anti-c-Myc mouse monoclonal antibody (1:2000, FUJIFILM Wako, 9E10, catalog no. 011-21874) overnight at 4 °C. Then, the membranes were washed three times with TBST and incubated with HRP-conjugated goat anti-mouse IgG (H + L) (1:20,000, Jackson Immunoresearch, West Grove, PA, USA, catalog no. 115-035-003) at RT for 1 h. The membranes were washed three times with TBST and incubated with the ECL solution (Solution 1: 2.5 mM luminol, 4 mM 4-IPBA, and 200 mM Tris-HCl, pH 8.8; Solution 2: 10.6 mM H2O2). Equal volumes of each solution were mixed immediately before use. Membranes were imaged using an EZ-capture MG instrument (ATTO, Tokyo, Japan). Quantitative densitometry was used to measure the levels of protein fragments with a CS Analyzer (ATTO). Recombinant human procollagen 3A1 (1.41 ng, R&D, catalog no. 7294-CL-020) was mixed with 4–8 ng of recombinant human BMP1 and leupeptin (final 20 µM, Nacalai Tesque, Kyoto, Japan, catalog no. 20454-76) in assay buffer (25 mM HEPES-NaOH, pH 7.5, and 0.01% Brij35). NPL1010, NPL3008, or UK383,367 was added to the mixture (the final concentrations of each compound were 10, 25, or 50 µM). The total volume of the reaction mixtures was adjusted to 20 µL. The mixtures were incubated at 37 °C for 1 h, an equal volume (20 µL) of 2× sample buffer was added to the mixtures, and mixed samples were heated at 98 °C for 5 min. Heated samples were separated on 10% SDS–PAGE gels and stained with Oriole Fluorescent Gel Stain (Bio–Rad, Hercules, CA, USA, catalog no. 1610496) according to the manufacturer’s instructions. Stained gels were imaged using ImageQuant LAS4000 (Cytiva, Marlborough, MA, USA), and the relative amount of proteins was analyzed using CS Analyzer (ATTO). BMP1-dependent cleavage of COL3A1 was calculated from the amount of each COL3A1 fragment by subtracting BMP1-independent degradation of COL3A1 based on the molecular weight of each fragment. Zebrafish were raised and maintained under standard conditions with the approval of the Chiba University Institutional Animal Care and Use Committee (Nos. 1-174, 2-178, 3-66, and 4-11). Zebrafish embryos were obtained from the natural spawning of wild-type adults. One to five zebrafish embryos were placed in 24-well plates with 1 mL of E3 medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, and 0.33 mM MgSO4) and exposed to several concentrations of compounds from 3 to 11 hpf or 3 to 24 hpf; the phenotype was observed after incubation. Images were captured using a stereomicroscope (Leica, Wetzlar, Hesse, Germany, MZ16) and Axio Cam MRc (Zeiss, Oberkochen, Baden-Württemberg, Germany). For examination of the NPL1010 and NPL3008 effects on fin morphogenesis and bone mineralization, the chorion was removed at 1 dpf, and embryos were treated with NPL1010 or NPL3008 from 1 dpf to 3 or 5 dpf. Each RNA probe was previously described. The cDNA fragments for chrd [26], ved [29], szl [27,28], krox20 [49], and myoD [50] were utilized as templates for the antisense probes. The antisense probes were synthesized with T3, T7 and Sp6 RNA polymerase using linearized templates. The embryos were collected in a 1.5 mL tube and fixed with 4% paraformaldehyde (PFA)/1× phosphate-buffered saline (PBS) at 4 °C overnight. The embryos were dechorionated and dehydrated by sequential treatment with 25% methanol/1× PBS supplemented with 0.1% Tween 20 (1× PBSTw), 50% methanol/1× PBSTw, and 75% methanol/1× PBSTw. The embryos were incubated with each solution for 5 min at room temperature (RT). Then, the embryos were treated with 100% methanol at −30 °C for 30 min. The embryos were hydrated by sequential treatment with 75% methanol/1× PBSTw, 50% methanol/1× PBSTw, and 25% methanol/1× PBSTw for 5 min at RT each. The embryos were washed with 1× PBSTw for 5 min at RT twice and prehybridized with hybridization buffer (HYB, 50% formamide, 5× saline sodium citrate (SSC), 0.1% Tween 20, and 50 µg/mL heparin) at 65 °C for 1 h. Then, the prehybridization HYB was replaced with HYB containing a probe, and the embryos were hybridized at 65 °C overnight. The embryos were washed with 100% HYB, 50% HYB/2× SSC supplemented with 0.1% Tween 20 (2× SSCTw), and 2× SSCTw at 65 °C for 15 min each, followed by two washes with 0.2× SSCTw at 65 °C for 30 min. Then, the embryos were treated with 1× MABDT (1× maric acid buffer (MAB), 1% DMSO, and 0.1% Tween 20) at RT for 15 min and a blocking solution (1% Blocking Reagent (Merck catalog no. 11096176001), 1× MAB, 10% FBS, 0.1% Tween 20, 1% DMSO) for 1 h at room temperature. Then, the embryos were incubated with anti-digoxigenin-AP Fab fragments (Roche, Basel, Switzerland, catalog no. 11093274910) diluted 1/5000 in blocking solution at RT for 3 h. The embryos were washed with 1× PBSTw eight times at RT for 15 min and held at 4 °C overnight. The embryos were washed with NTMT (100 mM NaCl, 500 mM MgCl2, 100 mM Tris-HCl, pH 9.5, and 0.1% Tween 20) twice at RT for 5 min and stained with coloration solution (3.5 µL of BCIP solution (Merck, catalog no. 11383221001) and 2.3 µL of NBT (Roche catalog no. 11483213001) per 1 mL of NTMT) for approximately 2 h to overnight at RT or 4 °C in the dark. After color development was complete, the embryos were washed with 1× PBSTw three times for 5 min at RT and stored in 4% PFA/1× PBS at 4 °C. Images were captured using a stereomicroscope (Leica MZ16) and Axio Cam MRc (Zeiss). The measurement of the angle of the chrd expression domain was performed using Fiji software [51]. The fertilized eggs were dechorionated at 1 dpf and treated with DMSO, NPL1010 (2.5 or 5 μM in E3 medium), NPL3008 (2.5 or 5 μM in E3 medium), or dorsomorphin (5 μM in E3 medium) until 5 dpf. Staining was performed as described (Walker and Kimmel, 2007). Briefly, 5 dpf embryos were collected in a 1.5 mL tube and fixed with 4% PFA for 2 h. Then, the embryos were dehydrated with 50% ethanol at room temperature for 10 min and stained with staining solution (0.02% Alcian blue, 0.05% Alizarin red, 200 mM MgCl2, and 70% EtOH) at room temperature overnight. After staining, embryos were treated with 1.5% H2O2 and 1% KOH at room temperature for 20 min. Then, the embryos were cleared by sequential treatment with 0.375% KOH, 25% glycerol; 0.375% KOH, 50% glycerol; and 0.125% KOH, 75% glycerol at room temperature for 1–3 days and stored in 100% glycerol at 4 °C. Images were captured using a stereomicroscope (Leica MZ16) and Axio Cam MRc (Zeiss). The bmp2b, bmp1a, tll1, and chrd mRNAs were generated from pCS2 + kzbmp2, pCS2 + bmp1a [11], pCS2 + tll1 [52], and pCS2 + chrd-myc [48] by restriction digestion with NotI and AmpliCap SP6 High Yield Message Maker Kit (CellScript, Madison, WI, USA). The synthesized capped mRNA was purified using a SigmaSpin™ PostReaction Clean-Up Column (Merck) and stored at −80 °C. The generated mRNA was injected into zebrafish embryos at the one-cell stage using an IM300 microinjector (NARISHIGE, Tokyo, Japan). In the rescue experiment, the mRNA injected embryos were exposed to the compounds at 3–24 hpf as described above, and the phenotype was observed. First, the structure of zebrafish BMP1 (BMP1_DANRE) for the docking study was modeled using the SWISS-MODEL server with the human BMP1 structure as the template (PDB: 6BSL). Then, hydrogen atoms were added and subjected to further docking experiments with AutoDock Vina v1.1.2 according to the instructions [53]. The grid box size was defined around a known BMP1 inhibitor (hydroximate Compound 22) binding site with dimensions of 40 × 40 × 40 Å grid points. Ligand conformations of NPL1010 and NPL3008 were obtained from the virtual screening database LIGANDBOX (version 1306) [54]. Molecules were docked using AutoDock Vina with exhaustiveness grade 8 and a maximum of 100 poses searched per molecule. The lowest energy conformations for NPL1010 and the second-lowest energy conformation for NPL3008, which exhibited good agreement with NPL1010 binding, were selected. The results were visualized using PyMOL (opensource version, Schroedinger). AlphaFold simulation was performed using AlphaFold Colab notebook on Google Colaboratory [42,43]. The results were visualized using PyMOL. Statistical analyses were performed using Prism 8 (GraphPad Software). Information about error bars, sample size, and statistical test methods is described in the corresponding figures and figure legends.

PMC10001958

Amaia Ezkurdia,María J. Ramírez,Maite Solas

Metabolic Syndrome as a Risk Factor for Alzheimer’s Disease: A Focus on Insulin Resistance

22-02-2023

hypertension,hyperlipidemia,obesity,diabetes mellitus,insulin resistance,astrocyte

Alzheimer’s disease (AD) is the main type of dementia and is a disease with a profound socioeconomic burden due to the lack of effective treatment. In addition to genetics and environmental factors, AD is highly associated with metabolic syndrome, defined as the combination of hypertension, hyperlipidemia, obesity and type 2 diabetes mellitus (T2DM). Among these risk factors, the connection between AD and T2DM has been deeply studied. It has been suggested that the mechanism linking both conditions is insulin resistance. Insulin is an important hormone that regulates not only peripheral energy homeostasis but also brain functions, such as cognition. Insulin desensitization, therefore, could impact normal brain function increasing the risk of developing neurodegenerative disorders in later life. Paradoxically, it has been demonstrated that decreased neuronal insulin signalling can also have a protective role in aging and protein-aggregation-associated diseases, as is the case in AD. This controversy is fed by studies focused on neuronal insulin signalling. However, the role of insulin action on other brain cell types, such as astrocytes, is still unexplored. Therefore, it is worthwhile exploring the involvement of the astrocytic insulin receptor in cognition, as well as in the onset and/or development of AD.

Metabolic Syndrome as a Risk Factor for Alzheimer’s Disease: A Focus on Insulin Resistance Alzheimer’s disease (AD) is the main type of dementia and is a disease with a profound socioeconomic burden due to the lack of effective treatment. In addition to genetics and environmental factors, AD is highly associated with metabolic syndrome, defined as the combination of hypertension, hyperlipidemia, obesity and type 2 diabetes mellitus (T2DM). Among these risk factors, the connection between AD and T2DM has been deeply studied. It has been suggested that the mechanism linking both conditions is insulin resistance. Insulin is an important hormone that regulates not only peripheral energy homeostasis but also brain functions, such as cognition. Insulin desensitization, therefore, could impact normal brain function increasing the risk of developing neurodegenerative disorders in later life. Paradoxically, it has been demonstrated that decreased neuronal insulin signalling can also have a protective role in aging and protein-aggregation-associated diseases, as is the case in AD. This controversy is fed by studies focused on neuronal insulin signalling. However, the role of insulin action on other brain cell types, such as astrocytes, is still unexplored. Therefore, it is worthwhile exploring the involvement of the astrocytic insulin receptor in cognition, as well as in the onset and/or development of AD. Alzheimer’s disease (AD) affects 27 million people worldwide and is the most common type of dementia [1]. Indeed, AD makes up 60 to 70% of all dementia cases [2]. The impact on the life of a patient’s family as well as the financial cost to society of the occurrence of the disease is very large, especially due to the fact that, to date, there is no curative treatment for AD [3]. Although AD is a public health issue, to date, only two classes of drugs have been approved for its treatment: cholinesterase enzyme inhibitors (rivastigmine, galantamine and donepezil) and NMDA receptor antagonists (memantine). Even though these two classes of drug show therapeutic effects, they are only effective at treating AD symptoms not preventing the disease [4,5,6]. Regrettably, a low number of clinical trials on AD were launched in the last decade and their outcome was a big failure. A plethora of treatment options are now being intensively studied [6]. However, it is crucial to identify the risk factors involved in the disease as the control of these factors may help to prevent and probably combat the course of the disease. The definition of risk factors can radically change the treatment and diagnosis of AD from conventional approaches towards precision medicine, offering a personalized approach to disease management [7]. The main and earliest manifestation of the disease is the memory impairment that evolves over several years. During the disease progression, intellectual skills deteriorate, behavioural problems and delusion appear, while the patient loses control over essential body functions. Pathologically, the main AD hallmarks are the accumulation of beta-amyloid peptide (Aβ) outside neurons and the hyperphosphorylation and Tau protein aggregation inside neurons [3,6,8,9]. Anatomically, AD patients present a profound brain atrophy, especially at the hippocampal and the neocortical areas [10]. Moreover, in AD brains, reduced levels of acetylcholine, norepinephrine and dopamine have been detected [11,12]. Furthermore, in light of epidemiological and experimental evidence, several pathological events that are not specific to AD have been identified, including brain energy deregulation [13,14,15], synaptic dysfunction [16], oxidative/ER stress [17], mitochondrial alterations [16], autophagy deterioration [18], inflammation [19] and the blood–brain barrier (BBB) [20] and neurovasculature breakdown [21]. In this point, a fundamental question is the following: are these dysfunctions directly connected to the amyloid and Tau pathology? In contrast, are these pathological features occurring parallel to Aβ and Tau? Even more importantly, how are these pathological events induced? Notably, many of those alterations have been directly associated to AD risk factors, pointing towards the importance of the deep study of risk factors that precipitate AD pathology. This review summarizes our current understanding of the impact of metabolic syndrome in the development of dementia, especially AD. We mainly focus on mechanisms and clinical outcomes of type 2 diabetes mellitus (T2DM), address the apparent paradox of how impaired neuronal insulin can protect from the development of neurodegenerative disorders and propose astrocytic insulin signalling as a new target to explore. Genetic variants as well as environmental or non-genetic factors have been linked to the onset and/or the progression of AD [22,23,24,25,26,27,28]. Some of the most studied environmental risk factors include aging, cardiovascular disease [29,30,31], T2DM [26,32], obesity [33], depression, dyslipidaemia [23], substance abuse or smoking. These risk factors may lead to the above-mentioned pathogenic features, i.e., reduced glucose utilization, oxidative stress, chronic inflammation, mitochondrial dysfunction or brain energy metabolism breakdown among others, that could cause or aggravate AD development [33,34,35,36]. Environmental AD risk factors rarely exist alone. Obesity, hypertension, dyslipidemia and diabetes often coexist, and their effects are hard to tease apart. Usually, most of the studies focus on one factor, neglecting the possibility that different risk factors could increase the risk of AD in an additive or synergistic manner. Therefore, a more accurate approach is the study of a cluster of risk factors such as the metabolic syndrome, defined as the coexistence of obesity, hypertension, hyperlipidemia and diabetes (Figure 1) [33]. The association of these risk factors to AD development is described in the following subsections. It has been suggested that the association between hypertension and AD occurs through cerebrovascular disease. Specifically, hypertension alters the vascular walls leading to hypoperfusion, ischemia and cerebral hypoxia, triggering AD development. According to the literature, cerebral ischemia precipitates the accumulation of Aβ and induces the expression of presenilin that is involved in Aβ synthesis. Moreover, hypertension promotes BBB breakdown, a feature intimately linked to AD pathology [37]. According to epidemiological studies, an association between hypertension and dementia has been described [38]. Notably, the most compelling evidence is obtained when hypertension is present in middle age and precipitates AD and vascular dementia 15 to 20 years later [39]. Previous studies have already demonstrated that patients with AD show 10% higher cholesterol levels compared to healthy individuals [40], proposing cholesterol as a risk factor for the development of AD. Elevated levels of cholesterol compromises BBB integrity [41], consequently increasing AD risk. Moreover, investigations in experimental animal models have shown that hypercholesterolemia increases Aβ peptide deposition, Tau hyperphosphorylation, neuroinflammation, cognitive deficiency, cholinergic neuron dysfunction and cerebral microhaemorrhages and is compatible with AD [42,43]. Observational studies have pointed out that statin users show a reduced incidence of AD or even an improvement in the disease progression [44,45,46]. However, most clinical studies have not demonstrated the efficacy of statins against AD onset and/or progression at various stages of the disease [47,48,49,50,51], in contrast to the study conducted by Song et al. [52] that observed a lower risk of AD in statin users. Apart from cholesterol, chronic high free fatty acid levels have been shown to induce pernicious outcomes, including low-grade inflammation that could lead to insulin resistance. Although the capacity of fatty acids to pass through the BBB is limited [53], PET studies have demonstrated fatty acids uptake by the brain [54]. Notably, metabolic syndrome induces the brain’s uptake and accumulation of fatty acids that can be reversed by weight reduction [54]. Interestingly, it has been extensively reported that the exposure to a high fat diet promotes AD pathogenesis, and diets enriched in polyunsaturated fatty acids such as docosahexaenoic acid (DHA) show a protective effect against AD [55]. Saturated fatty acids could promote the brain inflammatory response through TLR4 activation [56]. Indeed, the loss of function of TLR4 protects against high-fat-diet-induced deleterious effects [56]. Moreover, the molecular link between high levels of fatty acids and AD could be beta-amyloid and Tau, as free fatty acids have been shown to stimulate the assembly of both amyloid and tau filaments in vitro [57] leading to cognitive dysfunction. The prevalence of obesity and overweight is exponentially increasing, with an estimation of the existence of 1.35 billion overweight and 573 million obese people around the world by 2030 [58]. The risk of suffering dementia is significantly increased upon obese conditions. Indeed, in a longitudinal study where participant’s sagittal abdominal diameter was measured, a larger diameter was directly correlated with nearly a three-fold risk of developing dementia [59]. In the same line, another study demonstrated that a lower hippocampal volume is observed in subjects with higher waist–hip ratio [60]. The connection between midlife obesity and the risk of suffering dementia in the elderly has been investigated by several authors [59,61,62,63,64]. In the study performed by Xu et al., midlife overweight (BMI > 25–30) and obese (BMI > 30) individuals showed a higher dementia probability [65]. Another cohort study demonstrated that while obesity in midlife significantly increases the risk of suffering dementia later in life, this risk is decreased when obesity occurs in old age [66]. Remarkably, a cohort projection model based on an Australian population demonstrated that if midlife obesity is decreased by 20%, dementia in aging could be lowered by 10% [67]. In contrast with all the above-mentioned studies that suggests a direct link between midlife obesity and late life dementia, a cohort study showed that being underweight in middle age carries an increased risk of dementia in later life [68]. Based on these conflicting results, the possible connection between obesity and dementia needs a deeper investigation. It is worth mentioning that it has been proposed that the linking mechanism between obesity and AD could be obesity-induced insulin resistance. The association between diabetes mellitus (DM) and AD has been extensively studied, with most research showing a clear link between T2DM and a higher risk of developing AD. The suggested mechanisms underlying this association include, among others, insulin deficiency, insulin resistance, insulin receptor impairment, hyperglycaemia-induced toxicity, advanced glycation end products (AGEs)-induced adverse effects, inflammation and cerebrovascular damage [69]. The connection between T2DM and AD has been studied using different approaches, extensively described in Section 4 of the present review. For instance, post-mortem brains of AD patients present a deregulation of the insulin receptor (IR) intracellular signalling [70,71,72]. The exposition of AD transgenic mouse models to a high fat and/or high sugar diet exacerbates the AD pathology [73,74,75,76,77]. Upon antidiabetic treatment, a slight cognitive improvement, as well as the alleviation of inflammation, apoptosis and synaptic failure, has been described in human and AD mouse models [78,79]. Finally, DM murine models recapitulate the alteration in glucose metabolism, IR signalling, neuroinflammation, Tau hyperphosphorylation and Aβ aberrant processing characteristic of AD pathology [80,81]. However, the extensive literature, employing very different approaches, has produced conflicting results based on variables such as the model used, the type of diet, the duration of the study, etc. Thus, it is still difficult to have a definite idea of how T2DM is linked to AD. This conundrum will be discussed below. Traditionally, it has been thought that the brain is an insulin-insensitive organ. However, during the last few years, new evidence showing a high concentration of insulin in brain extracts, and expression of IR throughout the brain, supports that it is actually an organ that responds to this hormone [82]. Insulin is an important regulator of glucose homeostasis and metabolism. Nevertheless, its role in the central nervous system is a field that is not yet fully known. It has been suggested that it can modulate central functions such as feeding, depression and cognitive behaviour [83]. It has been proposed that insulin reaches the brain in two ways: across the BBB or synthesizing de novo in the brain. However, whether insulin can be locally synthesized in the brain remains under debate since the synthesis of insulin in the central nervous system (CNS) seems to depend on the animal species under study. In vitro experiments have shown that insulin can be synthesized and secreted by rabbit CNS neuronal cells in culture but not in glia [84]. Moreover, the expression of insulin mRNA has been detected in rat and mouse brains [85,86,87]. In humans, peptide C (by-product of insulin synthesis) has also been observed in several brain regions [88,89]. However, these studies have been questioned and others have not been able to demonstrate the presence of insulin mRNA in appreciable amounts in the brain. In general, there is a lack of data suggesting that the human brain produces and secretes significant amounts of insulin locally, and it is widely believed that the insulin found in the brain parenchyma comes primarily from the pancreas [90]. Insulin enters the brain across the capillary endothelial cells of the BBB via selective, saturable transport. Insulin levels in cerebrospinal fluid (CSF) are much lower than in plasma. Even so, there is a connection between both since the increase in peripheral insulin concentration acutely increases concentration in the brain and CSF, indicating that most of the insulin that reaches the brain derives from circulating pancreatic insulin [91,92]. This transport can be modulated by a vast array of factors such as DM, inflammation, obesity and circulating triglyceride levels. IRs are widely distributed throughout the CNS, following a selective regional pattern. In rodents, the highest IR expression is found in the olfactory bulb, hypothalamus, cerebral cortex, hippocampus and cerebellum [93,94]. Thus, IRs are present in brain areas related to glucose and energy homeostasis as well as cognitive processes (i.e., the hypothalamus and the cortex/hippocampus, respectively) [95]. Studies that have been carried out on insulin and IRs in the brain demonstrate a key role of central insulin signalling in the regulation of peripheral functions [96]. Insulin acts within the hypothalamus to regulate various processes of metabolism such as energy homeostasis (body weight and food intake), glucose metabolism and the regulation of reproduction. Insulin acts in two neuronal populations in the arcuate nucleus of the hypothalamus (ARC): the orexigenic neurons (coexpressing neuropeptide Y, NPY, and agouti-related peptide, AgRP) and the anorexigenic neurons (which produce proopiomelanocortin, POMC, and cocaine- and amphetamine-related transcript, CART). In the ARC, the role of insulin signalling on neuronal activity seems to be complex, as it acts by hyperpolarizing both anorexigenic POMC neurons and orexigenic AgRP/NPY neurons [97]. Intracerebrovascular administration of insulin has an anorexigenic effect; it inhibits food intake, resulting in a reduction in body weight. By contrast, inhibition of insulin signalling in the brain has an orexigenic effect, it increases food intake, and as a result increases the body weight [98]. At a cellular level, insulin regulates transcriptional events, by inducing Pomc transcription, and decreasing Agrp expression, resulting in an increase in the anorexigenic tone upon feeding [99]. Furthermore, in the postprandial state, the precursor protein POMC is converted into α-melanocyte-stimulating hormone (α-MSH) and released from POMC neuron synaptic endings located in ARC to paraventricular hypothalamic nucleus (PNV) where it activates melanocortin 3 and 4 receptors (MC3/4-R). Melanocortin receptor (MC-R) activation decreases food intake, increases energy expenditure and regulates glucose metabolism [100]. In addition to its anorexigenic effect, insulin plays an important role in the CNS-dependent regulation of peripheral glucose metabolism. Central insulin infusion results in a decrease in hepatic glucose production (HGP) [101,102]. This effect is not observed in neuronal IR knockout mice (NIRKO mice), nor in AgRP-restricted IR knockout mice, suggesting that insulin action in AgRP-expressing neurons is required for the suppression of HGP [101]. The anorexigenic effect and ability to suppress HGP of insulin are mediated by the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway, mainly through forkhead box protein O1 (FOXO1) and the activation of the adenosine triphosphate (ATP)-dependent potassium channel [95,97,98]. Nuclear exclusion of FOXO1 increases the transcription of POMC and decreases AgRP and G-protein-coupled receptor 17 (GPCR17). GPCR17 might control food intake and HGP by the modulation of ion channel activity and the resultant increase in orexigenic neuropeptide release [103]. Insulin also acts via activation of the ATP-dependent potassium channel, leading to neuronal hyperpolarization and inhibiting the secretion of neuropeptides [95,97,98]. It has been shown that insulin is involved in the CNS and brain function. However, the role it can play is not yet fully understood. Some studies pointed out the effects of insulin on learning and memory due to insulin and IRs presence in the hippocampus and cerebral cortex [104]. Indeed, it has been proposed that insulin regulates cognition modulating synaptic plasticity, synapse density and neurotransmission, and even by regulating adult neurogenesis [105]. Neuronal plasticity encompasses all the processes through which neurons adapt to changes in functional demands, and insulin-like peptides (ILPs) act by modulating these processes [105]. ILPs affect synaptic efficacy by modulating various components of the synapse and their expression. In the past decades electrophysiology studies have revealed the importance of synaptic signalling in long-term potentiation (LTP) and long-term depression (LTD) [106]. In vitro experiments using cultured neurons showed that ILPs could modulate LTP and LTD, regulating the synthesis of glutamate and GABA receptor subunits and changing the phosphorylation of glutamate receptor subunits [107,108]. Insulin can regulate the AMPA receptor that underlies synaptic transmission and synaptic plasticity [109]. It is seen that insulin induces glutamatergic AMPA receptor internalization leading to LTD [110]. Moreover, LTD induced by AMPA receptor internalization has been demonstrated to regulate the extinction of the fear-conditioned memory process [111], thereby linking the insulin-involved synaptic activities to cognition. Conversely, the downregulation of hippocampal IR function impairs LTP and spatial memory [112]. Insulin also potentiates NMDA receptor activity by expressing NMDA receptors to the cell surface [113] and through NMDA receptor phosphorylation [114], processes that may induce long-lasting changes. In addition to AMPA and NMDA receptors, insulin has been shown to be a regulator of GABA(A) receptors. Specifically, it increases inhibitory synaptic transmission by recruiting β2 subunits of GABA(A) receptors at the postsynaptic level [108]. Moreover, ILPs contribute to modulating synaptic strength by regulating the synthesis and release of neurotransmitters such as acetylcholine in the hippocampus [105]. PI3K/AKT and mitogen-activated protein kinase (MAPK) pathways include components that have been implicated in dendritic arbor formation dynamics. IRSp53, an IR substrate localized in the postsynaptic density, has been hypothesized to interact and stabilize the proteins of the cytoskeleton in the postsynaptic density region [115]. Moreover, in cell cultures, it has been demonstrated that increased expression of IRSp53 promotes dendritic arbor development [116], while RNA-interference-treated cells against IRSp53 showed the opposite effect [117]. It is seen that IR signalling promotes cell survival under oxygen and glucose deprivation in rat hippocampal cell culture studies. Although the exact mechanism remains unclear, the authors proposed increased GABA signalling as a potential mechanism of insulin action to protect neurons from oxygen–glucose-deprivation-induced cell death [118]. Intranasal administration of insulin has shown to improve memory and learning in rats. Other studies in healthy humans demonstrated that CNS insulin administration via the intranasal route have beneficial cognitive effects [119,120], indeed this improvement could even be intensified by the administration of rapid-acting insulin analogue, named insulin aspart [121]. Furthermore, systemic infusion of insulin in healthy humans improved verbal memory and selective attention [122]. Gene expression of IR has been increased in the hippocampal dentate gyrus and CA1 region of the rat after spatial memory training [104]. Although the mechanisms involved in these processes are not fully known, it is seen that the PI3K pathway is involved since the central administration of insulin increases memory in a PI3K-dependent manner [123]. Nevertheless, NIRKO mice showed unaltered spatial learning during a Morris Water Maze task, indicating that the insulin pathway does not play a primary role in memory formation [124]. Based on the above-mentioned important role of insulin in cognitive functions, the interaction between DM and AD has gained great attention in the past two decades [36]. Indeed, not only insulin deficiency but also insulin resistance has pernicious consequences on the brain [125,126]. There are multiple plausible mechanisms that could explain the connection between insulin resistance and AD. Insulin resistance is defined by the diminished response to insulin that occurs as a response to certain genetic and/or environmental factors [127]. Upon an insulin resistance state, insulin secretion from the pancreas is increased in an attempt to achieve normoglycemia. This hyperinsulinemia leads to a vicious cycle that worsens the insulin resistance [128,129] and could be implicated in AD pathogenesis [34,130,131]. Peripheral hyperinsulinemia can also affect its own transport across the BBB [125,132]. Once in the brain, insulin is degraded by the insulin-degrading enzyme (IDE), an enzyme able to degrade the Aβ protein as well [125,133]. Notably, insulin and Aβ compete for the binding to the IDE, the insulin affinity being higher compared to Aβ [133]. Therefore, in a state of hyperinsulinemia, insulin binds the IDE leading to Aβ protein accumulation [125,133]. Moreover, Aβ protein levels in the extracellular space are increased upon high insulin levels [133]. Interestingly, Aβ binds and avoids the insulin binding to IR, exacerbating insulin resistance [34,36,133,134]. In this line, considering the close link between Aβ and impaired insulin signalling, it is not surprising that impaired cerebral glucose metabolism usually precedes AD signs and symptoms by several years [35,135,136]. Notably, hyperglycaemia as well as insulin resistance induces cognitive deficiencies, one of the core symptoms of AD [137]. Insulin resistance has also been associated to Tau hyperphosphorylation [138]. Glycogen synthase kinase 3 (GSK3β) is a metalloprotease that acts by phosphorylating the Tau protein, which, when hyperphosphorylated, precipitates and accumulates in the form of neurofibrillary tangles (NFTs). Insulin is an inhibitor of GSK3β, thus preventing Tau from being phosphorylated. Therefore, it is suggested that in situations of insulin resistance, the activity of GSK3β is increased and leads to the phosphorylation of Tau and formation of NFTs [139,140]. Furthermore, insulin resistance reduces acetylcholine levels in the brain leading to cholinergic perturbations, which are largely involved in AD pathology [11,141,142]. The chronic state of hyperinsulinemia interferes with the saturable transport of insulin through the BBB. Thus, at some point, brain insulin resistance could be accompanied by reduced insulin levels at the CNS [36,125,132] and this brain insulin deficiency has also been associated with AD onset and progression [125,143]. In summary, AD can be considered a situation in which insulin deficiency as well as insulin resistance are profoundly implicated [144,145]. Other possible mechanisms linking DM and AD have also been described, such as chronic inflammation and oxidative stress [25,34,146]. High levels of inflammatory cytokines, such as interleukin-1 β, interleukin-6 and interferon gamma are located close to Aβ plaques and macrophage cells suggesting the important role of neuroinflammation in the pathology of AD. Peripheral hyperinsulinemia leads to the maintenance of inflammation through its inhibition of AMP-activated protein kinase [147]. These inflammatory processes could mediate the relationship between T2DM and AD. Another proposed pathogenic pathway is the alteration of gut microbiota and the subsequent disruption of the gut–brain axis that occurs upon insulin resistance [148,149,150,151,152,153]. Gut microbiota disturbance has been associated with AD [154,155]. Finally, another plausible mechanism linking DM and AD could be cerebrovascular diseases (CVD), as insulin resistance and the subsequent hyperinsulinemia are involved in CVD [156,157,158,159] that could eventually lead to AD pathology. In the light of all those shared mechanisms between DM and dementia, AD has been described as type 3 diabetes [160]. However, this concept is still under intense debate, as many studies in the literature do not support this idea. For instance, Hardy et al. tested for genetic correlation between T2DM and AD using a genome-wide association study (GWAS), without finding convincing evidence for a genetic overlap between both disorders. Specifically, none of the single nucleotide polymorphisms (SNP) found in one disease were relevant in the other disease genome [161]. Moreover, there are conflicting findings on the neuroprotective effects of the insulin signalling pathway (Figure 2). Indeed, it has been seen that decreased neuronal insulin signalling has a beneficial effect on lifespan regulation, and even delay age-related processes. In Caenorhabditis elegans, the human insulin and insulin-like growth factor 1 (IGF-1) receptors are codified by a single ortholog, DAF-2 [162]. Dillin et al. showed that adult-specific RNAi knockdown of DAF-2 increases lifespan [163]. In a similar manner, the group led by Ewald CY found that using an auxin-inducible degradation system, the depletion of DAF-2 at post-reproduction stages resulted in a 70–135% lifespan extension without inducing adverse phenotypes such as growth retardation, germline shrinkage, egg retention or reduced brood size. Impressively, at geriatric ages, auxin-mediated depletion of DAF-2 protein resulted in a doubling of lifespan [164]. In mammals, it is seen that midlife administration of IGF-1 receptor monoclonal antibodies is sufficient to improve health and increase lifespan, preferentially in females [165]. Furthermore, loss-of-function mutations in genes encoding for insulin receptor substrate 1 and 2 (IRS1/2) [166,167] and IGF-1 receptor [167,168] increased longevity in rodents. Decreased insulin signalling has also been associated with an improvement in age-related intracellular and behavioural events that are related to neurodegenerative disorders, and thus cognition [95]. In particular, IGF-1 deficiency in a genetic model of AD reversed premature mortality associated with AD and delayed Aβ accumulation [167]. Additionally, deletion of IRS-2 reduces Aβ deposition and rescues behavioural deficits in APP transgenic mice [169]. Due to the paradoxical effects of insulin signalling, the current state of our understanding is insufficient to differentiate clearly between the beneficial vs. negative impact on aging and neurodegenerative diseases of reduced insulin signalling (Figure 2). Notably, this debate is fed by results obtained from studies focused exclusively on neuronal insulin signalling; however, the role of insulin action on other brain cell types, such as astrocytes, is still unexplored. Indeed, the possibility that insulin signalling in astrocytes plays a functional role in cognitive performance and AD has never been studied. Therefore, it is worthwhile exploring the involvement of astrocytic IR in cognition, as well as in the onset and/or development of neuropsychopathologies. There is increasing evidence that non-neuronal cells contribute to the onset, progression and pathology of diverse neurodegenerative diseases. Interestingly, astrocytes are the most abundant cells in the brain and they are closely associated to neurons [170]. They provide an adequate microenvironment to guarantee the correct neuronal function, including the control and formation of synaptic plasticity. They participate in neurogenesis, regulation of the vascular tone of the brain, maintenance of the BBB and regulation of energy homeostasis [171]. Moreover, they can sense neurotransmitters and respond with increases in intracellular calcium level, and they can also release gliotransmitters to neurons [172]. Astrocytes are altered in neurodegenerative disorders [173,174], where dysregulation of astrocyte calcium activity, glutamate uptake and mitochondrial respiration could be seen, leading to damage to neural health [175,176]. For years, Aβ plaques have been considered the main pathological hallmark of AD and responsible for triggering astrocytic reactivity, as some studies showed a higher density of reactive astrocytes around plaques [177]. The glymphatic system participates in a correct fluid clearance where interstitial fluid is cleared along paravenous routes [178], and astrocytes participate actively in this process. They contain high levels of aquaporin 4 (AQ4), which facilitates astrocyte clearance of factors towards the systemic circulation. In AD, there is a downregulation of AQ4 levels, which can result in decreased clearance of Aβ and the subsequent possible protein accumulation [179]. Indeed, AQ4 knockout in an AD mouse model increased Aβ accumulation in the brain, and resulted in cognitive impairment [180]. Neurons are isolated from systemic circulation by the BBB and rely on astrocytes to obtain metabolic substrates [181]. A well described mechanism is the astrocyte–neuron lactate shuttle (ANLS) [182]. As known, glucose is converted to lactate in astrocytes and then exported to neurons for energy use or memory function among other things [183]. Based on the evidence that the energy homeostasis required for synaptic activity is altered in neurodegeneration, a central role of astrocytes in AD is suggested. In physiological conditions, calcium signalling in astrocytes is associated with multiple processes, such as neuronal activity or the response to synaptic stimulation [184]. Ca2+ dynamics in astrocytes were studied in a three-dimensional two-photon imaging approach. It was found that Ca2+ activity in astrocyte processes and endfeet displayed frequent fast activity, whereas the soma was infrequently active. Astrocytes responded locally to minimal axonal firing with time-correlated Ca2+ spots [185]. Dysregulation of astrocyte calcium signalling has been observed in mouse models of AD before the appearance of astrogliosis, suggesting that this alteration occurs early in the pathology. A study performed in the APP-PS1 mouse model of AD showed that chronic blockade of the calcium channel TRPA1 was sufficient to normalize astrocytic activity, avoid perisynaptic astrocytic process withdrawal, prevent neuronal dysfunction and preserve structural synaptic integrity, thus preserving spatial working memory in that model. In that study, they also showed that brief exposure of hippocampal slices to Aβ oligomers failed to induce synaptic dysfunction when calcium level elevation was blocked in astrocytes, suggesting that astrocyte calcium signalling is associated with synapses [186]. Astrocytes participate in neurotransmitter uptake and recycling. The main excitatory neurotransmitter in the brain is glutamate, which is taken up by transporters localized in astrocytes [187]. Neuronal death caused by excessive postsynaptic receptor activity at glutamatergic synapses has been proposed to explain the pathogenesis of AD [188]. Neuronal death leads to alteration at synapses, where astrocytes could contribute. The overactivation of extra synaptic glutamatergic NMDA receptors, which is a consequence of glutamate release from astrocytes after exposure to Aβ, connects glutamate dyshomeostasis and the loss of synapses observed in AD [189,190]. Not only the release of glutamate from astrocytes, but also the release of ATP [191], D-serine [186] and GABA [192] are increased in AD, suggesting that astrocytes contribute to synaptic alteration and cognitive performance. The role of insulin in neurons has been widely studied. Nevertheless, the role of insulin action in astrocytes and neurobehaviours remains less well studied. In 2016, García-Cáceres et al. reported that insulin signalling in astrocytes co-regulates behavioural responses and metabolic processes via the regulation of glucose uptake across the BBB. On the one hand, astrocytic IR ablation affects hypothalamic astrocyte morphology, mitochondrial function and circuit connectivity. On the other hand, it reduces glucose-induced activation of hypothalamic POMC neurons and impairs physiological responses to changes in glucose availability [171]. In 2018, Cai et al. demonstrated for the first time that a mice model with IR knockout in astrocytes showed anxiety- and depressive-like behaviours. Mechanistically, the loss of insulin signalling in astrocytes impairs the tyrosine phosphorylation of Munc18c and appears to decrease the exocytosis of ATP from astrocytes, leading to a reduction in dopamine release from the nucleus accumbens among others, affecting neuronal circuits involved in cognition and mood [193]. It is also suggested that insulin action in astrocytes plays an important role in the regulation of cognition and mood [194,195]. APP/PS1 mice showed elevated basal and stimulus-evoked hippocampal glutamate release, astrogliosis and impaired insulin sensitivity that was exacerbated in a high fat diet (HFD). The elevated astrogliotic response surrounding the plaques in APP/PS1 HFD mice could be a compensatory mechanism to control Aβ accumulation [196]. Likewise, streptozotocin treatment in a rat astrocytoma cell line, resulted in an IR mRNA decrease and protein expression, and induced neuroinflammation and amyloidogenesis [197]. Additionally, other studies support that astrocyte-released insulin and IGF-1 protect neurons from synaptotoxic Aβ peptide oligomers [198]. Although astrocytes actively coordinate brain energy homeostasis, and there is increasing evidence of the connection between astrocytic insulin signalling and pathophysiological mechanisms related with AD, many questions still have to be answered [199]. AD is a dramatic disease without any effective treatment. In this scenario, it is essential to tackle the modifiable risk factors such as obesity, diabetes, hypertension and dyslipidemia (i.e., metabolic syndrome) in an attempt to prevent the onset and progression of AD and improve the quality of life of patients. In this context, DM has gained central attention in the last decades. However, acting on neuronal insulin signalling has controversial effects on the pathogenesis of AD. Therefore, since no current drug intervention can modify the pathophysiological mechanisms related to the development of this devastating disease, in order to find an effective treatment, the focus should move towards other brain cells such as astrocytes, as they could be the main actors in the pathology of AD. In this regard, targeting astrocytic insulin signalling may be more relevant as a way to treat AD because this may not only halt disease progression but also bypass the paradoxical actions of neuronal insulin. In this sense, it is crucial to clarify these discrepancies regarding the role of neuronal vs. astrocytic insulin signalling in brain function and to delineate the exact mechanisms that lead independently and synergistically to the onset of T2DM and AD. This will ultimately open new avenues in the development of more efficient preventive and therapeutic strategies.

PMC10001964

Luwen Xing,Yiwen Zhang,Saiyu Li,Minghui Tong,Kaishun Bi,Qian Zhang,Qing Li

A Dual Coverage Monitoring of the Bile Acids Profile in the Liver–Gut Axis throughout the Whole Inflammation-Cancer Transformation Progressive: Reveal Hepatocellular Carcinoma Pathogenesis

21-02-2023

hepatocellular carcinoma,inflammation-cancer transformation process,bile acids,enterohepatic circulation,BAAT,liver–gut axis

Hepatocellular carcinoma (HCC) is the terminal phase of multiple chronic liver diseases, and evidence supports chronic uncontrollable inflammation being one of the potential mechanisms leading to HCC formation. The dysregulation of bile acid homeostasis in the enterohepatic circulation has become a hot research issue concerning revealing the pathogenesis of the inflammatory-cancerous transformation process. We reproduced the development of HCC through an N-nitrosodiethylamine (DEN)-induced rat model in 20 weeks. We achieved the monitoring of the bile acid profile in the plasma, liver, and intestine during the evolution of “hepatitis-cirrhosis-HCC” by using an ultra-performance liquid chromatography-tandem mass spectrometer for absolute quantification of bile acids. We observed differences in the level of primary and secondary bile acids both in plasma, liver, and intestine when compared to controls, particularly a sustained reduction of intestine taurine-conjugated bile acid level. Moreover, we identified chenodeoxycholic acid, lithocholic acid, ursodeoxycholic acid, and glycolithocholic acid in plasma as biomarkers for early diagnosis of HCC. We also identified bile acid-CoA:amino acid N-acyltransferase (BAAT) by gene set enrichment analysis, which dominates the final step in the synthesis of conjugated bile acids associated with the inflammatory-cancer transformation process. In conclusion, our study provided comprehensive bile acid metabolic fingerprinting in the liver–gut axis during the inflammation-cancer transformation process, laying the foundation for providing a new perspective for the diagnosis, prevention, and treatment of HCC.

A Dual Coverage Monitoring of the Bile Acids Profile in the Liver–Gut Axis throughout the Whole Inflammation-Cancer Transformation Progressive: Reveal Hepatocellular Carcinoma Pathogenesis Hepatocellular carcinoma (HCC) is the terminal phase of multiple chronic liver diseases, and evidence supports chronic uncontrollable inflammation being one of the potential mechanisms leading to HCC formation. The dysregulation of bile acid homeostasis in the enterohepatic circulation has become a hot research issue concerning revealing the pathogenesis of the inflammatory-cancerous transformation process. We reproduced the development of HCC through an N-nitrosodiethylamine (DEN)-induced rat model in 20 weeks. We achieved the monitoring of the bile acid profile in the plasma, liver, and intestine during the evolution of “hepatitis-cirrhosis-HCC” by using an ultra-performance liquid chromatography-tandem mass spectrometer for absolute quantification of bile acids. We observed differences in the level of primary and secondary bile acids both in plasma, liver, and intestine when compared to controls, particularly a sustained reduction of intestine taurine-conjugated bile acid level. Moreover, we identified chenodeoxycholic acid, lithocholic acid, ursodeoxycholic acid, and glycolithocholic acid in plasma as biomarkers for early diagnosis of HCC. We also identified bile acid-CoA:amino acid N-acyltransferase (BAAT) by gene set enrichment analysis, which dominates the final step in the synthesis of conjugated bile acids associated with the inflammatory-cancer transformation process. In conclusion, our study provided comprehensive bile acid metabolic fingerprinting in the liver–gut axis during the inflammation-cancer transformation process, laying the foundation for providing a new perspective for the diagnosis, prevention, and treatment of HCC. HCC is one of the most serious malignancy tumors threatening human health, the third leading cause of cancer-related death in the world [1,2,3]. Persistent inflammation leading to the formation of the tumor microenvironment is an important factor in the formation of HCC, whose mechanism is very complicated. The morbidity trend of HCC appears to be closely related to hepatitis B (HBV) infection, and it has been reported that HCC patients caused by HBV still account for more than half of the global cases [4,5,6]. Although the inflammation-cancer transformation process of “hepatitis-cirrhosis-HCC” has become a research highlight to reveal the pathogenesis of HCC, there is still no effective clinical treatment strategy. Hence, it has become important to clarify the pathogenesis of the process to achieve early diagnosis of HCC and identify new therapeutic targets. Among the various endogenous metabolites originating from the co-metabolism of the liver–gut axis, bile acids (BAs) have received increasing attention because of their neoplasm-promoting properties [7,8,9,10]. BAs are synthesized in the liver, and the size and composition of the liver bile acid pool are closely regulated by translocation proteins [11]. When liver organic solute transporter-alpha/beta (OSTα/OSTβ) expression is downregulated, abnormal retention of BAs in hepatocytes within the organism occurs, leading to chronic liver injury [12], while patients diagnosed with HCC between 13 and 52 months concerning bile acid transporter deficiency resulted in a suppression of liver bile acid efflux [13]. In addition to the effect of liver bile acid accumulation on hepatocarcinogenesis, disruption of intestinal bile acid pool homeostasis can contribute to cancer development and a variety of chronic disease phenotypes. Elevated levels of secondary BAs in feces are capable of causing structural and functional abnormalities in the colonic epithelium through various mechanisms, including oxidative damage to DNA, activation of nuclear factor kappa-B, and enhanced cell proliferation [14]. However, depicting the spectrum of BAs and their interactions in plasma, liver, and intestine, covering the entire enterohepatic circulation, during the overall disease course of “health-hepatitis-cirrhosis-HCC” still requires research. In this paper, based on an N,N-diethyl-1,4-butanediamine (DEABA) derivatization method for absolute quantification of BAs, the systematic bile acid profiles in plasma, liver, and intestine in the whole progression of HCC have been obtained. Combined with analysis of independent sample t-tests, principal component analysis (PCA), orthogonal partial least squares discrimination analysis (OPLS-DA), and bayesian linear discriminant analysis (BLDA), key BAs biomarkers were screened out to distinguish different disease stages, which was valuable for the early diagnosis of HCC. Next, gene set enrichment analysis (GSEA) and the cancer genome atlas (TCGA) database were employed to explore the effect of core genes on the distribution of bile acid pools, which was crucial for promoting the development of HCC. Our study revealed the change of BAs in the liver–gut axis during the inflammation-cancer transformation process and provided a novel perspective for treating HCC. Changes in total bile acid (TBA) levels can reflect the physiological status and injury degree of the organism. Studies have confirmed that the TBA profiles of patients with HCC have unique metabolic characteristics, and the homeostasis of TBA is dependent on liver synthesis and intestinal absorption [15,16]. To elucidate the etiopathogenesis of HCC underlying TBA metabolism disorders, the present study evaluated the various canceration stages of DEN-induced rats based on the results of hematoxylin-eosin (H & E) stained liver tissue sections and quantified the TBA level in rats plasma, liver, and intestine at different stages of HCC progression. H & E staining showed that hepatocytes began to exhibit severe impairment in the 8th week compared to healthy controls (Figure 1A), termed the hepatitis stage (Figure 1B). The liver tissue was infiltrated with lymphocyte-dominated inflammatory cells, with a small amount of bile duct hyperplasia and localized vascular stasis. The cirrhosis stage occurred in the 12th week (Figure 1C), with an obvious structural disorder of liver lobules, the proliferation of perivenous connective tissue, formation of pseudo lobules with hepatocyte regeneration nodules, and bile duct hyperplasia. The 16th week was the initial stage of HCC (Figure 1D). Microscopically, hepatocyte empty valve degeneration and a small number of adenoid structures were observed, and a large amount of bile duct hyperplasia was visible. At the same time, massive vascular stasis and brownish-yellow pigmentation were observed. The 20th week was described as an advanced HCC stage (Figure 1E). The hepatic tissue showed obvious adenoid structures, all cells had enlarged deep-stained nuclei, and different degrees of vacuolar degeneration were observed. Based on the histological results, we found that TBA levels significantly increased in all disease groups (Figure 2A). The TBA level of intestinal contents samples gradually decreased with disease progression, which showed an opposite trend to plasma and liver samples (Hepatitis & Cirrhosis vs. Control ** p < 0.01; HCC & Advanced HCC vs. Control * p < 0.05), while the TBA levels in plasma and liver gradually increased in all stages (* p < 0.05, ** p < 0.01). Therefore, we speculate that there is a close relationship between the inflammation-cancer transformation process and enterohepatic circulation. To further analyze the specific reasons for the gradual decrease of TBA levels in the intestine, we subsequently analyzed total primary and secondary BAs in plasma, liver, and intestinal contents. We found that total primary and secondary BAs were markedly elevated in plasma and intestinal contents. However, we observed a specific phenomenon of elevated total primary BAs but decreased secondary BAs in liver samples only (* p < 0.05, ** p < 0.01). With the development of HCC, total primary BAs in the intestine decline in the advanced HCC stages, in contrast to the continuous increment of total primary BAs in the plasma and liver (Figure 2B). In addition, it is noteworthy that the total secondary BA level in plasma and liver showed an abnormal rebound at the advanced HCC stage, which was not seen in intestinal contents (Figure 2C). To figure out the key driving BAs for the evolving of HCC, we quantified the changes in the levels of 5 free BAs (cholalic acid, CA; chenodeoxycholic acid, CDCA; ursodeoxycholic acid, UDCA; lithocholic acid, LCA; deoxycholic acid, DCA; Figure 3), and their associated 10 conjugated BAs in plasma, liver, and intestine (Figure 4). The quantitative results of 15 BAs in plasma, liver, and intestinal contents samples from different disease stages of HCC and healthy controls are included in Table S3, and the results are expressed as mean ± SD. For free BAs, we found the same trend in three samples, with a significant increase in CA, CDCA, and DCA and a marked decline in LCA (* p < 0.05, ** p < 0.01). In addition, the different phenomena in UDCA are noteworthy, which were reduced in the liver and intestinal contents but elevated in plasma. In rodents, free BAs are more likely to be coupled to taurine, the glycine-conjugated BAs accounting for a small proportion of conjugated BAs [17]. The report supports that glycine-conjugated BAs are present at low levels in rats [18]. Due to the low levels and some errors in the quantitative analysis, individual disease groups did not show significant differences compared to the control group. However, from an overall perspective, glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), and glycoursodeoxycholic acid (GUDCA) all showed similar trends to their prototypes in three samples (Figure 4A–E). Taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), tauroursodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), and tauroursodeoxycholic acid (TUDCA) showed consistent trends concerning prototypic BAs only in plasma and liver. Surprisingly, all five taurine-conjugated BAs were reduced in the intestine and found a progressive decrease in TCA, TUDCA, and TDCA with disease progression (* p < 0.05, ** p < 0.01, Figure 4F–J). Next, the association between discrepancies in bile acid levels and inflammatory-cancer transformation was established by two multivariate modeling approaches, PCA and OPLS-DA. The results showed that they were distinguished by respective disease stages. As the HCC progresses, the PCA score plot demonstrated a definite trend, confirming the potential of BAs to predict disease staging (Figure 5A–C). Next, combining the contribution degree of OPLS-DA (VIP > 1) and the significance of independent t-test (p < 0.05), CDCA, LCA, UDCA, and GLCA in plasma, and CDCA in liver and intestine were seen as biomarkers that have a positive role in the early diagnosis of HCC (Figure 5D–F). To date, liver biopsy is currently the gold standard for early diagnosis of HCC, but patient acceptance of this standard invasive technique is poor. A BLDA diagnostic model was constructed by CDCA, LCA, UDCA, and GLCA in plasma to achieve non-invasive detection. The coefficients of the four biomarkers and constants in the BLDA diagnostic model are listed in Table 1. By substituting the bile acid concentrations into the respective equations, the probability of being classified in the corresponding disease group was calculated. The result indicated a reliable model; 86.7% of the samples could be correctly distinguished (Table S4). To explore the potential mechanisms of bile acid metabolism changes in HCC patients and to screen out valuable key target genes, 373 HCC samples and 50 healthy samples from the TCGA database were involved in the present analysis. GSEA enrichment analysis was used to screen out 15 gene sets related to the biological functions of bile acid (Table S5, Figure S1). We obtained 125 genes from 15 gene sets to import into the STRING database to complete the visualization of Protein-Protein Interaction (PPI) Networks with a confidence level > 0.4 (Figure 6). Finally, Cytoscape software was applied to calculate the key node genes based on the cyto Hubba plug-in and maximal clique centrality (MCC) algorithm, the most core gene BAAT was obtained, ranking first. BAAT is the final modification before catalyzing the generation of conjugated BAs from free BAs into the enterohepatic circulation [19]. Evidence indicates that BAAT promotes glycine-conjugating BAs with extremely low efficiency but efficient conjugating with taurine in rats [20,21]. The TCGA database supports that BAAT is significantly under-expressed in HCC cases (** p < 0.01, Figure S2), suggesting that BAAT deficiency is partly responsible for the decrease in taurine-conjugated BAs in the intestine, which would alter the composition of the intestinal bile acid pool and increase its toxicity, thereby promoting the progression of inflammation to HCC. In recent years, HBV infection has progressively developed into a major cause of HCC. At the same time, 80–90% of new cases occur in the context of cirrhosis, suggesting that hepatitis and cirrhosis play important roles in the precancerous liver environment [22,23]. It is confirmed through research that early diagnosis of HCC by monitoring BAs may improve prognosis and the feasibility of curative treatment [24]. Meanwhile, the bidirectional communication of the liver–gut axis is an essential part of coordinating the dynamic balance of the bile acid pool in the body [25]. However, there are few existing articles describing whole bile acid profiling in enterohepatic circulation during the process of “hepatitis-cirrhosis-HCC”. The pathogenesis of HCC has not been clear till now. We clarified the four disease stages of HCC development based on the previous literature [26,27] and histopathological analysis, first achieving the dual coverage monitoring of the dynamic changes of bile acid levels and distribution during the enterohepatic circulation and the evolution of “hepatitis-cirrhosis-HCC”, and found that the imbalance of the enterohepatic circulation system was the key driver of the inflammation-cancer transformation process, which contributes to cognitive the pathogenesis of HCC. This study indicated a significant sludge of BAs in the liver–gut axis, while TBA levels in plasma and liver are positively correlated with HCC progression. High levels of bile acid environment have been known to induce reactive oxygen species production and apoptosis in hepatocytes, further leading to impaired liver function [28]. It was accepted that the gradual accumulation of TBA is a major risk factor for the development of HCC, while it is well established that TBA levels and enterohepatic circulation profoundly influence each other [29]. Enterohepatic circulation is the process by which BAs pass from the liver to the intestine and then return to the liver through reabsorption from the portal vein [25,30]. The above process is intricately linked to processes that mainly undergo extensive feedback and feed-forward regulation by specialized absorption and excretion transport systems in the liver and intestine [31]. Furthermore, defective expression and function of bile acid export, as well as reabsorption, have been recognized as important causes of progressive cholestasis in the liver and plasma [32,33]. BAs in the above process are circulated through specialized absorption and excretion transport systems in the liver and intestine. Bile salt export pump (BSEP) and multidrug resistance-associated protein (MRP2) are key transport proteins for the hepatic efflux of BAs, while sodium bile acid/taurocholic synergistic polypeptide (NTCP) and organic anion transport peptide (OATP) are the main transport proteins in the liver responsible for uptake of circulating BAs in the portal vein [34,35]. Reports on patients with HCC also indicate that BSEP, MRP2, NTCP, and OATP expression is downregulated [29,36], corroborating the disruption of enterohepatic circulation in the development of HCC. The intestine is the site of secondary BA synthesis. Primary BAs synthesized in the liver are further metabolized in the intestine [37]. We provide dysregulation of the primary and secondary BAs in the liver–gut axis, revealing a unique metabolic regulation of BAs in the intestine. The organic solute transporter-alpha/beta (OSTα/OSTβ) are exporters of BAs from the intestine and are an important link in enterohepatic circulation [38]. It has been confirmed in the literature [39] that the absence of OSTα/OSTβ expression causes an increased level of BAs in the intestinal contents as well as in the small intestine. Our quantitative results showed that total secondary BAs were most significantly elevated in the intestine, in addition to being equally elevated in plasma but reduced in the liver, a characteristic phenomenon that likewise suggests a deficiency of the liver bile acid transport system. Mechanisms underlying the failure of the intestinal barrier and the development of a leaky gut are not fully understood. Still, abnormal retention of toxic BAs is recognized as an important contributing factor [40,41,42]. Secondary BAs are generated from primary BAs through reactions such as 7α-dehydroxylation, so they have the highest hydrophobicity compared to all BAs, a property thought to be linked to hepatotoxicity [43]. On the other side, secondary BAs and their derivatives are a major component of the intestinal bile acid pool, and their elevation represents a change in the toxicity of the intestinal bile acid pool [44]. With the progressive development of HCC, we concluded that due to the large accumulation of secondary BAs in the intestinal epithelium, the intestinal permeability is altered, which eventually causes intestinal fistula. Therefore, we believe that the phenomenon of an abnormal rebound of total secondary bile acids in plasma and the liver is caused by the development of intestinal fistula and the massive efflux of toxic substances accumulated in the intestine at the advanced HCC stage. The above processes also coincided with a progressive decrease of total and secondary BAs in the intestine of the disease group. CA and CDCA are two primary BAs, and DCA and LCA are secondary BAs from their conversion, respectively. According to the report that the hydrophobic-hydrophilic balance of BAs is closely related to metabolic homeostasis in vivo [45], more hydrophobic BAs can act as cancer promoters and further amplify the development of HCC [46,47]. The high hydrophobicity of CDCA and DCA makes them cytotoxic and pro-inflammatory [48,49]. CA is not highly hydrophobic, but studies have shown that feeding mice with CA increases the size and hydrophobicity of the bile acid pool while causing cholestasis and hepatic steatosis [50]. LCA also has hydrophobic properties, but that’s a small fraction of BAs. UDCA is a primary bile acid in rats, a non-toxic hydrophilic bile acid [51]. Evidence supports the ability of UDCA to accelerate enterohepatic circulation and its cytoprotective properties [52,53]. Therefore, the elevation of CA, CDCA, and DCA in the liver and intestine and the downregulation of LCA and UDCA imply a hydrophobic change in the composition of BAs and a progressive accumulation of toxic BAs that inhibit the enterohepatic circulation. Bile flow is primarily dependent on the drive of conjugated BAs. Congenital defects in BA conjugating can lead to malabsorption of fat-soluble vitamins and, thus, severe liver disease [54,55]. BAAT is the key enzyme capable of mediating bile acid coupling [19]. As mentioned earlier, it has been demonstrated that BAAT -/- mice are almost completely devoid of taurine-conjugated BAs in the liver, suggesting that BAAT is the primary taurine-coupled enzyme in mice [56,57]. Our figures showed that the TBA level in the intestines remained significantly elevated. At the same time, all the taurine-conjugated BAs were continuously reductive in the intestine of model rats. We speculate that the down-regulation of BAAT expression is the key reason for the above phenomenon. Consistent with this, the gene enrichment results confirm our previous speculation about the variation of taurine-conjugated BAs level in the intestine. Acetonitrile, isopropanol, and methanol were purchased from Fisher Scientific (Fair Lawn, NJ, USA), while formic acid, dimethyl sulfoxide, and acetone were purchased from Yuwang Co. Ltd. (Yucheng, China). The distilled water used in the experiments was purchased from Wahaha Group Co., Ltd. (Hangzhou, China). DEN used in animal experiments was purchased from Sigma-Aldrich (St. Louis, MO, USA). The commercial standards selected for this study, the bile acid used for quantitative analysis, their abbreviations, CAS numbers, and manufacturers are included in Table S1. For this study, Wistar male rats, weighing 100 ± 20 g, purchased from by the Animal Ethical Committee of Changsheng Biotechnology (IACUC No. CSE202106002), were used and provided a constant relative humidity of 65 ± 15% and a temperature of 23 ± 2 °C environment with 12 h-light dark cycles. At the same time, the rats have full access to food and water. The rats were fed and acclimatized to their environment for one week prior to the experiment. Then, 64 rats were randomly divided into two groups, the HCC model group and the healthy control group. Rats in the model group (n = 32) were injected intraperitoneally with DEN solution at a dose of 70 mg/kg once a week for 10 weeks, while rats in the control group (n = 32) were injected intraperitoneally with an equal volume of saline as a control. Liver tissue sections were deparaffinized with xylene and dehydrated in ethanol. Making tissue into 3 µm slice samples and then stained with H&E. Images were acquired using a NIKON digital sight DS-FI2 imaging system after observation with a NIKON Eclipse ci optical microscope. A previously published method by our group was used to quantify the BAs [58]. The method was based on a polar response homogeneous dispersion strategy with DEABA labeling, which reduces the polarity and response gap of the analytes and improves selectivity compared to non-derivatization. The ultra-performance liquid chromatography—tandem mass spectrometer (UPLC-MS/MS) systems and chromatographic column were used for the analysis, and liquid phase conditions can be found in previous methods. The positive ion gradient elution program was: 0.01–10.00 min, 20%B→50%B; 10.00–17.00 min, 50%B→85%B; 17.00–22.00 min, 85%B→90%B. The negative ion gradient elution program was 0.01–4.00 min, 20%B→35%B; 4.00–6.00 min, 35%B→70%B; 6.00–10.00 min, 70%B→85%B. 10.00–10.10 min, 85%B→90%B, and continued with 90% B running at 10.10–12.00 min. We used the electrospray ionization (ESI) source in both positive and negative ion form to accomplish the analysis and determination of BAs by multiple reaction monitoring (MRM) modes. The ion spray voltage was 5500 V(+)/4500 V(−), and the other parameters of the mass spectrum were as follows: curtain gas (N2), 20 psi; nebulizer gas (gas 1, N2), 50 psi; heater gas (gas 2, N2), 50 psi; and source temperature, 500 °C(+)/500 °C(−). The corresponding mass spectrometer (MS) parameters for the 15 BAs can be found in Table S2. For plasma samples, the whole blood samples were collected from each group following forbidden food for 12 h, placed in heparinized sterile eppendorf tubes, and centrifuged at 10,142× g for 10 min at 4 °C to transfer plasma. Then, BAs were extracted from plasma samples as described in the previous method [58]. For liver samples, rats in each group were killed by cervical dislocation after plasma collection. Liver tissue was immediately peeled out, bathed in physiological saline, blotted through filter paper, and transferred to a dry ice box soon afterward. Liver tissue samples (50.00 ± 0.50 mg each) were homogenized in 100 μL physiological saline for 5 cycles (5 s at 300 w, with 3 s between each cycle) by using an ultrasonic cell disruptor (JY92-IIDN, SCIENTZ, Zhengjiang, China) in an ice bath. One liver homogenate was added to 10 µL of internal standard and 10 µL of methanol, the same internal standard used for plasma samples. After vortex shaking for 30 s, 500 µL of precipitated protein reagent, methanol:isopropanol (v/v, 1:2), was added. The homogenate was centrifuged (4 °C, 10,142× g) with vortex shaking for 5 min for 10 min, and the upper layer was dried under a stream of nitrogen. The dried liver samples were derivatized in the same manner as the plasma samples and then subjected to subsequent analysis. For intestinal contents samples, on the day before the rats were killed, the rats were placed in metabolic cages to collect 24 h intestinal contents. The collected intestinal contents samples were lyophilized for 48 h and ground into powder. 50 mg ± 0.50 mg was taken from intestinal contents lyophilized powder and spiked with 500 µL of physiological saline, then vortexed for 10 min to obtain Intestinal contents homogenate. The pretreatment procedure for intestinal contents samples was approximately the same as for liver samples. The difference is that for protein precipitation, 600 µL of methanol:acetonitrile:acetone (v/v/v, 1:1:1) was added to the intestinal contents sample, and the supernatant before drying was filtered through 0.22 μm organic filter membrane. The dried intestinal contents sample were derivatized in the same manner as the plasma samples and then subjected to subsequent analysis. We collected samples from The TCGA genomic data commons data portal (https://portal.gdc.cancer.gov/ (accessed on 15 September 2022)) and obtained their RNA sequencing fragments per kilobase million data. In this study, we selected the gene sets associated with biological functions of bile acid (shown in Table S3) from the GSEA data set (https://www.gsea-msigdb.org/ (accessed on 5 September 2022)) and performed enrichment analysis between the two groups by GSEA software (version 4.2.3). Among them, gene sets whose p-value < 0.05, false discovery rate (FDR) < 0.05, and normalized enrichment score (NES) > 1.5 were collected for subsequence procession. We visualized the PPI network using STRING 11.5 (https://cn.string-db.org/ (accessed on 18 November 2022 )) and the cytoHubba plug-in of Cytoscape (version 3.9.1) software for screening key genes. The generated raw data files were processed using the Analyst® application (version 1.5.1, AB SCIEX™, Foster City, CA, USA), based on which standard curves were created, and all BAs were quantified. The significant differences between the experimental groups were determined using the SPSS Statistics (version 26.0, CHI, Chicago, IL, USA) and GraphPad Prism (version 9.2.0, GraphPad Software Inc., San Diego, CA, USA). The BLDA discriminant analysis was carried out with SPSS software, while PCA and OPLS-DA analysis used the SIMCA-P program (version 14.1, Umetrics, Malmö, Sweden). When the p-value < 0.05 or less, we considered the data evidently different and statistically significant. In this study, we achieved a dual coverage monitoring of the bile acid profile in the liver–gut axis throughout the whole inflammation-cancer transformation progression. We found that the enterohepatic circulation is disrupted during HCC development after intensively researching the differences in levels of TBA, primary/secondary BAs, and single BAs. Next, we used GSEA gene enrichment analysis to obtain the key node gene BAAT, which dominates the synthesis of taurine-conjugated BAs in rats. We also validated our specific phenomenon of taurine-conjugated BAs in the intestine. In summary, our results suggest that the disruption of the enterohepatic circulation in the internal environment is an important factor dominating the inflammation-cancer transformation process. The lack of BAAT may be one of the potential mechanisms interrupting the enterohepatic circulation. Additionally, we developed the BLAD diagnostic model, and found that GLCA, CDCA, UDCA, and LCA in plasma samples can be used as biomarkers to distinguish the different disease stages of HCC, enabling early diagnosis of HCC from the perspective of non-invasive detection. However, immunotherapy has been a hot research topic for treating HCC. It has been recently suggested that regulatory T cells, the most abundant immunosuppressive cell population of the HCC-related tumor microenvironment, might suggest a potential target for HCC immunotherapy [59]. Evidence supports that intestinal flora influences the differentiation, accumulation, and function of regulatory T cells [60], and the influence of intestinal flora on BAs metabolism is well established [61,62]. In future studies, it is of great interest and necessity to focus on the link between BAs metabolism, intestinal flora, and the immune cell population of the tumor microenvironment, which will contribute to the further development of HCC therapy.

PMC10001983

Lyndsey A. Reich,Ana S. Leal,Edmund Ellsworth,Karen T. Liby

The Novel RXR Agonist MSU-42011 Differentially Regulates Gene Expression in Mammary Tumors of MMTV-Neu Mice

21-02-2023

RXR agonist,breast cancer,transcriptomics

Retinoid X receptor (RXR) agonists, which activate the RXR nuclear receptor, are effective in multiple preclinical cancer models for both treatment and prevention. While RXR is the direct target of these compounds, the downstream changes in gene expression differ between compounds. RNA sequencing was used to elucidate the effects of the novel RXRα agonist MSU-42011 on the transcriptome in mammary tumors of HER2+ mouse mammary tumor virus (MMTV)-Neu mice. For comparison, mammary tumors treated with the FDA approved RXR agonist bexarotene were also analyzed. Each treatment differentially regulated cancer-relevant gene categories, including focal adhesion, extracellular matrix, and immune pathways. The most prominent genes altered by RXR agonists positively correlate with survival in breast cancer patients. While MSU-42011 and bexarotene act on many common pathways, these experiments highlight the differences in gene expression between these two RXR agonists. MSU-42011 targets immune regulatory and biosynthetic pathways, while bexarotene acts on several proteoglycan and matrix metalloproteinase pathways. Exploration of these differential effects on gene transcription may lead to an increased understanding of the complex biology behind RXR agonists and how the activities of this diverse class of compounds can be utilized to treat cancer.

The Novel RXR Agonist MSU-42011 Differentially Regulates Gene Expression in Mammary Tumors of MMTV-Neu Mice Retinoid X receptor (RXR) agonists, which activate the RXR nuclear receptor, are effective in multiple preclinical cancer models for both treatment and prevention. While RXR is the direct target of these compounds, the downstream changes in gene expression differ between compounds. RNA sequencing was used to elucidate the effects of the novel RXRα agonist MSU-42011 on the transcriptome in mammary tumors of HER2+ mouse mammary tumor virus (MMTV)-Neu mice. For comparison, mammary tumors treated with the FDA approved RXR agonist bexarotene were also analyzed. Each treatment differentially regulated cancer-relevant gene categories, including focal adhesion, extracellular matrix, and immune pathways. The most prominent genes altered by RXR agonists positively correlate with survival in breast cancer patients. While MSU-42011 and bexarotene act on many common pathways, these experiments highlight the differences in gene expression between these two RXR agonists. MSU-42011 targets immune regulatory and biosynthetic pathways, while bexarotene acts on several proteoglycan and matrix metalloproteinase pathways. Exploration of these differential effects on gene transcription may lead to an increased understanding of the complex biology behind RXR agonists and how the activities of this diverse class of compounds can be utilized to treat cancer. Retinoid X receptor (RXR) agonists bind to and activate the nuclear receptor RXR. RXR is a type II nuclear receptor, which is found in the nucleus bound to DNA and corepressor proteins [1,2]. Upon activation by a ligand, conformational changes in the structure of RXR promote dissociation of corepressor proteins and recruitment of diverse coactivator proteins. Because of its flexible dimerization domain, RXR homodimerizes or heterodimerizes with other nuclear receptors, including peroxisome proliferator-activated receptor (PPAR), liver X receptor (LXR), pregnane X receptor (PXR), or vitamin D receptor (VDR), to initiate transcription [3]. Upon activation, RXR regulates the transcription of target genes, involved in proliferation, differentiation, survival, and immune cell function [4]. Bexarotene is an RXR agonist, currently FDA approved to treat cutaneous T cell lymphoma (CTCL) [5]. Bexarotene has been tested in clinical trials for breast and non-small cell lung cancer but failed to attain approval for these indications, despite promising responses in some patients and manageable side effects [6,7]. Many have sought to improve the efficacy of bexarotene via novel drug delivery systems and formulations [8] or have made structural modifications to identify new RXR agonists [9,10]. Our new analog, MSU-42011, is effective for treatment in the MMTV-Neu model of HER2+ breast cancer [11], an established mouse model which recapitulates the human disease, as has been validated by gene expression profiling [12,13]. This model expresses wild-type, unactivated Neu in mammary tissue under the mouse mammary tumor virus (MMTV) promoter [14]. MSU-42011 also effectively reduces established tumor burden in the A/J mouse model of carcinogen-induced lung cancer [9]. In both of these preclinical models, changes in immune cell populations differed in the tumors of mice treated with MSU-42011 vs. bexarotene [9], suggesting that these compounds have distinct patterns of immunomodulatory activity. Nuclear receptor biology is complex, and gene transcription varies based on the nuclear receptor binding partner of RXR [15]. For example, target pathways under the control of RXR:RAR heterodimers include genes which induce the enzymes phosphoenolpyruvate carboxykinase (PEPCK) and tissue transglutaminase 2 (TG2), immune-related genes such as B cell translocation gene 2 (Btg2), and retinoic acid response genes such as aberrant cellular retinol binding protein 1 (Crbp1) and cellular retinoic acid-binding protein 1 (Crabp1) [16]. Several genes involved in lipogenesis (Agpat2, Acsl1, Gpat3) and glucose metabolism (Hk2, Taldo1) are regulated by RXR:PPAR dimerization in adipocytes [17]. VDR, another nuclear receptor for which RXR is an obligate heterodimer, regulates expression of an extensive list of genes which act as VDR response elements. In quiescent hepatic stellate cells, binding of calcipotriol to the VDR nuclear receptor initiates binding to a cistrome of 6281 target sites, which expands to 24,984 sites when these cells are activated by lipopolysaccharide (LPS) or transforming growth factor beta (TGFβ) [18]. Through dimerization with the PXR nuclear receptor, RXR regulates transcription of genes involved in xenobiotic and endobiotic metabolism, cytoprotective mechanisms, and detoxification, including enzymes such as CYP3A4 and efflux pumps such as MDR1 [19,20]. Because the network of nuclear receptor target genes is vast, the biological effects of RXR activation are numerous and diverse. Others have previously investigated the effects of bexarotene on the transcriptional regulatory network in mammary glands of mouse models of breast cancer [21], but to date no one has analyzed gene expression data from tumors treated with different RXR agonists. To this end, we used RNA sequencing to compare pathways activated by treatment with MSU-42011 versus pathways activated by bexarotene and validated selected genes by qPCR and immunohistochemistry. These data provide additional information about the cancer-relevant transcriptional regulation of RXR agonists and the diversity of activities of these compounds. To characterize differential expression across the whole transcriptome, high-throughput techniques such as RNA sequencing (RNA-seq) allow us to parse differentially expressed genes into biological pathways for comprehensive analysis of RXR agonist response in tumors. For these studies, MMTV-neu mice (four per group) were fed control diet, MSU-42011 (100 mg/kg diet), or bexarotene (100 mg/kg diet) for 10 days. Tumors were harvested and RNA was analyzed by RNA-seq (Figure 1A). Relative to control tumors, tumors treated with both RXR agonists had higher expression of canonical immune pathways such as binding of antigen presenting cells and proliferation of immune cells, mononuclear leukocytes, and lymphocytes (Figure 1B). Causal network analysis [22], a means of identifying upstream regulators of differentially expressed genes from RNA-seq, identified SMAD4, IRF3, IRF7, and ZBTB10 as possible regulatory nodes. Differential expression analysis revealed a list of genes (GSE211290) differentially expressed in control tumors vs. tumors from mice treated with MSU-42011 vs. tumors from mice treated with bexarotene. This list of 289 significantly (padj < 0.05) upregulated or significantly downregulated genes was sorted by adjusted p value. Of the top 10 most significant differentially expressed genes, high levels of expression of five genes correlate with improved overall survival in breast cancer patients—GRIA3 (logrank p = 3.1 × 10−7) (Figure 2A), CLEC10 (logrank p = 0.0035) (Figure 2B), FNDC1 (logrank p = 9.7 × 10−5) (Figure 2C), ISLR2 (logrank p = 4.8 × 10−5) (Figure 2D), and ITGA11 (logrank p = 2.4 × 10−6) [23] (Figure 2E). Survival curves were generated using the Kaplan–Meier Plotter (KmPlot) [24], without further stratification of breast cancer patients. These genes code for a glutamate receptor linked to migration and invasion (GRIA3) [25]; a c-type lectin with a role in cellular adhesion, signaling, and inflammation which serves as a dendritic cell marker (CLEC10) [26]; a fibronectin protein associated with invasion and chemoresistance (FNDC1) [27]; a member of the immunoglobulin superfamily which participates in nervous system development (ISLR2) [28]; and an alpha integrin which regulates adhesion to the extracellular matrix and the organization of collagen (ITGA11) [29]. Enrichment analysis on control vs. MSU-42011 vs. bexarotene differential expression data using EnrichR reveals a set of pathways regulated by treatment with the various RXR agonists (Figure 3). The KEGG 2019 mouse database was used for these analyses; analysis using the Wikipathways 2019 mouse database is also shown (Supplemental Figure S1). Identified pathways include genes associated with ECM-receptor interaction, chemokine signaling, focal adhesion, PI3K-Akt signaling, complement and coagulation cascades, and the phagosome. Genes within these pathways encode for macromolecules involved in cellular structure and function, cellular behavior such as adhesion and migration, and downstream signaling pathways. Enrichment analysis was used to compare differentially expressed genes in control vs. MSU-42011 and control vs. bexarotene groups. Bar charts of these analyses reveal enrichment of shared pathways (focal adhesion, ECM-receptor interaction), as well as pathways unique to MSU-42011 (rheumatoid arthritis, ribosome) and pathways unique to bexarotene (PI3K-Akt signaling pathway, Rap1 signaling pathway) (Figure 4A,B). These unique pathways include genes which code for critical components related to cellular proliferation, immunity, and cellular migration and invasion. Scatterplot depictions of pathways regulated by MSU-42011 (Figure 4C) and by bexarotene (Figure 4D) highlight the similarities and differences in pathway enrichment within a particular cluster across different drug treatments. Volcano plot depictions of pathways regulated by MSU-42011 (Figure 4E) and bexarotene (Figure 4F) highlight the pathways unique to MSU-42011, especially the ribosome pathway. This pathway contains genes which encode for components necessary for rapid cellular turnover, which is particularly relevant to tumor biology [30,31]. KEGG 2019 was used as a database for these analyses. Several genes were selected from the differential expression analysis for validation of mRNA expression by qPCR and protein levels by IHC. Collagen type VI a3 chain (COL6A3) is an extracellular matrix protein which is altered in several types of cancer [32]. Col6a3 mRNA expression (Figure 5A) is increased in tumors treated with MSU-42011 (p = 0.0425) but not in tumors treated with bexarotene. IHC (Figure 5B) demonstrates a 41% increase in Col6a3 protein levels in tumors treated with MSU-42011 (p = 0.0096), and no apparent increase in Col6a3 in bexarotene-treated tumors (Supplemental Figure S2D). Kmplot was used to investigate the relevance of Col6a3 expression in human breast tumors (Figure 5C). High expression of COL6A3 is correlated with increased relapse-free survival (p = 0.031) in HER2+ breast cancer patients. qPCR (Figure 5D) also confirms a significant (p = 0.0026) increase in Map9 mRNA in MSU-42011-treated tumors, while there was no significant increase observed in bexarotene-treated tumors. MAP9 is a microtubule-associated protein which regulates cell cycle and the DNA damage response [33]. High expression of MAP9 is positively correlated with relapse-free survival (Figure 5E) in breast cancer patients (p = 0.0023). As shown in Figure 4, the rheumatoid arthritis pathway is differentially regulated by MSU-42011 but not by bexarotene. The genes within this pathway include immune response genes which may contribute to the anti-tumor immunomodulatory activity of MSU-42011 [34]. The cytokine IL-18 was selected from the rheumatoid arthritis pathway for validation (Figure 6A). In tumors of mice treated with MSU-42011, but not bexarotene (Supplemental Figure S2A), mRNA expression of IL-18 increased (p = 0.0116). IHC (Figure 6B) revealed an increase in IL-18 in tumors treated with MSU-42011 (p = 0.04825). Interestingly, tumors from the bexarotene group display an apparent paucity of IL-18, even in comparison to control tumors (Supplemental Figure S2B). Importantly, Kmplot analysis reveals that high IL-18 expression is correlated with increased relapse-free survival in breast cancer patients (p = 0.00022) (Figure 6C). MSU-42011-treated tumors also demonstrate a significant (p = 0.040822) upregulation of the gene coding for major histocompatibility complex (MHC) component H2-AA by qPCR (Figure 6D). RXR agonists regulate pathways relevant to the function of the immune system, such as rheumatoid arthritis, complement and coagulation cascade, and cytokine–cytokine receptor interaction. To validate and further characterize the immunomodulatory activity of these compounds, BMDMs treated with RXR agonists were evaluated for expression of cancer-relevant genes within these pathways. Monocytes were harvested and differentiated with MCSF (20 ng/mL). On Day 5, BMDMs were treated with conditioned media from E18-14C-27 cells, derived from MMTV-Neu mammary tumors, to induce a tumor-educated macrophage phenotype. BMDMs were treated with conditioned media alone, or with 300 nM of either MSU-42011 or bexarotene. After 24 h, the relative proportion of F4/80+CD206+ macrophages was significantly (p = 0.02726) lower in BMDMs treated with conditioned media and 300 nM MSU-42011 compared to conditioned media alone (Figure 7A) In comparison, treatment with 300 nM bexarotene and conditioned media did not significantly alter the relative proportion of F4/80 + CD206+ BMDMs (p = 0.9423). Treatment with 300 nM of either RXR agonist significantly (p = 0.0016) decreased mRNA expression of IL-13, an immunosuppressive cytokine (Figure 7B). A trend of increasing TLR9 and IRF1 mRNA expression, associated with a pro-inflammatory, anti-tumor phenotype was observed in BMDMs treated with both RXR agonists (Figure 7C,D). RXR agonists also induce a significant (p = 0.00015) increase in expression of CCL6, a pro-inflammatory cytokine (Figure 7E). RXR agonists are a class of drugs with anti-tumor activity in preclinical models of breast and lung cancer [9,10,35]. While the known target of these drugs is the nuclear receptor RXR, different RXR agonists have markedly different effects on downstream gene expression. The nature of nuclear receptors—their ability to homodimerize or to heterodimerize with other nuclear receptors, the diversity of the structures of their ligands, and the vast number of target genes—makes RXR an interesting drug target. These characteristics likely differ among RXR agonists, potentially initiating heterodimerization with different nuclear receptor partners or recruiting different coactivators, leading to variations in resulting gene expression which may be clinically beneficial. For the first time, using RNA-seq, we compared pathway activation and biological activity of the novel RXR agonist MSU-42011 and the FDA-approved bexarotene. The regulation of many similar pathways, including focal adhesion and extracellular matrix components, are shared by these two molecules (Figure 4). Immune-related pathways such as cytokine signaling pathways, complement activation, and genes related to phagosome activity are also shared by both MSU-42011 and bexarotene. Interestingly, validation of individual genes within these pathways shows that while one RXR agonist upregulates an immune- or ECM-related gene, the other RXR agonist does not. For example, MSU-42011 increases expression of Il-18 and Col6a3 at both the mRNA and protein level (Figure 5 and Figure 6), but neither of these two gene products are increased in tumors treated with bexarotene. Several pathways were identified through enrichment analysis that were unique to a single RXR agonist. For example, the ribosome pathway and the fatty acid biosynthesis pathway, through which macromolecules critical to cellular function are synthesized, were prominent in enrichment analysis for MSU-42011 but not bexarotene. Conversely, the proteoglycans in cancer pathway, containing genes which code for matrix metalloproteinases (MMP), WNT signaling molecules, and growth factors such as IGF1 and FGF2, is prominent in bexarotene differential expression analysis but not MSU-42011. The increase in Il-18 expression seen at both the level of mRNA and protein in tumors treated with MSU-42011, but not bexarotene, suggests that this RXR agonist promotes a pro-inflammatory tumor microenvironment, which can be harnessed for breast cancer treatment. IL-18 expression has been investigated as a possible prognostic indicator in breast cancer patients [36] and augments the cytotoxicity of NK cells [37]. Further investigation into the mechanism of MSU-42011 is necessary to determine if Il-18 is a critical mediator of anti-tumor immune response, and if it can be used as an indicator of response to therapy. Furthermore, the increase in H2-Aa mRNA observed in tumors treated with MSU-42011 provides further evidence of its immune modulatory properties. H2-AA is an MHC class II component, higher expression of which is correlated with increased survival in ovarian cancer [38]. MHC II is responsible for antigen presentation to CD4+ T cells, which have recently gained recognition supporting the activation of cytotoxic T cells and mediating checkpoint inhibition response in cancer [39]. The MHC II pathway is necessary for antitumor immunity in several cancer types and is upregulated by treatment with histone deacetylase (HDAC) inhibitors [40,41]. In triple negative breast cancer, high expression of genes associated with the MHC II pathway correlates with progression-free survival [42]. Pharmacologic means of augmenting MHC II signaling may be a valuable therapeutic strategy for enhancing anti-tumor immunity. The increase in expression in Il-18 mRNA and protein and H2-aa mRNA observed in tumors treated with MSU-42011, but not bexarotene, may provide insight into the unique immunomodulatory properties of these two RXR agonists. While COL6A3 expression has been explored as a prognostic biomarker in colorectal cancer [43], less is known about the role of COL6A3 in breast cancer. There is a trend of decreased COL6A3 expression with increasing tumor stage in breast cancer patients [32], which suggests a propensity for invasion and metastasis in these tumors [44]. Further, increased expression of COL6A3 in breast cancer after chemotherapy may predict for responsiveness to chemotherapy [45]. Finally, a cleavage fragment of COL6A3 known as endotrophin recruits macrophages through induction of monocyte chemoattractant protein-1 (MCP1) and increases IL-6 and TNFα in the tumor microenvironment [46]. Similarly, in obesity, collagen VI expression in omental white adipose tissue is correlated with expression of MCP-1, CD68, and CD86, providing further evidence that this collagen influences macrophage infiltration and phenotype [47]. As the role of COL6A3 is complex and can vary between cancer types and across tumor staging, the increase in expression of Col6a3 mRNA and protein in tumors treated with MSU-42011 and resultant effect on invasion and immunity merits further investigation. The expression of the microtubule-associated protein MAP9 is altered in both colorectal cancer and breast cancer, leading to cell cycle dysregulation [33]. MAP9 hypermethylation in breast cancer leads to decreased expression and may have utility as an epigenetic biomarker [48]. Further, MAP9 transcription is induced upon DNA damage, and MAP9 protein interacts with and stabilizes p53 in Sa-OS-2 cells, leading to increased tumor suppressor activity [49]. As mRNA expression of Map9 is increased in tumors treated with MSU-42011, an exploration of the effects of MSU-42011 on cell cycle control and the ways this may be exploited for therapeutic purposes is warranted. Based on our RNA sequencing data, particularly differentially expressed genes and pathways relating to immunity, we investigated the effects of MSU-42011 treatment on cell surface marker and gene expression in BMDMs (Figure 7). MSU-42011 decreased the relative proportion of F4/80 + CD206+ BMDMs by flow cytometry, indicating that treatment with MSU-42011 decreases immunosuppressive macrophages, while bexarotene did not have any effect. Further markers of immunosuppressive and pro-inflammatory macrophages were evaluated in BMDMs treated with RXR agonists by qPCR. MSU-42011 decreased expression of Il-13, an immunosuppressive cytokine, and increased expression of Ccl6, a pro-inflammatory cytokine. Furthermore, treatment with MSU-42011 increased expression of Tlr9 and Irf1, an interferon-regulatory factor known to be induced by ligation of TLR9. The TLR9-IRF1-IFN signaling axis has been implicated in macrophage polarization [50]. Taken together, these data provide additional evidence that MSU-42011 skews macrophages away from a tumor-promoting, immunosuppressive phenotype and toward an anti-tumor, proinflammatory phenotype. This effect on macrophages may be important for the anti-tumor activity of MSU-42011. In conclusion, treatment with RXR agonists results in modulation of gene expression that are consistent with effective cancer treatments. As a drug class, RXR agonists display a broad range of activities, regulating different genes and biological pathways. The diversity of these compounds may allow them to be utilized for targeted or personalized cancer therapy. MSU-42011 was prepared as previously described [9,10,11]. Bexarotene was purchased from LC Laboratories (Woburn, MA, USA). For in vivo studies, RXR agonists were dissolved in a vehicle of 1 part ethanol: 3 parts highly purified coconut oil (Neobee oil, Thermo Fisher Scientific, Waltham, MA, USA). A total of 50 mL vehicle or drug dissolved in vehicle was mixed into 1 kg of powdered 5002 rodent chow (PMI Nutrition, St. Louis, MO, USA) using a stand mixer (KitchenAid, Benton Harbor, MI, USA). Bone marrow-derived macrophages (BMDM) were isolated from femurs of adult C57BL/6 mice and differentiated using 20 ng/mL MCSF (Biolegend #576406, San Diego, CA, USA), as previously described [51]. Conditioned media was harvested from E18-14C-27 cells, derived from MMTV-Neu tumors, after 48 h of culture. BMDMs were treated using 75% conditioned media supplemented with 25% fresh media, with or without 300 nM RXR agonists for 24 h. IL-4 (10 ng/mL)(Biolegend #574304) was used as a positive control to induce a CD206+ immunosuppressive macrophage phenotype. BMDMs were harvested after 24 h treatment with conditioned media, with or without RXR agonists, filtered, and stained with fluorescent antibodies against F4/80 (APC, BM8, Biolegend) and CD206 (PE, MR6F3, Thermo Fisher Scientific). Live/dead green (Thermo Fisher Scientific) was used as a viability dye. Samples were run on BD Accuri C6 (BD Biosciences, San Jose, CA, USA. MMTV-Neu mice [14] from our breeding colony (founders were purchased from Jackson Laboratory, Bar Harbor, ME, USA) were fed pelleted chow and palpated for tumors. Once tumors were detected, mice were switched to powder 5002 chow. Tumors were measured twice weekly with a caliper until 4 mm in diameter, at which time mice were randomized and fed control diet or 100 mg per kg per day diet of RXR agonist diet (~25 mg per kg per day body weight) for 10 days. Tumors were harvested and sections were either flash frozen for RNA-seq/qPCR or saved in neutral buffered formalin for immunohistochemistry. Frozen tumor sections (4 samples per treatment group) were weighed and homogenized. RNA was extracted using a RNeasy Mini Kit (Qiagen, Hilden, Germany), and the quality of the RNA confirmed with an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA sequencing was completed by Novogene (Sacramento, CA, USA) as described previously [52]. Raw read counts were analyzed using the DESeq2 package in R (R for Windows v. 4.1.2; R Studio v. 1.4.1717) to generate differential expression profiles, and EnrichR and Ingenuity Pathway Analysis (Qiagen) were used for enrichment analysis. Raw and processed date were deposited in the Gene Expression Omnibus and are available through GSE211290. RNA harvested from frozen tumor sections was normalized across samples using Nanodrop (Thermo Fisher Scientific), and 500 ng of RNA was used to synthesize cDNA using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). PCR was run on QuantStudio 7 Flex (Thermo Fisher Scientific) using SYBR green fluorescence. PCR data was analyzed using the delta-delta CT method using GAPDH as a housekeeping control. Error bars represent standard error of biological replicates, as indicated in figure legends. The following forward/reverse primers (Integrated DNA Technologies, Coralville, IA, USA) were used: IL-18, 5′-TCCTTGAAGTTGACGCAAGA-3′/5′-TCCAGCATCAGGACAAAGAA-3′, Col6a3, 5′ AAGGACCGTTTCCTGCTTGTT-3′/5′-GGTATGTGGGTTTCCGTTGAG-3′. Map9, 5′-GAAGAGTGCTACAGCCAACAC-3′/5′-ACAACAAGGTTTTTCCCCTTCC-3′, H2-AA, 5′-TCAGTCGCAGACGGTGTTTAT-3′/5′-GGGGGCTGGAATCTCAGGT-3′. Formalin-fixed tissues were embedded in paraffin and sectioned by the Histology Core. Boiling citrate buffer was used for antigen retrieval, and endogenous peroxidase activity was quenched using hydrogen peroxide. Tissue sections were stained with antibodies against IL-18 (1 μg/mL, PA5-79481, Thermo Fisher Scientific), and Col6a3 (20 µg/mL, PA5-49914, Thermo Fisher Scientific), as described [34]. Sections were then labeled with biotinylated secondary antibodies (anti-rabbit, Cell Signaling Technology, Danvers, MA, USA; anti-rat, Vector Labs, Burlingame, CA, USA), as previously described. [34] A DAB substrate (Cell Signaling) was used for signal detection, as per manufacturer-provided protocols, and sections were counterstained with hematoxylin (Vector Labs). The Fiji ImageJ image processing package (version ImageJ2) was used for quantification of intensity of DAB staining by the color deconvolution method [53] and mean gray value was used to calculate optical density by the formula OD = log (max intensity/mean intensity, with a maximum intensity of 255 for 8–bit images [54]. Survival curves were generated using Kaplan–Meier Plotter (https://kmplot.com/analysis/, accessed on 26 July 2022). This tool allows for correlation of gene expression to publicly available patient survival data [23]. KmPlot sources this patient data from GEO, EGA, and TCGA databases. The patient samples are split into two groups, high and low expression of the gene in question, using a robust autoselect algorithm to determine the most appropriate cutoff [24]. Breast cancer data was used, and overall or relapse free survival was compared. Results were expressed as the mean ± standard error. Data from tumor qPCR experiments were analyzed by one-tailed t test. p < 0.05 was considered statistically significant throughout all experiments. For RNAseq, differential expression analysis was performed using DESeq2. Outliers are detected by Cook’s distance and removed [55]. p values were adjusted to correct for multiple comparisons using the Benjamini and Hochberg method, and padj < 0.05 was considered statistically significant [56]. Data from in vitro experiments were analyzed using one-way ANOVA, and significant differences between groups were determined by the Tukey HSD multiple comparisons test.

PMC10001986

Endrit Shahini,Giuseppe Pasculli,Antonio Giovanni Solimando,Claudio Tiribelli,Raffaele Cozzolongo,Gianluigi Giannelli

Updating the Clinical Application of Blood Biomarkers and Their Algorithms in the Diagnosis and Surveillance of Hepatocellular Carcinoma: A Critical Review

21-02-2023

alpha-fetoprotein (α-FP),des-γ-carboxy prothrombin (DCP),hepatocellular carcinoma,screening,surveillance,algorithm

The most common primary liver cancer is hepatocellular carcinoma (HCC), and its mortality rate is increasing globally. The overall 5-year survival of patients with liver cancer is currently 10–20%. Moreover, because early diagnosis can significantly improve prognosis, which is highly correlated with tumor stage, early detection of HCC is critical. International guidelines advise using α-FP biomarker with/without ultrasonography for HCC surveillance in patients with advanced liver disease. However, traditional biomarkers are sub-optimal for risk stratification of HCC development in high-risk populations, early diagnosis, prognostication, and treatment response prediction. Since about 20% of HCCs do not produce α-FP due to its biological diversity, combining α-FP with novel biomarkers can enhance HCC detection sensitivity. There is a chance to offer promising cancer management methods in high-risk populations by utilizing HCC screening strategies derived from new tumor biomarkers and prognostic scores created by combining biomarkers with distinct clinical parameters. Despite numerous efforts to identify molecules as potential biomarkers, there is no single ideal marker in HCC. When combined with other clinical parameters, the detection of some biomarkers has higher sensitivity and specificity in comparison with a single biomarker. Therefore, newer biomarkers and models, such as the Lens culinaris agglutinin-reactive fraction of Alpha-fetoprotein (α-FP), α-FP-L3, Des-γ-carboxy-prothrombin (DCP or PIVKA-II), and the GALAD score, are being used more frequently in the diagnosis and prognosis of HCC. Notably, the GALAD algorithm was effective in HCC prevention, particularly for cirrhotic patients, regardless of the cause of their liver disease. Although the role of these biomarkers in surveillance is still being researched, they may provide a more practical alternative to traditional imaging-based surveillance. Finally, looking for new diagnostic/surveillance tools may help improve patients’ survival. This review discusses the current roles of the most used biomarkers and prognostic scores that may aid in the clinical management of HCC patients.

Updating the Clinical Application of Blood Biomarkers and Their Algorithms in the Diagnosis and Surveillance of Hepatocellular Carcinoma: A Critical Review The most common primary liver cancer is hepatocellular carcinoma (HCC), and its mortality rate is increasing globally. The overall 5-year survival of patients with liver cancer is currently 10–20%. Moreover, because early diagnosis can significantly improve prognosis, which is highly correlated with tumor stage, early detection of HCC is critical. International guidelines advise using α-FP biomarker with/without ultrasonography for HCC surveillance in patients with advanced liver disease. However, traditional biomarkers are sub-optimal for risk stratification of HCC development in high-risk populations, early diagnosis, prognostication, and treatment response prediction. Since about 20% of HCCs do not produce α-FP due to its biological diversity, combining α-FP with novel biomarkers can enhance HCC detection sensitivity. There is a chance to offer promising cancer management methods in high-risk populations by utilizing HCC screening strategies derived from new tumor biomarkers and prognostic scores created by combining biomarkers with distinct clinical parameters. Despite numerous efforts to identify molecules as potential biomarkers, there is no single ideal marker in HCC. When combined with other clinical parameters, the detection of some biomarkers has higher sensitivity and specificity in comparison with a single biomarker. Therefore, newer biomarkers and models, such as the Lens culinaris agglutinin-reactive fraction of Alpha-fetoprotein (α-FP), α-FP-L3, Des-γ-carboxy-prothrombin (DCP or PIVKA-II), and the GALAD score, are being used more frequently in the diagnosis and prognosis of HCC. Notably, the GALAD algorithm was effective in HCC prevention, particularly for cirrhotic patients, regardless of the cause of their liver disease. Although the role of these biomarkers in surveillance is still being researched, they may provide a more practical alternative to traditional imaging-based surveillance. Finally, looking for new diagnostic/surveillance tools may help improve patients’ survival. This review discusses the current roles of the most used biomarkers and prognostic scores that may aid in the clinical management of HCC patients. Hepatocellular carcinoma (HCC) is the main type of primary liver cancer [1]. HCC is the third most common cause of cancer death worldwide, as it is the second most common cause of cancer [2]. The risk factors vary by region, but patients with any type of cirrhosis, particularly those between the ages of 40 and 60 in Western countries [1], are at high risk of developing HCC, with an annual risk ranging from 1% to 8% [3,4]. HCC affects 70–95% of patients with chronic liver disease (CLD), especially those with hepatitis B (HBV) or hepatitis C (HCV) virus infection [1,2,3,4], with the remainder caused by alcoholic liver cirrhosis (ALD) and the progressive form of non-alcoholic steatohepatitis (NASH), and less commonly by organochlorine pesticides, aflatoxins, Wilson’s disease, hemochromatosis, and alpha-antitrypsin deficiency [5]. Meanwhile, new etiological factors, such as metabolic liver disease, are becoming more relevant and must be considered separately. The observed decrease in HCC incidence in many countries, including China, is associated with the implementation of HBV vaccination and therapy [6], HCV treatment programs [6,7,8] or reduced aflatoxin exposure and may be mitigated or even overshadowed in the future by the recently rising prevalence of metabolic syndrome and NASH [1,3,5]. Figure 1 shows a depiction of a future possible scenario of prognostic scores in the context of HCC. Current guidelines for HCC screening include every six months abdominal ultrasound (US), with or without serum alpha-fetoprotein (α-FP), in patients with cirrhosis and subgroups with chronic hepatitis B virus (CHB) infection [1,9,10,11]. Limited randomized clinical trial (RCT) data among Asian patients with CHB and numerous cohort studies consistently demonstrate that screening is significantly associated with early HCC detection, increased curative treatment receipt, and improved survival [10]. Specifically, a 2018 meta-analysis found that when combined with US, α-FP can increase sensitivity for early-stage HCC detection (63% versus 45% with US alone), and a modelling study found that US and α-FP were the most cost-effective screening strategy across the majority of simulations [12]. However, insufficient early HCC detection/prevention and suboptimal risk stratification methods, a lack of therapeutic remedies for those people detected at late stages, irregular implementation of curative therapies in clinical practice, and competing risks of mortality from underlying liver disease can all lead to increased HCC fatality in high-risk populations. Indeed, only about 30% of patients with HCC in Europe today are diagnosed at an early stage when curative treatments are available [1]. Similarly, patients with advanced-stage disease in the United States (USA) have a 5-year survival rate of less than 5%, compared to more than 70% in those with early-stage HCC [13]. In cirrhotic patients, lectin-bound α-FP (α-FP-L3), des-gamma carboxy-prothrombin (DCP), and other emerging biomarkers are frequently used as HCC surveillance tests. Based on the disease stage and aetiology, they do, however, perform differently in terms of early HCC detection [9]. Nonetheless, except for α-FP and DCP, which are recommended by Japanese HCC guidelines, none of the biomarkers have been validated in Phase III clinical trials and are used in clinical practice [14]. This is due to the high heterogeneity of HCC biology, in which changes in various biochemical pathways contribute to tumorigenesis and, as a result, diverse biomarker expressions [15,16]. Despite some flaws and pitfalls, the role of specific biomarkers as alternative or complementary diagnostic tools for the current standard of care for early-stage diagnosis of HCC is being intensively researched in this context (Figure 2) [5]. The following section discusses the contemporary role of the most common biomarkers and prognostic scores that could improve the clinical management of patients with HCC. We conducted a literature critical review on relevant international English articles published between 1984 and 2022 using the PubMed database. The following keywords were used and linked by “AND” to select scientific papers: “marker”, “biomarker”, “diagnosis”, “prognosis”, “predictive scores”, “hepatocellular carcinoma” and “HCC”. The following sections discuss HCC biomarkers and prognostic scores, as well as their use in screening tests, diagnosis, and surveillance. We present current information derived from clinical trials, prospective and cohort studies, and meta-analyses. We rejected all unrelated articles from a total of 64,626 articles. We chose articles that discussed the clinical applicability of biomarkers. Finally, we gathered 166 articles. Figure 3 depicts the timeline and number of articles published on the most validated biomarkers in HCC up to 2023. The α-FP is a glycoprotein linked to the growth and development of HCC, and it induces malignant transformation of liver cells as well as proliferation, migration, apoptosis, and immune escape [17]. The α-FP is the only widespread used biomarker for HCC identification and surveillance, although it is not assumed to have appropriate screening performance characteristics. α-FP elevations can be definitively caused by a variety of illnesses, leading to false positive results, especially in patients with active CHB and CHC infections [18]. According to European guidelines from 2001, α-FP has a low sensitivity of 39–64%, and specificity of 76–97% (at its traditional cut-off of 20 ng/mL), and a positive predictive value (PPV) of 9–32% for early-stage diagnosis of HCC [18], which Stefaniuk P et al. confirmed in a review study in 2010 [19]. When the sensitivity drops to 22% at higher cut-offs of 200 ng/mL, the specificity increases [1]. Notably, serial α-FP value changes have been shown to be superior to single α-FP values in the detection of early-stage HCC [20,21,22]. The serum levels of α-FP demonstrated good accuracy in HCC diagnosis, with the 400 ng/mL threshold outperforming the 200 ng/mL threshold in terms of sensitivity and specificity, whether α-FP was used alone or together with US [22]. Nevertheless, approximately two-thirds of patients with HCC 4 cm showed α-FP levels less than 200 ng/mL, and approximately 20% of HCC do not produce α-FP [19]. However, despite its poor sensitivity and specificity [23], the dosage of α-FP concentration is one of the historical biomarkers most used for detecting HCC in at-risk groups such as cirrhotic patients [18,24]. Moreover, among other potential applications, α-FP has been also used to predict postoperative prognosis after surgical liver resection [25], as well as the likelihood of neoplasm recurrence in patients following liver transplantation [26]. Nonetheless, according to recent data, the optimal α-FP screening threshold may now be lower (as low as 12–20 ng/mL) due to a reduction in false positive cases due to increased antiviral therapy use [1,27]. Ultimately, because α-FP is insufficient as a screening test on its own, it is likely to play a role in the early detection of HCC when combined with other tests. α-FP-L3, or lens culinaris agglutinin-reactive α-FP, is a fucosylated glycoform of α-FP that was proposed about three decades ago as an early detection biomarker for HCC [28]. Notably, fucosyltransferase modifies the α-FP carbohydrate chain during HCC development, resulting in α-FP-L3 with a sensitivity of 40–90% and a specificity of over 90% for detecting HCC based on cohort characteristics [19]. The ratio of fucosylated α-FP to total α-FP is expressed as the percentage of α-FP-L3 [19,29,30,31]. Additionally, despite the fact that α-FP-L3 levels can be elevated in severe hepatitis [32], it appears to be a useful marker for detecting HCC in its early stages and predicting recurrence [33]. Specifically, the pooled sensitivity, specificity, and positive (PLR) and negative likelihood ratios (NLR) of α-FP-L3% for the diagnosis of early HCC in a 2021 meta-analysis [34] of 2447 patients from six heterogeneous studies were 34% (95% confidence interval (CI) 30% to 39%, p < 0.0001), 92% (95% CI 91% to 93%, p < 0.0001), 4.46 (95% CI 2.94 to 6.77, p = 0.0033), and 0.71 (95% CI 0.61 to 0.82, p = 0.0004). The diagnostic odds ratio (OR) was 6.78 (95% CI 4.02 to 11.44, p = 0.0074). The summary receiver operating characteristic’s area under the curve (AUROC) was 75% (95% CI 57% to 94%). According to recent data from a Phase III cohort (n = 397) in the USA (Singal AG et al., 2022), α-FP-L3 had a sensitivity of 46.2% and a false positive rate (FPR) of 10% in the six months preceding the diagnosis of HCC [35]. A cut-off of 8.3% had a fixed FPR of 10% and a sensitivity of 40% for early-stage HCC [36]. Furthermore, measurements of α-FP-L3 before treatment with the highly sensitive method were more useful for the diagnosis and prognosis of HCC than measurements with the conventional method in patients with α-FP less than 20 ng/mL [34]. Overall, these findings indicate that α-FP-L3 does not perform well enough as a standalone biomarker for HCC, but it may be useful in a biomarker-panel-based screening strategy. Des-γ-carboxy-prothrombin (DCP) is immature prothrombin that lacks carboxylation to various glutamate residues [19]. Prothrombin is shaped after the γ-carboxylation of vitamin K-dependent propeptides. DCP is produced due to an acquired post-translational defect in malignant cells’ vitamin K-dependent carboxylase sequence [37]. Hence, DCP production does not increase in CLD or cirrhosis, although it is a potential marker for the early diagnosis of HCC [19,38]. However, DCP measurement has no prognostic value in cases of vitamin K deficiency or inhibition of vitamin K function (i.e., in subjects receiving dicumarol therapy), because its synthesis is also induced by vitamin K deficiency, resulting in false positives. As a result, DCP is also known as PIVKA-II (protein induced in vitamin K absence). Despite these limitations, many studies have shown that DCP has higher sensitivity (48–62%) and specificity (81–98%) than α-FP in distinguishing HCC from other CLD [19,39,40]. Liebman et al. [40] and Koike Y et al. [41] demonstrated that DCP can be used as a very specific diagnostic and prognostic marker in HCC patients in two multicentre and prospective studies involving 76 and 227 patients, respectively. DCP has subsequently undergone Phase II and early Phase III validation. DCP alone showed an AUROC of 0.72 in a Phase II study of 131 early HCC patients [31]. However, a Phase III study revealed low sensitivity in detecting early HCC (26.3%) with a fixed FPR of 10% [42]. The overall sensitivity, specificity, PLR and NLR of DCP for the detection of HCC were 67% (95% CI, 58% to 74%), 92% (95% CI, 88% to 94%), 7.9 (95% CI, 5.6 to 11.2), and 0.36 (95% CI, 0.29 to 0.46) in a 2012 bivariate meta-analysis of 20 publications with significant heterogeneity [43]. The area under the bivariate summary ROC curve was 89% (95% CI, 85% to 92%) [43]. Another similar meta-analysis later confirmed that DCP had moderate diagnostic accuracy in HCC [44]. Notwithstanding, other data have suggested that DCP may not significantly improve the discriminatory power of α-FP and α-FP-L3 in the early detection of HCC [45]. Additionally, Sagar VM et al. discovered that DCP levels correlated with treatment response in most patients across a variety of therapeutic modalities in a cohort of 141 UK patients and found that DCP levels were informative in 60% of cases among α-FP non-secretors [46]. The type II Golgi-localized transmembrane protein known as Golgi protein 73 (GP73) is primarily expressed by cells of the epithelial lineage, though hepatocytes in a healthy liver also express it in small amounts [47]. GP73 expression significantly rises in the liver of HBV and HCV virus-infected individuals with cirrhosis [48], or focal nodular hyperplasia [49]. Numerous tumour cell types exhibit high levels of the serum GP73, which can also be used to diagnose HCC [50,51]. Even though the underlying mechanisms that lead to elevated GP73 levels are unknown, their roles in its secretion and potential contribution to HCC diagnosis are extremely important [52,53]. GP73 plays a role in the development of HCC through multiple mechanisms, by promoting the epithelial–mesenchymal transition in HCC cells, by interacting with EGFR to control the latter’s cell-surface recycling [54,55], or partly by targeting TGF-β1/Smad2 signalling [56]. Additionally, GP73 works with MMP2 or MMP7 in HCC cells to encourage their secretion and movement, which aids in the metastasis of HCC cells [52,53]. It has been also shown that GP73 and α-FP work in concert by increasing α-FP secretion via direct binding to α-FP, and that GP73 can aid HCC cells that express α-FP and its receptor in cancer progression and metastasizing [50]. Furthermore, extracellular α-FP and GP73 worked together to intensify the HCC cells’ malignant phenotype [50], decreasing patient survival rates [50,51]. Interestingly, GP73 levels can also be used to assess the efficacy of an anti-cancer treatment [57], and to choose patients determining the likelihood of potential complications following hepatectomy [58]. According to a 2009 Chinese study by Li X et al. [59], GP73 tests have higher sensitivity and specificity for early detection of HCC than α-FP (sensitivity, 62% vs. 25%, and specificity, 88% vs. 97%; p < 0.0001, respectively), and GP73 serum levels increased with the malignant potential of CLD [58]. Medium GP73 levels were higher in an Asian population of 124 patients with various forms of CLD (p < 0.001) than in healthy individuals and patients with other diseases for the diagnosis of HBV-related HCC [60]. In patients with HBV-related HCC, GP73 had higher sensitivity, specificity, and AUROC than α-FP (87.1%, 83.9%, and 92% vs. 48.4%, 96.8%, and 77%, respectively) [60]. Instead, a 2012 meta-analysis of eight studies discovered that GP73 was just as reliable as α-FP in diagnosing HCC regardless of the aetiology of CLD. In the included studies, the summary estimates for GP73 and α-FP in diagnosing HCC were as follows: sensitivity, 76% (95% CI 51% to 91%) vs. 70% (47% to 86%); specificity, 86% (95% CI 65% to 95%) vs. 89% (95 % CI 69% to 96%); DOR, 18.59 (95% CI 5.33 to 64.91) vs. 18.00 (95% CI 9.41 to 34.46); and AUROC 88% (95% CI 77% to 99%) vs. 86% (95% CI 84% to 87%) [61]. More recently, GP73 levels, among other biomarkers, were significantly higher in HCC compared with the other groups (CHC with/without cirrhosis and healthy subjects) in a 2020 Egyptian study of 238 patients (p < 0.001). For GP73, the ROC curve analysis revealed 91% sensitivity, 85% specificity, 74.7% PPV, and 95% NPV (AUROC 96%) [62]. Therefore, GP73 serum levels may be comparable to α-FP as promising HCC predictor biomarkers. Glypican-3 (GPC-3) is a glycoprotein that belongs to the proteoglycan family that contains heparan sulfate and is expressed in 72–81% of HCC cases [63]. GPC-3 serum levels are associated with a poor prognosis, as well as advanced tumour stage detection, vascular invasion, and metastases [64]. Moreover, a rapid increase in GPC-3 expression is also linked to the progression of precancerous lesions to HCC [65] Moreover, GPC-3 detection allows HCC to be distinguished from healthy liver tissue, benign lesions, and liver cirrhosis [66]. Concurrent detection of GPC-3 and α-FP improves test sensitivity and specificity, allowing for earlier diagnosis and reducing the risk of misdiagnosis [67]. El-Saadany et al. (2018) investigated the use of GPC-3 to aid in the diagnosis of HCC by comparing it to 20 healthy controls and two groups of 80 patients with α-FP less than or greater than 400 ng/mL. GPC-3 levels were significantly higher in HCC patients than in healthy controls [68]. As a result of the lack of clinical evidence on the reliability of GPC-3’s HCC diagnosis, we are presently unable to remark on its chances. Osteopontin (OPN) is a highly phosphorylated glycoprotein that either stays inside the cell or is secreted as an inflammatory cytokine. It is only expressed by liver macrophages, Kupffer cells, and stellate cells. OPN mediates a wide range of biological functions in the immune and vascular systems and has previously been evaluated as a tumour marker [69]. Increased OPN expression plays a pivotal role in hepatic inflammation, tumorigenesis, angiogenesis, extracellular matrix degradation, cancer cell migration, and metastatic potential in liver cancer and other digestive system neoplasms [70,71,72,73]. Multiple studies have found higher serum and plasma levels of OPN in people with HCC when compared to people with liver cirrhosis and/or CLD controls [74,75]. The researchers used Asian cohorts; a large multicentre study using West African and European cohorts was able to replicate these findings [76]. Across most studies, OPN had an AUROC ranging from 70% to 89% for predicting HCC. In contrast, the diagnostic efficacy of OPN in detecting early-stage HCC vs. non-HCC patients varied significantly depending on the study. Shang S. et al. [77] and da Costa et al. [78] reported an AUROC of 73% and 0.70, respectively. In the latter case–control study from France, OPN at a cut-off level of 91 ng/mL, appeared to be effective in distinguishing CLD from HCC, whereas Ge T et al. reported an AUROC of 89% [75]. Interestingly, a prospective study of 115 Asian patients with CLD at risk of HCC revealed increased plasma OPN levels 24 months before diagnosis in 21 subjects who developed HCC [78]. OPN has also demonstrated promising results in the detection of α-FP-negative HCCs [74,79]. OPN and α-FP serum levels together better predicted HCC development than these markers separately [80]. According to a 2018 meta-analysis, the pooled sensitivity, specificity, and diagnostic OR for serum OPN were 81% (95% CI 67% to 90%), 87% (95% CI 77% to 93%), and 30.05 (95% CI 8.84 to 102.07), whereas 64% (95% CI 54% to 73%), 96% (95% CI 91% to 98%), and 41.52 (95% CI 13.69 to 125.93) for α-FP [81]. For the diagnosis of HCC, OPN showed a higher diagnostic accuracy than α-FP. Moreover, elevated OPN levels were associated with a worse prognosis and a shorter post-hepatectomy survival time because of HCC [82,83]. A meta-analysis involving 9150 patients in 2022 confirmed that OPN has the potential to be used as a promising predictive tumour biomarker in the early detection and prognosis of HCC [84]. As a result, combining α-FP and OPN can improve the sensitivity of early HCC diagnosis. Dickkopf-1 (DKK1) is a member of the DKK Family and a secretory glycoprotein [85], that is tumour-specific and highly expressed in adult liver and other gastrointestinal neoplasms [86]. It is unclear exactly how DKK1 contributes to the development and progression of HCC. However, by altering the tumour microenvironment and causing inflammation, DKK1 seems to promote tumour invasion and migration via TGF-β1 [87]. Additionally, as recently shown, DKK1 stimulates HCC angiogenesis and tumorigenesis via VEGFR2-mediated mTOR/p70S6K signalling [88]. Originally, DKK1 expression was primarily examined in HBV-induced HCC. The optimal diagnostic cut-off value for DKK1 was 550.93 ng/L in a 2017 Chinese study, and the percentage of plasma DKK1 was significantly higher in the HCC group than in the HBV-related liver cirrhosis, CHB, and healthy controls (p < 0.05) [89]. Nevertheless, when compared to α-FP, DKK1 has been deemed as less effective in the diagnosis of HCC [90]. However, in a 2012 Chinese study [91] enrolling 831 participants, DKK1 levels were significantly higher in HCC patients than controls, and similar findings were found for early-stage HCC (AUROC 86%, sensibility 70.9%, and specificity 90.5% in the test cohort; 90%, 73.8%, and 87.2% in the validation cohort). Additionally, when compared with all controls, DKK1 maintained diagnostic accuracy in patients with HCC who did not have elevated levels of α-FP (AUROC 84, sensitivity 70.4%, and specificity 90% in the test cohort), AUROC 87%, sensitivity 66.7%, and specificity 87.2%. This included patients with early-stage HCC (AUROC 87%, sensitivity 73.1%, and specificity 90.0% in the test cohort; AUROC 89%, sensitivity 72.2%, and specificity 87.2% in the validation cohort), compared with controls [91]. Additionally, the authors of a 2016 Egyptian study used DKK1 as a biomarker for early HCC detection in HCV-infected patients [92]. A significant drop in DKK1 five days after curative resection in a small group of patients who had their HCV-induced HCC surgically removed may have indicated it as a surveillance marker for recurrence [92]. Moreover, DKK1 has been linked to HCC metastasis and prognosis [93]. Indeed, overexpression of DKK1 was linked to beta-catenin cytoplasmic/nuclear accumulation in clinical HCC samples (p = 0.011, correlation coefficient = 0.144) in a group of 314 Chinese HCC patients, as a critical indicator of a poor clinical outcome in HCC patients (p = 0.011, correlation coefficient = 0.144) [93]. Instead, in a 2019 Egyptian study, DKK1 had an AUROC of 83% with 87.3% sensitivity and 82.9% specificity in HCC (HCV-related) patients at a cut-off point of 8.92 ng/ml [78]. DKK1 was found to be associated with tumour size, liver dysfunction, and poor performance status in HCC patients [94]. Conclusively, DKK1 may support α-FP in the diagnosis and surveillance of HCC, aid in identifying patients with α-FP-negative HCC, and help differentiate between HCC and benign CLD. Alpha-L-fucosidase (AFU), which has two isoforms, alpha-l fucosidase (AFU1) and AFU2, is an enzyme that can remove the terminal -l-fucose residues from glycoproteins. Interestingly, high alpha-l-fucose expression has been linked to a variety of cancers, including breast, thyroid, and colorectal cancers [95]. In the diagnosis of HCC, AFU has been shown to be a promising tumor marker particularly in patients with underlying viral hepatitis and cirrhosis [96]. A 2014 meta-analysis of 12 studies aimed at evaluating the diagnostic value of AFU for HCC found a pooled sensitivity of 72% and a pooled specificity of 78% for AFU. The AUROC value was 81%. As a result, as a serum marker, AFU was useful in the diagnosis of HCC [97]. Because of its poor diagnostic performance, it has been suggested that AFU be measured in conjunction with other biomarkers to improve HCC detection sensitivity. AXL is a potential serum marker for the diagnosis of HCC. The activation of hepatic stellate cells and modulation of hepatocyte differentiation by the receptor tyrosine kinase AXL and its ligand Gas6 is critical in the development of liver fibrosis and HCC [98]. When compared with healthy or cirrhotic controls, AXL outperforms α-FP in detecting very early HCC and has a high diagnostic accuracy in α-FP-negative patients [99,100]. Among α-FP-negative HCC patients with non-HCC patients, the cut-off was 1301 pg/mL (AUROC, 90%) with a sensitivity of 84.6%, a specificity of 76.3%. The optimal cut-off for AXL in differentiating all HCC and CLD patients was 1243 pg/mL (AUROC, 84%) with sensitivity 93.8%, specificity 61.9%. The combination of AXL and α-FP improved sensitivity for early HCC diagnosis [100]. Midkine (MDK) is a heparin-binding growth factor that was discovered as a retinoic acid responsive gene and is involved in cell growth, survival, migration, angiogenesis, and carcinogenesis [101]. MDK expression has been found to be abnormal in a variety of human carcinomas, including HCC [102]. A 2019 meta-analysis discovered that HCC diagnostic accuracy of serum MDK was moderate/excellent [92,93]. The sensitivity and specificity of MDK for HCC diagnosis were 85% (95% CI 78% to 91%) and 83% (95% CI 76% to 88%), respectively [103,104]. A PLR of 5.05 (95% CI 3.33 to 7.40), a NLR of 0.18 (95% CI 0.11 to 0.28), a diagnostic OR of 31.74 (95% CI 13.98 to 72.09), and an AUROC of 91% (95% CI 84% to 99%) were also discovered. When the cut-off value was greater than 0.5 ng/mL, subgroup analyses revealed that MDK provided the best detection efficiency [103,104]. MDK levels were significantly higher in HCC compared to controls in a 2020 Egyptian study of 238 patients (p < 0.001). The ROC curve analysis revealed that MDK had 88.5% sensitivity, 80.6% specificity, 69% PPV, 93.5% NPV, and AUROC, 91%; MDK levels were comparable to α-FP levels in HCC patients [62]. Similarly, a 2020 systematic review and meta-analysis of 2483 patients discovered that MDK is more accurate than α-FP in diagnosing HCC, especially in early-stage HCC and α-FP-negative cases. The analyses for recognizing HCC using MDK and α-FP were as specified: 83.5 vs. 44.4% sensitivity, 81.7 vs. 84.8% specificity, and 87% vs. 52% AUROC. The analyses for identifying α-FP-negative HCC using MDK were as follows: sensitivity, 88.5%, specificity 83.9%, and AUROC, 91% [105]. According to a 2021 Egyptian study, patients with HCC (86 HCV induced) had significantly higher MDK levels than patients with liver cirrhosis and healthy controls (p < 0.001). At a cut-off value above 5.1 ng/mL, MDK levels discriminated between cirrhosis and HCC with a sensitivity of 100% and a specificity of 90% [106]. MDK appears to be a promising biomarker for early detection of HCC, particularly in α-FP-negative cases, but more research is needed to validate it. Aldo-keto reductase family 1 member 10 (AKR1B10) was discovered to be over-expressed in many cancers from various organs after being isolated from HCC [107]. The over-expression of AKR1B10 in early stages of well and moderately differentiated tumours, as well as its down-regulation in advanced tumour stages, demonstrated that AKR1B10 may be a useful marker for HCC differentiation [108]. Over the last decade, AKR1B10, has emerged as a potential biomarker for the diagnosis and prognosis of HCC, with experimental studies demonstrating roles for this enzyme in biological pathways underlying the development and progression of HCC [109]. AKR1B10 also correlated with worse prognosis in HCC patients [110,111]. Serum AKR1B10 levels were found to be higher in patients with HBV/HCV-related HCC compared with patients with other liver disorders (p < 0.05). In early- and intermediate-stage HCC, AKR1B10 levels increased significantly more than in advanced- and terminal-stage HCC. At a cut-off value of 1.51 ng/mL, the sensitivity (81.0%) and specificity (60.9%) for HCC diagnosis with AKR1B10 were both high [112]. Indeed, AKR1B10 was found to be up-regulated in association with serum α-FP and to be an independent risk factor for HCC in CHC patients, implying a role in early-stage hepatocarcinogenesis [113]. AKR1B10 upregulation might play a role in the early stages of HBV-related hepatocarcinogenesis [114]. However, its high expression may predict a low risk of early tumour recurrence after liver resection in patients with HBV-related HCC [115]. Moreover, even after SVR, CHC patients with high levels of hepatic AKR1B10 had an increased risk of developing HCC [116]. A multicentre study [90] with 1244 participants found that serum AKR1B10 levels were significantly increased in HCC patients compared with those in non-HCC and were associated with α-FP, alanine/aspartate aminotransaminase, tumour size, vascular invasion, and TNM stage, with an AUROC of 87%, sensitivity of 72.7%, and specificity of 95.7% for the diagnosis of HCC, and these values were better than those of AFP (AUROC 82%, sensitivity 65.1%, and specificity 88.9%), and AKR1B10 exhibited a promising diagnostic value (AUROC 89%, sensitivity 71.2%, and specificity 92.6%), and a similar diagnostic performance was observed in AFP-negative early-stage HCC (AUROC 83.9%, sensitivity 63.4%, and specificity 90.7%). The ratio of AKR1B10 messenger RNA levels in HCC versus non-tumorous tissues may predict prognosis after curative hepatectomy, with low expression in HCC tissue indicating a poor prognosis [117]. Several studies have revealed Annexin A2 (ANXA2) expression characteristics and distribution have good diagnostic potential for HCC diagnosis [118,119]. Moreover, ANXA2 is found to promote cancer progression and therapeutic resistance [120]. ANXA2 showed an AUROC of 80% across the entire range of sensitivities and specificities, whereas AFP had an AUROC of 78%. Combining serum ANXA2 and AFP detection significantly improved diagnostic efficiency (96.52%) and negative predictive value (96.61%) for HCC [119]. In a 2015 study aimed at assessing the diagnostic role of annexin A2 (ANXA2) as serum marker for 50 HCC patients, Annexin A2 levels were significantly higher in HCC patients’ sera compared with CLD patients’ sera (p < 0.001). The AUROC for ANXA2 was 91% at a cut-off level of 29.3 ng/mL [121]. A highly significant difference in serum ANXA2 levels was found among 44 mostly HCV-positive Egyptian patients with HCC and CLD, as well as controls. The AUROC of ANXA2 was 86%; the cut-off value was set at 18 ng/mL, with a diagnostic sensitivity of 74% and a specificity of 88%, whereas the sensitivity and specificity of AFP at the 200 ng/dL cut-off value were 20% and 100%, respectively [122]. A 2019 study found elevated ANXA2P2 expression levels in HCC tissue compared to adjacent noncancerous tissue, as well as a poor prognosis for patients with high ANXA2P2 levels in HCC tissue [123]. Furthermore, ANXA2 expression, or co-expression with STAT3 proteins, has been linked to HCC recurrence and survival [124]. Squamous cell carcinoma antigen (SCCA), a serine protease inhibitor that is naturally present in skin, as well as immunocomplexes forms of SCCA and α-FP (SCCA-IC and AFPIC, respectively), have both been identified in HCC patients and have been suggested as potential useful markers for the detection of micro-metastases and for improving accuracy of HCC diagnosis of at-risk patients. A 2005 Italian study by Giannelli G et al., looking into the expression of SCCA in tumoral and peritumoral tissues, in the serum of 48 cirrhotic patients and 52 HCC patients found that in comparison to cirrhotic samples, HCC samples had significantly higher SCCA serum levels. Additionally, HCC tumoral tissue had a significantly higher level of SCCA expression than peritumoral tissue [125]. The same author found an inverse relationship between SCCA levels and tumour size in another study conducted in 2007 involving 961 patients. The AUROC for SCCA and SCCA-IC in smaller HCCs was 70% and 69.4%, respectively. Together, the use of AFPIC, SCCA, and SCCA-IC allowed for the detection of 25.6% HCC in patients with α-FP levels under 20 IU/mL [126]. Beale G. et al. compared 50 patients with HCC-, ALD-, or NAFLD-related conditions with controls with NASH-related cirrhosis in a cross-sectional study on various biomarkers in 2008. With SCCA-1 showing no greater benefit for HCC surveillance than α-FP and DCP, the authors demonstrated that the best biomarkers for HCC surveillance may depend on the underlying cause of CLD [127]. In a 2009 study with 27 cirrhotic patients and 55 HCC patients (36.4% with a single nodule less than 3 cm and 63.6% with a single nodule more than 3 cm (or multifocal)), the latter two groups demonstrated significantly higher serum SCCA levels than cirrhotic patients (1.6 and 2.2 ng/mL vs. 0.41 ng/mL, respectively), as well as higher SCCA values in hepatic tissue in cirrhotic patients (1163.2 microm2 and 625.8 vs. 263.8 microm2). The SCCA expression was significantly higher in smaller HCC [128]. In 103 patients with CHC, a 2012 multicentre prospective Italian study found that those who responded to HCV antiviral therapy had higher levels of SCCA-IC than those who did not respond (238 AU vs. 149 AU, respectively). Hence, SCCA-IC was also proposed to be used as a prognostic indicator of a patient’s response to anti-HCV therapy [129]. Additionally, a 2016 multicentre prospective Italian study demonstrated that angiopoietin-2 (ANGPT2), delta-like ligand 4 (DLL4), neuropilin (NRP)/tolloid (TLL)-like 2 (NETO2), endothelial cell-specific molecule-1 (ESM1), and nuclear receptor subfamily 4, group A, member 1 (NR4A1), in particular, were the liver five-gene signature associated with neoangiogenesis that reliably detected rapidly growing HCCs and predicted HCC-related mortality in cirrhotic patients of various aetiologies [130]. Glutamine synthetase (GS) is a metabolic enzyme that catalyzes glutamine synthesis (a major energy source for tumour cells) and has been identified as a sensitive and specific indicator for the development of HCC [131]. GS has been proposed as a promising marker for distinguishing between malignant and benign hepatocellular lesions. Early studies have shown that GS is a novel serum marker for early HCC, particularly in patients with low α-FP levels (less than 200 ng/mL) [132]. Di Tommaso L demonstrated that the GS sensitivity and specificity for detecting early HCC were 72% and 100%, respectively [133]. Liu P et al. [134] discovered that serum levels of GS were higher in HCC patients compared with liver cirrhosis patients and healthy controls, and the AUROCs of GS and α-FP for HCC diagnosis were 0.85 and 0.861, respectively, whereas the AUROC was 91% (sensitivity 81.9%, specificity 100%) for differentiating AFP-negative HCCs from healthy controls, and the sensitivity and specificity were 82.5% and 93% when combining GS with these findings suggest that GS could be a useful biomarker for HCC diagnosis, particularly in α-FP-negative cases. Exosomes and small nanoparticles have recently been described as extracellular carriers of a plethora of molecules and cellular compounds, particularly microRNAs (miRNAs), produced by liver cells and non-parenchymal immune cells [135]. They are detected in plasma and represent a type of liquid biopsy used to detect HCC early. Exosomes have been hypothesized to be a potentially useful liver biomarker due to their mechanism of transmitting effector molecules and signals between cells. Several recent reports on exosomal miRNAs have found that these particles are better biomarkers for the diagnosis and treatment of HCC than their serum-free counterparts [135]. MiRNAs may be involved in variant OPN expression by interfering with translation by binding OPN mRNA in 3′-untranslated regions [136]. Additionally, in HCC cell lines, MiRNA 181a was observed to reduce OPN expression, and this may endow HCC with metastatic properties [136]. According to a 2016 meta-analysis, serum miRNAs have a relatively high diagnostic accuracy for HCC diagnosis and can easily distinguish HCC from healthy subjects and those with CLD/cirrhosis [137]. Sun N et al. studied the extracellular vesicles chip performance from 36 patients with early-stage HCC and 26 controls with cirrhosis in a 2020 study, with a sensitivity of 94.4% and a specificity of 88.5% [138]. Specifically, exosomal miRNAs with clinical significance in the detection, prognosis, and, in some cases, as a therapeutic target of HCC include: miR-224, miR-21, miR-93, miR-1247-5p, miR-92b, miR-210-3p, miR-155, miR-665, miR-718, miR-122, miR-638, miR-125b, and miR-9-3p are all examples of microRNAs [139]. Von Felden J et al. [140] found 86% sensitivity and 91% specificity for detecting early HCC in 209 at-risk controls in a 2021 phase II case–control study (AUROC, 87%). The signature of 3-small RNA clusters was independent of α-FP (p < 0.0001), and a composite model yielded an AUROC of 93% [139]. Circulating tumour DNA, on the other hand, has a promising diagnostic potential in hepatocarcinogenesis. DNA methylation has been identified as an early-stage circulating marker that could be used to detect HCC in its early stages [141]. It is, however, insufficient on its own and should be combined with α-FP for HCC screening and detection [142]. Although several different methylation panels are currently under investigation, there has been limited data beyond Phase II to support clinical use of DNA methylation. In a Phase II validation case–control study, an algorithm called the multitarget HCC blood test (mt-HBT), which includes three methylated markers in combination with α-FP and sex, demonstrated 82% sensitivity for early-stage HCC, 87% specificity, and an AUROC of 91% [143]. In another Phase II study involving 122 patients with HCC and 125 patients with CLD, a further multi-analyte cell free DNA test HCC (HelioLiver) demonstrated early-stage HCC identification of 76% with a specificity of 91% [144]. Long noncoding RNAs (lncRNAs) are important players in oncogenesis and tumour development, according to mounting evidence [145]. FOXD2AS1 was found to be an oncogene in HCC, upregulating ANXA2 expression in part by sponging’ miR206 [146]. Lung-cancer-associated transcript 1 (LUCAT1) has been identified in several human cancers, but its role in HCC is unknown. However, LUCAT1 in HCC promotes tumorigenesis by inhibiting ANXA2 phosphorylation [147]. Cancer susceptibility candidate 11 (CASC11) has been shown to play an important role in a variety of cancers, including HCC [145]. CASC11 promoted the progression of HCC by means of EIF4A3-mediated E2F1 upregulation, indicating CASC11 is a promising diagnostic biomarker for HCC [145]. Zinc finger E-box binding homeobox 1 antisense 1 (ZEB1-AS1) is an oncogenic regulator found in a variety of cancers. A study discovered that ZEB1-AS1 could decoy miR-299-3p and upregulate E2F1 expression, elucidating the functions and mechanisms of ZEB1-AS1 in HCC tumorigenesis and progression and providing novel biomarkers for HCC [148]. In total, 131 lncRNAs were found to be differentially expressed in α-FP-negative HCC, and two lncRNAs (LINC00261, LINC00482) demonstrated good diagnostic power under the ROC curve [149]. Furthermore, it was recently discovered that LINC01133 promotes HCC progression by sponging miR-199a-5p and interacting with ANXA2. In patients with HCC, LINC01133 CNV gain predicts a poor prognosis [150]. High levels of RNA-binding proteins (RBPs), including lncRNAs, were found to be detrimental to patient survival in 21 cancer types, particularly HCC. The researchers discovered that RBP gene expression is altered in HCC and that RBPs perform additional functions beyond their normal physiological functions, which can be stimulated or intensified by lncRNAs and affect tumour growth [151]. HCC has an incredibly diverse and complex genetic landscape [152] that has been clearly defined over the past 20 years, and this includes homozygous deletions on chromosome 9 and high-level DNA amplifications on chromosomes 6p21 (VEGFA) and 11q13 (FGF19/CNND1) (CDKN2A). The majority of mutations (60%) affect the TERT promoter, which is linked to higher telomerase expression [153]. Along with low-frequency mutated genes (like AXIN1, ARID2, ARID1A, TSC1/TSC2, RPS6KA3, KEAP1, MLL2), TP53 and CTNNB1 are the next most common mutations, affecting 25%–30% of HCC patients. These mutations help define some of the key deregulated pathways in HCC [153]. Specific genetic and molecular programs involved in hepatocarcinogenesis have been clarified by recent technological advancements in next generation sequencing (NGS). The molecular landscape of HCCs with vascular invasion was also examined in a recent study, which discovered distinct transcriptional, epigenetic, and proteomic changes fuelled by the MYC oncogene. They demonstrated that MYC up-regulates the expression of fibronectin, which encourages HCC invasiveness [154]. The mechanisms by which cyclin dependent kinase inhibitor 2A (CDKN2A), a crucial regulator of immune cell functionality, promotes immune infiltration in HCC are still unknown. A 2018 meta-analysis revealed that CDKN2A promoter methylation was linked to an increased risk of HCC, played a significant part in the development of HCC, and may be useful as a triage marker for HCC [155]. Another study from 2021 found that CDKN2A expression may have influenced how tumour-associated macrophages are regulated and that it can be used as a prognostic biomarker to assess the prognosis and immune infiltration in HCC [156]. Additionally, 18% to 40% of HCC patients have been found to have CTNNB1 mutations. The metabolic regulation of the liver is greatly influenced by the oncogenic Wnt/catenin pathway, which is triggered by the mutated CTNNB1. The metabolic morphology of CTNNB1-mutated HCC is distinct, frequently cholestatic, and infrequently with steatosis [157]. The rate of CTNNB1 mutation detection in HCC patients was increased by combining analysis of ctDNA and tumour tissue [158]. A 2021 study identified some candidate diagnostic and prognostic biomarkers for AFP-negative HCC, providing the top ten hub genes, which included several protein-coding genes such as EZH2, CCNB1, E2F1, PBK, CHAF1A, ESR1, RRM2, CCNE1, MCM4, and ATAD2 [149]. On the other hand, because epigenetic alterations such as DNA hypermethylation or hypomethylation are thought to be early events in HCC onset, DNA methylation biomarkers can be used to detect HCC. In a large cohort study of 1098 patients with HCC and 835 healthy controls, an effective blood-based diagnostic prediction model combining 10-methylation markers (cg10428836, cg26668608, cg25754195, cg05205842, cg11606215, cg24067911, cg18196829, cg2321194, cg17213048, and cg25459300) was established, demonstrating the potential for HCC diagnosis with high sensitivity and specificity [141]. Furthermore, the sensitivity and specificity of this model’s ability to detect HCC were higher than those of AFP. The detection of six HCC-specific hypermethylated sites (cg23565942, cg21908638, cg11223367, cg03509671, cg05569109, and cg11481534) was found to be highly sensitive and specific (92% and 98%, respectively) in separating HCC from other tumour types [159]. This study involved patients with various cancer diseases. In combination, it is believed that circulating tumour DNA methylation markers are accurate for use in HCC screening, diagnosis, and prognosis. The recognition of tumour cells by leukocytes has been described for many different types of tumours, changing the interpretation of how circulating immune markers function in the oncogenesis process [160]. Because cancerous lesions release cytokines and chemokines into the bloodstream, they can be detected in at-risk patients. This is extremely crucial during the development of HCC because the tumour typically develops in the context of chronic hepatitis, where the excessive immune stimulation caused by the presence of an inflammatory response in the liver may lead to further alterations in measurable immune markers during the progress of HCC [161,162]. Chemokines are essential immune system response mediators because they aid in the activation and recruitment of leukocytes at acute inflammatory or harmed sites. Chemokines are also crucial in tumor progression. Chemokines and their receptors, such as the CXCL12-CXCR4 axis, the CX3CL1-CX3CR1 axis, and the CCL20-CCR6 axis, have received a lot of attention in research [163]. C-C motif ligand 4 (CCL4) and CCL5 bind to the same receptor, C-C receptor 5, which is expressed in effector and memory T cells, making this interaction important in the control of chronic viral infections [164]. In cirrhotic patients, high serum levels of inflammatory chemokines such as CCL4 and CCL5 indicate the presence of HCC. While CCL14 is a potential prognostic biomarker that influences cancer progression and is linked to tumor immune cell infiltration in HCC [163]. One study has examined serum levels of various chemokines in the context of HCC detection, and multivariate regression analysis revealed that serum CCL4 and CCL5 levels were higher in cirrhotic with HCC (N = 61) than in cirrhotic patients without HCC (N = 78), making them useful candidate diagnostic markers for HCC. CCL4 and CCL5 detection performance for HCC was similar, with an AUROC of 0.72 for CCL5 and relatively high sensitivity of 71% and specificity of 68% [165]. Kupffer cells, or resident hepatic macrophages, are considered tumour-associated macrophages of HCC and can produce a variety of cytokines, most notably interleukin (IL)-6, a pro-inflammatory marker, to promote HCC tumorigenesis [166]. In patients with benign liver disease or non-HCC tumours, serum IL-6 levels are not elevated [167]. Serum IL-6 levels were found to correlate positively with tumour size and a poor prognosis in HCC patients [168]. IL-6 has the potential to be a useful HCC tumour marker. In a 2008 study, the ROC curves used to distinguish HCC from cirrhotic patients only showed that IL-6 titres had higher discriminant power than AFP titres, with a cut-off value of 12 pg/mL (sensitivity 0.73, specificity 0.87, efficiency 0.8). The sensitivity, specificity, and efficiency rates for discriminant analysis on HCC and non-HCC subjects were 77%, 93%, and 88%, respectively [169]. In a 2013 meta-analysis of HCC screening, IL-6 was found to be comparable (p = 0.66) to AFP [170]. Higher serum IL-6 levels, in particular, were discovered to be an independent risk factor for HCC development in female CHC patients but not in male CHC patients [171]. Furthermore, higher serum IL-6 levels have been linked to an increased risk of HCC regardless of hepatitis virus infection, lifestyle factors, or radiation exposure. Obese people are more vulnerable to this link [172]. When IL-6 is associated with biomarkers such as AFP measurement, its diagnostic value increases. With the availability of newer biomarkers and their various combinations, it has recently been determined that a US-free approach is a viable option for the early diagnosis of HCC. Assays that combine multiple biomarkers will be clinically significant in HCC decision-making processes. When combined with DCP and α-FP, α-FP-L3 may be useful in HCC diagnostics and screening tests [173]. For the past 20 years, the α-FP-L3 and DCP have been routinely used in Japan [174,175], and the combination of these biomarkers has increased the likelihood of early detection of small HCC [19,174,175]. In a 2008 Japanese systematic review, α-FP performed worse than DCP, and α-FP-L3 in terms of diagnostic OR (4.50 vs. 8.16 and 10.50) and AUROC in patients with HCCs 5 cm or smaller with CLD or cirrhosis as controls (65% vs. 69% and 70%). For α-FP, DCP, and α-FP-L3 the optimal cut-off values were 200 ng/mL, 40 mAU/mL, and 15%, respectively [176]. In a large 2011 study of 270 Japanese HCC patients with serum α-FP levels less than 20 ng/mL, the combined use of the α-FP-L3% (using the highly sensitive detection method) and DCP biomarkers detected 49% of all HCC patients with a size less than 2 cm [34]. In a 2015 retrospective study of 1255 CHB Korean patients, Seo SI et al. found that adding DCP alone to α-FP increased the sensitivity of detecting early HCC to around 75%, with a specificity of almost 90% [177]. In a 2016 German cohort, the combined three biomarkers demonstrated sensitivity and specificity of 85% (N = 304 HCC patients versus N = 403 controls) [15]. In a 2017 meta-analysis and validation study, Chen H et al. found that DCP with α-FP (sensitivity, 84%, specificity, 86%; AUROC, 89%) performed better than DCP (sensitivity, 76%, specificity, 92%; AUROC, 84%) or α-FP (sensitivity, 73%, specificity, 92%; AUROC, 84%) alone [178]. The pooled sensitivity and specificity for the α-FP, α-FP-L3, and DCP integrated biomarkers were 88% and 79%, respectively, in a 2020 meta-analysis of thirteen studies, whereas the AUROC was 91%, and the diagnostic OR was 28.33 [174]. In a Phase II study conducted by Piratvisuth T et al. (2022), the combination of α-FP and DCP demonstrated the best clinical performance for detecting early-stage HCC when compared to other biomarkers [179]. Further advances in genomics and proteomics platforms, as well as biomarker assay techniques, have resulted in the identification of a variety of novel biomarkers, including GP-73, GPC-3, OPN, and microRNAs, that have improved HCC diagnosis [71]. Notably, α-FP-L3 or GP73 can also be used to diagnose α-FP-negative HCC, and though their combination improves diagnostic accuracy and sensitivity [180]. Intriguingly, testing the levels of α-FP, α-FP-L3, and GP73 in venous blood samples from the sublingual vein of high-risk populations, was also effective as a screening test for HCC, with the added benefit of being a simple, and inexpensive test [69]. El-Saadany et al. (2018) discovered that GPC-3 plus α-FP was the most sensitive and specific test for the diagnosis of HCC, with both sensitivity and specificity of 98.5% [68]. The combination of DKK1 and α-FP demonstrated a better diagnostic yield than α-FP alone [90,181,182]. Indeed, in a 2016 Turkish study, which included 39 healthy controls, 54 patients with cirrhosis, and 40 consecutive HCC patients, the α-FP levels varied in each group and could be used to distinguish between them (p < 0.001). The combined use of DKK1 and α-FP increased the diagnostic yield, with a sensitivity, specificity, PPV, and NPV of 87.5%, 92.3%, 92.1%, and 87.8%, respectively. The DKK1 levels could help distinguish the HCC group from the cirrhosis and control groups (p < 0.001) [90]. In a 2015 Chinese study by Ge T et al., AUROC was higher (95% vs. 83%) and sensitivity was higher (88.76 vs. 71.91%) than that of α-FP alone, when α-FP, DKK1, and OPN were used as a panel in 390 participants. Additionally, this combination demonstrated a significant improvement in early-stage HCC patient diagnosis [75]. On ROC analysis, serum MDK levels had better sensitivity and specificity than OPN and α-FP levels in the diagnosis of HCC (98.4%, 97.1%, and 97%) vs. (96.2%, 95.3%, and 95%) in a 2017 Egyptian study enrolling 170 patients [183]. Furthermore, combined analysis of both MDK and α-FP yielded a similar diagnostic value in the diagnosis of HCC as combined analysis of both OPN and α-FP (98% vs. 97.5%) [183]. As a result, serum MDK and OPN levels were comparable to α-FP levels as potential HCC diagnostic biomarkers in HCV patients with liver cirrhosis. The ROC curve analysis for GP73, MDK, and DKK-1 in a 2020 Egyptian study of 238 individuals revealed (1) 88.5% sensitivity, 80.6% specificity, 69% PPV, 93.5% NPV, and (AUROC 91%) for MDK; (2) 93.6%, 86.9%, 77.7%, 96.5% for DKK-1; (3) 91%, 85%, 74.7%, 95% (AUROC 96%) [62]. The combination of GP73 and DKK-1 had the highest AUROC value (99%), with 97.4% sensitivity and 93.1% specificity, followed by the combination of GP73 and α-FP (98%). As a result, serum levels of GP73, MDK, and DKK-1 were comparable to α-FP as promising predictor biomarkers for HCC patients. The two-marker panel of GP73 and DKK-1 demonstrated the highest specificity and sensitivity [62]. Similarly, a 2021 Egyptian study found that using GP73, DKK-1, and α-FP together improved the sensitivity and specificity for the diagnosis of HCC compared to using each one separately [86]. Finally, according to a 2021 meta-analysis, the sum of sensitivity and specificity of α-FP with GP73 was 1.76 (p = 0.0001), the best among all panels including multiple biomarkers. Moreover, the sum of the triple biomarker panel of α-FP, α-FP-L3, and DCP was greater (1.64, p = 0.0001) than any double biomarker panel [184]. The combination of ANXA2 and α-FP improved diagnostic sensitivity (98% specificity, LR + 41, and 97.6% PPV). Follistatin combined with α-FP provided 92% specificity but only 50% sensitivity. As a result, serum ANXA2 is a promising biomarker for HCC, especially when combined with α-FP. Follistatin combined with α-FP may improve HCC diagnosis specificity [121]. The integration of miRNAs and α-FP has also a promising future [185]. Interestingly, in a 2021 meta-analysis, the sensitivity of circular RNAs for HCC diagnosis was 82% (95% CI 78% to 85%), and the specificity was 82% (95% CI 78% to 86%), compared to 65% (95% CI 61% to 68%) and 90% (95% CI 85% to 93%) for α-FP. The AUROC for circular RNAs was 89% (95% CI 86% to 91%) and 77% (95% CI 74 to 81) for α-FP. The combination of circular RNAs and α-FP showed a sensitivity of 88% (95% CI 84% to 92%), a specificity of 86% (95% CI 80% to 91%), and an AUROC of 94% (95% CI 91% to 96%) [186]. Consequently, circular RNAs are reliable and promising biomarkers for detecting HCC, and their combination with α-FP may improve diagnostic accuracy. Several retrospective studies on biomarker panels for the diagnosis of HCC, combining blood biomarkers with patient characteristics, have been published. In 192 HCC patients, a panel consisting of miR-122+miR885-5p+miR-29b+α-FP reported a 1.0 AUROC in the diagnosis of HCC [187]. On a sample size of 1933 subjects, the panel with ten DNA methylation markers demonstrated an AUROC of 94% in the training set and 97% in the validation set, indicating high sensitivity and specificity in the diagnosis of HCC [141]. Another panel consisting of miR-3126-5p+miR-92a-3p+miR-107+α-FP reported a 99% AUROC in 155 subjects, which was useful in the diagnosis of HCC [188]. A summary table with the main cited biomarkers and their combinations as well as their algorithms can be found in Table 1 and Table 2. A summary with the main cited biomarkers (with their timeline)requiring additional validation can be found in the Supplementary Material (Figure S1 and Table S1). The American Association for the Study of Liver Diseases (AASLD), Asian Pacific Association for the Study of the Liver (APASL), and European Association for the Study of the Liver (EASL) guidelines recommend HCC surveillance in the following cases, regardless of aetiology (for Child-Pugh A-B) [1,3,4]: liver cirrhosis, Child-Pugh C listed for liver transplant, HBV carriers with a positive family history of HCC, Asian males aged >40 years, Asian females aged >50 years, and African males aged >20 years. A 2021 Cochrane meta-analysis by Colli A et al. [189] recommended combining α-FP (threshold around 20 ng/mL) with US in most clinical settings for diagnosing HCC in people with CLD. This resulted from the direct comparison in 11 studies (6674 participants) that revealed a higher sensitivity of US (81%, 95% CI 66% to 90%) versus α-FP (64%, 95% CI 56% to 71%) with comparable specificity: US 92% (95% CI 83% to 97%) versus α-FP 89% (95% CI 79% to 94%). A comparison of six studies (5044 participants) revealed that the combination of α-FP and US had higher sensitivity (96%, 95% CI 88% to 98%) and comparable results than US (76%, 95% CI 56% to 89%). For resectable HCC (two studies), US outperformed α-FP, and their combination outperformed them both, with sensitivity reaching up to 89% and specificity reaching up to 87% [189]. Despite the heterogeneity of the included studies, the combination of α-FP with US demonstrated the highest sensitivity, with fewer than 5% of HCC occurrences missed and approximately 15% false positive results. Six-monthly monitoring effectively reduced HCC mortality by 37% [11]. Furthermore, surveillance allows for earlier detection of HCC and more frequent curative treatments, as revealed by a retrospective analysis of 887 cases of HCC diagnosed between 2005 and 2010 in the United States National Veterans Administration [194]. Even in patients with CLD and advanced fibrosis (fibrosis F3), the EASL/European Organization for Research and Treatment of Cancer (EORTC) guidelines recommend HCC surveillance with half-yearly US [1,3,4]. According to the AASLD guidelines, cirrhotic of any aetiology and cirrhosis related to CHB patients with a specific ethnic origin, age, and genetic background should have a six-month US and α-FP surveillance [195]. However, there is some uncertainty regarding the monitoring of patients with cirrhosis related to CHC (fibrosis F3) and HCV clearance [1,3,4]. In this regard, the AASLD HCV guidelines recommend HCC surveillance in this subgroup, whereas the AASLD HCC guidelines recommend HCC surveillance only in the presence of metabolic liver cirrhosis [1,3,4]. Furthermore, in cirrhotic patients with HBV and/or HCV, the APASL guidelines recommend six-monthly US and α-FP surveillance [196]. The Japan Society of Hepatology (JSH) guidelines recommend six-monthly surveillance with US and α-FP, α-FP-L3, and DCP in cirrhotic and CHB/CHC patients [197]. It should be noted that HCC may occur even in non-cirrhotic NAFLD patients, for whom surveillance is not recommended due to annual incidence rates of less than 1% [4]. Survival after HCC diagnosis varies between about 18 months in Germany, according to a retrospective analysis of 1066 patients with HCC divided into two 6-year periods (N = 385; 1998–2003 and N = 681; 2004–2009) [198], and about 48 months in Japan [199]. On the other hand, survival in the Western countries today is still comparable to that of Japan in the 1980s. Specifically, in Europe, approximately 30% of all HCC patients receive curative treatment, whereas in Japan, more than 60% receive this treatment [199]. Many of the HBV-related CLD-specific scores were developed in Asian patients with CHB to stratify the risk of developing HCC during surveillance. Following the need for an accurate, precise, and easy-to-use score in clinical practice, especially in geographic areas where the burden of HBV infection is particularly high, these scores were validated in Caucasian cohorts, reporting acceptable performances. A panel made up of age, gender, α-FP, and DCP was able to predict HCC in Chinese CHB patients in 2925 subjects with an AUROC of 94% in the training set and 93% in the validation set [95]. However, most of the validation scores performed worse than the “training” cohorts [3]. Validation studies with Caucasian CHB patients revealed the following AUROC value ranges for the respective scores, including specific variables: GAG-HCC ((age, gender, HBV-DNA, liver cirrhosis): 74–86%), CU-HCC ((age, albumin, bilirubin, HBV-DNA, radiological cirrhosis): 62–91%), REACH-B ((age, gender, ALT, HBeAg status, HBV-DNA levels): 54–77%), and RWS-HCC ((age, gender, liver cirrhosis, α-FP): 85%). These findings suggest that certain population characteristics have a significant impact on the risk of developing HCC in CHB patients. In comparison to CHB, there are few studies on predictive models of HCC risk scores in CHC patients, and their main limitation is the lack of validation cohorts in most of them. In 2016, a single European study on a French cohort of 1323 CHC patients aimed to develop an individualized score for the prediction of HCC (age > 50 years, previous alcohol abuse, low platelet count, GGT > upper limit normal, and of sustained virologic response [SVR]) [3]. This score, however, lacked an external validation cohort. Finally, evidence that the risk of HCC decreases significantly over time in patients who have obtained sustained virological response (SVR) emphasizes the need for new algorithms to tailor HCC surveillance. The HCC risk scores identified in studies evaluating Caucasian cohorts in both the study population and the external validation group, regardless of the aetiology of the underlying CLD, are as follows [3]. The THRI (Toronto hepatocellular carcinoma [HCC] risk index) was developed to predict the 10-year risk of HCC using four variables (age, gender, aetiology, platelet count); its performance was studied in three external validation cohorts from the Netherlands, China, and Turkey with similar accuracy in predicting HCC development. In identifying the high-risk group of HCC, AUROC values ranged from 75% to 80%. The aMAP, a four-variable model (age–male–ALBI–platelets), was developed from a training cohort of 3688 Asian patients and validated in nine cohorts with different aetiologies and ethnicities. The optimal cut-off for predicting HCC was determined to be 50, with a sensitivity of 85.7–100% and a NPV of 99.3–100%. The aMAP score showed excellent discrimination and calibration in assessing the 5-year HCC risk among all the cohorts irrespective of aetiology and ethnicity [200]. Ideally, the use of serum biomarkers with sufficient sensitivity and specificity could allow for the early diagnosis of HCC, avoiding the need for US surveillance. Other than α-FP, several serum biomarkers have been included in some scoring systems for the prediction of HCC development [3]. However, except for α-FP and DCP, which are recommended by Japanese HCC guidelines, none of the biomarkers have been validated in Phase III clinical trials and are used in clinical practice [14]. This can be explained by the high heterogeneity of HCC biology, in which changes in various biochemical pathways play a role in tumorigenesis [16]. Indeed, HCC patients exhibit a wide range of patterns of positivity for HCC biomarkers [15]. Therefore, researchers have investigated combining biomarkers with patient-specific risk factors and/or diagnostic tools. El-Serag HB et al. [201] reported on the development and validation of a α-FP-based algorithm in a retrospective cohort of cirrhotic patients with active HCV in the national Department of Veterans Affairs Healthcare System in 2014. The Hepatocellular Carcinoma Early Detection Screening (HES) algorithm took the patient’s current α-FP level, rate of α-FP change, age, alanine aminotransferase level, and platelet count into account. When compared to α-FP alone, this α-FP-adjusted model improved predictive accuracy at various α-FP cut-offs within 6 months [201]. Specifically, based solely on 20 ng/mL α-FP, the probabilities of HCC were 3.5% and 11.4%, respectively. Patients with the same α-FP values (20 ng/mL and 120 ng/mL) who were 70 years old, with ALT levels of 40 IU/mL and platelet counts of 100,000 had 8.1% and 29.0% chances of developing HCC, respectively [201]. Tayob N et al., in 2018, used the same previous study population to develop a risk prediction model called the parametric empirical Bayes (PEB) algorithm, which incorporates routinely measured laboratory tests, age, and the rate of change in α-FP over the previous year, with the current α-FP [202]. The analysis cohort included 11,222 cirrhosis control patients and 902 HCC cases who had serial α-FP tests prior to their HCC diagnosis. The PEB algorithm had a higher early HCC detection sensitivity of 63.64% when compared with the current α-FP alone (53.88%) [202]. Tayob N et al. also conducted a 2019 validation study of the model presented [203] in which 4804 cases of HCC with cirrhosis of any aetiology, were evaluated on the long term at Veterans Affairs medical centres. Within six months before diagnosis, the HES algorithm had a modest superiority in identifying patients with HCC with 52.56% sensitivity compared to 48.13% sensitivity for the α-FP assay alone (p < 0.001). The authors estimated that the HES algorithm detected almost 199 HCC cases per 1000 imaging analyses versus 185 for the α-FP assay alone, resulting in the detection of 13 additional HCC cases (p < 0.001) [203]. The GALAD (Gender, Age, α-FP-L3, AFP and DCP) score combines serum-based markers (α-FP, α-FP-L3, and DCP) with demographic factors (gender and age). The GALAD score is a model developed using data from 833 patients (394 with HCC and 439 with CLD of mixed aetiologies) from the United Kingdom [204]. The GALAD score demonstrated exceptional overall performance (AUROC values of 95%, sensitivity of 92%, and specificity of 85%), which was maintained in the early detection of HCC (AUROC of 92%, sensitivity of 92%, and specificity of 79%) using the UK cohort [204]. The model was validated in independent cohorts from Japan, Germany, and Hong Kong (n = 6834; 2430 HCC (1038 early stage), and 4404 CLD) after comparison with the set of UK data, with an overall sensitivity ranging from 80% to 91%, specificity ranging from 81% to 90%, and AUROC values ranging from 85% to 95% across the populations [205]. The aetiologies in both the original and validation studies were mixed, ALD and CHC dominating. According to the cohort analysis, the GALAD score had better ROC curves than single markers regardless of ethnic origin or aetiology (HCV, HBV, and others), detecting early-stage tumours as efficiently as late-stage ones as emerged from the cohort analysis German (n = 275 HCC, n = 900 CLD; unifocal HCC < 3 cm = AUROC 87%; unifocal < 5 cm = AUROC 85%; unifocal < 4 cm = AUROC 87%; unifocal < 10 cm = AUROC 90%) [205]. A 2019 Phase II validation study in patients with NASH-related cirrhosis and early-stage HCC from a multicentre German cohort had a sensitivity of 68% and a specificity of 95% [206]. The GALAD score was also compared to US for the detection of HCC and found to be superior. In a Chinese study, Liu M et al. (2020) [207], evaluated GALAD performance and developed new models in a country where HBV is the leading cause of HCC. They created the GALAD-C model with the same five variables as GALAD, as well as the GAAP model with gender, age, α-FP, and DCP, using logistic regression on 242 patients with HCC and 283 patients with CLD and comparing results to patients with other malignant liver tumours and healthy controls (50 patients, respectively). GALAD-C and GAAP models performed similarly (AUROC, 92% vs. 91%), and both outperformed GALAD, DCP, α-FP, and α-FP-L3% (AUROCs, 89%, 87%, 75%, and 71%) for discriminating HCC from CLD. The GALAD, GALAD-C, and GAAP models had excellent AUROCs for the HCV subgroup (94%, 96%, and 94%, respectively), but were relatively lower for the HBV subgroup (85%, 89%, and 88%). In a cohort of Chinese patients, the GAAP and GALAD-C models performed better than the GALAD model. These three models performed better in the HCV subgroup than those with HBV [207]. In a 2021, retrospective German single-centre study by Schotten C et al. [208], in a Caucasian HBV/HCV cohort (182 patients with HBV and 223 with HCV), in the Barcelona clinic liver cancer (BCLC) 0/A cohort (7%), GALAD had a higher AUROC in distinguishing HCC from non-HCC, outperforming α-FP α-FP-L3 and DCP (94% vs. 86%, 83%, and 83%, respectively). GALAD achieved even a higher AUROC of 96% and 98% in the HBV and HCV populations, respectively. Furthermore, GALAD had a significantly higher specificity (89%) in HCV patients than α-FP (64%) alone in detecting early-stage HCC. Hence, the GALAD score was deemed potentially useful for HCC surveillance in Caucasian HBV/HCV patients. Interestingly, in a 2021 Japanese study by Toyoda H et al. [209], on HCC patients on dialysis, the α-FP, DCP, and GALAD scores all had high predictive values for HCC, with AUROC values greater than 85%. This effectiveness was maintained when focusing on small HCC (≤3 cm or ≤2 cm) or early-stage HCC, as well as after propensity score matching of HCC and non-HCC patient characteristics. DCP and GALAD scores had excellent predictive abilities for HCC. In conclusion, measuring serum HCC biomarkers can supplement imaging studies in the surveillance of HCC in dialysis patients, reducing the likelihood of advanced HCC at diagnosis. In Singal AG et al., 2022’s prospective multicentre study [35], 42 patients with cirrhosis (Child-Pugh A/B) developed HCC (57.1% early stage) over a median of 2.0 years. In comparison to single-time point GALAD (79%), α-FP (77%), and HCC early detection screening (76%), longitudinal GALAD had the highest AUROC for HCC detection (85%). The highest sensitivities for HCC detection were observed for single time point GALAD (72%) and longitudinal GALAD (64%), respectively, in patients with biomarker assessment within 6 months prior to HCC diagnosis. Tayob N et al. [36], on the other hand, conducted a 2022 prospective cohort Phase III biomarker study in the USA with 534 patients, 50 of whom developed HCC (68% early HCC) and 484 of whom had negative imaging. GALAD had the highest sensitivity (63.6%, 73.8%, and 71.4% for all HCC, respectively, and 53.8%, 63.3%, and 61.8% for early HCC within 6, 12, and 24 months), but a FPR (i.e., accuracy) of 21.5% to 22.9%. The AUROC, however, was comparable between GALAD, HES, α-FP-L3, or DCP. GALAD score outperformed in terms of increased false positive results, although a significant improvement in sensitivity for HCC detection. According to Huang C et al. (2022) [210], the GALAD performs exceptionally well in the early diagnosis, prognosis prediction, and risk monitoring of HCC in a cross-sectional and longitudinal multicentre case–control study of 1561 Chinese patients. They discovered that GALAD identified early-stage HCC at an AUROC greater than 85% and outperformed α-FP, DCP, α-FP-L3, and BALAD-2. Meanwhile, the GALAD score could divide HCC into two distinct subgroups based on overall survival and recurrence risk. The GALAD score could detect HCC 24 weeks (AUROC, 85%) or 48 weeks (AUROC, 83%) before clinical diagnosis. The GALAD score, on the other hand, performed worse in small recent Phase III cohorts, with one demonstrating a sensitivity of 53.8% and another demonstrating a sensitivity of 30.8% at a FPR of 10% [35,36]. These findings imply that GALAD results vary depending on clinical and not clinical parameters, and new findings from other studies are therefore awaited. The α-FP, α-FP-L3, and DCP levels were measured in 98 Italian patients (44 CLD patients without HCC and 54 HCC patients with predominantly HCV/HBV aetiology) using a mTASWako self-analyzer i30 (highly sensitive microchip capillary electrophoresis test and liquid phase binding assay) [211]. Serum levels of α-FP, α-FP-L3, and DCP were significantly higher in HCC patients than in CLD patients (p < 0.0001). The respective AUROCs values were 88%, 87%, and 87%. The AUROC of the three combined biomarkers was lower than that of the GALAD model (98% vs. 95%, p = 0.028) [211]. According to the findings of this Italian study, the combination of α-FP, α-FP-L3, and DCP was superior to a single biomarker in detecting HCC. Furthermore, the GALAD algorithm performs significantly better than the combination of these three biomarkers alone. NAFLD is quickly becoming the leading cause of CLD, HCC, and liver transplantation. A multicentre case–control study conducted in eight German centres evaluated the GALAD score’s ability to diagnose HCC in patients with NASH on 125 patients with HCC (20% within the Milan Criteria, BCLC A) and 231 patients without HCC (NASH controls). The researchers also looked at data from a pilot cohort study of 389 NASH patients who were being monitored for HCC for a median of 167 months in Japan [206]. The GALAD score had significantly higher AUROC than the values for serum α-FP alone, α-FP-L3, or DCP (96% vs. 88%, 86%, and 87%, respectively). The AUROC values for the GALAD score were consistent in patients with and without cirrhosis (93%, and 98%, respectively). The GALAD score achieved an AUROC of 91% for the detection of HCC using the Milan Criteria, with a sensitivity of 68% and a specificity of 95% at a cut-off of −0.63 [206]. The mean GALAD score was higher in patients with NASH who developed HCC than in those who did not develop HCC as early as 1.5 years before the diagnosis of HCC in the Japanese cohort pilot study [206]. As a result, the authors conclude that the GALAD score can detect HCC with high accuracy in patients with NASH, both with and without cirrhosis as similarly to that of viral hepatitis. The GALAD score can detect patients with early-stage HCC and may aid in the surveillance of patients with NASH, who are frequently obese, limiting the sensitivity of US detection of HCC [206]. In any case, an optimal threshold value must be defined to develop a shared approach and standardized surveillance algorithm in patients with NASH. A recent study (2019) by Yang JD et al. found that the GALAD score outperformed the US in the detection of HCC [212]. However, the addition of a new GALADUS score (because of the combination of GALAD and US scores) improved the GALAD score’s performance even more. We looked at a single-centre cohort of 111 patients with HCC and 180 controls with cirrhosis or CHB from the Early Detection Research Network’s (EDRN) Phase II study, as well as a multicentre cohort of 233 patients with early HCC and 412 patients with cirrhosis from the EDRN Phase III study. The AUROC of GALAD for the detection of HCC was greater than that of US (85% vs. 82%, p < 0.01). The GALAD score had a sensitivity of 91% and a specificity of 85% at a cut-off of −0.76. The AUROC of the GALAD score for detecting early-stage HCC was 92% (cut-off −1.18, sensitivity 92%, specificity 79%). In the EDRN cohort, the AUROC of the GALAD score for HCC detection was 88%. The GALADUS score improved the GALAD score’s performance in the monocentric cohort, reaching an AUROC of 98% (cut-off −0.18, sensitivity 95%, specificity 91%) [212]. The new model’s equation, known as the GALADUS score, is as follows: By analyzing serum samples from 267 patients with liver cirrhosis, in 2021 Lambrecht J et al. [213] tried to develop a novel blood-based scoring tool for the identification of early-stage HCC. They created the APAC score, which consisted of the parameters age, expression levels of soluble platelet-derived growth factor receptor beta (sPDGFR), α-FP, and creatinine and identified patients with HCC among cirrhotic with an AUROC which was significantly better than the GALAD score (95% vs. 90%, p = 0.0031). Furthermore, the APAC score’s diagnostic accuracy was independent of disease aetiology. The APAC score achieved an AUROC of 92% (sensitivity 85%, specificity 89%) and 95% (sensitivity 91%, specificity 85%) for detecting patients with (very) early (BCLC 0/A) HCC stage or within Milan criteria, respectively. As a result, the APAC score was a highly accurate serological tool for the early detection of HCC. Notably, the multitarget HCC blood test (mt-HBT), incorporating methylation biomarkers (i.e., HOXA1, TSPYL5, and B3GALT6), α-FP, and sex, demonstrated 72% sensitivity for early-stage HCC at 88% specificity in a 2022 multicentre US study by Chalasani NP et al. [143], with 136 HCC cases (60% early-stage) and 404 controls. An independent cohort of 156 HCC cases with 50% early-stage and 245 controls was used to validate the test performance, which showed 88% overall sensitivity, 82% early-stage sensitivity, and 87% specificity. Early-stage sensitivity in clinical validation was significantly higher than α-FP at 20 ng/mL or greater (40%; p = 0.0001) and GALAD at −0.63 or greater (71%; p = 0.03), despite α-FP and GALAD having higher specificities (100% and 93%, respectively) at these cut-off values. Finally, in a recent prospective, multicentre, case–control Phase II study by Piratvisuth T et al., the researchers looked at biomarkers mentioned in international guidelines for HCC surveillance and diagnosis to find combinations with high sensitivity and specificity for early-stage HCC in a group of patients with HBV, 32.9% HCV, 60.5% cirrhosis, and 40.6% with early-stage HCC. The combination of α-FP and DCP, and either published biomarker (i.e., IGFBP3, COMP, or MMP3), as well as age and gender, demonstrated the best clinical performance for detecting early- and late-stage HCC. These new panels’ performance was comparable to the GALAD score [179]. A summary table with the main cited algorithms can be found in Table 3. Because the majority of HCC patients are still diagnosed at advanced stages when locoregional treatments are no longer indicated [214], in this review we discuss recent advances in diverse HCC biomarker evaluation for HCC early detection and prognostic scores in at high-risk populations, as well as the difficulties and advantages of shifting beyond US-based HCC screening. Cirrhosis incidence increased slightly in Europe, Asia-Pacific, East Asia, and Southeast Asia between 2000 and 2015 [215]. According to data from a Canadian population-based cohort, the incidence of age-specific cirrhosis increased by 22% between 1997 and 2016 [214]. In 2017, 1.5 billion people worldwide had CLD, with the most common causes being NAFLD (60%), HBV (29%), HCV (9%), and ALD (2%) [214]. Cirrhosis affects half of all North American “baby boomers” (born 1945–1965), with Black and Hispanic people, and those with lower education levels having a higher prevalence, and incidental diagnosis increasing in younger Americans [215]. As a result of large-scale HBV vaccination and HCV treatment programs, the rising prevalence of metabolic syndrome, a concurrent increase in the use of injectable drugs, and an increase in alcohol abuse, the epidemiology of CLD and liver cirrhosis, and thus of HCC, is changing [215]. Aflatoxin consumption with food (especially in East Asia and Sub-Saharan Africa), hemochromatosis, α-1-antitrypsin deficiency, and tobacco use remain significant risk factors for HCC in the minority of cases [215]. Accordingly, HCC is more prevalent in Asia and Sub-Saharan Africa than in Europe and North America, which has been linked to the spread of viral hepatitis [2,3,4]. There were approximately 854,000 new cases of liver cancer in 2015 (a 75% increase since 1990) and 810,000 cancer-related deaths worldwide [215]. From 1999 to 2017, there were approximately 150,000 HCC-related deaths in the USA. Males, Asian/Islanders, Pacific people, and those aged 75 to 84 years had the highest mortality rates in 2017 [215]. Although mortality rates have decreased in East Asia, North Africa/Middle East, and high-income Asia-Pacific, deaths have increased in many other parts of the world, including South Asia, Central Asia, and Eastern Europe. Racial disparities in HCC mortality persist, which research suggests is due, in part, to treatment failures, lower rates of early diagnosis, and lower chances of curative treatment, including liver transplantation, among racial minorities and non-Hispanic whites [215]. China has currently the most HCC cases due to both an elevated rate (18.3 per 100,000 people) and the world’s largest population (1.4 billion people) [216]. However, in recent years, there has been an increase in the incidence of HCC in patients with non-viral aetiology, primarily of a metabolic nature [1,2,3]. In Italy the majority of HCCs are caused by CHC and ALD, followed by CHB and others, despite a recent significant increase in NASH/NAFLD-related HCC (https://www.registri-tumori.it/cms/pubblicazioni/i-numeri-del-cancro-italia-2020 (accessed on 20 November 2022)) [1]. Additionally, liver tumours are more common in the south of Italy in women, and in the centre of Italy in men. According to the Italian Cancer Registries Association, 33,800 Italian men were diagnosed with liver cancer in 2020. It is estimated that approximately 13,000 new cases of liver tumours will be diagnosed in the same period (Males/Females ratio, 2:1), with 7800 deaths and a net survival rate of 21% in males and 20% in females. The current international guidelines for HCC surveillance recommend US with or without α-FP every six months as the standard of care, which has been shown to be noninferior to three monthly intervals [217]. The sensitivity of the US for early-stage HCC was reported in a meta-analysis of cohort studies. The detection rate was only 45%, but with the addition of α-FP, it rose to 63% [211]. Nevertheless, this strategy has limitations in many countries worldwide due to patient characteristics (e.g., body habitus, NAFLD) as well as US operator experience [12,218], or diminished adherence of at-risk patients to US screening [219]. The other disadvantage of this strategy is the two-point assessment required, which includes radiologic imaging and a blood test, highlighting the need for more accurate single-point screening tests. However, a recent cost-effectiveness analysis that considered both the benefits and costs of HCC surveillance in patients with compensated cirrhosis found that US and α-FP are more convenient for surveillance than US alone or no surveillance at all [220]. Moreover, due to the biological diversity of HCCs, some people have normal α-FP levels but high α-FP-L3 or DCP levels, and vice versa. US exams are commonly performed and interpreted in real time by hepatologists and are regularly combined with the broad application of a variety of biomarkers such as DCP, α-FP, and α-FP-L3 [179]. According to the AASLD, it is critical to determine whether serum biomarkers, such as α-FP-L3, DCP, and other novel serum tests, complement the US. Presently, there are no conclusive results regarding the possible different influence of ethnicity on the outcome of biomarkers/algorithms for HCC. Colli A et al. discovered that between 1982 and 2020, there were no differences in sensitivity and specificity of α-FP (cut-off of 20 ng/mL) for detecting HCC between countries (Europe, 60% and 87%; America, 56% and 89%; Asia, 60% and 83%; Africa, 68% and 81%, respectively; p = 0.447) in 98 studies conducted in Asia, 22 in Europe, 7 in Africa, 19 in North and South America, and one in all three continents [189]. Zhou JM et al. included four Asian case–control studies (China, Taiwan, and Japan) and two Western (Germany and the United States) in a meta-analysis on α-FP-L3% performance for early HCC diagnosis, with diagnostic cut-offs ranging from 3.84% to 10%. Among Asians, sensitivity ranged from 13.3% to 52.6%, with specificity ranging from 84.2% to 98.4%, whereas in Western countries, sensitivity was lower (27.9%), with similar levels of specificity (91.3–96.9%) [190]. In the Gao P et al. meta-analysis evaluating DCP diagnostic performance for HCC detection, despite the difficulty of comparing six Caucasian and fourteen Asian studies because biomarker thresholds varied across studies (4.5–20.24 ng/mL, and 40–150 mAU/mL), the sensitivity of studies on Asian subjects was lower than that of Caucasian studies (63% vs. 76%), whereas specificity showed the reverse (93% vs. 88%). Nonetheless, these findings may be due to higher DCP values in Caucasian subjects without liver disease [43]. In a meta-analysis on the performance of GP73 using different cut-offs ranging from 4.36 ug/L to 400 ug/L, the four Chinese studies outperformed the four American and Turkish studies in terms of sensitivity (75–98% vs. 7–95%) and specificity (52–97% vs. 35–95%) [61]. Another meta-analysis to evaluate the HCC diagnostic performance of OPN found that the Western studies had slightly better sensitivity and specificity (75–100% vs. 26–90%; 66–100% vs. 64–93%, respectively) from seven Eastern studies (three Korean, two Chinese, one Thai, one Australian) and five Western studies (four Egyptian, two American). In a systematic review and network meta-analysis involving 9080 patients and evaluating DKK-1 sensitivity and specificity for HCC detection, the authors did not conduct ethnic subgroup analyses. However, most of the studies were Chinese, with only two studies conducted in Egypt [181]. Finally, a meta-analysis [105] comparing the accuracy of MDK for HCC detection from nine Eastern studies (4 Chinese, 5 Australian) and five Egyptian studies found that the latter population had slightly better sensitivity and specificity (China (86–87% and 84–90%), Australia (61–79% and 58–63%), vs. Egypt (82–100% and 83–97%), respectively). However, it should be noted that the etiology of CLD differed by country (HCV is extremely common in Egypt) and by the fact that the studies used different HCC diagnostic cut-offs ranging from 0.387 ng/mL to 1.683 ng/mL. Even though extensive clinical evidence supports the GALAD model’s superiority for HCC detection in Asian HBV and HCV cohorts, its use in Caucasian populations is still limited. A contemporary prospective study enrolled 6834 patients (2430 with HCC and 4404 with CLD) from Germany, Japan, and Hong Kong [205]. Despite no ethnicity subgroup analysis being performed, the sensitivity of the GALAD model for HCC was higher in the UK and Germany than in Japan, whereas the specificity and accuracy were similar (sensitivity (88.4–91.6% vs. 81.4%); specificity (88.2–89.7% vs. 89.1%); AUC (0.94–0.97 vs. 0.93)). There was no evidence of a difference in model performance between etiologies in the European cohorts. Although there was some proof of an etiological difference in the Japanese cohort, it was not clinically influential. Moreover, while a US-based strategy was successful in some settings, such as Japan, where screening methods incorporating the α-FP, α-FP-L3, and DCP biomarkers allowed for the detection of a significant percentage of tumours measuring less than 2 cm [221,222], it may be challenging to spread in others. Likewise, as a result, more than half of the Japanese with a diagnosis of HCC had undergone surgery or local ablation therapy, implying that increasing HCC screening uptake in other contexts may spare meaningful national public health resources. The pursuit of a single-stage test has resulted in increased research into newer biomarkers and their combinations, as well as newer radiologic imaging [218]. Because α-FP, α-FP-L3, GP73, and OPN are insufficient as standalone biomarkers for early detection of HCC and DCP, DKK1, and MDK seem promising among α-FP non-secretors, they may be helpful in a biomarker panel-based screening strategy. On the other hand, clinical evidence still requires further studies, particularly concerning the reliability of GPC-3, SCCA, SCCA-IC, serum miRNAs, and cell-free DNA. Despite their various combinations that may enhance the diagnostic accuracy of HCC in its early stages, currently, none of these biomarkers are recommended due to a lack of RCT and cost–benefit analyses. Previous and recent research has shown that the GALAD score has a higher sensitivity to both US and α-FP alone, as well as the combination of its component biomarkers [35,212]. Indeed, GALAD score increased sensitivity is associated with high rates of false positivity and 6–12 months pre-diagnosis, resulting in morbidity in terms of anxiety and cost due to additional tests to confirm the diagnosis [35,36]. Moreover, the same superiority was not demonstrated in the Phase III biomarker study in the USA by Tayob N et al. when the FPR was reduced to 10% [36]. Furthermore, while combining GALAD with US may alleviate these flaws, it defeats the purpose of single-point screening [33]. Moreover, while α-FP-L3 is not universally available and has a high cost of surveillance, the combination of α-FP and DCP has shown higher diagnostic accuracy than individual biomarkers and may be a viable strategy in viral hepatitis endemic countries [223]. Nevertheless, GALAD may still be an effective tool in HCC screening and surveillance, especially in diagnosing a subset of patients with a small tumour and negative α-FP and DCP [210,222], but it may not be the best strategy in the other cases [218]. Indeed, Sachan A et al. recently commented in a letter that, in the absence of clear superiority to current surveillance strategies, as well as the limitations of using the GALAD score universally in all HCC patients, its routine use for HCC screening and surveillance may no longer be feasible [218]. The addition of multiple biomarkers raises the cost of surveillance in a resource-constrained setting, and no cost–benefit analysis for the GALAD score is currently available. Similarly, the morbidity associated with a high FPR of 25% with the GALAD score versus 15% with the US and α-FP combination is concerning [36,189]. Additionally, NAFLD is quickly gaining importance as the leading cause of HCC, CLD, and liver transplantation even in the absence of liver cirrhosis [224]. According to the American Gastroenterology Association expert review published in 2020, only NAFLD patients with cirrhosis or advanced fibrosis as determined by non-invasive tests should be considered for HCC surveillance [225]. US surveillance is recommended, but it is limited in obese patients and those with NAFLD who are at high risk of having an inadequate imaging resolution. Of interest, GALAD score may play a keystone role in identifying NASH patients at high risk of developing HCC, where the role of US and DCP is still unknown [206]. Therefore, we emphasize the need for evaluation of the utilization of current and novel biomarkers in validation studies, paving the way for a shift away from a US-based paradigm for HCC screening, with significant potential benefits to patients in terms of reducing the burden of HCC in at-risk populations. In conclusion, more sensitive tests that work around these current barriers to HCC surveillance adherence are required due to several limitations of the US-based screening strategy. In particular, there is a chance to produce and validate better data supporting the use of HCC screening and prognostic scores as an efficient cancer control method in high-risk populations with the introduction of novel and promising HCC screening strategies based on various combinations of the most favourable biomarkers. Additional biomarkers, such as tumour and non-tumour components from liquid biopsy, and metabolomic- and proteomic-based tools, are expected to emerge in the context of an increasingly personalized medicine, but they will require independent validation and strong evidence before being implemented in clinical practice.

PMC10002013

Chenxu Zhao,Han Guo,Yangxiao Hou,Tong Lei,Dong Wei,Yong Zhao

Multiple Roles of the Stress Sensor GCN2 in Immune Cells

21-02-2023

GCN2,immune system,stress,protein kinase,tumor

The serine/threonine-protein kinase general control nonderepressible 2 (GCN2) is a well-known stress sensor that responds to amino acid starvation and other stresses, making it critical to the maintenance of cellular and organismal homeostasis. More than 20 years of research has revealed the molecular structure/complex, inducers/regulators, intracellular signaling pathways and bio-functions of GCN2 in various biological processes, across an organism’s lifespan, and in many diseases. Accumulated studies have demonstrated that the GCN2 kinase is also closely involved in the immune system and in various immune-related diseases, such as GCN2 acts as an important regulatory molecule to control macrophage functional polarization and CD4+ T cell subset differentiation. Herein, we comprehensively summarize the biological functions of GCN2 and discuss its roles in the immune system, including innate and adaptive immune cells. We also discuss the antagonism of GCN2 and mTOR pathways in immune cells. A better understanding of GCN2′s functions and signaling pathways in the immune system under physiological, stressful, and pathological situations will be beneficial to the development of potential therapies for many immune-relevant diseases.

Multiple Roles of the Stress Sensor GCN2 in Immune Cells The serine/threonine-protein kinase general control nonderepressible 2 (GCN2) is a well-known stress sensor that responds to amino acid starvation and other stresses, making it critical to the maintenance of cellular and organismal homeostasis. More than 20 years of research has revealed the molecular structure/complex, inducers/regulators, intracellular signaling pathways and bio-functions of GCN2 in various biological processes, across an organism’s lifespan, and in many diseases. Accumulated studies have demonstrated that the GCN2 kinase is also closely involved in the immune system and in various immune-related diseases, such as GCN2 acts as an important regulatory molecule to control macrophage functional polarization and CD4+ T cell subset differentiation. Herein, we comprehensively summarize the biological functions of GCN2 and discuss its roles in the immune system, including innate and adaptive immune cells. We also discuss the antagonism of GCN2 and mTOR pathways in immune cells. A better understanding of GCN2′s functions and signaling pathways in the immune system under physiological, stressful, and pathological situations will be beneficial to the development of potential therapies for many immune-relevant diseases. General control nonderepressible 2 (GCN2), which is encoded by eukaryotic translation initiation factor 2 alpha (eIF-2α) kinase 4 (EIF2AK4), is a serine/threonine-protein kinase that senses amino acid deficiencies through binding to uncharged transfer RNA (tRNA). It plays a key role in modulating amino acid metabolism in response to nutrient deprivation. Upon amino acid starvation, GCN2 promotes the phosphorylation of eIF2α, which leads to a repression of general translation and the initiation of gene reprogramming to facilitate adaptation to nutrient stress. In response to diverse stress stimuli, eukaryotic cells activate a common adaptive pathway, termed the integrated stress response (ISR), to restore cellular homeostasis [1]. ISR is a common cellular stress response that is initiated upon phosphorylation of eIF-2α at the residue serine-51 and which is critical for translational control to maintain cellular homeostasis and in response to various stress conditions in eukaryotes [2]. The four well-known sensors of ISR in mammals include GCN2, double-stranded RNA-dependent protein kinase K (PKR), heme-regulated inhibitor, and PKR-like endoplasmic reticulum kinase [3]. GCN2 activation and the phosphorylation of eIF-2α mainly inhibit the general translation of proteins not important for cell survival, promote the translation of proteins important for cell survival, and induce the transcription of several stress-responsive genes, including those involved in amino acid transport and antioxidation [4]. Recent studies have shown that GCN2 plays important roles in various physiological and pathological processes, in addition to maintaining organismal homeostasis under nutrient deprivation and other stresses. GCN2 mutations impair vascular and parenchymal remodeling during pulmonary fibrosis in rats and humans [5,6]. In this review, we summarize the recent progress on the signaling pathways and biological functions of GCN2 in mammals, with a primary focus on its diverse roles across different immune cell subpopulations. GCN2 was first discovered in the yeast Saccharomyces cerevisiae [7]. GCN2 is one of three eIF-2α kinases in yeast and one of four characterized eIF2α kinases in mammals [2,8,9]. All distinct eIF2α kinases share extensive identity in the kinase catalytic domain. In addition to the 12 conserved subdomains found in most protein kinases, they have other features including an insertion between subdomains IV and V that distinguish them from other serine/threonine kinases. Their regulatory regions share little similarity compared to the catalytic domain, which contributes to the different stress signals controlling each eIF2α kinase [10]. As an eIF-2α kinase conserved across all eukaryotes and which primarily responds to starvation of different amino acids, GCN2 is a multidomain protein. It possesses a typical eukaryotic protein kinase catalytic moiety and a paratactic domain homologous to histidyl-tRNA synthetase (HisRS), mediating the activation of GCN2 through direct interactions with multiple uncharged tRNAs that accumulate during a state of amino acid limitation [11,12]. In addition, GCN2 also contains RING finger and WD repeat containing and DEAD-like helicases (RWD) domain, partial kinase domain and C-terminal ribosome domain (CTD). RWD domain associates with the activator protein General control nonderepressible 1 (GCN1). Partial kinase domain is required for GCN2 activation. CTD is required for uncharged tRNA binding which facilitates GCN2 dimerization and ribosome association [8,13]. Recent studies have shown that GCN1 is essential for both the GCN2-dependent stress response and GCN2-independent cell cycle regulation [14]. The high concentrations of uncharged tRNAs that accumulate in starved cells favor binding to the HisRS-like domain and CTDs, which alters the conformation of GCN2, relieves inhibitory interactions between the HisRS-like domain, CTDs and the protein kinase domain and result in autophosphorylation of the activation loop of the enzyme, thereby ultimately activating GCN2 [13,15,16]. It should be noted that GCN2 isoforms lacking functional RWD domains are expressed in some organisms, such as in the parasite T. gondii, the malaria parasite P. falciparum and Dictyostelium, and can nevertheless be activated [17,18,19], indicating that these GCN2 isoforms are activated in a GCN1-independent way. This issue remains to be further addressed in mammals in the future. GCN2 expression and activation can be induced by many conditions and factors (Figure 1). The activation of GCN2 has been extensively studied, primarily in yeast, in response to amino acid starvation, including essential amino acids and some nonessential amino acids. It has recently been reported that methionine deprivation acts via a GCN2-independent mechanism, while all other essential amino acids are sensed by GCN2 in Drosophila [20]. Upon starvation, accumulated uncharged tRNAs bind to the HisRS-like domain of GCN2 and induce an activating conformational change [8,11,15]. Later research has demonstrated that GCN2 can also be activated by glucose deprivation, purine starvation, and a high-salt environment, as well as by stressors unrelated to nutrients, including UV irradiation, osmotic stress, high levels of urea, oxidative stress (H2O2), high salinity (NaCl), tryptophanol and other anticancer drugs that inhibit proteasomes or histone deacetylases, and viral infections [4,8,21,22,23,24,25,26] (Figure 1). Small-molecule kinase inhibitors represent a broad class of cancer therapeutics. Some Food and Drug Administration (FDA)-approved inhibitors, such erlotinib and sunitinib, can bound and activate GCN2 [27]. It has been reported that tRNA binding is required for GCN2 activation in response to UV-induced stress or oxidative stress [28]. However, ongoing translation is not required for UV or oxidative stress-induced GCN2 activation [28]. The detailed mechanisms by which these nutrient-unrelated stresses activate GCN2 need to be addressed in the future. GCN2 can be activated by stress conditions that inhibit aminoacyl-tRNA synthetases without amino acid starvation, such as intracellular acidification, DNA damage and some antitumor drugs, ultimately leading to uncharged tRNA accumulation [29,30,31]. The inhibition of proteasome activity by MG132 or Bortezomib decreases free cysteine, asparagine, and aspartate levels in the cytoplasm, resulting in the accumulation of deacylated tRNAs and the subsequent activation of GCN2 in mammalian cells, which can be rescued by adding these amino acids to the medium. Thus, blocking proteasome function leads to GCN2 activation [32]. It is also worth noting that changes unrelated to intracellular tRNA levels can also lead to GCN2 activation. Methylglyoxal, an endogenous metabolite derived from glycolysis and harmful to cells at high concentrations, can activate GCN2 with no detectable alteration to uncharged methionyl tRNAs [33,34,35]. Rapamycin activates GCN2 without increasing uncharged methionyl tRNAs or histidyl-tRNAs [35,36,37]. Rapamycin-mediated mammalian target of rapamycin (mTOR) inhibition increases the affinity of GCN2 for amino-deacylated tRNAs [36]. The phosphorylation of eIF-2α caused by leucine starvation is dependent on maintained mechanistic target of rapamycin complex 1 (mTORC1) activity [38]. In addition, GCN2 can be activated through serine/threonine-protein phosphatase PP1-1 (Sit4) phosphatase by the treatment of cells with rapamycin, the inhibition of mTOR, or culturing cells in a poor nitrogen source, such as γ-aminobutyric acid [39]. GCN2 activity is also regulated by the phosphoinositide 3-kinase (PI3K)/protein kinase B (PKB)/glycogen synthase kinase 3 beta (GSK-β) pathway in neurons and fibroblasts [40]. Inhibition of PI3K decreases GCN2 activity, eIF-2α phosphorylation, and levels of activating transcription factor 4 (ATF4) [40]. PI3K activation inhibits GSK-3β phosphorylation, and the inhibition of GSK-3β increases GCN2 auto-phosphorylation. In turn, eIF2α phosphorylation activates the PI3K pathway indirectly [41], potentially forming a feedback loop that enables crosstalk between the PI3K pathways and general amino acid control. GCN2 activity in cells must be effectively inhibited under normal circumstances to allow for the maximum rate of protein synthesis. Many molecules have been recognized to directly or indirectly modulate GCN2 activation (Figure 2). Researchers have identified several endogenous inhibitors of GCN2 kinase in yeast and mammals, including eukaryotic translation elongation factor 1A (eEF1A), yeast impact RWD domain protein (IMPACT) homolog 1 (Yih1), IMPACT and p58IPK. eEF1A has been confirmed as an inhibitor of GCN2 in Saccharomyces cerevisiae cells. Visweswaraiah et al. proved that the essential translation elongation factor eEF1A can interact with the CTD of GCN2, and this interaction can be attenuated by uncharged tRNAs when cells are faced with amino acid scarcity. Meanwhile, eEF1A reduces the ability of GCN2 to phosphorylate its substrate, eIF2α, possibly through preventing GCN2 from binding eIF2α; however, it does not affect the autophosphorylation of GCN2 [42]. IMPACT and its homolog in yeast, Yih1, are potent suppressors of GCN2. Both IMPACT and Yih1 contain an RWD domain that competes with GCN2 for GCN1 binding, which is vital for GCN2 activation [43,44]. IMPACT and its homolog function mainly through the GCN2 signaling pathway. For example, IMPACT has been found to be preferentially expressed in the nervous system, where it may modulate neurite outgrowth in a GCN2-dependent way [45]; Rafael C. Ferraz et al. found that knockdown of the IMPACT homolog impt-1 in C. elegans activates the ISR pathway and increases both lifespan and stress resistance in a GCN2-dependent manner [46]. In addition, p58IPK is another inhibitor of GCN2, which can suppress GCN2 phosphorylation and prolong protein synthesis under endoplasmic reticulum deficiency, hypothermic challenge, and prolonged culture stress conditions [47]. Chaperone proteins, such as heat shock protein 82 (Hsp82) in yeast and mammalian heat shock protein 90 (Hsp90), may play an essential role in the maturation of GCN2 to an active kinase [42,48]. A highly conserved AMP-activated serine/threonine protein kinase (AMPK) in mammals and sucrose non-fermenting 1 (Snf1) in yeast, is activated when the AMP/ATP ratio increases. This process will switch off energy-consuming anabolic pathways while turning on ATP-producing pathways [49]. Snf1 and GCN2 directly interact with each other. Snf1 is activated by amino acid starvation and is required for GCN2 auto-phosphorylation and eIF-2α phosphorylation [50]. Resistance to glucose repression protein 1 (REG1) is a negative regulator of Snf1, and REG1 deletion increases the phosphorylation of GCN2 Thr-822 and eIF-2α independent of Snf1 under amino acid starvation [51]. Receptor for activated C-kinase (Rack1) in mammalian cells and its orthologue activating signal cointegrator–1 (Asc1) in S. cerevisiae are highly conserved proteins consisting of seven tryptophan-aspartate repeats [52] that act as scaffolds for proteins in various signaling pathways and play essential roles in regulating a wide array of biological processes. Asc1 is required for starvation-induced GCN2 auto-phosphorylation and eIF-2α phosphorylation promoting expression of amino acid biosynthesis genes in Schizosaccharomyces pombe under amino acid starvation [53]. The eIF-2α molecule is the most important known target of GCN2 identified to date (Figure 3). The GCN2 protein can phosphorylate eIF2α in Drosophila and mice in vitro [10,54,55]. After being activated in response to nutritional deprivation or other stresses, GCN2 reduces eukaryotic initiation factor 2 (eIF-2) activity by phosphorylating the α subunit of eIF-2 at serine 51, which impairs the rate of GDP-GTP exchange catalyzed by the initiation factor eukaryotic translation initiation factor 2B (eIF2B) and blocks eIF-2 recycling [11,56,57]. Phosphorylated eIF-2α is unable to fulfill its normal role in helping the 40S ribosome subunit acquire methionyl initiator tRNA, and formation of the eIF2/tRNAiMet/GTP ternary complex, which is required for polysome formation and translation initiation, is decreased [58,59]. Furthermore, temperately increased phosphorylation of eIF-2α can completely inhibit protein synthesis initiation while favoring selective translation of some messenger RNA (mRNAs), including general control nondepressible 4 (GCN4) in yeast and ATF4 in mammals [60] (Figure 3). In yeast, phosphorylation of eIF-2α by phosphorylated GCN2 decreases eIF-2-GTP levels and eliminates the (ternary complex) TC’s inhibitory effects of the upstream open reading frames (uORFs) of GCN4 mRNA, thereby promoting GCN4 translation and blocking general translation [61]. GCN4 is a transcriptional activator of the general amino acid control pathway and promotes the expression of genes encoding amino acid biosynthetic enzymes to remedy nutrient deprivation [15]. Similarly, phosphorylation of eIF-2α by GCN2 in mammalian cells induces the translation of ATF4 mRNA, which be classified a transcription factor in the basic leucine zipper family, which includes Gcn4. The mechanism of ATF4 mRNA translation induced by GCN2 in mammals is similar to that of GCN4 in yeast, which is dependent on two ORFs associated with its mRNA [62,63,64]. ATF4 is a transcription factor that binds an amino acid response element located in the promoters of specific genes and induces their transcription [65]. In the mouse liver, GCN2 deletion attenuates high fat diet-induced eIF-2α phosphorylation and the induction of ATF4-C/EBP homologous protein (CHOP) to decrease the expression of peroxisome proliferator-activated receptor gamma (PPARγ), fatty acid synthase and metallothionein [66]. In addition to eIF-2α, methionyl-tRNA synthetase has also been identified as a substrate of GCN2 (Figure 4). Methionyl-tRNA synthetase plays essential roles in initiating translation by transferring methionyl to initiator tRNA. Under UV irradiation conditions, GCN2 can phosphorylate methionyl-tRNA synthetase, which inhibits its binding to methionyl-tRNA and contributes to a down-regulation of general translation [67]. A recent study using quantitative phosphoproteomics showed that GCN2 also targets auxiliary effectors to modulate translation [68]. In addition to phosphorylation of eIF2a, GCN2 also phosphorylates the beta-subunit of the trimeric eukaryotic translation initiation factor 2 gamma (eIF2G) protein complex to promote its association with eukaryotic translation initiation factor 5 (eIF5) and, subsequently, to restrict recycling of the initiator methionyl-tRNA-bound eIF2-GDP ternary complex in amino acid-starved cells [68]. The protein kinase GCN2 also regulates mRNA translation in mammals in an ATF4-independent manner (Figure 4). The translation of inducible nitric oxide synthase is negatively regulated by GCN2 upon UV irradiation (22). GCN2 negatively regulates translation of the oncogene erb-b2 receptor tyrosine kinase 2 (ErBb2) and of hypoxia inducible factor 1α (HIF1α), both of which are involved in the cell cycle and cell survival [69,70]. Another direct GCN2 substrate-regulating protein is methionyl-tRNA synthetase [42]. Activated GCN2 phosphorylates methionyl-tRNA synthetase at serine 662, which inhibits methionyl-tRNA synthetase activity and activates the ataxia-telangiectasia, mutated protein kinases (ATM)/ ATM and Rad3-related protein kinases (ATR) system for DNA repair [42]. GCN2 negatively controls the translation of 5′-terminal oligopyrimidine tracts (TOP) mRNAs, which encode protein biosynthesis factors, under amino acid starvation, and this process is dependent on the stress granule-associated proteins T-cell intracellular antigen 1 (TIA-1) and TIA1 cytotoxic granule-associated RNA binding protein-like 1 (TIAR) [71]. This regulation will re-direct TOP mRNAs from polysomes to stress granules, thereby allowing modulation of limited resource usage according to amino acid availability [71]. GCN2 can directly regulate forkhead box O class protein (Foxo) activity in human cells and in Drosophila [72]. However, protein kinase RNA-like ER kinase (PERK) can compensate for potential deficiencies in Foxo activity when GCN2 is absent in human cells [72]. GCN2 can directly interact with Foxo3 in cells and phosphorylate Foxo3 to promote its nuclear translocation and transcriptional activation in skeletal muscle [73]. Under certain conditions, IFN-γ has the ability to promote cancer initiation and progression [74]. Importantly, IFN-γ accelerates arginine depletion and induces malignant transformation through the NF-κB/GCN2/eIF-2α pathway in vitro and in vivo [75], and arginine addition can rescue these effects of IFN-γ [76]. GCN2 activation reduces the activity of succinate dehydrogenase, an iron-sulfur mitochondrial enzyme, and promotes the nuclear localization of the transcription factor DNA-binding transcription factor 1 (Aft1) in the yeast Saccharomyces cerevisiae [77]. In normal situations, an excess of amino acids would promote low levels of GCN2 activity, leading to inhibition of Aft1 and iron transporters. This repression of iron transporters could help prevent intracellular iron from reaching toxic levels due to reactive oxygen species (ROS) production [77]. GCN2 is responsible for pro-apoptotic TNF-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) upregulation, caspase-8 activation, and extrinsic apoptotic cell death in tumor cells under glutamine or methionine starvation [78]. In addition, the GCN2/ATF4 pathway is involved in amino acid starvation-induced expression of the stress-inducible gene p8 through an amino acid response element in the promoter of this gene [79]. Importantly, GCN2 also plays roles in regulating the amino acid starvation response in the nucleolus, in addition to its function in the cytosol. The nucleolar localization of GCN2 regulates small RNA transcripts, which may serve as alternative stress-sensing machinery for nutrient deficiency. Depletion of GCN2 by siRNA increases small RNA transcripts and induces activation of the p53 pathway, which is dependent on B-related factor 1 [80]. GCN2-deficient mice are viable and fertile. These mice display no obvious phenotypic abnormalities unless fed a diet lacking a single amino acid [81,82]. However, GCN2 has a wide array of biological functions across different cells and species [3,8,26,83,84]. GCN2β isoform (GCN2β), which is highly abundant in unfertilized mouse eggs, is active and elevates the phosphorylation of eIF-2α; however, phosphorylation of eIF-2α is reduced drastically after fertilization, suggesting that GCN2-mediated translational control may contribute to the regulation of oocyte maturation [85]. GCN2-mediated phosphorylation of eIF-2α is elevated in old/aging cells, potentially contributing to global translation reduction and lifespan extension [86]. GCN2 deficiency attenuates induced hepatic steatosis and insulin resistance in mice fed a high-fat diet, partially through shifting lipolysis to lipogenesis, as well as by decreasing oxidative and endoplasmic reticulum stress [66]. GCN2 is required for the growth of prostate cancer cells and regulates the expression of over 60 solute-carrier genes, including those involved in amino acid transport [87]. Inhibiting GCN2 in arginine-deprived hepatocellular carcinoma cells promotes a senescent phenotype, rendering these cells vulnerable to senolytic compound-induced death [88]. Heritable pulmonary veno-occlusive disease is related to biallelic mutations in EIF2AK4 which encodes GCN2. GCN2 deletion impairs the ability of hematopoietic stem cells to repopulate and regenerate by regulating metabolic alterations [89]. The GCN2 activator halofuginone significantly prevents cell proliferation and inhibits expression of anti-HLA class I antibodies-induced IL-8, monocyte chemoattractive protein-1, and transforming growth factor-beta 1 in human glomerular endothelial cells, suggesting that GCN2 activation may have a protective effect against antibody-mediated graft rejection [90]. Herein, we will briefly summarize the involvement of GCN2 in oxidative stress, cell survival, autophagy, and cell metabolism. Excessive ROS production or impaired ROS reduction efficiency can lead to oxidative stress. Oxidative stress is associated with cytotoxic effects and has been implicated in the etiology of various diseases. The oxidative stress response is highly associated with amino acid supply. GCN2 is a potential regulator of redox homeostasis. Arriazu et al. demonstrated that amino acid deprivation decreases intracellular ROS levels in hepatic stellate cells in vitro, with this effect shown to be dependent on GCN2 but not on downstream eIF-2α [91]. Deletion or pharmacological inhibition of GCN2 significantly delays collective cell migration and wound closure by impairing the maintenance of intracellular free amino acids, particularly cysteine, as well as disrupting Ras-related C3 botulinum toxin substrate 1 (RAC1)-GTP-driven ROS generation, lamellipodia formation, and focal adhesion dynamics in human epidermal keratinocytes during wound healing [92]. Furthermore, GCN2 and its downstream signaling pathway play important roles in protection against oxidative injuries induced by an amino acid imbalanced diet [93]. The GCN2 kinase has a broad impact on cell survival. The GCN2/eIF-2α/ATF4 signaling pathway can mediate cell survival [94]. GCN2 can sense stalled ribosomes and activate the eIF-2α/ATF4 downstream signaling pathway to promote ISR, which protects neurons against ribosome stalling-mediated cell death [95]. Cai et al. reported that the GCN2/eIF-2α/ATF3 signaling pathway is important for cells in the renal medulla during urea stress. The loss of GCN2 increases the sensitivity of cells to urea stress and promotes activated caspase-3 expression to decrease cell survival [24]. GCN2 deficiency decreases doxorubicin-induced cardiotoxicity by ameliorating apoptosis and oxidative stress through an eIF-2α/CHOP-dependent pathway [96]. The GCN2-associated signaling pathway seems required for development and lifespan extension. GCN2 deficiency impairs translational control and the expression of differentiation-associated genes, eventually inhibiting normal epidermal differentiation in organotypic skin culture [97]; these findings indicate that the GCN2/eIF-2α signaling pathway promotes the translational control and differential protein expression required for normal keratinocyte differentiation. Under conditions of amino acid restriction, activated GCN2 and its downstream transcription factor ATF4 mediate transcription of 4E-BP, which regulates lifespan extension in Drosophila and is important for the normal development of certain tissues [98,99]. Xiao et al. found that the inhibited cell proliferation and increased apoptosis of breast cancer cells observed under leucine deprivation is dependent on fatty acid synthase (FASN). Leucine deprivation decreases Fasn expression by activating the GCN2/eIF-2α signaling pathway, which can inhibit sterol regulatory element-binding protein 1C (SREBP1C), a transcription factor that directly binds to the Fasn promoter and regulates Fasn mRNA abundance [100]. However, the specific mechanism by which the GCN2 signaling pathway regulates SREBP1C requires further investigation. GCN2 can also exert proapoptotic functions in cancer cells through posttranslational mechanisms. GCN2 protein levels critically determine the sensitivity of cancer cells to Na+, K+-ATPase ligand-induced apoptosis, both in vitro and in vivo. Na+,K+-ATPase ligand treatment triggers phosphorylation of GCN2 at threonine 899, which increases GCN2 protein expression by disrupting formation of the GCN2-beta-arrestin-NEDD4-like E3 ubiquitin protein ligase (NEDD4L) ternary complex. These increased GCN2 levels, in turn, aggravate Na+,K+-ATPase ligand-induced cancer cell apoptosis, which is largely dependent on a molecule downstream of GCN2, CHOP [101]. In eukaryotic cells, GCN2 plays an important role in regulation of the cell cycle under various stresses. DNA-damaging agents or nitrogen starvation induce cell arrest in the G1 phase, accompanied by phosphorylation of eIF-2α [102,103]. Hamanaka et al. demonstrated that PERK and GCN2 function cooperatively to regulate eIF-2α phosphorylation and cyclin D1 translation in fibroblasts after unfolded protein response (UPR) activation [104]. In addition, activation of the GCN2/eIF-2α signaling pathway can selectively up-regulate translational expression of a p21 transcript variant that contributes to cell cycle arrest at the G1/S phase and promotes cell survival [105]. The GCN2 signaling pathway plays a major role in autophagy and the regulation of stress-induced autophagy gene expression. The GCN2 kinase and its downstream transcription factors, ATF4 and CHOP, are required to increase transcription of a set of genes implicated in the formation, elongation, and function of the autophagosome [106]. Fougeray et al. reported that GCN2 can mediate IFN-γ-induced autophagy in human renal epithelial cells. During this process, IFN-γ induces tryptophan metabolism, then activates the GCN2 kinase and downstream eIF2α, an activator of autophagy [107]. During the lactation cycle of the bovine mammary gland, autophagy is induced in bovine mammary epithelial cells (BMECs) as a survival mechanism to maintain cellular homeostasis. IFN-γ-induced autophagy in primary BMECs promotes dramatic primary BMEC transformation in vivo and in vitro [75]. GCN2/eIF-2α signaling pathway-mediated IFN-γ-induced autophagy in BMECs reduces milk synthesis, while arginine supplementation can attenuate IFN-γ-induced autophagy and restore milk protein and fat synthesis to some extent [108]. Autophagy and its regulatory mechanisms are involved in intestinal homeostasis and repair, supporting intestinal barrier function in response to cellular stress through tight junction regulation and protection from cell death [109]. Acute amino acid starvation-induced autophagy inhibits intestinal inflammation, a key mediator of the ISR, through a mechanism dependent on GCN2 [110]. GCN2 and mTORC1 are two amino acid-sensing kinases in mammalian cells, and their combined effects orchestrate cellular adaptation to amino acid levels. GCN2 kinase is typically activated by increased levels of uncharged tRNAs during amino acid scarcity. The mTORC1 kinase can be activated by amino acid sufficiency, and enhanced mTORC1 activity promotes high levels of translation to maintain cell growth and proliferation by phosphorylating two important translational regulators, the S6 kinase (S6K) and eIF4E-binding protein (4E-BP1) [111,112]. GCN2 and mTORC1 signaling pathways are both regulated by amino acids and share some common functions. These two amino acid-sensing systems are linked. mTORC1 is inactivated by certain nutrient deprivation conditions. GCN2 is involved in the inhibition of mTORC1; however, GCN2 alone is not sufficient to inhibit mTORC1 activity upon leucine or arginine deprivation, indicating the existence of additional mechanisms by which leucine and arginine regulate mTORC1 activity. Moreover, GCN2 inhibits mTORC1 activity through phosphorylation of eIF2α but independently of the downstream transcription factor ATF4 [113]. In contrast, Ye et al. reported that GCN2 inhibits mTORC1 activity through ATF4 [114]. Deprivation of various amino acids can activate GCN2, up-regulate ATF4 expression and promote expression of the stress response protein Sestrin2, which is required to sustain mTORC1 repression by blocking its lysosomal localization [114]. Furthermore, mTORC1 activity also seems to impact GCN2 activity. Pharmacological inhibition of mTORC1 leads to GCN2 activation and eIF-2α phosphorylation dependent on the catalytic subunit of protein phosphatase 6 (PP6C) [115]. Inhibition of mTORC1, either by rapamycin, TOR2 mutation or nitrogen deprivation, induces GCN2-dependent phosphorylation of eIF-2α [9]. In response to UV irradiation and oxidative stress, GCN2 is fully activated without any detectable change in target of rapamycin 2(TOR2) activity. However, during amino acid starvation, activation of GCN2 is dependent on maintained TOR2 activity, and GCN2 is required for timely inactivation of the mTOR pathway. Deprivation of glutamine, arginine, methionine, or lysine, but not of 16 other amino acids, has been shown to induce PKB activation in non-small cell lung cancer cells via GCN2-ATF4-regulated in development and DNA damage responses 1 (REDD1) axis-mediated mechanistic target of rapamycin complex 2 (mTORC2) activation [116]. Thus, the GCN2 and mTOR signaling pathways likely act in a coordinated manner to prevent the translational machinery from using excessive amounts of vital resources during nutrient-limited conditions. Nevertheless, the relationship between GCN2 and mTOR is complicates, and more research is needed to clearly define the regulatory interplay between GCN2 and mTOR. GCN2 is closely linked to various metabolism-associated processes, including fatty acid and amino acid synthesis, gluconeogenesis and the hexosamine biosynthetic pathway. In primary human CD4+ T cells, indolamine 2,3-dioxygenase (IDO) activates GCN2 kinase through tryptophan depletion, resulting in down-regulation of key enzymes directly and indirectly involved in fatty acid synthesis [117]. The GCN2/ATF4 pathway can up-regulate serine synthesis enzymes in response to amino acid deprivation, which synergizes with PKM2-dependent accumulation of glycolytic precursors to maintain serine synthesis [118]. Gluconeogenesis normally plays a key role in the maintenance of peripheral glucose homeostasis. GCN2-deficient mice exhibit reduced gluconeogenesis upon administration of pyruvate, a gluconeogenic substrate. Xu et al. reported that GCN2 is important for maintaining gluconeogenesis in the liver through regulating expression of CCAAT enhancer-binding protein-beta (C/EBPβ) [119]. Amino acid catabolism is closely related to innate and adaptive immunity. GCN2 plays vital roles in the immune system and in the maintenance of immune homeostasis [3]. For example, GCN2/ATF4 signaling pathway-induced 4E-BP contributes to innate immunity by biasing mRNA translation toward cap-independent mechanisms, thus enhancing AMP synthesis during infection [120]. GCN2-mediated ISR impacts immune homeostasis in the intestine. GCN2 controls intestinal inflammation by suppressing inflammasome activation in response to amino acid starvation. During amino acid starvation or intestinal inflammation, GCN2-mediated ISR is activated in intestinal antigen presenting cells (APCs) and epithelial cells. GCN2 deficiency in CD11c+ APCs or intestinal epithelial cells leads to reduced autophagy and increased ROS levels, promoting inflammasome activation and interleukin (IL)-1β production and ultimately enhancing intestinal inflammation and T helper 17 cell (Th17) responses [110]. The regulatory roles of GCN2 in various immune cells will be discussed in the following sections. GCN2 participates in the protective innate immunity and in organismal stress responses caused by pathogen-induced amino acid starvation. During the process of bacterial infection, such as by Shigella or Salmonella, damage to the host cell membrane triggers an acute intracellular amino acid starvation response, then activates the GCN2/eIF-2α/ATF4/ATF3 ISR pathway and simultaneously decreases mTOR activity [121]. A systems biology approach revealed that there is a significant correlation between GCN2 gene expression in peripheral blood mononuclear cells and CD8+ T cell responses in humans vaccinated with the yellow fever vaccine YF-17D [122]. The increase in GCN2 gene expression in PBMCs is positively correlated with the increase of the proportion of activated CD8+ T cells, and the authors use experiments to confirm that after treatment of PBMCs with YF-17D, eIF2α phosphorylation is enhanced over time and Stress granules appear. It shows that in the time sequence, the expression of GCN2 in PBMC increases first, and then the activation of CD8+T increases [122]. Furthermore, Ravindran et al. demonstrated that YF-17D-induced GCN2 activation in dendritic cells initiates autophagy and enhances antigen presentation ability to both CD4+ and CD8+ T cells [123]. No studies addressing the direct role of GCN2 in granulocytes have been reported to date. Julia et al. provide a perspective on the signaling of pattern recognition receptors (PRRs) and cytokine receptors recruits nutrient-sensing and autophagic machinery to shape the immune response to microbes. Amino acid starvation during Shigella flexneri activates GCN2, which results in an Activating transcription factor 3 (ATF3)-dependent transcriptional signature, e.g., increased CHOP. Ravindran et al. recently reported that mice deficient in GCN2 exhibit substantially high levels of ROS and, subsequently, of the proinflammatory mediator IL-1β in response to cellular stress. This phenomenon is likely due to a lack of autophagy in GCN2-deficient mice, whereas mice fed a reduced-amino-acid diet show significantly lower levels of oxidative stress and of inflammatory responses to cellular stress [123]. Further mechanistic insights reveal that GCN2-induced autophagy interferes with oxidative stress and inflammasome activation, thereby controlling inflammation (Figure 2). It has recently been reported that GCN2 is a key driver of the induction of anti-inflammatory macrophage functional polarization and myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment, depending on ATF4 and altered oxidative metabolism and myeloid-lineage deletion of GCN2 can changes in the immune microenvironment with increased proinflammatory activation of macrophages and MDSCs and IFNγ expression in intratumoral CD8+ T cells [6]. It has been reported that both IL-6 and GCN2 are required to promote neovascularization in IFN-γ-induced IDO1-expressing asialo-GM1highCD11c+Gr-1+CD11blow cells with high autofluorescence [124]. Treatment with a GCN2 inhibitor alleviates MDSCs-related T cell suppression and restores T cell proliferation to enhance host anti-tumor immunity in mice [125]. In response to amino acid abundance, mTOR activates host protein translation, a mechanism that coronaviruses use for their own protein synthesis and replication. In contrast, the amino acid starvation sensor GCN2 activates pathways that limit inflammation and viral replication [126]. On the other hand, GCN2 signaling in myeloid cells can also promote inflammatory reactions in certain cases. The stress response kinase GCN2 promotes macrophage inflammation both in vitro and in vivo. Amino acid deficiency enhances the sensitivity of macrophages to lipopolysaccharides, as evidenced by increased IL-6 production dependent on the GCN2-ATF4 signaling pathway. In a mouse model of septicemia, mice with a myeloid cell-specific GCN2 deficiency were shown to exhibit reduced inflammatory responses and mortality, accompanied by decreased levels of TNF-α, IL-6, and IL-12 [127]. In mice, the GCN2 kinase is a key regulator of fibrogenesis and of acute and chronic liver injury induced by carbon tetrachloride. GCN2 plays roles in hepatic fibrogenesis and in the response to acute or chronic liver injury. Upon deficiency of the essential amino acid histidine, GCN2 is activated in primary and immortalized human hepatic stellate cells, resulting in decreased mRNA and protein expression of collagen type I, which is important for fibrogenesis. GCN2-deficient mice exhibit increased susceptibility to CCl4-induced acute or chronic liver damage [128]. GCN2 can be activated by IDO-driven tryptophan consumption to mediate the biological effects of IDO1 in immune cells. In vitro studies have shown that GCN2 kinase is required for IL-6 production by IDO-expressing macrophages in response to LPS stimulation in tryptophan-free RPMI 1640 medium [127]. Deletion of GCN2 specifically in myeloid cells decreases LPS-induced mouse septic mortality and decreases LPS-induced IL-6 levels, although not TNF-α production [127]. Clearance and tolerance to autoantigens produced by apoptotic cells are important to the maintenance of homeostasis and to prevent the initiation of inflammatory autoimmunity. Increased cell death and defective clearance can lead to the initiation and development of the autoimmune disease systemic lupus erythematosus (SLE) [129]. As a type of innate scavenging cell, macrophages are important for the clearance of apoptotic cell-associated self-antigens and the maintenance of self-tolerance. The tryptophan catabolizing enzyme IDO1 limits innate and adaptive immunity to apoptotic self-antigens, and IDO inhibits the inflammatory pathology caused by systemic autoimmune diseases [130]. The protein kinase GCN2, a primary downstream effector of IDO1, is involved in apoptotic cell-driven immune suppression. Upon activation of the GCN2 signaling pathway in macrophages, IDO1 enhances production of the apoptotic cell-driven anti-inflammatory cytokine IL-10 while reducing production of the proinflammatory cytokine IL-12 [130]. IDO1 fails to promote IL-10 protein expression in GCN2-deleted macrophages due to alterations in ribosomal association with cytokine mRNA transcripts [130]. Myeloid-specific deletion of GCN2 abrogates regulatory cytokine production and promotes inflammatory T-cell responses to apoptotic cell antigens and failure of tolerance induction. Consistently, myeloid deletion of GCN2 in lupus-prone mice results in increased immune cell activation, humoral autoimmunity, renal pathology, and mortality, while activation of GCN2 was found to significantly reduce these symptoms of systemic autoimmune disease [131]. Compared to wild-type mice, GCN2-deficient mice display resistance to anemia during a number of stress conditions, including hemolysis, amino acid deficiency and hypoxia. GCN2-deleted liver macrophages exhibit defects in the ATF4- NRF2 pathway that impair erythrophagocytosis and lysosome maturation, indicating that GCN2 is an important regulator of red blood cell clearance and iron recycling in liver macrophages [132]. GCN2 deletion in macrophages blocks leucine deprivation-induced browning and lipolysis in mouse white adipose tissue [133]. Further studies have revealed that GCN2 activation in macrophages reduces monoamine oxidase A expression and increases secretion of norepinephrine from macrophages to adipocytes, enhancing white adipose tissue browning and lipolysis under conditions of leucine deprivation [133]. Injection of the beta3-adrenergic receptor agonist CL316,243 and inhibition of monoamine oxidase A were both shown to increase norepinephrine levels, enhancing browning and lipolysis in white adipose tissue under leucine deprivation conditions [133]. Therefore, GCN2 expression in macrophages is involved in the balance between white and brown adipose tissues. GCN2 activation is important for T cell proliferation, activation, and differentiation. It has been reported that the type of amino acid transporter can dictate T cell differentiation fate and that T cells lacking the leucine transporter solute carrier family 7 member 5 (SLC7A5) are unable to evoke a robust response to antigen exposure or differentiate into effector T cells [134]. IDO inhibits T cell proliferation through the activation of GCN2 involves many pathways, such as GCN2 down-regulating TCR-complex ζ-chain, c-Myc, aerobic glycolysis and glutaminolysis levels by reducing levels of lactate dehydrogenase A (LDH-A) and glutaminase, GCN2-mediated down-regulation of key enzymes involved in fatty acid synthesis and GCN2 activated to decreasing glucose influx, and altering key enzymes involved in metabolism to decrease aerobic glycolysis and glutaminolysis [117,135,136]. In this way, IDO could be a constraining factor for alloreactive T cell proliferation and differentiation into effector T cell subtypes [136]. However, Sonner et al. found that GCN2 deficiency in T cells did not affect immunity to B16 tumors [137]. IDO-expressing plasmacytoid DCs suppress T cell proliferation through GCN2 activation in responding T cells via tryptophan catabolism. T cell-specific GCN2 deletion has been shown to weaken IDO-mediated suppression and eliminate IDO-mediated T cell anergy in vitro [138]. Consistently, proliferation of GCN2-deficient T cells is not inhibited by IDO-expressing DCs in vivo [138]. However, it has been reported that GCN2 is not involved in the suppression of anti-tumor T cell responses via tryptophan catabolism in melanoma-bearing mice [137]. It cannot be ignored the kynenurine (KYN) pathway metabolites KYN (catalyzed by IDO1) are endogenous agonists of the aryl hydrocarbon receptor (AhR). AhR can promote the development of type 1 regulatory T cells; another major source of IL-10. IL-10 is a potent anti-inflammatory cytokine that promotes differentiation, proliferation and maintenance of Treg cells, which impair CD8+ T cell maturation and cytotoxicity. IL10 also promote the development and proliferation of tolerogenic DCs, tumor-associated macrophages (TAMs), and MDSCs that populate the tumor microenvironment—to support angiogenesis immune escape of cancer cells [139]. The TME is the environment around a tumor. Tumors can affect their microenvironment by releasing cell signaling molecules, promote angiogenesis around the tumor and induce immune tolerance, while immune cells in the microenvironment can affect the growth and development of cancer cells [140]. A well-known animal model of experimental autoimmune encephalomyelitis (EAE) is widely employed to study the mechanisms and pathogenesis of multiple sclerosis. The invasion of Th1 and Th17 cells leads to central nervous system (CNS) demyelination and lesion formation during the peak of murine EAE. Spontaneous remission of EAE may occur after the peak of the disease, during which regulatory T cells (Treg) accumulate in the inflamed CNS, suppress effector T cell responses and reduce inflammation. GCN2 participates in the remission phase of EAE by affecting a variety of T cells, including effector T cells and Treg cells. Orsini et al. reported that the peak of the disease is characterized by high IDO expression and a high frequency of plasmacytoid DCs in the CNS of wild-type C57BL/6 mice. However, GCN2-deficient mice with EAE fail to reach the remission phase and are characterized by higher levels of CNS inflammation, accompanied by the increased presence of effector T cells (Th1/Th17) and a lower frequency of Treg cells [141]. Consistently, Keil et al. reported that GCN2-deficiency in mice exacerbates chronic disease progression during the remission period of EAE, mainly due to the low frequency of Treg cells. They demonstrated that GCN2 deficiency does not impact the survival, proliferation or suppressive capacity of Treg cells; rather, it decreases the infiltration of Treg cells into the inflamed CNS by reducing their ability to respond to CCL2 gradients [142]. However, GCN2-deficiency does not impact the migration of effector T cells. A subsequent study proved that the decreased infiltration capacity of Treg cells to the CNS in GCN2-deficient mice is not due to reduced surface receptor expression. These results suggest that, under an IDO-mediated immunoregulatory environment, the GCN2 kinase may be activated to restrict effector T cell responses and promote the accumulation of Treg cells in the inflamed CNS, mediating the remission phase of EAE. On the other hand, we recently demonstrated that GCN2 controls Th9 cell differentiation through a HIF1α-dependent glycolytic pathway [143]. GCN2-deficient mice are resistant to OVA-induced allergic airway inflammation [143]. Further studies should focus on the intrinsic roles of GCN2 in the differentiation and function of different T cell subsets. The GCN2-mediated amino acid starvation response pathway is important for Th17 cell differentiation. Th17 cells, which mainly produce IL-17, have been recognized as a type of pro-inflammatory CD4+ T cell and implicated in various autoimmune and inflammatory disorders. Sundrud et al. reported that halofuginone can selectively inhibit mouse and human Th17 differentiation by activating the GCN2-mediated amino acid starvation response [144,145]. Consistently, the addition of excess amino acids rescued the inhibition of Th17 differentiation by halofuginone. This halofuginone-induced GCN2-mediated amino acid starvation response significantly reduces Th17 cell differentiation and alleviates Th17-associated EAE in vivo [144]. Halofuginone can activate the GCN2-mediated amino acid starvation response by binding glutamyl-prolyl-tRNA synthetase and inhibiting prolyl-tRNA synthetase activity; this effect can be reversed by the addition of exogenous proline or glutamyl-prolyl-tRNA synthetase [146]. Other natural product derivatives in the febrifugine family have also been shown to inhibit glutamyl-prolyl-tRNA synthetase [146]. In addition, halofuginone blocks IL-23-induced signal transducer and activator of transcription 3 (STAT3) phosphorylation and IL-23-dependent proinflammatory cytokine expression in CCR6+ Th17 cells via activation of the amino acid starvation response pathway [147]. Thus, targeting GCN2 is an effective approach to autoimmunity prevention, a critical rationale for the development of tools to manipulate GCN2 function for the treatment of inflammatory immune disease. In murine models of glioblastoma, GCN2 deficiency limits CD8+ T cell activation and cytotoxic marker expression. Adoptive transfer of GCN2-deleted antigen-specific CD8+ T cells failed to control tumor burden compared to wild-type CD8+ T cells. This is likely because GCN2-eficient CD8+ T cells are sensitized to become rapidly necrotic when faced by reduced levels of PKCθ and p-PKCθ. This study demonstrates the importance of GCN2 to CD8+ T cell function and survival in mice [148]. GCN2-deficient CD8+ T cells, but not CD4+ T cells, exhibit defects in proliferation and trafficking in vitro and in vivo, indicating that GCN2 is required for efficient cytotoxic T cell function [149]. However, using an experimental B16 melanoma model with T cell-specific GCN2 knockout mice, Sonner et al. showed that GCN2 in T cells did not affect immunity against B16 tumors, suggesting that GCN2 is not intrinsically involved in the functional alteration of tumor-infiltrating T cells [137]. As a kinase that can sense amino acid scarcity and other stresses, the GCN2 kinase plays important roles in a variety of biological processes and diseases. Due to the important roles of GCN2 in many immunities, researchers continue to explore GCN2 inhibitors and activators. Several GCN2 inhibitors and activators have been approved by Food and Drug Administration (FDA), including erlotinib and sunitinib [27]. Neratinib has the ability to bind and activate GCN2 [27]. Inhibition of GCN2 activity in TAM and tumor cells in the tumor microenvironment can play an anti-tumor effect. The majority of existing studies focus on the effects of GCN2 deficiency or GCN2 activation under stress conditions, and the detailed molecular mechanisms have yet to be fully illuminated. The role of GCN2 in T cells and macrophages has been the focus of current research, but little is known about its role in neutrophils. In future studies, more attention should be paid to the antagonism of GCN2 and mTOR in different immune systems. Future research efforts should emphasize the ATF4-independent signaling pathways involved in GCN2-mediated biological regulation. Further studies should also seek to define in more detail the biological functions of GCN2 in the immune system and in immune-related diseases.

PMC10002025

Wei Yan,Guangyu Li,Qiqi Lu,Jianjun Hou,Meiqi Pan,Maomin Peng,Xitian Peng,Hui Wan,Xixia Liu,Qin Wu

Molecular Mechanisms of Tebuconazole Affecting the Social Behavior and Reproduction of Zebrafish

22-02-2023

tebuconazole,reproductive toxicity,social behavior,HPG axis,zebrafiah,mechanism

The aim of this study was to explore the underlying mechanism of adverse effects caused by tebuconazole (TEB) on the reproduction of aquatic organisms In the present study, in order to explore the effects of TEB on reproduction, four-month-old zebrafish were exposed to TEB (0, DMSO, 0.4 mg/L, 0.8 mg/L, and 1.6 mg/L) for 21 days. After exposure, the accumulations of TEB in gonads were observed and the cumulative egg production was evidently decreased. The decline of fertilization rate in F1 embryos was also observed. Then the changes in sperm motility and histomorphology of gonads were discovered, evaluating that TEB had adverse effects on gonadal development. Additionally, we also found the alternations of social behavior, 17β-estradiol (E2) level, and testosterone (T) level. Furthermore, the expression levels of genes involved in the hypothalamic-pituitary-gonadal (HPG) axis and social behavior were remarkably altered. Taken together, it could be concluded that TEB affected the egg production and fertilization rate by interfering with gonadal development, sex hormone secretion, and social behavior, which were eventually attributed to the disruption of the expressions of genes associated with the HPG axis and social behavior. This study provides a new perspective to understanding the mechanism of TEB-induced reproductive toxicity.

Molecular Mechanisms of Tebuconazole Affecting the Social Behavior and Reproduction of Zebrafish The aim of this study was to explore the underlying mechanism of adverse effects caused by tebuconazole (TEB) on the reproduction of aquatic organisms In the present study, in order to explore the effects of TEB on reproduction, four-month-old zebrafish were exposed to TEB (0, DMSO, 0.4 mg/L, 0.8 mg/L, and 1.6 mg/L) for 21 days. After exposure, the accumulations of TEB in gonads were observed and the cumulative egg production was evidently decreased. The decline of fertilization rate in F1 embryos was also observed. Then the changes in sperm motility and histomorphology of gonads were discovered, evaluating that TEB had adverse effects on gonadal development. Additionally, we also found the alternations of social behavior, 17β-estradiol (E2) level, and testosterone (T) level. Furthermore, the expression levels of genes involved in the hypothalamic-pituitary-gonadal (HPG) axis and social behavior were remarkably altered. Taken together, it could be concluded that TEB affected the egg production and fertilization rate by interfering with gonadal development, sex hormone secretion, and social behavior, which were eventually attributed to the disruption of the expressions of genes associated with the HPG axis and social behavior. This study provides a new perspective to understanding the mechanism of TEB-induced reproductive toxicity. Tebuconazole (TEB) is one of the most efficient and broad-spectrum 1,2,4-triazole fungicides, which can treat multiple fungal diseases in rice, wheat, peanuts, and vegetables [1]. Because of its high quality and widespread use, TEB can enter different environmental media through the natural water cycle, such as air, water, and soil [2]. The degradation half-life (time required to reach 50% degradation) of TEB in soil and water (25 °C) reached 216.6 days and 180 days [1,3]. Owing to its stable nature and high residue, TEB can be frequently detected in the natural environment. The concentration of TEB detected in surface water had reached 0.6–200 μg/L [4,5]. The maximum concentrations of TEB reported in agricultural runoff of Europe were 81 μg/L [6]. As a matter of fact, it was reported that TEB could accumulate in the organisms living in these environments [1,4]. For instance, the highest residual amount of TEB in brown rice was 0.9 mg/kg and the content of TEB in the muscles of fish (Cyprinus carpio) was 23.8 to 39.9 µg/kg [7,8]. There is even a study showing that TEB can be detected in the human body. Mercadante et al. [9] found that the metabolites of TEB detected in the urine of agricultural workers ranged from 3 to 473 μg/L. By this token, TEB, an aquatic pollutant of emerging concern, will pose a potential threat to the health of aquatic organisms and humans. TEB was reported to induce multiple toxic effects on organisms living in these environments, mainly including developmental toxicity, hepatotoxicity, immunotoxicity, neurotoxicity, and reproductive toxicity [10,11,12,13,14]. It is well demonstrated that TEB is an endocrine disruptor. Numerous experimental data indicated that it might cause adverse effects on the reproduction of multiple species (eg. rat, earthworm, bird, Xenopu laevis, zebrafish). In male rats, TEB exhibits anti-androgen activity, leading to the decline of testosterone (T) levels in the offspring and interfering the sexual differentiation [15]. In addition, the egg production of birds that were fed with the seeds treated with TEB was decreased. Meanwhile, the 17β-estradiol (E2) level in the plasma of these exposed birds was notably reduced and the genes encoding key enzymes related to the biosynthesis of sterols and steroid hormones were also affected [16]. In earthworms, TEB disrupted the earthworm’s reproductive through the AMP pathway [17]. Concerning amphibians, the concentrations of E2 of the plasma in Xenopus laevis were greatly reduced after exposure to TEB for 27 days [10]. Regarding zebrafish, TEB could decrease the fecundity of zebrafish by interfering with the synthesis of steroid hormones [11]. Comprehensive the above literature, it can be concluded that TEB has been shown to have a negative impact on the reproduction of organisms by impairing the development of gonads and the levels of sex hormones. Apart from hormone levels and reproductive parameters, growing evidence has shown that behaviors are critical for zebrafish reproduction [18,19,20]. A large number of studies have confirmed if normal patterns of reproductive behaviors were disrupted, reproductive success would be seriously impaired [21,22,23]. However, in recent years, some interesting studies have focused on non-reproductive behaviors which indicate that non-reproductive behaviors are key components of reproductive functions because they are essential for successful fertilization [19,24]. In teleosts, reproduction is not only dependent on the occurrence of reproductive behaviors, but also closely related to other non-reproductive behaviors, such as social behavior, and swimming behavior [19]. Social behaviors, which are key components of reproduction, provide many mating opportunities to conspecifics and reflect the mating intention, eventually interfering with mating behavior [18]. Meanwhile, swimming behavior reflects the locomotor activity of fish, which can maintain a series of normal social activities [19]. Therefore, these non-reproductive behaviors play vital roles in achieving the correct and effective reproductive behaviors, which has been verified in fish. In turquoise killifish, the sociability of the males exposed to fluoxetine was enhanced, contributing to the increase of mating frequency and reproductive output in fish populations [24]. Similarly, the male mosquitofish showed less intimacy and mating interest towards the gestodene-exposed females, indicating the time spent on attending, following, and mating behaviors decreased [25]. According to the above research, it is clear that the social interaction of female-male influences the mating behavior of fish. Now a growing stream of research suggests that TEB is capable of altering the behavioral responses of fish [26,27]. After being exposed to the TEB, the male zebrafish lost coordination of movements and resting stage at the bottom of the tank [27]. In tilapia, the behavior responses were significantly affected by TEB [26]. Integrating the above research, TEB has adverse effects on fish behavior. Hence, it is reasonable to speculate that TEB might induce reproductive toxicity by affecting social behavior in zebrafish. For the sake of exploring the potential mechanism of reproductive toxicity induced by TEB, zebrafish were employed as the experimental animal in this study. We first examined the fecundity, sperm motility, and fertilization rate to assess the effect of TEB on reproduction. Meanwhile, gonadal histopathology, social behaviors, and contents of sex hormones were detected. In order to further elucidate the molecular mechanism of TEB-induced reproductive toxicity, the expression levels of genes associated with the HPG axis and social behavior were analyzed. This study will enrich the toxic mechanism of TEB from a new perspective. TEB (CAS 107534-96-3, 99% purity) was purchased from Sigma-Aldrich. The TRIzol reagent was obtained from Invitrogen. PrimeScript® RT Reagent Kits and SYBR® Green PCR kits were bought from TakaRa (Dalian, China). Dimethyl sulfoxide (DMSO, Fisher Scientific, Fair Lawn, NJ, USA) was used as a solvent. All the chemicals used in this study were of analytical grade. Four-month-old zebrafish (Danio rerio) of the wild type (AB strain) were purchased from a commercial supplier. All fishes were housed in a fish breeding room with a light/dark cycle of 14/10 h. Charcoal-filtered tap water used in the fish breeding room was kept at 27 ± 0.5 °C. The relative humidity of the air was held at about 50%. The fish were fed at least for 14 d to adapt to the environment and each tank contains 15 females and 15 males. The mortality of zebrafish in this study was less than 5%. Culturing and breeding of adult fish were according to the described methods in OECD test guideline 229, which were fed with newly hatched brine shrimp (Artemia nauplii) three times a day in a quantity that was consumed within 5 min [28]. TEB was dissolved in DMSO, the solvent control group and exposure groups received 0.01% (v/v) DMSO. According to LC50 (median lethal concentration, 96 h) of TEB [27,29], the concentrations of TEB were set at 0, 0.4, 0.8, and 1.6 mg/L. The exposure solution with 0.01% DMSO was regarded as the solvent control group. There were three replicates for each group. Before the exposure experiment, in order to eliminate the influence of the breeding environment on spawning, the fecundity of fish (three male fish and three female fish) that were randomly chosen from each tank were tested by recording the number of eggs for 14 d. During the exposure duration, the six fish were paired every night in each tank for 21 d. The number of egg were counted in next morning after approximately 30 min of lighting. After counting, the cumulative production of eggs was computed. Then embryos were cultured in fresh water and the fertilization rate was also counted. The exposure water was renewed with the same fresh solution every two days which has been verified that the frequency of water exchange can maintain the concentration of toxin as the target concentration [11]. At the end of the exposure, the length and the total weight of each fish were recorded. After being taken blood, each fish was dissected to obtain the brain, liver, and gonad. Then brain-somatic index (BSI, brain weight × 100/body weight), hepatosomatic somatic index (HSI, liver weight × 100/body weight), and gonad somatic index (GSI, gonad weight×100/body weight) were calculated. The ovary and testis of 10 fish were separately collected to detect the accumulations of TEB in the gonads of zebrafish after exposure. Meanwhile, the contents of TEB in exposure water were also detected. The quantification method of TEB was according to Li et al. [11] and had been partially improved, which was described in Supporting Information (Test S1). On the last day of the exposure experiment, the fish were paired but the partition in the spawning box was not removed in next morning. We collected semen by pressing the abdomen of males per replicate (n = 10, each concentration). Then an equal amount of semen was stored in Hank’s solution for excluding the effect of sperm concentration on sperm motility. The activated sperm was determined in the HT CASAII animal system, the measured concentration of sperm was between 200–300 sperm per observation field (4× microscope) to avoid being too large or too small. The experiments were carried out at room temperature (25 ± 1 °C). We recorded three classical metrics of sperm motility, including average movement rate (VAP), linear movement rate (VSL), and curve movement rate (VCL) by using the Computer-Assisted Sperm Analysis System (CASA). Histological analysis was conducted according to the methods previously described in [19]. The gonad tissues of fish (n = 6, each concentration) were fixed in Bouin’s solution for 24 h and then dehydrated in graded ethanol. After being embedded in paraffin, samples were sliced into sagittal sections (5 µm). Specimens were sealed with neutral gum after being stained by hematoxylin and eosin. The stained gonads can be observed by using a microscope (Soptop EX31, Sunny, Ningbo, China). The developmental stages of spermatocytes were classified into spermatogonia (SG), spermatocytes (SC), and spermatids (ST). Meanwhile, the development of oocytes was examined and classified into four stages: primary oocyte (PO), cortical alveolar oocyte (CAO), early vitellogenic oocyte (EVO), and late/mature oocyte (LO). The percentage of ovarian follicles in each developmental stage was expressed as a ratio of the number of corresponding ovarian follicles occupying the total follicles. The percentage of sperm cells at each stage was expressed as the ratio of the total sperm area occupied by the corresponding sperm cells. After exposure for 21 days, a DanioVision system accompanied by EthoVision XT computer tracking software 15 (Noldus Information Technology) was utilized to detect swimming speed and distance in zebrafish. The testing method was according to the previous study [30]. In detail, each group was placed individually in water tanks (10 × 12.5 × 15 cm, high × wide × long) with 1 L water, and their swimming behavior was recorded (n = 16, each concentration). To assess social behaviors, fish were placed in water tanks (10 × 10 × 20 cm, high × wide × long) at a rearing density of a pair of fish, while fish from all exposed groups were examined (n = 16, each concentration). The distance threshold was set to 1.5 cm according to Wu et al. [30]. Two fish were thought to be in contact when a distance was less than 1.5 cm. Each pair of fish was recorded for 10 min. Using a video tracking system with the OpenOfficeOrg 2.4 software can analyze the number of contacts between fish and the time spent in the contact. Two sex hormones, E2 and T, were measured in the blood plasma of male and female fish. The method was referred to published protocols [31,32]. The blood of fish in each tank was collected. After taking a 5 μL plasma sample in the sterilized glass tube, and the plasma sample was diluted with 400 μL ultrapure water, then 2 mL ethyl ether was added to the diluted sample. The mix was centrifuged at 3000 r/min at 4 °C for 10 min. After mixing, the upper organic phase was absorbed into a new sterilized glass tube. The sample was repeated extraction twice according to the above steps. The collected organic phase was placed in the same dilution, which slowly dried the liquid with a nitrogen blower. Firstly, adding 60 μL buffer in the same dilution to again dissolve, which could be used in the determination of hormones. Then, E2 and T levels of plasma in zebrafish were examined by using the corresponding ELISA kits (Cayman Chemical Company, Ann Arbor, MI, USA), the detection limits of which were 15 and 6 pg/mL. Total RNA was extracted from samples of brains, livers, ovaries, and testes by using TRIzol, and their quality was evaluated by a spectrophotometer. RT-qPCR analysis was carried out as described previously [30]. Briefly, 1 μg total RNA was used to synthesize cDNA by use of PrimeScript™ RT reagent Kits. RT-qPCR amplifications with SYBR™ Premix Ex Taq™ reagent Kits and primers were used to quantify the mRNA of all target genes. Thermocycling protocols were as follows: 30 s at 95 °C, 40 cycles of 5 s at 95 °C, and 30 s at 60 °C. Expression of β-action was stable and used as a housekeeping gene. Expressions of the detected genes were normalized to β-action by use of the 2−ΔΔCt method, which was not significantly different between control and exposure groups. Statistical analyses were conducted by using SPSS 17.0 software and data were expressed as mean ± standard error of the mean (SEM). The Kolmogorov-Smirnov test and Levene’s test were employed to examine the normality of the data and the homogeneity of variances. Statistically significant differences were determined by the use of one-way analysis of variance (ANOVA) with Tukey’s multiple range test. Significant differences were taken as p < 0.05. The accumulations of TEB in gonads and water were shown in (Table 1). From the results, the content of TEB in the ovary from exposure groups (0.4 mg/L, 0.8 mg/L, and 1.6 mg/L) were 5.65 ± 0.06, 8.87 ± 0.07, and 14.72 ± 0.04 µg/g·WW (wet weight), respectively. Meanwhile, the accumulations of TEB in testis were 5.78 ± 0.05 (0.4 mg/L group), 7.95 ± 0.09 (0.8 mg/L group), and 13.83 ± 0.07 µg/g·WW (1.6 mg/L group). The recovery rates in the ovary and testis were 90.56 ± 11.23% and 89.23 ± 5.83%. The results showed the TEB concentrations of exposure water were stable and relatively close to the target concentrations. The mean cumulative egg production during the pre-exposure (14 days) and exposure periods (21 days) were shown in Figure 1. There was no significant difference in egg production among all groups during the pre-exposure period. Compared to the control, the cumulative egg production in the 1.6 mg/L TEB group was significantly reduced by 46%, while it was not remarkably changed in other exposure groups. Meanwhile, the fertilization rate of F1 embryos was notably decreased only in the 1.6 mg/L TEB group (Figure 1C). Somatic indexes are important indicators to evaluate fish reproduction shown in Supporting Information (Table S1). No significant effect was observed in the BSI of zebrafish in all the exposure groups. However, the HSI was notably increased in female fish from the 1.6 mg/L TEB group and in male fish from all the exposure groups. GSI remarkably declined in females of the 1.6 mg/L TEB group, while there was no significant difference in males, which demonstrated that females were more sensitive to TEB than males. In this study, the development of the ovary and testis in adult zebrafish was inhibited. The proportions of various cells in the DMSO group were not remarkably different from the blank group, which indicated that the solvent has little effect on the development of the gonads. For female fish, the PO, CAO, EVO, and LO in the control groups and TEB groups showed normal characteristics (Figure 2A). The proportion of PO in 0.8 mg/L and 1.6 mg/L TEB groups significantly increased by 25.2% and 38.0%. The proportion of CAO was notably decreased in all exposure groups (52.6%, 0.4 mg/L TEB group; 37.9%, 0.8 mg/L TEB group; 50.1%, 1.6 mg/L TEB group) (Figure 2B). The proportion of LO was significantly reduced by 25.5% in the 1.6 mg/L TEB group. Similarly, the percentage of SG, SC, and ST in males was also detected (Figure 2C). Compared with the control group, the percentages of SG in all the exposure groups were notably decreased (46.0%, 0.4 mg/L TEB group; 38.9%, 0.8 mg/L TEB group; 47.2%, 1.6 mg/L TEB group), while the percentages of SC in 0.4 mg/L, 0.8 mg/L, and 1.6 mg/L TEB groups were significantly increased by 102.1%, 120.9%, and 134.3%. However, the percentages of ST were notably decreased in the 0.8 mg/L (37.8%) and 1.6 mg/L TEB group (40.7%). It was shown that the three motor parameters of VCL, VSL, and VAP have a stronger association with the rate of fertilization compared with the other parameters. The three motor parameters were not dramatically affected in the solvent control group compared to the blank control. Nevertheless, the three indicators of sperm swimming velocity (VAP, VSL, and VCL) were remarkably reduced by 19.9%, 21.6%, and 13.7% in the 1.6 mg/L TEB group, respectively (p < 0.05) (Figure 3). And VAP, VSL, and VCL of sperms in other exposure groups were not notably altered. The average velocity of female zebrafish in the highest concentration group was significantly decreased by 48.8% (p < 0.05) (Figure 4A). The average velocity of males in exposure groups was not remarkably affected, while it showed a downward trend. The cumulative swimming distances of zebrafish were consistent with changes in the mean velocity, and remarkable reductions of 48.3% and 25.0% were observed in the females and males from the 1.6 mg/L TEB group (Figure 4B,C). Changes in swimming movement in females were greater than that in males, indicating that females might be more sensitive to TEB. The effects of TEB on the social performance of zebrafish were shown in Figure 4D,E. In female-male, the time of contact with interactions was notably decreased by 48.1% in the 0.8 mg/L TEB group. The number of contacts significantly decreased by 47.8% and 39.1% in the 0.4 mg/L and 1.6 mg/L TEB groups. In male-male, the number of contacts was remarkably decreased by 52.4% in the 0.8 mg/L TEB group, while there was no notable change in the time of contacts. In addition, the time and number of contacts in female-female were affected after exposure to TEB. The contact time was remarkably decreased by 56.8% and 43.2% in the 0.4 mg/L and 0.8 mg/L TEB groups. In addition, the contact number was also significantly decreased in all the exposure groups (60.0%, 0.4 mg/L TEB group; 50.0%, 0.8 mg/L TEB group; 55.0%, 1.6 mg/L TEB group). The sex hormone levels in both females and males were altered after exposure to TEB. In females, the plasma E2 and T levels were observably reduced by 34.1% and 14.7% in the 1.6 mg/L TEB group (Figure 5A). In males, the concentration of T was notably decreased by 11.6% and 14.7% after exposure to 0.8 mg/L and 1.6 mg/L TEB (Figure 5B). However, the E2 level was not evidently affected. The expression levels of genes involved in the HPG axis and social behavior were evaluated in the present study after exposure to TEB for 21 days (Figure 6), the detailed expression levels of which were summarized in the Supporting Information (Tables S2 and S3). In the brain of females, the transcription levels of gnrh2, oxt, scg2b, and lhβ (1.36-, 1.73-, 1.18-, and 1.33-fold) in 1.6 mg/L TEB group were significantly down-regulated. The gene expression levels of gnrhr3 (1.20- and 0.94-fold) and avp (1.72- and 2.31-fold) showed obvious declines in the 0.8 and 1.6 mg/L TEB groups. There were no notable changes in expression levels of the gnrh3, fshβ, and scg2a genes. In the female liver, the expression levels of the vtg3 and erα genes were both decreased by 1.35-fold in the 1.6 mg/L TEB group. However, erβ and vtg1 were not remarkably affected. In the ovary, the transcription of star (2.32-fold) involved in the steroidogenic pathway was prominently up-regulated after exposure to 1.6 mg/L TEB, while down-regulated transcriptions of cyp19a, 17β-hsd, and fshr (1.92-, 1.35-, and 1.46-fold) were observed. In addition, cyp11a was notably up-regulated in 0.8 (1.64-fold) and 1.6 mg/L (1.44-fold) TEB groups. In male fish, gnrhr3, oxt, and scg2b genes in the brain (1.00-, 1.45-, and 0.68-fold) were significantly down-regulated after exposure to 1.6 mg/L TEB, whereas, the transcription of lhβ (2.26-fold) gene was remarkably up-regulated in 1.6 mg/L TEB group. There were no changes in expression levels of gnrh2, gnrh3, fshβ, avp, scg2a, and scg2b genes. In the liver, the notable down-regulation of vtg3 and erβ (1.45- and 2.24-fold) was observed in 1.6 mg/L TEB. In testis, the erα and fshr (1.91- and 1.04-fold) genes were significantly down-regulated in the 1.6 mg/L TEB group. Additionally, the expression level of the lhr gene was notably decreased by 1.02-, 0.96-, and 2.38-fold in 0.4, 0.8, and 1.6 mg/L TEB groups, respectively. In the present study, the accumulation of TEB in gonads and the decrease of egg production in females were found after exposure to TEB, indicating TEB accumulation could evidently decline the reproductive capacity of zebrafish and show negative effects on the reproduction system of zebrafish which was in accordance with the previous study [11]. Meanwhile, the GSI of zebrafish after exposure was remarkably decreased. GSI is a parameter in toxicological studies and is considered to be related to egg production [31]. Judging from this, the alternation of GSI was consistent with the decrease in egg production. Additionally, the fertilization rate of F1 embryos was also significantly affected by TEB. Taking all these results together, it was indicated that the TEB-elicited reproductive toxicity through affecting the reproductive capacity of parental zebrafish and the fertilization rate of F1 embryos. For the sake of exploring the underlying mechanism of the decreased egg production and fertilization rate, the development of gonads and sperm motility were examined. The development of gonads was inseparable from the normal formation of the germ cells, which eventually contributed to the reproductive success of zebrafish [33,34]. Through the results of histopathological examination in this study, it was found that the proportion of each cell type (CAO and LO in the ovary; SC and ST in the testis) at each stage were significantly decreased. Consistent with our results, Lu et al. [34] found that TEB could disrupt the development of gonads in Caenorhabditis elegans. A similar phenomenon was found in Hyla intermediate membrane larvae. The testis was hypoplastic and the seminal sac leaflets were barely recognizable after exposure to TEB [33]. Hence, all this evidence indicated TEB had an adverse effect on the development of gonads. And the abnormal development of gonads in zebrafish led to the reduction of GSI and egg production. Furthermore, a reduction in sperm viability was found in this study. There is evidence that sperm viability is closely related to reproductive success in zebrafish [35]. For example, long-term exposure to nonylphenol could inhibit the sperm motility of male zebrafish, ultimately leading to a decrease in the fertilization rate [36]. Chen et al. [22] also found this phenomenon in zebrafish. Additionally, sperm quality has interwovenness with the development of the testis. Thus, from the above evidence, it can be concluded that TEB exerted an interfering effect on gonadal development and function which contributed to the decrease in sperm viability, egg production, and fertilization rate. There was growing evidence that confirmed that the synthesis and secretion disorders of steroid hormones (T and E2) and vitellogenin (VTG) could affect the process of gonads development [32,37]. Hence, we investigated the contents of T and E2 in the plasma of zebrafish as well as the variety in gene expressions of the HPG axis to explore how TEB exerted toxic effects on the development of gonads. T and E2 directly affect spermatogenesis and the proliferation of oocytes [37]. Afterward, after binding to estrogen receptors (ERs) in the liver, E2 could induce the generation of VTG which was an important precursor for the synthesis of vitellogenin, and then affect the development of oocytes [38]. It was reported that the HPG axis occupied an important position in the process of reproduction by regulating the synthesis and secretion of hormones (gonadotropin-releasing hormone (GnRH), gonadotropin (Follicle stimulating hormone (FSH), and lutein (LH)), VTG, and sex hormones (T and E2) in the endocrine system [39]. In this study, the levels of E2 and T in plasma were dramatically decreased in zebrafish after exposure to TEB. Meanwhile, TEB exposure altered transcription levels of these genes involved in the biosynthesis of the HPG axis (gnrh2, lh, lhr, vtg3, er, star, and cyp19α in females; gnrhr3, lh, vtg3, and fshr in males). As reported in previous studies, the delay of gonadal development caused by contaminants (tris (1,3-dichloro-2-propyl) phosphate; microcystin-LR; pyriproxyfen; bisphenol AF) was attributed to the changes in E2, T or VTG [39,40,41,42]. Equally, these phenomena were observed in pregnant Sprague-Dawley rats exposed to TEB for 10 days [43]. Hence, based on the above findings, it could be suggested that TEB might induce toxic effects on the gonadal development of zebrafish by inhibiting the synthesis of steroid hormones and interfering with the expression of genes along the HPG axis, ultimately contributing to the decline of egg production and sperm viability. Apart from the normal development of the gonads, reproductive success in fish also depends on reproductive behaviors and non-reproductive behaviors [44,45]. Non-reproductive behaviors (such as social behavior, and swimming behavior) have been recognized as sensitive endpoints which are critical for successful reproduction [45]. Among these behaviors, social behaviors, are crucial components of reproduction, which are necessary for successful fertilization [46]. Besides fertilization, social behaviors between females and males (males rapidly swing their tail against the female side) trigger the spawning of females. Actually, in the process of mate choice and mating, social interaction also plays a vital role [47,48]. Therefore, alternations in social behavior may influence reproductive behaviors, causing a decrease in egg production. In this study, both the number and time of interaction in zebrafish declined dramatically, implying mating attempts were affected after exposure to TEB. Simultaneously, the swimming speed of zebrafish from exposure groups was notably slower than that of the control group. A decrease in social behavior may be associated with a decrease in motility. Consequently, combined with the above literature and the results in this study, there were reasons to consider that the effect of TEB on social behavior might contribute to the spawning and fertilization of the egg, eventually inducing reproductive toxicity in zebrafish. The social behaviors of fish are mainly controlled by the central nervous system (CNS) and the neuroendocrine system [21]. In the neuroendocrine system, neuropeptides synthesized and secreted in the hypothalamus may influence the social behavior of fish [49]. Among these neuropeptides, secretoneurin (SN) generated by the two secretogranin-2 (SCG2) subtype precursor proteins is a member of a peptide family that is the key to the modulation of social behaviors and reproductive [50]. Mutation of the scg2 genes reduced social sexual behavior, oviposition, and fertility in zebrafish [51]. Additionally, isotocin (OXT) and vasotocin (AVP) which are co-expressed with SCG2 are well-known regulators of social behavior [50,52]. A growing body of evidence supports that SCG2, OXT, and AVP are associated with social behavior [53,54]. Hence, for the sake of exploring the mechanisms of TEB affecting social behavior, we further examined the transcriptions of behavior-related genes (scg2a, scg2b, oxt, and avp). After exposure to TEB, scg2b, avp, and oxt genes in the brain were notably down-regulated, which was consistent with the previous studies [51]. Maruska et al. [55] also reported that the abnormality of social behavior was owing to the interruption of the expression levels of these hypothalamic neuropeptide genes or the activity of hypothalamic neurons, resulting in abnormal reproductive function. Meanwhile, in electric fish, SN and AVP were also confirmed to modulate social behavior [50]. There is also evidence that these hypothalamic neuropeptides (SN, OXT, and AVP) regulate social behaviors by regulating the HPG axis [53,54]. Consequently, TEB might influence the social behavior of zebrafish by interfering with the expression of genes involved in social behavior and the HPG axis, eventually affecting the reproduction of zebrafish. Taken together with all the results, it could be concluded that TEB could affect the egg production and fertilization rate by interfering with gonadal development, sex hormone secretion, and social behavior, eventually leading to adverse effects on the reproduction of zebrafish. After further analysis, the reasons for the decline of fecundity and fertilization rate caused by TEB were the disruption of the expressions of genes associated with social behavior and the HPG axis. Therefore, our study provides a new perspective to understanding the mechanism of TEB-induced reproductive toxicity, contributing to the ecological risk evaluation of TEB.

PMC10002047

Hina Agraval,Taylor Crue,Niccolette Schaunaman,Mari Numata,Brian J. Day,Hong Wei Chu

Electronic Cigarette Exposure Increases the Severity of Influenza a Virus Infection via TRAIL Dysregulation in Human Precision-Cut Lung Slices

21-02-2023

electronic cigarettes,PCLS,Influenza A virus,TRAIL

The use of electronic nicotine dispensing systems (ENDS), also known as electronic cigarettes (ECs), is common among adolescents and young adults with limited knowledge about the detrimental effects on lung health such as respiratory viral infections and underlying mechanisms. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), a protein of the TNF family involved in cell apoptosis, is upregulated in COPD patients and during influenza A virus (IAV) infections, but its role in viral infection during EC exposures remains unclear. This study was aimed to investigate the effect of ECs on viral infection and TRAIL release in a human lung precision-cut lung slices (PCLS) model, and the role of TRAIL in regulating IAV infection. PCLS prepared from lungs of nonsmoker healthy human donors were exposed to EC juice (E-juice) and IAV for up to 3 days during which viral load, TRAIL, lactate dehydrogenase (LDH), and TNF-α in the tissue and supernatants were determined. TRAIL neutralizing antibody and recombinant TRAIL were utilized to determine the contribution of TRAIL to viral infection during EC exposures. E-juice increased viral load, TRAIL, TNF-α release and cytotoxicity in IAV-infected PCLS. TRAIL neutralizing antibody increased tissue viral load but reduced viral release into supernatants. Conversely, recombinant TRAIL decreased tissue viral load but increased viral release into supernatants. Further, recombinant TRAIL enhanced the expression of interferon-β and interferon-λ induced by E-juice exposure in IAV-infected PCLS. Our results suggest that EC exposure in human distal lungs amplifies viral infection and TRAIL release, and that TRAIL may serve as a mechanism to regulate viral infection. Appropriate levels of TRAIL may be important to control IAV infection in EC users.

Electronic Cigarette Exposure Increases the Severity of Influenza a Virus Infection via TRAIL Dysregulation in Human Precision-Cut Lung Slices The use of electronic nicotine dispensing systems (ENDS), also known as electronic cigarettes (ECs), is common among adolescents and young adults with limited knowledge about the detrimental effects on lung health such as respiratory viral infections and underlying mechanisms. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL), a protein of the TNF family involved in cell apoptosis, is upregulated in COPD patients and during influenza A virus (IAV) infections, but its role in viral infection during EC exposures remains unclear. This study was aimed to investigate the effect of ECs on viral infection and TRAIL release in a human lung precision-cut lung slices (PCLS) model, and the role of TRAIL in regulating IAV infection. PCLS prepared from lungs of nonsmoker healthy human donors were exposed to EC juice (E-juice) and IAV for up to 3 days during which viral load, TRAIL, lactate dehydrogenase (LDH), and TNF-α in the tissue and supernatants were determined. TRAIL neutralizing antibody and recombinant TRAIL were utilized to determine the contribution of TRAIL to viral infection during EC exposures. E-juice increased viral load, TRAIL, TNF-α release and cytotoxicity in IAV-infected PCLS. TRAIL neutralizing antibody increased tissue viral load but reduced viral release into supernatants. Conversely, recombinant TRAIL decreased tissue viral load but increased viral release into supernatants. Further, recombinant TRAIL enhanced the expression of interferon-β and interferon-λ induced by E-juice exposure in IAV-infected PCLS. Our results suggest that EC exposure in human distal lungs amplifies viral infection and TRAIL release, and that TRAIL may serve as a mechanism to regulate viral infection. Appropriate levels of TRAIL may be important to control IAV infection in EC users. Cigarette smoking is a well-recognized risk factor for various lung pathologies including chronic obstructive pulmonary disease (COPD), respiratory infections, asthma, and lung cancer [1,2]. Electronic cigarettes (ECs) are commonly used by youth and young adults with a rapidly increasing number of users. E-cigarette or vaping product use-associated lung injury (EVALI) was first reported in 2019–2020 in the USA [3]. This outbreak resulted in 68 deaths and 2807 hospitalizations and raised concern about the safety and hazardous effects of vaping on health. However, the adverse effects and underlying mechanisms of vaping on lung health, especially on distal lungs are poorly understood [4]. Recently, various in vitro studies reported detrimental effects of ECs including inflammation, cytotoxicity, and oxidative stress in different types of cells [5,6,7,8]. In a murine model study, EC exposure induced airway inflammation in both control and asthmatic groups [9]. In another study, acute exposure to ECs increased the release of pro-inflammatory cytokines IL-6 and IL-1β in mice whereas chronic EC exposure was associated with more severe effects including chronic inflammation and emphysema [10]. Influenza A virus (IAV), a respiratory pathogen from the Orthomyxoviridae family, is known to contribute to COPD exacerbations [11,12]. It has been suggested that cigarette smoke may increase the risk of respiratory viral infections [13]. However, whether EC exposures directly increase the risk of IAV infection in human distal lungs, the site of emphysema, remains unclear. Recently, a study from our group reported that EC exposure aggravates the pro-inflammatory response of human distal airway epithelium during IAV infection [8]. A limitation of our previous study was that it only examined airway epithelial cells but not the entire human lung tissue. Precision-cut lung slices (PCLS) are uniform tissue slices representing an ex vivo organotypic model [14]. PCLS retains a three-dimensional tissue structure including small airways, lung parenchyma, mechanical properties, and other organ-specific features. It also contains most types of lung cells including fibroblasts, alveolar type I (ATI) and alveolar type II (ATII) cells, monocytes, macrophages, T cells, and natural killer cells, which makes PCLS an ideal model to mimic human lung responses to EC exposures and viral infection [14,15]. The mechanisms by which ECs impact IAV infection have not been well explored. IAV infection is known to induce apoptosis in infected epithelial cells as a defense mechanism to eliminate infected/damaged cells [16]. A key apoptotic pathway is related to the activation by tumor necrosis factor (TNF) superfamily members [17,18,19,20,21]. TNF-related apoptosis-inducing ligand (TRAIL), a type II transmembrane protein belonging to the TNF family, can be secreted by various cell types to induce apoptosis. TRAIL is up-regulated in IAV-infected epithelial cells and immune cells [22]. TRAIL binds to the cell surface receptors DR4/TRAIL-R1 and DR5/TRAIL-R2 to transduce the death signals and initiate apoptosis [23]. Levels of TRAIL and its death receptors are elevated in the airways and serum of COPD patients where TRAIL is associated with regulation of inflammation, apoptosis, and remodeling [24,25,26]. In the present study, we hypothesized that EC exposures worsen IAV infection in human PCLS through dysregulation of TRAIL expression. To test this hypothesis, we examined the effect of EC juice (E-juice) on TRAIL/TNF alpha release and viral infection and tested the role of TRAIL in IAV infection in E-juice exposed PCLS. Cigarette smoke exposure has been shown to increase the severity of respiratory viral infections [27]. To determine the effect of E-juice on IAV infection, we analyzed the changes in viral RNA levels in PCLS supernatants collected at 24, 48, and 72 h post-infection, and tissues collected at 72 h post-infection. In PCLS treated with IAV alone, viral load in supernatants trended to decrease over time (from 24 to 72 h). In contrast, in PCLS treated with both E-juice and IAV, viral levels in the supernatants increased from 24 h to 72 h. At 48 h, and especially at 72 h post-infection, viral load was significantly increased (~1.6 fold) in supernatants collected from PCLS exposed to both E-juice and IAV as compared to IAV infection alone. Similarly, the tissue viral level in PCLS treated with E-juice and IAV for 72 h was significantly higher (~1.5 fold) than that in PCLS treated with IAV alone (Figure 1A,B). To determine the potential mechanisms by which EC exposures increase viral levels in PCLS, we measured TRAIL release in supernatants. TRAIL-mediated immunity may be important for the host cells to clear viruses [28] as IAV titers and morbidity increased in TRAIL deficient mice as compared to wild-type mice [29]. At 72 h post viral infection, E-juice alone or IAV alone moderately increased the release of TRAIL in the supernatants of PCLS. Combined E-juice and IAV treatment significantly amplified the release of TRAIL compared with the control (~5 fold) (Figure 2A). TNF-α is mainly produced by activated macrophages, T lymphocytes, and natural killer cells during acute inflammation [30]. TNF-α is involved in the pro-inflammatory response to cigarette smoke exposure [31,32]. TNF-α may induce apoptosis of various types of cells under pathological conditions such as viral infection. To examine the effect of E-juice on TNF-α release, TNF-α in supernatants of human lung PCLS were measured after 72 h of viral infection. IAV infection alone increased TNF-α release which was amplified by E-juice (~2.5-fold increase vs. IAV treatment alone) (Figure 2B). Having shown increased TRAIL and TNF in PCLS treated with both E-juice and IAV, we evaluated their effect on cytotoxicity. Cellular LDH release into supernatants of PCLS were used to indicate the cytotoxic effect of E-juice and IAV treatment. The level of LDH was increased by IAV alone at 48 h, which was moderately enhanced by E-juice. E-juice increased LDH levels in IAV-infected PCLS after 72 h of infection (~2.2-fold increase vs. IAV treatment alone) (Figure 2C–E). Overall, these results suggest that E-juice may enhance cell injury during IAV infection. TRAIL signaling was blocked using a TRAIL neutralizing antibody to examine the role of TRAIL in IAV release from lung tissue cells. TRAIL neutralizing antibody significantly reduced viral levels (~53% reduction over IgG control) at 72 h, but not at 24 and 48 h, in supernatants of PCLS treated with both E-juice and IAV (Figure 3A–C). In contrast, viral levels in the lung tissue were increased by the TRAIL neutralizing antibody as compared to the IgG control (~2.1 fold) (Figure 3D). The TRAIL neutralizing antibody trended to reduce the LDH levels in PCLS treated with both IAV and E-juice (Figure 3E). Our data suggest that blocking TRAIL signaling may reduce cell injury, coupled with increased intracellular viral load, while decreasing viral release into the supernatants. To further evaluate the role of TRAIL in IAV infection, recombinant TRAIL protein (10 ng/mL) was added to PCLS treated with IAV alone or with combination of IAV and E-juice for 72 h. TRAIL, as compared to the BSA control, significantly decreased the tissue viral load in PCLS exposed to both E-juice and IAV (Figure 4A). In contrast, TRAIL increased IAV release into supernatants (Figure 4B) of PCLS treated with both E-juice and IAV (Figure 4B). TRAIL trended to increase LDH levels in PCLS treated with both E-juice and IAV (Figure 4C). Together, these data suggest that TRAIL may promote cell injury and the release of viruses from the intracellular compartment into the supernatant. Interferon production serves as a critical mechanism to eliminate viruses from infected cells [28]. It was also reported that TRAIL may up-regulate IFN-β and IFN-regulated genes [33,34]. To determine if TRAIL-mediated reduction of viral load in the tissue is associated with enhanced IFN responses, we measured IFN-β and -λ mRNA levels in PCLS. Recombinant TRAIL amplified IFN-β and IFN-λ mRNA expression in PCLS treated with both E-juice and IAV (Figure 5A,B). In line with the viral load data, E-juice and IAV co-treatment in the absence of recombinant TRAIL enhanced the expression of IFN-β (~6 fold over BSA control) and IFN-λ (~4.5 fold over BSA control). Interestingly, TRAIL treatment in control PCLS also increase the levels of IFN-β and IFN-λ mRNA. Application of the human PCLS model in our study, for the first time, clearly demonstrated the direct effects of E-cigarettes on viral load during IAV infection. Further, we explored the role of TRAIL in viral infection in the context of EC exposure. Our data suggest that E-juice significantly increased IAV levels as well as TRAIL release. TRAIL may serve as a mechanism to regulate viral infection during lung exposures to E-cigarettes. The distal lung compartment, the major site of emphysema and loss of lung function [8,35,36], is vulnerable to environmentally hazardous agents. Various cell culture and animal models have been used to study the effect of vaping on viral infection [8,37]. However, these models do not authentically represent the microenvironment in human distal lungs [1] or cannot reflect the complexity of human genetic and physiological responses [38]. The human lung PCLS model offers several major advantages over other models including intact tissue/organ architecture, native microenvironment, organ-specific features such as metabolic activity, spatially correct interstitial matrix, and tissue homeostasis [1,39,40,41]. Novel application of the human PCLS model demonstrated that E-cigarette exposures increased the IAV load. In our recently published study using the human distal airway epithelial culture, we did not observe increased viral load following E-cigarette exposures although we found an exaggerated pro-inflammatory response [8]. This may be explained by the lack of cell–cell interactions in the airway epithelial cell culture model. For example, alveolar epithelial cells can also be infected by IAV and their responses to vaping remain unclear. Thus, PCLS preserving all types of lung cells may serve as a more physiologically relevant model to study the vaping effects and perhaps EVALI in humans [41]. A recent human clinical trial comparing the level of live-attenuated IAV in nasal lavage fluid samples from volunteers with or without E-cigarette exposure demonstrated a trend of increased viral load (not statistically different) in E-cigarette users compared to non-smoker controls [42]. We propose that such a finding may be in part related to the location (i.e., nasal) of the samples collected, and future human studies may extend the sample collection site to the distal lung area. Nonetheless, our PCLS model data supports a previous mouse study where E-cigarette exposures were shown to increase lung IAV titers [43]. Together, our human PCLS model may provide a promising platform to study the mechanisms and new therapeutic targets. Induction of TRAIL by IAV infection has been suggested to serve as a defense mechanism to remove virus-producing cells from the tissue [22,29,44], but whether TRAIL is involved in E-cigarette-mediated amplification of IAV infection has not been investigated. Using our PCLS model, we found that the combination of IAV and E-juice further increased TRAIL and TNF-α release. To evaluate the role of TRAIL during IAV infection with or without E-cigarette exposures, TRAIL neutralizing antibody and recombinant TRAIL protein were utilized to demonstrate their effect on viral load in the tissue and supernatants, indication of viral replication and release, respectively. The complementary data from our experiments suggest that TRAIL reduced tissue viral load or replication while increasing the release of viruses into the supernatants in the absence or particularly in the presence of E-cigarette exposures. Our data supports the protective role of TRAIL in IAV infection as reported in a mouse model [22,29,44]. How TRAIL protects against viral infection remains incompletely understood. It is generally believed that TRAIL-mediated apoptosis or cell death represents one of the major mechanisms to remove cells infected with viruses [29,45,46]. In our study, E-juice alone or IAV alone slightly or moderately increased the levels of cytotoxicity. Our data is in line with previous studies showing that electronic cigarettes have less cytotoxic effects than tobacco smoke [47,48,49]. However, combination of E-cigarettes and IAV significantly reduced cell viability, suggesting that E-juice exacerbated tissue injury following viral infection. In our current study, application of TRAIL neutralizing antibody and recombinant TRAIL protein trended to decrease and increase LDH levels, respectively. This may, in part, explain the effect of TRAIL on viral infection; however, other mechanisms may also be involved. It has been shown that type I and type III IFNs induce TRAIL expression [50,51,52,53]. However, TRAIL may serve as a feedback loop mechanism to induce IFN expression in cancer cell lines without viral infection [33]. We found that E-juice-mediated amplification of viral load and TRAIL was associated with increased expression of IFN-β and IFN-λ, suggesting a potential role of IFNs in TRAIL production. Notably, our finding of increased IFN-β and IFN-λ expression following recombinant TRAIL treatment in PCLS without IAV infection indicates that TRAIL alone may promote IFN expression. In the presence of IAV infection, TRAIL further enhanced IFN-β and IFN-λ expression in lung slices treated with both E-juice and IAV. TRAIL-mediated IFN expression may represent an additional mechanism for observed changes of viral load in the lung tissue and supernatants following TRAIL treatment in PCLS exposed to E-juice and IAV. One of the intriguing questions raised in our study is why the E-juice-mediated increase of TRAIL release in IAV-infected PCLS was associated with an increase, but not a decrease, of viral load in the tissue and supernatants. We propose that E-juice may fail to induce sufficient levels of TRAIL expression or activity to reduce viral levels. To test this possibility, we performed a pilot study where we treated PCLS with various doses of recombinant TRAIL. Only the higher dose (10 ng/mL) of recombinant TRAIL was able to reduce the viral load in the tissue (Figure 6A,B). By using recombinant TRAIL as a standard, we performed the western blot to determine the levels of TRAIL in PCLS supernatants and found that TRAIL levels were below 10 ng/mL under all the conditions (data not shown). Our data suggests that TRAIL levels released in the media of PCLS treated with E-juice and IAV may not be sufficient to completely control IAV infection. Although we revealed the contribution of TRAIL to viral infection in EC-exposed human lung tissue, there are other potential mechanisms including the inhibitory effect of EC on host defense protein SPLUNC1 [54,55] and EC-mediated disruption of lung lipid homeostasis involved in innate immunity [56]. These additional mechanisms can be further explored in our future studies using the human PCLS model. There are several limitations to this study. First, we examined the effect of the short duration (72 h) of E-juice treatment on human PCLS, which may not reveal the long-term impact of vaping on the lung health of EC users. Second, we focused on the effect of E-cigarettes on IAV-mediated TRAIL release and subsequent IAV infection but did not explore the effect of downstream signaling of TRAIL such as apoptosis or apoptotic molecules on viral infection. Third, E-cigarettes contain proprietary components, and our study did not clarify which components were responsible for altered TRAIL expression and viral infection. There are multiple types of cells in PCLS, but we do not know exactly how each type of cell may play a role in regulating IFN and TRAIL expression and subsequently contribute to EC-mediated exacerbation of viral infection. We performed a preliminary single-cell RNA sequencing experiment using PCLS from a healthy non-smoking donor. We found that IAV significantly increased the expression of TRAIL in airway epithelial cells, alveolar type I and type II epithelial cells as well as lung macrophages. E-juice appeared to further increase TRAIL expression by alveolar epithelial cells and macrophages (Supplementary Figure S1, Supplementary Table S1) in IAV-infected PCLS although the difference was not statistically significant likely due to the use of a single subject for this analysis. Lastly, in our PCLS model, IAV and EC were not delivered through inhalation due to the technical limitation. Unlike the air–liquid interface culture model for airway epithelial cells, PCLS were cultured under the submerged condition, which may not mimic the physiological route of airway epithelial exposure to viruses and vaping products although it is relevant to the in vivo exposure of lung structural cells (e.g., endothelial cells and fibroblasts) to vaping [57,58]. By leveraging our access to human donor lungs, we have demonstrated that E-cigarette exposures worsened distal lung tissue viral infection, which was associated with dysregulated TRAIL and IFN expression (Figure 6C). Maintenance of appropriate host responses such as TRAIL production to viruses in E-cigarette users may be beneficial to attenuate viral infection and tissue damage. The upper lobes of the right lung from healthy, non-smoking donors were obtained from the International Institute for the Advancement of Medicine (Philadelphia, PA, USA) or the Donor Alliance of Colorado (Denver, CO, USA). All the donor lungs were selected based on the non-smoking status and no history of lung disease/infection. The detailed donor demographic information is given in Table 1. Lungs were inflated with 1.5% low-melting agarose (42 °C) and sliced into consecutive 450 µm thickness sections using a Compresstome® VF-300 vibratome (Precisionary Instruments, Natick, MA, USA). The slices were transferred to 24-well plates containing Dulbecco’s Modified Eagle’s Medium (DMEM, Thermo Fisher Scientific, Waltham, MA, USA) with antifungal agents and antibiotics and incubated in a humidified incubator at 37 °C supplemented with 5% CO2. We used the pandemic influenza A/California/07/2009 virus which was initially and generously provided by Dr. Kevin Harrod from the University of Alabama at Birmingham and further propagated by Dr. Mari Numata Nakamura at National Jewish Health, Denver [59]. IAV was propagated in Madin-Darby canine kidney cells (MDCK; ATCC, Manassas, VA, USA) [8,60,61,62,63]. MDCK cells were grown in DMEM (Thermo Fisher Scientific, Waltham, MA, USA) as described previously [59]. IAV was harvested after 72 h post-infection and titered by plaque assay using MDCK cells [59]. Human PCLS were incubated with 1.5 µg/mL of TPCK-treated trypsin [control] (Thermo Fisher Scientific, Waltham, MA, USA) or IAV (3 × 105 PFU/well) in 250 µL of DMEM media supplied with antibiotics for 2 h at 37 °C and 5% CO2. After 2 h, the virus-containing medium was removed and PCLS were washed three times with warm 1X PBS to remove the unbound virus. The dose of IAV was selected based on our previous publication [59]. E-juice with Virginia tobacco flavor from JUUL Labs (Washington D.C.), which contains nicotine at 35 mg/mL, was used in this study. 0.05% E-juice with a final nicotine concentration of 17.5 µg/mL was added to PCLS during the 2 h IAV infection and maintained after the viruses were removed from the supernatants. A previous study [64] measured nicotine in the serum samples of EC users. Serum nicotine concentrations after 5 min of EC use ranged from 5 to 45 ng/mL. Serum nicotine concentrations were determined to be about 1000 times lower than those in the airway epithelial lining fluid [64,65,66]. Therefore, our nicotine added to human PCLS at 17.5 µg/mL is considered to be within the physiological range of human EC users. To demonstrate the role of TRAIL in viral infection, a TRAIL neutralizing antibody (50 ng/mL, Peprotech, Cranbury, NJ, USA) or an IgG antibody control (50 ng/mL, Jackson Immuno-research, West Grove, PA, USA) was added to PCLS exposed to 0.05% E-juice with or without IAV infection for up to 72 h. Similarly, recombinant human TRAIL (0.1–10 ng/mL, Peprotech, Cranbury, NJ, USA) or bovine serum albumin (BSA) was applied to PCLS exposed to E-juice with or without IAV infection. After 24, 48, and 72 h time points, supernatants and tissues were collected. Expression of interferon beta (IFN-β) and interferon lambda (IFN-λ) mRNA, intracellular and extracellular IAV RNA was measured by reverse transcription and quantitative real-time PCR (RT-PCR). To extract total RNA, PCLS was homogenized using the TRIzol reagent, followed by using Mini Spin Columns for RNA extraction (Epoch Life Science Inc., Missouri City, TX, USA) according to the manufacturer’s instructions. cDNA was generated through a Bio-Rad T100 thermocycler. RT-PCR was performed using a probe-based method where 18s RNA (ThermoFisher, Waltham, MA, USA) was used as a housekeeping gene. The custom-made (Integrated DNA Technologies, Coralville, IA, USA) specific primers and probes for human IFN-λ1 were forward: 5′-GGGAACCTGTGTCTGAGAACGT-3′; reverse: 5′-GAGTAGGGCTCA GCGCATAAATA-3′; probe: 5′-CTGAGTCCACCTGACACCCCACACC-3′), for IFN-β forward: 5′-GACGGAGAAGATGCAGAAGAG-3′, reverse:5′-CCACCCAGTGCTGGA GAA -3′, probe: 5′-TGCCTTTGCCATCCAAGAGAT-3′; and for IAV were forward: 5′-GAC CRATCCTGTCACCTCTGAC-3′, reverse:5′-AGGGCATTYTGGACAAAKCGTCTA-3′, probe: 5′-TGCAGTCCTCGCTCACTGGGCACG-3′. The comparative cycle of threshold (ΔΔCT) method was used with the housekeeping gene 18S rRNA as an internal control to calculate the relative gene expression levels. TRAIL released in PCLS supernatants was measured using western blotting. As TRAIL levels in the raw supernatants were low, supernatants were concentrated using Amicon ultra centrifugal filters (MilliporeSigma, Burlington, MA, USA). An equal volume of concentrated supernatants was separated on 15% SDS-polyacrylamide gels, transferred onto PVDF membranes, blocked with western blocking buffer, and incubated with a mouse anti-human TRAIL antibody (R&D Systems, Minneapolis, MN, USA) overnight at 4 °C. The next day, the membranes were washed in PBS with 0.1% Tween-20, incubated in HRP-conjugated IgG secondary mouse antibody (EMD Millipore, Burlington, MA, USA) followed by developing using a Fotodyne imaging system (Fotodyne, Inc., Hartland, WI, USA). Image J software (National Institutes of Health, Bethesda, MD, USA) was used to measure the intensity of TRAIL protein expression and calculate the fold change values of treatment groups versus the non-treated control groups. TNF-α levels were measured in PCLS supernatants using a Human TNF-α DuoSet ELISA kit (R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s instructions. To determine the cytotoxic effects of IAV and E-juice, LDH levels in PCLS supernatants were measured using an LDH detection kit (Roche Diagnostics, Indianapolis, IN, USA) according to the manufacturer’s instructions. Data were expressed as the fold changes of various treatment groups versus the control groups. GraphPad Prism version 8.0 software was used for all statistical analyses. For parametric tests, a Student’s t-test was performed for two-group comparisons. Non-parametric data were analyzed using the Mann–Whitney test for two group comparisons. p < 0.05 was considered statistically significant.

PMC10002054

Amaya Urdánoz-Casado,Javier Sánchez-Ruiz de Gordoa,Maitane Robles,Miren Roldan,Mónica Macías Conde,Blanca Acha,Idoia Blanco-Luquin,Maite Mendioroz

circRNA from APP Gene Changes in Alzheimer’s Disease Human Brain

21-02-2023

Alzheimer’s disease,entorhinal cortex,circRNA,mRNA,Amyloid beta,APP

Alzheimer’s disease (AD) is the most common cause of age-related dementia. Amyloid precursor protein (APP) is the precursor of Aβ peptides, and its role in AD has been widely investigated. Recently, it has been reported that a circular RNA (circRNA) originated from APP gene can serve as a template for Aβ synthesis, postulating it as an alternative pathway for the Aβ biogenesis. Moreover, circRNAs play important roles in brain development and in neurological diseases. Therefore, our aim was to study the expression of a circAPP (hsa_circ_0007556) and its linear cognate in AD human entorhinal cortex, a brain region most vulnerable to AD pathology. First, we confirmed the presence of circAPP (hsa_circ_0007556) in human entorhinal cortex samples using RT-PCR and Sanger sequencing of PCR products. Next, a 0.49-fold decrease in circAPP (hsa_circ_0007556) levels was observed in entorhinal cortex of AD cases compared to controls (p-value < 0.05) by qPCR. In contrast, APP mRNA expression did not show changes in the entorhinal cortex between AD cases and controls (Fold-change = 1.06; p-value = 0.81). A negative correlation was found between Aβ deposits and circAPP (hsa_circ_0007556) and APP expression levels (Rho Spearman = −0.56, p-value < 0.001 and Rho Spearman = −0.44, p-values < 0.001, respectively). Finally, by using bioinformatics tools, 17 miRNAs were predicted to bind circAPP (hsa_circ_0007556), and the functional analysis predicted that they were involved in some pathways, such as the Wnt-signaling pathway (p = 3.32 × 10−6). Long-term potentiation (p = 2.86 × 10−5), among others, is known to be altered in AD. To sum up, we show that circAPP (hsa_circ_0007556) is deregulated in the entorhinal cortex of AD patients. These results add to the notion that circAPP (hsa_circ_0007556) could be playing a role in the pathogenesis of AD disease.

circRNA from APP Gene Changes in Alzheimer’s Disease Human Brain Alzheimer’s disease (AD) is the most common cause of age-related dementia. Amyloid precursor protein (APP) is the precursor of Aβ peptides, and its role in AD has been widely investigated. Recently, it has been reported that a circular RNA (circRNA) originated from APP gene can serve as a template for Aβ synthesis, postulating it as an alternative pathway for the Aβ biogenesis. Moreover, circRNAs play important roles in brain development and in neurological diseases. Therefore, our aim was to study the expression of a circAPP (hsa_circ_0007556) and its linear cognate in AD human entorhinal cortex, a brain region most vulnerable to AD pathology. First, we confirmed the presence of circAPP (hsa_circ_0007556) in human entorhinal cortex samples using RT-PCR and Sanger sequencing of PCR products. Next, a 0.49-fold decrease in circAPP (hsa_circ_0007556) levels was observed in entorhinal cortex of AD cases compared to controls (p-value < 0.05) by qPCR. In contrast, APP mRNA expression did not show changes in the entorhinal cortex between AD cases and controls (Fold-change = 1.06; p-value = 0.81). A negative correlation was found between Aβ deposits and circAPP (hsa_circ_0007556) and APP expression levels (Rho Spearman = −0.56, p-value < 0.001 and Rho Spearman = −0.44, p-values < 0.001, respectively). Finally, by using bioinformatics tools, 17 miRNAs were predicted to bind circAPP (hsa_circ_0007556), and the functional analysis predicted that they were involved in some pathways, such as the Wnt-signaling pathway (p = 3.32 × 10−6). Long-term potentiation (p = 2.86 × 10−5), among others, is known to be altered in AD. To sum up, we show that circAPP (hsa_circ_0007556) is deregulated in the entorhinal cortex of AD patients. These results add to the notion that circAPP (hsa_circ_0007556) could be playing a role in the pathogenesis of AD disease. Alzheimer’s disease (AD) is a chronic and irreversible neurodegenerative disease [1]. AD is the leading cause of age-related dementia and is also the most common neurodegenerative disease [2,3]. The main anatomopathological features of AD are the brain deposition of intraneuronal neurofibrillary tangles (NFTs) of hyperphosphorylated tau protein and extracellular plaques of Amyloid beta peptide (Aβ) in the parenchyma and blood vessels. AD can be classified into two types, familial AD and sporadic AD. The former constitutes 5% of AD cases and is characterized by mutations in the Amyloid Precursor Protein (APP), Presenilin 1 (PSEN1) or Presenilin 2 (PSEN2) genes, all of which are related to the synthesis and processing of Aβ-peptide. Regarding sporadic AD, the cause of the disease remains unknown, although it is considered a multifactorial disease in which genetic and environmental risk factors contribute to its development [2]. APP is a membrane protein which performs several crucial cellular functions. So far, a number of APP isoforms have been described by alternative splicing; one of which is commonly expressed in the brain, where it participates in synaptogenesis and synaptic plasticity [4,5]. APP can be processed through the amyloidogenic pathway, in which the final products are going to be the Aβ40 and Aβ42 peptides, components of amyloid plaque, and the APP intracellular domain (amyloid precursor protein intracellular domain, AICD), following proteolytic cleavage of the APP protein carried out by the enzymes β-secretase (BACE1) and γ-secretase. In the non-amyloidogenic pathway, the enzymes in charge of processing the APP protein are α-secretase (ADAM metallopeptidase domain 10, ADAM10) and γ-secretase, and the final product will be irrelevant peptides for this pathology [1,6]. Although the role of Aβ in the AD pathogenesis is yet not well understood [7], and some authors believe that aberrant Aβ expression may not be the primary cause of all EOAD [8], the amyloid cascade theory still remains the most widely accepted pathogenic model [6]. This theory proposes that Aβ deposition is the first critical event that triggers a cascade of molecular phenomena leading to neurofibrillary deposits, synaptic failure, neuronal death, neurodegeneration and, finally, AD dementia. It seems clear that deposition of Aβ occurs as a consequence of altered processing of APP or Aβ clearance. However, it is still not well understood how the peptide is produced [9]. Recently, it has been described that alternative splicing of APP results in the generation of several circRNAs. Mo et al. even demonstrated that Aβ can be transcribed from a specific circRNA (hsa_circ_0007556) [9]. circRNAs are single-stranded RNA molecules characterized, as their name suggests, by a circular structure. They lack 5′ and 3′ ends since, after transcription, a covalent bond is established between these ends [10,11]. circRNAs can act as miRNA sponges, protein templates or transcriptional regulators, among other described functions [12,13]. circRNAs are evolutionarily conserved and expressed in a large number of body tissues [10,11,14,15]. However, it is in the brain that they are most highly expressed; in fact, 20% of brain genes encode circRNAs [16,17]. Interestingly, neuronal specialization has been related to a high level of alternative splicing [18], and this process could also explain the enormous amount of brain-specific circRNAs [14]. Nevertheless, the expression levels of circRNAs vary from one brain region to another, with the synapse being the site where their expression is highest [19]. Thus, alterations in the expression levels of circRNAs have been described in different neurological diseases, such as multiple sclerosis (MS), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and AD, among others [20,21,22,23,24]. In the last few years, thanks to advances in massive sequencing and transcriptome analysis, knowledge about circRNAs involvement in AD has increased exponentially. Numerous studies, both in human brain or blood samples and in cellular and mouse models of AD, have shown dysregulation of circRNAs in this disease. A number of circRNAs are now known to play important regulatory functions in neuroinflammation (e.g., circ_0000950), oxidative stress (e.g., mmu_circRNA_013636 and mmu_circRNA_012180) and autophagy (e.g., circNF1-419), as well as Aβ production and degradation (e.g., circHDAC9, CDR1as, circHOMER1 or circCORO1C) [25,26,27,28,29,30,31,32,33]. In addition, significant changes in circRNAs expression have been detected whose genes of origin are closely related to AD pathology, involved in synaptic plasticity and neuronal survival (e.g., HOMER1, DOCK1, NTRK2 and APC) or vesicular trafficking (e.g., DGL1/SAP97, TRAPPC9 and KIF1B) [34]. From the APP and the MAPT genes, which encode the main proteins involved in AD [9,35], or from the APOE gene [36], circRNAs also originate. Likewise, several possible candidate circRNAs for diagnosis, prognosis and disease progression have been postulated, such as circHOMER1, circCOROC1 or hsa_circ_0003391 [25,26,37,38,39,40]. Of all these, circRNAs derived from the APP gene are particularly interesting. Thirty-three circRNAs derived from the APP gene (circAPPs) have been described by in silico analysis of RNA sequencing data, and 17 circAPP have been observed by RT-PCR and Sanger sequencing. Interestingly, only two of them, namely hsa_circ_0007556 and hsa_circ_0115725, were in both lists, with hsa_circ_0007556 being the most abundant one [9,14,41,42]. It is known that this circAPP (hsa_circ_0007556) can be detected in two brain regions, hippocampus and prefrontal lobe, but its expression in the entorhinal cortex is unknown. Therefore, our aim was to study the expression of circAPP (hsa_circ_0007556) and its related linear form in human entorhinal cortex, given that knowledge of the circAPP (hsa_circ_0007556) expression pattern of in both healthy and AD-affected human entorhinal cortex is scarce, and this region constitutes one of the brain areas most vulnerable to the development of AD. Neuronal loss at entorhinal cortex occurs very early in AD [43], and dysfunction in this region is implicated in memory and learning impairment [44]. In addition, entorhinal cortex is the main communication pathway between the hippocampus and neocortex [44]. Furthermore, gene expression profiling changes related with memory and learning functions have been identified in entorhinal cortex from AD and control post-mortem brain samples [45]. First, we wanted to test whether circAPP (hsa_circ_0007556) was expressed in the human entorhinal cortex. For this purpose, APP-specific divergent primers were designed to amplify circular but not linear RNA. Then, RT-PCR was performed on RNA samples isolated from the entorhinal cortex of AD patients and controls. After electrophoresis, PCR products were selected from the agarose gel for Sanger sequencing analysis (Figure 1). As a result, we succeeded to amplify circAPP (hsa_circ_0007556) in the human entorhinal cortex in both AD patients and controls. In order to study the expression levels of circAPP (hsa_circ_0007556) detected in the previous section and to explore whether there were expression differences in entorhinal cortex samples between AD and controls, RT-qPCR technique was performed. A total of 29 entorhinal cortex samples from AD patients and 16 controls were studied. All samples passed the RNA quality criteria. It should be noted that statistically significant differences in age (mean SD, 55.94 ± 5.31 in controls versus 82.07 ± 1.96 in AD, p-value < 0.001) and gender (% female, 31.25% in controls versus 62.07% in AD, p-value < 0.05) were found between AD samples and controls, so we fitted multivariate linear regression models for each transcript (circAPP (hsa_circ_0007556) and its corresponding linear mRNA), including the presence or absence of the disease as predictor and adjusting for age and gender as potential confounders, and it was found that only the presence of the disease significantly explained differences in the expression of circAPP transcript (p = 0.025). Regression coefficients and standard errors can be found in Table S1. Additionally, we checked whether circAPP and APP mRNA expression is decreased with age (Figure S1) or by gender. We observed no correlation between circAPP expression and age (p-value = 0.306) or gender (p-value = 0.655), nor between APP mRNA expression and age (p-value = 0.158) or gender (p-value = 0.063). A significant decrease (fold-change (FC) = 0.49, p-value < 0.05) in the circAPP (hsa_circ_0007556) levels was observed in the entorhinal cortex of AD cases compared to controls. However, APP mRNA expression (FC = 0.80, p-value = 0.81) showed no significant changes in the entorhinal cortex region of AD samples compared to controls (Figure 2A). To evaluate whether the magnitude of the differences in the transcript expression levels between AD cases and controls was gender-dependent, we included in the multivariate linear regression models the interaction between the presence/absence of the disease and gender, resulting in non-statistically significant interactions (circAPP (hsa_circ_0007556), p-value = 0.544 and for APP mRNA, p-value = 0.549). These results demonstrated the absence of sex dependence in the differential expression levels of the transcripts between AD cases and controls. We also wanted to analyze whether circAPP (hsa_circ_0007556) and APP mRNA expression changed across AD neuropathological stages, according to the ABC score. A significant decrease in circAPP (hsa_circ_0007556) expression was observed in the group with higher ABC score values with respect to controls (p-value < 0.05) (Figure 2B). For its part, APP linear transcript showed a significant decrease in expression in the group with high level of ABC score with respect to the intermediate group (p-value < 0.05) and the low group (p-value < 0.001) (Figure 2B). Next, using a univariate general linear model, where the dependent variable was the RNA expression levels and the independent variables were the specific RNA variants (circAPP (hsa_circ_0007556) and APP mRNA) and the presence of disease (AD or control), it was observed that the expression of the two transcripts together in AD cases was 37.75% lower (p-values < 0.05) with respect to the controls. Considering linear APP as a reference, circAPP (hsa_circ_0007556) expression was 99.93% lower (p-value < 0.0001). However, when the cases (AD and control) and the two RNA variants were studied together in the model, relative expression ratios of the two transcripts (circAPP/APP) were maintained in the AD cases and controls (99.94% and 99.90% lower circAPP (hsa_circ_0007556) relative to APP mRNA, respectively, p-value = 0.100), meaning that the decrease in expression of the 2 RNA variants is proportional within AD cases, although only circAPP (hsa_circ_0007556) was significantly decreased in samples with AD compared to controls (Figure S2). We repeated the above analysis, but taking into consideration the ABC score instead of the presence of the disease (AD or control). Relative expression ratios of the two transcripts (circAPP/APP) were also maintained in each level of ABC scores and controls (99.95% at the low level, 99.93% at the intermediate level, 99.93% at the high level, 99.90% at the control level and lower circAPP relative to APP mRNA, p-value = 0.455, p-value = 0.955, p-value = 0.362 between groups) (Figure S3). We can conclude that the decrease in expression of the different variants is proportional within AD or ABC score levels and control samples; in fact, the circAPP/APP ratio is maintained between AD patients or ABC score levels and controls. Since one of the main pathophysiological features of AD is the deposition of Aβ peptide, which derives from APP protein processing, we sought to study the relationship between quantitative assessment of Aβ deposits and APP-derived RNA transcripts expression levels. Thus, a negative correlation was found between Aβ deposits and both circAPP (hsa_circ_0007556) and linear APP expression (Rho Spearman = −0.48, p-value < 0.05 and Rho Spearman = −0.652, p-value <0.01, respectively) (Table 1, Figure S4). Additionally, we decided to study the correlation between circAPP (hsa_circ_007556) or APP mRNA expression and the ABC score and each group individually forming ABC score (Method of Thal, Braak and Braak classification, Method of CERAD). We observed a negative correlation between circAPP (hsa_circ_007556) expression and all pathological features analyzed (Method of Thal, Braak and Braak classification, Method of CERAD, ABC score and Global average area of Aβ deposits), while APP mRNA showed a negative correlation with all of them, except the Method of CERAD) (Table 1). It has been demonstrated that circAPP (hsa_circ_0007556) could be the template for Aβ peptide transcription, but little is known about the role of circAPP (hsa_circ_0007556) in the brain [9]. Since circRNAs may function as microRNAs sponges, we decided to identify those miRNAs that target circAPP (hsa_circ_0007556). For this purpose, we used the miRNA target sites tool from CircInteractome database [46]. We identified 17 miRNAs that could potentially target circAPP (hsa_circ_0007556) (Table 2). Some of these miRNAs have been described in different neurological disorders, such as major depression, epilepsy, ALS, PD or AD [47,48,49,50,51,52,53,54]. Moreover, we wanted to know the biological pathways in which these miRNAs are involve and tried to approach the biological function of circAPP (hsa_circ_0007556). We employed the microT-CDS tool of the DIANA mirPath v.3 software [55] and used the 17 miRNAs as the input. A significant association between these miRNAs and diverse KEGG pathways were found, as follows: Wnt-signaling pathway (p = 3.32 × 10−6), Long-term potentiation (p = 2.86 × 10−5), Glycosaminoglycan biosynthesis—heparan sulfate/heparin (hsa00534) (p = 3.38 × 10−5), Ubiquitin mediated proteolysis (p = 6.68 × 10−5), Axon guidance (7.01 × 10−5) and Glutamatergic synapse (p = 7.76 × 10−5), among others (Table S2). In this study, we observed downregulation of circAPP (hsa_circ_0007556) expression in the entorhinal cortex of AD patients versus controls. Furthermore, taking into account the progression of AD neuropathological change and according to the ABC score, circAPP (hsa_circ_0007556) expression was downregulated in the group with higher ABC score values with respect to controls and APP mRNA showed downregulation in the group with higher ABC score values with respect to intermediate and low groups. In addition, both transcripts showed a negative correlation with Aβ deposits in the brain tissue. The APP protein is the precursor of Aβ peptides and its role in AD has been extensively investigated [73]. Databases of circRNAs predict up to 33 different circRNAs derived from the APP gene. Some of these circRNAs were detected by massive sequencing in different brain regions such as cerebellum, frontal cortex, diencephalon, occipital lobe, parietal lobe and temporal lobe [14,41,42]. In recent years, the focus has been on studying the expression of circRNAs in different brain regions affected by AD. For example, Lo et al. [40] detected by RNA-seq, in four brain regions, i.e., the anterior prefrontal cortex, superior temporal lobe, inferior frontal lobe and hippocampus, five circRNAs originating from APP, but only one was differentially expressed in the inferior frontal lobe between AD patients and controls. However, they did not observe the circAPP (hsa_circ_0007556) studied in the present work. On the other hand, Dube et al. [25] surveyed the parietal cortex, inferior frontal lobe, frontal pole, superior temporal lobe and parahippocampal lobe also by RNA-seq but found none of the APP-derived circRNAs. That could be because they may not meet the minimum requirements for the subsequent differential analysis. It is worth noting that, of all the circAPPs revealed by massive sequencing, only hsa_circ_0007556 and hsa_circ_0115725 has been validated by another technique in two brain regions, the hippocampus and prefrontal lobe [9]. Following the results obtained in our study, the expression of hsa_circ_0007556 in the entorhinal cortex of both AD patients and controls can also be confirmed. Moreover, with this work, knowledge of the differential expression of circAPP (hsa_circ_0007556), which was found to be less expressed in samples with AD compared to controls, is added. A priori, an increase in its expression would have been expected, since the mechanism by which Aβ deposition is increased in AD brain is not entirely clear, and an increase in this circRNA could imply an increase in the production of Aβ [9]. In fact, it has been recently published that this circAPP (hsa_circ_0007556) may serve as a “template” for Aβ synthesis, postulating itself as an alternative pathway for this peptide biogenesis [9]. On the other hand, a negative correlation of peptide Aβ burden and circAPP (hsa_circ_0007556) expression levels is observed here (although when was taking into consideration the ABC score, only the group with higher ABC score values showed a significant decrease in circAPP (hsa_circ_0007556) expression respect to control, but this observation could be due to the reduced sample size by segmenting into ABC stages limits the power of the analysis), leading to hypothesize that it could play a regulatory role, directly or indirectly, on the expression of enzymes in charge of APP protein processing. For example, due to the upregulation of the peptide, a downregulation of circRNA could be induced to compensate for the excess of Aβ that could be produced from it. In fact, the expression of this circRNA seems to be reduced across the progression of the disease. An alternative explanation would be that enzymes involved in Aβ peptide processing may bind to circAPP (hsa_circ_0007556), so that circAPP (hsa_circ_0007556) would control the availability of them; a circAPP (hsa_circ_0007556) dysfunction would lead to the release of the enzymes, resulting in an increase of Aβ. In any case, our study is observational and merely shows a statistical association, not causality. Therefore, to elucidate whether there is a true involvement of circAPP (hsa_circ_0007556) in the generation of the peptide, further experimental studies would be necessary. In any case, it would be interesting to study several aspects of this circRNA, such as the expression of the protein that originates from circAPP (hsa_circ_0007556), the methylation of circAPP (hsa_circ_0007556), since it has been demonstrated that m6A RNA methylation can efficiently promote translation to proteins [27] or the interaction of this circRNA with other molecules to try to elucidate its function in the brain and its role in AD. It would also be interesting to study the localization of this circRNA at the cellular level, as it is known that the expression of circRNAs is enriched at the synapse [16], and synaptic dysfunction is one of the characteristics of AD [7]. Anyway, it must be taken into consideration that the decrease in the level of circAPP may be due to a number of reasons, such as downregulation of its expression, higher processing or destruction or sequestration of circRNA in the amyloid plaques. Although circAPP (hsa_circ_0007556) expression is downregulated, the expression of linear APP is not altered on the whole. These findings are in the same direction as others previously shown in the literature [9]. Nevertheless, one would expect that the ratio of circAPP (hsa_circ_0007556) to linear APP between patients and controls may be altered, given that circAPP (hsa_circ_0007556) shows expression changes in the face of AD while APP does not. However, what is observed is that the circAPP (hsa_circ_0007556)/APP ratio is maintained between AD patients and controls. This may be due to a decrease in APP expression, although not statistically significant, in AD patients and this small decrease may be sufficient to maintain the circAPP (hsa_circ_0007556)/APP ratio. Both RNA transcripts show a negative correlation with Aβ deposits, and this points to other mechanisms being involved in this process, such as those concerning miRNAs. In the CircInteractome database [46], circAPP (hsa_circ_0007556) is predicted to have a binding site for 17 miRNAs. Some of them have been associated with several diseases, but it is worth mentioning that at least five of them have been related with AD. For example, hsa-miR-598, hsa-miRNA-659 and hsa-miR-324-5p have been identified as candidate biomarkers in different fluids such as plasma or cerebrospinal fluid [47,48,49]. In an AD model in primary mouse hippocampal neurons, circ_0004381 was found to regulate PSEN1 expression through miR-647 [67]. has-miR-186 is a strong negative regulator of BACE1 expression, a protein involved in the amyloidogenic pathway [61]. Furthermore, in the review by He et al. [74], it is pointed out that different miRNAs participate in the metabolism of Aβ peptides acting at different levels and on different genes. Moreover, these 17 miRNAs found in the in silico functional analysis have been significantly associated with multiple pathways, some of which are altered in AD. For instance, Wnt-signaling pathway is implicated in neurodevelopment and neurogenesis and, in AD, is downregulated in several cell types in AD brains [75]. Other relevant pathways, such as synaptic long-term potentiation, axon guidance and dysregulation in ubiquitin-mediated proteolysis, which are implicated in synaptic plasticity and synaptic development, have been related to cognitive impairment observed in AD [76,77,78]. Glycosaminoglycan biosynthesis—heparan sulfate/heparin is associated with the formation of Aβ plaques [79,80,81], and the glutamatergic synapse pathway has been observed altered in AD, while glutamatergic receptors have been suggested as pharmacological targets [82]. However, this is still an in silico study, and other functional studies are required to confirm these predictions. Brain entorhinal cortex samples from 29 AD patients and 16 controls were provided by Navarrabiomed Brain Bank. After death, half-brain specimens from donors were cryopreserved at −80 °C. A neuropathological examination was completed following the usual recommendations [83] and according to the updated National Institute on Aging-Alzheimer’s Association guidelines [84]. Assessment of Aβ deposition was carried out by immunohistochemical staining of paraffin-embedded sections (3–5 μm thick) with a mouse monoclonal (S6 F/3D) anti-Aβ antibody (Leica Biosystems Newcastle Ltd., Newcastle upon Tyne, United Kingdom). Evaluation of neurofibrillary pathology was performed with a mouse monoclonal antibody anti-human PHF-TAU, clone AT-8 (Tau AT8) (Innogenetics, Gent, Belgium), which identifies hyperphosphorylated tau (p-tau) [85]. The reaction product was visualized using an automated slide immunostainer (Leica Bond Max) with Bond Polymer Refine Detection (Leica Biosystems, Newcastle Ltd., UK). Other protein deposits, such as synuclein deposits, were ruled out by a monoclonal antibody against α-synuclein (NCL-L-ASYN; Leica Biosystems, Wetzlar, Germany). The staging of AD was performed by using the ABC score according to the updated National Institute on Aging-Alzheimer’s Association guidelines [84]. ABC score combines histopathologic assessments of Aβ deposits determined by the method of Thal (A) [84], staging of neurofibrillary tangles by Braak and Braak classification (B) [85], and scoring of neuritic plaques by the method of CERAD (Consortium to Establish A Registry for Alzheimer’s Disease) (C) [86] to characterize AD neuropathological changes. Thus, the ABC score shows three levels of AD neuropathological severity: low, intermediate and high. A summary of the characteristics of subjects considered in this study is shown in Table S3. Total RNA, including small RNA species, was isolated from cells and entorhinal cortex samples with miRNAeasy mini Kit (QIAGEN, Redwood City, CA, USA) following the manufacturer’s instructions. Concentration and purity of RNA were both evaluated with NanoDrop spectrophotometer. Complementary DNA (cDNA) was reverse transcribed from 500 ng total RNA with SuperScript® III First-Strand Synthesis Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) after priming with random primers. RT-PCR was performed by using GoTaq® DNA polymerase (Promega, Madison, WI, USA) in an Applied Biosystems™ Veriti™ Thermal Cycler, 96-Well (Applied Biosystems, Foster City, CA, USA). PCR conditions were as follows: denaturation at 95 °C for 20 s, extension at 72 °C for 30 s and annealing temperatures 60 °C for 40s and cycles used were 40. Primer3 software was used for divergent primers design (Table S4). Candidate bands were selected after 1.8% agarose gel electrophoresis of RT-PCR products. Bands purification were made with Wizard® SV Gel and PCR Clean-Up System (Promega, Madison, WI, USA). Next, Sanger sequencing was performed and UCSC (University of California Santa Cruz) Genome Browser software was used for the sequence alignment [87,88]. Total RNA were isolated from the entorhinal cortex with RNAeasy Lipid Tissue mini Kit (QIAGEN, Redwood City, CA, USA) following the manufacturer’s instructions. Genomic DNA was removed with recombinant DNase (TURBO DNA-free™ Kit, Ambion, Austin, TX, USA). Concentration and purity of RNA were both evaluated with NanoDrop spectrophotometer. Complementary DNA (cDNA) was reverse transcribed from 500 ng total RNA with SuperScript® III First-Strand Synthesis Reverse Transcriptase (Invitrogen, Carlsbad, CA, USA) after priming with random primers. RT-qPCR reactions were performed in triplicate with Power SYBR Green PCR Master Mix (Invitrogen, Carlsbad, CA, USA) on the QuantStudio 12K Flex real-time PCR system (Applied Biosystems, Foster City, CA, USA) and repeated twice on independent cDNA samples. The sequences of convergent primer pairs for linear RNA detection were designed using the IDT real-time PCR tool (Coralville, IA, USA) and Primer3 software and are listed in Table S4. The relative expression level of mRNA in each sample was calculated using the delta delta-CT method and the geometric mean of GAPDH and ACTB genes was used as a reference to normalize the expression values [89]. In order to quantitatively assess the Aβ burden for further statistical analysis, we applied a method to quantify protein deposits. This method generates a numeric measurement that represents the extent of Aβ deposition. Sections of the entorhinal cortex were examined after performing immunostaining with anti Aβ antibody as described above in Human Entorhinal Samples. Three pictures were obtained for each immunostained section by using an Olympus BX51 microscope at ×10 magnification power. Focal deposit of Aβ, as described by Braak & Braak (neuritic, immature, and compact plaque) [85], was manually determined and was further edited and analyzed with ImageJ software. Then, the Aβ plaque count, referred to as amyloid plaque score (APS) and total area of Aβ deposition, was automatically measured by ImageJ and averaged for each section (Figure S5). With the help of public databases containing information about ncRNAs we can predict which genes, proteins and miRNAs candidate circRNAs interact with. For the prediction of miRNAs that could bind to circAPP (hsa_circ_0007556), the miRNA target sites tool from the CircInteractome database was used [46]. For the study of the potential biological pathways that miRNAs might be regulating, microT-CDS tool of the DIANA mirPath v.3 software [55] was employed, and as for input, we included the miRNAs obtained in the previous step. Statistical analysis was performed with SPSS 25.0 (IBM, Inc., Chicago, IL, USA). Before performing differential analysis, we checked whether continuous variables follow a normal distribution, as per one-sample Kolgomorov–Smirnov test and the normal quantil–quantil (Q–Q) plots. For the analysis of the differential expression of the distinct APP transcripts, we fitted multivariate linear regression models for each transcript (circAPP and its corresponding linear mRNA), including the presence or absence of the disease as predictor and adjusting for age and gender as potential confounders. All three explanatory variables were included using the Enter method (all variables are entered in a single step). To evaluate the homogeneity of variances, the Levene’s test was used, and the normality of the regression residuals was assessed by visualization of the histograms and Q–Q plots. All models met the aforementioned requirements. In order to analyze differences in the expression levels of the different transcripts studied between the ABC scale groups, a general univariate linear model adjusted for gender and age and the Bonferroni post hoc test were developed. On the other hand, a general linear univariate model was used to determine the proportions between the expression levels of the APP RNA variants in the AD samples versus the control samples and ABC score stages. For each gene, an expression variable was created where the log(expression) of each variant and another categorical variable with the type of transcript was collected, leaving a model where the expression variable was the dependent variable and as fixed factors the case variables ((AD or control) or (ABC score stages (control, low, intermediate and high) and the type of transcript were included. Spearman’s test was used to assess the correlation between the continuous variables circRNA or mRNA expression and Aβ deposition or Method of Thal or Braak and Braak classification or Method of CERAD or ABC score. GraphPad Prism version 9.00 for Windows (GraphPad Software, La Jolla, CA, USA) was used to draw graphs. We observed the expression of circAPP (hsa_circ_0007556) in the human entorhinal cortex, and we also show that circAPP (hsa_circ_0007556) is downregulated in the entorhinal cortex of AD patients compared to controls and at late stages of neuropathological changes. These results add to the notion that circAPP (hsa_circ_0007556) could be playing a role in the pathogenesis of AD disease.

PMC10002064

Francesca Celiberto,Giuseppe Losurdo,Maria Pricci,Bruna Girardi,Angela Marotti,Alfredo Di Leo,Enzo Ierardi

The State of the Art of Molecular Fecal Investigations for Helicobacter pylori (H. pylori) Antibiotic Resistances

22-02-2023

Helicobacter pylori,stools,antibiotic resistance,therapy,eradication,genotypic resistance

A new paradigm shift for the treatment of Helicobacter pylori (H. pylori) infection would be timely due to a progressive increase in antibiotic resistance. Such a shift in the perspective of the H. pylori approach should include the preliminary assessment of antibiotic resistance. However, the availability of sensitivity tests is not widespread and the guidelines have always indicated empirical treatments without taking into account the need to make sensitivity tests accessible, i.e., the necessary starting point for improving results in different geographical areas. Currently, the traditional tools for this purpose (culture) are based on performing an invasive investigation (endoscopy) and often involve technical difficulties; thus, they were only confined to the settings where multiple attempts at eradication have failed. In contrast, genotypic resistance testing of fecal samples using molecular biology methods is much less invasive and more acceptable to patients. The purpose of this review is to update the state of the art of molecular fecal susceptibility testing for the management of this infection and to extensively discuss the potential benefits of their large-scale deployment, i.e., novel pharmacological opportunities.

The State of the Art of Molecular Fecal Investigations for Helicobacter pylori (H. pylori) Antibiotic Resistances A new paradigm shift for the treatment of Helicobacter pylori (H. pylori) infection would be timely due to a progressive increase in antibiotic resistance. Such a shift in the perspective of the H. pylori approach should include the preliminary assessment of antibiotic resistance. However, the availability of sensitivity tests is not widespread and the guidelines have always indicated empirical treatments without taking into account the need to make sensitivity tests accessible, i.e., the necessary starting point for improving results in different geographical areas. Currently, the traditional tools for this purpose (culture) are based on performing an invasive investigation (endoscopy) and often involve technical difficulties; thus, they were only confined to the settings where multiple attempts at eradication have failed. In contrast, genotypic resistance testing of fecal samples using molecular biology methods is much less invasive and more acceptable to patients. The purpose of this review is to update the state of the art of molecular fecal susceptibility testing for the management of this infection and to extensively discuss the potential benefits of their large-scale deployment, i.e., novel pharmacological opportunities. It is well known that Helicobacter pylori (H. pylori) represents the infectious agent of chronic active gastritis, peptic ulcer disease, gastric carcinoma and MALT lymphoma as well as of some extra-gastric disorders, i.e., iron deficiency anemia and idiopathic thrombocytopenia. Its role in these conditions has brought about the recommendation that this bacterium should be eradicated whenever possible [1]. Nevertheless, the management of the infection at present is far from an optimal solution, since it displays some complex evidences: (i) infection is still widespread [2]; (ii) a large and puzzling number of empiric therapeutic schedules have never been proposed for any other infection [3]; (iii) the same regimen has given exciting and/or disappointing results at the same time [4]; (iv) currently, no available therapy has demonstrated a 100% successful rate [5]. It is unquestionable that these problems are mainly related to the progressive development of antibiotic resistances [6]. Undeniably, this matter has been growing all the time, thus inducing a continuous decline in the effectiveness of conventional therapies despite a variety of published treatment guidelines. These circumstances have led to the use of empirical treatments originally developed on the bases of clinical experiments and, even, breakdowns. Consequently, most of the guidelines have been based on increasingly less effective therapies, thus suggesting still a feasible use of antibiotics, such as clarithromycin, even after the spread of resistance had rendered them largely ineffective [7]. The same guidelines have never taken into account the need to make the sensitivity tests more accessible, i.e., the necessary starting point to improve the results in the various geographical areas, where it is known that the levels of resistance to various antibiotics are very different from zone to zone. It is evident that these concerns underline the importance of applying an antimicrobial management with regard to the treatment of H. pylori. In this context, the main problem has been the unavailability in first line of sensitivity tests, which enhanced the effects of microbial resistances on current therapy failures [8]. On these bases, it would be advantageous that next guidelines are adjusted according to susceptibility-based treatments rather than empirical clinical trials, meta-analyses based on them, and expert opinions [1,9]. Therefore, the purpose of this review is to testify the effectiveness and feasibility of current antibiotic resistance investigations and update the state of the art of molecular fecal susceptibility tests for the management of H. pylori infection, since they might be the most promising ones because of their noninvasive peculiarity and short time requiring results. The first susceptibility test was conventionally based on culture and antibiogram in H. pylori isolates (phenotypic resistance detection), even if it is recommended by current guidelines only after repeated treatment failures [1]. Indeed, it is almost impossible to use this method for first-line treatment selection, since a relatively high rate of false negative findings often resulting in a low sensitivity has weighed on the use of the test on a large scale. This intricacy is mainly due to the need to create and maintain a micro-aerophilic environment, in which the bacterium can grow. Other factors, that have prevented the widespread diffusion of H. pylori culture so far. are the following methodology-related issues: number of gastric biopsies, time-consuming endoscopic procedures, conditions and interval of biopsy samples transport, laboratory characteristics, long and unpredictable time needed to obtain the result of the investigation [10]. It is noteworthy that culture does not detect the H. pylori hetero-resistant state, i.e., the simultaneous presence of susceptible and resistant strains [11]. As an alternative to bacterium culture and antibiogram, real time polymerase chain reaction (RT-PCR)-based techniques have been developed (genotypic resistance detection) [12]. They are based on the principle of amplifying and detecting the point mutations responsible for antibiotic resistance of H. pylori DNA isolated from gastric biopsy samples. These culture-free approaches are accurate in revealing minimal traces of genotypic resistant strains as well as in finding out hetero-resistant status. Furthermore, the ability to evaluate resistant mutant genotypes by PCR not only on fresh samples, but also on archived paraffin-embedded biopsy specimens, which has been shown to provide an equally reliable substrate for the analysis of DNA as fresh material, has further emphasized the utility of these methods [13]. The pros and cons of the two methods (culture and RT-PCR on gastric biopsy samples) have been excellently summarized in a 2010 post hoc study enrolling 146 H. pylori positive patients and comparing phenotypic and genotypic methods for clarithromycin resistance analysis. Culture revealed an overall prevalence of phenotypic clarithromycin resistance significantly lower than RT-PCR with an agreement of 71.2% between the two techniques [14]. Three main factors may be invoked to explain the lack of a full agreement between the two methods: (i) the relatively low sensitivity of the phenotypic investigation, (ii) its lack of detection of hetero-resistance, (iii) the possibility that culture may identify resistant strains carrying rare and new-fangled point mutations that are different from commonly tested ones [11,14]. Based on what has been reported, molecular tests offer unquestionably advantages and guarantees of feasibility when compared to culture, even if they are not used in clinical practice. It is presumable that the need for an invasive endoscopic procedure has been the most significant limit to their diffusion. Therefore, a successive step has been represented by an attempt to overcome this drawback by pointing out an extensive and fitting analysis. At first, a pioneering study in 1996 showed that it was possible to isolate H. pylori DNA from stool samples. This finding was confirmed by another evidence only after seven years [15]. A further advance in this topic was represented by the possibility of detecting the point mutations that confer antibiotic resistances in fecal samples of bacterial DNA. In this regard, Table 1 reports the main clarithromycin sensitivity studies performed on H. pylori fecal DNA by RT-PCR starting from 2003 [16,17,18,19,20,21,22,23,24,25,26]. The most interesting aspect which emerged from these studies was that all showed high sensitivity and most of them also showed high specificity, when compared with culture and/or RT-PCR of gastric biopsies. Interestingly, the method appeared to be reliable even in diagnosing infection when compared with the most commonly used non-invasive diagnostic methods (13C-urea breath test and stool antigen detection). One of the first commercial non-invasive investigations using fecal RT-PCR (H. pylori ClariRes assay, Ingenetix, Vienna, Austria) tested the A2142G mutation for clarithromycin resistance and was used in a pediatric population (143 children). Its main limit was constituted by the presence of the other two main mutations responsible for resistance to this antibiotic in Western countries (A2143G and A2142C), whose search was not foreseen by this test [27]. Later, another commercial molecular test was developed, i.e., Genotype Helico-DR (Hain Lifescience GmbH, Nehren, Germany). It allowed the detection of the molecular resistances of H. pylori to both clarithromycin and fluoroquinolones, identifying both the most common point mutations for the resistance to clarithromycin (A2146C, A2146G and A2147G) and to the fluoroquinolones, i.e., the mutations of the gyrA gene located at positions 87 (N87K) and 91 (D91N, D91G, D91Y) [28,29]. This investigation was initially used on tissue samples and, only later, applied to H. pylori DNA isolated from fecal samples [29]. The limitation demonstrated by this test was the finding of an unexpected low agreement between the detection of resistance to clarithromycin and fluoroquinolones on stool and gastric biopsy samples, respectively. Simultaneously with the use of the HelicoDR test on stool samples, a new RT-PCR method (THD Fecal Test, THD S. p. A., Correggio, Italy) was preliminarily tested to study clarithromycin resistance mutations in stool bacterial DNA [30]. The procedure showed full agreement between the results obtained in tissue and stool in 52 consecutive patients at the first diagnosis of infection. The A2143G mutation was found in ten (19.2%), A2142G in four (7.7%) and A2142C in five (9.6%) patients with an overall clarithromycin resistance rate of 23% in a Southern Italian population. In order to better understand the diagnostic reliability of the test, the results of a preliminary “in vitro” experiment are of interest. This experiment demonstrated that the presence of components of fecal material, such as macromolecules and fibers, makes necessary that a certain amount of colony forming units (CFU)/mL are present to obtain a positive result from the isolation of bacterial DNA. In detail, a clear positivity was achieved by a concentration of 1.5 × 10 CFU/mL of pure bacteria and of 1.5 × 103 CFU/mL of a mixture of micro-organisms and feces. Therefore, fecal test results for H. pylori DNA may be influenced by a cut-off value for bacterial concentration in feces. On these bases, it is presumable that the lack of agreement between the results on the stool and the gastric biopsy samples observed with Gene Helico-DR could be due specifically to the fact that a cut-off value for bacterial concentration in feces had not been evaluated for this investigation. Kovacheva-Slavova et al. performed a study in 2021 enrolling 50 patients with dyspeptic symptoms aging 46.46 ± 15.10 year using a molecular test based on RT-PCR in fresh fecal samples (VIASURE H. pylori real-time PCR Detection Kit; CerTest Biotec S.L. Zaragoza, Spain). A stool antigen test was used as gold standard. They identified H. pylori infection in 24 patients (48.00%). Clarithromycin resistance was observed in seven of them (29.17%). None of the patients had been treated before. The molecular test showed 85.71% sensitivity and 100% specificity, with a diagnostic accuracy of 92.00% [22]. The study confirmed that this molecular test could be beneficial for its high accuracy and clarithromycin resistance. Its assessment could improve the outcome of eradication therapy. Marrero Rolon et al. developed a PCR assay using a customized extraction kit in 2021 (Mayo MicroLab Maxwell high-throughput fecal DNA purification kit chemistry; Promega, Madison, WI, USA) that employed methods to enhance inhibitor removal and maximize DNA extraction from stool samples for the simultaneous detection of H. pylori and genotypic markers of clarithromycin resistance (A2143G, A2142G, and A2142C) directly from stool specimens. The test resulted in 88.6% and 92.8% sensitivity in the validation and clinical study sets, respectively. A high value of specificity was observed (97%). Sequencing confirmed correct detection of clarithromycin resistance-associated mutations in all positive validation samples. The gold standard in this study was culture [23]. A set of 223 antigen-positive stool samples was tested and retrospective medical record review performed to define the clinical utility. The clarithromycin-based triple therapy success was very low in the presence of resistance detection by PCR (41%) when compared to the absence of resistance finding (70%; p = 0.03). A further prospective study on GenoType Helico DR assay was performed by Brennan et al. in 2016 in a study population of 616 subjects. Genetic identification of H. pylori and its resistance to clarithromycin and fluoroquinolones was performed on stool samples from patients with campylobacter-like organism positive endoscopy (389) and UBT-positive patients (227). A multiplex amplification of DNA regions of interest was performed using a combination of the biotinylated primers supplied in the GenoType HelicoDR kit (Hain Lifescience GmbH, Nehren, Germany) and the Hotstart Taq DNA polymerase kit (Quiagen, Hilden, Germany). PCR products were reverse hybridized to DNA strips containing probes for gene regions of interest. According to conventional techniques, the strips were analyzed for the presence of a conjugate control band (to indicate successful conjugate binding and substrate reaction), an amplification control band (to indicate a successful amplification reaction), a H. pylori control band (to document the presence of a H. pylori strain) and gene locus control bands for 23S (positions 2146 and 2147) and gyrA (codons 87 and 91) in order to indicate the successful detection of the gene regions of interest for clarithromycin and fluroquinolone resistance, respectively. In addition, the strips were analyzed for the presence of wild type and/or mutation bands. The assay was reported to be efficient at detecting mutations predictive of antibiotic resistance when applied to H. pylori cultures or gastric biopsy specimens, with a sensitivity and specificity of 94–100% and 86–99% for detecting clarithromycin resistance and 83–87% and 95–98.5% for detecting fluoroquinolone resistance, respectively. The gold standard of this study was PCR on gastric biopsy samples [24]. Nevertheless, authors emphasized that the GenoType HelicoDR assay was not suitable for the accurate detection of antibiotic resistance-mediating mutations using stool samples from H. pylori-infected patients and alternative PCR or DNA sequencing-based methods could show a better outcome. As reported above the limit of this test may have been due to the fact that a cut-off value for bacterial concentration in feces was been evaluated for this investigation. In 2020, Pichon et al. [25] described a real-time PCR (H. pylori ClariR) assay that allowed the amplification of samples from stool. The raw data were analyzed by using a fully automated analysis program (Amplidiag Analyzer) that accelerated this process, obtaining the same results (Amplidiag H. pylori + ClariR, Mobidiag, Espoo, Finland). The test provided results for the detection of H. pylori and for the detection of mutations conferring clarithromycin resistance (without distinction between the mutations). A prospective, multicenter study enclosed 1200 adult patients who underwent gastroduodenal endoscopy with gastric biopsy sampling and were naive for eradication treatment. The results were compared with those of culture/E test. Quadruplex real-time PCR was performed on two gastric biopsy samples (from the antrum and corpus) in order to detect the H. pylori glmM gene and mutations in the 23S rRNA genes conferring clarithromycin resistance. The sensitivity and specificity of the detection of H. pylori were 96.3% (95% confidence interval [CI], 92 to 98%) and 98.7% (95% CI, 97 to 99%), respectively. Positive and negative predictive values were found to be 92.2% (95% CI, 92 to 98%) and 99.3% (95% CI, 98 to 99%), respectively. In this cohort, 160 patients (14.7%) were found to be infected (positive by culture and/or PCR). The sensitivity and specificity for detecting resistance to clarithromycin were 100% (95% CI, 88 to 100%) and 98.4% (95% CI, 94 to 99%), respectively [25]. Another nested polymerase chain reaction-quenching probe (Nested PCR-QP) with a novel technique was pointed out by Kakiuchi et al. in 2020 in order to analyze 23S rRNA genetic mutations (A2142C, A2142G, and A2143G) that were associated with clarithromycin resistance in H. pylori [31]. In a sample of 57 H. pylori-positive subjects, a clarithromycin rate of resistance of 49% was observed. A further study demonstrated THD fecal test diagnostic accuracy when compared to 13C urea breath test. Two hundred and ninety participants completed the study. The THD fecal test showed the following results: sensitivity 90.2% (CI: 84.2–96.3%), specificity 98.5% (CI:96.8–100%), PPV 96.5% (CI: 92.6–100%), NPV 95.6% (CI: 92.8–98.4%), accuracy 95.9% (CI: 93.6–98.2%), positive LR 59.5(CI: 19.3–183.4), negative LR 0.10 (CI: 0.05–0.18). Out of 83 infected participants identified with the THD fecal test, 34 (41.0%) had bacterial genotypic changes consistent with antibiotic-resistant H. pylori infection. In detail, 27 subjects (32.5%) demonstrated bacterial strains resistant to clarithromycin, 3 (3.6%) to levofloxacin, and 4 (4.8%) to both antibiotics [26]. Since 2007, some studies evaluating guided versus empirical treatment after PCR resistance detection were conducted (Table 2). The first attempt was preliminarily performed in Japan by Furuta et al. with a surprising result. Indeed, susceptibility guided treatment success was lower than empirical treatment (75% vs. 84.4% at intention to treat—ITT). This study, however, was strongly limited by the very small number of enrolled patients (four) in the group of tailored therapy. Indeed, when the same authors carried out a second investigation on a larger sample (300 patients), a high success rate was found in guided (96%) when compared with the empirical therapy group (70%) at ITT [32]. A similar study was conducted in 2015 by Dong et al. from China. The results did not differ much from Japan, i.e., with an outcome of an eradication rate of 91.1% for guided versus 73.3% for empirical treatment at ITT [33]. Successively, in 2018, Liou et al. reported the results of two studies from Taiwan in the same paper. Resistance-associated mutations in 23S ribosomal RNA (clarithromycin) or gyrase A (fluroquinolones) were identified by polymerase chain reaction with direct sequencing. The differences between tailored and empirical treatment were evident in both preliminary (81% versus 60% at ITT in 41 patients) and final experiment (78% versus 72.2% at ITT in 410 patients) [34]. Despite this satisfactory outcome, authors expressed reservations regarding tailored therapy accessibility, cost, and patient preference. A further experiment from China was performed by Fan et al. In this study, PCR investigation was supplemented by sequencing. The study provided a comparison between clarithromycin containing quadruple therapy versus tailored quadruple therapy. AT ITT, the difference between guided (77.8% in 270 subjects) and empirical (65.3%; 274 subjects) treatment success rate was evident even if overall eradication percentage was almost low [35]. Nevertheless, this value markedly increased when per protocol analysis was performed: 86.4% versus 70.2%. The most recent experiment is that from Delchier et al., who used GenoType HelicoDR in order to compare tailored PCR-guided and empirical triple therapy. This French multicenter prospective open-label randomized study enclosed 207 subjects in guided therapy and 208 in empirical therapy. The results confirmed the superiority of guided (85.5%) when compared to empirical therapy (73.1%) [36]. Finally, Ma et al. recently reported a systematic review and meta-analysis about tailored therapy for H. pylori, enclosing both PCR and culture-based regimens. When the results were limited to PCR-guided therapy, a better outcome for tailored therapy was found, with an odds ratio of 1.24 at ITT (95% CI 1.12–1.36) [37]. The treatment of H. pylori infection shows some critical issues, due to the fact that the regimens proposed over the years are losing their effectiveness because of the development of antibiotic resistance. At the same time, in the last 20 years, the arsenal of available drugs has not changed in number, but the combinations have simply been flourished in order to improve their outcome. On these bases, it would be desirable that the treatment of the infection follows the criteria of “precision medicine” and that a personalized treatment can be achieved. The availability of susceptibility testing for H. pylori, therefore, may change the management of this infection, at least conceptually, in agreement with that of most infectious diseases. This potential advancement could modernize the current management, which basically consists of opinion-based recommendations, with a progression towards a susceptibility-based approach according to the current principles of antibiotic management. This will not be simple or fast for several reasons. Indeed, we currently have treatments that can provide a 90% success rate and are recommended by current guidelines. This could lead to the development of guidelines, which, at least in the near future, will likely continue to be inherent in a context, where the current principles of antibiotic management are not quick-witted and potential controversies may easily arise. An obvious objection that can be raised to the current therapeutic indications is that suggested regimens require the daily intake of a large number of tablets, respectively, fourteen for bismuth containing quadruple therapy, and eight for the concomitant regimen [1,9]. It is clear that this aspect can negatively influence the patient compliance. It is presumable that a complete adherence may be obtained from patients who may be motivated by the presence of major diseases, such as MALT lymphoma, a family history of gastric cancer or peptic ulcer, particularly if complicated by bleeding episodes. However, dyspeptic or asymptomatic subjects may not be guided by similar motivations, given that, in most cases, the eradication of the bacterium is not accompanied by a clear clinical benefit [38]. Indeed, the incomplete adherence to an antibiotic therapy may be an important cause of resistance emergence since sub-inhibitory concentrations could stimulate the selection of resistant mutants. In this regard, bismuth containing quadruple therapy encompasses the use of tetracycline, which currently shows very low resistance rates in Europe. However, tetracycline resistance rates of 19% have already been reported in Asia [39]. Therefore, as already occurred for other therapeutic regimens as triple therapy (Table 3) [40,41,42,43,44], in the future, there is the truthful risk of an increase in resistance to this antibiotic due to its large-scale use as well as patient incomplete adherence to its intake [45]. Conversely, concomitant therapy involves the use of three conventional antibiotics with the obvious aim of overcoming resistance to each individual drug by the overall combined effect of the regimen. This strategy, therefore, is based on hypothetical assessments rather than real susceptibility data and, presumably, encompasses the use of more antibiotics than needed. Conversely, triple therapy containing amoxicillin and clarithromycin is currently no longer recommended in areas with a 30% of clarithromycin resistance, where it has been proven to be ineffective in 40–50% of patients. The progressive reduction in the efficacy of this therapy in the decades 1997–2017 is summarized in Table 3. Nevertheless, despite its significant ineffectiveness, it could still be used for clarithromycin susceptible strains, if we have the possibility to evaluate this feature before prescribing an eradication treatment. Therefore, the availability of sensitivity tests might bring this and other issues into focus and could address their solution. It is, therefore, possible that the high efficacy rate of currently recommended therapies could lead to an excessive and superfluous consumption of antibiotics and that this aspect could reduce their efficacy in the future, favoring the development of increasingly resistant strains. Based on what has been reported above, it would be appropriate that susceptibility tests are available, which do not require invasive investigations and allow the immediate knowledge of the result. For these reasons, several attempts have been made at developing genotypic investigations of susceptibility on fecal samples, which potentially have all the required requisites. Molecular methods have few limitations since they are based on the amplification of small amounts of bacterial DNA and, therefore, are very sensitive. However, there are some rare mutations (such as A2115G, G2141A, and A2144T for clarithromycin), which are not detected by commercially available kits [46]. Furthermore, in some cases, melting curve-based methods may detect mutations that are neutral and do not confer any resistance, thus causing false positives [47]. At present, several studies have confirmed the reliability of these tests at least with regard to the evaluation of resistances to clarithromycin and fluoroquinolones [24,48], while the results concerning metronidazole have given controversial results and need to be further improved [49]. The data regarding amoxicillin and tetracycline are still insufficient and need to be validated by experiments on large samples. Other antibiotics that have shown efficacy in empirical regimens (rifabutin, doxycycline, minocycline) should be considered in the development of molecular tests of susceptibility for second-third line regimens [50,51,52]. Similarly, antibiotics that have been shown in vitro to be effective in strains with multiple resistances (tigecycline) could be considered and tested for rescue-therapies [53]. A separate discussion deserves the use of furazolidone for the treatment of H. pylori infection for the relevant ethical concerns arising with its use. This is an antibiotic that was used in the 1980s for parasitic infections. Studies were published in the 1990s that raised several concerns about this drug for its potential carcinogenicity. Therefore, the FDA withdrew its approval in March 2005. At the same time, the European Medicines Agency (EMEA; the equivalent of the FDA in the European Union) banned the drug in Europe. Therefore, its use is limited to countries outside the United States and the European Union and carries important risks for patients, who should at least be informed about them [54]. In order to optimize therapeutic regimens, it should be also considered that proton pump inhibitors (PPIs) differ markedly in terms of antisecretory activity and that intragastric pH control is a critical determinant of the success of a curative schedule for H. pylori infection. Currently, even the comparative studies of PPIs as adjuvants of therapy have been conducted with a certain superficiality. In fact, these are substances with largely different antisecretory efficacy. For example, 40 mg of pantoprazole has an antisecretory efficacy equal to 9 mg of omeprazole, while 20 mg of vonoprazan has an effect greater than 70 mg of omeprazole [55]. Therefore, it is evident that a legitimate comparison of therapeutic regimens would require the use of antisecretory drugs of equivalent potency. Based on what has been reported above, about the possibilities and limits of the use of genotypic methods for resistances, we would state once more that they are currently reliable only for the evaluation of resistance to clarithromycin and fluroquinolones [24,48]. Therefore, we conclude that genotypic techniques still require further development so that they give results that are completely comparable to the phenotypic method. Despite these concerns representing an undeniable reality, the relevant detail remains that culture method is not feasible as a front-line technique for guided therapy. A wide diffusion of sensitivity tests on fecal samples, even if currently validated and usable only for clarithromycin and fluoroquinolone resistance detection, could accelerate the process of simplifying and personalizing the therapeutic choice of H. pylori infection, thus inducing novel pharmacological chances even with the use of old drugs. In this regard, it is important to consider that none of the recommended therapies were optimized before their approval and no limit for an acceptable cure rate was established as relevant part of the approval. “Packed-up” therapies, often containing components that are difficult to find, limit the possibility of their “personalization” in terms of drug, dosage, and duration. Finally, the possibility of using susceptibility tests before prescribing an eradication treatment, albeit at the moment in a field limited to two classes of antibiotics, could lead to the following results: a. Regimens that contain unnecessary drugs could be no longer approved or used in order for them to contribute to an increase in resistances; b. Doubts about the dosages and duration of the therapies could be eliminated; c. Comparisons of PPIs should provide the relative antisecretory potency of the drugs necessary for their therapeutic efficacy. Indeed, a wise use of antibiotics is framed within the principles of antibiotics stewardship which, in the case of H. pylori, should rely on using therapies that are proved to be highly effective locally, performing a test-of-cure, and applying such data to confirm local effectiveness and share the results in the medical community [56].

PMC10002069

Wei-Cheng Fang,Cheng-Che E. Lan

The Epidermal Keratinocyte as a Therapeutic Target for Management of Diabetic Wounds

21-02-2023

diabetes mellitus,keratinocyte,diabetic wound healing

Diabetes mellitus (DM) is an important cause of chronic wounds and non-traumatic amputation. The prevalence and number of cases of diabetic mellitus are increasing worldwide. Keratinocytes, the outermost layer of the epidermis, play an important role in wound healing. A high glucose environment may disrupt the physiologic functions of keratinocytes, resulting in prolonged inflammation, impaired proliferation, and the migration of keratinocytes and impaired angiogenesis. This review provides an overview of keratinocyte dysfunctions in a high glucose environment. Effective and safe therapeutic approaches for promoting diabetic wound healing can be developed if molecular mechanisms responsible for keratinocyte dysfunction in high glucose environments are elucidated.

The Epidermal Keratinocyte as a Therapeutic Target for Management of Diabetic Wounds Diabetes mellitus (DM) is an important cause of chronic wounds and non-traumatic amputation. The prevalence and number of cases of diabetic mellitus are increasing worldwide. Keratinocytes, the outermost layer of the epidermis, play an important role in wound healing. A high glucose environment may disrupt the physiologic functions of keratinocytes, resulting in prolonged inflammation, impaired proliferation, and the migration of keratinocytes and impaired angiogenesis. This review provides an overview of keratinocyte dysfunctions in a high glucose environment. Effective and safe therapeutic approaches for promoting diabetic wound healing can be developed if molecular mechanisms responsible for keratinocyte dysfunction in high glucose environments are elucidated. Diabetes mellitus (DM), an important global health issue, is a metabolic disease characterized by impairment in regulating glucose homeostasis. The total number of diabetic patients is expected to increase from 171 million in 2000 to 366 million in 2030 [1]. A hyperglycemic state ultimately leads to the development of macrovascular and/or microvascular complications involving the eyes, kidneys, nerves, heart, and blood vessels [2]. Poor diabetic wound healing is one of the major complications of DM patients, leading to ulceration, infection, and ultimately amputation [3]. The incidence of foot ulcers in DM patients has been estimated to be 19 to 34% [4]. They remain a primary cause of morbidity and mortality in patients with diabetes [5]. Due to the increasing prevalence of diabetes worldwide, uncovering the underlying molecular mechanisms that are responsible for the poor wound healing of DM patients is a vital public health issue that needs to be addressed. Wound healing is a complicated multicellular process that includes coagulation, inflammation, proliferation, and remodeling phases. Platelets, inflammatory cells, fibroblasts, and endothelial cells have been known to play an important role in the wound healing process. In recent years, the key role of keratinocyte in wound healing has been investigated [6,7]. Keratinocytes can cover wound surfaces to regenerate an epithelial barrier with the outside environment. Keratinocytes secrete multiple cytokines to stimulate re-epithelialization, angiogenesis, and the production of a connective tissue matrix. Furthermore, keratinocytes are at the frontlines of innate immunity. After injury and the invasion of microorganisms, keratinocytes release various cytokines, chemokines, and antimicrobial peptides (AMPs) which activate immune cells and eliminate pathogens directly [8]. However, diabetic wounds have a microenvironment with hyperglycemia, advanced glycation end products (AGEs), mitochondrial dysfunction, reactive oxygen species (ROS), and inflammatory cytokines that may contribute to the impairment of keratinocyte functions. Physiological dysfunctions of keratinocytes in high glucose environments include prolonged inflammation, impaired proliferation, and migration ability, resulting in delayed wound healing. Herein, it is important to investigate the physiological functions and molecular mechanisms of keratinocytes in diabetic wound healing. The current standard treatment for diabetic foot ulcers includes surgical debridement, anti-infection treatments, wound dressing, pressure off-loading, and vascular surgery [9]. However, long-term surgical intervention and repeat dressings will cause severe pain and economic burden to the patients. Therapeutic approaches targeting the epidermal keratinocyte may bring new hope for optimal diabetic wound care. Wound healing is a complex multicellular process involving platelets, neutrophils, and macrophages, fibroblasts, endothelial cells, and keratinocytes. It follows four stages—the coagulation, inflammation, proliferation, and remodeling phases [10]. Coagulation is the first step of wound healing leading to clot formation and activation of the intrinsic and extrinsic coagulation cascade. Immediately after injury, vasoconstriction contributes to the reduction of bleeding and is followed by the accumulation and activation of platelets. Activated platelets release growth factors in alpha granules including platelet-derived growth factors (PDGF), insulin-like growth factors (IGF), epidermal growth factors (EGF), transforming growth factor-β (TGF-β), and platelet factor 4 [11,12,13] to recruit other platelets and inflammatory cells, and promote the proliferation and migration of fibroblasts and endothelial cells to the injury site [14]. The intrinsic and extrinsic coagulation cascades are initiated and result in the transformation of prothrombin into thrombin. Thrombin then catalyzes the conversion of fibrinogen to fibrin and activates Factor XIII. Activated Factor XIII functions to crosslink fibrin chains, leading to the clot formation that acts as a matrix for cell migration. Inflammation begins within 24 to 48 h after injury, and the characteristic of this phase is migration of inflammatory cells to the injury site. Neutrophils, the first arrived inflammatory cells, adhere to the vascular endothelium and further migrate into the extravascular space. Neutrophils have multiple functions, including antimicrobial ability and the production of proinflammatory cytokines, enzymes and oxygen-derived free radicals [15]. Macrophages typically appear within 72 h after injury. Macrophages are the most important regulatory cells in the inflammatory phase for the phagocytosis of necrotic material and bacteria, releasing proteolytic enzymes and growth factors for extracellular matrix definition (ECM) production, including platelet-derived growth factor, fibroblast growth factors (FGFs), and vascular endothelial growth factors (VEGFs), as well as TGF-β and TGF-α. Macrophages can be divided into M1 (classically activated) and M2 (alternatively activated) macrophages [16,17]. M1 macrophages, activated by interferon-γ (IFN-γ) and TNF-α, are represented as a pro-inflammatory phenotype, showing increased phagocytic and antigen presenting capacities, pro-inflammatory cytokine and oxidative metabolite production to promote host defense, and the elimination of necrotic tissues [18,19]. On the other hand, M2 macrophages demonstrate a phenotype in the resolution of inflammation by releasing anti-inflammatory cytokines such as IL-10 [20]. M2 macrophages are derived from resting macrophages after exposure to Th2 cytokines, such as IL-4 or IL-13 [21]. They arrive later in the wound healing process for granulation tissue formation. Notably, this M1/M2 terminology is determined based on in vitro experiments [22,23], and has been challenged by in vivo studies [24,25,26]. Actually, macrophages can coexpress both M1 and M2 markers during different stages of wound healing [27,28]. Using a small number of markers to categorize M1 or M2 macrophages is not accurate. Pang et al. used single cell RNA-sequencing and downstream analysis to reveal the different phenotypes and transitions of macrophages in the course of wound healing in mice [29]. The proliferative phase is characterized by fibroblast migration, ECM deposition, granulation tissue formation, neovascularization, and re-epithelialization. Fibroblasts are attracted by PDGF and TGF-β and produce components of ECM, including fibronectin, hyaluronan, collagen and proteoglycans. The formation of ECM is crucial for tissue repair and serves as a scaffold for cell growth and migration [30]. The main structural element of the ECM is collagen. The synthesis of collagen is stimulated by PDGF, basic FGF (bFGF), TGF-β, IL-1, and TNF. Integrins are transmembrane proteins binding the ECM to cytoskeletal structures and are important in cell–cell and cell–matrix adhesion [31]. M2 macrophages in this stage produce anti-inflammatory cytokines, VEGFs and TGF-β for induction of cell proliferation and the granulation of tissue formation [9]. Neovascularization, also a characteristic of this stage, is stimulated by different angiogenic factors, including VEGF and fibroblast growth factor-2 (FGF-2) secreted from keratinocytes, fibroblasts and inflammatory cells [32]. α3β1 integrin in keratinocytes induces the secretion of proangiogenic factors that promotes endothelial-cell migration leading to angiogenesis [33]. Re-epithelialization is a critical event in the proliferative phase and is regulated by the migration and proliferation of keratinocytes from the wound edges or skin adnexal structures [34,35]. Re-epithelialization is induced by growth factors such as the endothelial growth factor (EGF), the keratinocyte growth factor (KGF), and the FGF-2 secreted from keratinocytes and other cells [9]. During keratinocyte migration, matrix metalloproteinases (MMPs) are important for the detachment of keratinocytes from the hemidesmosome and desmosome. MMP-1 can bind the α2β1 integrin upon release from keratinocytes migrating on type I collagen [11]. MMP-9 plays a crucial role in breaking down Type IV and Type VII collagen, which are major components of the anchoring fibrils and basement membrane [36]. After breaking down these complicated structures that anchor the keratinocytes to the basement membrane and nearby keratinocytes, keratinocyte migration begins and is important for the resurfacing of the wound. In the normal tissue, MMP-9 is expressed at a low level, and is upregulated in wounds. As the wound heals, MMP-9 is downregulated [37]. However, the persistent expression of MMP-9 in chronic wounds contributes to impaired wound healing. The balance of the bimodal expression of MMP-9 is important to the epithelialization. Remodeling, the final phase of wound healing, occurs around 2–3 weeks after injury and may continue for months. During this phase, the granulation tissue is gradually replaced by mature scar tissue [38]. The remodeling of collagen including the synthesizing of new collagen and collagen degradation is mediated by fibroblasts and MMPs. Collagen type III is gradually replaced by collagen type I, which has greater tensile strength [39]. Fibroblasts interact with ECM, leading to wound contraction, which is influenced by multiple cytokines, including TGF-β, PDGF, and bFGF. The phenotypic switch from fibroblasts to myofibroblasts promotes wound contraction, leading to potential scar formation, which is induced by keratinocytes through TGF-β signals [40,41,42]. Diabetes is a metabolic disease characterized by hyperglycemia, and is a major cause of chronic wounds that may lead to amputation in affected patients. Diabetic wounds have a microenvironment with elevated levels of glucose, advanced glycation end products (AGEs), mitochondrial dysfunction, reactive oxygen species (ROS), and inflammatory cytokines that may contribute to the impairment of keratinocyte migration and proliferation, chronic inflammation, chronic infection, and impaired angiogenesis (Figure 1). Emerging evidence has shown that the hyperglycemic environment can increase oxidative stress, which indicates an imbalance between free radical formation and adequate antioxidant capacity [43,44]. The increased ROS level may contribute to the impairment of the ability for wound healing through altering the mitochondrial membrane potential, mass, and morphology in mononuclear cells of diabetic patients [45,46] and increasing TNF-α in mouse models [47]. ROS can also decrease the diversity of the skin microbiota that promotes biofilm formation and further prolongs wound healing [48]. Our previous study showed that elevated ROS levels in a high-glucose environment contribute to the increase in IL-8 production from keratinocytes, and neutrophil infiltration results in impaired wound healing in a diabetic rat model [49]. ROS can upregulate MMP-9 through the activation of nuclear factor kappa beta (NF-κB) in human keratinocytes, leading to the impairment of keratinocyte migration [50,51]. In addition, the mitochondria, a main source of ROS production, can generate huge amounts of ROS in a high glucose environment, followed by the hampering of the antioxidant ability of the cell and resulting in mitochondria damage [52,53]. Excessive ROS then causes the loss of mitochondrial membrane potential and further mtDNA fragmentation. The fragmented mtDNA translocate into to cytosol and involve cGAS-STING-IRF3 activation via the ERK1/2-PI3K/Akt-tuberin-mTOR pathways [54,55]. Activated interferon regulatory factor 3 (IRF3) then promotes the inflammatory reaction and triggers keratinocyte apoptosis [56]. MMPs are endopeptidases involved in degrading extracellular matrix elements such as collagen, fibronectin and laminin, and have been revealed to play critical roles in wound healing due to influencing keratinocyte migration. The activities of MMPs are mediated by the tissue inhibitors of MMPs (TIMPs), and the abnormal expression of MMPs and TIMPs have been linked to delayed wound healing in diabetes. Our previous work revealed that a high glucose environment suppressed keratinocyte migration, reduced mRNA levels and the activity of MMP-2 and MMP-9, but increased the expression of TIMP-1 in cultured keratinocyte [57]. We also demonstrated that keratinocyte cultured in a high glucose environment decreased the expression of MMP-1 and α2β1 integrin, which are crucial for the migration of keratinocytes on type I collagen. These events contribute to delayed diabetic wound healing [58]. Additionally, keratinocyte derived MMPs may be mediated by cytokines produced by circulating mononuclear cells. Our previous study showed that the decreased expression of IL-22 from peripheral blood mononuclear cells may suppress the production of MMP-3 in cultured keratinocytes and the wounds of diabetic rats, leading to impaired keratinocyte migration in high glucose environments [59]. Chang et al. revealed that infected diabetic wounds increase active MMP-9, increases inflammation, and decreases angiogenesis leading to prolonged wound healing. (R)-ND-336, a potent and selective inhibitor of MMP-9, can promote the healing of infected diabetic wounds in a mouse model [60]. Keratinocyte migration is important in the re-epithelialization stage of wound healing. Our previous study revealed that a high glucose environment downregulated the expression of phosphorylated p125FAK (pp125FAK) in cultured human keratinocytes, which is a crucial factor in the organization of cytoskeletal protein and cell migration [57]. The hyperglycemic environment promotes the polyol pathway, resulting in increasing intracellular sorbitol and further stimulating the formation of AGEs and pro-inflammatory cytokines. Keratinocytes cultured with AGE modified human serum albumin showed impairment of keratinocyte adhesion and migration as well as the decreasing expression of integrin alpha 3 [61]. In addition, increased O-linked N-acetylglucosamine (O-GlcNAc) glycosylation in a high glucose environment is responsible for reduced Gal-7 expression in cultured human keratinocytes, which plays an important role in keratinocyte migration [62]. p38/mitogen-activated protein kinase (MAPK) is also an important kinase promoting keratinocyte migration and proliferation through the reorganization of the cytoskeleton [63,64,65,66]. Autophagy, a downstream target of the p38/MAPK pathway for the degradation of misfolded proteins [67,68,69], has been revealed as a regulator in early differentiation [70,71], cell death [72,73], and the cell migration of keratinocytes [74,75]. Li et al. demonstrated that the p38/MAPK pathway in human immortalized keratinocyte HaCaT cells is downregulated and followed by the inactivation of autophagy in a high glucose environment, leading to the impairment of keratinocyte migration [76]. The migration of keratinocytes from the perilesional area is essential for re-epithelialization. Our previous study revealed that an increased percentage of M1 macrophages and a high level of TNF-α were detected in the perilesional area of diabetic rats. We further found that a high glucose environment induces M1 macrophage infiltration followed by the increased secretion of TNF-α, which upregulates the TIMP-1 expression in keratinocytes, resulting in impaired keratinocyte migration. The recovery rate of a wound can be significantly improved after the administration of a TNF-α inhibitor to the perilesional area of diabetic rats [77]. Leucine-rich repeat LGI family member 3 (LGI3) has various functions involved in neuronal exocytosis, β-amyloid endocytosis, and it induces neuronal differentiation. A recent study found that the increasing expression of LGI3 can restore cell migration in a high glucose environment and reduce LGI3 expression by siRNA into HaCaT cells, inhibiting wound closure [78]. KIM et al. revealed that LGI3 in HaCaT cells can regulate the migration of keratinocytes via the Akt pathway, which also plays an important role in keratinocyte migration and differentiation, influencing wound healing ability [79] through the phosphorylation of forkhead box protein O1 (FOXO1) [80] and β-catenin [81]. Recent studies revealed that increased FOXO1 in keratinocytes can diminish the expression of TGF-β1 but stimulate the expression of MMP-9, CCL20, IL-36γ, and SERPINB2, leading to the impairment of re-epithelialization, connective tissue healing, and angiogenesis in diabetic conditions [7]. In addition to keratinocyte migration, the proliferation of keratinocytes and the dynamic expression of gap junctions between keratinocytes are also a critical step in re-epithelialization. Previous studies have shown that decreased basal epidermal proliferation and decreased induction of keratinocyte mitogens including KGF are noted in the wound healing of diabetic mice [82,83]. The mechanism of this phenomenon is currently unclear, but may be related to the abnormal expression of apoptotic proteins [84], impaired K16 expression [58] or the increased expression of suppressors of cytokine signaling (SOCS)-3 in keratinocytes [85] in a high glucose environment. Increased Connexin 43 (Cx43) expression, a gap junction protein, has been demonstrated in keratinocytes from the wound edge in diabetic rats [86,87]. Further knockout Cx43 in mice can accelerate re-epithelialization in wound healing [88]. Acetylcholine (Ach) is not only a cholinergic neurotransmitter, but also has non-neuronal functions in the activation of cholinergic signaling in nonneuronal cells. Interestingly, keratinocytes are one of the nonneuronal cells which is responsive to Ach with an unknown role and mechanism in re-epithelialization [89,90]. A recent study demonstrated that Ach could upregulate the expression of TGFβRII in cultured human keratinocytes by activating the Src-ERK pathway to promote TGFβ1-SMAD2-mediated epithelial mesenchymal transition (EMT). However, keratinocytes in a high glucose environment were resistant to Ach due to the activation of the p38 kinase pathway, which inhibits the Src-ERK cascade leading to reduced TGFβRII, the impairment of the TGFβ1-mediated signaling pathway, and delayed EMT in diabetic mice [91]. Therefore, a high glucose environment may inhibit keratinocytes’ response to Ach and further impair diabetic wound healing. Figure 2 summarizes the factors that affect the proliferation and migration ability of the keratinocytes in a high glucose environment. In wound healing, the acute inflammatory phase may last 2 weeks. The prolongation of inflammation may impair wound healing. Previous studies revealed that more neutrophils and macrophages were noted in diabetic wounds [9,15,49,92]. Increased pro-inflammatory cytokines such as IL-1, IL-6, IL-8 and TNF-α were also found [9,93]. Neutrophils play a crucial role in the inflammatory phase, and it can generate ROS and serine proteases for preventing wound infections. However, prolonged neutrophil infiltration may impair wound healing. Our previous study demonstrated that reducing pro-inflammatory cytokines and decreasing neutrophil infiltration promoted diabetic wound healing in a diabetic rat model [49]. Recent studies revealed the important role of keratinocytes, which secrete various chemokines and pro-inflammatory cytokines in the chronic inflammation of diabetic wounds. We have revealed that increased IL-8 expression from keratinocytes in a high-glucose environment is known to recruit and activate neutrophils that produce ROS, contributing to impaired diabetic wound healing [49,93]. The activation of TNF-α and toll-like receptor 4 (TLR4) signaling pathways in monocytes and endothelial cells due to increased oxidative stress in the high-glucose environment have been found in diabetic patients and animal models [94]. Cheng et al. revealed the association between TNF-α and TLR4 in keratinocytes stimulated by the high-glucose environment in animal models [95]. Wang et al. revealed that Wnt family member 7A (wnt7a) in human umbilical vein endothelial cells speeds up wound healing through the promotion of angiogenesis and the amelioration of local inflammation. The decreased expression of wnt7a is noted in wounds of diabetic rats [96]. Exogenous Wnt7a can reverse the high glucose-induced TNF-α production, TNF-α related TLR4 signaling, and high glucose-induced excessive autophagy in HaCaT cells [97]. Other keratinocyte-derived cytokines involved in different mechanisms were also found. The increased expression of macrophage inflammatory protein-2 (MIP-2) and macrophage chemoattractant protein-1 (MCP1) from keratinocytes at the wound edges were noted in the diabetic wounds of mice [92]. Kampfer et al. demonstrated that decreased cyclooxygenase-1 (COX-1) expression and increased COX-2 expression in wound margin keratinocytes of diabetic mice may influence inflammatory responses due to the abnormal production of prostaglandin [98]. A diabetic wound is characterized by an increased risk of infection, and an infected wound may further delay wound healing because of a prolonged inflammatory phase [99,100]. Increased ROS due to chronic inflammation may reduce the diversity of microbiota and promote biofilm formation [48]. The strain-level variation of Staphylococcus aureus, one of the dominant bacteria in human skin, is correlated with delayed re-epithelialization in diabetic wounds [101,102]. Bacteria may further provoke inflammation that deteriorates the re-epithelialization process and directly influences epidermal keratinocytes, such as by increasing apoptosis and diminishing keratinocyte migration and proliferation [103]. As a defense mechanism, keratinocytes play a key role in cutaneous innate immunity through the secretion of antimicrobial peptides including human β-defensins (HBD), cathelicidins and Psoriasin for defending bacteria, fungi, and viruses. Our previous study revealed that keratinocytes cultured in a high-glucose environment show decreasing mRNA and protein levels of HBD-2 and HBD-3. The reduction of HBD-2 is mediated by AGEs and the signal transducer and activator of the transcription (STAT)-1 pathway in human umbilical vein endothelial cells [104] and reduced HBD-3 is regulated by AGEs and the inhibition of the p38/MAPK pathway in diabetic rats [105]. Apart from its antimicrobial activity, HBD-2 has been shown to induce keratinocyte proliferation, migration, and angiogenesis through stimulating the proliferation and migration of human umbilical vein endothelial cells [106]. Cathelicidin, the other keratinocyte derived antimicrobial peptide, also showed decreased expression of mRNA and protein levels in human keratinocytes cultured in a high glucose environment, leading to decreased antimicrobial protection [107,108]. Angiogenesis is an important step in achieving proper wound healing by the formation of blood vessels. Several angiogenic factors including VEGF, FGF, angiogenin (RNase 5), and angiopoietins (Ang1 and Ang2) were generated by endothelial cells and keratinocytes [109,110]. The defective angiogenesis in diabetes has been linked to the impaired recruitment and migration of endothelial cells and endothelial progenitor cells (EPCs) [111]. Galiano et al. showed that the administration of VEGF in diabetic wounds can enhance angiogenesis due to the increased mobilization of EPCs from the bone marrow, which have the ability to differentiate into endothelial cells [112]. Marin-Luevano et al. revealed that synthetic innate defense regulator-1018 (IDR-1018) can promote VEGF-165 (pro-angiogenic molecules) in a cultured human endothelial cell line and HaCaT cells and reduce hypoxia-induced transcription factor-1 (HIF-1) (anti-angiogenic molecules) to stimulate endothelial cell migration [113]. We previously showed that the increased expression of the angiogenesis inhibitor Thrombospondin-1 (TSP-1), is mediated by increased DNA hypomethylation at the promoter region of TSP-1 and increased oxidative stress in cultured human keratinocytes exposed to a high glucose environment. The administration of antioxidants can normalize TSP-1 expression and improve wound healing in diabetic rats [114]. These studies indicate the important role of oxidative stress-derived TSP-1 in defective angiogenesis in diabetic wounds. In addition, the impaired expression of VEGF in keratinocytes can also induce abnormal angiogenesis, leading to chronic diabetic wounds [115,116]. Multiple mechanisms are involved in impaired diabetic wound healing, as described above. Targeting these pathways and correcting the physiologic functions of keratinocytes may provide novel therapeutic methods to improve wound healing in diabetic patients. For example, our previous study revealed that the administration of a TNF-α inhibitor can significantly improve wound healing in diabetic rats, since increased TNF-α in the wound environment may impair keratinocyte migration [77]. The p38/MAPK pathway is known to be involved in influencing keratinocyte migration through different mechanisms. The increased expression of FOXO1 in a high glucose environment may impair re-epithelialization and angiogenesis, which are important steps in wound healing. Targeting the p38/MAPK pathway or inhibiting FOXO1 expression may be a potential adjunctive treatment for promoting diabetic wound healing. In addition, Kulkarni et al. found that topical esmolol hydrochloride (Galnobax) can improve wound healing in diabetes through pleiotropic mechanisms [117]. It can inhibit aldose reductase and the formation of sorbitol and AGEs which interfere with keratinocyte migration, induce autophagy, and modulate macrophage polarization. Esmolol hydrochloride can also induce NO production, promoting keratinocyte proliferation and angiogenesis, which is significantly reduced in diabetic wounds [118]. Moreover, it can reduce caspase-3 and upregulate B-cell lymphoma 2 (Bcl-2) to prevent necrosis of the wound bed in animal models [119]. Therefore, esmolol hydrochloride may be a new option for the treatment of diabetic ulcers. MicroRNAs (miRNAs) are endogenous noncoding small RNAs participating in cell proliferation, apoptosis, and cell differentiation through the regulation of gene and protein expression [120]. Etich et al. showed that the changing expression of miR-204 was noted during wound healing [121]. Further studies demonstrated that the overexpression of miR-204-3p in cultured human keratinocytes can increase the expression of TGF-β and Bcl-2 in a high glucose environment, promoting the proliferation and migration of keratinocytes via suppressing levels of Bax and cleaved caspase-3. These results show that the overexpression of miR-204-3p can improve the functional impairment of keratinocytes in a high glucose environment, and it may be a novel therapeutic target for the treatment of diabetic wounds in the future [122]. In recent years, nanotechnology-based diabetic foot ulcer therapies have been developed. Nanomaterials can not only deliver drugs or cytokines to the cells, but they can also remodel the microenvironment of diabetic wounds [123]. Yoon et al. used a chemokine-loaded hydrogel to promote angiogenesis, collagen deposition, and re-epithelialization [124]. Lipid nanoparticles implanted with recombinant human EGF can promote re-epithelialization through stimulating fibroblast and keratinocyte proliferation in animal models [125]. In addition to mechanism-based therapies, various clinical trials focused on cell-based products and cell-based therapies including allogeneic keratinocyte sheets [126], autologous fibroblasts and keratinocytes implants/grafts [127,128] have been developed in the treatment of diabetic wound healing. Cell therapy is a highly promising method for diabetic wound treatment, and it can correct the factors that lead to prolonged wound healing through various mechanisms [129]. Autologous and allogeneic keratinocytes transplanted to the wound can improve wound healing via increasing the expression of growth factors [130] and ECM proteins [131]. Although allogeneic keratinocytes cannot permanently remain in the wound, they can stimulate the migration and proliferation of native keratinocytes from the wound edges in chronic leg ulcers [132]. In addition, the topical application of keratinocyte sheets has shown its effectiveness in the treatment of diabetic wounds in patients [126,133]. Furthermore, mesenchymal stem cells (MSCs) have been shown to enhance angiogenesis in pre-clinical and clinical studies [134,135]. Paracrine signaling and the ability of stem cells to differentiate into specialized cells including fibroblasts, vascular endothelial cells, and keratinocytes contribute to promote angiogenesis, neovascularization, and re-epithelialization [136]. Intravenously injected MSCs can migrate to the acute wound area and differentiate into keratinocytes, endothelial cells, monocytes, and pericytes in mice [137]. Although several studies have shown that cell therapy is a potent tool for the treatment of chronic diabetic wounds, adverse effects have also been reported [138]. Major adverse events include pulmonary and renal thromboembolism, heart failure, and liver fibrosis [139,140]. Therefore, it is important to evaluate the effectiveness of treatment and patients’ safety under cell therapy. Keratinocytes play an important role in wound healing. A high glucose environment can change the gene and protein expression in keratinocytes, leading to prolonged inflammation, impaired proliferation, and the migration of keratinocytes and impaired angiogenesis during wound healing (Table 1). Elucidating the precise molecular dysfunction in keratinocytes will likely result in the development of effective and safe therapeutic approaches for optimal wound healing in patients with diabetes, as topical treatments are likely to succeed if treatment targets dysfunctional keratinocytes in the high glucose environment.

PMC10002072

Yiming Zhu,Lingtao Duan,Chengqi Zhu,Li Wang,Zhenrui He,Mei Yang,Erxun Zhou

Dual Transcriptome Analysis Reveals That ChATG8 Is Required for Fungal Development, Melanization and Pathogenicity during the Interaction between Colletotrichum higginsianum and Arabidopsis thaliana

22-02-2023

pathogen–host interaction,Colletotrichum higginsianum,ChATG8,ChTHR1,dual RNA-seq

Anthracnose disease of cruciferous plants caused by Colletotrichum higginsianum is a serious fungal disease that affects cruciferous crops such as Chinese cabbage, Chinese flowering cabbage, broccoli, mustard plant, as well as the model plant Arabidopsis thaliana. Dual transcriptome analysis is commonly used to identify the potential mechanisms of interaction between host and pathogen. In order to identify differentially expressed genes (DEGs) in both the pathogen and host, the conidia of wild-type (ChWT) and Chatg8 mutant (Chatg8Δ) strains were inoculated onto leaves of A. thaliana, and the infected leaves of A. thaliana at 8, 22, 40, and 60 h post-inoculation (hpi) were subjected to dual RNA-seq analysis. The results showed that comparison of gene expression between the ‘ChWT’ and ‘Chatg8Δ’ samples detected 900 DEGs (306 upregulated and 594 down-regulated) at 8 hpi, 692 DEGs (283 upregulated and 409 down-regulated) at 22 hpi, 496 DEGs (220 upregulated and 276 down-regulated) at 40 hpi, and 3159 DEGs (1544 upregulated and 1615 down-regulated) at 60 hpi. GO and KEGG analyses found that the DEGs were mainly involved in fungal development, biosynthesis of secondary metabolites, plant–fungal interactions, and phytohormone signaling. The regulatory network of key genes annotated in the Pathogen–Host Interactions database (PHI-base) and Plant Resistance Genes database (PRGdb), as well as a number of key genes highly correlated with the 8, 22, 40, and 60 hpi, were identified during the infection. Among the key genes, the most significant enrichment was in the gene encoding the trihydroxynaphthalene reductase (THR1) in the melanin biosynthesis pathway. Both Chatg8Δ and Chthr1Δ strains showed varying degrees of reduction of melanin in appressoria and colonies. The pathogenicity of the Chthr1Δ strain was lost. In addition, six DEGs from C. higginsianum and six DEGs from A. thaliana were selected for real-time quantitative PCR (RT-qPCR) to confirm the RNA-seq results. The information gathered from this study enriches the resources available for research into the role of the gene ChATG8 during the infection of A. thaliana by C. higginsianum, such as potential links between melanin biosynthesis and autophagy, and the response of A. thaliana to different fungal strains, thereby providing a theoretical basis for the breeding of cruciferous green leaf vegetable cultivars with resistance to anthracnose disease.

Dual Transcriptome Analysis Reveals That ChATG8 Is Required for Fungal Development, Melanization and Pathogenicity during the Interaction between Colletotrichum higginsianum and Arabidopsis thaliana Anthracnose disease of cruciferous plants caused by Colletotrichum higginsianum is a serious fungal disease that affects cruciferous crops such as Chinese cabbage, Chinese flowering cabbage, broccoli, mustard plant, as well as the model plant Arabidopsis thaliana. Dual transcriptome analysis is commonly used to identify the potential mechanisms of interaction between host and pathogen. In order to identify differentially expressed genes (DEGs) in both the pathogen and host, the conidia of wild-type (ChWT) and Chatg8 mutant (Chatg8Δ) strains were inoculated onto leaves of A. thaliana, and the infected leaves of A. thaliana at 8, 22, 40, and 60 h post-inoculation (hpi) were subjected to dual RNA-seq analysis. The results showed that comparison of gene expression between the ‘ChWT’ and ‘Chatg8Δ’ samples detected 900 DEGs (306 upregulated and 594 down-regulated) at 8 hpi, 692 DEGs (283 upregulated and 409 down-regulated) at 22 hpi, 496 DEGs (220 upregulated and 276 down-regulated) at 40 hpi, and 3159 DEGs (1544 upregulated and 1615 down-regulated) at 60 hpi. GO and KEGG analyses found that the DEGs were mainly involved in fungal development, biosynthesis of secondary metabolites, plant–fungal interactions, and phytohormone signaling. The regulatory network of key genes annotated in the Pathogen–Host Interactions database (PHI-base) and Plant Resistance Genes database (PRGdb), as well as a number of key genes highly correlated with the 8, 22, 40, and 60 hpi, were identified during the infection. Among the key genes, the most significant enrichment was in the gene encoding the trihydroxynaphthalene reductase (THR1) in the melanin biosynthesis pathway. Both Chatg8Δ and Chthr1Δ strains showed varying degrees of reduction of melanin in appressoria and colonies. The pathogenicity of the Chthr1Δ strain was lost. In addition, six DEGs from C. higginsianum and six DEGs from A. thaliana were selected for real-time quantitative PCR (RT-qPCR) to confirm the RNA-seq results. The information gathered from this study enriches the resources available for research into the role of the gene ChATG8 during the infection of A. thaliana by C. higginsianum, such as potential links between melanin biosynthesis and autophagy, and the response of A. thaliana to different fungal strains, thereby providing a theoretical basis for the breeding of cruciferous green leaf vegetable cultivars with resistance to anthracnose disease. Cruciferous plants include a wide number of species that are economically and nutritionally important all over the world. This category contains vegetables such as Chinese cabbage, Chinese flowering cabbage, cabbages, broccoli, radish, cauliflower, mustards, and also oilseeds such as rapeseed and canola [1]. When cultivated on a large scale, cruciferous horticulture crops are particularly vulnerable to pathogen invasion during production. Cruciferous anthracnose disease, caused by the filamentous fungus Colletotrichum higginsianum, is one of the most important threats to cruciferous crops. Moreover, most Arabidopsis ecotypes are also susceptible to C. higginsianum, so Colletotrichum–Arabidopsis interactions can serve as a model pathological system to help us gaining more insight into the interactions between pathogens and plants [2,3]. For C. higginsianum, the appressoria produced by the conidia are essential structures for the infection of the host plant. Conidia began to germinate when they land on the leaves of plant and form a mature appressorium, which penetrate plant epidermal cells by a penetration peg. Following this, bulbous hyphae are formed from the penetration peg within the initially infected epidermal cells. At this stage, which is called the “biotrophic infection phase”, infected cells remain normally plasmolysed, and the host plasmalemma and tonoplast remained functional. Then, the bulbous hyphae develop rapidly to produce narrow hyphae and neighboring cells are colonized. At this stage, narrow hyphae grow rapidly and eventually cause necrotic lesions that appear as water-soaked lesions on the surface of the infected host [2,3,4,5]. At high temperatures and during wet seasons, cruciferous anthracnose disease is common in fields [4]. Currently, the use of fungicides is the primary method to control cruciferous anthracnose. Continued exposure to fungicides, on the other hand, increases the risk of environmental contamination and pathogen resistance. Gaining a better comprehension of the defense mechanisms used by cruciferous plants in response to C. higginsianum infection, as well as the pathogenic mechanisms of C. higginsianum, will allow us to breed resistant cultivars of cruciferous crops, develop novel fungicides, and design new and safer control strategies for anthracnose diseases of cruciferous crops. Autophagy (ATG) is a conserved cytoprotective mechanism that facilitates the degradation of damaged or unwanted cellular components, which are referred to collectively as cargo. Atg8 protein plays key functions during macroautophagy, upon conjugation to double-membrane vesicles, termed autophagosomes. Finally, the autophagosomes sequester cargo for lysosomal/vacuolar degradation [6]. In many fungal pathogens, autophagy is important for pathogenicity and the gene ATG8 is also required for conidiation and pathogenicity [7]. For example, Pyricularia oryzae (=Magnaporthe oryzae) had varied degrees of loss or decrease in conidiation and pathogenicity when it lost its autophagic core gene, MoAtg8 [8,9,10]. CoATG8 has been shown to be involved in conidiation and pathogenicity in the genus Colletotrichum [11]. Recent studies have shown that ATG8s also function in single-membrane organelles in addition to their traditional roles resulting in significantly diverse degradative or secretory fates, vesicle maturation, and cargo identification. ATG8s are associated with many vesicles through complex regulatory processes that are not being completely understood [6,12]. Therefore, a better understanding of the role of Atg8 in the process of plant infection by pathogenic fungi is particularly important. High-throughput sequencing (HTS) technologies allow more detailed monitoring of molecular changes in plants under various stresses. Among them, RNA-seq has been widely used in plant–pathogen interaction studies in many agricultural crops such as citrus, apple, soybean, and tomato plants [13,14,15,16]. RNA-seq was also used to study interactions between A. thaliana and C. higginsianum [3,17]. Most of the studies mentioned above were restricted to a single transcription study of C. higginsianum, and it is still unclear how A. thaliana responds to the pathogen’s attack. Recently, dual RNA-seq technology, which simultaneously sequences and analyzes the gene expression profiles of two (or more) species by simply sharing the same cDNA library, has provided us with a powerful tool to study in vivo interactions between pathogenic fungi and their host plants [18], thus revealing dynamic changes in gene expression between two interacting species [19], and specifically identify genes associated with the dynamic expression profiles of host–pathogen interactions [20]. To date, the role of ChATG8 during the infection and counterattack response of A. thaliana against ChWT and Chatg8Δ mutant strains are unknown. The present study used dual RNA-seq analysis to investigate the changes in A. thaliana infected with ChWT and Chatg8Δ mutant strains. cDNA libraries were constructed and further analyzed for identified DEGs. The expression of fungal genes was also investigated at four infection stages (8, 22, 40, 60 hpi) to discover potential role of the ChATG8 gene in the infection process. Furthermore, RT-qPCR experiments were performed to validate the reliability of the dual RNA-seq data. Through this study, we hope to gain insight into the interaction between ChWT strain and A. thaliana, the potential pathogenic factors associated with ChAtg8 during pathogenesis, and the defense response of A. thaliana to help us better control cruciferous anthracnose. When the conidia of C. higginsianum land on the host surface, they begin to germinate and produce germ tubes by 8 hpi. Then, the appressorium that swells from the germ tube apex fully matures at 22 hpi, followed by the biotrophic infection phase at 40 hpi, and finally the necrotrophic infection phase at 60 hpi [2,3,4]. To investigate the role of autophagy in the infection process of C. higginsianum and the response of A. thaliana to the pathogen during this process, the conidial suspensions of WT and Chatg8Δ mutant strains were sprayed onto Arabidopsis plants (Figure 1A). The results showed that there were no disease lesions on Arabidopsis leaves inoculated with the WT conidial suspensions at 8 hpi and 22 hpi, but water-soaked, necrotic collapsed anthracnose lesions and yellowing symptoms could be seen on the infected leaves of Arabidopsis plants from 40 hpi to 60 hpi. In contrast, under the same conditions, the Chatg8Δ mutant hardly caused any necrotic lesions to the inoculated leaves at all time points (Figure 1B). According to the observations, four time points (8, 22, 40, and 60 hpi) were chosen as sampling time points for RNA-seq analysis so as to obtain sufficient transcripts of the gene ChATG8 in C. higginsianum and to investigate the dynamic transcript changes in both A. thaliana and the fungal strains. Leaves infected with WT and Chatg8Δ strains were sampled at four time points (ChWT-8 hpi-1/2/3, ChWT-22 hpi-1/2/3, ChWT-40 hpi-1/2/3, ChWT-60 hpi-1/2/3, Chatg8Δ-8 hpi-1/2/3, Chatg8Δ-22 hpi-1/2/3, Chatg8Δ-40 hpi-1/2/3, Chatg8Δ-60 hpi-1/2/3) with three biological replicates at each time point. Table S1 displays the summary statistics of raw reads and filtered clean reads at each time point for the three replicates. The 24 samples generated 1.90 billion raw reads and 1.89 billion filtered clean reads. Furthermore, at each time point, each sample contained an average of 11.8 Gb of clean data. According to the ratios of Q20 and Q30, which were higher than 97% and 92%, respectively, the quality of the sequencing data were sufficient for further study. After redundant deletion and species identification, de novo assembly was conducted using HISAT2. 2.4 software using paired-end methods, and 23,670 unigenes of A. thaliana and 13,677 unigenes of C. higginsianum were eventually generated and utilized as reference transcripts for further research. Principal component analysis (PCA) was carried out to analyze the relationships of biological replicates in the samples as well as differences between samples. The result of PCA in C. higginsianum indicated that the three biological replicates at each time point clustered closely, indicating acceptable variation within the replicates at each time point (Figure 2A). Furthermore, the fungal samples could be separated into four groups. Group ‘one’ contained two samples, ChWT-8 hpi and Chatg8Δ-8 hpi. This suggests that the fungal samples between WT and Chatg8Δ had a similar pattern of gene expression at 8 hpi. The Group ‘one’ samples also clustered far from the others which suggest that there were distinct patterns of gene expression. Group ‘two’ contained only the sample of ChWT-60 hpi. Group ‘three’ included the closely clustered samples of Chatg8Δ at 22 hpi, 40 hpi, and 60 hpi. Group ‘four’ consisted of the samples of ChWT at 22 hpi and 40 hpi (Figure 2A). In addition, hierarchical clustering (HCL) was implemented to assess the biological variability among all samples, and the results revealed a strong correlation between replicates of a single condition, and a clear separation between independent conditions (Figure 2B). Comparison of gene expression between the ‘ChWT’ and ‘Chatg8Δ’ sample series detected 900 DEGs (306 upregulated and 594 down-regulated) for ChWT-8 hpi vs. Chatg8Δ-8 hpi, 692 DEGs (283 upregulated and 409 down-regulated) for ChWT-22 hpi vs. Chatg8Δ-22 hpi, 496 DEGs (220 upregulated and 276 down-regulated) for ChWT-40 hpi vs. Chatg8Δ-40 hpi, and 3159 DEGs (1544 upregulated and 1615 down-regulated) for ChWT-60 hpi vs. Chatg8Δ-60 hpi (Figure 2C, Supplementary Data S1). The volcano plot shown in Figure 2D illustrates more details of the DEGs and the non-DEGs at each time point. For A. thaliana, the PCA and HCL were performed on the biological variability across all samples of A. thaliana. The results revealed that the three biological replicates at each time point clustered closely, indicating that the variation within the replicates at each time point and condition was acceptable (Figure 2E,F). The numbers of DEGs in A. thaliana at each time point are shown in Figure 2G. The results showed that the numbers of DEGs increased slowly at 8, 22, and 40 hpi for ChWT vs. Chatg8Δ. However, the DEGs increased sharply at 60 hpi, which indicates that A. thaliana differed very much in transcript levels at 60 hpi with ChWT and Chatg8Δ (Figure 2G,H, Supplementary Data S2). Furthermore, the phenotypic differences between A. thaliana plants inoculated with ChWT and Chatg8Δ was greatest at 60 hpi, as shown in Figure 1B. In short, the combined transcriptomic data from this series of pathogen and plant samples suggest that the transcriptional changes could support further studies. The numbers of DEGs in both the plant and pathogen samples reached the maximum at 60 hpi. To provide insights into the biological functions of the DEGs in the two fungal strains and A. thaliana, GO and KEGG enrichment analyses were performed. For the fungal strains, the GO terms of intracellular ribonucleoprotein complex, ribonucleoprotein complex, and ribosomal subunit in the cellular component terms, carboxylic acid biosynthetic process, organic acid biosynthetic process, and oxoacid metabolic process in the biological process terms, and structural molecule activity in molecular function terms were the most enriched terms in the ChWT-8 hpi vs. Chatg8Δ-8 hpi comparison group (Figure 3A). The 20 most enriched GO terms in the ChWT-20 hpi vs. Chatg8Δ-20 hpi comparison group are shown in Figure 3B. The cellular component terms of intrinsic component of membrane, membrane part, and membrane were the most enriched categories. In terms of molecular function, nucleotide binding, nucleoside phosphate binding, and transmembrane transporter activity are the most enriched categories. The single-organism process was the only biological process categories in the top 20 enriched GO terms. The 20 most significantly enriched GO terms in the ChWT-40 hpi vs. Chatg8Δ-40 hpi comparison group are showed in Figure 3C. The molecular functions of pattern binding, polysaccharide binding, and carbohydrate binding were the most enriched categories. The cellular component of intrinsic component of membrane, membrane part, and membrane were the most enriched categories. In terms of biological process, nucleophagy, membrane invagination, and lysosomal microautophagy were the most enriched categories. The DEGs in the ChWT-60 hpi vs. Chatg8Δ-60 hpi comparison group shown in Figure 3D were the most enriched in the cellular components of intracellular ribonucleoprotein complex, ribonucleoprotein complex, and preribosome, and the biological processes of purine ribonucleotide biosynthetic process, rRNA metabolic process, and purine nucleoside monophosphate metabolic process. For A. thaliana, the 20 most significantly enriched GO terms at 8 hpi, 22 hpi, 40 hpi and 60 hpi were concentrated under the category of molecular functions. Figure 3E shows that the DEGs in the ChWT-8 hpi vs. Chatg8Δ-8 hpi comparison group were most significantly enriched in response to oxygen-containing compound, response to wounding, and response to acid chemical. The DEGs in the ChWT-22 hpi vs. Chatg8Δ-22 hpi comparison group were most significantly enriched in response to organic substance, response to chemical, and response to stimulus. The DEGs in the ChWT-40 hpi vs. Chatg8Δ-40 hpi comparison group were most significantly enriched in response to chemical, response to stimulus, and response to oxygen-containing compound. At 60 hpi, necrotic lesions were already evident on the leaves of Arabidopsis inoculated with the ChWT strain, but not on plants inoculated with the Chatg8Δ strains. The GO terms of response to stimulus, response to chemical, and oxoacid metabolic process in the biological process terms, and plastid part, chloroplast part, and plastid in the cellular component terms were the most enriched terms. KEGG analysis assigned the DEGs in the ChWT vs. Chatg8Δ comparison groups from C. higginsianum to 371 pathways for all time points during the infection. Figure 4A shows the top 20 enriched pathways in the ChWT-8 hpi vs. Chatg8Δ-8 hpi comparison group. DEGs in this comparison group were most enriched in ribosome, glycerolipid metabolism, biosynthesis of secondary metabolites, 2-oxocarboxylic acid metabolism, and valine, leucine, and isoleucine biosynthesis. DEGs in the ChWT-22 hpi vs. Chatg8Δ-22 hpi comparison group were most enriched in ribosome, nitrogen metabolism, fatty acid degradation, alanine, aspartate, and glutamate metabolism, and glycerolipid metabolism (Figure 4B). DEGs in the ChWT-40 hpi vs. Chatg8Δ-40 hpi comparison group were most enriched in nitrogen metabolism, autophagy—yeast, autophagy—other eukaryotes, starch and sucrose metabolism, and methane metabolism (Figure 4C). DEGs in the ChWT-60 hpi vs. Chatg8Δ-60 hpi comparison group were most enriched in ribosome, ribosome biogenesis in eukaryotes, oxidative phosphorylation, one carbon pool by folate, and fructose and mannose metabolism (Figure 4D). For A. thaliana, KEGG analysis assigned the DEGs in the ChWT vs. Chatg8Δ comparison groups to 337 pathways for all the timepoints during the infection. The DEGs were primarily enriched in phenylpropanoid biosynthesis, biosynthesis of secondary metabolites, nitrogen metabolism, starch and sucrose metabolism, and tryptophan metabolism at 8 hpi (Figure 4E); photosynthesis-antenna proteins, phenylpropanoid biosynthesis, glutathione metabolism, plant hormone signal transduction, and biosynthesis of secondary metabolites at 22 hpi (Figure 4F); zeatin biosynthesis, glutathione metabolism, plant hormone signal transduction, biosynthesis of secondary metabolites, and phenylpropanoid biosynthesis at 40 hpi (Figure 4G); and biosynthesis of secondary metabolites, metabolic pathways, photosynthesis, carbon metabolism, and biosynthesis of amino acids at 60 hpi (Figure 4H). In the phenotypic experiments above, we found that the ChAtg8Δ mutant did not cause necrosis in the host plant A. thaliana. Based on the transcriptome analysis, we hoped to answer what happens at this stage for the plant. PRGdb is a bioinformatics platform for the investigation of plant resistance genes [21]. A total of 2639 genes in A. thaliana that were annotated by PRGdb were analyzed for expression profiles in the order of ChWT-8 hpi, ChWT-22 hpi, ChWT-40 hpi, and ChWT-60 hpi (Supplementary Data S3). Among them, 497 genes were significantly enriched in one of the four infection stages with up-regulated expression in profiles 10, 19, 16, 6, and 13 (Figure 5E). As illustrated in Figure 5F, 135 of the 497 key genes were significantly down-regulated in the ChWT-22 hpi vs. Chatg8Δ-22 hpi or ChWT-40 hpi vs. Chatg8Δ-40 hpi comparison groups. Next, these 135 genes were investigated by GO and KEGG enrichment analyses. In this gene set, almost all of for the genes were associated with biological processes related to plant immune-related and stress responses such as defense responses, response to stimulus, response to stress, response to chemical, and immune system processes (Figure 5G). The KEGG enrichment results showed that the 135 genes were found to be mainly associated with metabolism and signaling related pathways, such as starch and sucrose metabolism, cyanoamino acid metabolism, phenylpropanoid biosynthesis, and MAPK signaling pathway—plants (Figure 5H). These results imply that the Arabidopsis plants inoculated with conidia of the Chatg8Δ strains did not seem to have an immune response. The related hormonal pathways were schematically illustrated in Figure 6. Many phytohormones are involved and play crucial regulatory functions in plant–pathogen interactions, including abscisic acid (ABA), auxin (AUX), ethylene (ET), jasmonic acid (JA), brassionosteroid (BR), and salicylic acid (SA) [22]. The related DEGs of several hormone signaling pathways were analyzed in the infected leaves of Arabidopsis plants (Figure 6 and Table S2). Several DEGs involved in auxin signaling had differential expression, e.g., ARF, SAUR, and auxin-responsive GH3 homologues, which showed notably down-regulated expression. All DEGs in the SA signaling pathway were down-regulated during the whole process and two PR-1 homologs were significantly down-regulated at 22 hpi. The only AHP homolog in the cytokinine signaling pathway was down-regulated. The PP2C homolog involved in the ABA signaling pathway was down-regulated at 8 hpi and 60 hpi. ERF1/2 homologs known to be ET responsive were differentially expressed; one of them was down-regulated during the whole process and the others were down-regulated at 22 hpi and 60 hpi. Previous studies revealed that BR belongs to a distinct family of growth-promoting steroid hormones, which are known to be important regulators of plant immunity [23]. The TCH4 homolog involved in BR signaling cascades was significantly down-regulated at 22 hpi. Finally, the JAZ homolog in the JA signaling pathway was notably down-regulated at 60 hpi. PHI-base is a database that provides regulatory information on the molecules and biology of genes that have been shown to influence the outcome of host–pathogen interactions [24]. To identify the important avirulence/virulence factors of C. higginsianum, 3402 key genes were annotated by PHI-base and analyzed for expression profiles in the order of ChWT-8 hpi, ChWT-22 hpi, ChWT-40 hpi, and ChWT-60 hpi (Supplementary Data S4). A total of 1018 genes were significantly enriched in one of the four infection stages with up-regulated expression in profiles 16, 10, 18, 17, 13, and 19 (Figure 5A). As previously reported, virulence factors including effectors of C. higginsianum mainly accumulated at the biotrophic interfacial bodies and are secreted to the host cell from the biotrophic interfacial bodies [2]. The role of autophagy in the infection of C. higginsianum is unknown. To investigate the role of autophagy during the transition from the biotrophic infection phase to the necrotrophic infection phase, two comparison groups (ChWT-22 hpi vs. Chatg8Δ-22 hpi and ChWT-40 hpi vs. Chatg8Δ-40 hpi) were chosen. Compared to the ChWT, a total of 111 genes were significantly down-regulated, and 82 genes were significantly down-regulated in the ChWT-22 hpi vs. Chatg8Δ-22 hpi comparison group. Meanwhile, 71 genes were significantly down-regulated in the ChWT-40 hpi vs. Chatg8Δ-40 hpi comparison group, and 42 genes were significantly down-regulated in both two comparison groups (Figure 5B). To further study the biological functions of the above 111 genes, GO and KEGG enrichment analyses were performed. Peptidase activity, acting on L-amino acid peptides, pattern binding, and polysaccharide binding were the most enriched GO terms (Figure 5C). Furthermore, the involvement of a total of 20 KEGG pathways was demonstrated. Pathways with the highest DEG representation were for ‘biosynthesis of unsaturated fatty acids’, followed by ‘pentose and glucuronate interconversions’, ‘biotin metabolism’, and ‘phenylalanine metabolism’ (Figure 5D). The above findings demonstrated that during the infection, C. higginsianum stimulated a number of metabolic processes that produced energy and toxic metabolites to attack host cells. Additionally, the gene ChATG8 is highly related to these processes. The KEGG enrichment analysis of the 111 genes selected above revealed that the gene CH63R_08913 was most significantly enriched and was classified in the biosynthesis of unsaturated fatty acids pathway. The gene CH63R_08913 was expressed only at 22 hpi, an important time point for the melanization and maturation of C. higginsianum appressoria [3]. By gene sequence comparison, CH63R_08913 was found to encode a homolog of trihydroxynaphthalene reductase (THR1), which is an important enzyme in the melanin biosynthesis pathway in many fungi [25,26,27]. To investigate the relationship between the gene ChATG8 and the biosynthesis of melanin, conidia of WT and Chatg8Δ strains were observed, and the result showed that more than 80% conidia of WT strain germinated properly on a hydrophobic surface to form a single unbranched germ tube subtending a single appressorium (Type 1), but 42% conidia of the Chatg8Δ strains produced a slender, bifurcated germ tube without appressoria (Type 2), 23% conidia of Chatg8Δ strains produced two germs without appressoria at one end (Type 3), nearly 20% of conidia germinated and formed appressoria without melanin (Type 4), and nearly 18% of conidia did not germinate (Type 5) (Figure 7A). This result indicates that 80% of the conidia of the Chatg8Δ strains were unable to form an appressorium, while about 20% of the conidia could form an appressorium, but this appressorium could not accumulate melanin normally. Next, to verify whether the gene CH63R_08913 (ChTHR1) also plays a role as a trihydroxynaphthalene reductase in C. higginsianum, we knocked down the gene CH63R_08913 (ChTHR1) (Figure S1). As shown in Figure 7B, the appressorium of the Chthr1Δ strains also failed to accumulate melanin normally. Unlike the Chatg8Δ strains, most germination types of Chthr1Δ strains conidia were only type 4, with very low numbers of types 1, 2, 3, and 5. In addition, the colony morphology of the Chatg8Δ strains and Chthr1Δ strains were also altered to a greater extent. The colonies of the Chatg8Δ strains showed an orange color, and the colonies of Chthr1Δ strains were brown, but the melanin in their colonies was significantly lower than that of WT (Figure 7C). Similar to the Chatg8Δ strains, the Chthr1Δ strains also lost pathogenicity. These results suggest that the ChATG8 is not only involved in the pathway of melanin biosynthesis, but also affects the growth and development and pathogenicity of C. higginsianum in other ways. To ascertain the reliability of the generated dual RNA-seq data, the expression of 12 DEGs were analyzed using RT-qPCR assays, of which six were derived from C. higginsianum (CH63R_01708, CH63R_03174, CH63R_01918, CH63R_09049, CH63R_03670, CH63R_06437) and six were from A. thaliana (AT3G60120.1, AT5G50760.1, AT5G59220.1, AT1G64780.1, AT5G58310.1, AT4G35090.1) (Figure 8). Comparable up- or down-regulation expression patterns were seen between the RNA-seq and the RT-qPCR results. The RT-qPCR results indicated that these DEGs were in good agreement with the RNA-Seq results, showing a correlation coefficient (R2) > 0.8957 (Figure S2). Minor variations in expression levels can point to different sensitivity between the two approaches. Overall, the outcomes validated the accuracy of the RNA-seq data. To identify any dynamic changes in the plant tissue and gain a better understanding of the function of ChATG8 in host–pathogen interactions, dual RNA-seq of A. thaliana leaves infected by the ChWT and Chatg8Δ strains of C. higginsianum was performed. This study compared the gene expression of A. thaliana, infected by ChWT and Chatg8Δ strains, at four infection stages [3,28]. A total of 15055 and 23956 sequenced genes were identified in C. higginsianum and A. thaliana, respectively. The numbers of DEGs found in C. higginsianum and A. thaliana were 5247 and 8879, respectively. For the pathogen, the analysis of these DEGs focused on the effect of the gene ChATG8 on the pathogenicity of C. higginsianum during infection. For the host plant, the analysis of these DEGs focused on the changes in the immune response of A. thaliana to the infections of both ChWT and Chatg8Δ strains. For genus Colletotrichum and P. oryzae, the most important infection structures are appressoria, which are required for infection [29]. Research in the past few decades showed that several signaling pathways, such as the MAPK pathway, TOR pathway, and autophagy pathway, were involved in appressorium formation and invasive growth [7,30,31,32]. Our study showed that Tec1 located in the MAPK signal pathway induced by starvation was significantly down-regulated in the two comparison groups ChWT-8 hpi vs. Chatg8Δ-8 hpi and ChWT-22 hpi vs. Chatg8Δ-22 hpi. Slt2 involved in the MAPK pathway is induced by cell wall stress and was significantly down-regulated at 22 hpi and 40 hpi. In addition, many DEGs at the four time points were involved in the phosphatidylinositol signaling pathway, which implies an important underlying link between the phosphatidylinositol signaling pathway and the autophagic pathway during C. higginsianum infection of host plants. PHI-base contains molecular and biological information on genes that have been proven to affect the outcome of pathogen–host interactions [24]. We also aligned all the DEGs to the PHI database, and then the DEGs obtained from the alignment were subjected to expression pattern analysis, and finally the DEGs whose expressions were up-regulated at any of the four time points were selected for GO and KEGG analyses. It was hypothesized that the relationship between autophagy and these pathogenic factors would be discovered. For instance, the gene CH63R_08913 (THR1) was the most prominently enriched gene in the biosynthesis of unsaturated fatty acids in the KEGG pathway analysis. We proved that the gene CH63R_08913 (THR1), encoding a homolog of trihydroxynaphthalene reductase (THR1), was involved in the melanin biosynthesis pathway in C. higginsianum. By knocking out the gene ChTHR1, we found that the appressoria of Chthr1Δ were unable to accumulate melanin, which is also one of the phenotypes of the appressoria of Chatg8Δ. The accumulation of melanin in Chthr1Δ colonies was also lower than that of WT colonies. However, their colony colors were also different from those of Chatg8Δ; this may be due to the fact that the gene ChATG8 not only affected the melanin synthesis pathway, but also affected other color-related secondary metabolite pathways. In C. lagenarium, the mutant strain that lost the gene THR1 formed nonmelanized appressoria, and the mutant was unable to infect its host plant because the abnormal appressoria could not penetrate the host plant’s epidermal cells [33]. This is similar to the phenotype of the A. thaliana that were inoculated with Chatg8Δ and Chthr1Δ in our study. In melanocytes, the autophagy proteins Atg8 and Atg4B have been reported to mediate melanosome trafficking on the cytoskeletal tracks [34]. However, to date, there are no reports related to the involvement of autophagy or ATG8 in melanin biosynthesis in fungi. From the perspective of plants, phytohormones are critical in the developmental processes and signaling networks that regulate plant responses to various stresses [34]. Cell death and immune responses are regulated by ET signaling elements such EIN2, EIN3, EBF1/2, and ERF1/2 [35]. In response to diverse biotic and abiotic challenges, JA signaling is systemically activated, boosting the resistance of host plants to some pathogens [36]. Inducing defense and resistance in response to pathogen assaults is another critical function of SA [37]. In our study, 21 DEGs involved in the auxin, SA, cytokinine, ABA, ET, BR, and JA pathways were significantly down-regulated. Their interplay induced defense responses to C. higginsianum infection. The involvement and characteristics of DEGs in the intricate phytohormone signaling pathways suggest that these signals were not just simple linear and isolated cascades in response to C. higginsianum infection, but also collaborated with one another. To further determine the immune response of A. thaliana during different infection stages (8, 22, 40, and 60 hpi), all DEGs from A. thaliana were aligned to the phi-database, which is a bioinformatics platform for plant resistance gene analysis [21]. For example, the gene SBT3.3 (AT1G32960) of A. thaliana, encoding a serine protease homologous to the tomato P69C subtilase, was significantly up-regulated at 22 and 40 hpi. Previous studies found that the protein SBT3.3 may be involved in the process of pathogen recognition and activation of signaling pathways [38]. Two amidohydrolases IAR3 (AT1G51760) and ILL6 (AT1G51760), which play essential roles for proper JA–Ile homeostasis upon fungal attack, were also identified [39]. Moreover, the 135 key genes obtained by expression pattern analysis and PHI database alignment may be highly correlated with the response of Arabidopsis to pathogenic fungal infection. Currently, the role of ChAtg8 in the infection of A. thaliana by C. higginsianum is unclear. Previous studies showed that loss of the gene ATG8 results in the inability of the pathogenic fungus to successfully penetrate plant epidermal cells [29], and our study also indicated the same result. For C. higginsianum, by screening secondary metabolite genes and candidate pathogenesis-related genes, we confirmed the involvement of the gene ChATG8 and autophagy in melanin biosynthesis, and provided a reference for subsequent studies. For A. thaliana, we screened for key genes that may be relevant to its disease resistance and contribute to our deeper understanding of the pathogen–plant interaction mechanism. In this study, a powerful methodology for dual transcriptome analysis of host plants and pathogenic fungi was designed to establish the basis for a comprehensive study of the pathogenicity-related genes and pathogenesis of cruciferous anthracnose. In all pathogenicity assays, the ecotype Col-0 of A. thaliana was proposed as the susceptible line. Arabidopsis plants were cultivated in growth chambers and four-week-old seedlings were used for inoculation assays. The genome-sequenced C. higginsianum strain IMI349063 [3] was kindly contributed by Prof. Junbin Huang from Huazhong Agricultural University (Wuhan, China). C. higginsianum strains were incubated on potato dextrose agar (PDA, potato 200 g/L, dextrose 20 g/L, agar 20 g/L) medium at 27 °C. For artificial inoculation, strains were first incubated on PDA in dark conditions in a 27 °C incubator for 7 d before conidia were harvested from the fungal colony of PDA plates and suspended in 5 mL of sterile distilled water. Conidial suspensions were checked with a hemocytometer and diluted to a final concentration of 1 × 105 conidia/mL with sterile distilled water. Subsequently, the leaves of A. thaliana plants in a pot were uniformly sprayed with 10 mL of the conidial suspension. The inoculated plants were placed in a 12 h/12 h light/dark dew chamber at 27 °C with almost 100% relative humidity and all samples of inoculated leaves were harvested at 8, 22, 40, and 60 hpi (three independent biological replicates for the two treatments of leaves infected by ChWT and Chatg8Δ strains), and immediately frozen in liquid nitrogen and stored at −80 °C until further use. Fungal genomic DNA was extracted using the Ezup Column Fungi Genomic DNA Purification Kit (Sangon Biotech, Shanghai, China). PCR amplification was performed using Phanta Max Super-Fidelity DNA Polymerase (Vazyme Biotech, Nanjing, China). The Universal UNlQ-10 Column DNA Purification Kit (Sangon Biotech, Shanghai, China) was used to purify the PCR product. In the Southern blot assay, NEB (MA, USA) restriction enzymes Bsu36I and EcoRI were used for digestion of genomic DNA. The DIG-High Prime DNA Labeling and Detection Starter Kit I (Roche, IN, USA) was used for probe labeling. Amersham Hybond TM-N (GE Healthcare, WI, USA) membrane was used for blotting. NBT/BCIP Stock Solution (Roche, IN, USA) was used as the probe for protein detection. To replacement of ChTHR1 gene by the HPH1 (hygromycin phosphotransferase) gene follows the strategy described in [40,41] (Figure S1). Fragments approximately 1500 bp in size of the upstream and downstream sequences flanking the gene were amplified with primers THR1upF/THR1upR and THR1dsF/THR1dsR (Table S3). The Agrobacterium Transfer-DNA vector pFGL821 was digested with HindIII (NEB, MA, USA) and EcoRI (NEB, MA, USA), and then the upstream and downstream PCR products were fused into this vector to constitute the ChTHR1 knockout vector, named p821-ChTHR1KO. The construct was transferred into Agrobacterium tumefaciens strains AGL1, then transformed into the WT strains using Agrobacterium tumefaciens-mediated transformation (ATMT). After transforming the replacement vector into WT strains, hygromycin-resistant transformants were isolated and tested for resistance to hygromycin and confirmed by Southern blotting (Figure S1) [42]. Total RNA extraction and quality assessment, cDNA libraries preparation, data assembly, sequence alignment of reference genomes, and unigene annotation were performed by Gene Denovo Biotechnology Co. (Guangzhou, China). Following the manufacturer’s instructions, total RNA was extracted from each sample using Trizol reagent (Invitrogen, MA, USA). The cDNA libraries were sequenced using an Illumina NovaSeq™ 4000 instrument that generated paired-end reads lengths of 200 bp, and the clean reads were aligned to the C. higginsianum or A. thaliana genome assembly with HISAT2 [43]. Stringtie was used to reconstruct the transcripts and calculate the expression of all genes in each sample, presented as FPKM value (Supplementary Datas S1 and S2) [44]. DESeq2 [45] was used to identify DEGs with an FDR (false discovery rate) < 0.05 and |log2FC| > 1. Then, DEGs were analyzed for enrichment of Gene Ontology (GO, http://www.geneontology.org/, accessed on 15 Octorber 2022) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/, accessed on 15 Octorber 2022) pathways. Key genes from C. higginsianum were annotated by the PHI-base (Pathogen–Host Interactions database, www.phibase.org/, accessed on 2 September 2022) (Supplementary Data S4), which incorporates molecular and biological information on genes proven to influence the outcome of pathogen–host interactions [24]. Key genes from A. thaliana were annotated by the PRGdb (Plant Resistance Genes database, www.prgdb.org/prgdb4/, accessed on 2 September 2022) (Supplementary Data S3), which is a bioinformatics platform for the analysis of plant resistance genes [21]. The RNA-seq data were confirmed by selecting 12 DEGs and measuring their expression levels by RT-qPCR. Following the manufacturer’s instructions, cDNAs were synthesized from total RNA (1 μg) using a TransScript® One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China) in a reaction containing Anchored Oligo(dT)18 Primer, 2 × TS Reaction Mix, TransScript® RT/RI Enzyme Mix, gDNA Remover, RNase-free Water, and total RNA. The ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) was used to perform RT-qPCR on a Bio-Rad CFX96 Real-Time PCR System according to the manufacturer’s instructions. The PCR reactions were performed in a total volume of 20 μL including 10 μL 2 × ChamQ Universal SYBR qPCR Master Mix, 0.4 μL Primer-F (10 µM), 0.4 μL Primer-R (10 µM), 6 μL cDNA (50 ng/μL), and 3.2 μL ddH2O. The RT-qPCR program included an initial denaturation step at 95 °C for 30 s, followed by 40 cycles of 10 s at 95 °C and 30 s at 60 °C. The expression levels were normalized to the expression of the reference genes ChACT (CH63R_04240) [42] and TUB2 (NM_125664.4) [46]. The relative expression levels of the genes were calculated with the formula 2−ΔΔCt. The expression levels of each gene were expressed as a ratio relative to the stages of infection (ChWT-8 hpi), which was set as 1. All primers used in this study are listed in Table S3. In this study, the leaf samples of A. thaliana inoculated with ChWT and Chatg8Δ strains at four infection stages, i.e., 8 hpi (germ tube emergence), 22 hpi (appressorial matured), 40 hpi (biotrophic infection phase), and 60 hpi (necrotrophic infection phase), were used for dual RNA-seq analysis. A total of 900, 692, 496, and 3149 DEGs of C. higginsianum and 285, 575, 971, and 7048 DEGs of A. thaliana were identified in the ChWT vs. Chatg8Δ-8, 22, 40, 60 hpi comparison groups, respectively. During Arabidopsis infection, a series of key genes highly correlated at the 8, 22, 40, or 60 hpi timepoints and annotated in PRGdb or PHI-base were identified. Highly correlated genes were identified at 60 hpi, expanding our understanding of the role of the autophagy-related gene ChATG8 in C. higginsianum and the changes in A. thaliana inoculated with ChWT or Chatg8Δ mutants. We found that ChAtg8 affects ChThr1 and is involved in melanin biosynthesis. Additionally, six DEGs each from C. higginsianum and A. thaliana were selected for RT-qPCR assays to validate the output of the RNA-seq. In summary, this work enriches the resources available for research into the role of ChATG8 during A. thaliana infection and the response of A. thaliana to different fungal strains, thereby providing a theoretical basis for the breeding of resistant cultivars of cruciferous crops, and to develop novel fungicides as well as to design new and safer control strategies for anthracnose diseases of cruciferous crops.

PMC10002073

Jung Woo Eun,Hye Ri Ahn,Geum Ok Baek,Moon Gyeong Yoon,Ju A Son,Ji Hyang Weon,Jung Hwan Yoon,Hyung Seok Kim,Ji Eun Han,Soon Sun Kim,Jae Youn Cheong,Bong-wan Kim,Hyo Jung Cho

Aberrantly Expressed MicroRNAs in Cancer-Associated Fibroblasts and Their Target Oncogenic Signatures in Hepatocellular Carcinoma

21-02-2023

hepatocellular carcinoma,cancer-associated fibroblast,hsa-microRNA-101-3p,hsa-microRNA-490-3p,TGFBR1

Cancer-associated fibroblasts (CAFs) contribute to tumor progression, and microRNAs (miRs) play an important role in regulating the tumor-promoting properties of CAFs. The objectives of this study were to clarify the specific miR expression profile in CAFs of hepatocellular carcinoma (HCC) and identify its target gene signatures. Small-RNA-sequencing data were generated from nine pairs of CAFs and para-cancer fibroblasts isolated from human HCC and para-tumor tissues, respectively. Bioinformatic analyses were performed to identify the HCC-CAF-specific miR expression profile and the target gene signatures of the deregulated miRs in CAFs. Clinical and immunological implications of the target gene signatures were evaluated in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA_LIHC) database using Cox regression and TIMER analysis. The expressions of hsa-miR-101-3p and hsa-miR-490-3p were significantly downregulated in HCC-CAFs. Their expression in HCC tissue gradually decreased as HCC stage progressed in the clinical staging analysis. Bioinformatic network analysis using miRWalks, miRDB, and miRTarBase databases pointed to TGFBR1 as a common target gene of hsa-miR-101-3p and hsa-miR-490-3p. TGFBR1 expression was negatively correlated with miR-101-3p and miR-490-3p expression in HCC tissues and was also decreased by ectopic miR-101-3p and miR-490-3p expression. HCC patients with TGFBR1 overexpression and downregulated hsa-miR-101-3p and hsa-miR-490-3p demonstrated a significantly poorer prognosis in TCGA_LIHC. TGFBR1 expression was positively correlated with the infiltration of myeloid-derived suppressor cells, regulatory T cells, and M2 macrophages in a TIMER analysis. In conclusion, hsa-miR-101-3p and hsa-miR-490-3p were substantially downregulated miRs in CAFs of HCC, and their common target gene was TGFBR1. The downregulation of hsa-miR-101-3p and hsa-miR-490-3p, as well as high TGFBR1 expression, was associated with poor clinical outcome in HCC patients. In addition, TGFBR1 expression was correlated with the infiltration of immunosuppressive immune cells.

Aberrantly Expressed MicroRNAs in Cancer-Associated Fibroblasts and Their Target Oncogenic Signatures in Hepatocellular Carcinoma Cancer-associated fibroblasts (CAFs) contribute to tumor progression, and microRNAs (miRs) play an important role in regulating the tumor-promoting properties of CAFs. The objectives of this study were to clarify the specific miR expression profile in CAFs of hepatocellular carcinoma (HCC) and identify its target gene signatures. Small-RNA-sequencing data were generated from nine pairs of CAFs and para-cancer fibroblasts isolated from human HCC and para-tumor tissues, respectively. Bioinformatic analyses were performed to identify the HCC-CAF-specific miR expression profile and the target gene signatures of the deregulated miRs in CAFs. Clinical and immunological implications of the target gene signatures were evaluated in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA_LIHC) database using Cox regression and TIMER analysis. The expressions of hsa-miR-101-3p and hsa-miR-490-3p were significantly downregulated in HCC-CAFs. Their expression in HCC tissue gradually decreased as HCC stage progressed in the clinical staging analysis. Bioinformatic network analysis using miRWalks, miRDB, and miRTarBase databases pointed to TGFBR1 as a common target gene of hsa-miR-101-3p and hsa-miR-490-3p. TGFBR1 expression was negatively correlated with miR-101-3p and miR-490-3p expression in HCC tissues and was also decreased by ectopic miR-101-3p and miR-490-3p expression. HCC patients with TGFBR1 overexpression and downregulated hsa-miR-101-3p and hsa-miR-490-3p demonstrated a significantly poorer prognosis in TCGA_LIHC. TGFBR1 expression was positively correlated with the infiltration of myeloid-derived suppressor cells, regulatory T cells, and M2 macrophages in a TIMER analysis. In conclusion, hsa-miR-101-3p and hsa-miR-490-3p were substantially downregulated miRs in CAFs of HCC, and their common target gene was TGFBR1. The downregulation of hsa-miR-101-3p and hsa-miR-490-3p, as well as high TGFBR1 expression, was associated with poor clinical outcome in HCC patients. In addition, TGFBR1 expression was correlated with the infiltration of immunosuppressive immune cells. Hepatocellular carcinoma (HCC) is the fifth most common cancer and fourth leading cause of cancer-related mortality worldwide [1]. Although significant developments in therapeutic strategies have been made in the last 20 years, the long-term survival of HCC patients remains unsatisfactory. The development of novel therapeutic strategies based on an in-depth understanding of the molecular features of HCC is required to improve the prognosis of HCC patients. The tumor microenvironment (TME) is a highly complex and dynamic ecosystem consisting of tumor cells, cancer-associated fibroblasts (CAFs), and a variety of immune cells [2]. In recent years, most studies examining the TME have focused on better understanding the role of TME components to improve immunotherapy efficacy [3]. CAFs make up a major cell type in tumor stroma that produce an extracellular matrix [4]. CAFs contribute to tumor growth, angiogenesis, invasiveness, and metastasis, not only by directly regulating the aggressiveness of malignant cells, but also by indirectly promoting an immunosuppressive TME [5]. MicroRNAs (miRs) are small, non-coding RNAs (usually ~22 nucleotides) that play a key role in RNA silencing and regulating target gene expression [6]. In participating in tumor cell proliferation, differentiation, and metastasis, miRs can act as tumor suppressors or oncogenes by negatively regulating the expression of target mRNAs in nearly all cancer types, including HCC [7,8]. Accumulating evidence suggests that miRs are key players in regulating the tumor-promoting properties of CAFs; however, the role of miRs in CAFs of HCC (HCC-CAFs) remains poorly elucidated [9,10]. In the present study, to better understand the molecular mechanisms of HCC-CAFs, aberrantly expressed miR signatures in HCC-CAFs were evaluated using miR-sequencing data from primary cultured HCC-CAFs, para-cancer fibroblasts (PAFs), and normal fibroblasts (NFs). In addition, target gene signatures of the aberrantly expressed miRs in HCC-CAFs, as well as the clinical and immunological implications of these target genes, were evaluated using bioinformatic analyses. CAFs were isolated from HCC tissues, and PAFs were isolated from paired non-tumor tissues adjacent to the HCC (Figure 1a, middle and right panel). NFs were isolated and cultured from a normal liver tissue, acquired from a patient who did not have any chronic liver disease but had undergone surgical resection for a gradually growing benign tumor (Figure 1a, left panel). A flowchart of this study’s protocol is available in Figure 1b. The differential expression patterns of miRs in NFs, PAFs, and CAFs are presented in Figure 1c. While certain miRs were upregulated in CAFs compared to NFs and PAFs (Figure 1c, right), other miRs were relatively downregulated in CAFs (Figure 1c, left). Figure 1d shows a heatmap of 31 miRs that were significantly differently expressed in HCC-CAFs compared to NFs and PAFs, including 17 downregulated miRs (left panel) and 14 upregulated miRs (right panel). Integrative analyses were performed to identify the aberrantly expressed miRs showing clinical significance in HCC patients. Figure 1e displays a Venn diagram of differently expressed miRs between HCC-CAFs and The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA_LIHC) dataset. Among the 31 differently expressed miRs in HCC-CAFs, hsa-miR-101-3p and hsa-miR-490-3p were significantly downregulated in CAFs compared to PAFs, and their expression was also downregulated in tumor tissue compared to non-tumor tissue in TCGA_LIHC (Figure 1f). Furthermore, hsa-miR-95-3p was upregulated in CAFs, as well as in HCC tissue in TCGA_LIHC (Figure 1f). To evaluate the clinical significance of these three miRs in HCC progression, a clinical-stage analysis was performed using gene expression data from Catholic_LIHC and Tsinghua_LIHC. In this analysis, expression of hsa-miR-101-3p and hsa-miR-490-3p decreased significantly as HCC stage progressed in Catholic_LIHC, but hsa-miR-95-3p expression was not significant (Figure 1g). This expression pattern was also observed in the Tsinghua_LIHC dataset. The expression of hsa-miR-101-3p and hsa-miR-490-3p was lower in tumor and portal vein tumor thrombosis compared to normal liver tissue, while there was no significant difference in the expression of hsa-miR-95-3p (Figure 1h). In addition, the qRT-PCR analysis results also revealed that the paired CAFs exhibited the lowest levels of expression when compared to non-tumor tissues adjacent to HCC and tumor tissues from the same patient (Supplementary Figure S1). Thus, hsa-miR-101-3p and hsa-miR-490-3p were selected for further analysis, as their expression was associated with tumor progression, suggesting that they might have a central oncogenic role in HCC-CAFs. The target genes of hsa-miR-101-3p and hsa-miR-490-3p were screened using the ENCORI tool (https://starbase.sysu.edu.cn/ accessed on 12 January 2022) and CLIP-seq data. The candidate target genes were selected only when (1) they were identified as targets of the miRs by at least three of six ENCORI prediction tools and (2) they had at least one binding site for the miRs. As a result, a total of 1235 genes were selected as targets of hsa-miR-101-3p, while 352 genes were selected as targets of hsa-miR-490-3p (Figure 2a). To verify the association between the selected target gene signatures and miRs, miRTarBase, a representative target prediction tool, was used. The target gene signatures selected by the ENCORI tool and CLIP-seq data showed the closest correlation with hsa-miR-101-3p and hsa-miR-490-3p, respectively (Figure 2b). Gene Ontology analysis considering biological processes (BP; left), molecular functions (MF; middle), and cellular components (CC; right) was performed to elucidate the functional role of the identified target gene signatures (Figure 2c,d). The target genes of hsa-miR-101-3p were enriched in “coronary vasculature morphogenesis” for BP, “phosphatidylinositol monophosphate phosphatase activity” for MF, and “ISWI-type complex” for CC (Figure 2c). The target genes of hsa-miR-490-3p were enriched in “positive regulation of protein acetylation” for BP, “cAMP-dependent protein kinase activity” for MF, and “NSL complex” for CC (Figure 2d). Next, pathway enrichment analysis using KEGG 2021 Human and MSigDB Hallmark 2020 was performed. In analyses using KEGG 2021, the target genes of hsa-miR-101-3p were enriched in proteoglycans in cancer, while UV response was downregulated in MSigDB analyses (Figure 2e). Meanwhile, target genes of hsa-miR-490-3p were enriched in ferroptosis in KEGG, and in myc target V1 in MSigDB (Figure 2f). Common pathways included hedgehog signaling, TGF-beta signaling, and hypoxia, which are closely associated with hepatocarcinogenesis. The possible target gene network of hsa-miR-101-3p and hsa-miR-490-3p was predicted by using the miRWalks database (http://mirwalk.umm.uni-heidelberg.de/ accessed on 8 April 2022), miRDB, and miRTarBase. Genes were selected when they (1) exceeded a 0.95 score and bound to 3’UTR in miRWalks and (2) were predicted as bounding genes of hsa-miR-101-3p and hsa-miR-490-3p in miRDB and miRTarBase. As a result, GEN1, CLCC1, and SMARCD1 were predicted as target genes of hsa-miR-490-3p, and TRIB1, PSPC1, ACVR2B, GRSF1, MCL1, LMNB1, FBN2, LIFR, DCBLD2, and RAP1B were determined to be possible target genes of hsa-miR-101-3p. TGFBR1 was predicted as a common target gene of both hsa-miR-101-3p and hsa-miR-490-3p (Figure 3a). Expression of the 14 target gene signatures was significantly associated with the prognosis of HCC patients in the TCGA_LIHC database. Specifically, patients with higher expression of the target gene signatures had a significantly poorer prognosis (Figure 3b, left panel). Subgroup analysis was performed according to iCluster classification. iCluster involves integrative clusters classified by genomic, expression, and epigenetic data [11]. Interestingly, only in iCluster 1, which is known as an immune-low cluster, patients with higher expression of the target gene signatures had a significantly poorer prognosis, while there was no difference in iCluster 2/3 (Figure 3b, middle and right panel). In the enrichment analysis, the 14 target genes were highly enriched in TGF-beta signaling in both KEGG 2021 and MSigDB (Figure 3c). In CBioPortal analyses, alterations of the target gene signatures were related to the TGFB-SMAD and Activin-SMAD pathways, which were associated with cancer cell proliferation and stem/progenitor phenotypes (Figure 3d). These results suggest that the target genes of the CAF-related miRs contribute to HCC progression by activating the TGF-beta/SMAD pathway. We performed further analyses to evaluate whether the miRs regulated TGFBR1 expression. First, to determine the specific binding sites of hsa-miR-101-3p and hsa-miR-490-3p in 3′-untranslated regions (3′-UTR) of TGFBR1 mRNA, we analyzed the binding sites and context ++ scores with the TargetScan algorithm. This analysis result showed that both miRNAs bind to the specific sites of TGFBR1 3′-UTR and the absolute binding context scores indicates a high probability of the direct regulation of TGFBR1 by miRs (Figure 4a and Supplementary Table S1). Next, TGFBR1 expression in CAFs and PAFs were evaluated (Figure 4b). TGFBR1 was significantly upregulated in CAFs compared to PAFs. In correlation analyses, TGFBR1 expression was significantly inversely correlated with expression of hsa-miR-101-3p and hsa-miR-490-3p (Figure 4c). In the TCGA_LIHC database, TGFBR1 was upregulated in HCC tissue compared to non-tumor tissue (Figure 4d, left panel: comparison across entire TCGA_LIHC cohort; right panel: paired comparison of tumor and non-tumor tissue). TGFBR1 expression levels were also evaluated in several other studies in the Gene Expression Omnibus (GEO) and International Cancer Genomic Consortium (ICGC) databases. The expression of TGFBR1 in HCC tissues was generally upregulated compared with that in adjacent non-tumor tissues (Figure 4e). We next obtained the expression of TGFBR1 in HCC tissues from the Human Protein Atlas (HPA) database. TGFBR1 was mainly expressed in the cytoplasm/membrane and showed a positive expression of 54.5% in liver cancer tissues (Figure 4f). Next, correlation of TGFBR1 and expression of the miRs were evaluated in twenty pairs of surgically resected HCC tissues and corresponding non-tumor tissues from the Ajou University Hospital (Suwon, South Korea). TGFBR1 was found to be significantly upregulated in tumor tissue compared to non-tumor tissue in 18 of 20 patients (Figure 4g). In addition, TGFBR1 expression was inversely correlated with expression of hsa-miR-101-3p (p = 0.01), while there was no significant correlation between the expression of TGFBR1 and hsa-miR-490-3p (Figure 4h). To validate the regulatory effect of hsa-miR-101-3p on TGFBR1 expression, an hsa-miR-101-3p mimic was transfected into Huh-7 cells and TGFBR1 expression was measured using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Interestingly, when the hsa-miR-101-3p mimic was transfected, TGFBR1 expression was markedly lower. Similar results were also observed with the hsa-miR-490-3p mimic transfection; however, the negative regulatory effect on TGFBR1 expression was more potent when hsa-miR-101-3p was transfected (Figure 4i). The Western blot analysis results confirmed the decrease in TGFBR1 protein expression in HCC cells upon treatment with CAF-conditioned medium (CAF-CM) when the miR mimics were transfected. Additionally, the phosphorylation of Smad2, a downstream molecule of the TGF-beta signaling pathway, was significantly reduced in HCC cells when they were treated with CAF-CM and transfected with the miR mimics (Figure 4j). In the survival analysis, patients exhibiting higher expression of TGFBR1 had significantly poorer overall survival (OS) and progression free survival (PFS) in TCGA_LIHC (Figure 5a). In an analysis of hsa-miR-101-3p and hsa-miR-490-3p expression, patients with higher expression of both miRs demonstrated a significantly better prognosis in OS, disease free survival (DFS), and PFS (Figure 5b). Interestingly, combinations of TGFBR1 expression and expression of the two miRs demonstrated more potent prognostic implications on OS, DFS, and PFS than any one gene’s expression (Figure 5c). Patients with high expression of TGFBR1 and low expression of the two miRs demonstrated significantly poorer OS (Hazard ratio (HR) = 2.08, p = 0.022), DFS (HR = 2.63, p = 0.003), and PFS (HR = 2.44, p = 0.002) than patients with low TGFBR1 and high miR expression. The immunological implication of TGFBR1 expression was evaluated in TCGA datasets using a TIMER analysis (Figure 5d). As expected, TGFBR1 expression was highly correlated with CAF infiltration. TGFBR1 expression was positively correlated with the infiltration of M2 macrophages, regulatory T cells, and myeloid-derived suppressor cells (MDSCs), and negatively correlated with the infiltration of CD8+ T cells. Further, we also evaluated the correlation between TGFBR1 expression and regulatory T-cell markers (ENTPD1 and CCR8), M2 macrophage markers (PPARD and STAT3), and MDSC markers (LOX and CD83). Interestingly, TGFBR1 expression was significantly positively correlated with Treg, M2, and MDSC markers (Figure 5e). Accumulating evidence indicates that miRs are involved in carcinogenic transformation in the TME [12]. In particular, miRs have been shown to further the ability of CAFs to promote tumor progression [13,14]. However, the oncogenic role of miRs in HCC-CAFs remains poorly evaluated. In this study, hsa-miR-101-3p and hsa-miR-490-3p were identified as major downregulated miRs in HCC-CAFs, and lower expression of hsa-miR-101-3p and hsa-miR-490-3p was associated with a poor prognosis in HCC patients. Bioinformatic analyses revealed that TGFBR1 was a common target gene, and a validation study demonstrated that the expression of TGFBR1 was directly regulated by hsa-miR-101-3p and hsa-miR-490-3p in HCC. Both hsa-miR-101-3p and miR-490-3p are known as tumor suppressors in many cancers. Aberrant expression of miR-101-3p in CAFs has been reported in lung cancer and breast cancer [15,16]. Guo et al. [15] demonstrated that CAFs promote migration and invasion of cancer cells via miR-101-3p-mediated VEGFA secretion in non-small cell lung cancer. In a study of HCC, Yang et al. [17] reported that CAF-derived TGF-β and SDF1 promote vascular mimicry formation, which was reversed by miR-101. Several prior studies reported that miR-490-3p inhibited migration, invasion, and epithelial–mesenchymal transition of cancer cells by suppressing TGFβR1 expression in colorectal and ovarian cancer [18,19]. In the present study, expression of hsa-miR-101-3p and hsa-miR-490-3p was consistently and significantly downregulated in HCC-CAFs and significantly associated with poor OS. In addition, overexpression of TGFBR1, which was identified as a common target gene of hsa-miR-101-3p and hsa-miR-490-3p, was associated with a poor prognosis in HCC patients. In the same context, previous studies have demonstrated that TGFBR1 acts as a potent modifier of cancer risk, and TGFBR1 overexpression has been associated with cancer cell aggressiveness and poor clinical outcomes in many malignancies [20,21,22,23,24]. TGFBR1 is associated with the TGF-β /SMAD pathway, as demonstrated in Figure 3d [25]. The TGF-β signaling pathway contributes to HCC progression and is known as one of the major oncogenic pathways of CAFs [26,27]. iCluster performs HCC subtyping based on multi-omics technology, including evaluations of DNA copy number and methylation, as well as mRNA, microRNA, and protein arrays, proposed by the TCGA research network [28]. iCluster 1, known as the immune-low cluster, is characterized by a high tumor grade and the presence of macrovascular invasion with significantly worse prognosis [11,29]. The target gene signatures of hsa-miR-101-3p and hsa-miR-490-3p showed significant prognostic implications in iCluster 1. It suggests that aberrant expression of hsa-miR-101-3p and hsa-miR-490-3p in CAFs may play a specific role in iCluster 1 by regulating target gene expression. Further, this role may relate to creating immune-suppressive TMEs. Thus, we evaluated the immunological implication of TGFBR1, which is the common target gene of the two miRs. TGF-β signaling plays a central role in enabling tumor immune evasion, and recent studies have reported that it is associated with poor responses to cancer immunotherapy. The present study revealed that TGFBR1 had a consistent, positive correlation with the infiltration of MDSCs, Treg cells, and M2 macrophages, which are known as key players in promoting an immune-suppressive TME [30]. We also showed that TGFBR1 expression was negatively correlated with CD8+ T-cell infiltration. This study has several limitations. First, although CAF-specific dysregulated miR signatures were identified in this study, it is difficult to say that these findings are representative of all HCC-CAFs, as the number of included CAF and PAF pairs is only nine. Second, several recent studies have revealed the heterogeneity of the CAF population through single-cell RNA-sequencing (scRNA-seq) [31,32], but this study was based on bulk RNA-sequencing data and did not reflect the heterogeneity of HCC-CAFs. Third, although this study revealed the dysregulated profile of miRs and their target gene signatures in HCC-CAFs through bioinformatic analysis, with attempts to demonstrate its clinical and immunological implications, these results are inferences based on analytical methods. Additional in vitro and in vivo study is required to validate these results. Fourth, only one biological sample of NF was included in this study. Acquiring normal liver tissue for NF primary culture was very difficult, because most of patients with benign liver tumor followed up without surgical resection. Fifth, although we demonstrated significant downregulation of hsa-miR-101-3p and hsa-miR-490-3p in CAFs compared to their paired tumor and non-tumor tissue (Supporting Figure 1), selective downregulation of these miRs in CAFs compared to the other cells in the tumor microenvironment could not be evaluated in the present study. To accurately demonstrate the selective downregulation of these miRs in CAFs, the use of scRNA-seq would be ideal. However, there are currently no studies that have analyzed miRNA expression in HCC tissue using scRNA-seq. The Biobank of Ajou University Hospital, a member of the Korea Biobank Network, provided HCC tissues, corresponding adjacent para-tumor tissues, and a normal liver tissue used in this study. All experiments were performed according to the Declaration of Helsinki and the study protocol was approved by the Institutional Review Board of Ajou University Hospital (approval no. AJIRB-BMR-SMP-17-188; 28 July 2017). HCC tissues and paired para-tumor tissues were collected from HCC patients who underwent surgical resection at Ajou University Hospital (Suwon, South Korea) between 2017 and 2019. Fresh liver tissues were washed with phosphate-buffered saline (GenDEPOT, Barker, TX, USA) and finely minced into small fragments (<1 mm3). Then, the tissue fragments were placed in a culture dish and incubated in fresh culture medium with a cover slip to promote fibroblast attachment. Isolated fibroblasts were maintained in Dulbecco’s modified Eagle’s medium (DMEM, GenDEPOT) containing 10% fetal bovine serum (FBS; Invitrogen, Waltham, MA, USA) and 100 units/mL penicillin–streptomycin (GenDEPOT) and kept at 37 °C in a humidified incubator with 5% CO2. Small RNA libraries were constructed from total RNA using the Illumina HiSeq 2000 system (Illumina Inc., San Diego, CA, USA). After small-RNA-sequencing, the cutadapt program was used to remove adapters and low-quality sequences, trimming reads to 18~26 bp in length considering the length of mature miR. Then, the trimmed reads were collapsed to remove duplicates and estimate sequence abundance and annotated using BLAST with miRBase. To enable comparisons between samples, counts of each sample were normalized in units of transcripts per million. To assess the expression level of miRs and candidate target genes in HCC patients, RNA-sequencing data were obtained from TCGA_LIHC, ICGC, and the GEO databases from the National Center for Biotechnology Information (NCBI) projects: GSE114564; Catholic_LIHC, GSE76903; Tsinghua_LIHC, GSE22058, GSE14520, GSE54236, GSE64041, and GSE76427. To investigate the gene candidates targeted by hsa-miR-101-3p and hsa-miR-490-3p, we used the Encyclopedia of RNA Interactomes (ENCORI, http://starbase.sysu.edu.cn/index.php) tool (accessed on 12 January 2022). ENCORI identifies miR–target gene interactions based on miR target prediction programs and supports the published Argonaute-crosslinking and immunoprecipitation (AGO-CLIP) data for miR target predictions. To predict the specific binding probability scores and sites of hsa-miR-101-3p and hsa-miR-490-3p, we used TargetScan (https://www.targetscan.org/ accessed on 18 January 2022). To identify the biological functions and molecular pathways related to the target candidates of hsa-miR-101-3p and hsa-miR-490-3p, Gene Ontology (GO), KEGG 2021 Human, and MSidDB Hallmark 2020 databases were used in Enrichr (https://maayanlab.cloud/Enrichr/ accessed on 5 April 2022). A p-value < 0.05 was defined as significant in both GO and pathway enrichment analyses. miRWalk (version 3.0, http://mirwalk.umm.uni-heidelberg.de/ accessed on 8 April 2022) was used to predict the network between hub-target genes and two CAF-related miRs. In the miRWalk platform, for each miR, we considered all experimentally validated targets reported by the miRTarBase tool and predicted targets identified by both TargetScan and miRDB tools. The relationship between the expression of 14 signatures and LIHC prognosis was analyzed through the Gene Expression Profiling and Interactive Analysis (GEPIA2) database. The GEPIA2 survival analysis tool was used to evaluate this relationship based on gene expression levels, and the Log rank test was applied for hypothesis testing. The expression of the 14 signatures was divided into high- and low-expression cohorts, with the median value of 50% used as the threshold in GEPIA2. Patients with expression levels above 50% were categorized as the high-expression cohort (high 14 signatures), while those with expression levels below 50% were categorized as the low-expression cohort (low 14 signatures). The Cox proportional hazard ratio and 95% confidence interval were included in the survival plots. To evaluate OS, DFS, and PFS for two miRNAs and TGFBR1, clinical data of liver hepatocellular carcinoma (TCGA, PanCancer Atlas) from cBioPortal (https://www.cbioportal.org/ accessed on 5 September 2022) were downloaded and analyzed. The levels of TFGBR1 were analyzed using the median value as the threshold and the combination of two miRNAs was categorized as low/high based on the median value of each individual miRNA. If both miRNAs were found to be high, they were grouped as “2miR_High,” and if both miRNAs were found to be low, they were grouped as “2miR_Low” and analyzed. In cases where the TGFBR1 and two miRNAs showed contrasting results, patients with high TGFBR1 and low 2 miRNAs were designated as “TGFBR1_High & 2 miR_Low,” and patients with low TGFBR1 and high 2 miRNAs were designated as “TGFBR1_Low & 2 miR_High” and analyzed accordingly. The Human Protein Atlas (https://www.proteinatlas.org accessed on 20 September 2022) was used to analyze the TGFBR1 protein expression level in human HCC tissues. The HPA is a Swedish program initiated in 2003 that aims to map all human proteins in cells, tissues, and organs by integrating various omics technologies, including antibody-based imaging. The representative immunohistochemistry pictures were downloaded from the Tissue Atlas and Pathology Atlas in the HPA. CAF cells were derived from patient hepatocellular carcinoma tissues. CAF cells were cultured in DMEM with 10% FBS and Huh-7 cells (Korean Cell Line Bank, Seoul, South Korea) were cultured in RPMI-1640 (Sigma-Aldrich) containing 10%; both CAF and Huh-7 cells contained 100 units/mL penicillin-streptomycin (GenDEPOT), and were kept at 37 °C in a humidified incubator with 5% CO2. To analyze the effects of secreted factors from CAFs on tumor cells, CAFs were cultured with the respective media for 48 h. Cell-free conditioned media was collected and stored at −70 °C until used. Synthetic miR mimics (Genolution, Seoul, South Korea) or miR NC mimics (Bioneer, Daejeon, South Korea) were transfected into Huh-7 cells using Lipofectamine 2000 (Invitrogen), according to the manufacturer’s instructions. We transfected 3 × 105 Huh-7 cells treated with or without CAF-CM with the miR mimic and cultured for 48 h. Thereafter, the whole-cell extracts were prepared from Huh-7 cells treated with or without CAF-CM with the miR mimic. Cells were harvested, washed with ice-cold phosphate-buffered saline (PBS), and lysed in RIPA buffer (10 mM Tris (pH 7.2), 150 mM NaCl, 1% Nonidet P-40, 0.5% sodium deoxycholate, 0.1% SDS, 1.0% Triton X-100, and 5 mM EDTA) supplemented with protease inhibitors for 30 min on ice. In this study, the absence of mycoplasma in the cultures was confirmed (Supplementary Figure S2). QIAzol reagent (Qiagen, Hilden, Germany) was used to extract the total RNA from tissues and cells. cDNA was synthesized from 500 ng of RNA using the miScript RT II kit (Qiagen) or PrimeScript™ RT Master Mix (Takara Bio, Shiga, Japan), in accordance with the manufacturers’ instructions. qRT-PCR was performed using the amfiSure qGreen Q-PCR Master Mix (GenDEPOT) and monitored in real time using a CFX Connect Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA). The cycling conditions were as follows: 95 °C for 2 min, 40 cycles of 95 °C for 15 s, 58−62 °C for 34 s, and 72 °C for 30 s, followed by a dissociation stage at 95 °C for 10 s, 65 °C for 5 s, and 95 °C for 5 s. Relative expression levels were calculated using the 2−ΔΔCq method. Utilized primer sequences are listed in Table S2. All assays were performed in triplicate. Total cell lysates were separated by SDS-PAGE, transferred to polyvinylidene fluoride (PVDF) membranes (Merck Millipore, Burlington, MA, USA), and then subjected to immunoblot analysis. The antibodies used for immunoblotting were as follows: rabbit anti-TGFBR1 (1:1000; Abcam, Cambridge, MA, USA), rabbit anti-Smad2/3 (1:1000; Cell signaling, Danvers, MA, USA), rabbit anti-phospho-Smad2/3 (1:1000; Cell signaling), and mouse anti-GAPDH (1:1000; Santa Cruz Biotechnology, Santa Cruz, CA, USA). Chemiluminescence signals were detected using Clarity™ Western ECL Substrate and ChemiDoc (both from Bio-Rad Laboratories). The relative band density was quantified using ImageJ software version 1.49 (Laboratory for Optical and Computational Instrumentation, Madison, WI, USA). All experiments were performed at least three times and all samples were analyzed in triplicate. Between-group differences were analyzed using a paired t-test, unpaired Welch’s t-test, or two-way ANOVA with GraphPad Prism version 8.0 software (GraphPad Software Inc., San Diego, CA, USA). Differences were considered statistically significant when p < 0.05. In conclusion, hsa-miR-101-3p and hsa-miR-490-3p were downregulated in HCC-CAFs, and their common target gene was identified as TGFBR1. The downregulated hsa-miR-101-3p and hsa-miR-490-3p and upregulated TGFBR1 was associated with a poor clinical outcome in HCC patients. TGFBR1 expression was correlated with immunosuppressive immune cell infiltration, involving MDSCs, M2 macrophages, and Treg cells. This is the first study to analyze the aberrant expression of miRs in HCC-CAFs and their target gene signatures through bioinformatic analysis. The results of this study enhance scientific understanding of the molecular signatures of HCC-CAFs and may support further study of HCC therapeutics and biomarkers.

PMC10002075

Martina Sandonà,Giorgia Cavioli,Alessandra Renzini,Alessia Cedola,Giuseppe Gigli,Dario Coletti,Timothy A. McKinsey,Viviana Moresi,Valentina Saccone

Histone Deacetylases: Molecular Mechanisms and Therapeutic Implications for Muscular Dystrophies

21-02-2023

histone deacetylase,muscular dystrophies,Duchenne Muscular Dystrophy,clinical trials

Histone deacetylases (HDACs) are enzymes that regulate the deacetylation of numerous histone and non-histone proteins, thereby affecting a wide range of cellular processes. Deregulation of HDAC expression or activity is often associated with several pathologies, suggesting potential for targeting these enzymes for therapeutic purposes. For example, HDAC expression and activity are higher in dystrophic skeletal muscles. General pharmacological blockade of HDACs, by means of pan-HDAC inhibitors (HDACi), ameliorates both muscle histological abnormalities and function in preclinical studies. A phase II clinical trial of the pan-HDACi givinostat revealed partial histological improvement and functional recovery of Duchenne Muscular Dystrophy (DMD) muscles; results of an ongoing phase III clinical trial that is assessing the long-term safety and efficacy of givinostat in DMD patients are pending. Here we review the current knowledge about the HDAC functions in distinct cell types in skeletal muscle, identified by genetic and -omic approaches. We describe the signaling events that are affected by HDACs and contribute to muscular dystrophy pathogenesis by altering muscle regeneration and/or repair processes. Reviewing recent insights into HDAC cellular functions in dystrophic muscles provides new perspectives for the development of more effective therapeutic approaches based on drugs that target these critical enzymes.

Histone Deacetylases: Molecular Mechanisms and Therapeutic Implications for Muscular Dystrophies Histone deacetylases (HDACs) are enzymes that regulate the deacetylation of numerous histone and non-histone proteins, thereby affecting a wide range of cellular processes. Deregulation of HDAC expression or activity is often associated with several pathologies, suggesting potential for targeting these enzymes for therapeutic purposes. For example, HDAC expression and activity are higher in dystrophic skeletal muscles. General pharmacological blockade of HDACs, by means of pan-HDAC inhibitors (HDACi), ameliorates both muscle histological abnormalities and function in preclinical studies. A phase II clinical trial of the pan-HDACi givinostat revealed partial histological improvement and functional recovery of Duchenne Muscular Dystrophy (DMD) muscles; results of an ongoing phase III clinical trial that is assessing the long-term safety and efficacy of givinostat in DMD patients are pending. Here we review the current knowledge about the HDAC functions in distinct cell types in skeletal muscle, identified by genetic and -omic approaches. We describe the signaling events that are affected by HDACs and contribute to muscular dystrophy pathogenesis by altering muscle regeneration and/or repair processes. Reviewing recent insights into HDAC cellular functions in dystrophic muscles provides new perspectives for the development of more effective therapeutic approaches based on drugs that target these critical enzymes. Transcriptional regulation in eukaryotes is strongly influenced by post-translational modifications (PTMs) of histones, the core proteins of chromatin, such as phosphorylation, methylation, and acetylation. Histone acetylation is probably the most well-characterized of these modifications, with hyperacetylation leading to an increase in gene expression, due to the relaxation of chromatin structure, while hypoacetylation has the opposite effect. The latter is mediated by histone deacetylases (HDACs) [1]. By doing this, HDACs influence the delicate balance between euchromatin and heterochromatin, thereby widely affecting gene expression in a prolonged fashion [2]. Therefore, the balance between the levels of histone deacetylation and acetylation plays a key role in the modulation of gene transcription and governs numerous developmental processes, being involved in the regulation of various genes associated with signal transduction, cell growth, and cell death, as well as disease states, including fluid and electrolyte disorders or cancers [3,4]. In addition, HDACs deacetylate non-histone proteins, such as p53 [5,6] as one of the first identified HDAC targets, thus regulating their activity. The numerous HDACs have a wide range of expression and function in multiple cell types and tissues. In spite of the lack of complete knowledge of their roles, a global inhibition of deacetylase activity in the human body has been proposed as a therapeutical approach for various disease states, including muscle dystrophy. Before approaching this issue, it is therefore important to provide an overview of the HDAC family, focusing on the different roles HDACs play in striated muscle (Table 1). According to their sequence similarities with yeast orthologs and the use of either Zn2+ or NAD+ as cofactors [3,7,8], 18 human HDACs have been identified and grouped into four classes. Class I HDACs shows similarity to the yeast deacetylase Rpd3p enzyme and include HDAC1, 2, 3 and 8. They are Zn2+-dependent, ubiquitously expressed enzymes, which are localized prevalently in the nucleus, playing a key role in the lysine deacetylation of N-terminal histone tails [9]. They are essential regulators of gene expression, being recruited to specific chromatin loci as a part of multi-protein complexes that control the acetylation state of histones and other chromatin-associated factors [10], resulting in chromatin condensation and transcriptional silencing [11]. The best-studied complexes include the NuRD, Sin3 and CoREST complexes, which contain HDAC1/2, and the SMRT/NCoR complex, which contains HDAC3 [12,13,14]. Thanks to tissue-specific knock-out (KO) mouse models, it has been established that HDAC1 and 2 often play redundant functions in the development or homeostasis maintenance of numerous tissues and cell types. In the heart, HDAC1 and 2 repress genes encoding contractile proteins and calcium channels [15], while, in skeletal muscle, they control autophagic flux and muscle metabolism [16]. HDAC3 is required for normal mouse development and tissue-specific functions by epigenetically controlling metabolism and circadian rhythms [17]. Cell type- or tissue-specific deletion reveal a role of HDAC3 in cardiac development and cardiomyocyte metabolism, since its absence leads to severe underdevelopment of the ventricular walls and to ventricular septal defects [18,19,20]. Of note, some of these functions in cardiac development are independent of its deacetylase activity; rather HDAC3 regulates gene transcription by recruiting other epigenetic factors to the NCOR complex [19] or by tethering peripheral heterochromatin to the nuclear lamina [18]. As a demonstration of the importance of HDAC3 in the whole-body metabolism, the deletion of Hdac3 in skeletal muscle causes severe systemic and skeletal muscle-specific insulin resistance, impaired insulin and glucose tolerance, and diminished glucose uptake into skeletal muscle, overall impacting on muscle performance [21]. HDAC8 controls processes different from the other class I HDAC members and has a multifaceted role in human pathophysiology [22]. However, to date, no specific role in striated muscle has been reported. Class II HDACs are similar to the yeast Hda1 deacetylase enzyme. This class is further subdivided into class IIa (HDACs 4, 5, 7 and 9) and class IIb (HDACs 6 and 10). While class IIa HDACs localize both in the nucleus and in the cytoplasm, class IIb HDACs are primarily in the cytoplasm and contain two catalytic sites. Class IIa HDACs are characterized by an extended N-terminal domain, containing conserved serine (Ser) residues, which are subjected to phosphorylation by several kinases, such as CaMK or SIK [23,24,25], facilitating HDAC nuclear export. Moreover, because of a Tyr-to-His mutation in their catalytic pocket, class IIa HDACs possess very low deacetylase activity compared to class I and class IIb HDACs [26]. This finding implies that class IIa HDACs regulate gene transcription via acting as scaffold proteins to recruit class I HDACs to specific genes, or as tethering proteins to anchor chromatin regions, or via sterically block transcription factor activity. Among the members of class IIa, HDAC4 plays crucial functions in striated muscles. Increased expression of HDAC4 has been detected in skeletal muscle in different diseases, such as Duchenne Muscular Dystrophy (DMD) [27] and Amyotrophic Lateral Sclerosis (ALS) [28,29]: importantly, the observations in pre-clinical models have been validated in patients. Despite binding and repressing the activity of two major myogenic factors, i.e., MEF2 [30] and SRF [31], mice harboring a skeletal-muscle specific deletion of Hdac4 are viable and do not display obvious defects in skeletal muscle [32]. While class IIa HDACs play redundant roles in the establishment of the metabolic pattern of skeletal muscle fibers, by repressing MEF2 [33], HDAC4 per se is necessary and sufficient to mediate responses upon different stimuli in skeletal muscle. For instance, deletion of Hdac4 in differentiating skeletal muscle cells via myogenin:Cre recombinase, hampers muscle regeneration, due to the release of soluble factors that inhibit muscle precursor cell differentiation [34]; if the deletion of Hdac4 occurs earlier in the myogenic cells, such as in Pax7+ cells, it compromises muscle stem cell (MuSC) proliferation and differentiation [35]. Together with HDAC5, HDAC4 connects neural activity to skeletal muscle transcription upon denervation, via both epigenetic regulation of gene expression [36,37,38] and by modulating nuclear and cytoplasmic non-histone protein acetylation [39,40], thereby mediating neurogenic muscle atrophy. Interestingly, deletion of Hdac4 in skeletal muscle is protective in experimental models of neurogenic muscle atrophy in the early phases after the surgical procedure [37]; however, it resulted detrimental effects following long-term denervation, causing muscle degeneration due to the impairment in the activation of multiple signaling, including the oxidative stress response, the ubiquitin-proteasome system and the autophagic pathway [32]. Consistently, deletion of Hdac4 in skeletal muscle in a mouse model of ALS worsened pathological features, advancing and exacerbating skeletal muscle atrophy and denervation by modulating several biological processes and gene networks [29]. Similar to ALS, HDAC4 expression is upregulated in DMD skeletal muscles [27], and, consistently, deletion of Hdac4 results detrimental in both disease states. Indeed, mdx mice with a skeletal muscle-specific deletion of HDAC4 show increased muscle damage and hampered muscle regeneration, overall leading to decreased muscle function. HDAC4 prevalently localizes in the cytoplasm of dystrophic muscles, where it mediates activation of the membrane repair mechanism, likely through a deacetylase-independent activity, thereby affecting muscle necrosis, satellite cell survival and myogenic capacity [27]. Overall, these studies suggest that skeletal muscle up-regulates HDAC4 expression upon stress as a response to a disease state. In the heart, the N-terminal proteolytically derived fragment of HDAC4 finely regulates lipid metabolism and glucose handling through MEF2-dependent gene expression, ultimately protecting from heart failure [41,42]. HDAC5 acts as a negative epigenetic regulator of IL-6 synthesis and release in skeletal muscle, and Hdac5 global KO mice show improved systemic glucose tolerance in response to exercise [43]. Of note, a non-deacetylase-dependent regulatory role of HDAC5 has been reported in cardiac cells. By using Hdac5 global KO mice, it has been illustrated that HDAC5 is required for the interaction of the class I HDAC/Sin3 co-repressor complex with the Nkx2.5 and YY1 transcription factors and the consequent recruitment of the complex to promoter regions of either the Ncx1 or Bnp gene, which are important for cardiac hypertrophy [44]. HDAC9 is highly expressed in cardiac muscle even though it does not affect heart development. Nonetheless, mutant mice lacking Hdac9 are sensitized to hypertrophic signals and exhibit stress-dependent cardiomegaly, suggesting that HDAC9 is a negative regulator of cardiomyocyte hypertrophy [45,46]. HDAC9 mutant mice showed an increase in slow fibers suggesting that its deletion results in enhanced slow-fiber gene expression [33]. Among class IIb HDACs, HDAC6 has been found associated with the class III deacetylase SIRT2 [47]. This complex interacts with poly-ubiquitin and poly-ubiquitinated proteins [48], and with tubulin and microtubules in the cytoplasm [49]. In particular, it has been observed that HDAC6 localizes at NMJs and its deletion protects against microtubule disorganization, markedly influencing NMJ structure [50]. Moreover, HDAC6 expression is upregulated during muscle atrophy, where it interacts with the E3-ubiquitin ligase MAFbx, participating to its activation; consistently, HDAC6 inactivation protects against muscle wasting in mice [51]. In the heart, HDAC6 was recently found to regulate myofibril stiffness and diastolic function [52]. HDAC10 mainly acts as polyamine deacetylase instead of lysine deacetylase [53], but its targets and functions in striated muscle are poorly characterized. Class III HDACs show similarity to the yeast Sir2. In humans, the family consists of seven members, named sirtuins (SIRT1-7), whose activity depend on NAD+ [54]. Sirt1 KO mice are sterile, smaller and present abnormalities in heart morphogenesis, due to p53 hyperacetylation and p53-dependent apoptosis [55]. Moreover, SIRT1 plays an essential role in the maintenance of mitochondrial integrity by modulating the MEF2 transcription factors in the heart [56]. Similarly to the yeast Sir2, SIRT1 exerts longevity effects against aging-associated pathologies, such as neurodegeneration, metabolic dysfunction [57], and cardiovascular diseases [58]. Consistently, SIRT1 levels decrease with age [59], promoting senescence. In skeletal muscle, SIRT1 inhibits FoxO1 and FoxO3 activity upon fasting, thereby protecting muscle from atrophy while promoting growth [60]. Importantly, SIRT1 is a sensor of energy metabolism, being triggered by AMPK, and deacetylates, thus activating, peroxisome proliferator activated receptor gamma coactivator 1α (PGC-1α) [61]. SIRT2 plays a pivotal role in regulating the whole-body metabolism. Upon high-fat condition, deletion of Sirt2 reduces muscle insulin sensitivity and contributes to liver insulin resistance in mice, potentially by affecting mitochondrial acetylation state [62], although opposite data were previously reported in vitro in muscle cells [63]. Moreover, SIRT2 inhibition impairs myoblast fusion [64] and promote autophagic flux [65]. Consistently, SIRT2 activation protects myotubes against dexamethasone-induced atrophy through inhibition of the autophagy system [65]. Similarly, in the heart, SIRT2 overexpression protects from Ang II-induced cardiac hypertrophy and fibrosis, promoting AMPK activation by deacetylating the kinase LKB1 [66,67]. SIRT3 localizes in the mitochondria and it functions to maintain mitochondrial homeostasis under stress, having as a target at least one fifth of all mitochondrial proteins and regulating their activity [68]. In skeletal muscle, SIRT3 expression is downregulated in diabetic or high-fat diet fed mice and, conversely, it is upregulated upon fasting [69]. Mice lacking SIRT3 show decreased oxygen consumption and simultaneous increase in reactive oxygen species production, as well as higher oxidative stress in muscle, overall impacting the insulin signaling [69]. Thus, SIRT3 is an additional, potential therapeutic target for regulating skeletal muscle insulin sensitivity. SIRT4 shows a mitochondrial localization and is a lysine deacylase that controls insulin secretion: Sirt4 KO mice progressively develop glucose intolerance and insulin resistance, highlighting the importance of this mitochondrial enzyme in regulating leucine metabolism [70]. Indeed, Sirt4 KO mice display deregulated lipid metabolism, leading to increased exercise tolerance and protection against diet-induced obesity, with elevated levels of malonyl CoA decarboxylase and decreased malonyl CoA in skeletal muscle and adipose tissues [71,72]. SIRT5 localizes in mitochondria and catalyzes the removal of PTMs on lysine residues, such as succinylation, malonylation, and glutarylation, thus regulating the activity of numerous enzymes involved in cellular metabolism, including fatty acid oxidation [73,74], ammonia cycle [75,76], ketogenesis [76] or respiratory chain [77] and redox metabolism [78]. SIRT5 deficiency suppresses mitochondrial ATP production and promotes AMPK activation in response to energy stress, which is sufficient to prevent left ventricular dilation and cardiac dysfunction in mice subjected to transverse aortic constriction [79]. SIRT6 localizes in the nucleus and has deacetylase [8], defatty-acylase [80], and mono-ADP-ribosylation [81] activities, playing important regulatory roles during physiological and pathological processes. In skeletal muscle, SIRT6 deficiency induces a reduction of AMPK activity and impaired glucose homeostasis and insulin sensitivity, leading to attenuated whole body energy expenditure, and weakened exercise performance [82]. In the heart, SIRT6 binds to and represses the promoter of IGF signaling–related genes, thereby acting as a negative regulator of cardiac hypertrophy. Consistently, SIRT6 KO mice develop cardiac hypertrophy and heart failure at around 8–12 weeks of age [83]. Recent efforts have identified SIRT7 involvement in various cellular processes such as ribosome biogenesis [84], gene expression and cellular metabolism [85,86], by promoting glucose production, and mitochondrial homeostasis [87,88]. Deletion of Sirt7 in mice leads to premature aging, progeroid phenotype, and lethal heart hypertrophy due to enhanced activation of p53 [89]. Class IV HDACs include only HDAC11, which presents similarities to both Class I and Class II. It has been found that HDAC11 promotes MuSC proliferation, by activating Notch signaling, and negatively affects skeletal muscle regeneration by reducing MyoD1 transcription [90,91,92]. HDAC11 deletion also increases the number of oxidative myofibers in skeletal muscle by promoting a glycolytic-to-oxidative muscle fibers switch [93]. In the heart, HDAC11 deletion improved several parameters of diabetes mellitus-associated cardiac injury, including oxidative stress, apoptosis, inflammation and cardiac function [94], pointing to HDAC11 suppression as a potential therapeutic target for treating such a cardiac condition. Several HDAC isoforms have been implicated in skeletal muscle remodeling, both in physiological and pathological conditions [95,96]. Ample work revealed that HDACs exert pivotal roles in regulating fiber type specification [96], muscle fiber size and innervation [29,37,97], metabolic fuel switching [16,98,99], muscle development [100], insulin sensitivity and exercise capacity [69,101,102], thus contributing to the maintenance of skeletal muscle homeostasis. The evidence of a wide variety of HDAC functions in skeletal muscle led to an increasing interest to clarify their roles in skeletal muscle disorders [29,96], including muscular dystrophies (MDs). MDs consist of a heterogeneous group of genetic disorders characterized by progressive weakness and degeneration of skeletal muscles resulting in impaired muscle function [103]. Traditionally classified by a patient’s clinical presentation, muscle group involvement, mode of inheritance, age of onset and overall disease progression, MDs have been linked to a variety of distinct single-gene mutations [104]. So far, molecular genetic mapping techniques have shown that MDs are caused by numerous mutations in several genes encoding structural and functional muscle proteins, resulting in degeneration or dysfunction of skeletal muscle [104]. The most severe and the most common adult form of MD is Duchenne Muscular Dystrophy (DMD), which affects 1 in 3500–6000 live male births, and is caused by the lack of functional dystrophin protein due to mutations in the dystrophin gene (DMD) [105]. The structural role of dystrophin is closely related to its centrality in assembling the sarcolemmal Dystrophin-Associated Protein Complex (DAPC), which provides the molecular link between the cytoskeleton and the extracellular matrix of skeletal myofibers [106,107]. Lack of dystrophin results in mechanical instability causing myofibers rupture during contraction. Moreover, being connected with multiple proteins, dystrophin modulates several signal transduction pathways, including Ca2+ entry, nitric oxide (NO), and reactive oxygen species (ROS) production [108,109,110]. The mdx mouse, harboring a nonsense point mutation in the exon 23 that aborts the full-length dystrophin expression, is the most widely used animal model for DMD research [111]. Despite the loss of dystrophin, mdx mice show minimal clinical features of the disease, if compared with DMD patients, probably due to compensatory mechanisms. The latter include muscle regeneration, which is more efficient in mdx mice compared to DMD patients, in part due to differences in telomere shortening and muscle stem cell regenerative capacity [112]. Among compensatory mechanisms triggered by the absence of dystrophin, the upregulation of utrophin has been reported in both DMD and mdx myofibers [113]. Utrophin is a structural and functional autosomal paralogue of dystrophin, normally located at the neuromuscular and myotendinous junctions in adult skeletal muscle in physiological condition [114], but enriched at the sarcolemma in dystrophic myofibers where it acts to preserving muscle function and mitigating necrosis [113]. Importantly, while the exogenous expression of utrophin attenuated the mdx dystrophic phenotype, its deletion in mdx mice worsened the pathology, thus confirming that utrophin protective functions in DMD [115,116,117]. In addition to dystrophin, another important member of the DAPC is the sarcoglycan complex, which is composed of four sarcoglycan (SG) proteins, α−, β−, δ−, and γ-SG, playing a key role to protect striated muscle membranes against contraction-induced damage [118,119]. Mutations in one of the four sarcoglycan genes (SGCA) cause a different form of autosomal recessive sarcoglycanopathies [120,121], a subgroup of Limb Girdle Muscular Dystrophies (LGMDs). Sarcoglycanopathies are more frequently found among the most severe forms of MDs, and the clinical phenotype closely resembles that of DMD, with onset during childhood [122,123]. The role of HDACs in MDs is not yet fully identified; indeed, most of our knowledge derives from studies with HDAC inhibitors in dystrophic contexts (discussed below). However, several studies revealed the deregulation of HDAC expression or activity in dystrophic muscles (Figure 1). Higher global deacetylase activity was first detected in muscles of mdx mice and in DMD patients [124,125], accompanied by selectively elevated levels of HDAC2 in MuSCs [124]. Further investigations revealed a molecular link among the DAPC, NO signaling and HDAC2 [124,125]. Indeed, in mdx mice, the loss of an essential component of the dystrophin–glycoprotein complex leads to the displacement of the muscle-specific variant of the neuronal nitric oxide synthase (nNOSm) enzyme, which is normally located at the sarcolemma in close contact with the DAPC complex, resulting in reduced generation of NO. In addition to impairing many processes, including mitochondrial biogenesis and glucose metabolism, reduced NO bioavailability alters S-nitrosylation of HDAC2, resulting in increased activity and constitutive inhibition of HDAC2-target genes in dystrophic muscle [124,126,127]. HDAC2 directly inhibits follistatin gene transcription in mdx muscle cells, which in turn blocks a powerful inhibitor of muscle growth, i.e., myostatin [128]. Consistently, the follistatin-myostatin axis has been identified as a target to ameliorate MDs; indeed, myostatin blockade at early stages of the disease provides a beneficial effect in both mdx and α-SG–deficient mice [129,130]. Moreover, HDAC2 modulates a specific subset of miRNAs, including miR-1 and miR-29, while HDAC1 specifically inhibits miR-206 in dystrophic MuSCs, thereby correlating with several pathogenetic traits of DMD [127]. HDAC2 downregulation by siRNA or NO-donor led to improved myogenesis of mdx MuSCs in vitro, in addition to ameliorating functional and morphological parameters in vivo [124]. Among the members of class I HDACs, HDAC3 has been shown to be directly involved in the pathogenesis of the X-linked Emery–Dreifuss muscular dystrophy (EDMD1) [131,132]. This disease is caused by mutations in the emerin gene, which encodes for a nuclear membrane protein that binds to and recruits HDAC3 to the nuclear lamina. The loss of emerin in muscle cells leads to aberrant nuclear envelope architecture and heterochromatin organization, which results in a more open conformation because of the delocalization and loss of activity of HDAC3. As a result, skeletal MuSCs are unable to differentiate, resulting in progressive skeletal muscle wasting and impaired skeletal muscle regeneration [133]. Moreover, muscles from EDMD1 patients and emerin-null mice show an increased and improper temporal expression of marker genes involved in muscle regeneration, including Pax7, MyoD, and Myf5 [134]. Importantly, activation of HDAC3 catalytic activity by theophylline treatment rescues myogenic differentiation in emerin-null mice, confirming HDAC3 as a master regulator in coordinating the spatiotemporal localization of gene loci to the nuclear envelope required for proper differentiation and muscle regeneration [135]. In a recent study, HDAC8 was found to be overexpressed in DMD human primary myoblasts and myotubes, and in a zebrafish DMD model [136]. In the same study, the authors clarified the role of HDAC8 in modulating cytoskeletal architecture and stability through the deacetylation of α-tubulin. Moreover, selective inhibition of HDAC8, by PCI-34051 administration, rescues the DMD phenotype in terms of increased human myoblast differentiation and reduced lesion extent in zebrafish embryos, overall restoring skeletal muscle histomorphology and reducing inflammation [136]. Differently from class I HDACs, which predominantly localize to the nucleus, where they mostly act as epigenetic regulators, class IIa HDACs shuttle between the nucleus and the cytoplasm, regulating numerous stress responses. HDAC4 has been shown to be crucial for proper MuSCs proliferation and differentiation [35] and muscle regeneration [34] following acute muscle injury. A recent paper revealed enhanced expression of HDAC4 in mdx and DMD muscles, characterized by a higher cytoplasmic abundance of HDAC4 [27], thus suggesting a potential role for HDAC4 in this pathology. Mdx mice carrying a skeletal muscle-specific deletion of HDAC4 developed a more severe MD pathology, with increased muscle damage and reduced muscle regeneration, overall showing decreased muscle performance. The protective role of HDAC4 in the cytoplasm of dystrophic muscles is independent of its deacetylase activity and depends on its involvement in the membrane repair process. Indeed, cytosolic HDAC4 mediates the activation of a compensatory mechanism of membrane repair in mdx muscles, thus promoting MuSCs survival and differentiation, ultimately improving muscle regeneration and function [27]. HDAC5 is downregulated in the nucleus of mdx muscle and MuSCs, compared with normal controls, and has been implicated in the epigenetic control of chromatin landscape during mdx MuSCs differentiation. Impaired NO-dependent protein phosphatase 2A activity induces a hyperphosphorylation of HDAC5, thus reducing the amount of nuclear HDAC5 in complex with HDAC3, and affecting mdx MuSCs differentiation [125]. Regarding class IIb HDACs, two independent groups identified interesting functions for HDAC6 in DMD [137,138]. HDAC6 exclusively localized in the cytoplasm, where it removes acetyl groups from non-histone proteins such as α-tubulin, modulating microtubule network stability and organization [49]. HDAC6 also possesses a non-enzymatic zinc-finger ubiquitin-binding domain at its C-terminus, through which HDAC6 interacts with components of the ubiquitin proteasome pathway, thus playing a critical role in the cellular response to misfolded and aggregated proteins [139]. Moreover, HDAC6 and its endogenous inhibitor paxillin, regulate acetyl choline receptors (AChR) clustering at the neuromuscular junctions, by mediating a fine balance of nonacetylated and acetylated microtubule network [50]. Increased HDAC6 protein expression has been reported in mdx muscles, with a concomitant reduction of acetylated α-tubulin, which contributes to the disorganization of microtubule network and to the impairment of the autophagic flux in DMD. The pharmacological inhibition of HDAC6, by tubastatin A administration, restores the microtubule acetylation and rescues the autophagic flux enhancing autophagosome-lysosome fusion in mdx mice, in addition to improve AChR clustering and distribution [137,138]. Moreover, HDAC6 inhibition downregulates transforming growth factor beta (TGF-β) signaling, through an increase of SMAD2/3 acetylation, thereby reducing muscle atrophy and fibrosis and improving protein synthesis in mdx muscles [137]. Members of class III HDACs rely on NAD+ to deacetylate their targets, thereby mediating several important functions in skeletal muscle physiology and diseases [95,140]. Although SIRT1 expression does not change between mdx and control muscles, the lack of dystrophin abrogates proper diurnal oscillation of SIRT1 mRNA expression [141]. Moreover, an increased level of phosphorylated SIRT1 (p-SIRT1) was observed in mdx muscles, with a concomitant increase of histone H3 acetylation at Lys9/Lys14, thus suggesting attenuated SIRT1 activity [142]. In addition, NAD+ concentration was found to be reduced in dystrophic muscles, supporting a model in which SIRT1 activity is downregulated in mdx mice [143,144]. Functional proof that SIRT1 downregulation contributes to MD pathogenesis comes from gain-of-function studies. Mdx mice overexpressing SIRT1 in skeletal muscle developed a less severe DMD pathology, with decreased myofiber necrosis, oxidative stress and fibrosis, accompanied by a fast-to-slow myofiber shift, and overall improvement of muscle performance [143]. Most of the improvements reported in mdx SIRT1 overexpressing mice have been proven to be mediated by the activation of peroxisome proliferator-activated receptor, gamma, coactivator 1 alpha (PGC-1α), a SIRT1 target known to protect and ameliorate dystrophic muscles [145,146]. Indeed, increased expression of PGC-1α in dystrophic muscle mimics, in part, mdx SIRT1 transgenic mice, enhancing mitochondrial biogenesis, improving the oxidative metabolism and driving a fast-to-slow fiber switch, and preventing muscle degeneration [147]. Skeletal muscle-specific Sirt1 knockout mice display a mild dystrophic phenotype, being more prone to suffer from exercise-induced muscle injury, probably due to defects in membrane resealing [148]. However, Sirt1 loss in skeletal muscle of mdx mice does not exacerbate the dystrophic phenotype [148], suggesting redundant protective mechanisms in skeletal muscle under stress conditions. SIRT2 modulates autophagy signaling, thereby affecting skeletal muscle atrophy and myoblast proliferation [65,149]. A recent role for SIRT2 in skeletal muscle following injury has been demonstrated. Sirt2 KO mice showed a delay in muscle regeneration due to a decreased expression of anabolic and cell cycle regulators genes, with a concomitant increase in catabolic genes and muscle atrophy [150]. Interestingly, a significant upregulation of SIRT2 mRNA has been reported in MuSCs derived from DMD patients [151]. These recent results illustrate that further research is needed to better understand the role of SIRT2 in MDs, since SIRT2 could be a promising new therapeutic target in those muscular pathologies where regeneration is inefficient. Moreover, SIRT2 has been proposed as new serum dystrophic marker, since it is upregulated in mdx serum while it is reversed to control levels by overexpressing utrophin in mdx mice [152]. SIRT3, SIRT4, and SIRT5 are exclusively localized to mitochondria and regulate a wide range of metabolism-oriented enzymes in skeletal muscle, thereby modulating energy metabolism in response to mitochondrial stress. Mitochondrial dysfunction is a pathological feature of several MDs [153,154], suggesting a possible involvement of these sirtuins in such pathologies. SIRT3, SIRT4 and SIRT5 mRNA expression have been found to be upregulated in MuSCs derived from DMD patients and mdx mice [151], although no further investigations elucidating their potential role in MDs have been performed. SIRT6 plays a pivotal role in heterochromatin stabilization through deacetylation of H3K9ac, H3K18ac and H3K56ac. In skeletal muscle, SIRT6 has been reported to negatively regulate myostatin expression via suppressing NF-κB signaling, in addition to modulating glucose homeostasis and insulin sensitivity [82,155]. SIRT6 expression has been found to be upregulated in skeletal muscle and in MuSCs of mdx mice, where it mostly acts on H3K56ac, thereby repressing several genes, including utrophin and myostatin [151]. Lack of SIRT6 reduces muscle fragility and damaged myofibers, increasing the physical activity of mdx mice. Interestingly, Sirt6-depleted MuSCs showed attenuated activation, characterized by a strong reduction of Pax7/MyoD double-positive cells, reduced proliferation rate, and decreased expression of stress response-related genes [151]. Overall, these results indicate that reducing the persistent and chronic activation of MuSCs in mdx muscles is protective, and that inactivating SIRT6 in DMD ameliorates the dystrophic phenotype in mice. The class IV HDAC11, which is a lysine de-fatty acylase [156,157,158], is highly expressed in skeletal muscle but it is dispensable for adult muscle growth. Interestingly, its genetic deletion accelerates regeneration in response to muscle injury [91,159]. The recent study on HDAC11-deficient mice show a more efficient muscle regeneration following acute injury [91] likely due in part to an increase in IL-10, which allows a faster transition from inflammatory to pro-regeneration environment. Since high levels of IL-10 have been demonstrated to ameliorate the pathology of mdx mice [160,161], these new results on HDAC11 functions are promising and open new avenues for the development of more specific HDAC inhibitors, such as specific HDAC11 inhibitors, as an effective approach to treat MDs. Further studies are needed to evaluate whether this HDAC is involved in the persistent and inefficient regeneration in MDs, and to verify whether HDAC11 is a candidate target to improve muscle repair in this pathological condition. Epigenetic mechanisms controlling transcriptional programs in tissue progenitors are becoming a critical area of interest in medicine. Indeed, current studies are focused on manipulating chromatin targets of individual signaling pathways to provide novel regenerative strategies based on epigenetic drug administration. Numerous studies have highlighted the fundamental role of HATs and HDACs in regulating muscle gene transcription and therefore, muscle development and differentiation. Moreover, cumulative in vitro and in vivo evidence in the last years has underscored the link between HDAC deregulation and the pathogenesis of several MDs, in particular of the most severe one, the DMD [162,163,164]. In this context, HDACi have been shown to act in a selective way, potentiating myogenesis through the hyperacetylation of genes regulated during development and resolving their epigenetic bivalency, a characteristic signature that identifies genes poised for transcription that typically are enriched in embryonic stem cells or pluripotent cells [165]. Starting from this evidence, by inhibiting HDACs and reestablishing the epigenetic events necessary to activate adult stem cells, it represents one of the most powerful approaches to restoring the downstream networks of muscle regeneration and muscle homeostasis, leading to increased functional and morphological recovery of dystrophic muscles. At first, focusing on the HDACi activity on skeletal muscle cells in vitro, it was observed that the pharmacological treatment targets myogenic differentiation [97,166]. Indeed, treatment of wild-type myoblasts with pan-HDACi, such as Trichostatin A (TSA), Valproic acid (VPA), or Sodium Butyrate (PhB), increases their differentiation potential and fusion capacity, due to different mechanisms: (i) the upregulation of MyoD acetylation; (ii) the modulation of histone acetylation at specific gene promoters and (iii) the increase of the expression of the pro-myogenic protein follistatin [97,166]. Several years ago, a link between dystrophin loss and HDAC activity was demonstrated [124,125]. In mdx whole muscles and primary myoblasts, an increase in global HDAC activity and HDAC2 expression was observed in association with a reduction in follistatin expression. Inhibition of HDAC2, by using the class I HDAC inhibitor MS-275 or siRNA, restores the level of global HDAC activity similar to healthy control muscles, leading to morphological and functional benefits in dystrophic muscles [124]. In more recent studies, increased activity of class I, class IIa and class I/IIb HDACs in muscles of 1.5-month-old mdx mice [27] and in Fibro-Adipogenic Progenitors (FAPs) isolated from 1.5 month- and 12 month-old mdx mice has been reported [167], further suggesting the involvement of HDACs in the pathogenesis of DMD. Next-generation sequencing studies have focused on the fine regulation of myogenesis by HDACi, paying attention to the epigenetic players that create changes in the epigenome, opening new therapeutic options in muscle diseases. It emerged that most of the beneficial effects of the HDACi on dystrophic muscles arise from their ability to selectively activate a microRNA-SWI/SNF-based epigenetic network in FAPs, a specific population of mesenchymal cells resident in muscle interstitium [168,169]. FAPs are a muscle cell population that, while in regenerating conditions support MuSCs differentiation, in pathological conditions, such as DMD, contribute to the progression of the disease, affecting fibrotic and fat deposition, decreasing muscle contractility, and altering metabolism [170,171,172]. Intriguingly, pan-HDACi manipulate cell fate determination that redirects the lineage commitment of FAPs from a fibro-adipogenic toward a myogenic one [169]. In the context of MDs, it is worth mentioning the sirtuins, which are class III histone/protein deacetylases, are able to modulate several important physiological mechanisms such as inflammation, apoptosis, glucose homeostasis, life span, and neuroprotection. Acting pharmacologically on these enzymes permits modification of the acetylation state of several intracellular messengers, thereby regulating downstream mechanisms. This approach likely has strong therapeutic potential for many human diseases such as metabolic disorders, and degenerative diseases such as MDs. As described above, the most studied of the sirtuins is SIRT1, which is expressed in many tissues, including skeletal muscle and heart, where it deacetylates and activates PGC-1α, a key modulator of muscle metabolism. The activated form of PGC-1α controls mitochondrial biogenesis and homeostasis, and therefore SIRT1 modulation was seen to be associated with muscle pathologies. It is now well known that PGC-1α overexpression in dystrophic mdx mice leads to milder signs of pathology and an improved function both in normal condition and after intense physical exercise [61,145]. Other mechanistic roles are attributed to SIRT1 modulation, supporting the beneficial effects on muscle pathologies. It has been described for example that SIRT1 stimulates and restores autophagy in muscle tissue through the deacetylation of autophagy components, including Atg5, Atg7, and Atg8, and activating FoxO3a a transcription factor that regulates autophagy in skeletal muscle [173,174]. Moreover, SIRT1 may modulate the activity of SMAD transcription factors, key TGF-β signaling components that are involved in myofibroblast differentiation. The activity of SMAD is regulated by lysine acetylation/deacetylation, which plays a critical role in tissue fibrosis [175]. All these data generated in vitro on cells (Figure 2), together with the in vivo evidence of deregulated activity of HDACs in MDs, have provided the rationale for using pan-HDACi and modulators of sirtuins in preclinical studies, with the aim of assessing the ability of these classes of compounds to improve muscle regeneration and counteract muscle degeneration in models of MD. In DMD muscles, the lack of dystrophin, in addition to the events previously described, leads to a deregulation in the expression and in post-transcriptional modifications of all the DAPC components, causing a strong fragility of muscle fibers after contraction [176,177]. Muscle degeneration in turn activates compensatory regeneration to reduce the muscle damage. These processes characterizing DMD pathology, together with the increase of HDAC activity, led to the hypothesis that epigenetic treatments, based on pan-HDACi, could represent an encouraging approach to reduce the DMD progression by enhancing the formation of multinucleated myotubes and therefore of new muscle fibers [124,162,163]. The three pan-HDACi studied in vitro on wild-type myoblasts, TSA, VPA, and PhB, were also tested in vivo in mdx mice, by daily intraperitoneal injections in young mdx mice in which the compensatory regeneration phase is still active [178]. The results of this study established the ability of pan-HDACi to improve the differentiation potential of MuSCs after 10 days of treatment. However, long-term treatment of mdx mice for 3 months revealed that TSA represents the best epigenetic compound used to treat mdx pathology, among the three pan-HDACi used. These conclusions were drawn based on a decrease of DMD biomarkers and creatine kinase levels in blood, in addition to the absence of side effects. Moreover, histological and functional analyses revealed that TSA improves muscle force and muscle size, as well as reducing fibrotic scars and fat deposition, slowing progression of the disease. These effects were associated with upregulation of follistatin expression in mdx MuSCs [178]. Deepening the effect of pan-HDACi on mdx mice in vivo, further studies revealed a stage-specific effect of the epigenetic drugs. Indeed, it was demonstrated that the beneficial effects of TSA in mdx mice are restricted to the active regeneration window of time, while if the treatment starts at late stages of the disease, where the regeneration potential is exhausted, the compound loses its beneficial effects on muscle regeneration [111,168]. During the last decade, different pan-HDACi were tested on mdx mice (Figure 3 and Table 2). A dose-dependent study of suberoylanilide hydroxamic acid (SAHA) treatment demonstrated its effectiveness in ameliorating both dystrophic muscle function and morphology, reducing inflammation and fibrosis and also attenuating cardiac arrhythmias [179,180]; in addition, a preclinical dose-dependent study of ITF2357 (givinostat) highlighted its ability to recover muscle function and counteract muscle degeneration of mdx mice [163]. At the cellular level, the beneficial effects of pan-HDACi in mdx mice have been mainly associated with FAPs [168,169,181]. It was shown that HDACi treatment of young regenerating mdx mice exerts a double positive effect on mdx FAPs: first, it converts their lineage commitment toward a pro-myogenic one, and secondly, it stimulates their positive interaction with MuSCs, promoting muscle regeneration. In particular, pan-HDACi treatment controls dystrophic FAP lineage commitment, reducing their ability to contribute to fibrotic scar infiltration and adipocyte accumulation while inducing their latent myogenic phenotype, confirming that FAPs represent one of the cellular targets of pan-HDACi treatment [168,169]. Deciphering the molecular mechanisms behind this effect of pan-HDACi, it was demonstrated that the treatment changes chromatin structure at muscle loci of FAPs, inducing the expression of muscle genes, such as BAF60C and MyoD, and of muscle-specific miRNAs (myo-miRs), including miR-1.2, miR-133, and miR-206 [169,182]. These studies identified the HDAC–myo-miR–BAF60 network as up-regulated by pan-HDACi treatment in FAPs. Briefly, myo-miRs target and repress the expression of the alternative BAF60 variants of the SWI/SNF complex, BAF60A and BAF60B, responsible for the fibro-adipogenic phenotype, favoring the 172expression of the BAF60C variant, which in turn activates the transcription of muscle genes leading to a promyogenic commitment of FAPs. Such an effect, again, was not observed in FAPs from old mdx mice [169,182]. The inefficacy of pan-HDACi to ameliorate the dystrophic phenotype at late stages of the disease has been recently investigated by using genome-wide approaches, and it was found to be related to aberrant HDAC activity and to a senescent state of FAPs that is not reversed by the treatment [167]. Regarding the ability of FAPs to support MuSC differentiation, the beneficial effect of pan-HDACi relies on their capability to fine tune the miRNAs cargo of the extracellular vesicles (EVs) released by FAPs [183]. In particular, pan-HDACi treatment upregulates a subset of promyogenic miRNAs into EVs released by dystrophic FAPs, creating EVs that improve MuSC differentiation in vitro and muscle regeneration in vivo of dystrophic mdx mice [183]. Of note, among the pan-HDACi tested in dystrophic mdx mice, givinostat represents the most encouraging one to date due to its safety profile. It is a hydroxamate. The fact that givinostat has already been successfully tested in a clinical study in pediatric populations affected by systemic-onset juvenile arthritis made it possible, following promising preclinical studies, for the transfer of this drug into a clinical trial for DMD [184]. Mdx mice treated daily with 5 mg/kg of givinostat for 3.5 months showed histological and functional muscle improvements; the HDACi exerted numerous beneficial effects ranging from reduction of inflammation and fibrosis to promotion of skeletal muscle regeneration [163]. The beneficial outcomes of givinostat were also dependent on its effects on mdx muscle metabolism and mitochondrial content and quality. Indeed, mitochondrial dysfunction has been implicated as an important actor in skeletal muscle diseases, including DMD [185]. Givinostat treatment increased acetylation of the promoter of the PGC-1α gene, a key regulator of mitochondriogenesis, and therefore its expression, in mdx mice. As a consequence, givinostat induced a recovery of mitochondrial biogenesis and oxidative fiber type switch in mdx muscles, classifying it for the first time as a metabolic remodeling drug [185,186]. Givinostat was also recently used to treat muscle progenitor cells (MPCs) generated from human-induced pluripotent stem cells, increasing MPC proliferation and motility in vitro. These MPCs treated with givinostat were then transplanted into injured muscles of dystrophic nude mice, as a possible test of cell-therapy. Indeed, MPCs treated with givinostat were locally engrafted into the muscle and were able to restore dystrophin levels to reduce inflammation, necrosis, and fibrosis as well as to repopulate the MuSC niche [187]. This study suggests another strategy for the treatment of DMD based on the use of pan-HDACi. All these studies characterized the functional, histological, and molecular beneficial effects of givinostat on a mild dystrophic phenotype, the mdx (C57BL10ScSn-Dmdmdx) mice. Of note, only in a recent study were the pharmacokinetic and muscle uptake properties of givinostat in mdx mice described, confirming a positive correlation between the doses of givinostat and the drug distribution in muscles and blood [188]. In the same study, givinostat was tested in a more severe mouse model of DMD, the D2.B10 mice. Long-term treatment with givinostat resulted in partial efficacy, improving muscle function and reducing muscle fibrosis in D2.B10 mice, although no significant effects were detected on myofiber cross sectional area or generation of myofibers [188]. In addition, different groups are focusing on studying the interactions of givinostat with other pharmacological interventions for DMD, such as steroids, to find a combinatory therapeutic strategy that successfully improves the beneficial effects on dystrophic muscles [184,188]. TSA was also tested in two different Danio rerio zebrafish models of DMD: the dmd morpholino (dmd-MO) knock-down model, in which an anti-sense morpholino cocktail was used to knock-down dystrophin, and the zebrafish dmd mutant line. In both models, the pan-HDACi was able to rescue muscle fiber damage [189]. The zebrafish DMD-MO model has been confirmed as a valid tool for rapid and cost-effective small molecule screening in another study. A novel chemical-combination screen of a library of epigenetic compounds identified a specific combination of the class I and II HDACi, oxamflatin, and the class III HDACi, salermide, able to ameliorate skeletal muscle phenotype in DMD mutant zebrafish, increasing the acetylation profile of histone H4 [190]. Salermide has also been described to protect muscle cells against oculopharyngeal muscular dystrophy (OPMD) in Caenorhabditis elegans [191]. The zebrafish model was also recently employed to study the effect of a new HDAC8 inhibitor, PCI-34051. Indeed, by using DMD patient-derived myotubes, Spreafico and colleagues observed an increase of HDAC8 activity, which was downregulated by PCI-34051 treatment [136]. Inhibition of HDAC8 in DMD-MO zebrafish treated with PCI-34051 in vivo ameliorates the dystrophic phenotype, partially repairing muscle lesions and reducing the inflammation process. At the molecular level, it was observed that the effect on the inflammation process is due to the ability of PCI-34051 to reduce IL-1b expression and, thus, immune cell recruitment. Interestingly, the beneficial effects of PCI-34051were similar to the achieved by the pan-HDACi givinostat, except for the reduction of inflammation, which was more pronounced in PCI-34051-treated zebrafish [136]. Almost ten years ago it was described how SIRT1 overexpression, in a transgenic mouse model, ameliorated the pathophysiology of DMD disease. SIRT1 overexpression decreases serum creatine kinase levels, tissue fibrosis, and myofibril damage, and increases oxidative fibers and the ability of mice to run long distances compared with control mdx mice [143]. SIRT1 activation improves skeletal muscle function and protects muscles of mdx mice by suppressing oxidative stress and also inducing the expression of antioxidative molecules such as SOD2 or catalase, acting on FoxO transcription factors [192]. For this recent evidence, SIRT1 activation is emerging as a novel therapeutic strategy for patients with MDs and drugs capable of activating the SIRT1/PGC-1α pathway may have positive effects in MD. Two small molecules were mainly used as basic direct activators of SIRT1: resveratrol and quercetin, at different doses and for different periods of treatment [193]. Resveratrol belongs to the class of flavonones and is a polyphenol compound found in foods such as grapes and red wine, and it has recently gained popularity due to its anti-inflammatory and oxidative metabolic enhancing properties [194,195]. In skeletal muscle, resveratrol may alleviate muscular dystrophic pathologies by activating the SIRT1/PGC-1α axis, therefore reducing inflammation and improving muscle function in a variety of disease models [196,197]. There are different independent studies that demonstrate how daily oral intake or intraperitoneal injections of resveratrol for several weeks in mdx mice contributed to the preservation of muscle function and muscle mass [198,199,200,201]. Gordon and collaborators tested, on 5-weeks-old mdx mice, different doses of resveratrol for a short period (10 days), and they observed with the optimal dosage (100 mg/kg) an increased expression and activity of SIRT1 and PGC-1α activation, leading to increased expression of PGC-1α and PGC-1α target genes. Unlike what chronic treatment showed in a previous study [200], this experiment also shows a reduction in the inflammatory infiltrate and increase in IL-6 [199]. Of note, the authors also observed an increase in utrophin gene expression. SIRT1 activation through chronic resveratrol daily administration has been shown to promote a fast to slow fiber shift in the muscle of mdx mice, helping the remodeling of dystrophic skeletal muscle towards a slower, more oxidative phenotype, which is known to be more resistant to the dystrophic pathology [202,203]. Long-term treatment with resveratrol also exerts beneficial effects on the hearts of mdx mice and of TO-2 hamster deficient in δ-sarcoglycan (LGMD2F animal model), resulting in inhibition of hypertrophy and fibrosis and improving cardiac function compared to untreated mdx by the downregulation of p300 protein levels and by the increase in the mitophagy process that promotes damaged mitochondrial deletion [192,204]. Similarly to resveratrol, quercetin proved to have beneficial effects against oxidative stress, neurogenic muscle atrophy [194] and DMD. Chronic quercetin dietary intake attenuates dystrophic cardio-pathology, decreasing inflammatory markers and cardiac tissue damage and increasing mitochondrial biogenesis and utrophin expression. Chronic treatment prevents loss of specific tension and fatigue resistance in skeletal muscle of dystrophic mice [193,205,206]. Quercetin treatment has also shown beneficial effects on dystrophic diaphragm muscles, improving respiratory function leading to an increase in the number of muscle fibers and reduced fibrotic area, but fails to increase utrophin levels, suggesting that PGC-1α/SIRT1 pathway is only partially activated in diaphragm muscle [207]. Abou-Samra et al. demonstrated that the hormone adiponectin (ApN), which has anti-inflammatory properties, is efficacious in mdx mice due to an effect on SIRT1 activation, which promotes upregulation of utrophin. In transgenic mdx mice overexpressing ApN, the investigators observed a decrease in muscle damage and enhanced muscle force compared to mdx mice [208]. The promising effects of using pan-HDACi have also been observed in other types of MDs (Table 2). The common pathological features of sarcolemma fragility shared by mdx and alpha-sarcoglycan (α-SG) null mice led to the hypothesis that pan-HDACi could be a powerful therapeutic approach also for LGMDs. Indeed, TSA treatment promotes the in vitro differentiation of MuSCs isolated from α-SG null mice. Furthermore, daily treatment of α-SG null mice with TSA induces muscle fiber size increase, while reducing fibrosis and inflammation [178]. The characterization of α-SG null mice identified numerous similarities with the mdx mouse, including the deregulation of NO synthesis due to the delocalization of the neuronal NO synthase to the sarcolemma, which causes changes in muscle metabolism, defects in mitochondrial biogenesis and dynamics, and modulation of HDAC activity [124,209,210]. In addition, Pambianco and colleagues demonstrated mitochondria defects in sarcoglycanopathy LGMD-2D skeletal muscle, associated with the sarcolemma instability. Indeed, LGMD-2D patients and α-SG null mice present reduced levels of PGC-1α and its target genes, reduced mitochondrial content and slow fiber-type composition and oxidative metabolism. Treatment of α-SG null mice with the pan-HDACi TSA increases the acetylation of histones within the PGC-1α promoter, thereby changing its chromatin assembly and enhancing gene expression, leading to a boost of mitochondrial biogenesis [211]. Exploiting drug screening associated with artificial intelligence-based predictive ADMET characterization of hits, givinostat was identified as a potential therapeutic drug for the sarcoglycanopathy LGMD-2D/R3, by inhibiting the autophagic pathway, likely by blocking HDAC6 activity, thereby leading to a partial α-SG protein rescue [212]. To strengthen these data, the authors also investigated the effect of another pan-HDACi, Belinostat, which induced a similar rescue of α-SG protein. Moreover, a synergistic effect was found by combining givinostat and the FDA-approved proteasome inhibitor Bortezomib, which blocks the proteasome activity and prevents the degradation of misfolded proteins: inhibition of both autophagic and proteasome pathways completely restored α-SG expression in the plasma membrane in mutant fibroblasts [212]. This evidence suggests a new therapeutic avenue for the treatment of LGMD-2D/R3, but also for other genetic diseases sharing similar protein degradation defects, as other sarcoglycanopathies. Pan-HDACi were also exploited for the treatment of Myotonic dystrophy Type 1 (DM1), a genetic rare disease characterized by the expansion of CTG trinucleotide repeats in the 3′ untranslated region of the DMPK gene, that leads to the nuclear sequestration of the alternative splicing factor Muscleblind-like protein 1 (MBNL1). A flow cytometry-based screen identified the HDAC6 inhibitor ISOX and the pan-HDACi Vorinostat as modulators of MBNL1 expression. The treatment of DM1 patient-derived fibroblasts with ISOX or Vorinostat resulted in an increased MBNL1 expression, and a partial rescue of the splicing defect caused by (CUG)exp repeats [213]. Establishing the beneficial effects of pan-HDACi in rescuing skeletal muscles in multiple animal models, taken overall, these findings provide the preclinical basis for a rapid translation into clinical studies with MD patients. Oculopharyngeal muscular dystrophy (OPMD) is caused by polyalanine expansion in nuclear protein PABPN1 [poly(A) binding protein nuclear 1] and characterized by muscle degeneration. Studies conducted on Caenorhabditis elegans transgenics expressing human PABPN1 with polyalanine expansion showed that deletion of sir-2.1 (homologous of SIRT1), of the transcription factor daf-16 (homologous of mammalian FoxO) and the aak-2 (homologous of AMPK) rescued C. elegans adult phenotypes, whereas increasing sir-2.1 dosage resulted detrimental [214]. Therefore, Sir2 inhibition protects against OPMD muscle pathology, whereas Sir2 activation is detrimental. This study suggested that the effect of resveratrol is context-dependent, increasing the resistance to muscle fatigue while enhancing the susceptibility to degeneration of the dystrophic muscle. The PABPN1 protein may have a role in muscle gene expression [215], and when mutated, may modify the beneficial effect of resveratrol on the control of energy metabolism in muscle leading to negative effects [214]. Based on the discovery that Sir2 inhibitors (sirtinol and splitomicin) promoted and the Sir2 activator (resveratrol) reduced muscle protection in PABPN1 nematodes, Pasco and collaborators, tested twelve SIRT1/2 inhibitors—sirtinol analogues—bearing different degrees of inhibition, for protection against mutant PABPN1 toxicity in Caenorhabditis elegans. Three compounds were highly efficient revealing a therapeutic potential for muscle cell protection in OPMD [191]. A high number of clinical trials involve pan-HDACi mainly in the treatment of hematologic neoplasms, but also of MDs, HIV infection, inflammatory diseases, neurodegenerative diseases, frontotemporal dementia, and Friedreich’s ataxia [184]. One of the main limitations for the use of pan-HDACi in clinics is their common adverse events, which include nausea, vomiting, anorexia, and thrombocytopenia [216,217]. Despite the positive results in preclinical studies obtained with numerous pan-HDACi for MDs, only one of them successfully arrived in a clinical trial, givinostat. The advantage of using givinostat in MD was suggested by the successful Phase I study in children affected by Systemic Onset Juvenile Arthritis (SOJIA) started in 2011 and concluded with the Phase II, in 2021, confirming that administration of givinostat (10 mg/mL oral suspension) is effective and safe [218,219] (www.Clinicaltrials.gov, clinical trial identifier: NCT00570661, accessed on 12 September 2006). Regarding the application of givinostat in DMD treatment, in 2013 the Phase II clinical study enrolled twenty boys, aged 7 to <11 years, with a diagnosis of DMD, under stable corticosteroids regimen, and able to complete the 6 min-walk test with a minimal distance of at least 250 m. This study confirmed the ability of givinostat administration to significantly counteract DMD progression after one year of treatment, proven by an increase of myofiber cross-sectional area, muscle fiber area fraction, and a reduction of fibrosis, necrosis, and fat replacement in DMD muscles. This study also determined that givinostat was safe and tolerated. No functional improvement was observed, probably due to the small size of the cohort analyzed; however, no decline of muscle performance was noted, highlighting the fact that the compound was well tolerated [220] (www.Clinicaltrials.gov, clinical trial identifier: NCT01761292, accessed on 10 April 2013). These encouraging data permitted, in 2016, the translation of givinostat into a phase III clinical study, which is focused on long-term safety, tolerability, and efficacy of the inhibitor in a large cohort of DMD patients (www.Clinicaltrials.gov, clinical trial identifier: NCT02851797 and NCT03373968, accessed on 6 June 2017). In 2017, a phase II clinical study of givinostat for the treatment of Becker Muscular Dystrophy was initiated (www.Clinicaltrials.gov, clinical trial identifier: NCT03238235, accessed on 12 December 2017). These trials are currently ongoing. For the many beneficial effects described above for Sirt1 activators, resveratrol could be tested on DMD patients. Only one pilot study has been conducted so far administrating resveratrol to patients with DMD, BMD, or FCMD for 24 weeks. The pilot clinical study had a limited number of patients, with three types of MDs and different clinical conditions; moreover, neither a placebo-treated group nor an untreated group could be added within the study. Nevertheless, consistent with the findings on mdx mice, resveratrol improved motor function and muscle power in the proximal muscles of patients under the study and creatine kinase levels decreased considerably, while longer-term administration of resveratrol may be necessary to reveal the cardiac function of resveratrol in MD patients. As adverse effects, diarrhea and abdominal pain were evaluated [221]. HDACs are certainly involved in the development and maintenance of muscle homeostasis in response to different insults or stimuli. Consistently, HDAC expression and activity have been found altered in MDs, suggesting a role for these enzymes in the progression of the disease. Preclinical studies showed the effectiveness of different HDACi in rescuing muscle force and morphology in MD animal models and, for some of them, the molecular mechanisms and target cells have been in part clarified. However, only the pan-HDACi givinostat has been endorsed for clinical trials in MDs, raising numerous questions about the real effectiveness of the use of these drugs on patients. Safety, dosage tolerance, and drug specificity are the main limitations associated with HDACi. Considering the specific, sometimes redundant roles, of the different HDAC isoforms, is not surprising if a pan-HDAC, which blocks the activity of numerous HDAC family members, belonging to different classes, exerts unwanted side effects. Further characterization of the kinetics of the different HDAC members in terms of expression, activity, and intracellular localization, taking into consideration the different cell types residing in striated muscles, is necessary to better define their specific involvement in MDs. Moreover, preclinical studies with isoform-specific HDACi, alone, or in combination with other FDA-approved drugs, are encouraged to propose new, more efficacious pharmacological treatments to ameliorate the burden of MDs.

PMC10002081

Xiaoping Guo,Xu Zuo,Zhengjie Zhou,Yinuo Gu,Haoyu Zheng,Xinlei Wang,Guoqiang Wang,Caina Xu,Fang Wang

PLGA-Based Micro/Nanoparticles: An Overview of Their Applications in Respiratory Diseases

22-02-2023

PLGA micro-/nanoparticles,treatment of respiratory diseases,asthma,chronic obstructive pulmonary disease (COPD),drug delivery

Respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), are critical areas of medical research, as millions of people are affected worldwide. In fact, more than 9 million deaths worldwide were associated with respiratory diseases in 2016, equivalent to 15% of global deaths, and the prevalence is increasing every year as the population ages. Due to inadequate treatment options, the treatments for many respiratory diseases are limited to relieving symptoms rather than curing the disease. Therefore, new therapeutic strategies for respiratory diseases are urgently needed. Poly (lactic-co-glycolic acid) micro/nanoparticles (PLGA M/NPs) have good biocompatibility, biodegradability and unique physical and chemical properties, making them one of the most popular and effective drug delivery polymers. In this review, we summarized the synthesis and modification methods of PLGA M/NPs and their applications in the treatment of respiratory diseases (asthma, COPD, cystic fibrosis (CF), etc.) and also discussed the research progress and current research status of PLGA M/NPs in respiratory diseases. It was concluded that PLGA M/NPs are the promising drug delivery vehicles for the treatment of respiratory diseases due to their advantages of low toxicity, high bioavailability, high drug loading capacity, plasticity and modifiability. And at the end, we presented an outlook on future research directions, aiming to provide some new ideas for future research directions and hopefully to promote their widespread application in clinical treatment.

PLGA-Based Micro/Nanoparticles: An Overview of Their Applications in Respiratory Diseases Respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), are critical areas of medical research, as millions of people are affected worldwide. In fact, more than 9 million deaths worldwide were associated with respiratory diseases in 2016, equivalent to 15% of global deaths, and the prevalence is increasing every year as the population ages. Due to inadequate treatment options, the treatments for many respiratory diseases are limited to relieving symptoms rather than curing the disease. Therefore, new therapeutic strategies for respiratory diseases are urgently needed. Poly (lactic-co-glycolic acid) micro/nanoparticles (PLGA M/NPs) have good biocompatibility, biodegradability and unique physical and chemical properties, making them one of the most popular and effective drug delivery polymers. In this review, we summarized the synthesis and modification methods of PLGA M/NPs and their applications in the treatment of respiratory diseases (asthma, COPD, cystic fibrosis (CF), etc.) and also discussed the research progress and current research status of PLGA M/NPs in respiratory diseases. It was concluded that PLGA M/NPs are the promising drug delivery vehicles for the treatment of respiratory diseases due to their advantages of low toxicity, high bioavailability, high drug loading capacity, plasticity and modifiability. And at the end, we presented an outlook on future research directions, aiming to provide some new ideas for future research directions and hopefully to promote their widespread application in clinical treatment. Respiratory diseases are common and frequent diseases, and the main lesions are in the trachea, bronchi, lungs and chest. Patients with mild cases often manifest as cough, chest pain and affected breathing, while patients with severe cases have difficulty breathing, a sense of oxygen deprivation, and even respiratory failure and eventually death [1]. Respiratory diseases have always been a major disease plaguing human beings, especially since the outbreak of COVID-19 in 2019; the number of patients with respiratory diseases has been increasing day by day, seriously threatening human life and property security [2]. In 2016, more than 9 million deaths were associated with respiratory diseases, equivalent to 15% of global deaths. Among them, chronic obstructive pulmonary disease (COPD), a relatively common respiratory disease, ranks third among the top ten causes of death worldwide, causing 3.2 million deaths annually, accounting for 81.7% of the total number of deaths from chronic respiratory disease [3]. Asthma, another common respiratory disease, is known to be the most common chronic disease of childhood, and its prevalence has been increasing over the past three decades [4]. Currently, more than 300 million people worldwide suffer from asthma, and the number of patients suffering from asthma may increase to 400 million by 2025 [5,6]. Currently, drugs used to treat respiratory diseases are mainly administered orally, intravenously or inhaled by nebulization into the body for therapeutic purposes [7]. Among them, oral administration is the most preferred route of administration for most drugs due to the advantages of high patient compliance, low cost, ease of administration, non-invasiveness and safety [8]. However, the absorption of orally administered drugs is a complex process, and factors such as the physicochemical properties of the drug, the nature of the dosage form and the physiology of the gastrointestinal tract (GIT) can all affect drug absorption [9]. As there are several absorption barriers in the GIT that prevent foreign substances from entering the body, only certain specific types of molecules can pass through GIT [8,10]. Compared to oral administration, intravenous administration for respiratory diseases can avoid the penetration of the mucosal barrier. However, it is an invasive route of administration that may lead to poor patient compliance and high medical costs during long-term treatment [11]. Moreover, both the intravenous and enteral routes are exposed to partial in vivo clearance mechanisms (e.g., mononuclear phagocytes in the liver and spleen or first chemical modifications in the liver) on the filtration or metabolism of the active ingredient of the drug and on the non-selective distribution of the drug, resulting in lower plasma concentrations of the drug and reduced distribution of the drug at the site of the lesion, directly affecting the therapeutic effect of the drug and increasing the toxic effects on normal tissues [12]. Nebulized inhalation drug delivery can be an alternative to intravenous drug delivery to some extent, and it is a promising administration strategy. Nebulized inhalation treatment of respiratory diseases is the use of the gas flow principle; the water droplets will be impacted into a fine aerosol suspended in the gas through the respiratory tract so that the drug is delivered directly to the lesion so as to achieve the purpose of treatment, with the advantages of easy operation, fast effects and drug delivery directly to the lesion [13,14]. Biomolecules such as peptides, proteins, deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) can be administered by nebulized inhalation, which can significantly increase the bioavailability of biomolecules compared to other drug delivery strategies [15]. The use of nebulized inhalation antibiotics for pulmonary infections can rapidly achieve effective concentrations at the lesion, avoid frequent oral or intravenous administration of high doses of antibiotics, thereby reducing systemic adverse effects, and has promising applications in the treatment of pulmonary bacterial infections and prevention of bacterial resistance [16]. However, nebulized inhalation drug delivery has some disadvantages. For example, alveolar macrophages rapidly remove some of the inhaled drug, resulting in a limited duration of action and a reduced concentration of bacterial inhibition [15,17]. Studies have found that the vast majority of patients with respiratory diseases have varying degrees of airway mucus layer thickening, making it difficult for conventional drugs to reach the lesion [18]. The shortcomings of conventional drug delivery methods include low mucosal penetration efficiency, poor patient compliance with invasive drug delivery, high medical costs, the unsatisfactory biodistribution of drugs, toxic effects on normal tissues and so on. The disadvantages of conventional drug delivery methods include the influence of the internal environment on drug absorption, poor patient compliance with invasive drug delivery, high medical costs, the poor biodistribution of drugs, low mucosal penetration efficiency and toxic effects on normal tissues. However, most of the above-mentioned disadvantages of conventional drug delivery can be solved by micro- and nanotechnology. Micro- and nanotechnology have been widely developed in modern medicine and pharmacy applications, and micro/nanoparticles have significant advantages in achieving targeted drug delivery, drug sustained release, enhancing drug solubility, improving pharmacokinetics, prolonging blood circulation time and reducing the toxic side effects of drugs [19]. Due to their unique physicochemical properties, such as controllable size, good biocompatibility and low cytotoxicity, micro/nanoparticles have become a hot topic of research for researchers in many fields [20,21,22]. Among many micro/nanomaterials, poly (D,L-lactide-co-glycolide) (PLGA) copolymers are approved for medical use by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) as polymeric organic compounds with excellent biocompatibility, biodegradability and unique physical and chemical properties, making them one of the most popular and efficient polymers for drug delivery [23,24,25]. Compared with other conventional drug delivery strategies, PLGA M/NPs have many advantages, such as low toxicity, high bioavailability, controlled drug release, and direct action on lesions through multiple routes of drug delivery. However, their application in the treatment of respiratory diseases is rarely reported. Therefore, this review focused on the application and further prospects of PLGA M/NPs in respiratory diseases. In this review, we firstly introduced the synthesis and modification methods of PLGA M/NPs and summarized their progress in the treatment of respiratory diseases, including asthma, COPD, CF, acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) and acute respiratory infections (ARIs) in the past few years. Common causes are as follows: allergen stimulation, such as animal hair, is a major factor in the development of asthma [26]; smoking is a major trigger for COPD [27]; CF is a genetic disorder usually caused by abnormal genes inherited from both parents [28]; intra- and extra-pulmonary factors such as blunt lung contusion are the main cause of ALI/ARDS [29]; and ARI is usually caused by pathogenic infections [30] (Figure 1). We hope that this review can stimulate scientific ideas to promote the early and widespread use of PLGA M/NPs in clinical applications for the treatment of respiratory diseases. PLGA copolymers are widely used in drug delivery systems for the encapsulation of both hydrophilic and hydrophobic drugs [25,31]. It has been used in cancer treatment, wound healing and antibacterial, antioxidant and anti-inflammatory applications [32,33,34,35,36]. The main products of PLGA decomposition are lactic acid and glycolic acid, which are easily metabolized and cleared by the body; thus, their safety is greatly guaranteed [37]. PLGA M/NPs are commonly prepared by emulsion solvent evaporation, precipitation, spray drying, coacervation and low-temperature spray extraction. The most commonly used methods are briefly described below. In recent years, several methods for the preparation of PLGA M/NPs have been reported in the literature, among which the most commonly used methods are single and double emulsions, which are capable of encapsulating a wide range of drugs with different solubility [38]. The single emulsion (oil-in-water or O/W) method is suitable for encapsulation hydrophobic drugs. In this method, PLGA copolymers and the drugs are dissolved in an organic phase with volatility (e.g., benzyl alcohol, dichloromethane, ethyl acetate, etc.), and the liquid is added drop by drop to an aqueous phase containing a surfactant (e.g., polyvinyl alcohol (PVA), sodium cholate, etc.) in the aqueous phase, and then the mixture is sonicated, followed by stirring and evaporation to remove the organic solvent, and the diffusion of the organic solvent, and the counter-diffusion of water in the emulsion droplets result in the formation of polymer nanoparticles [39]. When encapsulating hydrophilic drugs, the double emulsion method is used to dissolve the drug in the aqueous phase first and dissolve PLGA copolymers in the organic phase, and then add the aqueous phase to the organic phase and perform the ultrasonic treatment to form a primary water-in-oil (W/O) emulsion. Then the primary emulsion is added to the aqueous phase, dissolved with a surfactant, and then sonicated and stirred to volatilize the organic solvent so that the microparticles are formed by water-in-oil-in-water (W/O/W) emulsion [40]. The single emulsion method has shown good results in the treatment of neurological diseases [41]. Fernández et al. used rasagiline mesylate microencapsulated into PLGA microspheres by the single emulsion method in an animal model of Parkinson’s disease and found that it promoted neuroprotection during the disease process [42]. The double emulsion method is often used to load proteins, nucleic acids and antiviral drugs [43,44,45]. Azizi et al. applied this method to prepare spherical PLGA NPs loaded with chondroitinase ABC for application in the treatment of spinal cord injury, which had potential as drug candidates for spinal cord repair, functional recovery and axonal regeneration [46]. Both single and double emulsion methods can control the size of microparticles by varying the ratio of drugs to PLGA copolymers, PLGA copolymers concentration, organic solvent, surfactant concentration in the aqueous phase, the nature of the solvent and the stirring speed. However, both methods usually have batch-to-batch variation, and the stability of the carriers of protein drug carriers prepared by these methods is limited due to the degradation of the protein at the water interface and the huge pressure of homogenization leading to the unfolding of the protein sheets [47]. The basic principle of the spray drying method is to use the atomizer to disperse the material liquid into fine droplets and evaporate the solvent rapidly in the hot drying medium to form dry powder products, generally including four stages: (1) Atomization of the material liquid; (2) Contact mixing of the fog group with the hot drying medium; (3) Evaporation drying of the fog droplets; (4) Separation of the dry product from the drying medium. The spray drying method has the advantages of speed, convenience and few processing parameters and is suitable for industrially scalable processing [48,49]. This method requires the preparation of water in oil or solid in oil emulsions, which is then sprayed in a hot air stream to solidify the microparticles. The solvent used in the emulsion depends on the hydrophilic and hydrophobic nature of the encapsulated drug [40]. The spray drying method is suitable for wrapping hydrophilic and hydrophobic drugs, and due to the mild preparation conditions, it can also be used to wrap some condition-sensitive compounds. It has the advantages of speed, convenience and few processing parameters, making it suitable for industrial scale-up production. The main disadvantage of this method is that some of the emulsion will adhere to the inside of the nano-sprayer and cannot be recovered, leading to waste of the product [38,50,51,52]. In recent years, the olfactory mucosa has been recognized as a pathway to the brain and central nervous system, causing it to circumvent restrictions such as the blood-brain barrier [53]. Lena et al. used the spray drying method to develop homogeneous and reproducible PLGA NPs with high encapsulation rates and smooth surfaces and embedded them in chitosan particles, thus enhancing their absorption into the olfactory mucosa [54]. In a clinical study, poly (methacrylic acid-co-methyl methacrylate) (Eudragit-S100) encapsulated PLGA NPs prepared by spray drying method were demonstrated to have high oral bioavailability, enhanced sustained release and good targeting [55]. The precipitation method is a simple step, low cost, and the ability to build structure and function on demand synthesis technique [56]. Precipitation differs from emulsion-based methods (emulsification-diffusion, emulsion-evaporation and salting-out techniques) in that it is a rapid and straightforward method for the synthesis of micro/nanoparticles. In this method, PLGA copolymers and drugs are first dissolved into a polar organic solvent and then added to a large amount of aqueous phase, and the whole system undergoes phase separation, leading to the formation of PLGA M/NPs encapsulated with the drug, and then the organic solvent can be simply removed by evaporation [57]. Unfortunately, this method is not suitable for wrapping hydrophilic drugs because hydrophilic drugs cannot form favorable interactions with PLGA copolymers in the aqueous phase [58]. Razan et al. successfully prepared paclitaxel-PLGA-NPs to inhibit the proliferation of MCF-7 breast cancer cells using the nano-precipitation technique. They found that paclitaxel-loaded PLGA-NPs exhibited high efficacy against MCF-7 cells while showing no toxicity to normal MCF-10A cells [59]. In addition, Arjun et al. investigated PLGA-based NPs containing folic acid-targeted genistein loading by nanoprecipitation and demonstrated that they significantly enhanced cellular uptake and thus exerted better anticancer activity [60]. Coacervation is a method for preparing nanoscale or micron-scale biodegradable polymers by liquid-liquid phase separation techniques. It can facilitate polymer interactions by changing the ionic strength within the system, changing the temperature or adding a non-solvent to precipitate the polymer [61]. Coacervation methods are divided into mono-coacervation and complex-coacervation methods. The mono-coacervation method uses a hydrophilic electrolyte or a non-electrolyte as a coagulant to reduce the solubility of nanomaterials and cause them to agglomerate into microparticles. The complex-coacervation method uses two polymer materials with opposite charges to form a vesicle, which reduces the solubility and causes the microparticles to agglomerate and precipitate out of the solvent [40,62]. Peter et al. used the coacervation method to produce flexible PLGA-based implants for loading ciprofloxacin hydrochloride and showed that ciprofloxacin hydrochloride could be released at a controlled rate in vitro for up to 65 days [63]. When using the single and double emulsion method of particle preparation, the choice of solvent and mixing rate can affect the encapsulation efficiency and final particle size, so the particles are often used as injectable microsphere formulations [40]. Due to the mild preparation conditions of the spray drying method, it is often used to encapsulate environmentally sensitive contents such as nucleic acids and proteins to protect them from significant loss of activity. However, the disadvantage is that the particles adhere to the inner wall of the spray dryer during the synthesis process, causing some degree of loss [50,64,65,66]. The precipitation method is the simplest laboratory-based method for the preparation of polymer particles that has been consistently reported to date [67]. This synthetic method allows the synthesis of amphiphilic particles and is prepared without the need for high-shear homogenization techniques, ultracentrifugation or surfactants. However, this method has significant disadvantages, such as the concentration of the resulting particles is usually very low, and the particles cannot be lyophilized using freeze-drying techniques [68]. Furthermore, the coacervation method is a process focused on the preparation of micron-scale biodegradable polymer encapsulation formulations through liquid-liquid phase separation techniques. The desired morphology and size of the microspheres can be obtained by adjusting processing parameters such as polymer concentration, quenching temperature, quenching time and solvent composition [69,70,71]. In summary, different synthetic methods allow easy processing and fabrication of PLGA particles of various shapes and sizes that can be adapted to deliver different drugs as well as different delivery methods. This reinforces the fact that PLGA polymer is a very promising drug delivery vehicle. The following is the summary of the above synthesis methods and their application (Table 1). In order to make PLGA M/NPs well-targeted, long-circulating in vivo and readily available for cellular uptake, the particles are generally modified by cationic, hydrophilic and targeting modifications [93]. The fundamental purpose of employing M/NPs in drug delivery is to use these carriers to carry and protect the loaded drugs and ultimately deliver them to the intended site. In practice, however, the fate of PLGA M/NPs in vivo is influenced by many factors, such as their surface charge, hydrophilicity/hydrophobicity and in vivo targeting ability [25]. In order to synthesize PLGA M/NPs with the desired properties, their precise design is required. To this end, surface modification has become the main strategy for solving these problems. [93]. PLGA M/NPs are widely used as a carrier for drug delivery systems due to their excellent biocompatibility and biodegradability, but they cannot easily adhere to certain negatively charged mucous membranes in the body because of their inherent negative charge. The intestinal mucosa contains many mucins with sialic acid, which are negatively charged, and it is difficult for PLGA M/NPs to adhere to its surface due to mutual repulsion of the same charges [94]. Therefore, cationic modification of PLGA M/NPs can greatly improve their ability to adhere to certain sites in vivo. Commonly used cationic modification substances include cetyltrimethylammonium bromide (CATB), chitosan (CS) and so on. Ramovatar et al. tested the anticancer activity of PLGA NPs encapsulated with curcumin modified with CATB in a triple negative breast cancer cell line (MDA-MB-231 cells) and showed significantly better cellular uptake, cytotoxicity and anticancer activity than unmodified PLGA NPs [93]. In addition, Dilip et al. demonstrated that chitosan-modified PLGA NPs had clearance, were readily absorbed by the body and induced significant systemic and mucosal immune responses in nasal vaccine delivery [95]. Polyethylene glycol (PEG) is the most commonly used hydrophilic modified copolymer and is now used in many applications because of its good biocompatibility, water dispersibility, stability and ease of grafting [96]. Vllasaliu et al. and Owens et al. demonstrated that after PEG modification, the hydrated layer of PEG chains could effectively prevent the recognition and binding of tonin proteins to plasma proteins and reduce the phagocytosis of the reticuloendothelial system (RES), thus increasing the stability of drug-loaded nanoparticles, prolonging the in vivo circulation time and achieving controlled drug release [97,98]. Besides, Zohreh et al. used low molecular weight chitosan (LMWC) as a surface coating encapsulated on the outside of PLGA NPs. The hydrophilic LMWC layer protected the nanodrug under neutral pH conditions, effectively avoiding the clearance of nanoparticles by J774A.1 macrophages [99]. In recent years, advanced drug delivery systems have evolved from basic drug loading to intelligent drug delivery systems, which now focus on two main aspects, including targeting and stimulus responsiveness. Targeted micro/nanoparticles are able to target specific organs, tumors or inflammatory sites with significantly better therapeutic effects than non-targeted systems, which can largely reduce adverse drug reactions and multidrug resistance problems [100,101]. Targeted micro/nanoparticles are generally coated with specific ligands such as antibodies, small molecule peptides and aptamers that bind specifically and with high affinity to the target on a specific cell surface. Yang et al. achieved the targeting ability to the site of inflammation by transplanting γ3 peptide on the surface of PLGA NPs, through which the γ3 peptide could specifically bind to intercellular adhesion molecule-1 (ICAM-1). The drug-loaded γ3 PLGA NPs exhibited excellent antibacterial ability and effectively reduced inflammation and immune response in mice with acute lung infection [102]. In addition, Zhang et al. found that PLGA MPs loaded with (2-[(Aminocarbonyl)amino]-5-(4-fluorophenyl)-3-thiophenecarboxamide (TPCA-1) and coated with anti-ICAM-1 antibody on the surface were able to target inflamed lungs by intravenous injection, thereby reducing lung inflammation and injury [103]. Besides, Spence et al. prepared PLGA NPs modified with the natural Siglec ligand, di(α2→8) N-acetylneuraminic acid (α2,8 NANA-NP) and demonstrated that the modified PLGA NPs blocked lipopolysaccharide-induced production of inflammatory cytokines [104]. Asthma is a heterogeneous disease characterized by chronic airway inflammation and airway hyperresponsiveness. The main features include chronic airway inflammation, high airway reactivity to multiple stimuli, variable and reversible airflow limitation and a series of airway structural changes over the course of the disease, known as airway remodeling [105]. Clinical manifestations include recurrent episodes of wheezing, shortness of breath, chest tightness or coughing, often occurring or worsening at night and in the early hours of the morning, with most patients relieved on their own or with treatment [106]. The common drugs used to treat asthma are mainly bronchodilators and inhaled corticosteroids. However, long-term application of corticosteroids can cause a variety of serious side effects and increase the mortality rate of patients. Several studies have demonstrated that delivery using micro-nanocarriers is a promising strategy compared to conventional delivery methods due to their ability to target tissues, enhancing therapeutic efficacy while minimizing systemic side effects [12]. The morphology of the particles can affect their distribution in the organisms [107,108]. Decuzzi et al. found that the accumulation concentration of disc-shaped particles in the lung was significantly higher than that of spherical or quasi-hemispherical particles [109]. Park et al. prepared curcumin-containing PLGA-based microscale discoidal polymeric particles (Cur-PLGA-DPPs) and found that the number of inflammatory cells in asthmatic mice was significantly reduced by intravenous injection of Cur-PLGA-DPPs and the thickness of bronchial walls and the proliferation of cupped cells in asthmatic mice (Figure 2A) [110]. This might be attributed to enhanced curcumin bioavailability upon administration of curcumin as discoidal polymeric particles. Besides, whether the particles are porous or not also affects the distribution in the body. Oh et al. prepared porous PLGA MPs using ammonium bicarbonate as a porogenic agent and delivered PLGA MPs to mice via pulmonary administration. It was demonstrated that the lung absorption rate of porous PLGA MPs was significantly higher than that of non-porous PLGA MPs, and their therapeutic effects were also verified in asthmatic mice. Mice in the budesonide-loaded porous PLGA MPs group showed significantly lower levels of inflammation and significantly reduced airway hyperresponsiveness compared to the free budesonide and budesonide-loaded non-porous PLGA MPs-treated groups [88]. This might be because the lung uptake efficiency of porous PLGA particles is higher than that of non-porous PLGA particles. In addition, the size of the particles also affects how particles are deposited in the lungs. The study proved that large porous particles were more likely to accumulate in the lungs compared to small non-porous particles [111]. However, large nanoparticles have difficulty in crossing the mucus layer and maintaining good long circulation in vivo, whereas nanoparticles with a particle size of 200 nm or even smaller can easily pass through the mucus layer and maintain good long circulation in vivo [12]. Polyethylene glycol (PEG) modification is currently considered a strategy with the potential to penetrate the mucus layer of asthmatic lungs and reduce phagocytosis by macrophages. This is mainly because the hydrated layer of PEG chains effectively blocks the recognition of plasma proteins and myotonic proteins to inhibit their binding and reduces the phagocytosis of the reticuloendothelial system (RES), thus improving the stability of drug-loaded nanoparticles and enhancing the circulating half-life [97,98,112]. Li et al. investigated the effects of different PEG molecular weights and molar ratios on drug release, mucus penetration, macrophage uptake, lung accumulation and the in vivo distribution of PEG-modified PLGA MPs and showed that at a molar ratio of 1:1, PEG 2000 modified PLGA MPs could not only rapidly penetrate the mucus layer by pulmonary administration, but also accumulate in the lung for a long time (Figure 2B) [113]. With the development of treatment technology, some emerging treatments have also emerged, such as immunotherapy and gene therapy in the treatment of respiratory diseases. Allergen-specific immunotherapy (SIT) is currently an excellent treatment for allergic diseases. SIT can stimulate the increase of Treg cells in patients, suppress the production of specific IgE and inhibit the activity of allergen-specific effector T cells [114,115]. The tumor necrosis factor alpha-induced protein 3 (TNFAIP3/A20) has been shown to regulate a variety of immune cell functions and is involved in maintaining immune homeostasis in vivo [116]. Luo et al. synthesized the PLGA-based nanovaccine by wrapping ovalbumin (OVA) and A20 and applied this nanovaccine to a mouse model of asthma via nasal drops. The results showed that the as-prepared nanovaccine significantly inhibited the Th2 inflammatory response and promoted the production of Treg cells [117]. In addition, it has been demonstrated that thousands of long-stranded non-coding RNAs are differentially expressed in macrophage polarisation, with DNA methyltransferase 3A opposite strand (Dnmt3aos), located on the antisense strand of Dnmt3a, being a known lncRNA that plays a key role in macrophage polarisation and may therefore be a potential target for the treatment of allergic asthma [118]. Pei et al. wrapped a Dnmt3aossmart silencer consisting of three small interfering RNAs (siRNAs) and three antisense oligonucleotides (ASOs) inside PLGA and wrapped exosomal membranes from M2 macrophages around the overall surface of PLGA, exploiting the homing properties of the exosomal membranes for stable and efficient target delivery by intravenous injection (Figure 2C,D). The results suggested that by silencing Dnmt3aos, a key target gene in allergic asthma, airway inflammation could be reduced, leading to effective treatment of allergic asthma [119]. COPD was ranked as the third leading cause of death globally in 2016, and with approximately 300 million people worldwide reported to have COPD as early as 2017, it has now become a major public health problem characterized by persistent respiratory symptoms and airflow limitation, usually associated with airway and/or alveolar abnormalities caused by significant exposure to harmful particles or gases [120]. Currently commonly used therapeutic drugs include bronchodilators (such as β1 adrenergic agonists, anticholinergics and theophyllines), glucocorticoids and expectorants, etc. [121]. However, the therapeutic effects of these drugs on COPD need to be improved, and their use is limited to the clinical treatment of COPD, and some of them have certain adverse effects; therefore, there is an urgent to develop safe and efficient drugs for the clinical treatment of COPD [122]. In the past few decades, it has been demonstrated that macrophages could be activated in different ways, and in addition to the classical activation of pro-inflammatory activation leading to host defense responses, macrophages activated in different ways could instead promote the dissipation of inflammation and facilitate tissue repair [123,124,125]. Therefore, the activation of macrophages through specific pathways to promote inflammation regression may have potential clinical applications. Noort et al. demonstrated that the small heat-shock protein alpha B-crystallin (HSPB5) could exert anti-inflammatory effects by activating macrophages via endosomal/phagosomal CD14 and Toll-like receptors 1 and 2. Subsequently, HSPB5 was encapsulated in porous PLGA MPs for the treatment of COPD, and it was found that HSPB5-loaded PLGA MPs were selectively taken up by alveolar macrophages during intratracheal administration, and the anti-inflammatory effect was associated with activation of an immune-regulatory macrophage response via TLR1/2, and CD14 as an essential co-receptor, which significantly inhibited neutrophil and lymphocyte infiltration in the lungs. In contrast, a 30-fold higher dose of free soluble HSPB5 still had no significant therapeutic effect [126]. RNA-based therapies have become a hot topic with the advent of RNA interference (RNAi) technology [127]. Among them, microRNAs (miRNAs) are widely used for the treatment of respiratory diseases because of their endogenous and editable nature to inhibit gene expression [128,129,130]. For example, miR-146a plays a key role in the pathogenesis of COPD, and it has potential therapeutic value due to its ability to downregulate the expression of interleukin-1 receptor-associated kinase (IRAK-1) and thus inhibit IL-1R signaling [131,132]. However, the precise delivery of miRNA to the desired site is considered to be one of the major problems in the development of miRNA therapies, and secondly, free miRNAs are unstable, and their physicochemical properties all affect the efficiency of these molecules in penetrating biological barriers [133]. Mohamed et al. used a mixture of L-leucine and mannitol as dispersion enhancers and protective excipients to prepare the nanocomposite microparticles (NCMPs) with cations, enabling them to adsorb and protect negatively charged miR-146a for efficient deposition in the deep lung and to downregulate IRAK-1 expression for anti-inflammatory effects via pulmonary administration, demonstrating their potential for treating COPD (Figure 3A) [134]. In addition, some small molecule drugs, such as ibuprofen, have been shown to treat COPD, but their low permeability across the mucosal barrier precludes their clinical use. To address this issue, Vij et al. loaded ibuprofen into PLGA NPs modified with a maleimide-capped PEG and PEG (5:95) copolymer. The aim was to reduce the neutrophil-mediated inflammatory response in COPD by exploiting the mucus inertness of PEG and the coupling properties of maleimide to achieve penetration and targeting (Figure 3B) [135]. The results showed that the NPs were able to specifically bind and release the drug into neutrophils after airway transport, thereby treating COPD by controlling neutrophil inflammation. CF is an inherited disease caused by an abnormal gene inherited from both biological parents. CF affects nearly 70,000 patients worldwide and is most common, especially among non-Hispanic whites [136]. It is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene resulting in decreased salt transport, decreased water movement, mucus accumulation, and obstruction of the airway, leading to recurrent infections triggering lung inflammation and injury. Respiratory failure is the most common cause of death in CF patients [137,138]. The current treatment for CF patients is mainly daily nebulized inhaled medications to dilute the mucus and help the patient expel it from the airways, along with oral or inhaled antibiotics to fight the infection. Lung transplantation is recommended for some patients with severe CF [139]. Chronic lung infections caused by a number of bacteria are the leading cause of death in CF patients, of which Pseudomonas aeruginosa has been shown to be the main pathogen [140]. Since P. aeruginosa infection can form bacterial biofilms that protect the bacteria from reaching minimum inhibition concentration (MIC) at the site of action and kill the bacteria, which leads to the emergence of bacterial drug resistance [141]. Therefore, achieving the controlled release of antibiotics at the lesion and increasing the penetration of the drug into the bacterial biofilm may be an effective strategy for treating infections with drug-resistant strains of bacteria [142,143]. The ciprofloxacin-loaded PLGA NPs were synthesized with a size of 190.4 ± 28.6 nm, with a drug encapsulation rate of 79%, and the nanoparticles were shown to be safe and non-toxic at the minimum inhibitory concentration (MIC). Overall, in the context of pulmonary drug delivery, they were expected to be effective in penetrating the mucus layer and crossing bacterial biofilms to exert antibacterial effects (Figure 4A) [144]. Exotoxin A (ETA) is the most virulent extracellular component of P. aeruginosa and can be extracted from over 90% of P. aeruginosa clinical isolates [145]. ETA is a T-dependent antigen that stimulates T cells and leads to the production of memory cells [146]. A novel PLGA-based vaccine candidate containing ETA (ETA-PLGA NPs) against Pseudomonas aeruginosa was synthesized and characterized in vitro and in vivo. The results showed that ETA could act as a suitable immunogenic substance to stimulate the immune response. Compared to the free ETA-treated group, ETA-PLGA NPs by intramuscular injection promoted the expression of cytokines such as TNF-α, INF-γ, IL-17A and IL-4 and enhanced the IgG response in immunized mice, indicating that ETA-PLGA NPs could increase its functional activity by reducing bacterial transmission [147]. Because of the ability of phages to kill bacteria within bacterial biofilms, their ability to suppress infections caused by drug-resistant strains, and their specificity for specific pathogens, phage therapy has been developed in recent years to treat drug-resistant bacteria as an alternative to antibiotic therapy [148,149]. In addition to P. aeruginosa, Staphylococcus aureus is also a common pathogenic bacterium that can cause pulmonary infections [150]. Kalelkar et al. designed PLGA MPs for the endotracheal delivery of phages active against S. aureus (Figure 4B). Phage loading onto the surface of MPs was effectively achieved by incubating MPS in a phage solution. In vitro, the MPs were able to infect and lyse S. aureus and were also observed to inhibit the growth of S. aureus in the supernatant of mucoid sputum and sputum specimens from CF patients. The MPs also showed effective inhibition of S. aureus in a mouse model of acute lung infection [151]. Since the airway is the primary target site for CF treatment, inhaled drug delivery has become the primary treatment for CF [149,152]. However, the airways of patients with severe respiratory disease are often covered by the mucus layer, a complex barrier composed of highly cross-linked mucin chains, water and other gel-like components that can prevent the accumulation of various drugs in the lungs and interfere with their efficacy [153,154,155,156]. N-acetyl cysteine (NAC) is a commonly used mucolytic agent that cleaves the disulfide bonds of mucin fibers and thus acts to reduce the viscosity and enlarge the pore size of mucus/sputum in vitro and in vivo [157]. Cristallini et al. synthesized PLGA MPs or gellan gum, and NAC was encapsulated into PLGA MPs to achieve the mucolytic effects (Figure 4C). The results showed that NAC could be released from the PLGA MPs and diffuse into the mucus, thereby achieving dilution of the mucus layer. Therefore, the authors concluded that the as-prepared PLGA MPs had potential as effective carriers for the treatment of CF [158]. ARDS refers to acute diffuse lung injury and subsequent development of acute respiratory failure caused by various intra- and extra-pulmonary pathogenic factors and is a common and severe clinical syndrome with high incidence and mortality [159,160]. Blunt chest trauma, pneumonia, inhalation injuries and ventilator-induced lung injury (VILI) have been reported as the main or pulmonary causes of ARDS [29]. The main pathological features are damage to the pulmonary microvascular endothelium and alveolar epithelium due to inflammation and increased vascular permeability, which in turn leads to pulmonary edema and hyaline membrane formation. The main pathophysiological changes are reduced lung volumes, reduced lung compliance and severe ventilation/blood flow ratio imbalance [161,162]. ALI and ARDS are two stages of the same disease process, with ALI representing the early and less severe stage and ARDS representing the later and more severe stage. There is no specific effective therapy for ALI/ARDS, and early administration of dexamethasone can greatly reduce the duration of mechanical ventilation and mortality in patients with ARDS [163]. However, there are significant side effects associated with the long-term use of dexamethasone [164], and there is a need to develop delivery systems with lung-targeting potential. Kotta et al. synthesized dexamethasone-loaded lipopolymer PLGA MPs with pulmonary targeting using the single emulsion technique, administered intravenously to rats with lipopolysaccharide-induced ARDS model and were able to significantly inhibit the inflammatory response and reduce lung tissue damage. Therefore, this delivery system would contribute to the reduction of dose and frequency of drug administration, thereby reducing side effects [72]. Inflamed endothelial cells have been shown to upregulate vascular cell adhesion molecule-1 (VCAM-1) expression as a means of recruiting immune cells, such as leukocytes expressing very late antigen-4 (VLA-4) [165]. Park et al. used wild-type cells genetically engineered to express VLA-4 consisting of integrins α4 and β1, which were then wrapped in dexamethasone-loaded PLGA NPs through the plasma membrane of the genetically engineered cells, resulting in the synthesis of a cell membrane-encapsulated DEX-NP expressing VLA-4 (VLA-DEX-NP), which was administered intravenously to mice (Figure 5A) [166]. The study further confirmed the therapeutic effect of VLA-DEX-NP on lung inflammation through hematoxylin and eosin (H&E) stained lung sections (Figure 5B). Compared with the control group, free DEX group, and WT-DEX-NP group, the VLA-DEX-NP group showed the least inflammatory cell infiltration and no significant thickening of the bronchial wall, confirming the significant therapeutic effect of VLA-DEX-NP on lung inflammation. In addition, oxidative stress has been reported to play a crucial role in the development and progression of ALI, and mitochondria are the main organelles attacked by reactive oxygen species (ROS). Therefore, the possibility of targeted delivery of antioxidants to mitochondria has become a difficult and hot topic in ALI research [167,168]. Jin et al. designed sialic acid (SA)-modified lung-targeted MPs, which encapsulated antioxidant-active curcumin inside, combined with E-selectin expressed on inflamed lung endothelial cells and increased PLGA MPs accumulation at the site of inflammation, targeted mitochondria for sustained release of curcumin [169]. The results showed that these MPs could increase the cellular uptake of inflammatory cells by binding to E-selectin receptors and inhibit apoptosis by reducing ROS production. In addition, the therapeutic effect of such MPs on murine models of ALI can be demonstrated in terms of both oxidative stress production and pro-inflammatory cytokine expression. ARIs, also known as acute respiratory syndromes (ARSs), are responsible for about 4 million deaths annually and are the major cause of death among children under 5 years old [170,171]. Infections of the lower respiratory tract are mainly caused by bacteria and viruses, including conditions such as the common cold, bronchitis pharyngitis, laryngitis, tuberculosis and pneumonia [30]. With the emergence of new respiratory viruses and related diseases, such as the novel coronavirus SARS-CoV-2, which emerged in late 2019, and COVID-19 caused by its infection, the severity and extent of ARI are increasing each year [172,173]. COVID-19 mainly affects the lower respiratory system, and the most common symptoms in patients are fever, cough and shortness of breath. Other symptoms include malaise, vomiting, diarrhea, decreased sense of taste and smell, etc. In addition, vital organs such as the liver, heart, kidneys and gastrointestinal tract can be affected, leading to multi-organ complications [174,175]. In order to control the global spread of COVID-19, many preventive measures have been taken, such as home quarantine, home office and minimizing the movement of people. Unfortunately, even with these precautions, the cumulative number of confirmed cases per day in each country continues to rise, as there is no specific treatment or vaccine available for disease control [176,177]. Remdesivir (RDV) was one of the first drugs approved by the US Food and Drug Administration for the treatment of COVID-19, but its use has not been rationalized due to poor patient compliance with intravenous administration and the increased risk of transmission of infection from further interpersonal contact due to hospital infusions [178,179]. Patki et al. developed the self-injectable extended-release subcutaneous injection of RDV (SelfExRem) by using PLGA MPs, minimizing face-to-face contact, hospitalization and frequency of dosing, thereby significantly increasing the availability of RDV for patients in the early stages of infection and for those with limited hospital or clinical facilities (Figure 6A) [180]. Cytokine storm syndrome (CSS) is strongly associated with poor prognosis in COVID-19 [181]. Tan et al. developed an intravenous drug delivery system based on bionanocarriers for the anti-inflammatory and antiviral treatment of COVID-19 (Figure 6B). Lopinavir (LPV) was first encapsulated in PLGA NPs to form PLGA-LPV NPs, and then macrophage membranes were wrapped to form drug-loaded macrophage bionanocarriers (PLGA-LPV@M) due to the inherited surface receptors of macrophage membranes, PLGA@M could masquerade as mini-macrophages and competitively took up a variety of pro-inflammatory substances to inhibit the activation of macrophages and neutrophils, ultimately alleviating or stopping the progression of CSS so that they could significantly relieve inflammation and reduce tissue viral loading capacity. This study might have great application in the treatment of COVID-19 [182]. In addition, ivermectin (IVM) has been found to inhibit SARS-CoV-2 replication in vitro; however, the low plasma concentration of IVM in the lesion after oral administration makes oral-free IVM unable to achieve the desired therapeutic effect [183]. Zheng et al. synthesized PLGA NPs loaded with IVM, coated with chitosan (CS), and finally adsorbed on the surface of red blood cells (RBC) to prepare RBC-CSPNPs [184]. As the intravenously injected nanoparticles do not resist the shear stress between the RBC and the small capillaries, they are dislodged from the pulmonary capillaries [185], thus achieving the ability to target nanoparticles to the lung (Figure 6C). The study has shown that nanoparticles modified with CS significantly increase the adsorption efficiency of nanoparticles on RBC, resulting in enhanced targeting to the lung, prolonged circulation of nanoparticles and reduced clearance of nanoparticles in vivo. Staphylococcus aureus has gradually become an important pathogen of pneumonia, and the emergence and infection of methicillin-resistant Staphylococcus aureus (MRSA) has aggravated the morbidity and mortality of patients [186,187]. Studies have shown that about 81% of patients with community-acquired pneumonia (CAP) caused by S. aureus infections become seriously ill, requiring intensive care therapy, with a mortality rate of 29% [188]. The production of virulence factors panton-valentine leucocidin (PVL), alpha-hemolysin (Hla) and biofilms are currently considered as several factors that may contribute to exacerbations of the disease [189,190]. Lin et al. designed lysozyme-encapsulated PLGA MPs for intravenous injection, which had a good affinity with the lung, high stability, effectively eliminated MRSA, reduced biofilm formation and had a good anti-inflammatory effect, thus improving the survival rate of mice [191]. In addition to S. aureus, Pseudomonas aeruginosa and Klebsiella are also the main pathogens of pneumonia [192,193]. Encapsulation of P. aeruginosa type III secretion system protein POPB and its chaperone protein pCRH in PLGA NPs could be used as nasal vaccination to protect mice from acutely fatal P. aeruginosa pneumonia [194]. In addition, Qu et al. synthesized PLGA MPs loaded with cefquinolone (CEQ) for the treatment of Klebsiella pneumonia. The results showed that intravenous administration of CEQ-loaded PLGA MPs had good lung accumulation and reduced lung bacterial colony counts and expression of inflammatory cytokines. Therefore, targeted delivery of CEQ was expected to be an alternative method for the control of important zoonotic pathogens [195]. At present, the research and applications of micro- and nanotechnology in drug delivery are developing rapidly [196,197]. PLGA M/NPs are considered promising drug delivery vehicles because of their biocompatibility, high bioavailability, biodegradability, surface modifiability and active targeting [198,199]. Although the application of PLGA M/NPs offers more options for the prevention and treatment of respiratory diseases, there are still some shortcomings. For example, PLGA M/NPs have an impact on the stability of the loaded proteins during preparation and storage, mainly due to the accumulation of acidic monomers and lactic and glycolic acids generated by their hydrolysis within the delivery system, which leads to a significant decrease in the pH of the local microenvironment denaturing the encapsulated proteins [200]. And different methods of preparing PLGA M/NPs can also have different degrees of adverse effects on the secondary structure of certain proteins [201]. Besides, there may be potential safety issues with nanomaterials, and there are many studies showing that nanoparticles may have adverse effects on healthy tissues and organs [202,203]. What’s more, the lack of uniformity in particle size of nanomedicines resulting in poor reproducibility [204] and the high cost of preparation for commercialization and mass production are still many challenges [205]. In recent years, with the efforts of researchers, some solutions to the above problems have been found. To address the problems associated with protein degradation, loading, etc. There has been research into encapsulating proteins by complexing them with zinc or adding antacid excipients to the buffer to protect the protein activity [206]. To address potential safety issues, we can use means to accumulate as many M/NPs as possible in the diseased lungs. For example, positively charged micro-nanoparticles that do not effectively penetrate airway mucus can be modified with a cationic carrier on their surface to have a neutral or anionic group. In addition, active targeting could provide new ideas for optimizing drug delivery systems by exploring receptors that are expressed only at the lung site and modifying the surface of the M/NPs so that it can specifically bind to that receptor, thus enabling active targeting of the pathogenic site. Besides, M/NPs with responsive drug release capability can be designed to release drugs only at the site of disease, thereby improving therapeutic efficiency and reducing systemic toxicity [153,207,208,209,210,211]. Synthetic strategies for drug delivery based on micro- and nanotechnology have shown potential possibilities for the treatment of respiratory diseases at both the research and clinical levels. Currently, researchers have developed a number of PLGA M/NPs for the treatment of respiratory system diseases for in vitro experiments regarding pharmacology, biocompatibility and safety, followed by in vivo evaluation using corresponding animal models. To better utilize this technology, researchers should focus on understanding the mechanism of action of the drug or drug delivery system in vivo while taking into account the production methods to ensure a more rational design of M/NPs in the future. More importantly, we still have not established international standards for in vitro and in vivo studies of micro/nanomedicines, and the establishment of uniform standards will facilitate effective intercomparison of studies and thus promote the development of micro/nanomedicines. Development and challenges go hand in hand, and only the existence of currently unsolved problems can drive the development and depth of scientific research. In general, micro/nanomedicines are a technology with extremely long-term development value, and we believe that someday in the future, we will overcome these difficulties and realize the widespread application of micro/nanomedicines in clinical practice.

PMC10002089

Lara Slavec,Ksenija Geršak,Andreja Eberlinc,Tinka Hovnik,Luca Lovrečić,Irena Mlinarič-Raščan,Nataša Karas Kuželički

A Comprehensive Genetic Analysis of Slovenian Families with Multiple Cases of Orofacial Clefts Reveals Novel Variants in the Genes IRF6, GRHL3, and TBX22

21-02-2023

genetics,family study,non-syndromic orofacial cleft,Van der Woude syndrome,X-linked cleft palate with or without ankyloglossia,IRF6,GRHL3,TBX22,whole exome sequencing

Although the aetiology of non-syndromic orofacial clefts (nsOFCs) is usually multifactorial, syndromic OFCs (syOFCs) are often caused by single mutations in known genes. Some syndromes, e.g., Van der Woude syndrome (VWS1; VWS2) and X-linked cleft palate with or without ankyloglossia (CPX), show only minor clinical signs in addition to OFC and are sometimes difficult to differentiate from nsOFCs. We recruited 34 Slovenian multi-case families with apparent nsOFCs (isolated OFCs or OFCs with minor additional facial signs). First, we examined IRF6, GRHL3, and TBX22 by Sanger or whole exome sequencing to identify VWS and CPX families. Next, we examined 72 additional nsOFC genes in the remaining families. Variant validation and co-segregation analysis were performed for each identified variant using Sanger sequencing, real-time quantitative PCR and microarray-based comparative genomic hybridization. We identified six disease-causing variants (three novel) in IRF6, GRHL3, and TBX22 in 21% of families with apparent nsOFCs, suggesting that our sequencing approach is useful for distinguishing syOFCs from nsOFCs. The novel variants, a frameshift variant in exon 7 of IRF6, a splice-altering variant in GRHL3, and a deletion of the coding exons of TBX22, indicate VWS1, VWS2, and CPX, respectively. We also identified five rare variants in nsOFC genes in families without VWS or CPX, but they could not be conclusively linked to nsOFC.

A Comprehensive Genetic Analysis of Slovenian Families with Multiple Cases of Orofacial Clefts Reveals Novel Variants in the Genes IRF6, GRHL3, and TBX22 Although the aetiology of non-syndromic orofacial clefts (nsOFCs) is usually multifactorial, syndromic OFCs (syOFCs) are often caused by single mutations in known genes. Some syndromes, e.g., Van der Woude syndrome (VWS1; VWS2) and X-linked cleft palate with or without ankyloglossia (CPX), show only minor clinical signs in addition to OFC and are sometimes difficult to differentiate from nsOFCs. We recruited 34 Slovenian multi-case families with apparent nsOFCs (isolated OFCs or OFCs with minor additional facial signs). First, we examined IRF6, GRHL3, and TBX22 by Sanger or whole exome sequencing to identify VWS and CPX families. Next, we examined 72 additional nsOFC genes in the remaining families. Variant validation and co-segregation analysis were performed for each identified variant using Sanger sequencing, real-time quantitative PCR and microarray-based comparative genomic hybridization. We identified six disease-causing variants (three novel) in IRF6, GRHL3, and TBX22 in 21% of families with apparent nsOFCs, suggesting that our sequencing approach is useful for distinguishing syOFCs from nsOFCs. The novel variants, a frameshift variant in exon 7 of IRF6, a splice-altering variant in GRHL3, and a deletion of the coding exons of TBX22, indicate VWS1, VWS2, and CPX, respectively. We also identified five rare variants in nsOFC genes in families without VWS or CPX, but they could not be conclusively linked to nsOFC. Orofacial clefts (OFCs), characterised by the incomplete fusion of certain facial or oral structures, are the most common congenital craniofacial anomalies with global widely varying incidence rates by race and ethnicity. In Slovenia, the average incidence of OFCs is around 1/600 live births (period from 1993 to 2012), which is comparable to other European populations where it ranges from 1/500 to 1/1000 [1,2]. OFCs affect various parts of the oral cavity and face (i.e., palate, alveolus, lip, nose) and, accordingly, are often classified into the following groups: cleft lip; cleft lip and alveolus; cleft lip, alveolus, and palate (CLP); and cleft palate (CP). CP is further divided into complete CP (i.e., hard and soft), soft CP, submucous CP, and bifid uvula. Historically, due to common developmental mechanisms and epidemiological aspects, cleft lip, cleft lip and alveolus, and CLP have often been grouped and studied together as cleft lip with or without cleft palate (CL/P) [3]. However, CP is supposed to have a separate aetiology, but with some overlap [3,4]. Typically, CL/P affects one side (unilateral) or both sides (bilateral) of the lip, alveolus, and/or palate, and appears in varying degrees of severity. Most OFCs (approximately 70%) are isolated or non-syndromic (nsOFCs) and occur without other structural and/or functional abnormalities, whereas the remaining 30% of OFCs occur as part of various syndromes (syOFCs), caused by single-gene mutations (i.e., Mendelian inheritance), chromosomal aberrations, or teratogenic factors [5,6]. Most common syOFCs include 22q11.2 deletion syndrome (i.e., DiGeorge or velocardiofacial syndrome), Van der Woude syndrome (VWS), and Pierre Robin sequence (PRS) [7]. Many syndromes in which clefting is a major feature have clearly noticeable phenotypes and are easily diagnosed. Nevertheless, there are some syndromes (e.g., X-linked cleft palate with or without ankyloglossia (CPX), PRS, and VWS) where the clinical signs, apart from OFC, are minor or sometimes even unrecognizable, making some syOFCs difficult to distinguish from nsOFCs. Van der Woude syndrome is one of the most common forms of syOFCs, representing 2% of all OFC cases [8]. The genetic cause can be identified in 75% of VWS cases, with mutations in IRF6 (VWS1; MIM#119300) in approximately 70% of cases and mutations in GRHL3 (VWS2; MIM#606713) in the remaining 5% of cases [9,10]. This autosomal dominant syndrome is inherited with high penetrance but variable phenotypic expression [11]. In addition to OFC, VWS is characterized by congenital lower lip pits and in some cases hypodontia [12]. Interestingly, CP and CL/P may both occur in a single VWS family, which is rare in families with nsOFCs [13]. Further, whereas the lip pit phenotype in VWS patients varies from a single, barely visible elevation/depression to pronounced bilateral lower lip pits, 15% of patients lack lip pits altogether [11,12]. Interestingly, deleterious variants in IRF6 and GRHL3 were also found in nsOFCs [14,15] in addition to VWS. X-linked cleft palate with or without ankyloglossia (MIM#303400) is a rare disorder with a semidominant X-linked inheritance of mutations in TBX22 [16]. It is characterised by a CP phenotype that is most often present in males and ranges from a high-arched palate, bifid uvula, submucous CP, soft CP, to complete CP [17,18]. The main characteristic that divides CPX from nsOFCs is ankyloglossia that is frequently but not always present in affected males and also in female carriers [18]. Family history or pedigree size are sometimes not informative enough to predict the X-linked mode of inheritance, and the diagnosis of CPX may be overlooked [16,19]. Unlike most syOFCs that have a known genetic cause, the aetiology of nsOFCs is complex, since nsOFCs are considered multifactorial disorders that develop due to interactions between genetic and intrauterine environmental factors. Approximately 20% of nsOFC patients come from multi-case families [20]. Individuals with nsOFCs have a significantly increased risk of recurrence in their first-degree relatives (parents, siblings, and offspring) [21]. Moreover, phenotype concordance is 40–60% in monozygotic twins, whereas it is only 3–5% in dizygotic twins, suggesting a significant genetic component in the aetiology of nsOFCs [22,23]. The identification of genetic risk factors for nsOFCs is challenging. To date, many approaches have been used to find candidate regions or genes associated with nsOFCs: cytogenetic studies, linkage analyses, candidate gene association studies (i.e., family- and population-based studies), direct sequencing studies of candidate genes, genome-wide association studies, and studies on animal models [24]. Moreover, in recent years, next-generation sequencing methods, in particular whole exome sequencing (WES), have been increasingly used to determine the genetics of nsOFCs [25,26]. It is a widely accepted hypothesis that complex diseases such as nsOFCs arise from the accumulation of disease-causing variants with relatively high population frequencies (minor allele frequency (MAF) > 5%) [27,28]; however, studies that used different genetic approaches to confirm this hypothesis (i.e., association studies), have explained only a small fraction of heritability of nsOFCs [28,29]. On the other hand, some studies have successfully used WES to identify rare deleterious variants in multi-case families with nsOFCs [30,31,32,33,34,35,36]. In this study, we present the first comprehensive analysis of genetic risk factors for OFCs in the Slovenian population alongside our aim to establish the best diagnostic approach to distinguish between nsOFCs and syOFCs in a cohort of phenotypes resembling nsOFCs and to evaluate a diagnostic gene panel for nsOFCs. A total of 34 Slovenian families with multiple cases of apparent nsOFCs (isolated OFCs or OFCs with minor additional facial signs) were included in the study. Our stepwise diagnostic approach initially examined only three genes implicated in VWS and CPX (i.e., IRF6, GRHL3, and TBX22) using WES and Sanger sequencing. To further determine genetic risk factors for OFCs in Slovenian multi-case families, we later examined 72 additional genes using WES. Utilizing a two-step diagnostic approach enabled us to differentiate between syOFC cases and nsOFC cases. However, the gene panel was not as informative in families with nsOFCs. We were able to identify the genetic cause of OFCs in 21% of families as we discovered three novel genetic variants causing VWS1, VWS2, and CPX. We examined two genes implicated in VWS, IRF6 and GRHL3, and the gene implicated in CPX, TBX22, in 34 multi-case families with apparent nsOFCs (isolated OFCs or OFCs with minor additional facial signs). We identified causal variants confirming VWS in 6 families and CPX in 1 family. In total, 7 of the 34 multi-case families with apparent nsOFCs had at least one member with lip pits, suggesting the diagnosis of VWS. We detected one novel and three previously described heterozygous disease-causing variants in IRF6 (Table 1, Figure 1 and Figure S1) in 5 of the 7 families with suspected VWS. One frameshift, one missense, and two nonsense variants were located in different exons of IRF6 (3, 6, 7, or 9). To the best of our knowledge, the novel variant, a frameshift variant in exon 7, has not yet been described in the literature, in HGMD Professional 2022.2, or in the ClinVar database. No disease-causing variants in IRF6 were identified in families with nsOFCs. IRF6 is intolerant for loss-of-function (LoF) variants (pLI = 1) and shows a degree of intolerance to missense variants (Z = 2.74) as indicated by gnomAD. A missense variant in exon 3 of the IRF6 (NM_006147.4:c.134G>A; rs121434229) was identified in the proband of family 1 (F-1) (Table 1). The substitution was previously detected once in a heterozygous state in gnomAD v2.1.1 (1/251,482 alleles), specifically in one African/African American female. In silico deleteriousness tools for missense substitutions unanimously supported a deleterious effect of the variant on the gene product. The variant is classified as likely pathogenic (PP2, PP3, PP5, PM1, PM2) by ACMG guidelines. The proband of family 1 is female (F-1; IV-1) with a complete CP and two indistinct lower lip pits, but no other detectable congenital abnormalities. She is the second child in the family, and her older male sibling (F-1; IV-2) is unaffected. The proband’s mother (F-1; III-2) was also born with CP and two lower lip pits. The mother’s cousin (F-1; III-5) apparently had CP and died at the age of 1. Other family members were reportedly unaffected although they were not clinically assessed by a medical professional. Co-segregation analysis has shown that the variant is present in all three examined subjects of the family, the proband, affected mother, and unaffected male sibling (Figure 1). In the proband of family 2 (F-2), we detected a nonsense variant in exon 6 of the IRF6 (NM_006147.4:c.622C>T) (Table 1). The presence of this variant results in a premature termination codon. It is not present in gnomAD v2.1.1 and is expected to be a loss-of-function variant. It may also activate nonsense-mediated RNA decay (NMD), resulting in haploinsufficiency. The variant is classified as pathogenic (PVS1, PP5, PM2) by ACMG guidelines. The proband of family 2 is female (F-2; III-3), an only child born with bilateral CLP, two lower lip pits, and dental anomalies, including several missing teeth (hypodontia). The proband’s mother (F-2; II-3) was also born with bilateral CLP, two lower lip pits, and hypodontia. A co-segregation analysis revealed the mother as the affected carrier of the variant. Other family members were reportedly healthy, and the ones available for analysis (F-2; I-1, II-2, II-4, III-1) did not carry the variant (Figure 1). In the proband of family 3 (F-3), we identified a novel 1 bp deletion in exon 7 of the IRF6 (NM_006147.4:c.687delG) (Table 1). This frameshift variant disrupts the reading frame of the sequence and leads to a premature termination codon, which results in the protein product being truncated. This loss-of-function variant may also activate nonsense-mediated RNA decay (NMD), resulting in haploinsufficiency. The variant is not present in gnomAD v2.1.1 and has not been reported before. It is classified as likely pathogenic (PVS1, PM2) using ACMG guidelines. The proband of family 3 is male (F-3; III-1), an only child born with complete CP, two lower lip pits, and hypodontia (aplasia of several teeth). The proband’s mother (F-3; II-2) was also born with CP and lower lip pits, and the maternal grandmother (F-3; I-2) had CP, but they were not available for further phenotyping. Other family members were reportedly unaffected. Only the proband’s mother was available for co-segregation analysis, and she was found to be the variant carrier (Figure 1). In the probands of families 4 (F-4) and 5 (F-5), we identified a nonsense variant in the exon 9 of the IRF6 (NM_006147.4:c.1234C>T; rs1553247595) (Table 1). It leads to the formation of a premature termination codon and is not present in gnomAD v2.1.1. The variant has been shown to reduce IRF6 activity by promoting its degradation on the protein level [44]. Therefore, it is classified as pathogenic (PVS1, PP5, PM2) by ACMG guidelines. The proband of family 4 is male (F-4; III-1), an only child born with unilateral CLP and two lower lip pits. His father (F-4; II-1) has bilateral CLP and lip pits. The proband’s mother (F-4; II-2) and other family members were reportedly unaffected. In addition to the proband, the variant was detected in the affected father, but not the unaffected mother (Figure 1). The proband of family 5 is also a male (F-5; IV-1) and an only child. He has soft CP and two lower lip pits. His father (F-5; III-1) was born with unilateral CLP and two lower lip pits, and the father’s sister (F-5; III-4), mother (F-5; II-2) and aunt (F-5; II-4) all have lower lip pits, whereas the proband’s mother (F-5; III-2) is unaffected. Other family members were also reportedly unaffected. The variant was identified in the affected father, whereas samples from other affected members of his family were not available for the analysis (Figure 1). The remaining 2 of the 7 families with suspected VWS did not have disease-causing variants in IRF6 and no causal variants in GRHL3. Interestingly, we identified a splice-altering variant in a family without suspected syOFC. GRHL3 is intolerant for LoF variants (pLI = 0.99) and shows a small degree of intolerance to missense variants (Z = 1.42) as indicated by gnomAD. In the proband of family 6 (F-6), we identified a novel donor splice site variant located at the position of the last nucleotide of exon 10 in GRHL3 (NM_198173.3:c.1285G>T) (Table 1, Figure 2 and Figure S1). This variant is not present in gnomAD v2.1.1, is not listed in dbSNP154, and has not yet been reported in association with VWS. In silico splice site prediction tools unanimously supported a deleterious effect of the variant. Moreover, it is predicted to be deleterious by MutationTester and CADD (score of 35). The tools’ results indicate that the variant most probably affects splicing and is classified as a variant of uncertain significance (VUS) (PM2, PP3) by ACMG guidelines. The proband of family 6 is female (F-6; III-2), an only child with complete CP. Her father was also born with complete CP (F-6; II-1). Initially, the possibility of VWS was ruled out since they lack lower lip pits. However, a subsequent examination showed an asymmetric lower lip in both the affected father and daughter (Figure 2B), which may subtly indicate the presence of VWS. In addition, the father presents with hypodontia. Other family members were reportedly unaffected. Only the proband’s parents were available for the co-segregation analysis, and the variant was confirmed in the sample of the affected father, but not the unaffected mother (F-6; II-2) (Figure 2A). With the further analysis of the WES data (i.e., computing copy number variations (CNVs)) in families with suspected nsOFC, we have discovered the deletion of TBX22 on the X-chromosome in the proband of family 7 (F-7). Using the Twist Human Core Exome Plus Kit (Twist Bioscience, San Francisco, USA), we covered only the coding exons of TBX22 gene (exons 2–9) and established that the deletion is located in the region with the inner start-stop coordinates chrX:g.79,277,769–79,286,610 (hg19) and spans at least 8.8 kb, affecting the entire gene. We did not detect any deletions of the coding regions of adjacent genes or other coding exons on the proband’s X chromosome. Using microarray-based comparative genomic hybridization (array CGH) analysis on the same DNA sample, we further confirmed a hemizygous deletion of 9.91 kb (arr[GRCh37] Xq21.1(79,277,377_79,287,288)x0) encompassing exons 2–9 of the TBX22 gene (Figure S2). This analysis showed that the non-coding exon 1 of TBX22 is intact and also revealed the first signal 3.8 kb downstream of the TBX22 gene, limiting the size of the deletion and confirming that it does not include other genetic material. The identified deletion, encompassing only TBX22, has not been reported before and is classified as pathogenic by ACMG standards. The proband of family 7 is male (F-7; IV-2), born with complete CP (Figure 3). His brother (F-7; IV-3), father (F-7; III-3), and mother (F-7; III-4) are apparently unaffected. The OFC is inherited through the maternal side. The mother’s grandfather was born with bifid uvula (F-7; I-1), her father (F-7; II-1) with soft CP, and her uncle (F-7; II-3) with an unknown kind of CP. The mother’s two sisters (F-7; III-2, III-6) each have one son with soft CP (F-7; IV-1, IV-4). The family history was reassessed after genetic testing. Ankyloglossia was identified in the proband (F-7; IV-2), his unaffected brother (F-7; IV-3), his mother (F-7; III-4), one of his unaffected aunts (F-7; III-2), both affected cousins (F-7; IV-1, IV-4), his affected grandfather (F-7; II-1), and his affected great-grandfather (F-7; I-1). In some cases, ankyloglossia was corrected immediately after birth or later in life and not recorded in the medical records. Moreover, the family also reported that the proband’s affected cousins (F-7; IV-1, IV-4) had hypotonia. Other family members are reportedly unaffected. The hemizygous loss of all coding exons of TBX22 detected by WES and array CGH in the proband (F-7; IV-2) was confirmed by real-time quantitative PCR (qPCR). His mother (F-7; III-4) was found to be a carrier, and the variant was also confirmed in his affected cousin (F-7; IV-1) and aunt (F-7; III-2). Other samples were not available for the analysis. The qPCR results are reported in Table S1. The loss of TBX22 in this family suggest the diagnosis of X-linked cleft palate with or without ankyloglossia. The X-linked inheritance mode does not match with the proband’s great-grandfather’s (F-7; I-1) phenotype. There is no evidence of a consanguinity between his great-grandparents and no history of OFC in his great-grandmother’s (F-7; I-2) family. Further genetic risk factors for OFCs in Slovenia were determined by examining 72 additional genes in multi-case families lacking disease-causing variants in IRF6, GRHL3, or TBX22 or with no VWS or CPX diagnosis (n = 27). Thus, we identified 14 rare variants that fit our inclusion criteria: five rare variants with inconclusive involvement in OFCs (Table S2); nine rare variants that were excluded after co-segregation analysis (Table S3). The involvement of five rare variants in nsOFCs could not be conclusively determined based on the results of in silico prediction tools, co-segregation analysis, and the literature (Table S2). In the proband of one family with nsOFC, we identified in-frame insertion in FGFR1 (NM_023110.3:c.396_398dup) and a missense variant in JAG2 (NM_002226.5:c.3004A>G) in another. Both variants co-segregate with the disease phenotype but are also present in the unaffected siblings of the probands, suggesting that the variant is either not causal or that its penetrance is reduced. In addition, c.3004A>G (JAG2) was not predicted to be damaging by the majority of in silico tools used, although it was predicted uncertain by Franklin’s aggregated prediction. The variant in TBX22 (NM_001109878.2:c.1489G>A) segregates with the disease phenotype in the family but occurs at the end of last exon (exon 9) and is predicted to be benign by the majority of in silico tools (uncertain by Franklin’s aggregated prediction). A co-segregation analysis failed to yield an informative result for the variant in DLG1 (NM_001366207.1:c.2048-22_2048-4del) due to the absence of the sample from the affected sibling, whereas the unaffected mother does not carry the variant. Finally, the variant in BMP4 (NM_001202.6:c.272C>G) is unanimously predicted to be deleterious by in silico tools and co-segregates with disease phenotypes in the family, but in the ClinVar database, researchers provided conflicting interpretations of pathogenicity, ranging from uncertain significance to likely benign. We also report nine rare variants that were studied for their involvement in OFCs in our cohort but were excluded after co-segregation analysis because they did not segregate with the OFC phenotype (Table S3). The present study employed genetic analysis to examine 34 Slovenian families with multiple cases of apparent nsOFCs (isolated OFCs or OFCs with minor additional facial signs) to identify rare disease-causing variants and found 6 deleterious variants in 7 families, 3 of which were novel. All variants were found in three genes, IRF6, GRHL3, and TBX22, which are involved in the known syndromes, VWS and CPX. In addition, we discovered five rare variants in probands with nsOFCs, where their involvement in the disease could not be conclusively determined. In five of seven families with suspected VWS (71.4%), we found four heterozygous variants in IRF6 that are classified as pathogenic or likely pathogenic according to ACMG guidelines. The figure is consistent with previous studies in which IRF6 variants were detected in approximately 67% of VWS cases [9,45]. In addition, we discovered a heterozygous likely causal splice-altering variant in GRHL3 in one family with suspected nsOFC, which is classified as VUS according to ACMG guidelines. On subsequent examination of the family, we recognized atypical but identifiable signs of VWS. IRF6, the first gene of interest, has 9 exons, 7 of which are coding (exons 3–9) [46], and they encode a protein with a highly conserved N-terminal DNA-binding domain (helix-turn-helix) (exons 3 and 4) and the less conserved C-terminal protein-binding domain called SMIR (exons 7 and 8) [4,47]. Researchers have identified numerous IRF6 variants associated with VWS, allowing them to examine their distribution among coding exons [9] and to define the IRF6 domains in which variants are most likely to affect IRF6 function [48]. De Lima et al. showed that deleterious variants in IRF6 occur significantly more frequently in exons 3, 4, 7, and 9. In addition, they observed frameshift and nonsense variants (protein truncating variants) in all IRF6 exons of the VWS families, whereas missense variants and in-frame indels are significantly overrepresented in the exons encoding conserved DNA-binding or SMIR domain [9]. Leslie et al. further demonstrated that syndromic features arise from rare variants in the coding sequence of IRF6 (particularly the DNA-binding domain), because these variants are very rare in controls [48]. The high frequency of protein-truncating variants in VWS [9] and data from functional studies [44] suggest that the cause of VWS is most likely haploinsufficiency of IRF6. In our cohort of VWS families, there were four IRF6 variants. The missense variant c.134G>A (rs121434229), located in the DNA-binding domain (exon 3), was identified in the affected mother and daughter with complete CP and lower lip pits, and in the unaffected son (F-1). The in silico tools unanimously supported a deleterious effect of the variant, although we noted incomplete penetrance. The variant was previously described in a Japanese VWS family where one patient had CL and lip pits, whereas the father and uncle only had lip pits [37]. These data suggest that this variant is associated with phenotypic variability. A nonsense variant c.622C>T in IRF6 (exon 6) was found in both affected individuals in one family (F-2), the mother and daughter with bilateral CLP, lower lip pits, and hypodontia. This loss-of-function variant was previously identified in a male Honduran VWS patient with unknown family history who had unilateral CL/P and two lower lip pits [38]. We also identified a novel variant c.687delG, a frameshift deletion located in the SMIR domain (exon 7) of IRF6, which is not present in gnomAD v2.1.1. This loss-of-function variant was confirmed in both mother and son with complete CP and lower lip pits (F-3). Lastly, we identified another nonsense variant (in exon 9) c.1234C>T (rs1553247595) in two families. This loss-of-function variant is located within a CpG dinucleotide and could result from a cytosine methylation/deamination process [9,49]. Phenotypic variability was observed in both families. In the first family (F-4), the phenotype ranges from bilateral CLP and lip pits in the father to unilateral CLP and lip pits in the son, and in the second family (F-5), the father presents with unilateral CLP and lip pits and the son with soft CP and lip pits. The variant is one of the five most common variants in VWS [9], having been identified previously in numerous VWS families with variable phenotypic expressions from Brazil, China, Honduras, northern Europe, Pakistan, and Singapore [4,9,38,39,40,41,42,43]. Observed phenotypic variability and incomplete penetrance are common features of VWS and may be due to stochastic effects and/or genetic modifiers. In contrast to VWS1, which arises from rare protein-altering IRF6 variants [4], nsOFCs are significantly associated with common IRF6 variants in European populations [14,50]. Lately, scientists focused on rare deleterious variants in numerous genes that might explain some heritability of complex nsOFC aetiology [13,25,26,30,31,32,33,34,35,36,45,51]. In the study by Leslie et al. [13], more than 1500 nsOFC families were screened for variants in IRF6, and the literature on similar studies was reviewed to determine that rare IRF6 variants occur in less than 0.5% of probands with nsOFCs. Even though we included only families with multiple cases of nsOFCs, it is not surprising that we were unsuccessful in finding rare IRF6 variants in our small cohort. This further supports the thesis that rare coding variants are unlikely to play a major role in nsOFCs [13]. Another gene of interest, GRHL3, has 10 protein-coding transcripts that differ in both length and exon number. The Ensembl canonical transcript has 16 coding exons [46,52]. GRHL3 encodes a protein with transactivation (exons 2–3), DNA-binding (exons 6–10), and dimerization (exons 13–16) domains (according to the GRHL3 protein NP_937816.1). In vivo studies suggest that proteins encoded by mutated GRHL3 cause VWS through a cell-autonomous dominant-negative effect [10]. According to HGMD, variants in GRHL3 (missense/nonsense variants, splicing substitutions, and small indels) cause either VWS2, non-syndromic cleft palate, or spina bifida. In one of the families with suspected nsOFC (F-6), we identified a novel splice site variant c.1285G>T in exon 10 of GRHL3 (within the DNA-binding domain). The variant is predicted to alter the donor splice site and is not present in gnomAD v2.1.1. It was detected in both affected individuals, father and daughter, both presenting with complete CP. Subsequent examination revealed a somewhat asymmetric lower lip with elevations in both and hypodontia in the father. The daughter was too young to have permanent teeth and was not available for dental anomaly examination with dental imaging techniques. Other phenotypes that were present in addition to OFC suggested the diagnosis of VWS2 in this family. Two studies identified deleterious variants in close proximity to c.1285G>T. In a patient with non-syndromic cleft palate Eshete et al. [51] identified a dominant-negative missense/splice-site variant c.1282A>C (GRHL3), which is three nucleotides upstream of our variant. The presence of lip pits and dental anomalies was not referenced. In addition, Mangold et al. [15] reported a donor splice-altering variant c.1285+2delT (GRHL3), located only two nucleotides downstream of the variant reported herein in a nsOFC family with a phenotype highly similar to the one observed in two affected individuals from the present study (F-6). Two half-sisters had a complete CP and a slightly asymmetric lower lip with elevation on the left side resembling lower lip pits, which could be interpreted as a subtle VWS sign. Hypodontia or dental abnormalities were not indicated [15]. Because of incomplete penetrance and variable phenotypic expression in VWS, the phenotype can mimic nsOFC. A family with VWS may exhibit barely visible lip pits/anomalies, dental abnormalities, or even no phenotypic abnormalities. Families are usually recruited for genetic studies based on the phenotype of the proband, so VWS may be overlooked if the proband does not display typical signs of VWS. This was demonstrated in a study by Leslie et al., when an a posteriori review of cases with suspected nsOFCs and deleterious IRF6 variants revealed lip pits in many of the families [13]. Individuals with VWS2 (causal variants in GRHL3) are more likely to have CP and less likely to have CL/P and lip pits compared to individuals with VWS1 (causal variants in IRF6) [10], making the VWS2 phenotype even more similar to nsOFC. Furthermore, although nsOFCs are traditionally described as isolated anomalies without the presence of other malformations, patients with nsOFCs often have subphenotypes, such as dental anomalies [53], suggesting that the distinction between syOFCs and nsOFCs is imprecise. Based on this, it is questionable whether individuals with isolated clefts and IRF6 or GRHL3 variants really have nsOFCs. Nevertheless, Mangold et al. [15] have shown that deleterious GRHL3 variants are more common in families with multiple CP cases, even if non-syndromic, and are inherited in an autosomal dominant manner, a fact not to be overlooked in genetic counselling. Individuals with non-syndromic CP and a GRHL3 variant have a higher recurrence risk for CP with possible VWS signs in their offspring. The following gene of interest, TBX22, has 9 exons, 8 of which are coding (exons 2–9) [46], and they encode a transcription factor with conserved T-box DNA-binding domain [16]. According to HGMD, missense, nonsense, splicing, and regulatory variants as well as small indels have been associated with CPX. Due to the location of TBX22 on the X-chromosome, deleterious variants lead to a complete loss of function in males [16], which was also demonstrated in functional studies [54,55]. Although loss-of-function variants show high penetrance in males (CP in 96% and ankyloglossia in 79% of cases), haploinsufficient females usually show a milder phenotype (ankyloglossia only or no phenotype) [19]. In this study, we present a family (F-7) with a history of CP in males suggestive of an X-linked mode of inheritance, but the pattern did not match completely because the proband’s great-grandfather had bifid uvula and two sons with CP. We would like to emphasize the importance of using WES as a diagnostic tool, as without performing WES, we would not be able to detect CNVs in this family, so the deletion of TBX22 would be missed. After analysing the data from WES and identifying the loss of all coding exons of TBX22 in the proband with complete CP, we re-evaluated the family history and found ankyloglossia in individuals of all generations of the family, including putatively unaffected females. We validated the TBX22 deletion by qPCR and confirmed the variant in two males (proband and one of his affected cousins) and their mothers with ankyloglossia and without CP. Samples from other family members were not available for the analysis. The phenotype of the family corresponds to the diagnosis of CPX. There is no evidence of a consanguine marriage between proband’s great-grandparents, possibly making the great-grandfather’s phenotype the result of different genetic or environmental factors. Interestingly, the great-grandfather also had ankyloglossia, a characteristic of CPX, indicating that there is also the probability that paternal heterodisomy of sex chromosomes occurred in his sons [56,57]. Marçano et al. [19] similarly identified a missense variant in TBX22 in a family in which both the proband and his father had CP, but later, ankyloglossia was found in the proband’s mother and his maternal uncle, indicating that CPX was inherited from the mother and not from the father. To complement the above findings, we examined WES data for 72 additional genes in the families without disease-causing variants in IRF6, GRHL3, or TBX22 or without the diagnosis of VWS or CPX. We identified five rare variants, whose involvement in nsOFCs could not be clearly determined based on the available data, and nine rare variants that were excluded after the co-segregation analysis. Reporting these variants is important because it provides other researchers or clinicians with the knowledge that the specific variant has already been identified in an OFC case and helps them to include or exclude that variant as potentially causative in their cases. It also improves the classification of variants according to ACMG standards. The reason for being unsuccessful in finding any disease-causing variants in nsOFC cases might lie in our study design. Although we included all available Slovenian multi-case families, the number of families studied is small. Moreover, we screened a relatively small gene panel. Genes were selected through a systematic review of the genetic markers obtained from population case–control studies of nsOFCs [50]. Although we focused on screening genes implicated in nsOFCs in populations of European ancestry, some other studies have successfully screened nsOFC families using a broader range of candidate genes (more than 500) implicated in each form of OFCs (syOFCs and nsOFCs) and ethnicity [31,34]. This suggests that we may be successful in identifying monogenic causes in Slovenian nsOFC families if we expand the gene panel. In addition, selected genes were obtained from association studies examining disease-causing variants with relatively high population frequencies. The present study focused only on monogenic causes of nsOFCs, despite the fact that nsOFCs are commonly considered multifactorial disorders. We sought to reduce the impact of interactions between genetic and environmental factors in our cohort by including only families with multiple affected cases. Nevertheless, there is a likelihood that selected genes are involved in the complex aetiology in these families through the polygenic inheritance of variants with higher population frequencies. We recruited families with multiple cases of apparent nsOFCs (phenotypes resembling nsOFCs), that is, OFC families without or with additional minor facial clinical signs. In some families, additional facial signs were present in only some members. Our cohort mainly included multi-case families with nsOFCs but also multi-case families with suspected VWS and PRS. Exclusion criteria included single-case families, families where the subjects had OFC in combination with defects of other organ systems (e.g., congenital heart defects), or with previously confirmed chromosomal abnormalities. The majority of the probands and their affected and non-affected family members were recruited from September 2019 to February 2021 at the Department of Maxillofacial and Oral Surgery, University Medical Centre Ljubljana in Ljubljana, Slovenia. The probands’ mothers were asked to fill in the questionnaire in order to determine the family history and evaluate their medical conditions or exposure to environmental risk factors during pregnancy. The diagnosis of OFC was based on a thorough clinical examination and assessment of the diagnostic data from medical records by a maxillofacial surgeon (A.E.). Overall, we included 34 families with two or more members affected with apparent nsOFCs, where 24 families had members with nsOFCs, three families had at least one member with signs of PRS, and seven families had at least one member with lip pits, suggesting the diagnosis of VWS. As all the cases of OFCs in Slovenia are treated in one tertiary centre (Department of Maxillofacial and Oral Surgery, University Medical Centre Ljubljana), we included all of the available multi-case families from Slovenia. All probands and their family members were of European descent. Altogether, the initial analysis included the selection of 39 affected subjects (22 males, 17 females) drawn from 34 families; one affected subject in the case of 29 families, and two affected siblings/cousins in the case of five families. Apart from lip pits, seven subjects from seven families with presumably VWS had bilateral CLP (n = 1), unilateral CLP (n = 2), complete CP (n = 3), or soft CP (n = 1). The remaining 32 subjects from 27 families had bilateral CL/P (n = 6), unilateral CL/P (n = 13), complete CP (n = 7), soft CP (n = 3), or PRS (n = 3). We recruited between 1 and 7 affected and non-affected family members per multi-case family, depending on their family history and willingness to cooperate. All the subjects or their parents/legal guardians (for subjects under 15 years) signed the informed consent form. The study protocols were approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-211/2019/3). EDTA blood (venous/capillary) samples or buccal swab samples were collected, and genomic DNA was extracted using three different commercial kits: FlexiGene DNA kit (Qiagen, Hilden, Germany), QIAamp DNA Mini kit (Qiagen, Hilden, Germany), or MasterPure complete DNA and RNA purification kit (Epicentre (Illumina), Madison, WI, USA), according to the manufacturers’ instructions. The Multiplex ligation-dependent probe amplification (MLPA) assay was performed on samples of all the probands using the SALSA MLPA Probemix P245-B1 Microdeletion Syndromes-1A (MRC-Holland, Amsterdam, The Netherlands), according to the manufacturer’s instructions. The kit tested for the presence of deletions/duplications in various chromosomal regions involved in selected microdeletion and microduplication syndromes, including the 22q11.2 region, but no aberrations were detected. The first step of the sequence analysis comprised screening the probands for disease-causing variants in the three genes known to be implicated in VWS and CPX: IRF6, GRHL3, and TBX22. In seven affected subjects from six families, Sanger sequencing was used to analyse the three genes due to the lack of high-quality DNA. First, protein-coding exons and flanking intronic regions were amplified by PCR using a HOT FIREPol® DNA Polymerase kit (Solis BioDyne, Tartu, Estonia) and in-house primer pairs designed using Primer3 (v4.1.0) software (Table S4) [58]. The PCRs were performed according to the manufacturer’s instructions. The PCR products and primers were subsequently sent to McLab (San Francisco, CA, USA) for Sanger sequencing. Sufficient high-quality DNA was available for the remainder of the affected subjects (n = 32), so IRF6, GRHL3, and TBX22 were analysed in these samples by WES. In the second step of the sequence analysis, the WES data of the subjects lacking disease-causing variants in the three selected genes were further filtered for variants in 72 additional genes (Table S5). The selection criteria for nominating candidate genes were based on information from an extensive systematic review in which we compiled data from 84 population-based case–control studies and investigated genetic risk factors for nsOFCs in populations of European ancestry. A meta-analysis was performed for repeatedly reported genetic variants from 43 of these studies. The genetic variants from 84 studies that were not included in the meta-analysis were only reviewed [50]. We selected all genes that were included in the meta-analysis (statistically significant and not significant) because these genes were most frequently studied in populations of European ancestry. Candidate genes were also selected based on variants that were not included in the meta-analysis but were significantly associated with nsOFCs in one of the 84 studies. A few studies investigated rare variants by sequencing the coding regions of specific genes. Because it is more difficult to demonstrate a statistically significant association with the abnormality for rare variants, we also included genes that were studied in this way. On the other hand, we did not consider genetic variants located in non-coding regions or variants for which the corresponding gene was not mentioned in the studies. WES was carried out at the CeGaT GmbH (Tübingen, Germany) using the Twist Human Core Exome Plus Kit (Twist Bioscience, San Francisco, CA, USA). The paired-end sequencing (2 × 100 bp reads) was performed on a NovaSeq 6000 (Illumina, San Diego, CA, USA). After sequencing, reads were demultiplexed (Illumina bcl2fastq 2.20) and adapters were trimmed (Skewer 0.2.2) [59]. The generated reads were aligned to the human reference genome (hg19-cegat) using a Burrows-Wheeler Aligner (BWA-mem version 0.7.17-cegat) [60]. Reads at the target regions were locally realigned using ABRA (version 2.18) to improve indel detection [61]. A CeGaT proprietary tool was used to discard duplicated reads and reads that aligned with identical mapping scores to more than one locus. The achieved average coverage was >107x. The variants were detected and annotated using an additional CeGaT proprietary software. The annotation was performed using various public databases (Ensembl (v100) [46], RefSeq Curated (20200723) [62], CCDS (r22) [63], GnomAD (2.1.1 (exonic), 3.1 (genomic)) [52], dbSNP154 [64], Gencode 34 [65]). The CNVs were also computed using CeGaT internally developed method. The method compares the expected number of reads on the target loci (coverage in a number of CeGaT reference samples) with the observed number (coverage in the tested samples) [66]. The resulting variants were analysed in the affected subjects of each family independently. We have only considered rare variants with a MAF ≤0.01 in gnomAD and/or dbSNP154, of which we have only examined LoF variants (e.g., nonsense, nonstop, initiation codon, essential/canonical splice site variants, frameshift indels, and single-exon or multi-exon deletions), microduplications, splice-region variants, missense single nucleotide variants (SNVs), and in-frame indels. Other variants were discarded. Tools provided by The Ensembl Variant Effect Predictor (VEP) [67] and Franklin (Genoox) [68] platform were used to predict the consequences of each variant in silico. Missense SNVs were suspected to be protein-altering if predicted to be deleterious/damaging by at least three in silico deleteriousness/conservation prediction tools (SIFT [69], PolyPhen-2 [70], MutationAssessor [71], MutationTaster [72], FATHMM [73], CADD [74], MetaLR [75], REVEL [76] or GERP++ [77]) or predicted “deleterious”/“uncertain” by Franklin’s aggregated prediction. The potential of splice-region SNVs to alter splicing was predicted by using splice site prediction tools (MaxEntScan [78], dbscSNV Ada [79], and SpliceAI [80]), MutationTester, and CADD. All the variants that met the inclusion criteria were visually inspected by Integrative Genomics Viewer (IGV) [81] to confirm them as “true” variants. Franklin (Genoox) platform [68] was also used to classify variants based on the ACMG guidelines [82]. In accordance with these criteria, the variants were classified into five groups as benign, likely benign, variant of uncertain significance (VUS), likely pathogenic, and pathogenic. Our analysis focused only on variants that were classified as VUS, likely pathogenic, and pathogenic [82]. Lastly, we reviewed the literature, HGMD Professional 2022.2 (Qiagen, Hilden, Germany), ClinVar database [83], and DECIPHER v11.12 [84] to identify known disease-causing variants. All the putative variants found in the probands were validated, and co-segregation analysis was also performed on their available affected and non-affected family members. The SNVs/indels and CNVs were confirmed using Sanger sequencing and qPCR, respectively. To further confirm the presence of CNVs and more precisely determine their location and size, we also performed array CGH on the proband. DNA sequences with the SNVs or indels were amplified by PCR using HOT FIREPol® DNA Polymerase kit (Solis BioDyne, Tartu, Estonia) and in-house primer pairs designed using Primer3 (v4.1.0) (Table S6). The reactions were performed according to the manufacturer’s instructions. PCR products and primers were later sent to McLab (San Francisco, USA) for Sanger sequencing. The qPCR was used to confirm CNVs, i.e., the deletion of all the coding exons of TBX22. We modified the method described by Weksberg et al. [85]. Reactions were performed with HOT FIREPol® EvaGreen® qPCR Supermix (Solis BioDyne, Tartu, Estonia) and in-house primer pairs designed using Primer3 (v4.1.0). Designed primer pairs targeted eight coding exons of TBX22 (exons 2–9) and two exons of the two selected reference genes, G6PD (exon 3) and IRF6 (exon 5). We chose G6PD because it is a commonly used X-linked housekeeping gene [85] and IRF6 because primer pair was available. Primer-BLAST (NCBI) [86] was used to ensure the primers were specific for the target sequences. We optimized the concentration and annealing temperature for each primer pair, which are listed in Table S7 along with the genomic targets, amplicon sizes, and optimized conditions. The qPCR was performed according to the manufacturers’ instructions using the LightCycler® 480 Real-Time PCR System (Roche, Basel, Switzerland), and the resulting data were analysed with LightCycler® 480 software release 1.5.1.62 SP3 (Roche, Basel, Switzerland). Melting curve analysis was performed to confirm the specificity of each amplification. Due to the location of TBX22 on the X chromosome and the associated difference in allele numbers between the sexes, male (n = 3) and female (n = 4) genomic control DNA samples were included in the analysis. In addition, two separate standard curves were generated for all qPCR reactions using twofold dilution series of a male and a female control DNA sample. Reactions were performed in triplicate and PCR-grade water was used as a blank. Instructions by Weksberg et al. [85] were followed for data analysis and calculation of the fold change in copy number (∆KCt) for each sample. The average Ct values of the target region (TBX22 exons) for each control and test sample were normalized using the average Ct values of the reference gene (G6PD or IRF6) and slope values derived from standard curves. To control for variability between sexes as a result of different allele numbers, we employed the equation of Weksberg et al. [85] for male and female (control and test) samples separately. The fold change in copy number (∆KCt; copy number of each TBX22 exon) was then determined by comparing the normalized data of the control and test samples (male–male and female–female). ∆KCt values of 0 ± 0.35 indicate no copy number change or no genetic abnormality (in males and females), whereas −1 ± 0.35 indicates a loss of one allelic copy (the deletion of the TBX22 exon) in females, who normally carry two copies. In the male samples, the loss of a single allelic copy of each TBX22 exon was detected when no qPCR product was present or the Ct value was similar to the blank Ct value (i.e., no peak was generated in the melting curve analysis). The quality of the DNA from these samples was verified by the presence of a qPCR product when reference genes were amplified. In addition, array CGH was performed on the sample from the proband with the TBX22 deletion to localise the identified CNV and its size. Array CGH analysis was performed using a commercial oligonucleotide array (Agilent 180K Baylor Oligo, Agilent Technologies, Santa Clara, CA, USA) and a sex-matched human reference DNA sample (Agilent Technologies, Santa Clara, CA, USA). Data were analysed using Cytogenomics 5.1.2.1 Software (Agilent Technologies, Santa Clara, CA, USA). The present comprehensive genetic study is the first study investigating Slovenian families with multiple cases of OFCs. Its main outcome is the identification of novel genetic variants in known OFC genes and their potential application as a diagnostic approach to distinguish between nsOFCs and syOFCs. The sequencing of known OFC genes is clearly a powerful tool to make or improve a diagnosis. We recruited families with apparent nsOFCs (i.e., OFC families without or with additional minor facial signs). Using WES and Sanger sequencing, we screened the selected 75 genes and identified six disease-causing variants in 7 of 34 families (20.6%). These variants were located in 3 genes, IRF6, GRHL3, and TBX22. With the identification of four disease-causing SNVs in IRF6, one of which was novel, we confirmed the VWS1 diagnosis in five families with OFC and lip pits. Interestingly, we also identified two syndromic forms of OFCs in our cohort of suspected nsOFCs. A novel splice-altering SNV in GRHL3 identified a family with VWS2, and the novel CNV, the deletion of TBX22 coding exons, revealed a familial CPX. Although we also identified and analysed many rare variants in probands with nsOFCs, the involvement of nine SNVs/indels was excluded after co-segregation analysis, whereas the results for five SNVs/indels are inconclusive. Our sequencing approach and gene selection were successful in identifying syOFC families with monogenic inheritance patterns in a cohort of apparent nsOFCs, suggesting that WES is useful for diagnostic purposes in OFC families with minor additional clinical signs and multiple cases. Our results show that the sequencing of IRF6, GRHL3, and TBX22 has a high diagnostic yield. This is particularly important in cases where the phenotype is complex and difficult to characterize clinically. However, our approach was unsuccessful in identifying the monogenic cause of nsOFCs. Additional approaches that consider multifactorial aetiology should be used to identify the complete genetic aetiology of nsOFCs.

PMC10002092

Alexandra Sack,Elena N. Naumova,Lori Lyn Price,Guang Xu,Stephen M. Rich

Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019

28-02-2023

Ixodes scapularis,tick-borne diseases,Borrelia burgdorferi,Anaplasma phagocytophilum,Babesia microti,Borrelia miyamotoi

This study aimed to analyze human-biting Ixodes scapularis ticks submitted to TickReport tick testing service from 2015–2019 in Massachusetts to (1) examine possible patterns of pathogen-positive adult and nymphal ticks over time and (2) explore how socioeconomic factors can influence tick submissions. A passive surveillance data set of ticks and tick-borne pathogens was conducted over 5 years (2015–2019) in Massachusetts. The percentages of four tick-borne pathogens: Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi were determined by Massachusetts county and by month and year. Regression models were used to examine the association between zip-code-level socioeconomic factors and submissions. A total of 13,598 I. scapularis ticks were submitted to TickReport from Massachusetts residents. The infection rate of B. burgdorferi, A. phagocytophilum, and B. microti was 39%, 8%, and 7% in adult ticks; 23%, 6%, and 5% in nymphal ticks, respectively. A relatively higher level of education was associated with high tick submission. Passive surveillance of human-biting ticks and associated pathogens is important for monitoring tick-borne diseases, detecting areas with potentially high risks, and providing public information. Socioeconomic factors should be considered to produce more generalizable passive surveillance data and to target potentially underserved areas.

Passive Surveillance of Human-Biting Ixodes scapularis Ticks in Massachusetts from 2015–2019 This study aimed to analyze human-biting Ixodes scapularis ticks submitted to TickReport tick testing service from 2015–2019 in Massachusetts to (1) examine possible patterns of pathogen-positive adult and nymphal ticks over time and (2) explore how socioeconomic factors can influence tick submissions. A passive surveillance data set of ticks and tick-borne pathogens was conducted over 5 years (2015–2019) in Massachusetts. The percentages of four tick-borne pathogens: Borrelia burgdorferi, Anaplasma phagocytophilum, Babesia microti, and Borrelia miyamotoi were determined by Massachusetts county and by month and year. Regression models were used to examine the association between zip-code-level socioeconomic factors and submissions. A total of 13,598 I. scapularis ticks were submitted to TickReport from Massachusetts residents. The infection rate of B. burgdorferi, A. phagocytophilum, and B. microti was 39%, 8%, and 7% in adult ticks; 23%, 6%, and 5% in nymphal ticks, respectively. A relatively higher level of education was associated with high tick submission. Passive surveillance of human-biting ticks and associated pathogens is important for monitoring tick-borne diseases, detecting areas with potentially high risks, and providing public information. Socioeconomic factors should be considered to produce more generalizable passive surveillance data and to target potentially underserved areas. The causative pathogens of tick-borne diseases have been associated with increased tick density in Ixodes spp. ticks in multiple states in the United States [1,2]. Increased Ixodes spp. tick density has been associated with oak forests, higher humidity, and denser litter cover [1,3,4]; however, these studies involve collecting ticks from the environment and do not account for human exposure to ticks. Environmental factors can be considered to fall into two categories: those that affect tick mortality and a tick’s life cycle, and those that affect questing behavior [5]. For humans to be infected by a tick-borne pathogen, human land use and pathogen-positive ticks must spatially overlap [6]. Human behavior also plays an important role in determining access and land use, influencing tick bites and pathogen exposure [7,8]. Field collection of host-seeking ticks usually provides vector density and geographic locations of ticks. However, as human behavior also strongly affects risk, three surveillance methods, (1) human disease cases, (2) serology of domestic animals as sentinels, and (3) human-biting ticks, can help fill in knowledge gaps by accounting for factors associated with human behavior. [9,10,11,12]. Lyme disease patients often are unaware of a tick bite preceding the onset of symptoms; moreover, sentinel serology and human case reports relying on the place of residence may fail to account for travel history and may misattribute a high percentage of cases [13]. Our current paper not only examines the exposure risk from human-biting ticks but also explores the effect of socioeconomic factors on passive surveillance. Previous studies of human-biting Ixodes scapularis include those from Canada [12,14,15,16], single states in the United States [17,18,19,20,21,22,23], and two multistate studies [24,25]. These studies identified tick species and their life stages, the peak season of human-biting occurs, tick bite sites on the human body, and the prevalence of tick-borne pathogens. The human-biting tick data are important to public health, for it predicts spatial and inter-annual patterns of tick-borne disease case incidence. Passive surveillance provides important information about human-biting ticks and information on actual encounters with ticks and tick-borne pathogens. Analyzing the submission patterns for human-biting ticks provides insight into potential high-risk areas or groups to target with future public health surveillance programs. Massachusetts is a high-risk area for tick-borne diseases, with the most common diseases being Lyme Disease, Babesiosis, and Anaplasmosis [26,27,28]. Borrelia burgdorferi and/or Borrelia mayonii are causative pathogens of Lyme disease in humans, Anaplasma phagocytophilum causes Anaplasmosis, and Babesia microti causes Babesiosis. Borrelia miyamotoi causes Borrelia miyamotoi disease, an emerging disease in Massachusetts. All four of these diseases are spread by I. scapularis [26]. Strong seasonality with a peak in summer is recorded throughout New England in I. scapularis [18,29,30]; however not all tick-borne diseases carried by I. scapularis peak at the same time. Borrelia miyamotoi disease in humans occurs most commonly in July and August after the peak season for Lyme disease, which is in June and July [31]. Massachusetts currently performs tick exposure and syndromic disease surveillance but does not report diseases specific case numbers. Still, more than 0.2% of all emergency room visits were related to tick-borne diseases during the summer [32]; however, this also leaves a gap in current information about tick-borne disease exposure risks in the state. TickReport is a public outreach service at the University of Massachusetts at Amherst, providing individuals with information about potential pathogen exposures associated with tick bites. For the first several years of its existence, TickReport was small and served mostly communities close to the campus. As the service grew in popularity and appeal, the sampling density became more substantial such that this individual risk assessment service grew to comprise a passive surveillance network in aggregate [18]. In this paper, we analyzed human-biting I. scapularis ticks submitted to TickReport tick testing service from 2015–2019 in Massachusetts to (1) examine patterns of pathogen-positive adult and nymphal ticks over time and (2) explore how socioeconomic factors can influence tick submissions. The ticks submitted to TickReport and employed for this study were submitted voluntarily to the TickReport from January 2015 through December 2019. All submitters were asked to provide information about the presumed exposure location, the tick removal date, and the person’s sex, age, and residence location. Each submission corresponded with a single tick and was treated as a separate exposure. This service is subjected to a fee and is available for the entire United States. While there was a variety of tick species submitted and states covered by this service, the present work was focused on I. scapularis ticks received from the Massachusetts area due to having the most complete information temporally and geographically. Information about the biting tick’s species and transmitted pathogens were ascertained by an expert [13,18]. Ticks were first morphologically identified to stage and species levels [33,34,35], then confirmed by molecular assays targeting the tick mitochondrial 16S rRNA gene and ITS gene; see reference for a list of primers [13,18]. The total DNA was extracted from each tick using Epicenter Master Complete DNA and RNA Purification Kits (Epicenter Technologies, Madison, WI, USA) following the manufacturer’s protocols. B. burgdorferi s. l., B. miyamotoi, B. mayonii, B. microti, and A. phagocytophilum were detected by a multiplex TaqMan real-time PCR assay in 16 μL reaction volumes using the Brilliant III qPCR Master Mix (Agilent, La Jolla, CA, USA) in an Agilent MX3000P qPCR System. Cycling conditions included an initial activation of the Taq DNA polymerase at 95 °C for 10 minutes, followed by 40 cycles of 95 °C for 15 seconds and 60 °C for 1 minute. Borrelia detection was performed by first applying a Borrelia genus-specific detection assay for a conserved target, followed by specific qPCR assays for each of the three species (B. burgdorferi s. l., B. miyamotoi, and B. mayonii) [13,18]. In Massachusetts, the canonical B. burgdorferi species found in I. scapularis is B. burgdorferi stricto sensu, the sole species of Lyme Disease in North America [36]. Only ticks with a location of exposure in Massachusetts were included. The percentage of pathogen-positive ticks and tick submissions by life stage were calculated by month and total submissions were calculated by month and year with 95% confidence intervals (95% CI). The total tick submissions and the percentage of each of the four tick-borne pathogens were calculated per zip code tabulation area (ZCTA) and by county with 95% confidence intervals. The percentages of pathogen-positive adult and nymphal ticks per ZCTA and county also were calculated for each year and the study period. The annual trend of pathogen prevalence was analyzed by the Mann-Kendall Test at p < 0.05 level. Only submissions with a valid Massachusetts ZCTA (from residences in ZCTAs within Massachusetts) were included for the socioeconomic analysis. Median household income, percentage of self-reporting race as white, percentage of the population with a high school education or less, and population density, were collated for each ZCTA from the 2018 American Community Survey (ACS, 5-year estimates), or from the 2014 ACS or corresponding census blocks if 2018 data were not available. Distance from the TickReport laboratory was calculated as a straight-line distance from the ZCTA centroid. The land-use type for each ZCTA was calculated from the 2010 National Land Cover data set (NLCD) and was defined as the land-use type that made up the highest percentage in that ZCTA. The NLCD 2010 has twenty different categories based on 30-meter squares. All spatial analyses were performed using ArcMap 10.7.1 (ESRI, Redlands, CA, USA, 2020). Statistical analyses were run using RStudio 1.2.502 (R Studio Team, Boston, MA, USA, 2020). It was decided a priori to test the socioeconomic factors with and without Boston, due to the large range of socioeconomic statuses but consistently low tick submissions. Population density and median household income were transformed using a log transformation due to non-normal distributions. A negative binomial regression model was applied to examine associations between ZCTA-level socioeconomic variables and the tick submissions per ZCTA in both univariate and multivariable models. Socioeconomic variables that met the inclusion criteria of p < 0.10 in the univariate analysis were included in the multivariable analysis. Results from the regression models were reported as incident rate ratios. The land-use category that made up the highest percentage of each ZCTA was added to the final socioeconomic model to analyze the effect that the inclusion of land use had on the socioeconomic variables. Nagelkerke’s pseudo-R2 was measured for the models with and without land use [37,38]. Influence points were defined as ZCTAs with either residual greater than the absolute value of three and/or Cook’s value greater than 0.2. A total of 13,598 I. scapularis ticks were submitted to TickReport with a reported Massachusetts exposure: 76.7% of ticks were adults (n = 10,435), 21.6% were nymphs (n = 2935) and 1.7% were larva (n = 228). Men (n = 6743) and women (n = 6701) submitted a similar number of ticks from exposures (n = 154 chose not to include gender). Of all submissions, 96.6% reported exposure location at the ZCTA level. Also, 77.4% of submissions were from exposures to a tick in the same ZCTA as where the person lived. Over the five-year period, no ticks were submitted from 11.1% (n = 59/537) of Massachusetts ZCTAs (Figure 1). Most counties submitted more adults than nymphs; however, Nantucket and Dukes County submitted more nymphs than adults (74.2% and 57.1% of submissions, respectively), as only 16 adult ticks were submitted from Nantucket. In adult ticks, 39.0% (95% CI: 38.1–39.9%) were positive for B. burgdorferi, 8.1% (95% CI: 7.6–8.6%) for B. microti, 7.6% (95% CI: 7.1–8.1%) for A. phagocytophilum, and 2.0% (95% CI: 1.7–2.3%) for B. miyamotoi. In nymphal ticks, 23.1% (95% CI: 21.6–24.7%) were positive for B. burgdorferi, 6.4% (95% CI: 5.6–7.4%) for B. microti, 4.9% (95% CI: 4.2–5.8%) for A. phagocytophilum, and 1.3% (95% CI: 0.9–1.8%) for B. miyamotoi. No ticks were positive for B. mayonii. For adult and nymphal stages combined, 41.5% (95% CI: 40.7–42.3%) of ticks were infected by one tick-borne pathogen; 8.8% (95% CI: 8.3–9.3%) of ticks were infected by more than one pathogen. Five adult ticks were positive for all four pathogens. The prevalence of each pathogen varied by year, and no pathogen exhibited a significant linear trend (Table 1). Two larva each were positive for B. burgdorferi, A. phagocytophilum, and B. miyamotoi. Exposure to I. scapularis forms two peaks with the first in the spring and early summer, April–June, and the second in the fall, October–November. The start of the tick season and the proportion of ticks in each peak varied by year with 2018 having the highest summer peak and 2017 the highest fall peak proportionally. Two distinct peaks were seen for exposures to adult I. scapularis ticks with nymphal ticks showing a single peak in the late spring/early summer (Figure 2). Pathogen prevalence remained consistent throughout the year with a decrease in B. burgdorferi going into late fall. Prevalence can drop to zero in low submission months for both nymphs and adults. The average prevalence of each pathogen varied county by county, and B. burgdorferi had the highest prevalence in every county, except for nymphs in Hampden County (Table 2). Only Nantucket had greater than 50% of adult ticks test positive for B. burgdorferi. Adult ticks in Essex County tested the highest for B. microti and B. miyamotoi, and Berkshire County for A. phagocytophilum. For nymphs, Suffolk County tested the highest percent pathogen-positive for B. burgdorferi, Plymouth County for B. microti and B. miyamotoi, and Hampden County for A. phagocytophilum. For the socioeconomic analysis, we excluded 61 ZCTAs in the Boston area, as decided a priori. All socioeconomic variables met the inclusion criteria of p < 0.10 for the multivariable regression model (Table 3). ZCTAs with a higher percentage of the population self-reporting white race were positively associated with the total tick submissions per ZCTA (Table 3). Higher ZCTA-level median household income and percentage of the ZCTA with a high school education or less were associated with fewer ticks submitted from a ZCTA. Longer distance from the submitter’s residence to the TickReport lab was positively associated with the number of tick submissions (IRR = 1.003 per 1 km: 95% CI 1.002–1.006). Once land-use categories were added to the model, the percentage of self-reporting race as white became non-significant but was still insignificantly positively associated with submissions. The estimate for percent of the ZCTA with high school education or less and median household income changed by less than 10%. In both models, with and without land use, the log of population density was non-significant. Based on model diagnostics, the one ZCTA (01002 Amherst, n = 620 ticks submitted) that met the criteria for influence points was removed, and the model was rerun (Table 3). After removing the potential influence point, the direction of the association between the socioeconomic variables and the number of submissions did not change. However, the percentage of self-reporting race as white became significant, even with land use included, and median household income became insignificant. When examining the socioeconomic model with the Boston area included without land-use categories (Table 3), the direction of the association between the socioeconomic variables and the number of submissions did not change. However, the estimate changed towards the null for all variables but population density, which was significantly associated with fewer tick submissions. Passive surveillance of human-biting is an important surveillance strategy that provides insight into potential high-risk areas or groups. The percentage of I. scapularis positive for tick-borne pathogens in Massachusetts varied by location and life stage. B. burgdorferi was the most common pathogen, followed by B. microti and A. phagocytophilum. Furthermore, we found that tick submissions were more common from ZCTAs with a higher level of education, even after accounting for land use. Among ticks with a known exposure location, 77% of people experienced tick exposure in the same ZCTA as where they lived. Having exposure location thus prevented geographical misattribution for almost 25% of tick submissions and allowed for percent pathogen-positive and exposure risks to be mapped correctly. Even using the same diagnostic service with a similar percentage of adult ticks (77% vs. 81% in the previous report), the percentage of all ticks positive for B. burgdorferi has increased to 35.0% from 29.6%, which was reported by TickReport from 2006–2013. B. microti saw an even larger percentage increase from 4.6% to 7.6% and A. phagocytophilum from 1.8% to 6.9% of all ticks submitted from exposure in Massachusetts [18]. This is especially important as Massachusetts is not considered part of the I. scapularis expanding range [39]. Even if the pathogen prevalence in all questing ticks is not changing, people may be encountering more pathogen-positive ticks due to land use and behavior patterns. The exposure pattern for human-biting I. scapularis ticks followed the two seasonal peaks that are seen in field studies in the study area [40,41]. Adult submissions form two peaks with the nymphs forming a single peak [41]. This also follows the pattern of tick exposure visits in Massachusetts, though not disease visits [32]. Since this data set relies on tick submissions, there was a potential for human factors to affect this pattern. More work is needed to look at the seasonality of pathogen prevalence and not just seasonality in human cases, which depends on tick numbers and human behavior as well. Some climate variables have previously been associated with the percentage of ticks positive for B. burgdorferi, so seasonal prevalence trends may be more dependent on climate and local weather data than overall monthly trends [42,43,44,45]. Massachusetts is known to be an area of high risk for Lyme disease, and a high percentage of ticks are pathogen-positive [27,28]. While not significant, the number of ticks did increase across the study period; this is most likely due to increased awareness of services and not necessarily an increase in tick exposures. Two studies that collected questing ticks from high-risk areas in Massachusetts found B. burgdorferi in more than 60% of I. scapularis ticks [27,28], which was also seen in Nantucket in this study, even with a low number of adult tick submissions. Historically, babesiosis was diagnosed in Nantucket, Martha’s Vineyard, and coastal Massachusetts, but recently other inland areas have also had human cases [46]. A previous study from 2006–2012 using the TickReport passive surveillance system found B. microti to be almost entirely limited to Cape Cod and the islands [18]. In our study, the highest percent positive for B. microti was in Cape Cod (Barnstable County) and Essex County; however, B. microti was found in ticks in all counties, except Suffolk County, which may be due to having the lowest tick submissions. These areas represent the expanding range of B. microti. TickReport data from 2006–2012 found A. phagocytophilum limited to the eastern half of the state, and the highest percent positive is still in eastern Massachusetts. Fewer studies have been performed on the range of B. miyamotoi. A 2010–2012 study of ticks collected from Cape Cod found that 2.8% of female, adult ticks were infected with B. miyamotoi [40], which is similar to the percentage of adult ticks in the current study. Risks for tick-borne diseases are even higher when considering all four pathogens, as almost 10% of human-biting I. scapularis were positive for multiple pathogens. In nymphs, the overall percent positive for any pathogen was lower, as expected for fewer blood meals. It has been suggested that human-biting nymphs are especially important for assessing disease risk to humans [47]. Due to their small size, nymphs tend to be attached longer than adults before removal or go unnoticed [48]. Nymphs are assumed to be the source of most human cases, especially when no tick exposure is reported [47]. Host-seeking infected nymphs tend to be clustered spatially at a smaller spatial scale than adults, so human land usage likely plays a large role in exposure to pathogen-positive nymphs [49]. There is a fee associated with submission and previous socioeconomic associations have been reported with human Lyme disease cases, emphasizing the importance of examining socioeconomic factors associated with tick submissions. Amherst residents submitted the most ticks of any locale, by a large margin, likely due to TickReport’s location at the University of Massachusetts, Amherst. ZCTAs with higher percentages of post-high school education levels were associated with increased tick submissions, as was a higher percentage of residents self-reporting race as white once Amherst was removed from the model. These socioeconomic variables remained significant even after accounting for land use, indicating it was more than just that certain socioeconomic statuses could live in areas with higher risk, such as more forested areas. Land use such as deciduous forests, edge forests, and non-agricultural land has been previously associated with tick numbers [50,51,52]. The inclusion of Boston resulting in population density being the most significant factor was expected due to the high population density compared to the rest of the state and low tick submissions. Studies of Lyme disease cases in the United States have found that cases are associated with relatively higher proportions of the population reporting race as white, higher levels of education, and lower levels of poverty [53,54,55]. For Anaplasmosis, one study found a relatively higher proportion of race reported as white and lower unemployment were associated with more human cases [54]. This may reflect both risk for infection, the costs of medical care, and the awareness of tick-borne diseases that is needed to seek diagnosis and treatment. Our study examined median household income, and not poverty, which might explain why after adjusting for other factors, income was negatively associated with the number of tick submissions. Median household income at the ZCTA level in Massachusetts outside of Boston is relatively high with only 15 ZCTAs having a median household income of less than $35,000, according to the 2018 ACS. As there is a fee associated with submission, we would hypothesize that an analysis at the household level would find household income associated with submission. Further research is needed to look at how to overcome this barrier, such as working with public health departments to subsidize at-risk underserved areas or even insurance coverage of testing fees to allow for accurate, earlier exposure risk assessments for different areas. There may also be opportunity costs in underserved areas that affect submission rates that would need to be addressed. A major strength of this study was the use of a large data set with minimal missing data. Almost 14,000 human-biting ticks were submitted over a five-year period, allowing an examination of variability in percent positive and range for all four pathogens, including the more rare but emerging B. miyamotoi. Study limitations were that TickReport is a passive surveillance system, so there was high variation in tick submissions. The human risk depends not just on the pathogen-positive percentage but on the total number of ticks in an area as well, which is outside this data set. The demographic data were based at the ZCTA level, so individuals submitting ticks may not match the average demographic characteristics of those residing in a ZCTA. Passive surveillance of human-biting ticks is an important part of monitoring tickborne diseases. In Massachusetts, the three pathogens previously measured using this data set increased in human-biting ticks since 2012. As messaging consistent with B. burgdorferi transmission risk may not be applicable for the less common tick-borne disease pathogens [56], awareness of the variation of the range of high-risk areas for all four pathogens is important to inform public health messages. The association of submissions with education level and other socioeconomic factors at the ZCTA level should be considered to produce more generalizable passive surveillance data, especially when a fee is involved. Further investigation is needed to see if similar associations persist in citizen science projects. Passive surveillance of human-biting ticks and associated pathogens is important for monitoring tick-borne diseases, detecting areas with potentially high risks, and providing public information. We anticipate that the presented results can provide support for medical, public health, and veterinary professionals to continue surveillance for tick-borne disease pathogens and to include socioeconomic determinants of health.

PMC10002094

Giuseppe Mannino,Luca Pietro Casacci,Giorgia Bianco Dolino,Giuseppe Badolato,Massimo Emilio Maffei,Francesca Barbero

The Geomagnetic Field (GMF) Is Necessary for Black Garden Ant (Lasius niger L.) Foraging and Modulates Orientation Potentially through Aminergic Regulation and MagR Expression

23-02-2023

nearly null magnetic field,dopamine,serotonin,melatonin,phylogenetic analysis,antioxidant enzymes,oxidative stress,Lasius niger

The geomagnetic field (GMF) can affect a wide range of animal behaviors in various habitats, primarily providing orientation cues for homing or migratory events. Foraging patterns, such as those implemented by Lasius niger, are excellent models to delve into the effects of GMF on orientation abilities. In this work, we assessed the role of GMF by comparing the L. niger foraging and orientation performance, brain biogenic amine (BA) contents, and the expression of genes related to the magnetosensory complex and reactive oxygen species (ROS) of workers exposed to near-null magnetic fields (NNMF, ~40 nT) and GMF (~42 µT). NNMF affected workers’ orientation by increasing the time needed to find the food source and return to the nest. Moreover, under NNMF conditions, a general drop in BAs, but not melatonin, suggested that the lower foraging performance might be correlated to a decrease in locomotory and chemical perception abilities, potentially driven by dopaminergic and serotoninergic regulations, respectively. The variation in the regulation of genes related to the magnetosensory complex in NNMF shed light on the mechanism of ant GMF perception. Overall, our work provides evidence that the GMF, along with chemical and visual cues, is necessary for the L. niger orientation process.

The Geomagnetic Field (GMF) Is Necessary for Black Garden Ant (Lasius niger L.) Foraging and Modulates Orientation Potentially through Aminergic Regulation and MagR Expression The geomagnetic field (GMF) can affect a wide range of animal behaviors in various habitats, primarily providing orientation cues for homing or migratory events. Foraging patterns, such as those implemented by Lasius niger, are excellent models to delve into the effects of GMF on orientation abilities. In this work, we assessed the role of GMF by comparing the L. niger foraging and orientation performance, brain biogenic amine (BA) contents, and the expression of genes related to the magnetosensory complex and reactive oxygen species (ROS) of workers exposed to near-null magnetic fields (NNMF, ~40 nT) and GMF (~42 µT). NNMF affected workers’ orientation by increasing the time needed to find the food source and return to the nest. Moreover, under NNMF conditions, a general drop in BAs, but not melatonin, suggested that the lower foraging performance might be correlated to a decrease in locomotory and chemical perception abilities, potentially driven by dopaminergic and serotoninergic regulations, respectively. The variation in the regulation of genes related to the magnetosensory complex in NNMF shed light on the mechanism of ant GMF perception. Overall, our work provides evidence that the GMF, along with chemical and visual cues, is necessary for the L. niger orientation process. The geomagnetic field (GMF) is one of the abiotic components that has interacted continuously with living organisms since the beginning of life on Earth [1]. All living organisms are affected by the GMF, from bacteria to plants [2,3,4], up to invertebrates [5,6] and vertebrates [7,8,9,10]. Birds can use the GMF vectors during homing or migratory events as a compass for orientation [11]. Four different mechanisms of magnetoperception have been described: (i) the radical pair mechanism (i.e., magnetically sensitive chemical intermediates that are formed by photoexcitation of cryptochrome [12,13], which is present in animals [14], humans [15], and plants [16]); (ii) the presence of magnetic field (MF) sensory receptors described in magnetotactic bacteria [17]; (iii) the presence of electroreceptors in elasmobranch animals [18]; (iv) the biocompass model based on the MagR/Cry complex, demonstrated in the model insect Drosophila melanogaster [19]. Among the four possible mechanisms of magnetoreception, at least two (the radical pair mechanism of chemical magnetosensing and the MagR/Cry biocompass) adequately explain the alterations in the MF by the rates of redox reactions and subsequently altered concentrations of free radicals and reactive oxygen species (ROS) observed in different organisms [16,20,21,22]. Ants and other social insects could represent outstanding models to study the effects of GMF on orientation patterns. Because they live in colonies, sometimes counting hundreds of individuals, their persistence and success rely on a complex organization maintained through a multimodal communication system [23,24]. The needs of the whole colony are fulfilled by collective decision making, coordinating, and regulating several workers engaged in distinct labors without central control [23]. Thus, ants show a wide variety of foraging strategies to optimize the resource intake for the whole colony. The efficiency of the foraging behavior is strictly linked to the ant’s orientation abilities which can be based on “egocentric” or “geocentric” cues [25,26], or by a combination of the two reference systems during homing or food searching. The former includes proprioceptive signals continuously obtained while walking out and integrated to return to the nest, while the latter is landmark-based information (visual or chemical) used to infer the target position [26,27]. Lasius niger L. (black garden ant) workers show a plastic foraging strategy based on the combination of several pieces of information, such as pheromone trails, visual cues, and encounters with nestmates [28,29,30,31,32]. Although the foraging behavior of black garden ants has been extensively studied (see [32] and references therein), to our knowledge, there is no experimental evidence of a potential role of the GMF on their orientation. To evaluate the effect of GMF on the orientation abilities of L. niger, we used a triaxial Helmholtz coils system able to reduce the local GMF (42.20 ± 0.02 µT) to near-null magnetic field (NNMF, 45 ± 6 nT) values and observed how ant foraging performance varied. The system used (see [33] for a technical explanation) allows rearing ants at the same light, temperature, humidity, and magnetic field (MF) inclination and declination as the GMF controls, varying only the MF intensity. Because biogenic amines (BAs), which act as neurotransmitters, neuromodulators, or neurohormones, are involved in the ant behavioral plasticity [34,35,36], we tested the GMF-dependent aminergic regulation of ant foraging behavior by reducing the GMF to NNMF conditions. Recently, the study of BAs has attracted great interest in entomology, as they are responsible for behavioral modifications, including muscle performance, locomotory, learning processes, memory, aggression, and both aggressive and nonaggressive social interactions [37,38]. Among other BAs, tyramine (TA), dopamine (DA), l-3,4-dihydroxyphenylalanine (L-DOPA), and serotonin (Ser) are well known as essential transmitters not only in vertebrates but also in invertebrates [35,37,39]. DA and L-DOPA are involved in food-searching behavior, while Ser antagonizes the effects of octopamine (OA) in the control of rhythmic behaviors, but it is more entailed in learning and memory processes [35,37,40,41]. In invertebrates, melatonin (Mel) has been recently investigated in abiotic tolerance to stress [39,42], whereas TA and OA, which have no physiological significance in vertebrates, play essential roles in controlling different behaviors, including muscle contraction and sense organ sensitivity [34]. Here, we show that reducing GMF to NNMF decreases workers’ orientation performances, potentially through dopaminergic and serotoninergic regulations. Moreover, we show that the regulation of genes related to the magnetosensory complex [43,44] implies the perception of GMF in black garden ants. Overall, we provide evidence that the GMF, along with chemical and visual cues, is pivotal in the L. niger orientation process. During foraging, L. niger gathers spatial information by combining chemical and visual cues [45]; therefore, we designed our bioassays to deliberately include both chemical trails and conspicuous landmarks, along with the reduction in the GMF. Increasing or reverting the MF has been correlated with significant changes in ants’ foraging strategies, leading to variations in the path trajectories, as revealed for Pheidole sp. [46], or increasing the time necessary to lay trails in Solenopsis invicta [47], suggesting the ants’ ability to respond to MF variations by perceiving or releasing chemical signals [46]. However, the use of Helmholtz coils placed directly in the arenas or mazes used for the bioassays might generate short-range environmental variations such as temperature increase [46,48], thus creating confounding factors. Our experimental approach does not introduce other variations, such as temperature increase, compared to the GMF conditions within the artificial arenas, but the reduction to NNMF values. In NNMF (Phase 1), L. niger workers took on average 43.78 ± 10.37 s to enter the arena by leaving their colony, while, in GMF, the time was 29.50 ± 6.00 s; however, this difference was not statistically significant (GLMM, LR χ21,55 = 0.310, p = 0.578) (Figure 1A). Furthermore, we did not record any significant difference in the time spent at the food source by the first worker who started the foraging activity between NNMF and GMF conditions (GLMM, LR χ21,54 = 0.0168, p = 0.897) (Figure 1C). The latter observation is in contrast with the findings of Pereira and coworkers [46], who found differences when applying a static MF of 60 µT. Therefore, the experimental conditions we used in our system neither stressed worker ants nor generated any disturbance in the ant ability to forage. However, we found that GMF plays a crucial role in L. niger orientation. Even if all other cues allowing the ant orientation were present, in NNMF, workers took twice as much time (262.04 ± 60.02 s; Video S1) to discover the food source (LMM, LR χ21,56 = 6.251, p = 0.012) compared to workers in GMF (132.10 ± 25.54 s) (Figure 1B; Video S2), and the first worker took on average longer time (62.75 ± 20.94 s) to return from the food source to the center of the arena, with respect to the colonies observed in GMF (22.56 ± 3.12 s) (LMM, LR χ21,51 = 5.385, p = 0.020) (Figure 1D). By considering the average number of errors made along the way (i.e., the number of times that the forager walked in a labyrinth arm that did not lead to the food), we found that, in NNMF, the first worker made more errors to reach the food source (10.82 ± 1.81) than in GMF (5.14 ± 1.21) (GLMM, LR χ21,56 = 5.917, p = 0.015) (Figure 1E). In addition, the number of workers who made mistakes was extremely variable in NNMF and lower in GMF (Figure 1E). On the other hand, no significant differences (GLMM, LR χ21,51 = 0.0150, p = 0.903) were observed between GMF and NNMF in the number of errors made by the first forager while returning to the nest after feeding at the food source, even if the returning time was longer (Figure 1F). It is, therefore, possible that NNMF conditions affected the locomotory ability of the first scouting worker, by extending the return walk time. However, the correct path could still be found on the basis of visual cues, because L. niger ants make a reduced use of chemical trails in the first phase of foraging [45,49]. After the first worker returned to the colonies, all nest members were allowed to enter the experimental arena leading to a collective foraging, according to the exchange of signals between nestmates and the deposition and perception of pheromone trails (so-called Phase 2) [49]. During Phase 2 of the experiment, the first recruited worker took, on average, significantly longer to reach the food source in NNMF (Video S3) than those in colonies exposed to GMF (GLMM, LR χ21,52 = 5.651, p = 0.017) (Figure 2A; Video S4). By following all workers who fed at the food source for at least 30 s, we observed that foragers took an average longer time to return to the center of the arena in NNMF, compared to workers in GMF (LMM, LR χ21,498 = 20.095, p < 0.001) (Figure 2B). Even the number of errors made by the workers while returning to the nest was found to be significantly higher in NNMF (GLMM, LR χ21,498 = 14.777, p < 0.001) (Figure 2C). The best models (ΔAICc < 4) explaining the time to return to the nest during Phase 2 included the presence/absence of the GMF, the number of errors made by the foragers while returning to the nest, the interaction between these two terms, and the worker sequential order of arrival at the center of the arena (see Supplementary Table S1). The first model contained all entered terms, and only the presence and absence of the GMF and the errors made were statistically significant (GLMM, GMF/NNMF: LR χ21,498 = 10.555, p = 0.001; errors: LR χ21,498 = 56.446, p = 5.775e−14; GMF/NNMF × errors: LR χ21,498 = 2.138, p = 0.144; order of arrival: LR χ21,498 = 3.221, p = 0.073). As expected, the return time to the center of the arena increased as more errors were made along the way; however, the MF presence is the most important explanatory variable, providing other supporting evidence that the GMF is a key factor for the orientation abilities of L. niger. During Phase 2, workers present in the arena interacted with each other. Many interactions (76.5 ± 8.3 events) occurred in GMF conditions between two workers and were found to be instantaneous, i.e., lasting less than 3 s. The workers generally interacted by touching each other with their antennae. In a limited number of cases (6.2 ± 1.1), two workers spent more time in the interaction, which involved, in addition to the antennating behavior, food exchange and allogrooming. The time of these prolonged interactions ranged from a few seconds to a few minutes. NNMF did not affect the number of interactions, both short (GLMM, LR χ21,53 = 1.0624, p = 0.303) (Figure 3A) and prolonged (GLMM, LR χ21,52 = 0.0296, df = 1, p = 0.863) (Figure 3B), or the time spent by the two workers in prolonged interactions (GLMM, LR χ21,36 = 0.615, p = 0.433) (Figure 3C). These interaction events among workers were monitored because they can mediate the transfer of cues that signal the food position or quality [32,50,51]. Indeed, in L. niger, no evidence supports an exchange of information through this modality, while the contact between the antennae of two workers could serve to recognize the nestmates rather than transferring food information [31]. Our data provide indirect evidence to support this last hypothesis, as no significant differences were found in any types of exchange analyzed in either GMF or NNMF conditions. In contrast, assessing the effects of GMF on the collective foraging behavior (Phase 2) shows that both the locomotory and the ability to perceive or release pheromone trails are GMF-dependent, as both the time and the number of misrouting events (errors) increase in NNMF (see also Section 2.2 for further discussion). Biogenic amines (BAs) are compounds derived from amino-acid decarboxylation [52]. Although many of these compounds are known to function as toxins, some BAs are physiologically produced by organisms and function as key factors in several biological processes [42,53]. BAs are extremely diverse, but only a few have shown physiological importance in invertebrates, playing a different role with respect to vertebrates [35,37,38,54]. Consequently, we investigated whether the modulation of ant locomotor abilities by GMF, which affects the foraging performance, was correlated to qualitative or quantitative changes of the BA profiles in L. niger brains. We evaluated the content of those BAs that are known to modify invertebrate behavior, such as TA, OA, DA, L-DOPA, Ser, and Mel, using HPLC analysis coupled to mass spectrometry. Figure 4 shows the chemical structure of the BAs under study. Exposure to NNMF did not produce any qualitative difference in the BA profile of L. niger brains with respect to GMF (Figure 5). In both experimental conditions, Ser was the most produced BA. From a quantitative point of view, a significant (p < 0.05) reduction in TA, OA, L-DOPA, DA, and Ser was found in the brain of L. niger ants exposed to NNMF. On the other hand, the content of Mel was significantly (p < 0.05) lower in GMF-exposed ants (Figure 5). Specifically, most BAs were 3.2- to 4.6-fold higher under GMF than NNMF conditions (Figure 5). These findings suggest a significant role of the GMF in the production of almost all BAs. Although a direct causal effect has rarely been demonstrated, the correlation between the variation of BA levels and behavioral plasticity has been shown in various animal species [34]. These correlations are species- and context-dependent, making it difficult to assess a unique function for each BA. However, it is assumed that TA and OA may be primarily involved in regulating muscle contraction, sense organ sensitivity, and nestmate recognition [37,38,47]; DA and L-DOPA may be associated with aggressive behaviors and locomotor activity, while Ser is supposedly involved in ants’ chemical trace responsiveness [55]. A few studies described the role of Mel in invertebrates, and its presence has been confirmed only recently in insects, linked to abiotic stress tolerance [39,42]. The significant decrease in TA and DA contents observed in NNMF conditions (Figure 5) might be associated with a locomotor inhibition, as observed in several insects, including ants [38], and might explain why NNMF-exposed ants experienced altered foraging performances, as shown in our bioassays (Figure 1 and Figure 2). Indeed, the behavior of L. niger exposed to NNMF is a general decrease in locomotor capacity and lower effectiveness in perceiving chemical cues. The significant decrease found in the content of Ser and OA in ants exposed to NNMF with respect to GMF might be responsible for the reduction in chemical perception because both compounds are primarily involved in sensing, while Ser is known to specifically modulate ant responsiveness to pheromone trails [47,55,56]. Overall, these findings suggested that GMF is necessary for L. niger orientation by modulating the ant brain BA contents. MagR is an evolutionarily ancient protein, identified for the first time in Drosophila CG8198, and consists of an iron–sulfur complex (ISCA). ISCA proteins are involved in a model of light–magnetism-coupled magnetoreception that shows both the magnetic properties of Fe–S proteins and the light-dependent properties of cryptochrome (Cry) [19]. MagR has recently been designated as a potential magnetoreceptor protein and consists of a linear polymerization of Fe–S clusters forming a rod-shaped biocompass surrounded by the photoreceptor Cry [43]. Cry acts as a photoreceptor to receive light signals, while MagR acts as a magnetic receptor within the complex to detect magnetic signals and complete the sensing of geomagnetic information through the opto-magnetic coupling mechanism [4]. The MagR/Cry complex is consequently referred to as the magnetosensory complex (MagS). Recently, the protein has been shown to participate in several biological processes in plants and animals, including iron delivery, sensory redox, electron transport in respiration, photosynthesis, nitrogen fixation, and DNA replication or repair [22]. To evaluate whether MagR could be expressed in ants and to identify conserved motifs in the amino-acid sequence, a phylogenetic analysis of homologous proteins was conducted, considering MagR from Drosophila melanogaster as the original entry. In addition, to better appreciate potential differences in sequence composition, the investigation also included analysis of species phylogenetically close to ants, namely, wasps and bees. By analyzing the sequences of MagR homologs of different ant, wasp, and bee species (Supplementary Data S1), we obtained a circular cladogram phylogenetic tree based on the inclusion of 96 amino-acid residues and exclusion of ambiguous positions (pairwise deletion option). Interestingly, the MagR protein was detected in both ants, wasps, and bees (Figure 6A). The amino acid composition resulting from an overall alignment of the sequences reported in Supplementary Data S1 is also reported (Figure 6B). Several similarities were found among bees, wasps, and ants. However, although many portions of the MagR gene were found to be highly conserved between species, some differences in amino-acid composition allowed the separation of ants from the other organisms (Figure 6A, blue shaded area). Moreover, although not as marked as for ants, wasps and bees were also found to be distinctly divided into different clusters for most of the cases (Figure 6A). Regarding the conserved regions, the amino-acid sequences of MagR of the species under study showed highly conserved sequence residues. In particular, four aromatic residues able to carry electrons (Y64, F112, F114, and F130) were 88% conserved during the evolution of MagR (Figure 6B). This result is in agreement with recent studies on the amino-acid sequence similarity of MagR in different organisms, suggesting that the position of these residues, their spacing between each other, and their location in the three-dimensional structure of the protein are functional for physiological electron tunneling ranging from 6 Å to 14 Å between the two neighboring residues [57]. This also indicates a possible role in intermolecular electron transport within the discoidal tetramer of MagR based on Marcus’s theory of electron transfer [57]. Moreover, in agreement with [57], we found two other residues (Y69 and Y104) to be highly conserved (Figure 6B). Notably, Y69 is tightly localized at the interface between Cry and the MagR tetramer, hypothesizing that it may contribute to electron transfer from/to Cry [57]. In order to investigate whether the GMF could affect the modulation of MagR and Cry, gene expression analysis was conducted in L. niger by qRT-PCR. In addition, because it was recently supposed that the effects derived from changes in MF might be a consequence of altered redox state ratio of Cry during photocycle [20,44,57], the expression of genes coding for enzymes responsible for cellular redox balance (cSOD, mSOD, eSOD, eSOD2, CAT, GPX, and GSR) was also assayed. Expression analysis on target genes was performed by first assessing the stability of reference genes under GMF or NNMF conditions. Specifically, the expression of β-actin, ef-1β, and three different GADPH isoforms was monitored. All the analyzed candidate reference genes were quite stable under GMF or NNMF conditions. Among them, ef-1β had a variation percentage of over 10% (Supplementary Figure S1). Our data point out that β-actin is the most suitable for the study of gene expression variation in L. niger ants under NNMF conditions. Regarding MagR and Cry expression, exposure to GMF increased the transcriptional levels of MagR 1.25-fold, while the expression of Cry was reduced 0.86-fold, with respect to NNMF (Figure 7A). Our analyses suggest that, although MagR is the only portion of MagS capable of perceiving MF intensity and orientation [19], at the transcriptional level, Cry appears to be oppositely influenced by variations in the MF, with respect to MagR. However, the MF may somehow influence the gene regulation and/or enzymatic activity of proteins deputed to cellular redox balance [58]. Because Cry is a protein susceptible to altered redox state, it is very likely that different regulation of Cry under GMF or NNMF conditions is a consequence of disturbances of reactive oxygen species (ROS) production. In order to investigate whether the GMF could influence the expression of genes coding for enzymes involved in the cellular oxidative balance, the expression of genes coding for isoforms of superoxide dismutase (SOD) (cSOD, mSOD, eSOD, and eSOD2), catalase (CAT), glutathione peroxidase (GPX), and glutathione reductase (GSR) were assayed, under both GMF and NNMF conditions. SOD catalyzes the dismutation of the superoxide radical (O2−) into molecular oxygen (O2) and hydrogen peroxide (H2O2). Several isoforms of SOD are constitutively present in almost all living organisms. Although functionally similar, the isoforms have different cellular localization, and the study of their differential expression can provide useful insights into the location of oxidative stress [59]. GMF induced a significant (p < 0.05) upregulation of soluble cytoplasmic SOD (cSOD) isoform, whereas no significant difference was found for the other isoforms of SOD (Figure 7B). H2O2 detoxification is catalyzed by CAT and GPX [60]. Unlike CAT, GPX needs to be reduced by GSR to exert its function [61]. Gene expression analyses showed that CAT and GPX expression was unaffected by MF variations. In contrast, GSR was downregulated by the GMF (Figure 7B). These data suggest a potential upregulation of genes coding for enzymes that increase the H2O2 content in the cytoplasmic compartment. In order to validate this hypothesis, we quantified the H2O2 content of ants exposed to either GMF or NNMF. Our data showed that L. niger ants under GMF conditions produced a higher amount of H2O2 with respect to NNMF, with a 45% reduction (Figure 7C). In addition to the effect of GMF on genes expressing for enzymes involved in ROS production, the reduced H2O2 content under NNMF condition might be associated with the increased amount of Mel (see Figure 5). Indeed, among the detected BAs, Mel is the only one exerting a strong antioxidant power, and, in vertebrates, it counteracts cellular oxidative stress [39,42]. The high antioxidant power of Mel has been attributed not only to its radical-scavenging activity but also to its degradation products [42]. Metabolism and/or catabolism mechanisms of Mel in invertebrates, including ants, are not fully disentangled; however, studies are underway and will be reported soon. Nests of Lasius niger were collected in June 2021 near the Stura river at Borgaro Torinese (45°09′29.7″ N, 7°38′15.6″ E; MF data: declination: 2.56’ E, inclination: 60.95’ down, intensity: ~42 µT). After collection, the nests were brought to the laboratory, where the species identification was confirmed using the key according to [62]. In the laboratory, the colonies were placed in plastic containers (45 × 35 × 30 cm; L × W × H) with the soil removed during collection. Colonies were fed three times a week, offering them a 10% (w/v) solution of honey, dead Galleria mellonella larvae, and water ad libitum. Before starting the training period (see Section 3.2.2), two groups (hereafter subsets), each containing 75 workers from each of the four colonies collected in the field were created. The workers of each subset were placed inside smaller plastic boxes (13 × 8 × 6 cm; W × L × H). A wet sponge, partially covered by a plastic lid, was added to each box to provide humidity and shelter. The colonies were supplied with water ad libitum. The lid of each box was perforated in the center to allow subsequent connection to the experimental foraging arena (see Section 3.2.1). The foraging arena was built to observe the foraging behavior of L. niger. The general structure of the experimental labyrinth includes two mirrored arms that begin from a common central square, representing the entrance to the arena. Each arm, in turn, divides itself into two other paths, which finally end with a T-shaped bifurcation (Supplementary Figure S2). In detail, to build the arena, a 28 × 12 × 1cm transparent plexiglass rectangle was used as a support base for the experimental labyrinth, and a second, same-size rectangle was positioned above the latter to create a completely isolated artificial environment. A small hole was opened in the center of the plexiglass base and was used to connect the arena with the underneath nest through a wooden stick, allowing the workers to enter the labyrinth. The walls that compose the path of the arena were designed using the AutoCAD software (version 2022), and subsequently replicated with a 3D printer. The food source, a solution of water and honey (10%), was always placed in the same position and aligned in the direction of the Earth's geomagnetic north. Therefore, hypothetically the direction chosen by the first workers entering the arena implies discrimination between north and south. The subsequent branches, on the other hand, allow workers to choose between different directional combinations, both alternating and repetitive (left–right: L/R/L; L/R/R; R/L/L; R/L/R). Before behavioral observations, colonies were trained for 2 weeks to search for the food source within the arena. The training phase began 4 days after removing the food from the nest boxes, and, from this moment on, the food was provided exclusively in the manner described hereafter. The food source was located inside the labyrinth in the same branch of the arena (used for the experimental phases, see Section 3.2.1) in such a way as to allow the workers to memorize the series of correct directional choices, which led from the center of the arena to the source of food. The food source was located in the direction of the Earth's geomagnetic north. Assuming that the ants can orient themselves according to the geomagnetic field, the workers should have associated the food source with a specific position relative to the magnetic compass. A blue landmark was placed in the upper part of the plexiglass roof of the arena, near the food source, to also offer the foragers a visual signal that could be used as a spatial reference. The color was chosen according to literature data showing that blue and yellow are particularly distinguishable by ants [63]. After the training phase, we conducted behavioral observations to verify the effect of the MF on the foraging activity of L. niger. We used the Triaxial Helmoholtz coil system to reduce the GMF to NNMF values as previously described [33]. The GMF conditions in the laboratory were as follows: declination, 2.56’ E; inclination, 60.95’ down; intensity, 42.20 ± 0.02 µT (see Supplementary Figure S3 for details). NNMF values ranged from 40 to 50 nT, with the same inclination and declination as the GMF. A three-axis sensor (model Mag-03, Bartington Instruments, Oxford, UK) was positioned at the center of the Helmholtz coils, in order to obtain a continuous real-time measurement of the intensity of the MF inside the chamber of exposure in which the experiments were conducted. One of the two subset colonies was tested in NNMF, while the other subset served as a control and was observed under GMF. To minimize the differences in other environmental variables, the observations of the controls were carried out within the triaxial Helmoholtz coil system, keeping the three Helmoholtz coils switched off. Each subset colony was observed seven times. Before starting the observations, the sub-colonies were left inside the device for 10 min acclimatation, to avoid any type of influence related to preparation of the experimental setup. The experimental protocol included two successive phases (Phase 1 and 2). Phase 1. The arena was connected to the artificial nest underneath using a wooden stick passing through the holes in the center of the plexiglass base and in the lid of the box constituting the nest. The stick had to be small enough to allow the workers to pass through the holes and reach the center of the arena. After one worker entered the arena, the stick was raised and positioned in such a way as to block the entrance to other workers. The first part of the observation ended when the scouting worker (referred to as the first worker) managed to find and use the food, and then returned to the center of the arena. Then, the nest entrance was reopened, to allow the first forager to leave the arena and transport the food within the nest. Phase 1 began when the first worker entered the arena (labyrinth) and ended when the forager returned to the nest after having found and fed on the food source. Phase 2. After the first forager had returned to the nest, we allowed all the workers to enter the arena by positioning the stick in the opening position. Phase 2 ended after a fixed time of 15 min. This time limit was decided on the basis of observations made during the training, in which 15 min was more than sufficient to allow the workers to complete their foraging activity after the first worker had returned. Video recordings of the experiments were performed with an E-M10 Mark Ⅳ digital camera (Olympus). The camera was positioned above the triaxial Helmoholtz coil system on a plexiglass panel. The camera was connected to a Hersmay PS-BLS5/BLS external power supply and used remotely via the Olympus Image Share App Version 4.5.1. For each subset, both phases were recorded in succession. At the end of each recording, the nest and the arena were removed from the triaxial Helmoholtz coil system, and the arena was washed with ethanol and then with water in order to remove the chemical traces released by the ants during the foraging activities. Immediately after the behavioral tests, 15 workers per three subsets in GMF and three subsets in NNMF (a total of 90 samples) were frozen in liquid nitrogen and stored at −80 °C for subsequent chemical (see Section 3.3) and genetic analysis (Section 3.5 and Section 3.6). After conducting and recording all the experimental tests, videos were processed using the software Boris v. 7.10.7 [64]. In Phase 1 and 2, six parameters were taken into consideration as reported in Supplementary Table S3. Time parameters were expressed as seconds, while errors made by workers when foraging or returning to the nest and contacts between workers as number of occurrences. BAs were extracted from L. niger as previously described [38]. Briefly, after a rapid beheading under a dissection microscope using micro-scissors, brains were extracted using 20 µL of 0.1% (v/v) HCl and 5 mM heptafluorobutanoic acid (HFBA). HFBA was used as an ion-pairing agent for the highly polar BAs. The extraction performance was monitored by adding a known amount of 2-phenylethylamine (PEA) to the solvent as internal standard. After homogenizing the ant brain with a micropaste within the extraction solvent, the solution was sonic-bathed for 10 min, and samples were centrifuged at 12,000× g for 20 min at 4 °C to separate solid from liquid components. Lastly, a solution of isopropanol/chloroform (1:4, v/v) was directly added to the supernatant in a 1:1 (v/v) ratio to remove undesirable compounds. Samples were centrifuged at 12,000× g for 10 min at 4 °C, and the aqueous upper-phase obtained from centrifugation was immediately injected into an HPLC–ESI-MS/MS (1200 HPLC, Agilent Technologies, Santa Clara, CA, USA) for quantification of BAs. Elution of BAs was performed at 0.2 mL·min−1 by a solvent gradient composed of 5 mM HFBA in H2O (Solvent A) and 5 mM HFBA in MeOH (Solvent B) on a Luna C18(2) reversed-phase column (150 mm × 3 mm, particle size 3 µm, pore size 100Å, Phenomenex, Bologna, Italy). The gradient was kept in isocratic conditions (20% of Solvent B) for the first 2 min; then, it reached 50% Solvent B in 1 min. From 3 to 10 min, the gradient was readjusted reaching 100% Solvent B and maintained for 5 min. Finally, the chromatographic condition was reconditioned for 6 min using the starting condition. Regarding the mass spectrometer, source parameters were set as follows: nebulizing gas flow, 3 L·min−1; desolvation line temperature, 250 °C; heat block temperature, 400 °C; drying gas flow, 10 L·min−1. The mass spectrometer operated in MRM positive ion mode, monitoring the transitions of 138.0 > 121.0 (tyramine; RT: 14.3 min), 154.0 > 137.0 (dopamine; RT: 16.8), 154.0 > 137 (octopamine; 15.3), 177.0 > 160.0 (serotonin; RT: 21.1), 198.0 > 181.0, 152.0 (L-DOPA; RT: 18.8), and 233.0 > 216.0, 191.0, 174.0 (melatonin; RT: 18.9). All standards used were purchased from MERCK (Germany). To find homologous sequences of the MagR protein across ants and closely related organisms (wasps and bees), a phylogenetic analysis was conducted as previously described [65], using the Drosophila melanogaster MagR amino-acid sequence as the original entry (Accession number: NP_573062.1). The database containing the sequences was completed in December 2022, and the search was conducted online at the National Center for Biotechnology Information (NCBI; https://www.nih.gov/, accessed on 5 December 2022) website. A BLASTp search was performed to search for MagR in ants, wasps, and bees, using nonredundant protein sequences (nr) as queries and limiting the query to organisms contained in specific libraries (taxid: 7399 and 7400). The search algorithm was then adjusted by setting the threshold for the expected number of random matches in a random pattern (expected threshold) to 0.05 and the seed length that initiates an alignment (word size) to 6. Regarding the scoring parameters, the BLOSUM62 matrix was used, and the gap cost for creating and extending a gap in an alignment was set to 11 and 1, respectively. After obtaining the database, all nonredundant results of these searches with E values ≤1 × 10−5 were extracted through an iterative screening process. The putative amino-acid sequences of the selected organisms were then used to perform a phylogenetic analysis using NCBI TreeViewer (ver. 1.19.4) to compute the distance tree of the results [65]. Hierarchical protein classification was performed by the neighbor joining method, with a maximum difference of 0.85, and arranged according to Grishin's visualization. In order to perform gene expression analysis on ant samples, RNA was extracted and reverse-transcribed. Briefly, L. niger was rapidly beheaded under a dissection microscope using micro-scissors, and, in order to prevent contamination by retinal pigments, a small medial–lateral incision was made directly behind the mandibles to remove the optic lobes. Brain RNA was extracted using an RNeasy Mini Kit R (Qiagen, Hilden, Germany). The quality and quantity of total RNA were checked using both a UV/visible nano-spectrophotometer (BioSpecnano, Shimadzu, Japan) and 1% (w/v) agarose gel electrophoresis. The isolated RNA was used as template for reverse transcription (cDNA Maxima H Minus First Strand, Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer’s instructions. The obtained cDNA was consequently used for quantitative real-time PCR by a QuantStudio 3 (Thermo Fisher Scientific, Waltham, MA, USA), Maxima SYBR Green qPCR Master Mix (Thermo Fisher Scientific, Waltham, MA, USA), and using the primers reported in Supplementary Table S4. After evaluating the stability of each reference gene (GAPDH1, GAPDH2, GAPDH3, and β-Actin) under the different experimental conditions, the expression levels of the target genes (GPX, GSH, SOD1, SOD2, SOD3, SOD4, MagR, and Cry) were calculated using the Pfaffl method [66]. Primers for target and reference genes were designed with Primer3 v.4.1.0 software. H2O2 content was measured using the MAK311 Peroxide Assay kit (Sigma-Aldrich, St. Louis, MI, USA) according to the manufacturer’s instructions. Briefly, whole ants exposed to GMF or NNMF were ground using a micropestle and extracted in milliQ water using a 1:2 (w/v) ratio. After centrifugation (15,000× g; 10 min; 4 °C), 4 uL of limpid supernatant was injected into 20 µL of reaction buffer provided by the manufacturer in the commercial kit. Simultaneously, 4 µL of hydrogen peroxide (Sigma-Aldrich, St. Louis, MI, USA) at different standard concentrations was incubated in 20 µL of the same reaction buffer in order to make a calibration curve (LOD: 1.2 mmol LOQ: 3.5 mmol; R2: 0.9998; y = 0.0246x + 0.0375). Both ant extracts and wells containing H2O2 at different concentrations were incubated for 30 min at room temperature. The absorbance resulting from the reaction was monitored at 585 nm using a BioSpec-nano spectrophotometer (Shimadzu, Kyoto, Japan). Kolmogorov–Smirnov tests were used to assess data distribution. Results were expressed as the mean ± standard deviation (SD). To verify if the presence/absence of the geomagnetic field affects the behavioral variables, we computed generalized linear mixed models with a negative binomial distribution to account for overdispersion including the colony identity as random factor using the glmer.nb function of the lme4 R package. In a few cases, where the assumptions were satisfied, we computed linear mixed models including the colony identity as random factor. For each model, the likelihood ratio chi-square was calculated using the function Anova in the car package. To understand which factors could influence the time spent by the foragers to return to the nest during Phase 2, we performed model selection using the dredge function from the MuMIn package, starting with a full model that included the presence/absence of the geomagnetic field, the number of errors and their interaction, and the sequential order of arrival of each forager. The colony identity was included as a random factor. We selected equally plausible models with ΔAICc < 4. For the best model, the likelihood ratio chi-square was calculated. The graphs related to the behavioral observations were produced using the ggplot2 R package. ANOVA followed by Tukey’s post hoc test was applied to determine significant differences in biogenic amine and transcriptomic data using SPSS Statistics ver. 29 (SPSS, Chicago, IL, USA). Overall, the results obtained in our study showed that the GMF is necessary to enhance the efficiency of L. niger foraging by decreasing the time and mistakes made to discover the food source and allowing a quicker return to the nest. These results are consistent with other studies (see [46] and reference therein) that observed the disorientation of ants after exposure to altered MF (e.g., [47]). Interestingly, we observed a decrease in the orientation performance in NNMF, despite the presence of all other signals (chemicals and visuals) used to find their bearings. Therefore, our findings suggest that the GMF is an essential orientation cue for L. niger even if landmarks or trail compounds are available. Our BA analyses shed light on the potential mechanism through which the GMF might affect ant orientation. Indeed, in NNMF, the reduction in BAs that are known to be correlated with a decrease in the locomotor ability (TA and DA) and chemical perception of trails (OA and Ser) is associated with a longer time and higher misrouting events occurring while L. niger searches for food or homing. Although the examination of the mechanism of MF perception in ants was beyond the scope of our work, our results confirm the modulation of MagR and Cry in L. niger and the regulation of genes coding for enzymes involved in maintaining the cellular oxidative state. In GMF-exposed ants, the upregulation of both SOD and the concomitant downregulation of GSR agreed with the increasing content of H2O2, suggesting increased oxidative stress in L. niger exposed to GMF throughout the time of the experiment. As recently demonstrated in Arabidopsis thaliana [44], these results indicate that the GMF induces a basic oxidative stress, characterized by the modulation of genes that contribute to ROS production. This circumstance has been hypothesized to generate a mild oxidative stress condition that organisms evolved in a GMF environment. Changes in MF would then induce changes in the redox status in L. niger, indicating a functional role of ant magnetoperception in response to stress. The GMF was present long before any organism evolved on Earth. Our results provide new insights into an emerging field of research trying to understand how the GMF contributed to life evolution and how invertebrates used GMF variations to infer directions and positions. Here, we showed that L. niger is able to orient itself in a GMF-dependent manner, and we demonstrated that this ant species possesses the magnetosensory complex to perceive MF variations. We hypothesize that GMF modulates ant behavior by interfering with the aminergic activity of their brains.

PMC10002095

Shoumeng Yan,Nan Yao,Xiaotong Li,Mengzi Sun,Yixue Yang,Weiwei Cui,Bo Li

The Association between the Differential Expression of lncRNA and Type 2 Diabetes Mellitus in People with Hypertriglyceridemia

21-02-2023

type 2 diabetes mellitus,lncRNA,hypertriglyceridemia,MIN6,ceRNA

Compared with diabetic patients with normal blood lipid, diabetic patients with dyslipidemia such as high triglycerides have a higher risk of clinical complications, and the disease is also more serious. For the subjects with hypertriglyceridemia, the lncRNAs affecting type 2 diabetes mellitus (T2DM) and the specific mechanisms remain unclear. Transcriptome sequencing was performed on peripheral blood samples of new-onset T2DM (six subjects) and normal blood control (six subjects) in hypertriglyceridemia patients using gene chip technology, and differentially expressed lncRNA profiles were constructed. Validated by the GEO database and RT-qPCR, lncRNA ENST00000462455.1 was selected. Subsequently, fluorescence in situ hybridization (FISH), real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8 assay, flow cytometry, and enzyme-linked immunosorbent assay (ELISA) were used to observe the effect of ENST00000462455.1 on MIN6. When silencing the ENST00000462455.1 for MIN6 in high glucose and high fat, the relative cell survival rate and insulin secretion decreased, the apoptosis rate increased, and the expression of the transcription factors Ins1, Pdx-1, Glut2, FoxO1, and ETS1 that maintained the function and activity of pancreatic β cells decreased (p < 0.05). In addition, we found that ENST00000462455.1/miR-204-3p/CACNA1C could be the core regulatory axis by using bioinformatics methods. Therefore, ENST00000462455.1 was a potential biomarker for hypertriglyceridemia patients with T2DM.

The Association between the Differential Expression of lncRNA and Type 2 Diabetes Mellitus in People with Hypertriglyceridemia Compared with diabetic patients with normal blood lipid, diabetic patients with dyslipidemia such as high triglycerides have a higher risk of clinical complications, and the disease is also more serious. For the subjects with hypertriglyceridemia, the lncRNAs affecting type 2 diabetes mellitus (T2DM) and the specific mechanisms remain unclear. Transcriptome sequencing was performed on peripheral blood samples of new-onset T2DM (six subjects) and normal blood control (six subjects) in hypertriglyceridemia patients using gene chip technology, and differentially expressed lncRNA profiles were constructed. Validated by the GEO database and RT-qPCR, lncRNA ENST00000462455.1 was selected. Subsequently, fluorescence in situ hybridization (FISH), real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8 assay, flow cytometry, and enzyme-linked immunosorbent assay (ELISA) were used to observe the effect of ENST00000462455.1 on MIN6. When silencing the ENST00000462455.1 for MIN6 in high glucose and high fat, the relative cell survival rate and insulin secretion decreased, the apoptosis rate increased, and the expression of the transcription factors Ins1, Pdx-1, Glut2, FoxO1, and ETS1 that maintained the function and activity of pancreatic β cells decreased (p < 0.05). In addition, we found that ENST00000462455.1/miR-204-3p/CACNA1C could be the core regulatory axis by using bioinformatics methods. Therefore, ENST00000462455.1 was a potential biomarker for hypertriglyceridemia patients with T2DM. Diabetes has become the third leading chronic disease that seriously endangers human health. In 2021, there were about 537 million people with diabetes worldwide, and this number is projected to reach 643 million by 2030 and 783 million by 2045. The prevalence of diabetes is on the rise, and over 6.7 million people will die from diabetes-related causes [1]. Type 2 diabetes mellitus (T2DM) is an endocrine and metabolic disease caused by a combination of genetic and environmental factors and characterized by fasting and postprandial hyperglycemia, which account for more than 90% of diabetes [2]. Existing evidence indicates that people with T2DM have a 15% increase in all-cause mortality compared with people without diabetes [3]. Pancreatic β cells play an essential role in maintaining glucose homeostasis [4]. Glucose is a major physiological regulator for pancreatic β cells and can be metabolized via pancreatic β cells, thereby stimulating insulin secretion [5,6]. However, in chronic hyperglycemic environments and sustained glucose metabolism, pancreatic β cells are prone to damage and dysfunction, resulting in defective insulin secretion [7]. In addition, dyslipidemia also plays an important role in the development of T2DM. On the one hand, the lipotoxicity caused by dyslipidemia could affect the development of insulin resistance, which in turn aggravates the occurrence of lipid metabolism disorders, and a vicious circle is established [8]. On the other hand, the accumulation of abnormally elevated triglycerides in pancreatic β cells leads to their dysfunction and the further apoptosis of pancreatic β cells, which eventually causes the disorder of insulin secretion and the increase of blood glucose, thus inducing T2DM [9]. Meanwhile, T2DM complicated with hyperlipidemia is more likely to induce complications such as cardiovascular and cerebrovascular diseases [10]. Therefore, whether from a public health or a clinical perspective, hypertriglyceridemia patients with T2DM should be paid more attention. Long noncoding RNAs (lncRNAs) represent a class of transcripts longer than 200 nucleotides with limited protein-coding potential [11]. They affect downstream gene expression and promote/inhibit disease development mainly by binding to targeted mRNAs or serving as endogenous competing RNAs for miRNAs [12]. Studies have found that lncRNAs are related to the development of T2DM and its related diseases. For example, lncRNA PVT1 can regulate insulin secretion and lipid metabolism by affecting miR-20a-5p expression, and it is also associated with end-stage renal disease in T2DM patients [13,14]. The lncRNA MALAT 1 plays an important role in the pathophysiology, inflammation, and progression of T2DM through regulating gene transcription [15]. MEG3 is overexpressed in patients with T2DM and is closely related to the occurrence of diabetic retinopathy [16]. Meanwhile, more than 1000 lncRNAs have been found in human islet cells, many of which are highly islet-specific, suggesting that they could have important and unique roles in regulating pancreatic function [13]. Our study aims to screen the differentially expressed lncRNA between new-onset T2DM and normal blood glucose control in hypertriglyceridemia subjects, and then explore the effects and possible mechanism of lncRNA on pancreatic β cell function and activity, thus providing some references for the prevention and treatment of T2DM in people with hypertriglyceridemia. Blood samples of six newly diagnosed T2DM patients and six patients with normal blood glucose were used to perform RNA sequencing. Basic information of subjects and the situation of data filtering are shown in Tables S1 and S2, respectively. The cleaned data is used for subsequent analysis to ensure the quality of the analysis. We obtained a total of 3163 differentially expressed lncRNAs (1439 up and 1724 down) between the T2DM group and the control group based on a p value less than or equal to 0.05. The corresponding volcano plot and heat map are shown in Figure S1. Meanwhile, a total of 25 differentially expressed lncRNAs (10 up and 15 down) were found between the above two groups based on an adjusted p value less than or equal to 0.05 (Table 1). Firstly, we analyzed the genes corresponding to the above 25 lncRNAs through the GSE 130,991 dataset, and a total of 13 genes were found in the dataset. Specially, the gene PLEKHM2, corresponding to the lncRNA ENST00000462455.1, was statistically significant (Table 2). Meanwhile, RT-qPCR was used to verify the expression levels of lncRNA ENST00000462455.1 in 120 hypertriglyceridemia T2DM patients and 120 hypertriglyceridemia patients with normal FPG. The results indicated that the expression level of ENST00000462455.1 in the T2DM subjects was decreased (t = 5.673, p < 0.001), and the same results were observed in gender and age subgroups (Figure 1). In addition, the ROC curve was used to assess the diagnostic power of ENST00000462455.1 (Figure S2 and Table S3). Firstly, we detected the localization and distribution of ENST00000462455.1 in MIN6 cells by FISH. As internal reference genes, 18S was almost located in the cytoplasm and U6 was almost located in the nucleus. The results of the FISH indicated that the ENST00000462455.1 was distributed in both the cytoplasm and the nucleus (Figure 2). Next, we analyzed the expression of ENST 00000462455.1 in MIN6 cells cultured for 24 h, 36 h, 48 h, 72 h, and 96 h for the control, HG, HF, and HG + HF groups. The results indicated that, compared with the HF group, the expression level of ENST00000462455.1 in MIN6 cells in the HG + HF group decreased after 48 h (HF vs. HG + HF: 1.92 ± 0.05 vs. 0.95 ± 0.17, p < 0.001), 72 h (HF vs. HG + HF: 2.06 ± 0.29 vs. 1.21 ± 0.17, p < 0.01), and 96 h (HF vs. HG + HF: 1.37 ± 0.05 vs. 1.07 ± 0.03, p < 0.01) of culture in the corresponding environment (Figure 3). To further explore the effect of lncRNA ENST00000462455.1 on the activity and function of MIN6, the siRNA against ENST00000462455.1 was transfected into MIN6 to silence the expression of the lncRNA. The results of RT-qPCR confirmed that the silencing effect was stable (Figure S3). Subsequently, we explored the effect of ENST00000462455.1 on MIN6 activity by the CCK-8 assay. Taking the HF group as a reference, we found that the relative survival rate of MIN6 in the HG + HF group with si-lncRNA was lower than that in the si-NC group (si-NC vs. si-lncRNA: 1.24 ± 0.21 vs. 1.06 ± 0.16, p < 0.05) (Figure 4A). Similarly, by flow cytometry, we observed that the relative apoptosis rate of MIN6 in the HG + HF group with si-lncRNA was higher than that in the si-NC group (Figure 4B). Meanwhile, the insulin level in the supernatant of the MIN6 cultured under the corresponding glycolipid environment for 48 h was detected by ELISA, thus assessing the effect of ENST00000462455.1 on the insulin secretion of MIN6. The results showed that the insulin secretion of MIN6 in the HG + HF group with si-lncRNA was lower than that in the si-NC group (si-NC vs. si-lncRNA: 12.06 ± 0.70 mIU/L vs. 9.07 ± 1.20 mIU/L, p < 0.001; si-NC vs. si-lncRNA(relative): 1.90 ± 0.11 vs. 1.33 ± 0.18, p < 0.001) (Figure 4C). In addition, RT-qPCR was also used to detect the expression levels of relevant key transcription factors. Taking the HF group as a reference, we found the expression levels of Ins1, Pdx-1, Glut2, FoxO1, and ETS1 in the HG + HF group with si-lncRNA were lower than those in the si-NC group (p < 0.05) (Figure 4D). Therefore, under a high-glucose and high-fat environment, the decreased expression of lncRNA ENST00000462455.1 could lead to the lowering of MIN6 cell activity and the occurrence of dysfunction. We further explored the possible mechanism of ENST00000462455.1 by constructing a ceRNA network which included lncRNA ENST00000462455.1 and its corresponding 14 miRNAs and 118 mRNAs (Figure 5). Given that miRNAs play an important role in the ceRNA network, we identified key miRNAs by searching the literature. Based on the available evidence, we found that miR-204-3p and miR-125a-3p were associated with type 2 diabetes or pancreatic β cells dysfunction, and 29 mRNAs corresponding to these two miRNAs were found in the ceRNA network (Table S4). Subsequently, GO and KEGG analysis were performed on these mRNAs (Figure 6A,B). The results indicated that CACNA1C, CSRP1, ANXA6, KCNIP2, and DPYSL2 are enriched in multiple pathways of BP, CC, and MF (Tables S5–S7). In particular, the results of the KEGG analysis showed that CACNA1C was enriched in multiple pathways including type 2 diabetes and insulin secretion (Table S8). Meanwhile, compared with the control group, the GSEA results found that CACNA1C was a core gene and the expression of it was decreased in hypertriglyceridemia subjects with T2DM (Table S9, Figure S4). In addition, we explored the interaction of key mRNAs in the ceRNA by establishing a PPI network, and a key network module was identified by cluster analysis: CSRP1-ANXA6-DPYSL2-CACNA1C-RCAN1-KCNIP2 (MCODE score: 2.8) (Figure 6C,D). Based on the above results, the possible ceRNA regulatory axis of lncRNA ENST00000462455.1 is shown in Figure 6E. Among them, ENST00000462455.1/miR-204-3p/CACNA1C may be the core regulatory axis. Protein-coding RNAs account for only about 2% of the genome [17,18]. Although noncoding RNAs do not have traditional RNA functions in protein translation, they have become novel basic regulators of gene expression. Existing evidence indicated that some lncRNAs in islet often map to the proximal end of related genes that related to function or development of pancreatic β cells and thus may have specific regulatory functions for the gene expression of pancreatic β cells [19,20,21]. In our study, transcriptome sequencing was first performed on whole blood samples of hypertriglyceridemia subjects with T2DM or normal FPG to get the differentially expressed lncRNAs. Subsequently, the differentially expressed lncRNA ENST00000462455.1 was verified by GEO and RT-qPCR, and its potential value in clinical settings was also assessed via ROC. In addition, compared with the HF environment, we found that the expression of ENST00000462455.1 in MIN6 cells decreased under the HG + HF environment. Therefore, lncRNA ENST00000462455.1 was viewed as a differentially expressed lncRNA in hypertriglyceridemia patients with T2DM and normal FPG. We further explored the effect of ENST00000462455.1 on the function and activity of MIN6 cells. After silencing ENST00000462455.1, we found that the activity of MIN6 cells decreased and the apoptosis rate increased. Meanwhile, the insulin secretion was also reduced. In addition, the expression levels of transcription factors, including Ins1, Pdx-1, Glut2, FoxO1, and ETS1, were decreased after silencing ENST00000462455.1. As an inherent regulatory gene of insulin, Ins1 is regulated by circulating levels of glucose and plays an important role in maintaining mature pancreatic β cells mass and function, insulin secretion and reserve, and glucose homeostasis [22,23]. Similarly, the function of Pdx-1 is to maintain mature islet function, mass, and the regeneration of pancreatic β cells [24]. Meanwhile, Pdx-1 may also be a key factor related to the adverse effects of lipid metabolism disorders on pancreatic islets [25]. FoxO1 could regulate the proliferation, apoptosis, and differentiation of pancreatic β cells and play a role in insulin secretion and resistance to oxidative stress [26]. Simultaneously, FoxO1 is closely related to Ins1 and Pdx-1. Previous study found that FoxO1 transgenic mice significantly elevated the expression levels of Ins1 and Pdx-1 [27]. In fact, the relationship between FoxO1 and Pdx-1 has been confirmed during the development of the body. FoxO1 can activate itself in the early stage of pancreatic development by mediating the expression of Pdx-1 [28]. Specially, although the function of Glut2 is merely to catalyze the passive transport of glucose across plasma membranes, this transport activity is important for the control of cellular mechanisms impinging on gene expression, the regulation of intracellular metabolic pathways, and the induction of hormonal and neuronal signals, which together form the basis of an integrated interorgan communication system to control glucose homeostasis [29]. In addition, previous study also found that the overexpression of Ets-1 in MIN6 cells could protect them from severe hypoxic injury in a mitochondria-dependent method [30]. One of the main mechanisms of lncRNAs is that they can become endogenous competing RNAs for miRNAs affecting the expression of downstream genes, thereby promoting or inhibiting the development of diseases. In our study, ENST00000462455.1 was observed in both the cytoplasm and nucleus by FISH. Existing evidence indicated that lncRNAs stably expressed in the cytoplasm are ideal ceRNAs (although recent studies also found that some nuclear-localized lncRNAs could also act as ceRNAs). Therefore, we further constructed the ceRNA network of ENST00000462455.1 by the bioinformatics method and found that ENST00000462455.1/miR-125a-3p/RCAN1/DPYSL2 may be one of the regulatory axes. Previous studies have shown that miR-125a-3p could inhibit the expression of insulin receptors via the insulin signaling pathway, resulting in insulin resistance, thus leading to lipid and carbohydrate metabolism disorder [31]. Meanwhile, miR-125a-3p is also related to diabetic cardiomyopathy and diabetic nephropathy [32]. RCAN1 has a role in the pancreatic β cell dysfunction for T2DM [33]. Some studies found that the acute induction of RCAN1 by increased reactive oxygen species and hyperglycemia could inhibit endocrine cell apoptosis and protect them from damage. However, some evidence indicated that chronic overexpression of RCAN1 could also adversely affect cells, leading to pathological changes in neurons and endocrine cells associated with T2DM [33]. Therefore, more studies for the molecular mechanisms of RCAN1 need to be performed. Another possible ceRNA regulatory axis is ENST00000462455.1/miR-204-3p/KCNIP2/CACNA1C/ANXA6/CSRP1. Among them, ENST00000462455.1/miR-204-3p/CACNA1C may be the core regulatory axis. Previous studies found that the expression of miR-204 is increased in pancreatic islets of T2DM and elevated serum miR-204 is a marker of ongoing pancreatic β cell death [34]. Meanwhile, miR-204 can directly target and inhibit the endoplasmic reticulum transmembrane factor protein kinase R-like endoplasmic reticulum kinase (PERK) and its downstream signaling pathways, thereby aggravating ER-stress-induced pancreatic β cell apoptosis [35]. As a chain of miR-204, miR-204-3p is involved in various diabetic complications. In diabetic cataract, miR-204-3p can regulate the migration and epithelial-to-mesenchymal transition in lens epithelial cells [36]. Meanwhile, miR-204-3p also plays a role in high-glucose-induced podocyte apoptosis and dysfunction [37]. In addition, for diabetic cardiomyopathy, miR-204-3p can regulate cardiomyocyte autophagy, thus affecting myocardial ischemia/reperfusion injury [38]. Voltage-gated calcium channels (VGCCs) and potassium channels are important to insulin secretion [39,40,41]. Among them, the L-type voltage-gated calcium channels (LVGCCs) are present in pancreatic β cells and are involved in glucose transport, lipolysis, and lipogenesis [42,43]. Although LVGCCs account for only ∼50% of the total Ca2+ current, their inhibition reduces glucose-induced insulin secretion by 80% and nearly abolishes insulin release in vivo [44]. In humans, the two main LVGCCs are Cav1.2 and Cav1.3, and CACNA1C is the encoding gene of Cav1.2. It was found that Cav1.2 was required for first-phase insulin secretion and rapid exocytosis in pancreatic β cells, and the expression level of CACNA1C was also high in the cells [45,46]. In mice, Cav1.2 was the only LVGCC and the knockout of CACNA1C was lethal (glucose intolerance and loss of first-phase insulin secretion were observed) [47]. In addition, CACNA1C is also involved in diabetic peripheral neuropathy, diabetic heart disease, and diabetic cataract [48,49,50]. KCNIP2 (encodes the KChIP2 protein) interacts with the subfamily of the voltage-gated potassium channel to increase the current density, accelerate the recovery from inactivation, and slow inactivation kinetics [51]. Existing evidence indicated that the lack of insulin signaling in the heart of T2DM patients may be one of the mechanisms for the decreased expression of KCNIP2, which in turn leads to abnormal changes in cardiac electrophysiology [52]. In addition, ANXA6 is involved in cholesterol transport, accumulation, and storage of TG, and plays an important role in the glucose and lipid balance by regulating the release of adiponectin [53,54,55]. Some limitations exist in this study. We used MIN6 cells, a mouse pancreatic beta cell line, for the experimental verification of lncRNA ENST00000462455.1 functions. Considering the species difference, the effect of this lncRNA on T2DM of human needs further evaluation. Moreover, the study lacked corresponding animal model verification. Meanwhile, our study only used bioinformatics methods to explore the possible ceRNA regulatory mechanism of ENST00000462455.1, and further experimental verification is required. Six newly diagnosed T2DM patients and six patients with normal blood glucose were recruited to perform RNA sequencing. All subjects were Han Chinese, aged 40–65 years, and were recruited at the First Hospital of Jilin University from July to September 2020. Patients were diagnosed based on the guidelines for the prevention and control of type 2 diabetes in China (2017 Edition): Patients with type 2 diabetes were defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L or oral glucose tolerance test (OGTT) two-hour blood glucose ≥ 11.1 mmol/L. FPG < 6.1 mmol/L and OGTT < 7.8 mmol/L were defined as the normal controls. Meanwhile, the level of triglycerides (TG) in all participants was ≥1.7 mmol/L according to the guidelines for prevention and treatment of dyslipidemia in China (2016 Edition). All patients had not previously controlled their blood glucose through drugs or other treatments. Moreover, the corresponding genes of the lncRNAs were verified via the GSE 130,991 dataset (910 samples). A total of 92 T2DM and 96 controls with hypertriglyceridemia were selected from the dataset based on the above guidelines. Meanwhile, we also collected 120 T2DM and 120 controls with hypertriglyceridemia to perform RT-qPCR validation at the First Hospital of Jilin University from July to August 2021.All patients with a history of coronary artery disease (CAD), hypertension, atrial fibrillation, myocardial infarction, tumor, acute infectious disease, immune disease, and hematological disease were excluded from the study. All participants provided written informed consent and the study was approved by Ethics Committee of the Public Health of the Jilin University, and the privacy of the participants are strictly confidential. Total RNA in blood was isolated and purified using a total RNA extraction kit. The NanoPhotometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA) and RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA) were used to assess the RNA purity and integrity, respectively. The chain-specific library was constructed by removing the ribosomal RNA. After the library was qualified, Illumina PE150 sequencing was performed according to the pooling of the effective concentration of the library and the data output requirements. Followed by the sequencing, data filtering was conducted: we removed reads with adapter and N (N means that the nucleobase information cannot be determined) ≥ 0.002, and the paired reads that contain low-quality nucleobases (>50%) in single-end reads were also removed. Meanwhile, the Q20, Q30, and GC content were calculated, and the clean reads were obtained. Subsequently, the mapping analysis was performed by the software Hisat2 for the corresponding clean reads. The reference database was GRCh38.p12 (human) and GRCm38.p6 (mouse). Based on the mapping results, we further assembled, filtered, and quantified the transcripts by using the Stringtie and Cuffmerge software. Finally, the expression level matrix was obtained. All analyses in the study were based on the data and the data could be found in GEO database (GSE193436). The total RNA was extracted using the MolPure® Blood RNA Kit (19241ES50, YEASEN) or MolPure® Cell RNA Kit (19231ES50, YEASEN) based on the sample type. Subsequently, we used the lnRcute lncRNA First-Strand cDNA Kit (KR202, TIANGEN) or FastKing gDNA Dispelling RT SuperMix (KR118, TIANGEN) to conduct reverse transcription. The cDNA was then analyzed by RT-qPCR using lnRcute lncRNA qPCR Kit (FP402, TIANGEN) or SuperReal PreMix Plus (SYBR Green) (FP205, TIANGEN) on the QuantStudio 3 system (Applied Biosystems, Waltham, MA, USA). The PCR primers are shown in Table S10. Expression data were normalized to the expression of β-actin with the 2−ΔΔCt method. MIN6 cells (mouse pancreatic beta cell line) were cultured in RPMI Medium 1640 (31800, Solarbio, Beijing, China) supplemented with 10% fetal bovine serum (FBS) (04-001-1A, Biological Industries, Cromwell, CT, USA) at 37 °C with 5% CO2. RiboTM lncRNA FISH Probe Mix (lnc11001001, RIBOBIO) and RiboTM Fluorescent in Situ Hybridization Kit (C10910, RIBOBIO) were used for the FISH of lncRNA, thus detecting the distribution of the target lncRNA. The cell slides were placed at the bottom of a 24-well plate and each well was plated with 1 × 105 cells. After the cells had grown to about 80%, the cells were washed with phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde. Subsequently, the cells were washed again and treated with permeabilization solution, then 200 μL of prehybridization solution was added and the cells were blocked at 37 °C for 30 min. The prehybridization solution was discarded and 100 μL of the hybridization solution containing the lncRNA FISH probe was added for overnight hybridization at 37 °C. Next day, the cells were washed by PBS and stained with DAPI and photographed by fluorescence microscopy, with 18S and U6 as the reference genes. Based on different glycolipid environments, our experiment was divided into four experimental groups: control (5 mmol/L D-glucose + PBS), high glucose (HG) (30 mmol/L D-glucose + PBS), high fat (HF) (5 mmol/L D-glucose + 400µmol/L sodium palmitate), and high glucose and high fat (HG + HF) (30 mmol/L D-glucose + 400µmol/L sodium palmitate) [56,57]. The expression of the target lncRNA in each group was determined by qRT-PCR after 24 h, 36 h, 48 h, 72 h, and 96 h. The siRNA was transfected by liposome reagent transfection to silence the target lncRNA. Corresponding sequence of siRNA was shown in Table S11. Firstly, six-well plates were seeded with 2 × 105 cells per well. After 24 h, siRNA against target lncRNA (GenePharma) was transfected into cells by using Lipofectamine 2000 (11668019, Invitrogen). After incubation at 37 °C with 5% CO2 for 6 h, the medium was changed to complete medium (supplemented with 10% FBS) for another 24 h. Subsequently, the RNA in the cells was directly extracted or further cultivated in different glycolipid environments for 48 h and the expression level of the target lncRNA in the negative control group (si-NC) and experimental group (si-lncRNA) was detected to evaluate the effect of transfection. The cells were seeded in 96-well plates (4 × 103 cells per well). After the lncRNA was silenced, corresponding glycolipid environment were constructed for 48 h, and then 10μL of CCK-8 reagent (CK04, Dojindo) was added to each well. Subsequently, the plate was incubated for another 1–4 h and the absorbance values were measured at 450 nm with an enzyme-linked immunometric meter. Cell apoptosis was detected by the FITC Annexin V Apoptosis Detection Kit I (556547, BD BIOSCIENCES PHARMINGEN). Firstly, cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, the corresponding glycolipid environments were constructed for 48 h. Then, the original medium in the plate was discarded and cold PBS was added to wash the cells. Subsequently, 1 × binding buffer was added to each well and the cells were stained with FITC and PI. After 15 min incubation protecting from light, flow cytometry analysis was performed by using a FACSCalibur (BD BIOSCIENCES PHARMINGEN). Insulin secretion was assessed by ELISA. Similarly, cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, the corresponding glycolipid environments were constructed for 48 h. Then, the supernatant was collected and detected by Mouse INS ELISA kit (ml001983, mlbio). All experiments were performed strictly in accordance with the manufacturer’s instructions. Cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, corresponding glycolipid environments were constructed for 48 h. Subsequently, RT-qPCR was used to detect the transcription factors of pancreatic β cell function and activity (Ins1, Pdx-1, MafA, Glut2, TCF7L2, FoxO1, ETS1, Pax6, Ngn3). Normal continues variables were described by mean and standard deviation. Meanwhile, median and interquartile ranges were used to describe the skewed continues variables. Correspondingly, the t-test and Wilcoxon rank-sum test were conducted based on the data distribution. Chi-square test was conducted for categorical variables. One-way ANOVA was used for comparison among multiple groups, and LSD was performed for pairwise comparison. The diagnostic value of the lncRNA for T2DM in hypertriglyceridemia subjects was evaluated by the ROC curve. All above analyses were mainly performed by SPSS 24.0 and GraphPad Prism 7.0 software. A 2-sided p value less than 0.05 was considered significant. Independent replicated experiments were conducted in our study. R 4.0.4, Cytoscape 3.8.2 and GSEA 4.2.1 software were used to conduct bioinformatics analysis. Differentially expressed genes were screened using the limma package [58] and the correlation between genes was analyzed by Pearson correlation. Meanwhile, the ggplot2 [59] and pheatmap [60] packages were used to draw the volcano plot and heat map, respectively. The ceRNA network construction strategy of the target lncRNA is shown in Figure S5, and Cytoscape was used to draw the networks. The clusterProfiler package [61] was used for GO (including Biological Process (BP), Cellular Component (CC), and Molecular Function (MF)) and KEGG enrichment analysis, and corresponding enrichment circle maps were drawn via the online analysis tool (https://www.omicsshare.com/tools/, accessed on 13 November 2021). Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. In addition, PPI network analysis was performed by STRING 11.5 (http://string-db.org, accessed on 12 November 2021) and Cytoscape, and the MCODE was used to conduct cluster analysis in PPI network. The lncRNA ENST00000462455.1 is a potential biomarker for hypertriglyceridemia patients with T2DM. More experimental studies are needed to verify the function of the lncRNA and analyze its possible mechanism.

PMC10002124

Irene Chamorro-Herrero,Alberto Zambrano

Modeling of Respiratory Diseases Evolving with Fibrosis from Organoids Derived from Human Pluripotent Stem Cells

23-02-2023

pluripotent stem cells,minilungs,disease modeling,fibrosis,myofibroblasts,idiopathic pulmonary fibrosis,IPF,cystic fibrosis,CF,chronic obstructive pulmonary disease,COPD,SARS-CoV-2,COVID-19

Respiratory disease is one of the leading causes of morbidity and mortality worldwide. There is no cure for most diseases, which are treated symptomatically. Hence, new strategies are required to deepen the understanding of the disease and development of therapeutic strategies. The advent of stem cell and organoid technology has enabled the development of human pluripotent stem cell lines and adequate differentiation protocols for developing both airways and lung organoids in different formats. These novel human-pluripotent-stem-cell-derived organoids have enabled relatively accurate disease modeling. Idiopathic pulmonary fibrosis is a fatal and debilitating disease that exhibits prototypical fibrotic features that may be, to some extent, extrapolated to other conditions. Thus, respiratory diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or the one caused by SARS-CoV-2 may reflect some fibrotic aspects reminiscent of those present in idiopathic pulmonary fibrosis. Modeling of fibrosis of the airways and the lung is a real challenge due to the large number of epithelial cells involved and interaction with other cell types of mesenchymal origin. This review will focus on the status of respiratory disease modeling from human-pluripotent-stem-cell-derived organoids, which are being used to model several representative respiratory diseases, such as idiopathic pulmonary fibrosis, cystic fibrosis, chronic obstructive pulmonary disease, and COVID-19.

Modeling of Respiratory Diseases Evolving with Fibrosis from Organoids Derived from Human Pluripotent Stem Cells Respiratory disease is one of the leading causes of morbidity and mortality worldwide. There is no cure for most diseases, which are treated symptomatically. Hence, new strategies are required to deepen the understanding of the disease and development of therapeutic strategies. The advent of stem cell and organoid technology has enabled the development of human pluripotent stem cell lines and adequate differentiation protocols for developing both airways and lung organoids in different formats. These novel human-pluripotent-stem-cell-derived organoids have enabled relatively accurate disease modeling. Idiopathic pulmonary fibrosis is a fatal and debilitating disease that exhibits prototypical fibrotic features that may be, to some extent, extrapolated to other conditions. Thus, respiratory diseases such as cystic fibrosis, chronic obstructive pulmonary disease, or the one caused by SARS-CoV-2 may reflect some fibrotic aspects reminiscent of those present in idiopathic pulmonary fibrosis. Modeling of fibrosis of the airways and the lung is a real challenge due to the large number of epithelial cells involved and interaction with other cell types of mesenchymal origin. This review will focus on the status of respiratory disease modeling from human-pluripotent-stem-cell-derived organoids, which are being used to model several representative respiratory diseases, such as idiopathic pulmonary fibrosis, cystic fibrosis, chronic obstructive pulmonary disease, and COVID-19. The respiratory system is composed of two main compartments: the airways and the alveoli. The airways consist of the nasal cavity, trachea, bronchi, and bronchioles. The airways conduct airflow to the distal alveoli in which gas exchange between the exterior and the underlying vasculature takes place. Clearance of microbes and suspended particles in the air is another essential function of the respiratory tree conducted by cells of the upper airway epithelium. The luminal surfaces of the airways have lining of ciliated pseudostratified columnar epithelium and contain goblet, ciliated, and basal cells, which are stem cells or progenitors of all the cells of the respiratory epithelium. As the degree of branching within the airway tree continues, the epithelium gradually changes from pseudostratified to simple cuboidal and the predominant cells become non-ciliated cells. Club cells are secretory cells of the bronchiolar epithelium and contribute to production of non-mucinous secretory proteins to the extracellular lining fluid. The alveoli are composed of two main epithelial cells: alveolar type I cells (ATI cells), implicated in gas exchange, and alveolar type II cells (ATII cells), responsible for secretion of the surfactant that reduces surface tension and promotes generation of ATI cells (Figure 1). Recent studies have described new epithelial cell types, such as CFTR-rich pulmonary ionocyte and respiratory airway secretory (RAS) cells [1,2]. The pulmonary ionocyte appears to be a major source of CFTR activity in the airway epithelium, suggesting a role in luminal pH regulation that could be relevant for cystic fibrosis physiopathology. RAS cells are primarily located in respiratory bronchioles and can serve as a distal lung progenitor for ATII cells. Respiratory diseases may be caused by genetic disorders, infections, tobacco smoke, particle inhalations, air pollution, etc., and affect one in five people. For instance, chronic obstructive pulmonary disease (COPD), lower respiratory infections, and airway and lung cancers are the third, fifth, and sixth largest causes of global death, respectively (World Health Organization—https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death, accessed on 20 February 2023; [3]). During or secondarily to progress of many respiratory conditions, fibrosis of the airways or the lung may eventually evolve. The fibrotic process is part of a general process represented by so-called “wound healing” in response to either endogenous and or exogenous damage and occurs in all organs in four common phases: (i) initiation due to injury of the tissue, (ii) inflammation and activation of effector cells, (iii) synthesis of extracellular matrix (ECM) components, and (iv) deposition of ECM to restore organ failure (remodeling phase) [4]. This process relies on multiple epithelial–fibroblast interactions. After tissue damage, the epithelial cells release inflammatory mediators regarding entry of leukocytes (neutrophils, macrophages, and T-cells) that secrete profibrotic cytokines, such as IL-1β, TNF, IL-13, and TGFβ. The activated macrophages and neutrophils remove dead cells and activate resident fibroblasts and other sources. Fibroblasts proliferate and differentiate into myofibroblasts that secrete ECM components (hyaluronic acid, fibronectin, proteoglycans, and interstitial collagens) to promote wound healing and restoration. Myofibroblasts exhibit increased synthetic capacities and are responsible for contractility of scar tissue [4,5,6,7]. Progenitors of myofibroblasts include resident fibroblasts, fibrocytes, smooth muscle cells, pericytes, epithelial and endothelial cells undergoing epithelial- or endothelial-to-mesenchymal transitions, stromal cells, or hepatic stellate cells [8]. Under normal conditions, this process of “wound healing” has a beginning and an end but may be impaired under persistent tissue damage, pathological states, or ageing. In these cases, resolution of the damage involves fibrotic lesions with excessive ECM deposition and presence of senescent and inflammatory cells [4,9,10]. The fibrotic tissue loses its elasticity due to ECM deposition, contracted fibroblasts, and reduced vasculature. Irreversible destruction of the lung architecture may lead to organ malfunction, disruption of gas exchange, and death from respiratory failure. Fibrotic remodeling of the lung may take place in the lung parenchyma (alveolus and lung matrix, e.g., in idiopathic pulmonary fibrosis, IPF) and the airways (e.g., in asthma, chronic obstructive pulmonary disease COPD, cystic fibrosis (CF)). However, IPF may involve airway-specific pathogenesis, such as bronchiolization of the distal airspace with abnormal airway cell-types and honeycomb cystic terminal airway-like structures [11]. Causes of pulmonary fibrosis include environmental pollutants, some drugs, radiation, connective tissue diseases, or interstitial lung disease; however, in most cases, no clear cause is found [4,12,13,14,15]. Thus, fibrosis of varying degrees has been described in smokers and several lung diseases, such as (i) obstructive lung diseases (asthma, bronchiolitis, chronic obstructive pulmonary disease (COPD), lung transplantation); (ii) infectious and suppurative lung diseases (cystic fibrosis (CF), pneumonia, etc.); (iii) adult respiratory distress syndrome (ARDS) and lung edemas; (iv) diseases specific to infants (chronic lung disease of prematurity, surfactant protein-B deficiency, etc.); and (v) interstitial lung diseases (sarcoidosis, idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis, silicosis, asbestosis), etc. [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]. Modeling of such complex pathogenic contexts that may affect lung structure and involve epithelial–mesenchymal interactions is challenging. During the last decade, we have observed great advances in biology of stem cells and establishment of human-stem-cell-derived organoid cultures in 3D structures. These 3D cultures consist of aggregates of cells that self-organize in structures that mimic relatively well the structure and function of the native organ [33]. The two most common types of organoids differ regarding the stem cells from which they emerge: pluripotent stem cells (PSCs) and organ-specific adult stem cells (ASCs). PSCs have a broader differentiation spectrum than ASCs and offer the possibility of generating unlimited individual-specific organoids. PSCs include both embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) [34,35,36,37]. Development of organoids-based models containing cells of the airways and the alveoli have enabled understanding of genetic disorders, chronic, and infectious diseases. This review discusses the use of PSCs-derived lung organoids (referred to here as minilungs) to model pulmonary fibrosis in the context of various human respiratory conditions, such as IPF, CF, COPD, and COVID-19. We will also discuss recent advances and propose possible lines of research development to study and model lung diseases that might evolve with fibrosis of different texture and magnitude. Development of 3D minilungs using biotechnology applied to pluripotent stem cells has contributed significantly to the understanding of respiratory diseases. To generate organoids, PSCs are subjected to a differentiation protocol that largely reflects the normal differentiation events that occur during embryonic development. Current protocols have been refined and improved in terms of manipulation of stem cells and efficiency of differentiation. In essence, these protocols depend on generation of definitive endoderm and generation of progenitors. The signaling pathways involved are those regulated by Activin A, TGFβ, WNT, NOTCH, and BMP4 [38,39,40,41,42,43,44,45,46,47]. Use of specific agonists or inhibitors allows turning the corresponding pathway on or off at certain times of the sequential protocol. Wnt3a, Wnt agonist CHI99021, and BMP4 inhibitor Noggin are commonly used to generate lung progenitors that are subsequently matured by addition of fibroblast growth factors FGF2, FGF7, and FGF10 and BMP4 [43,46,48,49,50,51,52]. Thus, PSCs are driven to produce epithelial cells from airways and alveoli. The differentiated cells can be displayed as a bidimensional array (2D minilung) or in 3D [50,51,53,54,55]. Gotoh et al. reported generation of 3D structures from anterior foregut endoderm containing cells from the bronchiolar epithelium and alveoli [56]. However, Chen et al. managed to create minilungs showing branching structures with proximal–distal compartmentalization consisting of cells from the airways and alveoli with some mesenchyme [53]. These organoids, named “Lung Buds Organoids” or LBOs, showed an expression profile corresponding to the second trimester of human development and were particularly enriched in ATII cells. One year later, we described generation of new minilungs structurally different from the lung buds, named “paddle-racquet lung organoids” or PRLOs [55]. These PRLOs present globose structures that enclose wider lumens than those expressed by LBOs, probably reflecting alveolar-like structures resembling the typical alveolospheres found after differentiation of ASCs from alveoli (Figure 2). Generation of such PRLOs was achieved via addition of dexamethasone to the maturation cocktail at certain times of differentiation. PSCs-derived minilungs, in 2D or 3D formats, have allowed, for instance, modeling of infection of human respiratory syncytial virus (HRSV) [53,54,57], influenza-virus-induced pneumonitis [58], Streptococcus pneumoniae interaction with the respiratory tract [59], fibrotic lung disease, including cystic fibrosis [60,61], surfactant deficiencies [62], Hermansky–Pudlak syndrome type 2 [63], and small-cell lung cancer [64]. IPF is a chronic and progressive fibrotic lung condition and the most common of idiopathic interstitial pneumonia. In Europe and North America, the incidence is estimated to range between 2.8 and 18 cases per 100,000 people per year [65,66]. IPF is more common in men and is rare in people younger than 50 years. The median survival time after diagnosis is 2–4 years. IPF is a consequence of multiple genetic and environmental risk factors leading to repetitive cycles consisting of local micro-injuries (damage) and aberrant regeneration of the alveolar epithelium. Because of altered epithelial–mesenchymal communication, continuous production of components of the extracellular matrix by myofibroblasts leads to aberrant fibrotic remodeling of the lung interstitium in detriment of functional respiratory tissue. This heterogeneous cellular context makes it very difficult to replicate through PSCs-derived minilungs. Chen et al. 2017, however, reported modeling of Hermansky–Pudlak syndrome (HPS) from PSCs-derived minilungs [53]. HPS is an autosomal recessive hereditary disease that may be complicated by progressive and potentially fatal interstitial pneumonia, characterized by an inflammatory process within the interstitial walls rather than the alveolar spaces [67]. HPS causes HPS-associated interstitial pneumonia (HPSIP), which is similar to IPF. HSP1 mutant organoids, generated by CRISPR/Cas9 technology, exhibited increased accumulation of mesenchymal content and enhanced deposition of collagens and fibronectin. This model recapitulated relatively well the clinical features present in HPSIP patients. Later, in 2019, Strikoudis et al. managed to introduce more HSP mutations associated with HPSIP into bona fine ESCs lines to create an IPF-like model of fibrogenic lung disease [61]. The mutant minilungs exhibited upregulation of interleukin-11 (IL-11) in ATII cells predominantly [61]. Athough the formal modeling of IPF is still elusive, this work is a good example of the applications of PSCs and organoid biotechnology to model complex respiratory and fibrogenic contexts. The fact that many of the IPF-associated mutations occur in genes encoding surfactant proteins (SFTPC or SFPTA2) has suggested direct involvement of ATII in pathogenesis of IPF [68,69,70,71]. In fact, the specific injury to ATII cells is a preeminent fact of the central fibrotic hypothesis regarding IPF [31,69,72,73,74]. Thus, damage inflicted on ATII cells can cause their entry into a state called cell senescence, mainly characterized by absence of proliferation [75,76,77]. The complex spectrum of molecules secreted by senescent cells leads to an exacerbated pro-inflammatory response, recruitment of inmmunitary cells, and fibrogenic activation of fibroblasts. Unfortunately, in the context of IPF, persistent damage and tissue repair lead to continuous fibrotic remodeling of the functional respiratory tissue and its aberrant regeneration. In addition, telomeric damage is a very important factor in development of IPF since 8–15% of patients with familial IPF have heterozygous mutations in genes associated with telomere maintenance and integrity, such as hTERT (reverse transcriptase) or hTERC (RNA component) [78,79,80]. Thus, the association between telomeric damage in ATII cells also contributes to alter their self-renewal and replacement of damaged ATI cells. ATII cells can enter easily a senescent state after genotoxic insults, such as that provoked by antibiotic bleomycin, which induces very efficiently DNA double-strand breaks (DSBs) [81,82,83], making bleomycin a very interesting model to analyze DNA damage. We and others have established in vitro and in vivo models of pulmonary fibrosis based on bleomycin, including ATII cellular systems, myofibroblasts, and mice sensitive to bleomycin-induced lung injury [83,84,85,86]. It should be noted here that, although bleomycin reproduces well many aspects of general pulmonary fibrosis and some lesions present in IPF, it has never been promoted as an experimental equivalent of IPF. However, the potential of bleomycin in induction of DSBs and senescence in many cell types is extraordinary. DSBs are excellent inducers of cellular senescence and can be accurately measured by different biotechniques. Expression of DNA damage and cellular senescence represent one of the early milestones of fibrogenic conditions, such as IPF, and many other conditions that evolve towards fibrosis [76,77,87]. We made use of the advantages of bleomycin to analyze expression of DNA damage in minilungs derived from hESCs. Moreover, we explored the influence of various vitamin-D-less hypercalcemic analogs that maintain their antifibrotic properties and act as very efficient DNA damage erasers [88]. This work represented a good example of use of PSC-derived minilungs to model early events that appear in the context of IPF and other fibrogenic conditions and as a platform for screening compounds of interest. Wilkinson et al. presented a method for generation of self-assembled human lung tissue and its potential for disease modeling and drug discovery [89]. They mounted a cohesive organoid from collagen-functionalized alginate beads and human fibroblasts in a rotational bioreactor, leading to structures recapitulating the native lung. Treatment with TGF-β1 showed a progressive scarring phenotype that resembles IPF. Schruf et al. generated a hiPSC-derived 2D airway-liquid interphase (ALI) culture model of ATII cell differentiation exposed to a pro-fibrotic environment to recapitulate phenotypic and functional features of aberrant epithelial remodeling in IPF lesions. The pro-fibrotic cocktail used was based on upregulated cytokines found in IPF patient bronchoalveolar lavage or sputum [90]. Another model for pulmonary fibrosis was reported by Suezawa et al., consisting of a co-culture model named fibroblast-dependent alveolar fibroblasts (FD-AOs) from hPSCs and primary human fetal lung fibroblasts. Recapitulation of epithelial–mesenchymal interactions by bleomycin treatment will serve for screening therapeutic agents to treat IPF [91]. CF is a multi-organ genetic disorder that affects more than 70,000 people worldwide. Although CF incidence varies by country, it has been estimated in 1/3000 births in Caucasians in North America and Europe (ECFS patient registry. 2022—https://www.ecfs.eu/sites/default/files/ECFSPR_Report_2020_v1.0%20%2807Jun2022%29_website.pdf, accessed on 20 February 2023; [92,93]). Mortality and morbidity in CF are mainly associated with lung dysfunction due to tissue rearrangements and fibrosis, recurrent infections, and inflammation. Development of small molecules to improve CFTR protein function, termed CFTR modulators, has substantially benefitted people with cystic fibrosis. Today, the median life expectancy of a CF patient is around 53 years [92,93,94] (ECFS patient registry. 2022; [95]). CF is caused by mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator (CFTR), a chloride and bicarbonate ion transport channel. Deletion of a phenylalanine at position 508 (Phe508del or ΔF508) is the most common mutation and represents more than two-thirds of all mutations [96,97,98,99,100,101,102]. In the lung, CFTR dysfunction deregulates transport of chloride and bicarbonate into the lumen of the airways, which normally regulates reabsorption of Na+ mediated by another channel (Na+ channel, or ENac). This leads to net water uptake by the respiratory epithelium, which results in dehydration of the liquid surface of the airways [103,104]. Consequently, the mucus of the mucociliary ladder becomes dehydrated and its clearance is compromised. A second hypothesis on CFTR dysfunction is related to the pH of the airway surface liquid. CFTR dysfunction would reduce bicarbonate secretion into the airway lumen, resulting in a decreased pH [105,106,107]. In any case, the CF lung context provides an excellent niche for colonization and infection of opportunistic pathogens and compromised antimicrobial defense. Thus, chronic airway obstruction, infection, and inflammation account for the majority of morbi-mortality associated to CF [108,109]. Regarding fibrosis, tissue remodeling with increased collagen deposition is common in distal airways of CF patients [110,111]. Modeling of CF through hPSCs also represents a challenge as CF affects specific cell subtypes in different regions of the lung. MacCauley et al. reported in 2017 generation of airway organoids from hPSCs based on specific modulation of the WNT pathway to achieve specification of respiratory progenitors [60]. The WNT pathway is involved in regulation of the proximodistal pattern of the human airways. In this remarkable work, the authors generated a new “low-Wnt” distal organoid differentiation protocol from hiPSCs-derived NKX2.1+ lung progenitors. They also applied CRISPR and TALENS gene editing on hiPSCs of genotype ΔF508/ΔF508, with a defect in forskolin-induced swelling, to correct the disease mutation. This approach engaged many applications in modeling and drug screening for airway diseases, such as CF and primary ciliary dyskinesia. Later, in 2020, Geurst et al. showed generation of a CF’s patient-derived intestinal organoid biobank to study gene editing mediated by adenine base editors (ABE editing). Conventional CRISPR/Cas9-mediated genome editing depends on introduction of DNA double-strand breaks (DSBs) at the target site [112]. The novel technology presented, based on versions of Cas9 endonucleases (SpCas9-ABE and xCas9-ABE), enables enzymatic conversion from A–T into G–C base pairs without introducing DSBs. This novel CRISPR-Cas9 technology enables efficient correction of mutations without genome-wide off-target effects and represents promise for hereditary diseases [112]. COPD is characterized by airflow limitation and abnormal inflammatory response of the airways to noxious particles and gases [113]. Currently, COPD is the third leading cause of death only behind ischemic heart disease and stroke (World Health Organization). The main causes of COPD include smoking tobacco, lung growth, environmental stimuli, and a complex background of genetic factors. The progressive course of COPD is frequently aggravated by exacerbations that reduce quality of life, accelerate disease progression, and increase risk of death. Several causes of exacerbations have been suggested, such as heart failure, pneumonia, pulmonary embolism, some medications, or inhalation of irritants. The most frequent cause of exacerbation is viral or bacterial infection [113,114]. The principal pathophysiological feature of COPD is obstruction of the airways. This is caused by increased mucus content, mucosal hyperplasia, infiltrations of inflammatory cells, and fibrotic remodeling due to excessive connective tissue deposition in the peribronchial space [18,115,116]. This progressive obliteration of the respiratory bronchioles is eventually accompanied by emphysema, which typically starts in the bronchioles. Thus, pulmonary emphysema and fibrosis are combined events that were first characterized in a homogeneous group of patients with both emphysema and interstitial lung disease (ILD) with pulmonary fibrosis in the lower lobes. Moreover, pulmonary emphysema is related to cellular senescence in terms of proliferation arrest, increased inflammation due to the pro-inflammatory properties of the senescent-associated secretory phenotype (SASP), aberrant cell regeneration, and fibrotic remodeling, and, eventually, carcinogenesis. Cigarette smoke and oxidative stress are also inducers of cell senescence; thus, COPD can be interpreted as a condition that accelerates aging, with several aging pathways involved in its pathogenesis [17,18,117,118,119]. Cigarette smoke can induce expression of senescence marker p21 in epithelial cells and fibroblasts. In addition, lungs with emphysema also show increased expression of p16, p19, and p21, all of which are cyclin kinase inhibitors and markers of cell senescence. Moreover, cigarette smoke exposure and mitochondrial dysfunction have been shown to lead to oxidative stress in COPD [120]. Oxidative stress arises primarily from presence of radical oxygen species (ROS). ROS are excellent inducers of DNA damage, DSBs being the most deleterious. Expression of DNA damage in lung cells and induction of permanent DNA damage response represent other hallmarks of the cellular senescence phenotype contributing to losing regenerative capacity and repair and progressive worsening of lung function [121,122]. CF and COPD share various characteristics, such as progressive airflow obstruction, chronic airway inflammation in the lumen, and recurrent infectious exacerbations, suggesting the possibility of common mechanisms [123]. Although COPD is mainly caused by environmental factors on a genetically susceptible background, the ideal modeling from PSCs and derived organoids would require similar strategies to those reported by MacCauley et al. for CF modeling [60]. Several works have reported generation of iPSCs in the context of COPD [124,125,126]. Ahmed et al. 2022 [124] managed to generate hiPSCS from peripheral blood mononuclear cells and differentiated them into an ALI bronchial epithelium without using purification of airway progenitors by cell sorting. This epithelium showed large zones with beating ciliated, basal, goblets, club cells, and neuroendocrine cells. However, we are still witnessing an initial development of adequate models for faithful modeling of the events that define COPD. These events include, among others, mucin production as observed in smokers and COPD patients, events related to fibrotic remodeling, and cellular senescence. This work represents an ideal experimental platform that could be enriched with mesenchymal cells, i.e., myofibroblasts, or used to assess the effect of tobacco smoke and pollution particles. Moreover, polymicrobial infections would help to reproduce the major exacerbations occurring in COPD patients. Figure 2 illustrates the usefulness of minilungs derived from PSCs to model COPD with exacerbations and other diseases. Another interesting context that can potentially be modeled and exploited via PSCs-derived minilungs is the one represented by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). COVID-19 patients may be asymptomatic or show symptoms such as those agglutinated in acute respiratory distress syndrome (ARDS) and pulmonary fibrosis. Pulmonary fibrosis is now recognized as a sequel of ARDS. Fibrotic lesions can be observed in high-resolution chest tomography (CT) scans of patients recovered from COVID-19, including ground glass opacity or a combination of irregular interlobular septal thickening and mild traction bronchiectasis [127,128,129,130,131,132]. These imaging findings are supported by autopsy reports. Reports on patients who died of COVID-19 pneumonia reveal features of diffuse alveolar damage with areas of consolidation by fibroblastic proliferation and deposition of extracellular matrix and fibrin in alveolar spaces [130]. We also know that patients with respiratory diseases compared to those without respiratory diseases have higher risk of hospitalization due to SARS-CoV-2 infection [133]. Moreover, patients with fibrotic interstitial lung disease (fibrotic ILD), especially those with IPF, have a higher risk of death after infection with SARS-CoV-2 [134]. All ILDs patients, especially IPF patients, can undergo acute exacerbations of different origins: internal accelerations of the fibrotic conditions, external events leading to acute lung injury, and diffuse alveolar injury. Acute lung damage can be triggered by viral infections and greatly enhanced by immune responses of the host. Respiratory failure in IPF patients due to these exacerbations accounts for higher hospital mortality, estimated in more than 50% in most cases [135,136]. The potential molecular mechanisms associated with the replicative cycle of SARS-CoV-2 by which fibrosis develops include [137]: (i) Interaction of viral spike “S” protein with angiotensin converting enzyme (ACE2) receptor. Binding of the virus to its receptor can downregulate level of ACE2, increase levels of Ang II, and decrease level of Ang1–7, thus promoting inflammation and fibrosis. (ii) Aberrant immune response leading to so-called “cytokine storm”, resulting in increased plasma levels of IL-1β, IL-2, IL-7, and IL-10, GCSF, MIP1α, IFNy, IP-10, IL-6, IL-8, TNFα, etc. Cytokine storm accelerates disease progression and aggravates ARDS and multiple organ failure. Release of pro-inflammatory cytokines and metalloproteinases during ARDS induces damage of the epithelium, endothelium, and fibrotic remodeling. (iii) Infection and damage of ATII cells. ATII cells are key mediators of the alveolar innate response and regeneration of the respiratory epithelia through its proliferation and differentiation into ATI cells. ATII can enter the senescence state and secrete a series of inflammatory mediators and metalloproteinases (SARS phenotype) that mediate tissue remodeling. For instance, TGFβ triggers proliferation and differentiation of fibroblasts into myofibroblasts, causing aberrant deposition of extracellular matrix proteins during abnormal fibrosis. (iv) Damage of endothelial cells. Endothelial cells, when injured, transform into a mesenchymal state (EndMT) with increased activity of mesenchymal protein secretion and matrix metalloproteinases, leading to accumulation of fibroblasts and myofibroblasts and induction of fibrotic remodeling in the interstitium of the lung. Transit of the virus into the lower respiratory tract the lung can be favored using mechanical ventilation. Presence of the virus in the lung may contribute to acute lung injury and secondary fibrosis. The course of the COVID-19 pandemic accelerated the search for suitable study models based on hPSCs. Yang et al., 2020 [138] proposed an experimental platform comprising cells and organoid derivatives of hPSCs to study SARS-CoV-2 tropism and infectivity. They studied permissiveness to viral infection of pancreatic endocrine cells, liver organoids, cardiomyocytes, and dopaminergic neurons, all derived from hPSCs. However, their platform did not include any airway or lung organoids, greatly limiting its impact on understanding viral interactions with the respiratory tract and the study of fibrotic events. In 2021, Han et al. [139], however, developed human minilungs and colonic organoids from hESCs (RUES2) based on previously reported stepwise strategies. As expected, both the minilungs and the colonic organoids generated were permissive to infection of SARS-CoV-2. A remarkable aspect of this work is use of this platform for high-throughput drug screening to identify candidate COVID-19 therapeutics. The authors identified several drugs that inhibit SARS-CoV-2 entry, including imatinib, MPA, and QNHC, both in vitro and in vivo. Bidimensional arrays of the airways and lung in different formats (multi-well plates, inserts, with or without an ALI) easily enable enrichment with other cell types, such as fibroblasts, myofibroblasts, and endothelial cells. These co-cultures, more or less complex, enable the study of the molecular events associated with infection in COVID-19 and elucidation of mechanisms underlying expression of DNA damage, cell senescence, and lung fibrosis. Generation of 2D minilungs enriched with myofibroblasts allowed us to study the infectivity of both epithelial cells and myofibroblasts by human respiratory syncytial virus (HRSV) [54]. Recently, we reported the usefulness of 2D and 3D minilungs to study the influence of DNA damage after treatment of potential pro-fibrotic compounds, such as vitamin D3, and some less hyper-calcemic vitamin D analogs [88]. Schruf et al. [90] managed to develop a more sophisticated co-culture model or “EpiAlveolar co-culture model” consisting of human primary alveolar epithelial cells, fibroblasts, and endothelial cells, with or without macrophages, to serve as a platform to predict long-term responses to aerosols. This work is a good example of how histological alterations can be modeled in hiPSC-derived ATII-like cell cultures maintained in ALI as a suitable model to study fibrosis. Proinflammatory and profibrotic responses could be assessed upon repeated subchronic exposures to different compounds. Other interesting experimental approaches to study molecular events in COVID-19 are based on use of Lung-On-ChiP (LOC) technologies consisting of biomimetic microsystems that reconstitute the critical functional alveolar–capillary interface of the human lung. These co-cultures are suitable to study the sequential events in COVID-19, at the air–blood barrier, in the presence of alveolar cells and peripheral immune cells and other cell types of interest [140]. Respiratory diseases represent a health problem of the first order and are one of the leading causes of global morbidity and mortality. Currently, there is no cure for most respiratory diseases, which are treated symptomatically. Thus, there is a critical need for adequate study models to delve into knowledge of the disease and explore novel therapeutic strategies. Research of human lung disease relied on cultures of immortalized cell cultures and animal models that, in many cases, did not faithfully reflect the human context. Isolation of primary cells from fragments of human material from the airways or the lung introduced new cell systems. These cultures reproduced relatively well the cellular context of the airways and lung displaying cells in bidimensional arrays or 3D structures, such as tracheospheres, bronchospheres, alveolospheres, etc., growing in suspensions or embedded in suitable hydrogels. The main drawbacks of these systems were availability of precious human material and the cell spectrum limited to a few types. The advent of PSCs biotechnology marked a qualitative leap in the field of airways and lung organoids and has increased our knowledge on the pathophysiology of the human lung and expanded our capacity and prospects for disease modeling and regenerative therapy. Exploitation of the cellular pathways that control lung development has enabled generation of airways and lung organoids through sequential differentiation of PSCs. The stepwise protocols available rely on generation of definitive endoderm and further differentiation to anterior foregut endoderm (AFE) and ventral AFE. Lung and airway progenitors can be further differentiated into mature epithelial cell types using FGFs, glucocorticoid agonists, etc. Essentially, the signaling pathways involved are those implicating TGFβ, WNT, BMP4, Activin A, Retinoic acid, glucocorticoids, and FGFs [46,47,48,49,50,51,52,53,56,60,62,141,142,143,144]. A plethora of signaling pathways have been described in animal models [141,142,145,146,147] and exhibit high levels of temporal and regional specificity by which they each promote differentiation and maturation of specific cell types at the expense of others. Modeling of respiratory diseases that evolve with some type of fibrosis of different magnitude and location is more challenging as it implies complex interactions between epithelial and mesenchymal cells. Differentiation of PSCs into lung and airways cells is normally accompanied by mesodermal cells. The source of mesoderm is probably due to a small population of contaminant cells that merged from spurious differentiation. It is well known that, during in vitro expansion of PSCs, colonies (“bad ones”) may be generated, with suboptimal quality. In addition, the differentiation efficiency of PSCs to any of the germ layers is rather far from 100%. Other possible sources of mesenchymal cells could be presence of traces of early mesendoderm specification in the definitive endoderm generated. Several reports have shown that the differentiation procedure will inevitably lead to presence of co-derived non-lung lineages [43,46,48,49,51,52,56,144]. Three noteworthy works are those reported by McCauley et al., Chen et al., and Stridoukis et al. [53,60,61]. The approach described by McCauley et al. produced reliable production of “epithelial-only” clean human airway organoids from hiPSCs to model CF and other genetic disorders [60]. Modeling of HPSIP, however, a clinical IPF-like disease described first by Chen et al. and then followed by Stridoukis et al., is based on appearance of mesodermal content in their HPS-mutant minilungs [53,61]. These mutant organoids expressed markers of extracellular cell matrix (ECM) and mesenchymal markers and a particular gene expression signature resembling the one of human IPF and rat lungs treated with bleomycin. Currently, there is a need to find suitable models of respiratory diseases, especially those that evolve with more or less fibrosis. This is a challenge as modeling of fibrotic conditions implies replication of complex epithelial and mesenchymal cells interactions. PSCs-based models are already a reality and enable generation of cells containing phenotypic and functional markers or mature airways and lung epithelial cell types. The derived organoids will help to better understand the disease, reduce use of animal models, and serve as a platform for drug screening and regenerative medicine. Among the main advantages of these models are unlimited availability of differentiated material, the possibility of gradually increasing the cellular spectrum by specific enrichment with cell types of interest, and the possibility of making individual-specific organoids. These specific contexts will enable application of gene editing strategies to correct mutations prior to production of organoids for disease modeling, drug screening, or subsequent in vivo tissue regeneration. However, PSCs differentiation also has several potential disadvantages, such as limitless differentiation capabilities that are difficult to control and their inherent ability to develop spurious differentiation. Differentiation protocols and large-scale production of hPSCs will need to be optimized and investigated prior to clinical use. Use of hPSCs carries ethical concerns, and both ESCs and iPSCs have been shown to form teratomas [148,149]. Nonetheless, hPSCs can be used to generate specific lung progenitors so that gene editing techniques—large-scale production from bioreactors and tissue bioprinting, for instance—could be applied and implemented with more efficiency. It has been reported that iPSCs partially retain the epigenetic memory of their tissue of origin, which could lead to limitations [150]. The large variety of organoid formats available, such as bidimensional arrays, ALI cultures, and enriched co-cultures on inserts, or even LOC biotechniques, on which high-throughput techniques can be applied, will enable rapid analysis of a multitude of biological measurements. These techniques will enable accurate analysis of the infectivity features of pathogens in specific genetic contexts, tissue damage, DNA damage and oxidative stress expression, cell senescence, and pro-fibrotic and pro-inflammatory responses.

PMC10002134

Viviana Costa,Marcello De Fine,Lavinia Raimondi,Daniele Bellavia,Aurora Cordaro,Valeria Carina,Riccardo Alessandro,Giovanni Pignatti,Milena Fini,Gianluca Giavaresi,Angela De Luca

Timing Expression of miR203a-3p during OA Disease: Preliminary In Vitro Evidence

21-02-2023

osteoarthritis,microRNAs,osteoblasts,interleukines,CX-43,SP-1,TAZ

Osteoarthritis (OA) is a degenerative bone disease that involves the microenvironment and macroenvironment of joints. Progressive joint tissue degradation and loss of extracellular matrix elements, together with different grades of inflammation, are important hallmarks of OA disease. Therefore, the identification of specific biomarkers to distinguish the stages of disease becomes a primary necessity in clinical practice. To this aim, we investigated the role of miR203a-3p in OA progression starting from the evidence obtained by osteoblasts isolated from joint tissues of OA patients classified according to different Kellgren and Lawrence (KL) grading (KL ≤ 3 and KL > 3) and hMSCs treated with IL-1β. Through qRT-PCR analysis, it was found that osteoblasts (OBs) derived from the KL ≤ 3 group expressed high levels of miR203a-3p and low levels of ILs compared with those of OBs derived from the KL > 3 group. The stimulation with IL-1β improved the expression of miR203a-3p and the methylation of the IL-6 promoter gene, favoring an increase in relative protein expression. The gain and loss of function studies showed that the transfection with miR203a-3p inhibitor alone or in co-treatments with IL-1β was able to induce the expression of CX-43 and SP-1 and to modulate the expression of TAZ, in OBs derived from OA patients with KL ≤ 3 compared with KL > 3. These events, confirmed also by qRT-PCR analysis, Western blot, and ELISA assay performed on hMSCs stimulated with IL-1β, supported our hypothesis about the role of miR203a-3p in OA progression. The results suggested that during the early stage, miR203a-3p displayed a protective role reducing the inflammatory effects on CX-43, SP-1, and TAZ. During the OA progression the downregulation of miR203a-3p and consequently the upregulation of CX-43/SP-1 and TAZ expression improved the inflammatory response and the reorganization of the cytoskeleton. This role led to the subsequent stage of the disease, where the aberrant inflammatory and fibrotic responses determined the destruction of the joint.

Timing Expression of miR203a-3p during OA Disease: Preliminary In Vitro Evidence Osteoarthritis (OA) is a degenerative bone disease that involves the microenvironment and macroenvironment of joints. Progressive joint tissue degradation and loss of extracellular matrix elements, together with different grades of inflammation, are important hallmarks of OA disease. Therefore, the identification of specific biomarkers to distinguish the stages of disease becomes a primary necessity in clinical practice. To this aim, we investigated the role of miR203a-3p in OA progression starting from the evidence obtained by osteoblasts isolated from joint tissues of OA patients classified according to different Kellgren and Lawrence (KL) grading (KL ≤ 3 and KL > 3) and hMSCs treated with IL-1β. Through qRT-PCR analysis, it was found that osteoblasts (OBs) derived from the KL ≤ 3 group expressed high levels of miR203a-3p and low levels of ILs compared with those of OBs derived from the KL > 3 group. The stimulation with IL-1β improved the expression of miR203a-3p and the methylation of the IL-6 promoter gene, favoring an increase in relative protein expression. The gain and loss of function studies showed that the transfection with miR203a-3p inhibitor alone or in co-treatments with IL-1β was able to induce the expression of CX-43 and SP-1 and to modulate the expression of TAZ, in OBs derived from OA patients with KL ≤ 3 compared with KL > 3. These events, confirmed also by qRT-PCR analysis, Western blot, and ELISA assay performed on hMSCs stimulated with IL-1β, supported our hypothesis about the role of miR203a-3p in OA progression. The results suggested that during the early stage, miR203a-3p displayed a protective role reducing the inflammatory effects on CX-43, SP-1, and TAZ. During the OA progression the downregulation of miR203a-3p and consequently the upregulation of CX-43/SP-1 and TAZ expression improved the inflammatory response and the reorganization of the cytoskeleton. This role led to the subsequent stage of the disease, where the aberrant inflammatory and fibrotic responses determined the destruction of the joint. Osteoarthritis (OA) is a chronic degenerative disease characterized by progressive cartilage erosion and lesions in subchondral bone as well as in other joint tissues. The OA niche is enriched of catabolic factors, such as matrix metalloproteinases (e.g., MMP-1 and −13), aggrecans (e.g., ADAMTS-4 and ADAMTS-5), and pro-inflammatory factors/cytokines (e.g., IL-1β, IL-6, TNFα, nitric oxide, and prostaglandin E2 (PGE2)) released by synovial cells, osteoblasts, and articular chondrocytes that contribute to joint destruction and establishing aggressive inflammatory process [1,2,3,4]. The role of inflammatory processes and mediators, such as IL-1β, Toll-like receptors, IL-15/IL-17, IL-6, adipokines, collagen derivatives of nitrous oxide, and reactive oxygen species, in the initiation and progression of disease has been well studied [5]. However, the role of IL-1β might be considered controversial: (1) only a subpopulation of individuals with OA presented elevated levels of IL-1β in their synovial fluid compared with the normal individuals [6]; (2) IL-1β might have a role at the very early stages, triggering a rapid destruction of cells in the joint cartilage, but it is not clear how far its effect is extended [7]; (3) IL-1β induces apoptosis and inflammation in chondrocytes by suppression of the Nuclear factor kappaB (NF-Kb) pathway [8]; (4) IL-1β induces the upregulation of OA-relative genes and others inflammatory cytokines such as IL-6, IL-8, and tumor necrosis factor-α (TNF-α) [7,9,10], which in turn cause the release of matrix-degrading enzymes including matrix metalloproteinases (MMPs) and aggrecanases and finally lead to articular cartilage destruction [11,12]; and (5) IL-1β induces the upregulation of miRNA [13] and consequently regulation of cell functions. Based on the evidence of these effects, it could be hypothesized that the regulation of IL-1β signaling was activated by the cooperation of different proteins and/or miRNAs. It is known that the regulation of the inflammatory process in the OA niche is mediated by the involvement of cell-to-cell communication through gap junction proteins or through the release of exosomes enriched by miRNAs or proteins; one of these proteins is Connexin 43 (Cx-43) [14,15,16,17,18,19]. Cx-43 displays many cell functions, including cell proliferation, migration, and differentiation, and it is involved in wound healing and inflammation. Regarding OA disease, it is demonstrated that Cx-43 is involved in the (1) nuclear translocation of Twist-1, improving the chondrocyAte-mesenchymal transition; (2) increase in the expression of proinflammatory mediators; (3) interaction with the astroglial– mesenchymal transition via nuclear translocation of the Yes-associated protein (YAP), a potent transcription coactivator of the cell differentiation process; (4) alteration of the recruitment of specificity protein 1 (Sp-1) into specific promoter binding sites of TWIST and COL2A-1 genes; and (5) Cx-43 involvement in the regulation of Sp-1 recruitment in OA osteoblasts and chondrocyte-derived cells by the regulation of miR-31-5p and miR-33a-5p [20]. In addition, Varela-Eirín et al. [21] revealed that the small extracellular vesicles (sEVs) released by human OA-derived chondrocytes contained high levels of Cx-43 and induced a senescent phenotype in the targeted chondrocytes and synovial and bone cells contributing to the formation of an inflammatory and degenerative joint environment by the secretion of senescence-associated secretory associated phenotype (SASP) molecules, including IL-1β, IL-6, and MMPs. Progressive joint tissue degradation and loss of extracellular matrix (ECM) elements, along with varying degrees of inflammation, are important hallmarks of OA disease. The identification of specific biomarkers to measure the various stages of the disease becomes a primary necessity in clinical practice. This need combined with the possibility of using multiple sources for the identification of biomarkers, such as urine, serum, biopsy tissue, and synovial fluid, led to an acceleration of these studies. For example, the possibility to detect microRNAs in tissues, cells, or blood with different methods makes them an excellent method for identifying key biomarkers for the step-by-step diagnosis and understanding of the disease [6,22,23]. MiRNAs are small non-coding RNAs that are part of the miRNA-induced silencing complex (RISC) and are involved in the regulation or deregulation of the gene expression of numerous physiological processes and pathological conditions [20,24,25,26]. A recent meta-analysis study reported the major role of 27 miRNAs and their targets in OA progression [22]. MiR-140 and miR-199 are two downregulated miRNAs in the synovial tissues of OA patients compared with healthy controls that were identified. Their expressions have been shown to decrease during OA and have been inversely correlated with the severity of disease [12,27]. MiR-22 that targets BMP7, a factor inducing chondrocyte terminal differentiation, and miR-27b that targets MMP13, a key remodeling enzyme in hypertrophic terminally differentiated chondrocyte [28], were also identified as mediators of the middle stage of OA progression. Recently, our results also contributed to the understanding of the role of different miRNAs in the differentiation of human mesenchymal stromal cells (hMSCs) into osteoblasts and in the OA disease. We identified the miR-675-5p, miR-31-5p, and miR33a family as modulators of hMSC osteoblast differentiation, LIPUS-mechanosensitive miRNA, and regulators of YAP and EGFR signaling in the differentiation of hMSCs into osteoblasts, respectively. In addition, miR-33a-3p and miR-33a-5p were identified as mediators of the different expression of CX-43 and SP-1 in osteoblasts and chondrocytes derived from patients with OA [20,24,25,26]. The present study aimed to highlight the role of miR203a-3p during the evolution/progression of OA as a possible biomarker of disease, through the identification of the related molecular mechanisms in which miR203a-3p was involved during the progression of OA. Here we performed our investigations starting from evidence recovery from osteoblasts in OA patient-derived cells to hMSCs treated with IL-1β to mimic different OA progression stage in vitro [9]. MiR203a-3p was well identified as possible tumor suppressor miRNA because it is able (1) to improve apoptosis signaling through the downregulation of ZNF217 in colorectal cancer [29]; (2) to regulate ERα signaling in endometrial carcinoma in which blocked or modified cell proliferation [30]; and (3) to alter the expression of Smad9 in MSCs derived from multiple myeloma patients, which modified the osteogenic differentiation ability [31]. However, only a few studies have investigated the biological effects of miR203a-3p in bone disease until now [31,32,33,34]. While miR203a-3p regulates the transition from osteogenic to adipogenic differentiation of hMSCs in postmenopausal osteoporotic microenvironments, downregulating its target gene DKK1, it appears to have different actions in OA [35]. It was demonstrated that (1) it enhances cellular inflammatory responses and cell damage and reduces aggrecan and Col2A1 levels [9]; (2) it binds with ERα and exerts its effects in OA development through this axis [12]; (3) it is promoted by IL-1β stimulation leading to chondrocyte injury, improving the inflammation process and diminishing aggrecan and Col2A1 expression [36]; and (4) miR203a expression is dysregulated in the knee articular cartilage of OA patients compared with the controls in three or more independent studies [12]. Overall, in this study, we investigated the role of miR203a-3p and the related molecular mechanisms in which it was involved during OA progression, such as the inflammatory response and the reorganization of the cytoskeleton, starting from evidence obtained by osteoblasts derived from OA patients to hMSCs treated with IL-1β to mimic in vitro different OA progression stages [6]. To understand the inflammatory conditions of OBs isolated from OA patients with different levels of KL grades (OB-KL ≤ 3 and OB-KL > 3) [37], we evaluated the expression of IL-1β, a master pro-inflammatory cytokine, IL-6, and IL-8 by qRT-PCR analysis. The gene expression analysis of these soluble factors revealed that OBs expressed them at different levels based on the grade of OA disease. In fact, OB-KL > 3 showed higher levels of IL-1β and IL-8 compared with OB-KL ≤ 3; on the contrary, IL-6 was upregulated in OB-KL ≤ 3 compared with OB-KL > 3. However, the released IL-6 and IL-8 proteins both were increased in the OB-KL > 3 group compared with OB-KL ≤ 3. These data were opposed to those of mRNA expression, probably suggesting the different mRNA methylation patterns on their promoter genes [38,39]. It is reported that IL-1β regulates the miR203a-3p expression during different diseases [3,10,12,40,41,42]. To investigate this evidence and the data reported in Figure 1, we performed qRT-PCR analysis on OB cells derived from OA patients (OB-OA). As shown in Figure 2, both cells expressed miR203a-3p, which was significantly higher in the OB-KL ≤ 3 group compared with the OB-KL > 3 group. To investigate the involvement of IL-1β on miR203a-3p expression, we treated OA-derived cells with IL-1β at 20 ng/mL for 48 h (Figure S1) [12]. The qRT-PCR analysis demonstrated the ability of these cells to express high levels of miR203a-3p after IL-1β treatments, compared with untreated groups (Figure 2B). Through a gain and loss of function study on OBs derived from patients, these data were verified. We overexpressed miR203a-3p inhibitor in OBs, and as expected, miR203a-3p was downregulated after transfection compared with untreated cells (Figure 2C); this was also true after co-treatments of OBs derived from OA patients with IL-1β and miR203a-3p inhibitor transfection. As shown in Figure 2D, miR203a-3p was downregulated after co-treatment compared with untreated groups and compared with IL-1β-treated cells (Figure 2E). To understand the role of the current miRNA data, a bioinformatic investigation through a target prediction scan was performed, revealing that miR203a-3p targets different genes involved in the OA process or OB differentiation. To validate these bioinformatic data, the expression levels of TRPV4 were evaluated on OB-OA-derived cells [43,44,45]. Figure 3 shows that OB cells had a low level of this miRNA target gene, and its expression was restored after miR203a-3p inhibitor transfection (A) or after co-treatments with IL-1β plus miR203a-3p inhibitor transfection (B) compared with untreated groups. To understand the link between miR203a-3p expression and inflammatory interleukins in OA disease, we first investigated the modulation in terms of mRNAs and the protein releases of IL-6 and IL-8 in OB-OA samples through gain and loss of function studies. The data obtained from IL-6 gene and protein expression (Figure 4A–D) showed that IL-1β alone and in co-treatments with miR203a-3p was able to induce the upregulation of IL-6 mRNA and protein compared with untreated groups; in particular, the co-treatments with IL-1β and miR203a-3p inhibitor improved a strong upregulation of the IL-6 protein release. In contrast, mimic and inhibitor transfection induced a lower upregulation compared with untreated cells of IL-6 mRNA and proteins in both OB groups. The analysis of the IL-8 expression after treatments with IL-1β, transfection with miR203a inhibitor or mimic, and co-treatments of IL-1β and miR203a-3p inhibitor showed the same modulation revealed for the IL-6 mRNA and protein released in both OB-OA-derived cells (Figure 4E–H). To validate the correlation between inflammation and miR203a-3p expression in OA disease, we investigated this relationship on the hMSC model of OA. We performed an in vitro evaluation on hMSCs treated with IL-1β at two different doses for 24 h and 48 h [33]. qRT-PCR analysis revealed that hMSCs presented an increase in IL-1β over time, in particular at 24 h, compared with the related untreated groups (Figure 5A), while showing the same increase in both doses of treatments after 48 h. Regarding miR203a-3p expression, it increased over time and was particularly higher in hMSCs treated with IL-1β 20 ng/mL for 24 h compared with the 48 h treatments, in which hMSCs maintained the upregulation of it but in a different amount (Figure 5B). The functionality of miRNA was demonstrated by the downregulation of TRPV4, in terms of RNA (Figure 5C) and protein (Figure 5D). To understand the role of IL-1β as a modulator of IL-6 release, a gene expression analysis of IL-6 was carried out on hMSCs treated with IL-1β to highlight the differences between the experimental times. As shown in Figure 5E, hMSCs displayed the similar trend of IL-6 expression that was displayed for the OBs derived from OA patients (Figure 1A,B); in fact, a low increase in mRNA and a strong upregulation of interleukin released compared with the untreated group (Figure 5F) were revealed at the same experimental times (p < 0.05). To better understand these differences, we performed a methylation analysis of the IL-6 promoter gene. The obtained data (Figure 5G) showed a downregulation after 48 h of treatments of its promoter methylation, justifying the different expression of IL-6 mRNA and protein revealed after treatments. Starting from the evidence about the role of Cx-43 in bone regeneration processes and as a modulator of inflammatory signaling through the activation of the NF-κB cascade [18,46], we investigated its involvement in hMSCs treated with IL-1β and in OB-OA-derived cells. The data reported in Figure 6 showed the different expressions of CX-43 levels during the experimental time points. After 48 h of treatments with IL-1β at the concentration of 20 ng/mL, hMSCs expressed high levels of CX-43 in terms of mRNA and protein compared with untreated cells (Figure 6A,B), while after 24 h of treatments, no modulation of CX-43 was observed compared with the untreated group. Through a gain and loss of function study on OBs, these data were verified. We overexpressed miR203a-3p in OBs, and as expected, a downregulation of CX-43 mRNAs levels (Figure 6C,D) in OBs transfected with miR203a-3p mimic was found compared with untransfected cells, following the same modulation highlighted after treatments with IL-1β. On the contrary, the overexpression of miR203a-3p inhibitor induced an increase in CX-43 expression into both OB-OA groups compared with those untransfected. These are also confirmed through the overexpression of miR203a-3p inhibitor after IL-1β treatment, in which OB-OA patient- derived cells, in OBs derived from patients with KL ≤ 3, improved the expression of CX-43 mRNA levels compared with untreated or IL-1β-treated cells (Figure S2). It is reported that Sp-1 regulates the CX-43 gene promoter in physiological and pathological conditions [20], and its expression is dependent on the amount of CX-43 at the cell membrane [15,47,48]. To investigate the regulative role of IL-1β on the SP-1 gene, its expression was evaluated in hMSCs treated with IL-1β and in OB-OA cells. As shown in Figure 7A, SP-1 mRNA was upregulated in hMSCs at each experimental time point compared with the untreated group. Through the gain and loss of function studies, we highlighted that SP-1 mRNA was significantly upregulated in the OB-KL ≤ 3 group (Figure 7B) after transfection with miR203a-3p inhibitor but also in a significant manner after co-treatments (IL-1β plus miR203a-3p inhibitor). While in the OB-KL > 3 group, SP-1 was downregulated independently by the treatment (Figure 7C), suggesting the probable role of SP-1 as a mediator of miR203a-3p signaling during the early stage of the OA disease in which the amount of miR203a-3p was higher compared with the severe stage of OA. Following these data, we evaluated the expression of one gene target of SP-1, Alkaline phosphatase (ALP), a specific osteoblast marker and an SP-1 target gene [49], on hMSCs after IL-1β stimulation; an upregulation of ALP mRNA compared with untreated cells (p < 0.0005) was identified (Figure 7D). Starting with the achieved evidence—a relationship in the expression of miR203a-3p and the axis SP-1/CX-43 and supported by the recent study about the interaction of CX-43 and YAP in OA disease—the expression of YAP and its related interactor TAZ in OBs derived from OA patients was investigated through a gain and loss of function study, hypothesizing an interaction between miR203a-3p-SP1/CX-43 and YAP/TAZ signaling during OA disease. The data showed that the strong regulation mediated by miR203a-3p during OA disease was suitable in TAZ mRNA expression and related proteins, while YAP mRNA did not appear to be modulated by the presence of IL-1β or by transfections with miR203a-3p mimic or inhibitor (Figure 8A,B) [50]. In fact, TAZ mRNA was modulated after miR203a-3p overexpression compared with untreated cells, while an increase in its mRNA expression was observed after miR203a-3p inhibitor transfection or after co-treatments (Figure 8C,D). To understand the difference in the regulation of the YAP/TAZ gene expression between both primary cell groups, we evaluated its expression on hMSCs treated with IL-1β. Our data showed that the protein complex was downregulated after IL-1β treatments as showed in Figure 8E,F. By considering the complexity of OA disease, the identification of specific biomarkers to monitor its various stages is still an important research focus and goal to reach. The molecular aspects of OA development and progression were investigated with the aim to provide valuable information for the most appropriate treatment, for the evaluation of the response to the treatment, and eventually for the definition of the predictive markers of the OA progression. MiRNAs were identified as suitable markers for various pathological conditions thanks to their simple availability of the source of analysis and for the simple method of their isolation and identification [51,52,53,54,55,56]. In the present study, we investigated the role of miR203a-3p in OA disease as a possible predictive biomarker of inflammatory aggressiveness and OA progression. MiR-203a-3p was mainly identified in various malignant tumors, in which was displayed a controversial role: it was upregulated in some tumors compared with healthy tissues and downregulated in others [13,33,57,58,59,60]. Recently, it was identified in bone as a mediator of cartilage degradation, synovial inflammation response, and OB dedifferentiation, even though evidence in OA progression is still poorly understood [2,22,44,61]. Through the investigation performed in OBs derived from OA patients with different KL grades (KL ≤ 3 and KL > 3) and in hMSCs treated with IL-1β to mimic the in vitro inflammatory conditions of OA, we identified the role of miR230a-3p and its relative targets during the various stages of disease progression. First, a difference in the inflammatory state of cells derived from OA patients was highlighted: OBs derived from patients with KL > 3 showed higher levels of IL-1β, IL6, and IL8 (mRNA and proteins) compared with those derived from patients with KL ≤ 3 (Figure 1). Second, the relationship between inflammatory progression in terms of IL-1β release and miR203a-3p expression was demonstrated. Recent studies suggested a double link between them: IL-1β was able to induce the expression of miRNAs, and in the same manner, miR203a-3p improved the expression of IL-1β probably blocking its transcriptional repressor or via NF-κB signaling [8,33]. Current data showed that miR203a-3p was upregulated in OBs with a low level of OA (KL ≤ 3) compared with a severe level (KL > 3), and the gain and loss of function studies suggested IL-1β was able to induce the expression of miRNAs in both cells (Figure 2) and consequently downregulate its target, TRPV-4 (Figure 3). Through the gain and loss of function studies (Figure 4), it was identified that the presence of IL-1β alone or in combination with miR203a-3p inhibitor induced differently the upregulation of IL6 and IL8 mRNAs and proteins in OB-KL ≤ 3 and KL > 3 groups compared with untreated cells. Moreover, the treatments with inhibitor and mimic also should increase the expression of ILs in terms of mRNA and proteins compared with untreated cells. These data were supported by many studies [38,41,62] reporting that miR203a-3p induced the expression of IL6 (mRNA and protein), modulating its major transcriptional factor NF-Κb [33], and by bioinformatic software analysis revealing interleukin 6 cytokine family signal transducer (IL6ST) as an miR203a-3p target. Working with primary cells can complicate the identification of the actor of a phenomenon, causing comprehension to become difficult. To overcome this aspect, we mimic OA disease in vitro using the model of hMSCs treated with IL-1β, to investigate the role of IL-1β as a modulator of IL6 and IL8 expression and as a promoter of miR203a-3p expression. The hMSC OA model analysis revealed that IL-1β was able to induce the expression of miR203a-3p and consequently downregulate its target TRPV-4 (Figure 5) in a different manner during the experimental time point, while the IL6 gene and protein analysis showed that the transcriptional modulation of mRNA did not correspond to the protein release regulation. The methylation analysis of IL6 and IL8 (Figure S3) promoter genes justified these findings; IL-1β treatments induced a lower methylation of both IL promoters during the experimental time point, inducing an increase in transcription and consequently in its protein release [38,39]. Concerning these data, we can hypothesize that miR203a-3p displayed a role at the transcriptional levels of IL6 and IL8 mRNA, but in the presence of IL-1β stimulation, the regulation of the promoters’ methylation overcame the modification induced by miR203a-3p. In our opinion, this was the reason because we found a different modulation of these ILs between the two groups of OB-OA-derived cells. Subsequently, the interrelated actions of IL-1β and miR203a-3p were investigated in CX-43 expression. In a previous study, we demonstrated in OBs and chondrocytes derived from OA patients that miR31-5p or miR33a-5p regulated CX-43 expression in a different manner based on the grade of OA and the cytotypes. The proteomic investigation suggested a direct role of Cx-43 in the development of OA, through an enrichment of Cx-43 interactors in OA samples compared with control samples. In addition, recent evidence suggested CX-43 is overexpressed in the middle stage of OA, favoring the maintenance of the chondrocytes in the fibrotic phenotype state leading to cartilage degeneration bringing on joint degeneration [46]. The preliminary current data reported that hMSCs treated with 20 ng/mL of IL-1β showed a significant increase in CX-43 mRNA and protein expression after 48 h of stimulation, while only an upregulation of CX-43 mRNA was found in OBs derived from the KL ≤ 3 group compared with the KL > 3 sample, suggesting the role of miR203a-3p as a regulator of CX-43 expression during the early stage of OA (Figure 6). To further deepen the comprehension of miR203a-3p’s role, the expression of Sp-1, a transcriptional promoter of the CX-43 gene, was assayed [63]. In OA disease, it was demonstrated that the downregulation of CX-43 in the membrane induced the reduction of Sp-1 recruitment to CxREs (CT-rich connexin response elements) and consequently caused less phosphorylation by the ERK cascade leading, for example, to the alteration of the cell phenotype. Our results highlighted that SP-1 was upregulated and able to modulate its target ALP, suggesting that the hMSCs retained their differentiation ability (Figure 7) and confirming that IL-1β induced the same phenomena activated during OA progression. The gain and loss of function studies revealed that the presence or lack of miR203a-3p altered the expression of SP-1 only in the OBs derived from the KL ≤ 3 group (Figure 8). These data allowed us to hypothesize the involvement of miR203a-3p in the regulation of SP-1 mRNAs during the early stage of OA but not in the severe stage of OA. From observing all data described until now, it seems that a direct link among Sp-1, CX-43, and miR203a-3p exists that should be investigated in planned future studies. Nevertheless, encouraged by this evidence, we evaluated the possible role of miR203a-3p induced by IL-1β in the regulation of YAP and TAZ expression during OA progression. Recently, the correlation was reported between CX-43 and YAP in astroglial mesenchymal transition, wherein downregulation of Cx-43 improved the CX-43/YAP complex dissociation and nuclear translocation of YAP [47] and consequently the activation of redifferentiation process in the target cells [64]. Through the gain and loss of function study we revealed that TAZ was upregulated after inhibitor transfection or co-treatments with IL-1β in OBs, while the mimic and IL-1β treatments induced the downregulation of its expression. In addition, in both OB groups, no significative variations in YAP expression were identified. On the contrary, the hMSCs treated with IL-1β showed a downregulation of YAP and TAZ, during the experimental times (Figure 8). This controversial expression revealed in OB groups was supported by the new evidence about the novel mechanism of YAP/TAZ regulation; it was demonstrated that YAP inversely regulates the abundance of TAZ protein by proteasomal degradation. Interestingly, this phenomenon was unidirectional since TAZ expression did not affect YAP abundance and TAZ degradation was a consequence of YAP-targeted gene transcription involving TEAD factors [50]. With regard to current YAP/TAZ results, we hypothesized that the different modulation on these proteins could follow this mechanism of regulation during OA progression. Finally, the current data highlighted the possibility to identify a role of specific molecules as biomarkers in the OA progression (Figure 9). During the early stage of OA, the disease niche was characterized by inflammatory microenvironments in which IL-1β displayed a central role. IL-1β induced the expression of miR203a-3p that improved the expression of IL-1β, self-creating a loop of activation and inducing the expression of IL-6 and IL-8. The progression of inflammatory conditions improved the evolution of OA, favoring probably a different methylation of miR203a-3p promoter or its precursor pre-miR203, leading to the downregulation of its expression during the OA progression. Regarding this hypothesis, many studies suggested the role of miR203a-3p as a tumor suppressor based on the methylation state of its promoter, which encouraged our idea [33,58,59,65,66,67,68,69]. At the middle stage of OA, the downregulation of miR203a-3p and the increase in inflammatory factors improved the expression of CX-43 and SP-1, allowing probably the release of CX-43 by exosomes, as recently demonstrated [19], or the activation of the EMT process or Hyppo signaling, as suggested by the upregulation of TAZ expression after miR203a-3p inhibitor transfection, preparing the cells to go through a fibrotic process that leads to the destruction of the joint, a typical feature of the severe state of OA (Figure 9) Osteoblasts (OBs) were isolated from waste surgical joint tissues (Protocol ID: CE AVEC 287/2018/Sper/IOR) of patients aged >40 years hospitalized for surgery of i) endoplasty or arthroplasty for OA with Kellgren and Lawrence (KL) grading > 3 (n = 4 patients) or ii) joint fractures (e.g., femoral neck fractures) requiring the implantation of a joint prosthesis that showed KL grading ≤ 3 (n = 4 patients). The demographic and clinical data of selected patients are reported in Table 1. OBs were isolated according to the appropriate protocol and maintained in culture in specific differentiated mediums (Osteoblast Growth Medium, iXCells Biotechnologies MD-0054). Commercially available human mesenchymal stromal cells (hMSCs; Lonza, Walkersville, MD, USA) were cultured in Mesenchymal Stem Cell Growth Medium (MSCGM™ Bullet Kit, Lonza, Walkersville, MD, USA). The culture medium was changed every three days, and cells were split at 70–80% of confluence using StemProAccutase (Gibco by Life Technologies Italia, Monza, Italy). All cells were maintained in culture in a humidified atmosphere of 5% CO2 at 37 °C until the third passages; then, the cells were plated to perform the following assays. For the IL-1β treatment, hMSCs were seeded at 100,000 cells/cm2 and treated with 10 or 20 ng/mL of IL-1β (200-01B, Preproteck) for 24 and 48 h to perform assay analyses. For cell transfection, Attractene Transfection Reagent (cat. number 1051531, Qiagen Srl, Milan, Italy) was used following the manufacturer’s indication. Briefly, cells seeded at 150,000 cells/cm2 were transfected with 30 pmol/mL of has-mir-203a-3p mimic (MC10152-MIMAT0000264mirVana miRNA mimics Life Technologies Italia), has-miR203a-3p inhibitor (MH10152-MIMAT0000264, mirVana miRNA inhibitors-Life technologies Italia), and scrambled negative controls (4464058mirVana negative control Life Technologies, Monza, Italy) for 24 h. These last controls are negative controls of tested miRNA mimics, and the target gene expressions from the negative control-transfected samples were used as baseline values for the evaluation of the effects of the control and experimental miRNA mimic or inhibitor on target gene expression. At each experimental time the cells were processed for the following assays. Total RNA was extracted using the commercially available NUCLEOZOL (FC140400T NucleoProtect RNA), according to the manufacturer’s instructions. RNA was reverse transcribed to cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, ThermoFisher Scientific, Waltham, MA, USA). Quantitative RT-PCR (qRT-PCR) analysis was performed in duplicates for each data point, using custom-made primers (Invitrogen, Life Technologies Italia) (Table 2) and Qiagen Primers (Table 3). The mean threshold cycle was used for the calculation of relative expression using the Livak method against ACTB [70,71]. For miRNA expression, 250 ng of RNA was reverse transcribed according to the manufacturer’s instructions (cat. number 4366596, TaqMan MicroRNA Reverse Transcription, Applied Biosystems, ThermoFisher Scientific, Waltham, MA, USA). TaqMan probes were used to analyze miR203a-3p (MI0000283-000507 Applied Biosystem, ThermoFisher Scientific, Waltham, MA, USA). Changes in the target miRNA content were calculated in relation to the housekeeping RNU6-1“RNA, U6 small nuclear 1” (4427975 Applied Biosystems, ThermoFisher Scientific, Waltham, MA, USA). Protein release was measured in the culture medium for IL-6 and IL-8 using (SEA079Hu for IL-6 and SEA080Hu for IL-8; Cloud-Clone Corp, 1304 Langham Creek Dr Ste 164, Houston, TX, USA) according to the manufacturer’s instructions. The data were expressed as fold of change (FOI) of protein release relative to the untreated group or in pg/mL amount for each sample tested. SDS-PAGE and Western blotting (WB) were performed according to standard protocols. Briefly, after transfection, cells were lysed in lysis buffer containing 15 mM Tris/HCl pH7.5, 120 mM NaCl, 25 mM KCl, 1 mM EDTA, 0.5% Triton X100, and Halt Protease Inhibitor Single-Use cocktail (100X, Fisher Scientific Italia, Rodano, Italy). Whole lysate (15 µg per lane) was separated using 4–12% NovexBis-Tris SDS-acrylamide gels (Invitrogen, Life Technologies Italia), electro-transferred on nitrocellulose membranes (Bio-Rad Laboratories Srl, Segrate, Milan, Italy), and immunoblotted with the appropriate antibodies. Antibodies against the following proteins were used: Sp1 (Sp1 (E-3) Antibody, sc-17824, Santa Cruz Biotechnology, Inc, Dallas, Texas, USA), Cx-43 (connexin 43 (F-7) Antibody, sc-271837, Santa Cruz Biotechnology, Inc, Dallas, Texas, USA), TRPV4 (TRPV4 Antibody #65893, Cell Signaling Technology), and α-Tubulin (monoclonal anti-α-Tubulin (TU-02), sc8035, Santa Cruz Biotechnology, Inc. Dallas, Texas, USA). All secondary antibodies were obtained from Fisher Scientific Italia. Immunofluorescence was detected and analyzed using a CCD high-resolution and high-sensitivity detection technology (ChemiDoc™ XRS+ System, Bio-Rad Laboratories Srl). The isolation of the genomic DNA of MSCs, under different treatments (IL-1β at 10 ng/mL or 20 ng/mL), was carried out with the PureLink Genomic DNA mini-Kit (Invitrogen™, Waltham, MA, USA). The DNA was quantified using the Nanodrop 2000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA), and the integrity was analyzed using electrophoresis on 0.8% agarose gel. The methylation sensitive restriction endonuclease–PCR (MSRE–PCR) analysis was performed to determine the methylation status of the CpG-rich sites, present in the proximal promotor regions of IL-6 and IL-8. The experiments were carried out as described elsewhere [38,72,73]. In brief, the PCR products were analyzed by 2% agarose gel electrophoresis, visualized by Gel Red staining (Biotium, Hayward, CA, USA) in a ChemiDoc apparatus (Bio-Rad Laboratories, Hercules, CA, USA), and densitometric analyses were obtained using the “Image Lab” application (version 5.2.1) of Bio-Rad Laboratories (Hercules, CA, USA). The statistical analysis was performed by using R software v.4.2.1 [71]. One- or two-way ANOVA was used to evaluate the significant effects and/or interactions of selected factors (“treatment” for one- and two-way and “experimental time” for two-way) on normally distributed (Shapiro–Wilk test) data with homogeneity of variance (Levene test). Then, selected pairwise multiple comparisons with p-values adjusted according to the Sidak–Holm or Dunnett method were carried out. The current results suggest that the increased expression of miR203a-3p, induced by the presence of pro-inflammatory mediators, has in turn an active role in inflammation, cell dedifferentiation, and transition, rendering cells unable to control the expression of SP-1, CX-43, and TAZ. These phenomena have led to the reduction of miR203a-3p expression, which, probably, will progressively degrade in a manner directly proportional to the increase in the severity of the disease, leading to the development of a microenvironment altered by a chronic inflammatory process and by an aberrant cellular dedifferentiation, until the final joint degeneration. To confirm this hypothesis, other investigations will be performed on the blood of OA patients from which will be isolated circulating miRNAs and exosomes, to evaluate their miR203a-3p enrichment and the proinflammatory cytokine amount. In addition, to validate our idea about the correlation between the inflammatory aggressiveness and miR203a-3p expression, we will perform the lymphocyte immunophenotyping by FACS analysis, to identify the possible enrichment of a specific lymphocyte subpopulation during the various steps of OA progression. These studies will support the protective role of miR203a-3p and its possible use as a predictive biomarker in OA progression.

PMC10002152

Nan Jia,Yuting Jiang,Xianyi Jian,Tong Cai,Qing Liu,Yuan Liu,Dan Xing,Yande Dong,Xiaoxia Guo,Tongyan Zhao

Transcriptome Analysis of Response to Zika Virus Infection in Two Aedes albopictus Strains with Different Vector Competence

21-02-2023

ZIKV,Aedes albopictus,vector competence,transcriptome analysis,cytochrome P450

Zika virus (ZIKV), which is mainly transmitted by Aedes albopictus in temperate zones, can causes serious neurological disorders. However, the molecular mechanisms that influence the vector competence of Ae. albopictus for ZIKV are poorly understood. In this study, the vector competence of Ae. albopictus mosquitoes from Jinghong (JH) and Guangzhou (GZ) Cities of China were evaluated, and transcripts in the midgut and salivary gland tissues were sequenced on 10 days post-infection. The results showed that both Ae. albopictus JH and GZ strains were susceptible to ZIKV, but the GZ strain was more competent. The categories and functions of differentially expressed genes (DEGs) in response to ZIKV infection were quite different between tissues and strains. Through a bioinformatics analysis, a total of 59 DEGs that may affect vector competence were screened—among which, cytochrome P450 304a1 (CYP304a1) was the only gene significantly downregulated in both tissues of two strains. However, CYP304a1 did not influence ZIKV infection and replication in Ae. albopictus under the conditions set in this study. Our results demonstrated that the different vector competence of Ae. albopictus for ZIKV may be determined by the transcripts in the midgut and salivary gland, which will contribute to understanding ZIKV–mosquito interactions and develop arbovirus disease prevention strategies.

Transcriptome Analysis of Response to Zika Virus Infection in Two Aedes albopictus Strains with Different Vector Competence Zika virus (ZIKV), which is mainly transmitted by Aedes albopictus in temperate zones, can causes serious neurological disorders. However, the molecular mechanisms that influence the vector competence of Ae. albopictus for ZIKV are poorly understood. In this study, the vector competence of Ae. albopictus mosquitoes from Jinghong (JH) and Guangzhou (GZ) Cities of China were evaluated, and transcripts in the midgut and salivary gland tissues were sequenced on 10 days post-infection. The results showed that both Ae. albopictus JH and GZ strains were susceptible to ZIKV, but the GZ strain was more competent. The categories and functions of differentially expressed genes (DEGs) in response to ZIKV infection were quite different between tissues and strains. Through a bioinformatics analysis, a total of 59 DEGs that may affect vector competence were screened—among which, cytochrome P450 304a1 (CYP304a1) was the only gene significantly downregulated in both tissues of two strains. However, CYP304a1 did not influence ZIKV infection and replication in Ae. albopictus under the conditions set in this study. Our results demonstrated that the different vector competence of Ae. albopictus for ZIKV may be determined by the transcripts in the midgut and salivary gland, which will contribute to understanding ZIKV–mosquito interactions and develop arbovirus disease prevention strategies. Zika virus (ZIKV), which belongs to the family Flaviviridae, genus Flavivirus, is a single-stranded RNA virus and primarily transmitted by Aedes mosquitos. ZIKV was discovered initially from a sentinel monkey in Uganda, Africa, in 1947 [1], and firstly detected in humans in 1952 [2]. In the past decade or so, ZIKV has continued to spread. It is currently recorded in 86 countries [3,4]. The clinical symptoms of Zika virus disease are variable, ranging from no or mild symptoms to severe neurological disorders such as microcephaly in infants born from infected mothers and Guillain-Barré syndrome in adults [5,6]. The spread of ZIKV poses a significant threat to public health. There is no specific drug or vaccine for ZIKV infection, and vector control remains the primary way to stop the spread of the virus [7,8]. However, with traditional vector control strategies becoming less effective, there is an urgent need for new methods to control the spread of arboviruses [9,10]. The female mosquito becomes infected with an arbovirus when it acquires a blood meal from an infected human. The ingested virus firstly invades the midgut tissue, where it replicates to produce viral particles. Then, the viral particles enter the hemolymph and spreads to secondary tissues, such as the trachea and salivary gland. Finally, the virus is released into salivary tubes and transmitted to uninfected vertebrate hosts by blood sucking for the next time [5,11,12]. Mosquito vector competence is influenced by the intrinsic factors and molecular mechanisms, and susceptibility to viruses varies among mosquito species and geographic strains [13,14,15]. This variability is driven by the compatibility of viruses with host factors and their ability to evade the action of the mosquito’s restriction factors, many of which are components of the insect’s innate immune system, such as Toll, immune deficiency (Imd), and the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathways [5]. Activation of these pathways leads to alterations in transcription factors, resulting in the production of multiple antipathogen effector molecules [16,17]. Furthermore, the RNAi pathway, a key antiviral defense system, can degrade viral RNAs and plays an important role during arbovirus infections [18,19]. The molecular interactions between Aedes aegypti and ZIKV have been extensively studied, but the interactions between Aedes albopictus and ZIKV have largely not been elucidated. Ae. albopictus is an effective vector for ZIKV and has also been responsible for several outbreaks of important arboviruses such as dengue virus (DENV) and chikungunya virus (CHIKV) [20,21,22]. Yunnan and Guangdong Provinces are located in the southernmost part of China and are the main links between Southeast Asia and South America. With the increasing frequency of trade and people exchanges in recent years, the epidemic situation cannot be ignored. Therefore, in this study, Ae. albopictus mosquitoes from Jinghong City (JH), Yunnan Province and Guangzhou City (GZ), Guangdong Province were collected, and their vector competence for ZIKV was assessed. The RNA-seq technique was used to analyze the changes in the transcriptome of the midgut and salivary gland tissues of the two strains of Ae. albopictus after infection with ZIKV. Furthermore, our comparative analysis of the ZIKV infection-responsive transcriptomes identified potential ZIKV infection-responsive genes, which could contribute to the development of new insect-borne disease prevention strategies. To evaluate the vector competence of Ae. albopictus JH and GZ strains for ZIKV, a low dose of 1 × 106 PFU/mL and a high dose of 1 × 107 PFU/mL ZIKV were used to orally infect the mosquitoes. Viral RNA in the midgut and salivary gland was detected at 4, 7, 10 and 14 days post-infection (dpi), and the infection rate and viral RNA copies were assessed (Figure 1). When the mosquitoes were infected with a low dose of ZIKV, viral RNA was detected as positive in all tissues at all sampling days, except the salivary gland of the JH strain at 4 and 7 dpi. The Infection rate in the midgut of the JH strain was maintained at a relatively low level, ranging from 30.0~33.3% at 4~10 dpi, and suddenly reached 80.0% at 14 dpi, but it was maintained at a high level of 80.0% to 96.7% at all sampling days in the midgut of the GZ strain (Figure 1A). The infection rate in the salivary gland of the GZ strain gradually increased from 3.3% at 4 dpi to 46.7% at 14 dpi. Viral RNA could not be detected until 10 dpi in the salivary gland of the JH strain with a 3.3% infection rate, and it reached 6.7% at 14 dpi (Figure 1B). The infection rate was significantly higher in the midgut of the GZ strain at 4, 7 and 10 dpi and in the salivary gland at 14 dpi when compared to that of the JH strain. The viral RNA copies in the positive samples were calculated and compared, but there was no significant difference between the JH and GZ strains in both the midgut and salivary gland (Figure 1C,D). When the mosquitoes were infected with a high dose of ZIKV, the infection rate in the midgut of the JH strain gradually increased from 50.0% at 4 dpi to 83.3% at 14 dpi, but it was maintained at a high level of 83.3% to 96.7% in the GZ strain at all sampling days (Figure 1E), similar to the low-dose infection experiments. The infection rate in the salivary gland exhibited a pattern of progressive increase both in the JH and GZ strain. It increased from 0.0% at 4 dpi to 26.7% at 14 dpi in the JH strain and from 10.0% at 4 dpi to 50.0% at 14 dpi in the GZ strain (Figure 1F). The infection rate of the GZ strain was significantly higher in the midgut at 4 dpi and in the salivary gland at 10 dpi than that of the JH strain. There was a significant difference in the viral RNA copies in the midgut tissues, with a higher load in the GZ strain than the JH strain (Figure 1G). However, it was not significantly different between the two strains in the salivary gland (Figure 1H). Taken together, it was shown that the Ae. albopictus GZ strain was more competent for ZIKV than the JH strain. Albeit both the Ae. albopictus JH and GZ strain were susceptible to ZIKV infection, the GZ strain was more competent for ZIKV than the JH strain. To screen genes that potentially influence the vector competence of Ae. Albopictus for ZIKV, the transcription profile in the midgut and salivary gland of these two mosquito strains that were infected with ZIKV (1 × 107 PFU/mL) at 10 dpi were obtained by Illumina sequencing, and the mosquitoes that were fed with uninfected blood meal were used as the control. A total of eight RNA-seq libraries were created, and 23.92 M raw reads were obtained from each library. Then, some reads were discarded in different libraries due to their low-quality scores or lack of adapter sequences. Finally, about 23.82–23.83 M clean reads were maintained in each library. Transcripts in the different libraries were analyzed, and fold changes of genes expression in the ZIKV-infected tissues compared to the non-infected group were calculated. The transcripts with a false discovery rate (FDR) < 0.001 in the group comparisons were defined as significantly regulated and those with FDR < 0.001 and |log2FC| > 1 as DEGs. A total of 211, 297, 228 and 1287 DEGs were identified in the midgut and salivary gland of Ae. albopictus JH and GZ strains in response to ZIKV infection, respectively (Figure 2). Interestingly, the most alterations in the mRNA profile were found in the salivary gland of the GZ strain, and the changes in mRNA expression were predominately downregulated (Figure 2D). To further analyze the related functions of DEGs and identify the biological pathways that play a key role in the biological processes, with the aim of revealing and understanding the basic molecular mechanism, Go and KEGG pathway analyses of the DEGs were performed. The GO functions enriched by DEGs were largely consistent in both two tissues and two strains (Figure S1). The DEGs were mostly enriched in (1) the biological process (BP) terms: cell process, metabolic process, biological regulation, regulation of biological processes, multicellular organismal process and response to stimulus; (2) the cellular component (CC) terms: cell, cell part, membrane, membrane part and organelle and (3) the molecular function (MF) terms: binding, catalytic activity and transporter activity. Unlike the GO analysis, the KEGG enrichment results showed that the KEGG pathways enriched were different between two tissues and two strains (Figure S2). For the JH strain, DEGs in the midgut are mainly involved in proximal tubule bicarbonate reclamation, tyrosine metabolism, dorsoventral axis formation and the glucagon signaling pathway (Figure S2A). DEGs in the salivary gland are mainly involved in phototransduction—fly, the oxytocin signaling pathway, metabolism of xenobiotics by cytochrome P450 and drug metabolism—and other enzymes (Figure S2B). For the GZ strain, DEGs in the midgut are mainly involved in galactose metabolism, pentose and glucuronate interconversions, the Fanconi anemia pathway and salivary secretion (Figure S2C). DEGs in the salivary gland are mainly involved in pancreatic secretion, protein digestion and absorption, the metabolism of xenobiotics by cytochrome P450, adrenergic signaling in cardiomyocytes, etc. (Figure S2D). The midgut and salivary gland play important roles in arbovirus infection and transmission via mosquitoes. Viruses have to overcome infection barrier and escape barrier in these two tissues to be secreted in the saliva and ready for infection of the vertebrate by blood sucking [23]. However, it was found that DEGs after ZIKV infection were quite different between the midgut and salivary gland in both the JH and GZ strains. There are only 7 (1.4%) DEGs shared by two tissues in the JH strain and 53 (3.6%) DEGs in the GZ strain. For these consensus DEGs, only one DEG was found in both the JH and GZ strains, namely LOC109405426 (cytochrome P450 304a1, CYP304a1) (Figure S3). The function of those 59 genes is summarized in Table 1. Differences in the DEGs between the JH and GZ strains in the midgut or salivary gland are also compared. There are only 24 (5.8%) consensus DEGs shared by two strains in the midgut and 41 (2.7%) consensus DEGs in the salivary gland (Figure S4). Similar to the KEGG enrichment analysis, these results showed that the transcriptome expression in response to virus infection were quite different between tissues and among mosquito strains. To validate the results of deep sequencing, 10 genes, including LOC109405426, in Table 1 were selected for RT-qPCR verification. The results showed that the expression levels of the DEGs obtained by RNA-Seq and RT-qPCR were consistent, indicating the results from the RNA-Seq were reliable (Figure 3). As previously described, only LOC109405426 (CYP304a1) was significantly downregulated in both two tissues and two strains after ZIKV infection, so it was chosen for further examination. The gene was knocked down by siRNA via thoracic microinjection, and its relative expression was examined 3 days after injection. The results showed that the transcription of CYP304a1 was significantly decreased after siRNA inoculation compared to the GFP control group (Figure 4A). Three days after gene silencing, the ZIKV suspension was microinjected into the mosquitoes, and the viral loads were assessed on 1 and 3 dpi via RT-qPCR. The results showed that the ZIKV load on 3 dpi was significantly higher than that on 1 dpi (p < 0.0001) in both the JH and GZ strains, but there was no significant difference between the CYP304a1 interference group and GFP group (Figure 4B,C), indicating that CYP304a1 did not influence ZIKV infection and replication in Ae. albopictus. Jinghong and Guangzhou, which are important port cities in South China, have close trade relations with Southeast Asia and South America and are the risk sites for the introduction of Zika virus. Ae. albopictus is a common mosquito species in tropical and subtropical areas of China and is an important vector for DENV, ZIKV and other Flaviruses. This study evaluated the vector competence of the Ae. albopictus Jinghong strain and Guangzhou strain for ZIKV, and the results showed that the GZ strain was a highly efficient vector for ZIKV, consistent with a previous study [15]. However, the JH strain showed a relative lower vector competence. When it was infected with a low dose of ZIKV, the infection rate of the midgut in the JH strain was significantly lower than that of GZ, and viral RNA was detected in the salivary gland until 10 dpi. When infected with a high dose of the virus, the viral RNA copies in the midgut of the JH strain were significantly lower than that of GZ, and viral RNA was detected in the salivary gland until 7 dpi. These results indicated that the JH strain has stronger resistance to ZIKV infection. However, in the high-dose experiment, there was no statistical difference of the virus infection rate and copy number in the salivary glands between two mosquito strains at the late infection stage (14 dpi), so the risk of ZIKV transmission by Ae. albopictus in Jinghong City should not be ignored. Different vector competence for ZIKV between geographic strains of the same mosquito species have been previously reported. For example, Ae. aegypti from Brazil, the Dominican Republic, and the United States were fed with artificial blood meals containing ZIKV, but only mosquitoes from the Dominican Republic transmitted the ZIKV Cambodia and Mexica strains [24]. Field Ae. aegypti from three Pacific islands were collected and orally exposed to ZIKV, and the results showed that the ZIKV infection rate was heterogeneous between the populations [25]. The vector competence of nine Ae. albopictus populations in China for DENV-2 was evaluated, and it was shown that significant differences of viral RNA copies existed among different populations [26]. The factors that influence vector competence are complicated, and the genetic background is one of them. In order to find the potential genes related to vector competence, a transcriptome sequencing was performed, and the DEGs in the midgut and salivary glands of two Ae. albopictus strains after ZIKV infection were screened and compared. The results showed that the DEGs were very different between two tissues and two mosquito strains. When comparing DEGs between tissues, the consensus DEGs account for only 1.4% of the total in the two tissues of the JH strain and 3.6% in the GZ strain. When comparing DEGs between strains, there were only 5.8% consensus DEGs shared by two strains in the midgut and 2.7% in the salivary gland. In the GO and KEGG pathway analyses, DEGs screened in four libraries were very similar in composition (Figure S1) but differed greatly in function (Figure S2). Subsequently, DEGs with the same regulated direction in both the midgut and salivary gland were screened, because genes that were upregulated in one tissue but downregulated in the other would make the following analysis and gene silencing validation complicated and contradictory. Such an analysis strategy might have omitted genes that influence the vector competence. However, the genes screened by this way had the highest probability of being related to vector competence. Finally, a total of 59 DEGs were screened, including defensin-A, RNA polymerase II subunit Rpb1, cytochrome P450, etc. Defensin-A, which was significantly downregulated after ZIKV infection both in the midgut and salivary gland of the GZ strain, is a member of the defensin family and the first biological line of defense against pathogen invasion [27]. Studies have shown that mosquito defensins are primarily active against Gram-positive bacteria [28]. However, recent studies have found that the defensin gene family plays a role after the mosquito is infected by the virus. For example, defensin-A is significantly reduced in DENV-1-infected Ae. aegypti, and it has been speculated that DENV-1 may inhibit the expression of certain factors required to induce defensin mRNA expression through the Toll pathway or directly target and inhibit gene transcription [29]. This is similar to the results of the present study and suggests that the defensin family is also involved in the defense process against ZIKV infection in Ae. albopictus. However, how ZIKV inhibit its expression and what effector molecules are involved remain to be determined. In addition, RNA polymerase II subunit Rpb1 was significantly downregulated in both the midgut and salivary gland of the GZ strain after ZIKV infection. At present, little is known about the function of this protein during infection. However, studies have reported that, after Semliki Forest Virus, Sindbis Virus or CHIKV infection, mRNA transcription in cells was inhibited by rapid degradation of the Rpb1 catalytic subunit of RNA polymerase II, thereby inhibiting cell antiviral reaction [30]. The results of this study indicated that RNA polymerase II subunit Rpb1 was involved in the process of ZIKV infection in Ae. Albopictus, but its clear mechanism and interaction with other molecules require further investigation. Among the 59 genes screened, one was significantly downregulated in two strains and two tissues, namely CYP304a1. The CYP enzymes are membrane-bound hemoproteins that play a pivotal role in the detoxification of xenobiotics, cellular metabolism and homeostasis [31]. It was reported that this gene played pivotal roles in the tolerance to toxic leaf litter for Ae. aegypti larvae [32], detoxification of pyrethroid insecticides for Anopheles minimus [33] and resistance to permethrin for Culex quinquefasciatus [34]. However, the relationship between mosquito CYP and virus infection has not been reported previously. In order to verify the effect of CYP304a1 on mosquito vector competence, interfering RNA for this gene were designed and intrathoracically injected into the mosquito to knock down its expression. Three days after interference, mosquitoes were infected with ZIKV by intrathoracic injection, and viral RNA were detected 1 and 3 days after infection. The results showed that the copy number of ZIKV virus was higher at 3 dpi than 1 dpi (p < 0.0001), but no significant difference between the CYP304a1 interference group and GFP group was found in this study, indicating that CYP304a1 had no effect on virus infection and replication under the conditions set in this study. However, the virus infection route and dose might have effects on the results. For example, virus infection by intrathoracic injection bypassed the midgut infection and escape barrier, where CYP304a1 may play a role. The high dose of virus injection used in this study (3000 PFU/mosquito) may have overloaded the antiviral effect of CYP304a1 or covered up its effect of promoting virus infection. Other infection routes (e.g., oral infection) or a lower virus dose may be used in future studies to further confirm the role of CYP304a1. The Aedes albopictus Jinghong strain was originally collected from Jinghong City, Yunnan Province (GPS location: 21°26′ N and 100°25′ E), in 2019. The Ae. albopictus Guangzhou strain was originally collected from Guangzhou City, Guangdong Province (GPS location: 23°07′ N and 113°16′ E), in 2019. Both two mosquito strains were reared under standard insectary conditions at 26 ± 1 °C and 75 ± 5% relative humidity, with a photoperiod of 14 h light:10 h dark cycles. Prior to the infectious feed, adult mosquitoes were provided with 8% sucrose solution. C6/36 (Ae. albopictus) cells were maintained in our laboratory and were cultured in RPMI 1640 medium (Gibco, Shanghai, China) supplemented with 10% fetal bovine serum (FBS) (Gibco, Shanghai, China) and 1% penicillin/streptomycin (Gibco, Shanghai, China) at 28 °C in an incubator of 5% CO2. The ZIKV SZ01 strain used in this study was obtained from the Microbial Culture Collection Center of the Beijing Institute of Microbiology and Epidemiology. This virus was originally isolated from a patient who returned from Samoa to China in 2016 (GenBank accession number: KU866423) [35]. The virus has been passaged in the C6/36 cell lines six times. Virus-infected blood meals were prepared by mixing 1:1 mouse blood and ZIKV SZ01 strain suspension supplemented with 2% FBS and 1% heparin sodium. Seven-day-old adult female mosquitoes that had been starved for 18 h were fed with this infected blood meal using a Hemotek membrane feeding system. The blood meal was kept at 37 °C during feeding. The non-infected group was supplied with a 1:1 mixture of mouse blood and RPMI 1640 medium supplemented with 2% FBS and 1% heparin sodium. After 1 h of feeding, mosquitoes were cold-anesthetized, and blood-engorged mosquitoes were transferred to and maintained in the standard rearing conditions. Thirty blood-engorged mosquitoes were respectively sampled at 4, 7, 10 and 14 dpi. The midguts and salivary glands of mosquitoes were dissected and collected carefully with sterile dissecting needles and individually transferred into 1.5 mL microtubes containing 1 mL of RNAiso Plus (TaKaRa, Dalian, China). Then, the total RNA from the midguts and salivary glands was extracted according to the manufacturer’s instructions. ZIKV genomic RNA was detected using the GoTaq® Probe 1-Step RT-qPCR System (Promega, Dalian, China), including forward primer: 5′-AAGTTTGCATGCTCCAAGAAAAT-3′, reverse primer: 5′-CAGCATTATCCGGTACTCCAGAT-3′ and probe: 5′-FAM-ACCGGGAAGAGCATCCAGCCAGA-TAMRA-3′ [36]. The following reagents were used for the RT-qPCR reactions: 2 μL of RNA sample, 10 μL of GoTaq® Probe qPCR Master Mix, 0.4 μL of GoScript™ RT Mix, 1 μL forward primer, 1 μL reverse primer, 1 μL probe and 4.6 μL of nuclease-free water to yield a 20 μL final reaction volume. Amplification reactions were performed in the QuantStudio™ 7 Flex Real-Time PCR system (Thermo Fisher Scientific, Waltham, MA, USA) and programmed as follows: 1 cycle at 45 °C for 15 min, 95 °C for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 1 min. Virus RNA copies were calculated by generating a standard curve using a recombinant plasmid-containing virus segment insertion. Seven-day-old female Ae. albopictus JH and GZ strains were fed with a ZIKV-infected blood meal or a blood meal devoid of ZIKV, as described previously. The midgut and salivary gland were dissected at 10 dpi. A total of 8 groups (2 strains × 2 tissues × 2 kinds of blood meal) were included for mRNA seq library preparation. Each group contained approximately 100 mosquitoes, from which tissues were collected into a 1.5 mL RNase-free microcentrifuge tube containing 1 mL RNAiso Plus (TaKaRa, Dalian, China) and stored at −80 °C until the subsequent RNA extraction. The RNA extraction, library preparation and sequencing analyses were performed by the BGI Company (Shenzhen, China). The total RNA was extracted from 8 groups using RNAiso Plus according to the manufacturer’s protocols. The quality and quantity of RNA were measured by the Agilent 2100 Bioanalyzer System (Agilent Technologies, Inc., Santa Clara, CA, USA). Each RNA sample was divided into two parts, with one used for mRNA library preparation and sequencing and the second part used for RT-qPCR validation. Oligo (dT) magnetic beads were used for the enrichment of mRNAs with a poly-A tail. Purified mRNA was fragmented into small pieces with fragment buffer at the appropriate temperature. Then, first-strand cDNA was generated using random hexamer-primed reverse transcription, followed by a second-strand cDNA synthesis. The purified double-stranded cDNA was repaired, A-tails were added to the ends and the products were purified again after PCR amplification to finally obtain a single-stranded circular DNA library. The quality of cDNA was checked using the Agilent 2100 Bioanalyzer system. mRNA sequencing was performed using the Illumina genomic analyzer. To ensure the quality and reliability of the data analysis, it was necessary to filter the original data. Reads with adapters, undetermined base information and low quality were removed with SOAPnuke v1.5.2 [37]. Clean reads were mapped to the reference genome using HISAT2 v2.0.4 [38] to obtain the localization information of the reads on the reference genome. Bowtie2 (v2.2.5) [39] was applied to align the clean reads to the reference coding gene set; then, the expression level of the gene was calculated by RSEM (v1.2.12) [40]. Finally, GO (http://www.geneontology.org/, accessed on 11 May 2022) and KEGG (https://www.kegg.jp/, accessed on 11 May 2022) enrichment analyses of the annotated DEGs were performed by Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution, accessed on 11 May 2022) based on the Hypergeometric test. An algorithm was used to identify differentially expressed mRNAs between ZIKV-infected samples and non-infected samples. p(χ) = e−λλχ/χ!, where χ is defined as the number of reads from mRNA and λ is the real transcripts of the mRNA. The method was described by Audic et al. [41]. When the FDR was <0.001, changes in the mRNA expression were considered to be significant. mRNAs with log2FC > 1 were designated as being significantly upregulated, and mRNAs with log2FC ≤ 1 were designated as significantly downregulated. To confirm the RNA-Seq data, the expression of 10 mRNA transcripts was verified by 2-step RT-qPCR. Firstly, RNA was reverse-transcribed into cDNA with the PrimeScript™ RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China). One microliter of RNA template, two microliters of 5× gDNA Eraser Buffer, one microliter of gDNA Eraser and six microliters of RNase-free water were mixed and incubated at 42 °C for 2 min. Then, 1 µL of PrimeScript RT Enzyme Mix I, 1 µL of RT Primer Mix, 4 µL of 5× PrimeScript Buffer II and 4 µL of RNase-free water were added to the above reaction solution, and the mixture was incubated at 37 °C for 15 min, then 85 °C for 5 s. The products were used as templates for qPCR validation in the next step. qPCR was performed using the PerfectStart™ Green qPCR SuperMix Kit (Transgene, Beijing, China). The reaction system consisted of 2 µL of cDNA template, 10 µL of SuperMix, 0.4 µL of Passive Reference Dye II, 0.4 µL F/R primer and 6.8 µL of RNase-free water. qPCR was performed using the QuantStudio™ 7 Flex Real-Time PCR system. The reaction procedure was as follows: 94 °C for 30 s for 1 cycle; 94 °C for 5 s and 60 °C for 30 s for 40 cycles for the dissociation stage. The 2−∆∆CT method [42] was used to analyze the qPCR results. The primer sequences of selected mRNA transcripts and the reference gene actin are shown in Table S1. CYP304a1 and GFP siRNA were designed by the DSIR website (http://biodev.extra.cea.fr/DSIR/DSIR.html, accessed on 9 September 2022), and the sequences are shown in Table S2. For gene silencing, female Ae. albopictus mosquitoes were cold-anaesthetized on a cold tray, and 0.01 nmol/300 nL of siRNA were injected into their thoraxes. The injected mosquitoes were allowed to recover 3 days under standard rearing conditions for viral infection. Then, the mosquitoes were thoracically microinjected with 300 nL of 1 × 107 PFU/mL ZIKV suspension and were then maintained in rearing conditions. The total RNA of the mosquitoes was extracted 3 days after siRNA injection for gene silencing validation or 1 and 3 days after virus infection for ZIKV detection. The methods of RNA extraction, CYP304a1 and ZIKV detection were the same as previously described in this study. The sample sizes of siRNA microinjection, ZIKV infection and ZIKV detection are summarized in Table S3. The data analysis was performed using GraphPad Prism (Version 8.0, GraphPad Software, San Diego, CA, USA). In the vector competence evaluation experiment, the infection rate between two strains was compared by Fisher’s exact test. Viral RNA copies in two Ae. albopictus strains were compared with the non-parametric Mann–Whitney test. In the CYP304a1 silencing experiment, the relative expression of CYP304a1 between two groups was compared by the non-parametric Mann–Whitney test. The viral loads in different strains at different times post-infection were analyzed by 2-way ANOVA with Šídák’s multiple comparisons test. The normality and heteroscedasticity of the residuals were evaluated by the Shapiro–Wilk test and Spearman’s test, respectively. p-values lower than 0.05 were considered statistically significant. In this study, the vector competence of Ae. albopictus from Jinghong and Guangzhou Cities were evaluated, and the results showed that both strains were susceptible to ZIKV, but the GZ strain was more competent. The transcription profile in the midgut and salivary gland of these two mosquito strains in response to ZIKV infection was examined and showed significant differences. A total of 59 DEGs that were simultaneously up- or downregulated in the midgut and salivary gland were screened out and were thought to influence the vector competence of Ae. albopictus. Particularly, CYP304a1 was significantly downregulated in both tissues and strains and might play a role in ZIKV–mosquito interactions, although it did not influence the virus infection and replication under the conditions set in this study. Our research provided new insights into mosquito–virus interactions, which could contribute to new strategy developments for arbovirus disease prevention.

PMC10002155

Saiyu Wang,Chengcheng Bai,Na Luo,Youwei Jiang,Yulu Wang,Yu Liu,Chunjie Chen,Yuxin Wang,Qiaoqiao Gan,Shurong Jin,Yu Ni

Brassica napus BnaC9.DEWAX1 Negatively Regulates Wax Biosynthesis via Transcriptional Suppression of BnCER1-2

21-02-2023

Brassica napus,cuticular wax biosynthesis,BnCER1-2,BnaC9.DEWAX1,transcription repressor

Very-long-chain alkane plays an important role as an aliphatic barrier. We previously reported that BnCER1-2 was responsible for alkane biosynthesis in Brassica napus and improved plant tolerance to drought. However, how the expression of BnCER1-2 is regulated is still unknown. Through yeast one-hybrid screening, we identified a transcriptional regulator of BnCER1-2, BnaC9.DEWAX1, which encodes AP2\ERF transcription factor. BnaC9.DEWAX1 targets the nucleus and displays transcriptional repression activity. Electrophoretic mobility shift and transient transcriptional assays suggested that BnaC9.DEWAX1 repressed the transcription of BnCER1-2 by directly interacting with its promoter. BnaC9.DEWAX1 was expressed predominantly in leaves and siliques, which was similar to the expression pattern of BnCER1-2. Hormone and major abiotic stresses such as drought and high salinity affected the expression of BnaC9.DEWAX1. Ectopic expression of BnaC9.DEWAX1 in Arabidopsis plants down-regulated CER1 transcription levels and resulted in a reduction in alkanes and total wax loads in leaves and stems when compared with the wild type, whereas the wax depositions in the dewax mutant returned to the wild type level after complementation of BnaC9.DEWAX1 in the mutant. Moreover, both altered cuticular wax composition and structure contribute to increased epidermal permeability in BnaC9.DEWAX1 overexpression lines. Collectively, these results support the notion that BnaC9.DEWAX1 negatively regulates wax biosynthesis by binding directly to the BnCER1-2 promoter, which provides insights into the regulatory mechanism of wax biosynthesis in B. napus.

Brassica napus BnaC9.DEWAX1 Negatively Regulates Wax Biosynthesis via Transcriptional Suppression of BnCER1-2 Very-long-chain alkane plays an important role as an aliphatic barrier. We previously reported that BnCER1-2 was responsible for alkane biosynthesis in Brassica napus and improved plant tolerance to drought. However, how the expression of BnCER1-2 is regulated is still unknown. Through yeast one-hybrid screening, we identified a transcriptional regulator of BnCER1-2, BnaC9.DEWAX1, which encodes AP2\ERF transcription factor. BnaC9.DEWAX1 targets the nucleus and displays transcriptional repression activity. Electrophoretic mobility shift and transient transcriptional assays suggested that BnaC9.DEWAX1 repressed the transcription of BnCER1-2 by directly interacting with its promoter. BnaC9.DEWAX1 was expressed predominantly in leaves and siliques, which was similar to the expression pattern of BnCER1-2. Hormone and major abiotic stresses such as drought and high salinity affected the expression of BnaC9.DEWAX1. Ectopic expression of BnaC9.DEWAX1 in Arabidopsis plants down-regulated CER1 transcription levels and resulted in a reduction in alkanes and total wax loads in leaves and stems when compared with the wild type, whereas the wax depositions in the dewax mutant returned to the wild type level after complementation of BnaC9.DEWAX1 in the mutant. Moreover, both altered cuticular wax composition and structure contribute to increased epidermal permeability in BnaC9.DEWAX1 overexpression lines. Collectively, these results support the notion that BnaC9.DEWAX1 negatively regulates wax biosynthesis by binding directly to the BnCER1-2 promoter, which provides insights into the regulatory mechanism of wax biosynthesis in B. napus. Drought is an environmental stress that limits the distribution of plants, affects plant growth and development, reduces crop productivity, and, therefore, leads to severe agroeconomic losses [1,2]. To withstand drought stress, plants have evolved many strategies to prevent water loss, to balance optimal water supply to all vital organs, and to maintain the cellular water content [2]. Land plants developed a hydrophobic cuticular wax layer that resists nonstomatal water loss, as well as various biotic and abiotic stresses [3,4,5]. Recent studies have focused on modifying cuticular waxes to improve plant tolerance to drought. For example, ectopic expression of wax-associated genes and transcription factors in transgenic plants can increase wax deposition and confer increased tolerance to water deficiency in some species [6,7,8,9]. Cuticular waxes are composed of very-long-chain fatty acids (VLCFAs) and their derivatives, such as aldehydes, alkanes, primary alcohols, secondary alcohols, ketones, and wax esters [10]. VLCFAs are elongated from C16 and C18 fatty acids by the fatty-acid elongase (FAE) complex in the endoplasmic reticulum (ER), and then converted to aldehydes, alkanes, secondary alcohols, and ketones via the alkane-forming pathway and primary alcohols and wax esters via the alcohol-forming pathway [10,11,12,13]. As the major wax compounds in Arabidopsis and many other species, alkanes contribute to cuticle characteristics associated with drought tolerance of the plant [14,15,16], and, therefore, are potential targets for crop improvement. It has been previously reported that CER1 interacts with both CER3 and CYTB5 to catalyze alkane formation, of which CER3 functions as a VLCFA-reductase-producing fatty aldehyde, whereas CER1 functions as an aldehyde-decarbonylase-producing n-alkane from aldehydes [14,17]. Furthermore, CER1-LIKE1 has been reported to be involved in the alkane-forming pathway with different acyl chain-length specificities from CER1 [18]. Some transcription factors involved in alkane formation have been reported in recent studies. The AP2/ERF-type transcription factor WIN1/SHN1 was first reported as a transcriptional activator that regulates cuticular wax biosynthesis and, therefore, improves plant drought tolerance; CER1, CER2, and KCS1 genes are regulated by WIN1/SHN1 [3]. DEWAX and DEWAX2, also members of AP2/ERF subfamily, have been found to negatively regulate cuticular wax biosynthesis in Arabidopsis by directly binding to the promoters of the cuticular wax biosynthetic gene LACS2 and CER1 [19,20]. Two Arabidopsis MYB-SHAQKYF transcription repressors regulate leaf wax biosynthesis via transcriptional suppression on DEWAX [21]. Furthermore, it has been found that SPL9 activates CER1 expression by directly binding to GTAC motifs in the CER1 promoter; meanwhile, SPL9 antagonistically acts with DEWAX to control CER1 expression and mediates light–dark, on–off switch-controlling wax synthesis [21]. An AP2/DREB transcription factor RAP2.4 was found to activate cuticular wax biosynthesis by increasing the expression of KCS2 and CER1 in Arabidopsis leaves under drought [22]. MYB transcription factors are also involved in the regulation of the alkane-formation pathway and plant tolerance to abiotic and biotic stresses. For example, MYB96 promotes wax biosynthesis in Arabidopsis by regulating the KCS1, KCS2, KCS6, KCR1, and CER3 genes under drought stress [23], whereas MIEL1 E3 ubiquitin ligase is involved in wax biosynthesis by controlling the protein stability of MYB96 and MYB30 [24]. Apart from transcriptional regulation, the alkane-forming pathway is also regulated at the post-transcriptional level and epigenetic level. For example, trans-acting small interfering RNAs (tasiRNAs) directly control CER3 expression at the post-transcriptional level and regulate stem wax deposition in Arabidopsis [25,26]. SAGL1 mediates proteasome-dependent degradation of CER3, thereby negatively regulating cuticular wax biosynthesis [27]. Histone H2B monoubiquitination has been reported to be involved in wax biosynthesis by targeting LACS2 and CER1 [28]. The Arabidopsis histone methyl transferases SDG8 and SDG25 contribute to wax accumulation through histone lysine methylation and/or H2B ubiquitination by targeting CER3 [29]. GCN5-mediated histone acetylation of CER3 also contributes to Arabidopsis cuticular wax biosynthesis [30]. As one of the most important oil crops worldwide, Brassica napus provides edible plant oil for humans and feed to animals; it also maintains soil fertility in crop rotations. Compared to other crops, B. napus is particularly susceptible to drought stress during the seedling and flowering stages and needs more water for its growth and development [31,32]. Therefore, improving the drought tolerance of B. napus has practical significance for increasing cultivation and stabilizing oilseed supply. A previous study reported that overexpression of BnCER1-2 in B. napus promotes the production of alkanes and total wax and increases plant tolerance to drought [33]. However, how the expression of BnCER1-2 in B. napus is regulated still remains unknown. With the goal of exploring the transcriptional regulation underlying the alkane-forming pathway in B. napus, we cloned the BnCER1-2 promoter fragment containing three GCC-like motifs as bait to perform a yeast-one-hybrid assay. The BnaC9.DEWAX1 gene, an Arabidopsis DEWAX ortholog in B. napus, was thereby identified. We revealed that BnaC9.DEWAX1 could repress the expression of the BnCER1-2 gene via direct binding to its promoter. Furthermore, overexpression of BnaC9.DEWAX1 in Arabidopsis inhibited alkane biosynthesis and total wax loads by down-regulating CER1 expression. Increased cuticle permeability in BnaC9.DEWAX1 transgenic Arabidopsis was observed and was attributed mainly to altered wax composition and crystallization in transgenic plants. BnCER1-2 is responsible for very-long-chain alkane biosynthesis of wax in B. napus [33]. To better understand the regulatory mechanisms underlying BnCER1-2 gene expression, we used a 195-bp BnCER1-2 promoter fragment containing three GCC-like motifs as bait in a yeast-one-hybrid assay. The result showed that BnaC09g05370D could interact with the BnCER1-2 promoter (Figure S1). Sequence analysis showed that BnaC09g05370D contained a typical AP2 DNA-binding domain and an acidic region in its N-terminus, and a predicted nuclear localization signal (NLS) in its C-terminus (Figure S2A). Phylogenetic analysis suggested that BnaC09g05370D was orthologous to Arabidopsis DEWAX (Figure S2B); therefore, it was designated as BnaC9.DEWAX1. Transient transfection of tobacco leaf with the cauliflower mosaic virus (CaMV) 35S promoter-driven eGFP:BnaC9.DEWAX1 construct showed that BnaC9.DEWAX1 was localized in the nucleus (Figure 1A,B), which was consistent with the predicted NLS in BnaC9.DEWAX1. To determine whether BnaC9.DEWAX1 harbors transcriptional activity, an effector construct which harbors BnaC9.DEWAX1 and the Gal4 DNA-binding domain (BD) and a reporter construct which contains a Gal4-binding site (BS) and a CaMV 35S minimal promoter-driven luciferase (LUC) were used in a transactivation assay with Arabidopsis protoplasts. Two other effectors, a BD vector and a VP16 vector which harbors a transcriptional activation domain, were used as control in transactivation assay (Figure 1C). As shown in Figure 1D, BnaC9.DEWAX1 was able to suppress 50% transcriptional activation by VP16. These results suggested that BnaC9.DEWAX1 could function as a transcriptional repressor. To further confirm that BnaC9.DEWAX1 binds to the BnCER1-2 promoter, we expressed the BnaC9.DEWAX1 protein as a GST fusion in E. coli, and the purified recombinant proteins were then used for an electrophoretic mobility shift assay (EMSA). Meanwhile, a 195-bp BnCER1-2 promoter fragment used in the yeast-one-hybrid assay was labeled for EMSA (Figure 2A). As shown in Figure 2B, the recombinant BnaC9.DEWAX1 protein was able to bind to the BnCER1-2 promoter fragment and caused a retarded band. In contrast, no retarded band was detected when the BnCER1-2 promoter fragment was incubated with GST protein alone. The addition of an unlabeled BnCER1-2 promoter fragment weakened the retarded band due to competition with the binding (Figure 2B). These results suggested that the BnaC9.DEWAX1 protein could bind to the BnCER1-2 promoter fragment. In subsequent co-transfection of Arabidopsis leaf protoplasts with the CaMV 35S promoter-driven BnaC9.DEWAX1 expression construct and the BnCER1-2 promoter-driven LUC reporter gene, the expression of BnaC9.DEWAX1 resulted in a substantial reduction in the expression of the reporter gene LUC, indicating that BnaC9.DEWAX1 was able to transcriptionally repress the expression of BnCER1-2 promoter (Figure 2C,D). To investigate the expression of the BnaC9.DEWAX1 gene in B. napus and its response to various abiotic stresses, total RNA was isolated from various B. napus organs and leaves treated by abiotic stresses and subjected to quantitative RT-PCR. The BnaC9.DEWAX1 gene was strongly expressed in leaves, siliques, and late developmental seeds, whereas it was least expressed in roots and early-stage seeds (Figure 3A). The expression level of BnaC9.DEWAX1 was up-regulated in response to SA, ACC, and NaCl stress, whereas it was down-regulated by MeJA, ABA, drought, and cold stresses (Figure 3B). The transcriptional repression of BnaC9.DEWAX1 on the expression of BnCER1-2 prompted us to investigate whether BnaC9.DEWAX1 regulated VLC-alkane biosynthesis and wax load. We generated transgenic Arabidopsis lines that overexpress BnaC9. DEWAX1 under the control of the CaMV 35S promoter in the wild type and the dewax mutant, respectively. The positive transgenic lines were identified by amplification of an 35S:BnaC9.DEWAX1 fragment and hygromycin antibiotic resistance. Compared with the WT and mutant, the growth and development of transgenic plants were delayed to varying degrees (Figure 4A). qRT-PCR analysis showed that the expression of BnaC9.DEWAX1 increased significantly in the overexpression lines OX#2, OX#3, and OX#5 (Figure 4B), whereas the expression of CER1 was decreased by approximately 57–77% in overexpressing lines relative to the WT (Figure 4C). Relative to the dewax, the expression of BnaC9.DEWAX1 in the complementation lines C#4, C#6, and C#9 increased significantly, whereas the expression of CER1 decreased significantly in complementation lines (Figure 4B,C). These results suggested that BnaC9.DEWAX1 negatively regulates the expression of CER1 involved in Arabidopsis cuticular wax biosynthesis. Overexpression of BnaC9.DEWAX1 in A. thaliana WT significantly reduced the contents of alkanes, secondary alcohols, ketones, primary alcohols, and total wax on stems (Figure 5A), and the contents of fatty acids, alkanes, and total wax on leaves (Figure 5C). For wax components, BnaC9.DEWAX1 gene overexpression mainly decreased the contents of C29 alkanes, C29 secondary alcohol, and C29 ketones, the three predominant compounds in total wax, as well as C28 aldehyde and C28 primary alcohols on the stem (Figure 5B). For leaf, BnaC9.DEWAX1 gene overexpression mainly decreased the contents of C26 fatty acid and C29, C31, and C33 alkanes (Figure 5D). As described previously [19], the dewax mutant plants showed higher contents of wax compositions and total wax on both stem and leaves compared with the WT. When the BnaC9.DEWAX1 gene driven by the CaMV 35S promoter was expressed in Arabidopsis dewax plants, no significant differences in wax depositions on stems and leaves were observed between the complementation lines and the WT (Figure 5). To examine whether altered amount of cuticular wax of BnaC9.DEWAX1 overexpression lines affected plant wax structure and cuticle permeability, we compared the epicuticular wax morphology between WT and overexpression lines. As Figure 6A shown, the overexpression lines showed reduced crystals on stems when compared with the WT. Next, we evaluated leaf cuticle permeability by water loss measurement and chlorophyll extraction. Compared with the WT, the leaves from the overexpression lines showed increased water loss rate and extracted chlorophylls rate (Figure 6B,C), suggesting that overexpression of BnaC9.DEWAX1 led to increased cuticle permeability. Cuticular waxes covering the plant’s outermost layer serve as the first protection barrier against environmental stresses, and its biosynthesis is tightly regulated by development and environmental factors [4]. However, the genetic control of wax biosynthesis in B. napus is less understood. B. napus is one of the most important oil crops worldwide. During the seedling and flowering stage, B. napus is sensitive to water shortage. We previously reported that overexpression of BnCER1-2 in B. napus promotes the production of alkanes and total wax and increases plant tolerance to drought [33]. BnCER1-2 is homologous to Arabidopsis CER1 and functions as an aldehyde decarbonylase producing n-alkanes from aldehydes [14]. Nonetheless, how the expression of BnCER1-2 in B. napus is regulated remains unknown. Transcriptional regulation is one of the most important strategies for regulating plant stress responses [34]. In this study, we identified an ERF-type transcription factor BnaC9.DEWAX1, one new component in B. napus wax biosynthesis. Sequence and phylogenetic analysis suggested that BnaC9.DEWAX1 is a member of the ERF subfamily B-3 of ERF/AP2 transcription factor family and is homologous to Arabidopsis DEWAX. Arabidopsis DEWAX is reported to bind to the classic GCC motifs or variants in the promoters of wax genes such as CER1, FAR6, LACS2, ACLA2, and ECR [19]. As shown in Figure 2 and Figure S1, BnaC9.DEWAX1 suppressed BnCER1-2 expression by directly binding on its promoter regions that harbor three GCC-like motifs. Transcription activity analysis suggested that BnaC9.DEWAX1 was a transcriptional repressor, though no EAR repression motif was found in BnaC9.DEWAX1. These results suggest that BnaC9.DEWAX1-mediated repression of BnCER1-2 transcription may contribute to the wax deposition in B. napus. BnaC9.DEWAX1 was predominantly expressed in leaves, siliques, and late developmental seeds but was not detected in roots (Figure 3A), which was similar to the expression pattern of BnCER1-2 being in mainly leaves and siliques [33], suggesting that BnaC9.DEWAX1 may be involved in the biosynthesis of leaf and silique wax. To prove the function of BnaC9.DEWAX1 in wax biosynthesis, transgenic Arabidopsis lines overexpressing BnaC9.DEWAX1 and complementation lines were generated. The CER1 expression levels were correlated with the altered wax loads in BnaC9.DEWAX1 overexpression and complementation plants (Figure 4). BnaC9.DEWAX1 gene overexpression mainly reduced the contents of C29 alkanes, C29 secondary alcohol, and C29 ketones, the three predominant compounds produced from the alkane-forming pathway, thereby reducing the total wax loads (Figure 5A,B). This mechanism could be due to down-regulated expression of CER1, which is responsible for alkane biosynthesis (Figure 4C). As reported previously [19], the Arabidopsis dewax mutant showed an increase in total leaf and stem wax loads. In this study, the excess wax phenotype of dewax was restored to wild type levels by overexpressing BnaC9.DEWAX1 in dewax mutant. Both promoters of BnCER1-2 and CER1 contain a GCC-like motif (Table S1). These results suggested that BnaC9.DEWAX1 negatively regulated alkane/wax biosynthesis, and this effect was achieved by inhibiting the expression of CER1/BnCER1-2. The overexpression of the BnaC9.DEWAX1 gene in Arabidopsis also reduced cuticle permeability (Figure 6B,C). Quantitative analysis of cuticular wax revealed that the amounts of long-chain alkanes and total waxes in both the leaf and stem of overexpressing lines were significantly lower than that in wild type plants (Figure 5). Currently, n-alkanes are one of the few wax components with an established specific contribution to cuticle properties. The accumulation of the n-alkanes and the reduction in cuticle permeability led to a better plant resistance to water stress [14,16,35]. Excepting the reduction in alkanes and total wax loads, overexpression of BnaC9.DEWAX1 also resulted in a reduction in the density of wax crystals on Arabidopsis stem (Figure 6A). The formation of three-dimensional epicuticular wax crystallites can significantly increase contact angles, which renders leaf surfaces essentially non-wettable [36]. Thus, both altered cuticular wax composition and structure contribute to increased epidermal permeability in BnaC9.DEWAX1 overexpression lines. Additionally, altered expression levels of BnaC9.DEWAX1 under abiotic stress and hormone treatments indicated the role of BnaC9.DEWAX1 in responses of plants to major abiotic stresses such as drought and high salinity (Figure 3B). Overall, these results suggested that the alkane synthesis was regulated by BnaC9.DEWAX1-BnCER1-2/CER1 module, which controlled water loss and played an important role in plant response to water deficiency. In summary, we identified a transcription repressor BnaC9.DEWAX1, which negatively regulated wax biosynthesis by suppressing BnCER1-2 expression. The results provide insight into understanding the regulatory mechanisms controlling cuticular wax accumulation in B. napus and also provide a promising target gene for improving the drought tolerance of rapeseed. B. napus cultivars Zhongshuang11 (ZS11) were used for gene cloning and gene expression analysis. The Arabidopsis T-DNA insertion mutant dewax (SALK_015182C) was obtained from the ABRC (http://www.arbidopsis.org; accessed on 31 March 2021). All plants (Arabidopsis, B. napus, Nicotiana tabacum) were grown on agar plates or in soil in a growth chamber (16 h light and 8 h darkness at 22 °C). To examine the effects of growth hormones and stress conditions on gene expression, leaves were harvested from eight-week-old plants (ZS11) at 6 h after incubation in a solution containing 100 μM SA, 10 μM MeJA, 10 μM ACC, 10 μM ABA, 1 μM IAA, or 150 mM NaCl. For drought stress, plants were air-dried for 6 h. For cold stress, plants were placed in a cold room (4 °C) for 6 h. Leaves from plants without treatments were used as control. To generate transgenic Arabidopsis overexpressing BnaC9.DEWAX1, the BnaC9.DEWAX1 coding sequence was amplified from B. napus using gene-specific primers (Table S2) and inserted between BamHI and HindIII sites of the pC1301-DsRED vector with the CaMV 35S promoter via homologous recombination. The recombinant vector was transformed into Arabidopsis wild type and mutant via the Agrobacterium-mediated floral-dip method [37]. Transgenic seeds were selected via fluorescence of a DsRed marker [38]. Furthermore, transgenic homozygous lines were selected via Hygromycin antibiotic resistance and verified via PCR genotyping from the T2 generation and confirmed in the T3 generation. Relative expression of genes was determined using quantitative realtime PCR. Total RNA was extracted from samples using a Total RNA Extraction Kit (Promega), and then 1.5 μg total RNA was reverse transcribed to first-strand cDNA using cDNA Synthesis SuperMix (Transgen). qRT-PCR was performed using a CFX96 System (Bio-Rad) with SYBR Premix Ex Taq (TaKaRa) according to the manufacturer’s instructions. For the real-time PCR, the following program was used: 95 °C for 30 s, 45 cycles of 95 °C 10 s, 58 °C for 30 s, and 65 °C for 10 s. The expression levels of target genes were normalized to that of EF1a in Arabidopsis and BnActin 7 in B.napus, respectively. The primer pairs used for real-time PCR are listed in Table S2. Three biological and three technical replicates were carried out for each sample. To perform yeast one-hybrid assays, a 195-bp BnCER1-2 promoter fragment containing three GCC-like motifs was cloned into the pAbAi bait vector, then transformed to Y1HGold yeast competent cell to generate a bait-specific reporter strain. The BnaC9.DEWAX1 coding sequence was cloned into the pGADT7 vector which harbors the GAL4 transcription activation domain (GAL4 AD) and then transformed to the Y1H Gold bait reporter strain. The colonies that can grow on synthetic defined (SD) medium in the absence of leucine (Leu) and containing AbA antibiotic were classified as protein–DNA interactions. Furthermore, the obtained yeast colonies were resuspended in water at equal densities, then serially spotted onto SD medium (-Leu/AbA) to check differential growth. pGADT7-53 was transformed into a p53-AbAi reporter strain as positive control, whereas it was transformed into the reporter strain containing the BnCER1-2 promoter fragment as negative control. The BnaC9.DEWAX1 coding sequence was inserted between EcoRI and BamHI sites of the pBI121 vector, which harbors the CaMV 35S promoter, eGFP, and the terminator of the nopaline synthase (Nos) gene. The generated eGFP:BnaC9.DEWAX1 plasmid was co-transfected with the nuclear marker mCherry-D53 into leaves of Nicotiana tabacum, as described previously [39]. The nuclear marker OsD53 fused with mCherry was used as positive control [40]. The GFP fluorescence was observed via a Zeiss LSM 780 confocal laser scanning microscope (Carl Zeiss, Germany) after incubation in the dark for 24 h. The activity of the BnaC9.DEWAX1 transcription factor was investigated using a transient expression system with Arabidopsis protoplasts. The BnaC9.DEWAX1 coding sequence was fused with the GAL4 DNA-binding domain (GAL4 BD) under the control of the 35S promoter to produce the 35S:BD-BnaC9.DEWAX1 effector construct. The 35S:BD-VP16 construct, which fuses the herpes virus protein VP16 activation domain to the GAL4 BD, and the empty 35S:BD construct were separately used as positive or negative control. The reporter construct harbors a LUC gene driven by the minimal TATA box plus five GAL4-binding elements and the CaMV 35S promoter. The pRL-TK vector, which harbors the Renilla luciferase gene driven by the CaMV 35S promoter, was used as an internal control. The effector, reporter, and internal control were simultaneously transformed into the Arabidopsis leaf protoplast cells, then kept in the dark for 16 h. The activities of LUC and REN were measured with a Dual-Luciferase Reporter Assay System and luminescence reader (GROMAX-20/20, Promega). Relative ratios of LUC/REN were used to evaluate the transcriptional activity of BnaC9.DEWAX1. The primers used are listed in Supplementary Table S1. To analyze the transcriptional repression activity of BnaC9.DEWAX1 on the promoter of the BnaCER1-2 gene, the BnaC9.DEWAX1 coding sequence was cloned into the pSKII plant expression vector containing the 35S promoter and Nos terminator. The same BnaCER1-2 promoter fragment used in the Y1H assay was cloned in the reporter vector pGreenII 0800-LUC under the control of the 35S promoter. Then, the reporter construct was transfected into the Arabidopsis protoplast together with an effector construct or empty pSKII vector. The transformed protoplasts were incubated under dark conditions for 14–18 h. The dual-luciferase assay was performed as described by the manufacturer (Dual-Luciferase® Reporter Assay, Promega, Madison, WI, USA), and the ratio of LUC/REN activity was detected using a multimode microplate reader (GloMax-20/20, Promega). Primers are listed in Supplemental Table S2. The BnaC9.DEWAX1 fused with GST was expressed in E. coli at 18 °C for 8 h with the induction of 0.1 mM isopropyl-beta-D-thiogalactopyranoside (IPTG), and the recombinant protein was purified using Glutathione Sepharose beads according to the manufacturer’s procedure. The 195-bp BnaCER1-2 promoter fragment which contains three GCC-like motifs that was used in the Y1H assay was labeled with biotin and then incubated with purified GST-BnaC9.DEWAX1 protein for 30 min at room temperature using the Chemiluminescent EMSA Kit (Beyotime, Shanghai, China). The reaction mixtures were separated by 6% native polyacrylamide gel electrophoresis and transferred to a nylon membrane (0.45 µm). After the membrane is crosslinked, the biotin end-labeled DNA is detected by Chemiluminescence. The extraction of Arabidopsis cuticular wax was performed as previously described with minor modifications [19]. Briefly, cuticular waxes were extracted from the stem and rosette leaves of six-week-old Arabidopsis plants by immersing them for 30 s in 5 mL chloroform solution containing 10 μg tetracosane as an internal standard. The extracts were transferred to vials; dried under nitrogen gas; derivatized by adding 20 μL of N, N-bis-trimethylsilyltrifluoroacetamide (BSTFA) and 20 μL of pyridine; and incubated for 45 min at 70 °C. Wax components were identified by GC-MS and quantified via gas chromatograph coupled to a flame ionization detector (GC–FID). The GC analysis was carried out with a 9790II gas chromatograph (Fu-Li, Hangzhou, China). The GC column was a DM-5 capillary column (30 m × 0.32 mm × 0.25 µm). Nitrogen served as the carrier gas. The GC oven was held at 80 °C for 10 min, heated at 5 °C/min to 260 °C, where the temperature remained 10 min. The temperature was then heated at 2 °C/min to 290 °C, and further heated at 5 °C/min to 320 °C, where the temperature was held for 10 min. Compounds were detected with a GCMS-QP2010 Ultra Mass Spectrometric Detector (Shimadzu Corp., Kyoto, Japan) using an HP-5 MS capillary column (30 m × 0.32 mm × 0.25 µm), and He served as the carrier gas. Compounds were identified by comparing their mass spectra with published data and authentic standards. Three biological replicates per genotype were performed. Five plants were used for each replicate. The amounts of wax were expressed per dry weight (µg mg−1). To view the epicuticular waxes, air-dried inflorescence stem sections from six-week-old Arabidopsis plants were mounted onto standard aluminum stubs, sputter-coated with gold particles with a Polaron SC-500 (Quorum, Lewes, UK), then viewed with a Quanta 200 microscope (FEI, Eindhoven, The Netherlands). Cuticle permeability was measured as described previously with minor modifications [35]. The six-week-old plants were dark acclimated for 3 h to ensure stomatal closure, and then the leaves were immersed into distilled water for 1 h and weighed. Next, the leaf samples were placed into a dark chamber for continuous dehydration, and their weights were determined at 15 min intervals for 120 min to record the water loss. Then, leaf samples were dried at 70 °C for 24 h and weighed. The water loss percentage was calculated as follows: Water loss (%) = (saturated weight—fresh weight)/ (saturated weight—dry weight) × 100%. For the chlorophyll leaching assay, leaves were detached, weighed, and soaked in 80% ethanol with shaking at 40 rpm at room temperature [41]. Aliquots of 500 μL were drawn from the solution at the individual time points and quantified for extracted chlorophyll amount by measuring the absorbance at 647 and 664 nm using an ultraviolet (UV) DU7300 spectrophotometer. Total micromoles of chlorophyll = 7.93 (A664) + 19.53 (A647). Data are expressed as percentages of the total chlorophyll extracted after 24 h in 80% ethanol. Five biological replicates per genotype were performed, and three plants were used for each replicate. BnaC9.DEWAX1 and the ERF subfamily proteins in Arabidopsis were aligned with ClustalX 2.1, and then the phylogenetic tree was generated using the neighbor-joining (N-J) method via MEGA 7.0 with bootstrap values of 1000 trials [42]. Statistical analysis was performed using the Student’s t-test (* p < 0.05; ** p < 0.01; *** p < 0.001). The data were presented as means ± SD of at least three biological replicates.

PMC10002156

Georgy A. Nevinsky,Andrey E. Urusov,Kseniya S. Aulova,Evgeny A. Ermakov

Experimental Autoimmune Encephalomyelitis of Mice: IgGs from the Sera of Mice Hydrolyze miRNAs

23-02-2023

C57BL/6 mice,EAE model of human multiple sclerosis,immunization with MOG,catalytic antibodies,hydrolysis of RNAs and micro-RNAs

It was shown that the spontaneous development of experimental encephalomyelitis (EAE) in C57BL/6 mice occurs due to changes in the profile of bone marrow stem cells differentiation. This leads to the appearance of lymphocytes producing antibodies-abzymes that hydrolyze DNA, myelin basic protein (MBP), and histones. The activity of abzymes in the hydrolysis of these auto-antigens slowly but constantly increases during the spontaneous development of EAE. Treatment of mice with myelin oligodendrocyte glycoprotein (MOG) leads to a sharp increase in the activity of these abzymes with their maximum at 20 days (acute phase) after immunization. In this work, we analyzed changes in the activity of IgG-abzymes hydrolyzing (pA)23, (pC)23, (pU)23, and six miRNAs (miR-9-5p, miR-219a-5p, miR-326, miR-155-5p, miR-21-3p, and miR-146a-3p) before and after mice immunization with MOG. Unlike abzymes hydrolyzing DNA, MBP, and histones, the spontaneous development of EAE leads not to an increase but to a permanent decrease of IgGs activity of hydrolysis of RNA-substrates. Treatment of mice with MOG resulted in a sharp but transient increase in the activity of antibodies by day 7 (onset of the disease), followed by a sharp decrease in activity 20–40 days after immunization. A significant difference in the production of abzymes against DNA, MBP, and histones before and after mice immunization with MOG with those against RNAs may be since the expression of many miRNAs decreased with age. This can lead to a decrease in the production of antibodies and abzymes that hydrolyze miRNAs with age mice.

Experimental Autoimmune Encephalomyelitis of Mice: IgGs from the Sera of Mice Hydrolyze miRNAs It was shown that the spontaneous development of experimental encephalomyelitis (EAE) in C57BL/6 mice occurs due to changes in the profile of bone marrow stem cells differentiation. This leads to the appearance of lymphocytes producing antibodies-abzymes that hydrolyze DNA, myelin basic protein (MBP), and histones. The activity of abzymes in the hydrolysis of these auto-antigens slowly but constantly increases during the spontaneous development of EAE. Treatment of mice with myelin oligodendrocyte glycoprotein (MOG) leads to a sharp increase in the activity of these abzymes with their maximum at 20 days (acute phase) after immunization. In this work, we analyzed changes in the activity of IgG-abzymes hydrolyzing (pA)23, (pC)23, (pU)23, and six miRNAs (miR-9-5p, miR-219a-5p, miR-326, miR-155-5p, miR-21-3p, and miR-146a-3p) before and after mice immunization with MOG. Unlike abzymes hydrolyzing DNA, MBP, and histones, the spontaneous development of EAE leads not to an increase but to a permanent decrease of IgGs activity of hydrolysis of RNA-substrates. Treatment of mice with MOG resulted in a sharp but transient increase in the activity of antibodies by day 7 (onset of the disease), followed by a sharp decrease in activity 20–40 days after immunization. A significant difference in the production of abzymes against DNA, MBP, and histones before and after mice immunization with MOG with those against RNAs may be since the expression of many miRNAs decreased with age. This can lead to a decrease in the production of antibodies and abzymes that hydrolyze miRNAs with age mice. Multiple sclerosis (MS) is a chronic autoimmune disease, the pathogenesis of which is characterized by the demyelination (plaques) of the gray and white matter of the brain and spinal cord, leading to neurodegeneration and brain atrophy [1,2]. The etiology of multiple sclerosis is still unclear; the most accepted theory of pathogenesis assigns the central role to the destruction of myelin-proteolipid shell axons resulting in inflammation bound with important autoimmune reactions ([3] and references therein). Natural abzymes (ABZs) splitting various oligosaccharides, lipids, peptides, proteins, DNAs, and RNAs, were revealed in the blood of patients with some autoimmune diseases (AIDs) and viral pathologies [4,5,6,7,8,9,10]. ABZs with insignificant activities splitting thyroglobulin [10], polysaccharides [11], and vasoactive neuropeptide [12] were revealed in the sera of some conditionally healthy volunteers. However, the blood of healthy people usually does not contain abzymes [4,5,6,7,8,9,10,13]. Similar to systemic lupus erythematosus (SLE) [9], the blood of MS patients contains abzymes hydrolyzing DNAs and RNAs [13,14,15,16], myelin basic protein (MBP) [17,18,19,20], histones [21], and oligosaccharides [12]. Relative activities (RAs) of IgGs from the cerebrospinal fluids degrading polysaccharides, MBP, and DNAs are, on average, from 35 to 60 times higher than those from the blood of the same MS patients [22,23,24]. MS is a multifactorial disease, the pathogenesis of which could depend on many various factors [25]. Micro-RNAs are small (22–25 nucleotides) non-coding RNAs participating in the post-transcriptional regulation of many genes [26,27], including transcription and neuroinflammation [26,27,28]. In MS and SLE, specific miRNAs characterized by increased expression are revealed in the cerebrospinal fluid and blood [28,29,30,31]. The extracellular miRNAs participate in signaling between cells and regulating neurogenesis, angiogenesis, and cell proliferation [32]. The change in miRNA expression in many cases is associated with pathological processes. As a result of inflammatory processes in SLE and MS, specific miRNAs’ processing, transcription, or maturation can be changed. Some miRNAs may be biomarkers of AIDs [33]. Thus, eliciting additional essential factors of MS pathogenesis and a possible role of miRNAs may be important. It is possible that not only miRNAs but also antibodies and abzymes against miRNA can also have a specific role in the pathogenesis of MS. The level of abzymes with different activities in patients with MS and other AIDs varies significantly from patient to patient [4,5,6,7,8,9,10]. However, it is difficult to accurately assess which factors, in the case of patients with AIDs, are the main ones in the development of pathologies. Several experimental autoimmune encephalomyelitis (EAE) mice models well mimic a specific facet of human MS are known (for review, see [34,35]). Analysis of the patterns of development of EAE in the case of two models of EAE-prone mice (C57BL/6 [36,37,38,39] and Th [40,41]) and one model of mice prone to systemic lupus erythematosus (MRL-lpr/lpr [42,43,44]) made it possible to identify several common factors essential for the development of AIDs. It was shown that the spontaneous and antigen-induced development of EAE [36,37,38,39,40,41] and SLE [42,43,44] occurs first of all due to specific changes in differentiation profiles of bone marrow hematopoietic stem cells (HSCs) associated with an increase in lymphocyte proliferation and apoptosis repression in different organs of these mice [36,37,38,39,40,41,42,43,44,45]. Changes in differentiation profiles in EAE and SLE mice during the development of these pathologies are very similar [36]. These changes in EAE-prone mice lead to the appearance of B lymphocytes producing catalytically active antibodies that hydrolyze DNA, MBP, mouse myelin oligodendrocyte glycoprotein peptide (MOG), and five histones (H1-H4) [36,37,38,39,40,41]. In the case of SLE-prone MRL-lpr/lpr mice, during the process of spontaneous and DNA-induced development of SLE was shown appearance in the blood of abzymes hydrolyzing DNA, ATP, and oligosaccharides [42,43,44]. Anti-DNA antibodies (Abs) in SLE, MS, and other AIDs are usually directed against nucleosomal histone-DNA complexes [45]. ABZs with DNA-hydrolyzing activity are cytotoxic: they penetrate the nucleus of the cells and split nuclear DNA leading to cell apoptosis [46,47], which increases concentrations of DNA and its complexes with histones in the blood and acceleration AIDs development [7,8,9,10]. In MS, Abs with MBP-hydrolyzing activity can attack MBP of the myelin-proteolipid sheath of the nerve tissue membranes, leading to a disruption of nerve impulses and providing a harmful role of such ABZs in MS pathogenesis [5]. Abzymes with micro-RNA hydrolyzing activity are found in the blood of patients with MS [48], SLE [49], and schizophrenia [50,51]. Several miRNAs regulate neuroinflammation and are characterized by impaired expression in MS [28]. Antibodies from the blood of MS patients efficiently hydrolyze these miRNAs [48]. A comparison of the development of MS in humans and EAE mice indicates that these processes are substantially similar. Therefore, it was expected that abzymes from the blood of C57BL/6 mice could hydrolyze miRNAs similar to IgGs of patients with MS. It was shown that C57BL/6 mice are characterized by a very slow spontaneous and MOG-induced development of EAE [33,34,35,36,37,38,39,40,41]. Some typical indicators of EAE development (optic neuritis and other clinical or histological evidence) appear in C57BL/6 mice only 1–2 years after spontaneous or MOG-accelerated evolution of EAE [33,34,35,36,37,38,39,40,41]. The appearance of auto-Abs hydrolyzing DNA, proteins and oligosaccharides was revealed as the earliest and statistically significant and undoubtedly important marker of the beginning of many autoimmune diseases in humans and mice prone to AIDs (for review, see [5,6,7,8,9,35,36,37,38,39,40,41]). Enzymatic activities of abzymes are veraciously detectable before the appearance of typical known medical and biochemical markers of different AIDs at the pre-disease stage [5,6,7,8,9,35,36,37,38,39,40,41]. At the pre-disease stage and onset of different AIDs, the concentrations of different auto-Abs usually correspond to the indices spans, which are typical for healthy humans and experimental mice. The emergence of abzymes may authentically testify about the beginning of AIDs, while the increase in their enzymatic activities is coupled with the development of deep pathologies [5,6,7,8,9,35,36,37,38,39,40,41]. In this work, we analyzed the changes in the catalase activity of antibodies at the early stages of the development of EAE in C57BL/6 mice. As shown earlier, during the spontaneous development of EAE, there is a relatively slow but gradual increase in hydrolysis efficiency by antibodies of DNA, MBP, MOG, and five histones [35,36,37,38,39,40]. Immunization of mice with MOG leads to a significant acceleration in EAE development and a sharp substantial increase in the activity of all abzymes. In this case, after mice immunization, three main stages of EAE development can be distinguished: the onset (6–8 days), the acute phase (18–20 days), and remission (>26–30 days). Already for 7 days, there is a significant increase in the activities of abzymes, which achieve maximum significance in the acute phase, and in the stage of remission, there may be a slight or moderate decrease in their activities [35,36,37,38,39,40]. Therefore, the analysis of miRNA hydrolysis by abzymes of EAE mice can provide additional opportunities for understanding at what stages of this disease development the accumulation of abzymes against miRNAs can occur. Considering the meaningful role of miRNAs in the proliferation, differentiation, and maturation of different neuronal cells and the possible role of miRNAs in the development of MS, we study the miRNA-hydrolyzing activity of IgGs of EAE-prone C57BL/6 mice in time before and after their immunization with MOG. The substrate specificity of mice antibodies in the miRNA splitting was compared with that of MS patients. It was of interest whether, as for IgGs of patients with MS, SLE, and schizophrenia [48,49,50,51], antibodies from the blood of EAE-prone C57BL/6 mice can exhibit RNase activity. The EAE development in C57BL/6 mice occurs spontaneously and may be accelerated by immunizing mice with MOG [35,36,37,38,39,40,41]. In addition, it was important to understand at what stages of mice EAE development abzymes that hydrolyze some micro-RNAs may appear. To study RNase activity of mice IgGs, we have used IgGs from the blood plasma of C57BL/6 mice corresponding spontaneous and MOG-induced EAE development. It is known that catalysis of various reactions by enzymes and abzymes occurs only after the formation of their specific complexes with substrates. First, it was shown that even IgGs of 3-months old mice possess RNase activity. The IgGmix (14 µg; a mixture of 14 mice preparations) was subjected to SDS-PAGE under non-reducing conditions without DTT. IgGmix was electrophoretically homogeneous (Figure 1A). The relative RNase activity (RA, %) in the hydrolysis of miRNA was estimated using eluates of 2–3 mm gel fragments (Figure 1B). The positions of RNase activity correspond to gel fragments containing intact IgGs, with no other protein bands or peaks of catalytic activities. Canonical RNases have vastly lower molecular masses (13–15 kDa) than IgGs (150 kDa). Thus, the coincidence of the positions of RNase activity peak and protein band of IgGs directly indicates that mice IgGmix hydrolyze miRNA, and it is not contaminated with classical RNases. Previously, we have shown that IgGs of CBA and BALB mice not prone to the development of autoimmune diseases do not have catalytic activities in the hydrolysis of DNA and RNA [35,36,37,38,39,40,41,42,43,44]. IgG antibodies of these lines of mice, isolated using the method developed by us to obtain antibodies containing no impurities of canonical enzymes, were used as a control. To analyze the sites of RNAs hydrolysis, we used three fluorescently 5′-labeled homo-oligonucleotides (ONs) 5′-Flu-(pC)23, 5′-Flu-(pU)23, and 5′-Flu-(pA)23, as well as six 5′-Flu-miRNAs; two neuroregulatory miRNAs (miR-219a-5p, miR-9-5p) and four of immunoregulatory miRNAs (miR-21-3p, miR-146a-3p, miR-155-5p, and miR-326) characterized by impaired expression in patients with human multiple sclerosis [28]. Several preparations of IgGs from different 3-months-old mice (zero time; the beginning of the experiment) corresponded to the spontaneous development of EAE disease during 40 days after the beginning of the experiment and 7–37 days after their immunization with MOG. As an example, Figure 2 shows typical data of hydrolysis homo-oligonucleotides (ONs) by some of the 35 IgG preparations used and IgGs of CBA and BALB mice. All IgGs hydrolyze 5′-Flu-(pC)23, 5′-Flu-(pU)23, and 5′-Flu-(pA)23, almost non-specifically at nearly all their internucleoside bonds. 5′-Flu-(pC)23 was hydrolyzed, leading mainly to short ONs (Figure 2A). In the case of 5′-Flu-(pU)23, formation ONs of completely different lengths with comparable efficiency (Figure 2B). The maximum rate of hydrolysis was observed for (pC)23 and significantly lower for (pU)23; (pA)23 hydrolysis was very weak. To detect (pA)23 hydrolysis, a higher concentration of IgGs and longer incubation times were used (Figure 2C). Incubation of (pA)23 with antibodies for 7–10 h leads to the formation of hydrolysis products with the formation of patterns similar to those for(pU)23. Under the conditions used, (pC)23, which is the best substrate for IgGs of C57BL/6 mice was not hydrolyzed by antibodies from CBA and BALB mice not prone to spontaneous development of autoimmune diseases before and 20 days after their immunization with MOG (Figure 2A). IgGs from plasma of CBA and BALB mice do not have RNase activity. Unlike homo-oligonucleotides, antibody-dependent hydrolysis of six miRNAs was site-specific but by varying degrees. Taking into account the different rates of various miRNAs cleavage by different IgGs to determine the common sites of hydrolysis of each miRNA, IgGs were used at different concentrations. The identification of major, moderate, and weak hydrolysis sites was carried out based on averaged data for sites of miRNAs cleavage by all 35 IgG preparations of IgGs corresponding to different stages of EAE development. The hydrolysis of miR-326 was exclusively site-specific with all 35 preparations (7 preparations corresponding to each time of blood sampling after immunization of mice with MOG: 0, 7, 12, 23, and 37 days). Figure 3A demonstrates typical patterns of miR-326 splitting by 13 of 35 preparations. During the hydrolysis of this micro-RNA, only one major product (site of the hydrolysis G10-C11) and one minor (C18-A19) product were formed. Some miRNAs’ hydrolysis efficiency could be very different for various IgG preparations. Figure 3B shows the data of miR-219a-5p hydrolysis by different IgGs at the same concentration. One can see that one of the preparations (lane 6) hydrolyzes this miRNA much more efficiently than other IgGs. Nevertheless, all 35 preparations showed five hydrolysis sites, two of which were major (C9-A19 and A10-A11), two minor (U5-G6 and G6-U7), and one, according to the averaged data for 35 preparations, can be attributed to the moderate site (C15-C15). As in the case of homooligonucleotides, IgG antibodies of CBA and BALB mice not prone to autoimmune diseases did not hydrolyze heterooligonucleotides Flu-miR-326 and Flu-miR-219-5p before and after mice immunization with MOG (Figure 3A,B). Figure 4 shows sites of hydrolysis by IgGs of Flu-miR-21-3p and Flu-miR-155-5p micro-RNAs. Six specific hydrolysis sites were found for hydrolysis of -21-3p with all antibody preparations (Figure 4A). However, in this case, four sites of specific hydrolysis were classified as major (A3-C4, A5, C6, C7-A8, and A8-G9) and two as moderate (G11-G12 and G9-G10). Specific hydrolysis of miR-155-5p by all 35 IgGs proceeded at eight sites but with somewhat different efficiencies (Figure 4B). Five hydrolysis sites could be attributed to major (A4-U5, G6-C7, U8-A9, A9-A10, and U17-A18) and one to undoubtedly minor (U5-G6) sites. For most preparations, these sites should have been classified as minor, but for three of the 35 IgGs, they were major (for example, lanes 7 and 9; Figure 4B). Hydrolysis at two clearly detectable sites (U14-G15 and C12-G13) was very different for IgGs preparations from the blood of other mice. Hydrolysis of miR-146-3p with all IgG preparations was site-specific at four major (U11-C12, C12-A13, C17-U18, and C20-A21) and one very minor site (C2-U3) (Figure 5A). A completely different picture was found in the case of miR-9-5p (Figure 5B). All antibodies hydrolyzed this miRNA with somewhat different efficiency. Based on averaging data for 35 preparations, three sites can be attributed to conditionally major (U8-U9, U9-A10, and C12-U13) and three minor sites (G6-G7, G18-U19, and A20-U21). However, in parallel with specific hydrolysis of this miRNA at six sites, nonspecific hydrolysis at all internucleoside phosphate groups of miRNA (Figure 5B), as in the case of homo-oligonucleotides (Figure 2), was observed. The spatial structures of six micro-RNAs having minimal free energy were calculated earlier [48,49,50,51]. The relative percent of every product of every micro-RNA splitting by individual IgGs was calculated. Then, using the data of three independent experiments for each IgG sample, the average percentage of each product corresponding to seven blood plasma IgGs was calculated. Figure 6 and Figure 7 demonstrate the location of splitting sites in the spatial structures of six micro-RNAs in the case of IgG antibodies. As mentioned above (Figure 2, Figure 3, Figure 4 and Figure 5), IgGs from plasmas of various mice hydrolyze six micro-RNAs with different efficiencies and, sometimes, at different sites. Finally, Figure 6 and Figure 7 demonstrate averaged data on the efficiency of six micro-RNAs hydrolysis by seven IgGs at each of the sites. The main sites for more efficient cleavage of miR9-5p by IgGs are located in the specific loop of this micro-RNA and its 3′-terminal part (Figure 6A). The major hydrolysis site of miR-148a-3p is also located in the specific loop (16.5%), but hydrolysis of this miRNA mainly occurs in the duplex structure and its 5′-terminal region (Figure 6B). One major miR-219a-5phydrolysis site is also located in a specific loop, and six of the eight others are distributed over the duplex part of the molecule, its 3′ and 5′ terminal parts (Figure 6C). The most significant number of hydrolysis sites was found for miR-326 (Figure 6D). Interestingly, two major sites (10.7 and 7.5% of the hydrolysis) and six other sites correspond to 5′ and 3′ duplex zones of this micro-RNA and only sites to its specific loop. In the case of miR-21-3p, hydrolysis sites in the specific loop and duplex part are absent and are mainly located in its 5′-terminal part of this RNA (Figure 7A). Nearly the same situation is observed for the hydrolysis of miR155-5p (Figure 7B); the main sites of hydrolysis are localized in the 5′-terminal part of this micro-RNA. Thus, the immune response (and antibody production) against each of the microRNAs is highly specific and individual. To evaluate the affinity of IgGs to micro-RNA, miR-155-5p and the conditions corresponding to the pseudo-first-order reaction were used-linear sections of the rate from the concentration of IgGs (0.2 μM) and the reaction time. Evaluation of the values of Km and Vmax (kcat) was performed using miR-155-5p and three IgG preparations corresponding to 7 days after the mice immunization with MOG (Figure 8). In the case of all three preparations, the same Km values were obtained, 5.8 ± 1.7 µM. At the same time, these three IgG preparations showed different kcat (kcat = Vmax (M/min)/[IgGs], M) values: 0.06 ± 0.01, 0.19 ± 0.03, and 0.25 ± 0.05 min−1 (Figure 8). The EAE development in C57BL/6 mice occurs spontaneously and may be accelerated by immunizing mice with MOG [35,36,37,38,39,40]. There are several stages of EAE development after mice immunization with MOG or complex DNA with histones: the onset at 7–8, the acute phase at 18–20, and the remission stage ≥25–30 days. The spontaneous and accelerated EAE development occurs as the result of certain changes in bone marrow HSCs differentiation profiles and an increase of lymphocyte proliferation in different organs associated with parallel production of abzymes splitting DNAs, MBP, MOG, and histones [35,36,37,38,39,40]. Supplementary Figures S1 and S2 show the changes in the differentiation profile of the bone marrow stem cells and lymphocyte proliferation in different organs of C57BL/6 mice during the spontaneous, MOG- and DNA-histones complex-induced development of EAE. The blood of mice was collected at various times up to 40 days after the start of the experiments (time zero) before and after mice immunization; days of blood sampling are shown in the Figures. The relative activity of abzymes hydrolyzing DNA, MOG, MBP, and histones during the spontaneous development of EAE in C57BL/6 mice increases slowly and smoothly—almost linearly [35,36,37,38,39,40]. A strong increase in the relative activities (RAs) of abzymes hydrolyzing these antigens-substrates occurs as early as 7 days after immunizing of mice with MOG and reaches their maximum values during the acute phase (18–20 days). During the period of remission, the relative activities of abzymes may decrease slightly or moderately [35,36,37,38,39,40,41]. Supplementary Figure S3 demonstrates a very strong increase in the RAs of ABZs in the hydrolysis of DNA, MOG, and myelin basic protein beginning from 7 days and the formation of antibodies with maximum activity by 20 days after the immunization of mice with MOG. It was interesting to analyze the change in the time of development of EAE in the relative activity of antibodies in the hydrolysis of micro-RNAs compared with the hydrolysis of DNA, MBP, and MOG before and after mice immunization with MOG. With this in mind, IgG antibodies were obtained from six groups of three-month-old C57BL/6 mice (seven mice per group), corresponding to 40 days of spontaneous development of EAE. Figure 9 shows data on changes in the average activity of abzymes corresponding to seven different IgGs of each group of mice in the hydrolysis of nine RNA-substrates before and after immunization of mice with MOG. In the case of spontaneous development of EAE, an absolutely unexpected result was obtained. While the activities of antibodies in the hydrolysis of DNA, MBP, and MOG in the process of spontaneous development of EAE increased intermittently but constantly (Supplementary Figure S3), starting from three months of mice age, the relative activity in hydrolysis of all nine RNA-substrates decreased slowly (Figure 9). At 3 months of life, the average relative activity of antibodies very strongly depended on the RNA-substrate and decreased in the following order: (pC)23 > miR-21-3p > miR-155-5p > miR-219a-5p ≈ miR-146a-3p > miR-9-3p > miR-326 ≈ (pU)23 > (pA)23 (Figure 9). Over 20 days after the start of the experiment, due to the spontaneous development of EAE, the decrease in the relative activity also depended on the RNA substrate (approximate % of initial values): (pC)23 (~98–99) > (pA)23 (75) > miR-219a-5p (57) ≈ miR-326 (55) > miR-155-5p (42) > miR-21-3p (20) ≈ miR9-5p (18) > (pU)23 (5) > miR-219a-5p (≈0). As showed in previous studies, the production of antibodies with various enzymatic activities is the earliest and statistically significant indicator of the development of autoimmune reactions (for review, see [4,5,6,7,8,9,10]. In the case of the initial onset stage of development of different AIDs, abzymes are detected at a time when the titers of autoantibodies to various self-antigens still correspond to the ranges of their variations in conditionally healthy donors. The detection of abzymes that hydrolyze DNA, MBP, MOG, and histones in mice as early as three months of life indicated that already at this age, the immune status of C57BL/6 mice is violated, and they demonstrated the initial forms of EAE. Previously, we used only 3-month-old C57BL/6 mice to study the mechanisms of EAE development. However, in this work, given the opposite nature of the change—a decrease in the relative activity of antibodies in micro-RNA hydrolysis during the spontaneous development of EAE in comparison with an increase in the hydrolysis of DNA, MBP, and MOG [35,36,37,38,39,40,41], we obtained an additional special set of IgGs from mice corresponding 50, 80 and 92 days (3 months) after their birth. The change in relative activity from 50 to 92 days of life in mice was highly dependent on RNA analyzed. The most significant decrease in antibody activity (79%) during this time was observed in hydrolysis (pC)23 (Figure 10A). Moreover, a strong statistically significant decrease in activity from 50 days to 3 months of mice life occurred in the case of (%): (pA)23 (81), (pU)23 (66), miR-9-5p (41) (Figure 10B), miR-146a-3p (62) (Figure 10C). A slight decrease in the relative activity of IgGs occurred in the case of miR-21-3p (26%; Figure 10A). There was no significant change in activity from 50 to 92 days in the case of miR-326 (Figure 10B), while a noticeable change in the hydrolysis of miR-219a-5p was observed only from 80 to 92 days (Figure 10C). Only in the case of miR-155-5p, during this period, there was an increase in the activity of antibodies in the hydrolysis by 25% (Figure 10C). Thus, in general, there is a tendency to reduce the relative activity of most of the abzymes that hydrolyze micro-RNAs in the period from 50 to 132 days of mice life. Therefore, it was interesting to compare the effect of MOG treatment of mice on RAs of IgGs in the hydrolysis of DNA and proteins with activity in the splitting of RNAs. Figure 9 shows data on changes in the activity of IgGs in the hydrolysis of nine RNAs before and after immunization. The spontaneous development of EAE leads to a significant decrease in the activity of antibodies in the hydrolysis of six out of nine RNAs. However, at the same time, in the case of two RNAs (miR-219a-5p and (pU)23), a slight temporary increase in antibody activity is observed at 10 days after the start of the experiment. After a noticeable decrease in the efficiency of miR-9-5p hydrolysis by 10 days (Figure 9C), a constant increase in the activity of IgGs is observed. Nevertheless, despite these features in the case of three RNAs, immunization of mice in all cases leads to a statistically significant (p < 0.05) sharp increase in the activity of antibodies in the hydrolysis of all nine RNAs compared to their activity at 3 months of age (-fold): (pA)23 (5.2), (pU)23 (4.6), miR-219a-5p (3.9), miR-9-5p (3.1), miR-326 (2.9), miR-146a-3p (2.5), miR-155-5p (1.9), miR-21-3p (1.2), and (pC)23 (1.2). As mentioned above, immunization of mice with MOG leads to a significant increase in the activity of antibodies in the hydrolysis of DNA, MOG, MBP, and histones. It is very important that the maximum activity in the hydrolysis of these four immunogens-substrates is observed in the acute phase of the disease—20 days (Supplementary Figure S3). A specific singularity of the sharp increase in the activity of IgGs in the splitting of RNAs and micro-RNAs after mice immunization with MOG is that the maximum of their activity is observed mainly in 7 and in some cases in 7–14 days—the initial stage of the pathology development (Figure 9). Then, by the 20th day of the acute phase of EAE, there is a strong decrease in the RAs of abzymes in the hydrolysis of all nine RNAs. Thus, immunization of C57BL/6 mice with MOG stimulates not only the production of abzymes that hydrolyze DNA, MOG, MBP, and histones but also homo-RNAs and micro-RNAs. As shown earlier, in SLE and EAE, in comparison with the norm before the disease, the first change in the differentiation profile of bone marrow stem cells occurs in the first stage of spontaneous or specific antigen-induced pre-disease conditions, and then, when moving to a deep pathology, an additional change of differentiation profile occurs (for review see [4,5,6,7,8,9,10]). These changes are associated with the appearance in the blood of mice of abzymes that hydrolyze DNA, MBP, ATP, and polysaccharides. It should be noted that the relative activity of antibodies from the cerebrospinal fluid of multiple sclerosis patients in the hydrolysis of DNA, MBP, and polysaccharides, depending on the substrate, is 30–60 times higher than that from the blood of the same patients [22,23,24]. Taking this into account, we believe that the development of autoimmune diseases can begin at the level of the cerebrospinal fluid and the brain. Previously, it was shown that antibodies from healthy donors are inactive in the hydrolysis of RNA and DNA [4,5,6,7,8,9,10]. At the same time, IgGs with DNase, RNase, proteolytic, and amylase activity are the earliest statistically significant markers of several autoimmune pathologies [4,5,6,7,8,9,10]. This work first showed that the hydrolysis of homo-RNAs and micro-RNAs, as in the case of patients with various AIDs [4,5,6,7,8,9,10], is an intrinsic property of IgGs of C57BL/6 mice predisposed to EAE. At the same time, hydrolysis of three homo-RNAs occurs non-specifically, while six micro-RNAs proceed at specific sites. A feature of miRNAs hydrolysis is that the activity of Abs is detected as early as 50 days after the birth of mice. In the period from 50 to 92 days (3-month-old mice), there is a significant decrease in the activity of IgGs in the hydrolysis of 8 out of 9 RNAs. Within 40 days after the start of the experiment, a further reduction in the activity of ABZs in the hydrolysis of miRNAs is mainly observed. This result is entirely inconsistent with the slow, gradual increase in the activity of antibodies in the hydrolysis of DNA, MBP, MOG, histones, and DNA during the spontaneous development of EAE in mice (Supplementary Figure S3) [35,36,37,38,39,40,41]. The constant production of abzymes that hydrolyze DNA, MBP, and histones during the spontaneous development of EAE is associated with some features of these autoantigens. The main antigen for producing antibodies against DNA and histones are DNA complexes with histones, which appear in the blood as a result of cell apoptosis [45]. Anti-DNA abzymes easily penetrate the outer and nuclear membranes of cells, hydrolyze the DNA of nuclear chromatin, and stimulate cell death through apoptosis [46,47]. This leads to an increase in the concentration of histones and DNA in the blood and antibodies-abzymes against them and, as a result, to the acceleration of several AIDs development [4,5,6,7,8,9,10]. The peculiarity of the production of abzymes that hydrolyze MBP is that histones and MBP have a high level of homology of their protein sequences. This leads to the fact that abzymes against histones effectively hydrolyze MBP and against MBP, on the contrary, all five histones ([52,53,54] and refs. therein). Since histones constantly appear in the blood due to cell apoptosis, this leads to a violation of the immune system and the synthesis by lymphocytes of Abs-abzymes against MBP. These factors underlie the continuous synthesis by lymphocytes producing antibodies against DNA, MBP, and histones and abzymes hydrolyzing these autoantigens [8,9,10]. One cannot exclude that the very important difference between abzymes against DNA (MBP and histones) and miRNAs is that micro-RNAs have many different biological functions, including regulating up to several hundred genes [55,56]. Different changes in microRNAs (microRNA-regulated gene networks) could result in the realignment in the expression of many genes in different cells. At various stages, the mice’s growth processes should be regulated by different micro-RNAs. It has been shown that the expression of many miRNAs changes with age. For example, CD1 mice show decreased expression of miR-148, miR-219, miR-199a, miR-214, miR-335, miR-411, and other micro-RNAs in the lungs of postweaning females and adult females compared to neonatal mice [57]. Similar data on decreased micro-RNA expression with age were obtained in peripheral blood mononuclear cells and plasma in humans [58,59]. However, some miRNAs’ expression may increase with age [59]. At the initial stages of the mice’s growth, the concentration of some miRNAs may be relatively high but then gradually decreases with mice age. A decrease in the concentration of miRNAs with age should lead to a reduction in the concentration of abzymes against these miRNAs. Therefore, in 50-day-old mice, the increased activity of abzymes in the hydrolysis of micro-RNAs may be associated with the production of antibodies-abzymes against micro-RNAs in mice in increased concentrations. Immunization of mice with MOG leads to severe impairment of their immune system leading to specific violations of an extended nature. A change in the profile of bone marrow stem cells differentiation leads to the production of lymphocytes that produce abzymes hydrolyzing not only MOG but also in parallel DNA, myelin basic protein, and histones. It cannot be ruled out that such changes in the differentiation profile may, in parallel, also lead to the production of lymphocytes producing abzymes to other external and self-antigens, including micro-RNAs. In this case, at the initial stage of EAE accelerated development after mice immunization with MOG, there may be the appearance of abzymes with higher activity in micro-RNAs hydrolysis. However, the effect of MOG on increased production of lymphocytes that synthesize anti-micro-RNAs antibodies with catalytic activity may be temporary. In connection with this, it should be noted that, in 7–14 days after mice immunization with MOG, the activity of abzymes hydrolyzing nine RNAs increases only 1.2–5.2-fold (Figure 9). At the same time, by the 20th day after immunization, the DNase activity of IgGs increases by 25 times [35,36,37,38]. It seems that the general trend of decreasing activity of some abzymes hydrolyzing miRNAs with age of mice leads to the significant reducing of the rise in their activity due to mice immunization with MOG. The general trend towards a decrease in the activity of abzymes hydrolyzing micro-RNAs over time due to a reduction in the concentration of micro-RNAs with age can probably lead to a recession in such antibodies as early as 20 days after immunization, which is observed in the experiment. All preparations were free from possible contaminants. All high-quality chemicals were from Sigma (St. Louis, MO, USA). Sorbents columns (Superdex 200 HR 10/30 (17-5175-01)) and Protein G-Sepharose (17061801)) were purchased from GE Healthcare (GE Healthcare, New York, NY, USA). We recently used inbred 3-months-age C57BL/6 mice to study possible mechanisms of spontaneous and MOG-induced EAE development [35,36,37,38,39,40,41]. The blood of mice was collected at various times up to 40 days after the start of the experiments (time zero) before and after mice immunization (0, 7, 12, 14, 20–23, and 37 days); days of blood sampling are shown in the Figures. All groups used consisted of 7 mice. Here, we also obtained an additional group of mice and their antibodies corresponding to 50, 80, and 92 days-old mice. These mice were grown in a special mouse vivarium of the Institute of Cytology and Genetics (ICG) using special conditions free of any pathogens. We also used seven-month-old CBA (CBAxC57BL-F1) and BALB mice not prone to spontaneous development of autoimmune diseases. All experiments with C57BL/6, CBA and BALB mice were carried out according to the protocol of the ICG Bioethical Committee (document number 134A of 7 September 2010), fulfilling the humane principles for operating with animals established by the Directive of European Communities Council (86/609/CEE). The Bioethical Committee of the institute has supported this study. Electrophoretically homogeneous polyclonal IgG antibodies from the plasma of mouse blood were first purified using affinity chromatography proteins of blood plasmas (7 mice in each group) on Protein G-Sepharose. In addition, they were isolated using FPLC gel-filtration (Fast protein liquid chromatography-gel filtration) on Superdex 200 HR 10/30 column [35,36,37,38,39,40,41]. After gel filtration, central parts of IgG preparations peaks were subjected to filtration through special filters (pore size 0.1 µm). In addition, 7 preparations of homogeneous IgG preparations were obtained from the blood plasma of CBA and BALB mice not prone to autoimmune diseases. The IgG preparations were subjected to the assay RNase activity after their SDS-PAGE using all eluates of gel fragments as in [35,36,37,38,39,40,41] to exclude possible traces of canonical RNases. It was shown that only intact IgG-antibodies demonstrate RNase activity, and no other protein bands or ribonuclease activities in different fragments of gel were found. The Supplementary Methods give a more detailed description of these experiments. SDS-PAGE analysis of Abs for homogeneity was performed using a 5–16% gradient gel containing 0.1% sodium dodecyl sulfate (SDS; Laemmli system) as in [35,36,37,38,39,40]. The IgGs were visualized by silver staining. Analysis of RNase activity of IgGs after SDS-PAGE was performed as in [48,49,50,51]. IgGs (10–40 μg) were pre-incubated at 30 °C for 35 min under non-reducing (1% SDS, 50 mM Tris-HCl, pH 7.5, and 10% glycerol) conditions. After SDS-PAGE electrophoresis of Abs to restore the RNase activity of IgGs, SDS was removed by incubating the gel for 1 h at 30 °C with 4.0 M urea and washed 12 times (8–10 min) with H2O. Then 2.5–3-mm cross sections of the gel long slices were cut up and then incubated with 50 μL 52 mM Tris-HCl (pH 7.5) supplemented with 50 mM NaCl for 7 days at 4 °C to allow IgGs refolding and eluting them from the gel. The solutions were removed from the gels using centrifugation and used for the assay of RNA hydrolysis, as described in the article. Parallel control lanes of gel were used for the detection of the position of IgG on the gel by silver staining. Fluorescein isothiocyanate (Flu) fluorescently labeled ribooligonucleotides 5′-Flu-(pA)23, 5′-Flu-(pU)23, and 5′-Flu-(pC)23, and several micro-RNAs characterized participating in the neuroinflammation regulation of by impaired expression in MS [28], were used in the study. Two neuroregulatory miRNAs are: miR-219a-5p (5′-Flu-UGAUUGUCCAAACGCAAUUCU) and miR-9-5p (5′-Flu-UCUUUGGUUAUCUAGCUGUAUGA). In addition, four immunoregulatory miRNAs were also used: miR-21-3p (5′-Flu-CAACACCAGUCGAUGGGCUGU), miR-146a-3p (5′-Flu-CCUCUGAAAUUCAGUUCUUCAG), miR-155-5p (5′-Flu-UUAAUGCUAAUCGUGAUAGGGGU), and miR-326 (5′-Flu-CCUCUGGGCCCUUCCUCCAG). The reaction mixture (10–15 μL) contained 50 mM Tris-HCl pH 7.5, 0.05 mg/mL one of miRNAs (1.3–1.6 μM depending on the miRNA used), 0.005 mg/mL (33.3 nM) IgGs. All mixtures were incubated for 2 h at 37 °C. The reaction was brought to a stop by the addition of a buffer (10–15 μL) containing 8.0 M urea and 0.025% xylene cyanol, and the product of hydrolysis analyzed by 20% PAGE using denaturing conditions (8 M urea, 0.1 Tris, 0.1 M boric acid, and 0.02 M Na2EDTA; pH 8.3). The gels were explored using FLA 9500 Typhoon laser scanner (GE Healthcare, New York, NY, USA). The markers of RNAs molecular weights were obtained by statistical alkaline hydrolysis of 3.2 μM miRNAs (at all internucleoside bonds) by substrates incubation in bicarbonate buffer (50 mM, pH 9.5) for 15 min at 95 °C. The relative RNase activity was estimated from a decrease in intact RNA substrates. Mean values of the relative activities in the hydrolysis of all substrates by antibodies were calculated by averaging activity values corresponding to seven mice in each group of mice. All initial rates of RNAs hydrolysis were measured using the reaction conditions of the pseudo-first-order corresponding to linear parts of dependencies on time (30–40% of RNAs hydrolysis) and concentrations of IgGs. The spatial models of six micro-RNAs (miR9-5p, miR219a-5p, miR-326, miR-155-5p, miR-21-3p, and miR-146a-3p) were generated previously [48,49,50,51] using Predict a Secondary Structure server: http://rna.urmc.rochester.edu/RNAstructureWeb/Servers/Predict1/Predict1.html (accessed on 14 February 2018), which uses a combination of four algorithms for predicting the secondary structure of RNA with minimal energy. The results corresponding to the average values (mean ± standard deviation) from three independent experiments for each preparation of IgGs and RNA-substrate, averaged over 7 different mice in every group. Based on the data on the hydrolysis of each of the homooligonucleotides and micro-RNAs, an estimation of relative activity was made using the loss of each of the initial substrates after their incubation with each of the individual antibody preparations (some examples of substrates hydrolysis are shown in Figure 2, Figure 3, Figure 4 and Figure 5). Then, the average value of antibody activity (mean ± standard deviation) was calculated using relative activities of seven IgG preparations of each of the analyzed groups of mice and shown in the Figure 9 and Figure 10. The efficiency of hydrolysis of all miRNAs at different sites by each of IgG preparations was estimated as a percentage relative to the sum of the relative efficiencies of spots (100%) of each of the hydrolysis products. The assessment of the relative average efficiency of hydrolysis in each of the sites was carried out by averaging the activity values for seven preparations of each of mice studied groups (Figure 6 and Figure 7). The apparent Km and Vmax (kcat) values for RNA were calculated from the dependencies of V versus [RNA] by non-linear fitting using Microcal Origin v5.0 software. Errors in the values were within 7–15%. The differences between characteristics of IgG samples of various groups were estimated by the non-parametric Kruskal–Wallis one-way analysis of variance, p < 0.05 was considered statistically significant. Here, we have first shown that IgG-abzymes from EAE-prone C57BL/6 mice efficiently hydrolyze nonspecifically at all sites homo-oligonucleotides and six miRNAs (miR-9-5p, miR-219a-5p, miR-326, miR-155-5p, miR-21-3p, and miR-146a-3p) in specific sites. During the spontaneous development of EAE from 50 to 132 days after birth, the relative activity of abzymes in the hydrolysis of all nine RNAs substrates is noticeably or significantly reduced. Immunization of three-month-old mice (at 92 days of age) with MOG leads to a sharp temporary increase in the activity of antibodies in micro-RNAs hydrolysis at 7 days (the initial stage of the disease), followed by a sharp decrease in their activity by 20 days. It was suggested that a temporary increase in the activity of abzymes in the splitting of micro-RNAs is associated with a tendency for a constant decrease in the concentration of micro-RNAs during mice growth and aging.

PMC10002165

Annarita Barone,Giuseppe De Simone,Mariateresa Ciccarelli,Elisabetta Filomena Buonaguro,Carmine Tomasetti,Anna Eramo,Licia Vellucci,Andrea de Bartolomeis

A Postsynaptic Density Immediate Early Gene-Based Connectome Analysis of Acute NMDAR Blockade and Reversal Effect of Antipsychotic Administration

22-02-2023

Homer1a,brain network,asenapine,antipsychotics,connectomics,functional connectivity,postsynaptic density,ketamine,psychosis,schizophrenia

Although antipsychotics’ mechanisms of action have been thoroughly investigated, they have not been fully elucidated at the network level. We tested the hypothesis that acute pre-treatment with ketamine (KET) and administration of asenapine (ASE) would modulate the functional connectivity of brain areas relevant to the pathophysiology of schizophrenia, based on transcript levels of Homer1a, an immediate early gene encoding a key molecule of the dendritic spine. Sprague–Dawley rats (n = 20) were assigned to KET (30 mg/kg) or vehicle (VEH). Each pre-treatment group (n = 10) was randomly split into two arms, receiving ASE (0.3 mg/kg), or VEH. Homer1a mRNA levels were evaluated by in situ hybridization in 33 regions of interest (ROIs). We computed all possible pairwise Pearson correlations and generated a network for each treatment group. Acute KET challenge was associated with negative correlations between the medial portion of cingulate cortex/indusium griseum and other ROIs, not detectable in other treatment groups. KET/ASE group showed significantly higher inter-correlations between medial cingulate cortex/indusium griseum and lateral putamen, the upper lip of the primary somatosensory cortex, septal area nuclei, and claustrum, in comparison to the KET/VEH network. ASE exposure was associated with changes in subcortical-cortical connectivity and an increase in centrality measures of the cingulate cortex and lateral septal nuclei. In conclusion, ASE was found to finely regulate brain connectivity by modelling the synaptic architecture and restoring a functional pattern of interregional co-activation.

A Postsynaptic Density Immediate Early Gene-Based Connectome Analysis of Acute NMDAR Blockade and Reversal Effect of Antipsychotic Administration Although antipsychotics’ mechanisms of action have been thoroughly investigated, they have not been fully elucidated at the network level. We tested the hypothesis that acute pre-treatment with ketamine (KET) and administration of asenapine (ASE) would modulate the functional connectivity of brain areas relevant to the pathophysiology of schizophrenia, based on transcript levels of Homer1a, an immediate early gene encoding a key molecule of the dendritic spine. Sprague–Dawley rats (n = 20) were assigned to KET (30 mg/kg) or vehicle (VEH). Each pre-treatment group (n = 10) was randomly split into two arms, receiving ASE (0.3 mg/kg), or VEH. Homer1a mRNA levels were evaluated by in situ hybridization in 33 regions of interest (ROIs). We computed all possible pairwise Pearson correlations and generated a network for each treatment group. Acute KET challenge was associated with negative correlations between the medial portion of cingulate cortex/indusium griseum and other ROIs, not detectable in other treatment groups. KET/ASE group showed significantly higher inter-correlations between medial cingulate cortex/indusium griseum and lateral putamen, the upper lip of the primary somatosensory cortex, septal area nuclei, and claustrum, in comparison to the KET/VEH network. ASE exposure was associated with changes in subcortical-cortical connectivity and an increase in centrality measures of the cingulate cortex and lateral septal nuclei. In conclusion, ASE was found to finely regulate brain connectivity by modelling the synaptic architecture and restoring a functional pattern of interregional co-activation. Administration of N-methyl-D-aspartate receptor (NMDAR) non-competitive antagonists has been considered as a proxy model for psychosis, characterized by NMDAR hypofunction, a molecular hallmark associated with the pathophysiology of schizophrenia, which affects several patterns of functional connectivity between brain regions [1], thus is suitable for testing the differential effects of antipsychotics. GWAS analyses [2], post-mortem studies [3], and preclinical models [4] of psychosis and schizophrenia have highlighted significant alterations in post-synaptic density, an electron-dense region localized at the postsynaptic sites of glutamatergic synapses, with selective involvement of smaller dendritic spines, which are strongly related to learning and behavioral flexibility, resulting in reduced plasticity of brain circuits [5,6,7]. For instance, a dysregulation in protein levels of several molecular components of dendritic spines, including postsynaptic density 95 (PSD-95), NMDAR subunit GluN1, spinophilin, and Homer 1, has been detected in multiple brain regions of patients affected by schizophrenia [3,6,8,9]. Antipsychotics have been found to modulate synaptic plasticity and metaplasticity, as well as affect postsynaptic sites. Nonetheless, if the ability of antipsychotics to induce IEGs and tune molecular processes involved in synaptic regulation, may finally hesitate in the restoration of functional connectivity, is yet to be clarified [10,11]. In a previous study, we generated a functional brain network by mapping the expression of Homer1a evoked by the acute administration of the typical antipsychotic haloperidol and investigated differences in discrete brain network properties, as compared to the vehicle (VEH) [12]. The present work was conceived to investigate the effects of an atypical antipsychotic, asenapine (ASE), both at the level of gene expression and functional connectivity in a pharmacological model of acute psychosis. Among the characteristics of its receptor profile, beyond D2 receptor (D2R) antagonism, asenapine shows a significant antagonism with relevant affinity at D1 receptors (D1Rs), compared to other antipsychotics [13]. The action at D1R sites is even more attractive since D1R and NMDAR couple at the postsynaptic site in dendritic spines, enhancing a reciprocal activity through a positive feedback mechanism [14]. Therefore, we aimed at exploring the ASE-induced changes in Homer1a expression, a direct marker of synaptic activity, in 33 regions of interest (ROIs) (listed in Table 1) within the cortex, the caudate-putamen, and the nucleus accumbens of rats previously challenged with acute ketamine (KET) administration by quantitative in situ hybridization histochemistry (ISHH). We opted to study the action of these compounds on cortical and striatal structures. Indeed, cortical connectivity appears to be impaired in schizophrenia, along with the morphological finding of grey matter volume loss [15] both in the disease and during the course of treatment. Furthermore, as striatal dopamine function has been related to the pathophysiology of schizophrenia [16], and striatal structures are known to be involved in the action of antipsychotic drugs, we also investigated gene expression and connectivity of these subcortical regions. A graph theoretical analysis and a statistical comparison between networks were applied to define group-individual differences within functional connectivity profiles. Homer1a gene expression was analyzed in 33 subcortical and cortical regions of the rat brain by quantitative ISHH and the resulting values were compared between groups of treatment. The dependent variable was normally distributed for each combination of the levels of the between- and within-subject factors, as revealed by a Shapiro–Wilk test, which did not give significant results. Mauchly’s test of sphericity indicated that the assumption of sphericity was violated, then we applied the Huynh–Feldt correction for degrees of freedom. There was a statistically significant two-way interaction between treatment and ROI, F(21.56, 115) = 2.096, p = 0.002, partial η2 = 0.308. The Student’s t test was used to compare the transcript values of Homer1a in each ROI between (i) VEH/VEH vs. KET/VEH groups to highlight the differences in Homer1a expression between a psychosis-like model and normal conditions (ii) VEH/VEH vs. VEH/ASE groups, to assess the impact of the antipsychotic on the gene expression under baseline conditions; (iii) KET/VEH vs. KET/ASE groups to evaluate the effects of the antipsychotic in an animal model of psychosis. The results from the comparisons between groups are displayed in Figure 1. The comparisons of Homer1a mRNA levels between VEH/VEH and KET/VEH groups are detailed in Table 2. With regard to the cortical regions, Homer1a expression was significantly lower in KET/VEH group compared to VEH/VEH group in the cingulate cortex (cg2 and cg1) (95% CI, −0.47 to −0.09, t(8) = −3.48, p = 0.008; 95% CI, −0.64 to −0.20, t(8) = −4.43, p = 0.002, respectively), in primary and supplementary motor cortex (95% CI, −0.51 to −0.10, t(8) = −3.47, p = 0.008; 95% CI, −0.59 to −0.12, t(8) = −3.56, p = 0.007, respectively), in the forelimb, jaw region, and dysgranular zone of the somatosensory cortex (CI, −0.44 to −0.07; t(8) = −3.22, p = 0.012; CI, −0.38 to −0.02, t(8) = −2.59, p = 0.032; CI, −0.37 to 0.00, t(8) = −2.28, p = 0.05, respectively). Homer1a expression was found reduced in the KET/VEH group compared to the VEH/VEH group even in subcortical areas. Specifically, in the KET/VEH group, reduced Homer1a mRNA levels were detected in all striatal subregions (in CPDM, CI, −0.55 to −0.05, t(4.96) = −3.14; p = 0.026; CPDL, CI, −0.57 to −0.11, t(5.81) = −3.63; p = 0.012; CPVL, CI, −0.57 to −0.03, t(4.65) = −2.93, p = 0.036; CPVM, CI, −0.69 to −0.15, t(4.72) = −4.09, p = 0.011), in the nucleus accumbens (the core, CI, −0.51 to −0.00, t(4.43) = −2.73, p = 0.047, the shell CI, −0.58 to 0.00, t(5.32) = −2.44, p = 0.05), the ventral region of the lateral septal nuclei (CI, −0.27 to −0.01, t(8) = −2.59, p = 0.033), the Calleja’s islands (CI, −0.24 to −0.05, t(8) = −3.55, p = 0.007), the ventral pallidum (CI, −0.32 to 0.10, t(8) = −4.21, p = 0.003), and olfactory tubercle (CI, −0.36 t −0.03, t(8) = −2.67, p = 0.028) (Figure 1). Nonetheless, significant values did not survive after the Bonferroni correction. VEH/VEH vs. VEH/ASE comparisons are shown in Table 3. Even though almost all of the values were not significant, ASE administration resulted in higher Homer1a transcript levels in cg2 (CI, −0.36 to −0.01, t(8) = −2.43, p = 0.041) and lower levels in the ventral pallidum (CI, −0.023 to −0.02, t(8) = −2.67, p = 0.028)(Figure 1). However, significant values did not survive after the Bonferroni correction. Noteworthy, the administration of ASE in an animal model of acute psychosis mimicked by acute KET exposure was able to restore Homer1a expression almost in all the regions considered, as outlined in Table 4. In cortical regions, the KET/ASE group exhibited higher significant levels of Homer1a mRNA compared to KET/VEH in the cingulate cortex (Cg2 and Cg1) (95% CI, −0.40 to −0.05, t(8) = −3.00, p = 0.017; 95% CI, −0.49 to −0.03, t(8) = −2.59, p = 0.032, respectively), in the supplementary and primary motor cortex (95% CI, −0.52 to −0.06, t(8) = −2.89, p = 0.020; 95% CI, −0.51 to −0.03, t(8) = −2.60, p = 0.032, respectively), in the forelimb region (95% CI, −0.55 to −0.09, t(8) = −3.19, p = 0.013), jaw region (95% CI, −0.53 to −0.00, t(8) = −2.32, p = 0.049), and dysgranular zone of the primary somatosensory cortex (95% CI, −0.53 to −0.02, t(8) = −2.50, p = 0.037), in the granular and dysgranular insular cortex (95% CI, −0.61 to −0.03, t(8) = −2.52, p = 0.036; 95% CI, −0.66 to −0.11, t(8) = −3.21, p = 0.012, respectively), dorsal and ventral agranular insular area (95% CI, −0.63 to −0.12, t(8) = −3.42, p = 0.009; 95% CI, −0.54 to −0.05, t(8) = −2.83, p = 0.022, respectively), in the claustrum (95% CI, −0.55 to −0.07, t(8) = −2.94, p = 0.019), and in the piriform cortex (95% CI, −0.50 to −0.03, t(8) = −2.66, p = 0.029), and in the dorsal endopiriform nucleus (95% CI, −0.36 to −0.04, t(8) = −2.91, p = 0.02). Following KET pre-treatment, several subcortical regions showed a higher Homer1a gene expression after ASE administration in comparison to VEH, including the lateral stripe of striatum (95% CI, −0.58 to −0.10, t(8) = −3.28, p = 0.011), the dorsomedial, dorsolateral, ventrolateral, and ventromedial caudate-putamen (95% CI, −0.52 to −0.21, t(8) = −5.41, p = 0.001; 95% CI, −0.7 to −0.32, t(8) = −6.24, p < 0.001; 95% CI, −0.84 to −0.42, t(8) = −6.93, p < 0.001; 95% CI, −0.71 to −0.21, t(8) = −4.32, p = 0.003, respectively), the core and shell of nucleus accumbens (95% CI, −0.58 to −0.11, t(8) = −3.36, p = 0.01; 95% CI, −0.63 to −0.13, t(8) = −3.49, p = 0.008, respectively), the dorsal, intermediate, and ventral septal nuclei (95% CI, −0.46 to −0.07, t(8) = −3.13, p < 0.014; 95% CI, −0.26 to −0.03, t(8) = −2.90, p < 0.02; 95% CI, −0.38 to −0.06, t(8) = −3.14, p = 0.014, respectively), the septohippocampal nucleus (95% CI, −0.36 to −0.08, t(8) = −3.58, p = 0.007), the medial septum (95% CI, −0.29 to −0.1, t(8) = −4.84, p = 0.001), the Calleja islands (95% CI, −0.47 to −0.04, t(8) = −2.71, p = 0.027), and the ventral pallidum (95% CI, −0.48 to −0.04, t(8) = −2.70, p = 0.027). No differences between groups were found in the Homer1a transcript values in the indusium griseum, the oral surface of the jaw region, and upper lip of the somatosensory cortex, the nucleus of the vertical limb of the diagonal band, and the olfactory tubercle. When multiple testing was considered using Bonferroni’s correction, significant differences survived only in the dorsolateral, dorsomedial, and ventrolateral caudate-putamen, as well as in the medial septum (p < 0.001). We used Homer1a expression levels to calculate all pairwise correlation coefficients between pairs of ROIs for each treatment group (please see Supplementary Tables S1–S4 for Pearson’s r and p-values) and generated four correlation matrices (please see Figure 2). It is noteworthy that the administration of ASE is associated with the appearance of multiple negative correlations between ROIs in the VEH/ASE matrix. It should be noted that methods for comparing brain networks largely ignore negative correlations [17] even if negative edges may be neurobiologically relevant and their significance is yet to be clarified [18]. In this case, the inter-correlation between ROIs, including the nucleus accumbens, cingulate cortex, motor cortex, and striatal subregions became negative. Moreover, acute KET challenge was associated with the appearance of negative correlations between the region corresponding to the medial part of the cingulate cortex and indusium griseum, and all remaining ROIs. Since the indusium griseum receives dense dopamine afferents and contains dopaminergic neurons, this region has been described as a common neuronal target of psychostimulant action [19,20]. Given the effects of KET on the dopamine function [21], it is possible that KET has similar effects to amphetamines on this specific brain region, which is classically considered as a part or a remnant of the hippocampus. The KET/ASE matrix was characterized by a pattern of stronger and positive connections, a large portion of which are significant or highly significant, as shown in Figure 2. The connections between caudate-putamen subdivisions, as well as between insular portions appear strong and positive, similar to what also occurs in the VEH/VEH matrix. In summary, the correlation matrix most closely resembling by visual inspection that observed under physiological conditions (i.e., VEH/VEH group was that associated with the KET/ASE treatment, in which glutamatergic dysfunctions were corrected by antipsychotic administration). By using the permutation test, we compared the edge weight of pairs of matrices (VEH/VEH vs. VEH/ASE; VEH/VEH vs. KET/VEH; KET/VEH vs. KET/ASE). Significant differences are graphically displayed in Figure 3. For a comprehensive acknowledgement of significant and non-significant permutated p-values, please refer to Supplementary Tables S5–S7. Among others, ASE administration was found to significantly impact correlations between subcortical and cortical ROIs in comparison to VEH injection. Specifically, the correlation between the ventral insular cortex and the ventrolateral (p-value after permutation test = 0.02) and dorsolateral (p-value after permutation test = 0.02) caudate-putamen were found to be reduced after ASE challenge, as well as the links between the somatosensory areas (S1FL and S1j) and the agranular ventral insular area (p-values after permutation test = 0.03 and 0.01, respectively), the claustrum (p-values after permutation test = 0.02 and 0.02, respectively), and the dorsal endopiriform nucleus (p-values after permutation test = 0.02 and <0.05, respectively). KET challenge resulted in a reduction of Pearson’s r coefficient in multiple pairs of correlation between insular ROIs and several cortical and subcortical regions. Of interest, the KET/VEH group, when compared to VEH/VEH, exhibited a significant reduction in the correlation between the intermediate lateral septal nucleus and indusium griseum (p-values after permutation test = 0.01), a remnant of the former part of the hippocampus in animals. The administration of the antipsychotic after acute KET challenge inverted Pearson’s r coefficient in multiple correlations between medial cingulate cortex/indusium griseum and several basal nuclei (Figure 3b), including the intermediate (p-values after permutation test = 0.01), dorsal (p-values after permutation test = 0.04), and ventral (p-values after permutation test = 0.04) lateral septal nuclei, the dorsolateral (p-values after permutation test = 0.04) and ventrolateral caudate-putamen (p-values after permutation test < 0.05), the upper lip of the primary somatosensory cortex (p-values after permutation test < 0.05), and the claustrum (p-values after permutation test < 0.05). In summary, the differences observed between VEH/VEH and VEH/ASE mainly affect the correlations between cortical-subcortical regions, probably mediating the therapeutic, as well as motor side effects of antipsychotics. Moreover, the differences between VEH/VEH and KET/VEH involve interconnections starting from insular, limbic, and hippocampal ROIs. Lastly, the administration of ASE after KET challenge appears to reverse the negative intercorrelations between the medial cingulate cortex/indusium griseum and several basal nuclei. Networks were drawn as indirect graphs, with edges indicating a two-way relationship. We retained only significant correlations with a p-value < 0.05 in order to achieve a trade-off between sensitivity and specificity. The color of the nodes was assigned depending on the degree. To facilitate interpretation, we have positioned the network nodes on the corresponding ROIs in the Paxinos rat atlas (please see Figure 4). Further, we calculated a series of parameters for each network, i.e., number of nodes, number of edges, network density, characteristic path length, connected components, clustering coefficient (please see Table 5), and centrality measures, including node degree and betweenness centrality (please see Supplementary Table S8). Then, we compared the overall network properties and centrality measures, such as betweenness (please see Supplementary Table S9) and node degree (please see Supplementary Table S10), by permutation testing. When comparing the networks in terms of global strength by permutation testing, the VEH/VEH network did not significantly differ from KET/VEH (p-value after permutation test = 0.547), nor KET/VEH differed from KET/ASE (p-value after permutation test = 0.45), whereas VEH/ASE exhibited a significantly reduced global strength compared to VEH/VEH (p-value after permutation test = 0.049). With regard to the node centrality metrics of VEH/VEH vs. VEH/ASE networks, the degree was significantly different in Cl (p-value after permutation test = 0.013), CPDL (p-value after permutation test = 0.018), CPDM (p-value after permutation test = 0.04), CPVL (p-value after permutation test = 0.024), S1FL (p-value after permutation test = 0.02), and Den (p-value after permutation test = 0.02), while the betweenness differed only in Cg2 (p-value after permutation test = 0.017) and LSV (p-value after permutation test = 0.031). By comparing VEH/VEH and KET/VEH networks, the degree was not different among nodes, while the betweenness was significantly higher in M2 (p-value after permutation test = 0.022) and MS (p-value after permutation test = 0.042) after acute KET challenge. Finally, the betweenness of S1DZ (p-value after permutation test = 0.009), CPDM (p-value after permutation test = 0.009), LSV (p-value after permutation test = 0.017), Ig (p-value after permutation test = 0.032), Shi (p-value after permutation test = 0.035), Pir (p-value after permutation test = 0.04), and Cg1 (p-value after permutation test = 0.045) was significantly different between KET/VEH and KET/ASE networks, while no difference in degree was observed. In particular, the betweenness of Ig, Cg1, Shi, and LSV was lower in the KET/ASE network, while the betweenness of S1DZ, CPDM, and Pir was higher in the KET/ASE network. Thus, the VEH/ASE group was associated with decreased betweenness of the cingulate cortex and lateral septal ROIs compared to the VEH/VEH group. The administration of KET after VEH pre-treatment was associated with a higher betweenness in the supplementary motor cortex and medial septum nodes compared to VEH/VEH. Finally, the administration of antipsychotics after the KET challenge was able to increase the betweenness of multiple nodes, while reducing the betweenness of others with respect to the KET/VEH network. We have summarized the main findings of our study in Table 6. In the present experiment, we evaluated whether the expression of the IEG Homer1a in multiple cortical and subcortical ROIs was affected by the treatment with the second-generation antipsychotic ASE, administered alone in naïve (i.e., VEH pre-treated) rats or KET pre-treated rats. Based on previously published papers, acute KET administration is regarded as a valuable and heuristic preclinical model of psychosis [22]. It has been documented that KET treatment in humans (at dose levels comparable to those utilized in our investigation) causes behavioral and neurochemical effects mimicking psychosis, including the multifaceted symptom presentation [23,24]. We did not observe a significant induction of Homer1a by acute KET administration at the timing chosen for the animal sacrifice after the treatments. Following the normalization of the data on values of gcc, a region that should not deliver signal intensity, Homer1a expression values in the KET/VEH group were even lower than in the control group, although the significance did not survive the Bonferroni correction. These results differ from a previous report from our group, which instead found a dose-dependent increase in Homer1a levels after KET administration [25]. However, the inconsistency in findings could be attributable to the different animal treatment procedures due to the administration of saline after KET challenge and the interval of additional 30 min before animal sacrifice in the present experiment. Hence, it is possible that, after acute KET exposure, Homer1a transcript levels increased for 90 min and returned to approximately baseline values in 120 min. We may therefore have captured different moments of the Homer1a expression curve following the challenge with an NMDAR antagonist. In a previous work by Buonaguro et al., 2017, exploring the effects at a post-synaptic level of antipsychotics and minocycline, both in a naturalistic context and after KET challenge, the authors did not perform gene expression comparisons between groups receiving different pre-treatments, and comparisons were separately carried out between VEH pre-treated groups on the one hand, and KET pre-treated groups on the other [26]. ASE administration in VEH pre-treated animals produced a region-specific pattern, inducing Homer1a only to a limited extent and never reaching significance. Again, after normalization, Homer1a values were higher in the Cg2 and vp in the control group than in the VEH/ASE group, although significance did not survive the Bonferroni correction. Lastly, the administration of ASE in an animal model of acute NMDAR dysfunction obtained by acute KET challenge was able to upregulate Homer1a almost in all of the regions considered. In particular, significant differences survived in the medial septum, dorsolateral, dorsomedial, and ventrolateral caudate-putamen when multiple testing correction was taken into account. These findings are consistent with previous reports showing that ASE only mildly impacts the cortical gene expression [27]. ASE relevant action in KET pre-treated rats, paralleled by the failure to detect an increase in Homer1a transcript levels in VEH pre-treated rats, may indicate that antipsychotics preferentially deliver their effects in a context of altered glutamatergic functions much more than under physiological conditions. It has been argued that the extent of Homer1a induction may be secondary to the degree of dopamine receptor blockade and the specific subtype [28]. Given the peculiar synergism of D1Rs and NMDARs, ASE effects on Homer1a may depend on its action at D1R sites [29]. Homer1a induction may also be triggered by 5-hydroxytryptamine 2A receptor (5-HT2AR) antagonism, which positively affects glutamatergic transmission [30]. However, repeated ASE exposure in animal models has been associated with a decreased 5-HT2AR binding in the medial prefrontal cortex and dorsolateral frontal cortex but not in other brain regions [31]. Moreover, striatal density of D1Rs is high whereas that of 5-HT2ARs is low, thus ASE-induced striatal gene expression could be mainly driven by the action at D1R sites [32]. As it can be inferred from the correlation matrices, the topographical organization of the four functional networks (i.e., VEH/VEH, VEH/ASE, KET/VEH, KET/ASE) varied widely, especially for the link between the cortex and striatum. In the present experiment, we observed that the VEH/VEH network was characterized by stronger functional connections between AIV and lateral caudate-putamen (CPVL and CPDL) compared to the VEH/ASE network. Ventral caudate-putamen and insular regions have been involved in the assignment of emotional value and reward magnitude expectation [33]. The links between the somatosensory areas (S1FL and S1j) and AIV, Cl, and Den (the latter two ROIs belonging to the amygdala complex [34]) are reduced in the VEH/ASE network. As well known, the insula receives sensory inputs from the somatosensory cortices relevant to pain sensitivity [35]. Antipsychotic ability to target discrete insular connections with striatal and somatosensorial regions might account for their effects on perception, motivation, and salience assignment. Acute KET challenge in the KET/VEH group is associated with negative correlations between the ROI corresponding to the medial portion of the cingulate cortex and the indusium griseum, and remaining ROIs, which are not observed in other treatment groups. It is noteworthy that indusium griseum has been described as a vestigial structure in humans and a remnant of the former part of the hippocampus in animals. Hippocampus is central in the neurobiology of psychotic disorders and the perturbation of functional connectivity within the hippocampus, as well as its extrinsic connections has been considered to contribute to schizophrenia deficits much more than psychotic symptoms [36]. However, only indusium griseum correlation with LSI (part of the septal area, the anterior portion of the limbic system) was significantly weakened in the comparison between VEH/VEH and KET/VEH. Moreover, it should be noted that the KET/ASE group showed significantly higher inter-correlations between the medial portion of the cingulate cortex and lateral putamen, the upper lip of the primary somatosensory cortex, septal area nuclei, and claustrum, in comparison to the KET/VEH group. Since brain sections were quantitated at the topographical level of the striatum, we were unable to directly investigate the connectivity of hippocampal regions. However, indusium griseum behaves as a single functional and neuroanatomical unit together with the anterior aspect of the hippocampus [37], a candidate region in the study of schizophrenia and a functional hub for multiple brain networks [38]. Altered hippocampal-striatal coupling has been reported to be involved in deficits in associative learning tasks [38]. In this context, it is noteworthy that ASE administration in KET pre-treated rats appears to reverse Pearson’s r coefficient in medial cingulate cortex-caudate correlations. Finally, we analyzed the global strength and indices of centrality in each network and identified discrete nodes with enhanced centrality metrics. Although ASE does not directly recruit these regions by inducing Homer1a expression, it was able to finely regulate the centrality of the cingulate cortex ROIs. The cingulate cortex is implicated in the regulation of cognitive, sensorimotor, affective, and visceral functions [39]. The centrality of several brain regions, including olfactory, medial, and superior frontal regions, anterior cingulate, medial temporal pole, and superior occipital regions has been found impaired in functional connectomic studies conducted on schizophrenia patients [40]. We may therefore conceive that ASE ability to modulate the betweenness of the cingulate cortex area 2 (corresponding to the anterior cingulate cortex) both in VEH and KET pre-treated rats may contribute to its antipsychotic action. Of interest, a magnetic resonance spectroscopy study suggested that elevations in glutamate and glutamate metabolites in the anterior cingulate cortex predicted a poor antipsychotic response [41]. Since Homer proteins are expressed limitedly at glutamatergic synapses, ASE ability to reduce the betweenness of this region in the Homer1a-based network is particularly attractive. The administration of ASE in the KET pre-treated group was found to significantly modify the betweenness of the LSV compared to KET/VEH. Since lateral septal nuclei have been recently identified as critical hubs linking hippocampal and prefrontal activity with subcortical areas, participating in cognitive functions and motivated behaviors, this action may account for the beneficial impact on negative symptoms in psychosis [42]. It is noteworthy that ASE administration after KET challenge was associated, in our study, with a decrease in the betweenness of Ig compared to the KET/VEH network. According to Kraguljac et al., glutamate levels in hippocampal regions, as detected by magnetic resonance spectroscopy, were significantly higher in schizophrenia patients compared to controls. Nonetheless, they failed to reveal any effect of the antipsychotic treatment with risperidone on hippocampal glutamate concentrations. Since Homer1a is a marker of glutamatergic connectivity, it follows that, although ASE does not significantly affect gene expression in the Ig, it may reduce the centrality of this region in the global glutamatergic connectivity network. We have tested the action of the antipsychotic compound on a pharmacological model of psychosis; however, further analyses of the gene expression connectome could take advantage of genetic rodent models mirroring persistent reorganization of neural circuitry characteristic of schizophrenia, such as disrupted in schizophrenia 1 (DISC1) L100P mutants [43]. In conclusion, we detected significant differences in node and edge measures across groups exposed to different treatments, highlighting ASE capability to finely regulate brain connectivity by shaping synaptic architecture and restoring a functional pattern of interregional co-activation. Further studies are warranted to disentangle the contribution of different components of the antipsychotics’ receptor profile in modifying the pattern of connectome alterations induced by NMDAR non-competitive blockade; this could be instrumental to design compounds that can better counteract the NMDAR hypofunction considered relevant for the pathophysiology of psychosis. According to a previously published protocol, we conducted the ISHH procedures in Sprague–Dawley rats [44], in light of the evidence supporting the use of ketamine-treated Sprague–Dawley rats for modelling schizophrenia [22,26,45]. Sprague–Dawley rats from Charles River Labs were chosen because they are among the most commonly used rat strains, easily purchased from the above vendor. Male Sprague–Dawley rats (n = 20) with an average weight of 250 g (Charles-River Labs, Lecco, Wilmington, MA, USA) were housed and adapted to human handling in a temperature and humidity-controlled colony room, kept on a 12 h/12 h light/dark cycle with ad libitum access to food and water. The experimental procedures and animal handling techniques were conducted in agreement with the NIH guide for care and use of laboratory animals (NIH publication no. 85–23, revised 1996) and approved by local animal care and use committee. All efforts have been made to minimize animal suffering. To assess gene expression both under vehicle treatment and after manipulation of the glutamatergic system, animals were randomly assigned to two groups (n = 10 for each pre-treatment group), receiving saline (VEH; NaCl 0.9%) or KET (30 mg/kg), respectively. Acute administration of KET at sub-anesthetic and sub-convulsant doses was chosen among validated preclinical models to reproduce the schizophrenia-like behavioral and neurochemical phenotype of schizophrenia [22]. Subsequently, each pre-treatment group was randomly split into two arms, receiving a second compound: the atypical antipsychotic ASE (0.3 mg/kg), or saline (VEH) (n = 5 for each treatment group). The second compound was administered intraperitoneally (i.p.) 30 min after pre-treatment. Since the Homer1a peak expression occurs 90–120 min after psychotropic drug challenges [46,47,48], we opted for a 30-min interval between injections in order to capture the Homer1a expression resulting from the combination of the two compounds, prior to the effect of the first compound no longer being detectable. ASE was administered at behaviorally active doses, known to induce gene expression according to previous published experimental protocols [49,50]. Thus, the following four treatment groups were obtained: (a) VEH + VEH, (b) VEH + ASE, (c) KET + ASE, and (d) KET + VEH. Animals were sacrificed by decapitation 90 min after the second injection. KET (Sigma-Aldrich, St. Louis, MO, USA) and ASE (Lundbeck A/S, Copenhagen, Denmark) were supplied as a powder and dissolved in saline solution (NaCl 0.9%), adjusted to physiological pH, and injected i.p. at the final volume of 1 mL/kg. Brains were quickly removed and frozen on dry powdered ice, and then stored at −70 °C until sectioning. Coronal brain slices (12 μm) were cut on a cryostat using the Paxinos rat atlas as a reference [51]. Sections were thaw-mounted onto gelatin-coated slides and stored at −70 °C for further analysis. The probes used for radioactive ISHH were oligodeoxyribonucleotide with a length of 48 bp, complementary to the mRNA sequence (bases 2527–2574) of the Homer1a gene (GenBank #U92079; MWG Biotech, Firenze, Italy). The probe sequence was derived from those used in our previous hybridization studies investigating the Homer1a expression [52]. The probes were labeled using 35S as a radioisotope and sections were processed for radioactive ISHH according to previously published protocols [44]. Hybridized sections were dried and exposed to Kodak-Biomax MR Autoradiographic film (Sigma-Aldrich, Milano, Italy). A slide containing a scale of 16 known amounts of 14C standards (ARC-146C, American Radiolabeled Chemical, Inc., St. Louis, MO, USA) was co-exposed with the samples. Each slide contained three adjacent brain sections of a single animal. The autoradiographic films were exposed in a time range of 10–45 days. The optimal time of exposure was chosen to maximize the signal-to-noise ratio but to prevent optical density from approaching the limits of saturation. Each slide contained three adjacent brain sections from a single animal. The autoradiographic films were exposed at a time interval between 10 and 45 days. The optimal exposure time was chosen to maximize the signal-to-noise ratio but to avoid the optical density approaching saturation limits. The autoradiographic films have been digitized by a transparency film scanner (Microtek 9800XL Plus TMA) preserving their original characteristics. Then, the optical density of the autoradiographic signal was quantitated by using ImageJ software (v. 1.46v, http://rsb.info.nih.gov/ij/ [accessed on 5 December 2022]) in 33 different ROIs at the topographical level of the striatum (from Bregma +1.68 to +1.44). The optical density evaluation was carried out by two independent investigators. For each animal, values from three adjacent sections were averaged and the mean value was reported with standard deviation in relative dpm. By averaging the measurements from three adjacent sections of each animal brain, we calculated the gene expression values in each region. Further, data were normalized by values of the Homer1a gene expression in the gcc, which should not deliver signal intensity. The Shapiro–Wilk test was used to determine if the relative dpm values were distributed normally. Repeated measures analysis of variance (ANOVA) was used to determine the individual contribution of each of the categorization factors on the outcome of the dependent variable (i.e., Homer1a gene expression). We analyzed the effect of the between-subjects variable (treatment) as well as the within-subject variable (ROI) effects and their interaction. Moreover, we used Student’s t test to compare the transcript values of Homer1a in (i) VEH/VEH vs. KET/VEH groups, in order to evaluate the Homer1a expression in the presence or absence of a challenge of the glutamatergic system; (ii) VEH/VEH vs. VEH/ASE groups, in order to understand the effect of the antipsychotic on gene expression in baseline conditions; (iii) KET/VEH vs. KET/ASE groups, to assess the effects of ASE on the Homer1a transcript levels in an animal model of schizophrenia. The comparisons were performed by using Student’s t-test. The threshold for comparisons’ significance was set at 0.05. Multiple testing error was managed by using the Bonferroni correction (adjusted p-value = 0.0015). For statistical analysis, SAS Institute Inc.’s JMP software version 9.0.1 and IBM SPSS 25 were adopted. By using the Homer1a signal intensity measures in each ROI as dependent variables, we calculated Pearson’s r for all possible pairwise correlations in each treatment group (i.e., VEH/VEH, VEH/ASE, KET/VEH, and KET/ASE) and four correlation matrices were generated. The statistical analyses and graphical outputs were obtained via the software R.4.2.1 with the “hmisc”, “corrplot”, and “dnt (differential network tests)” packages (http://www.r-project.org/ [accessed on 19 December 2022]), as well as Cytoscape software 3.8.2 (http://www.cytoscape.org/ [accessed on 19 December 2022]). We calculated the network properties, such as the characteristic path length, clustering coefficients, network density, and connected components. We used a function providing a permutation-based test for comparing networks and calculating significant differences between paired edges [53]. Further, we assessed the differences between networks in the global strength and other basic centrality properties, such as nodes’ degree and betweenness centrality through a permutation-based approach [53]. The significance threshold was set at a p-value of 0.05 as used in previous studies [53]. A large number of permutations (n = 1000) was employed to obtain reliable results [53]. Networks consisting of 33 nodes (as many as the ROIs investigated) were graphically generated; each network was summarized by its weighted adjacency matrix, where the edge weights between the two nodes refer to the corresponding r correlation coefficient value, ranging from −1 to +1, indicating the magnitude or strength of an edge. Graphical outputs were obtained by styling the edges based on their weights, and nodes based on the degree. In an effort to retain only relevant edges and avoid spurious ones, we filtered significant correlations with a minimum p-value < 0.05 to achieve a trade-off between sensitivity and specificity. The method applied in the present article, including the study protocol, ISHH procedures, the computation of correlation matrices, and network elaboration are summarized in the graphical abstract.