# 29 m6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation – 102 Transcriptomic Dataset Analyses

Authors: Ming Liu; Keman Xu; Fatma Saaoud; Ying Shao; Ruijing Zhang; Yifan Lu; Yu Sun; Charles Drummer; Li Li; Sheng Wu; Satya P. Kunapuli; Gerard J. Criner; Jianxin Sun; Huimin Shan; Xiaohua Jiang; Hong Wang; Xiaofeng Yang
Journal: Journal of Immunology Research
 (2022)

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## Abstract

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.


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## Body

## 1. Introduction

Cardiovascular diseases (CVDs) including coronary heart disease, cerebrovascular disease, and peripheral artery disease have risen to the top of the worldwide death toll [1, 2]. Previous studies showed that different risk factors including elevated plasma lipids [3, 4], elevated blood sugar [5], hyperhomocysteinemia [6, 7], and chronic kidney disease [8–10] promote vascular inflammation and atherosclerotic CVDs by different mechanisms including DNA methylation [11], microRNA regulation of mRNA stabilities [12], histone modifications including histone methylations [13–15], immune metabolic programming and trained immunity [16], innate immune activation [17] of endothelial cells (EC) [18–21], EC injury [22], Ly6C high mouse monocyte and CD40+ human monocyte differentiation [23, 24], decreased regulatory T cells (Treg) [25–27], and impaired bone marrow-derived progenitor cells’ vascular repairability [28, 29]. Furthermore, we recently proposed new models that include intracellular organelle dangers [30] and reactive oxygen species (ROS) as an integrated sensing system for metabolic homeostasis and alarming, which showed that metabolic remodeling and dysfunction trigger mitochondrial ROS [31–34], caspase-1/inflammasome activation [8, 10], histone modification enzyme downregulation [14], and increased expressions of trained immunity pathway enzymes. These reports have demonstrated that epigenomic mechanisms play significant roles in connecting metabolic reprogramming and dysfunction to inflammation initiation and gene transcription. However, the mechanism by which epitranscriptomic-RNA methylation controls the progression of many diseases is still unknown.RNA carries a spectrum of more than 100 chemical modifications including RNA methylation that play significant roles in the regulation of gene expression [35, 36]. As the most dominant mRNA modification, N6-methyladenosine (m6A), installed onto mRNA by the methyltransferase-like 3 (METTL3)/methyltransferase-like 14 (METTL14) methyltransferase complex, is at a frequency of 0.15-0.6% of all adenosines in polyadenylated RNA [37]. In addition, six other RNA methylations have also been reported such as pseudouridine (Ψ), 5-methylcytidine (m5C), N1-methyladenosine (m1A), N4-acetylcytidine (ac4C), ribose methylations (Nm), and N7-methylguanosine (m7G) [38]. At 25-60% of transcriptome, m6A methylation regulates gene expression by influencing numerous aspects of mRNA processes of RNA polymerase II-transcribed mRNAs such as pre-mRNA processing, nuclear export, decay, and translation as well as long noncoding RNAs (lncRNAs) [39]. In addition, m6A plays an important role on noncoding chromosome-associated regulatory RNAs (carRNAs) for gene expression, which includes enhancer RNAs (eRNAs), promoter-associated RNAs (paRNAs), and transposable element transcribed RNA (repeat RNAs). Similar to DNA methylation as a mode of epigenomic regulation, the m6A methylation that occurred in RNA as a mode of epitranscriptomic regulation becomes important in the crucial roles of m6A-mediated gene regulation in many physiological and disease processes [37]. Another important feature of m6A methylation is the reversibility, allowing for the regulation of m6A levels after initial deposition. Fat mass and obesity-associated protein (FTO) and AlkB Homolog 5 (ALKBH5) have been discovered as m6A demethylases (erasers).The m6A methylation also plays a significant role in human disease pathology. Loss of METTL14 has been shown to increase endometrial cancer cells’ proliferation and tumorigenicity. In contrast, METTL3/METTL14 have been found to play significant roles in acting as oncogenes in acute myeloid leukemia and glioblastoma and promoting or inhibiting roles in hepatocellular carcinoma [40]. Since METTL3/METTL14 have been indicated to be promising drug targets, phase 1 trials of small-molecule inhibitors for METTL3/METTL14 have been planned for 2021-2022 [37]. In addition, METTL3/METTL14 and m6A methylation have been reported to play roles in other diseases such as heart failure, viral infection, type 2 diabetes [37], cardiac remodeling, atherosclerosis, congenital heart disease, inflammation, obesity, insulin resistance, adipogenesis, and hypertension [41]. Moreover, decreased expressions of fat mass and obesity-associated protein (FTO) [42] have been related to heart failure, and overexpression of FTO in failing heart results in decrease of ischemia-triggered loss of cardiac function. Finally, YT521-B homology (YTH, m6A-dependent RNA binding) domain family (YTHDF) [43] reader proteins are also involved in pathogenic processes since YTHDF2 is essential for acute myeloid leukemia initiation and leukemia stem cell development [37].Despite major advancements in the discipline, the following questions remain unanswered: (1) whether the expressions of 29 m6A-RMRs are modulated in 41 diseases and cancers including acute inflammation, sepsis, acute respiratory distress syndrome, shock, trauma, cardiovascular diseases (CVDs), autoimmune diseases, and organ failures; (2) whether macrophages and CD4+Foxp3+ regulatory T cells (Treg) serve as the key cellular mechanisms underlying the roles of m6A-RMRs in various diseases and cancers; and (3) whether nine types of molecular mechanisms including danger-associated molecular pattern receptors (DAMP receptors); proinflammatory cytokines; immune checkpoint and costimulation receptors; methionine-homocysteine-methyl donation cycle enzymes; m6A-RMRs; proinflammatory transcription factors NF-κB and JAK/STAT; tumor suppressors TP53, PTEN, and APC; histone methyltransferases; and DNA methyltransferases play significant roles in modulating m6A-RMRs. After analyzing 102 transcriptomic datasets according to our flow chart (Figure 1), we made significant findings as summarized in Abstract. Our findings reveal new information about the roles of elevated m6A-RMRs in the pathogenesis of 41 illnesses and tumors, as well as new therapeutic targets for inflammation, sepsis, trauma, organ failures, autoimmunity, metabolic cardiovascular disorders, and cancers.Figure 1
Flow chart of the study. Data mining work includes two parts: (I) the expression changes of m6A-RMRs in diseases including acute inflammation, sepsis, acute respiratory distress syndrome, shock, trauma, cardiovascular diseases (CVDs), autoimmune diseases, organ failures, and cancers were examined; (II) cellular mechanisms including macrophages and CD4+Foxp3+ regulatory T cell (Treg) modulation and molecular mechanisms including the role of danger-associated molecular pattern receptors (DAMP receptors); proinflammatory cytokines; immune checkpoint and costimulation receptors; methionine-homocysteine-methyl donation cycle enzymes; m6A-RMRs; proinflammatory transcription factors NF-κB and JAK/STAT; tumor suppressors TP53, PTEN, and APC; histone methyltransferases; and DNA methyltransferases in regulation of m6A-RMRs were explored.


## 2. Materials and Methods

### 2.1. Expression Levels of m6A-RMRs in Patients with Various Inflammatory Disorders and Tumors

We collected 23 microarray datasets of acute inflammation, metabolic and cardiovascular diseases, autoimmune diseases, and organ failures (Table1); one microarray of Middle-East respiratory syndrome (MERS) coronavirus-infected human microvascular endothelial cells; one microarray dataset (nine comparisons) of subacute respiratory syndrome coronavirus- (SARS-CoV-) infected human airway epithelial cells; one microarray dataset (23 comparisons) of influenza virus-infected lung epithelial cells (Table 2); and eight microarray datasets of endothelial cells (Table 3) from National Institutes of Health- (NIH-) National Center for Biotechnology Information- (NCBI-) Gene Expression Omnibus (GEO) databases (https://www.ncbi.nlm.nih.gov/gds/). These datasets were analyzed with GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/). Some datasets were overlapped with our previous studies [44]. In addition, the Oncomine database (https://www.oncomine.org) was used to analyze the gene expression profile from 19 tumors [45], with threshold parameters of foldchange>2, p<0.05, and gene rank in the top 10%. Because these microarray studies employed diverse cell types, we were unable to compare the effects of illness circumstances on m6A-RMR regulation in the same cell types. It is worth noting that our strategy was well justified. For example, we and others frequently investigated gene expression in nonideal heterogeneous peripheral blood mononuclear cell populations (PBMCs) in pathophysiological conditions, which are made up of a variety of cell types (also see Discussion).Table 1
List of microarray datasets (23) of acute inflammations, metabolic diseases, autoimmune diseases, and organ failures in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) we collected in this study.
GEO IDDisease/phenotypeTissueComparisonNo. of samplesPMIDAcute inflammationGSE32707Lung injuryWhole bloodSepsis day 0 vs. no sepsis30/3422461369Lung injuryWhole bloodSepsis day 7 vs. no sepsis28/3422461369Lung injuryWhole bloodSepsis/ARDS day 0 vs. no sepsis ARDS18/3422461369Lung injuryWhole bloodSepsis/ARDS day 7 vs. no sepsis ARDS13/3422461369GSE13904Septic shockWhole bloodSepsis day 1 vs. normal32/1819325468Septic shockWhole bloodSepsis day 3 vs. normal20/1819325468Septic shockWhole bloodSepsis/shock day 1 vs. normal67/1819325468Septic shockWhole bloodSepsis/shock day 3 vs. normal39/1819325468GSE5580Severe traumaMonocytesSevere trauma vs. health7/717032758Severe traumaLeukocytesSevere trauma vs. health7/717032758Severe traumaT cellsSevere trauma vs. health7/717032758Metabolic diseaseGSE48964ObeseAdipose stem cellsMorbidly obese vs. nonobese3/324040759GSE55200MHOSubcutaneous adiposeMHO vs. LH8/724933025MUOSubcutaneous adiposeMUO vs. LH8/724933025GSE94752Obese IRAdipocytesObese IR vs. lean18/928570579Obese ISAdipocytesObese IS vs. lean21/928570579GSE23343T2DLiverT2D vs. normal glucose tolerance10/721035759GSE29221T2DSkeletal muscleT2D vs. nondiabetes12/1223308243GSE29226T2DSubcutaneous adiposeT2D vs. nondiabetes12/1223308243GSE29231T2DVisceral adiposeT2D vs. nondiabetes12/1223308243GSE28829AtherosclerosisCarotid arteryAdvanced plaque vs. early plaque16/1322388324GSE41571AtherosclerosisPlaque macrophagesRuptured plaques vs. stable plaque5/623122912GSE1010FCHLymphoblastic cellsFCH vs. normal12/1215388524GSE6054FHC and atherosclerosisMonocytesFHC homozygote vs. control4/1319040724GSE6088FHC and atherosclerosisT cellFHC homozygote vs. control3/1319040724Autoimmune diseaseGSE97779RAMacrophages∗RA vs. normal9/528813657GSE109248ACLESkinACL vs. normal7/1429889098CCLESkinCCL vs. normal6/1429889098PsoriasisSkinPsoriasis vs. normal17/1429889098SCLESkinSCL vs. normal12/1429889098GSE38713UCSigmoid colon or rectumUC active involved vs. normal22/1323135761GSE3365UCPBMCUC vs. normal26/4216436634CDPBMCCD vs. normal59/4216436634Organ failureGSE76701Heart failureLeft ventricleFailing heart vs. nonfailing heart4/426756417GSE38941HBV-ALFLiverHBV-ALF vs. normal17/1023185381GSE37171ESRFWhole bloodChronic renal failure vs. healthy controls75/4023809614GSE15072CKD hemodialysisPBMCHemodialysis vs. healthy controls17/819698090Abbreviations: No. of samples:numberofsamples=numberofdiseases/numberofcontrols; ARDS: acute respiratory distress syndrome; ALI: acute liver injury; ALF: acute liver failure; PBMC: peripheral blood mononuclear cell; IS: insulin sensitive; IR: insulin resistance; MHO: metabolically healthy obese; MUO: metabolically unhealthy obese; LH: lean health; T2D: type 2 diabetes; FCH: familial combined hyperlipidemia; FHC: familial hypercholesterolemia; RA: rheumatoid arthritis; ACLE: acute cutaneous lupus; CCLE: chronic cutaneous lupus; SCLE: subacute cutaneous lupus; UC: ulcerative colitis; CD: Crohn’s disease; HBV-ALF: hepatitis B virus-associated acute liver failure; ESRF: end-stage renal failure; CKD: chronic kidney disease; PBMC: peripheral blood mononuclear cell, macrophages; ∗macrophages from synovial fluids for RA and blood-derived macrophages for control. All the studies and samples are from human.Table 2
Three microarray datasets of respiratory virus infections with time course including Middle-East respiratory syndrome (MERS) coronavirus, severe acute respiratory syndrome (SARS) coronavirus, and avian influenza virus infection in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected to analyze the expression changes of m6A-RNA methylation regulators.
GEO IDComparisonCell/tissueNo. of samplesPMIDGSE79218icMERS-inoculated vs. mock-inoculated (0 hour)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (12 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (24 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (36 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (48 hours)HMEC5/428830941GSE47960SARS-CoV-infected vs. mock-infected (0 hour)HAEC4/323935999SARS-CoV-infected vs. mock-infected (12 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (24 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (36 hours)HAEC2/323935999SARS-CoV-infected vs. mock-infected (48 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (60 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (72 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (84 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (96 hours)HAEC4/423935999H1N1-infected vs. mock-infected (0 hour)HAEC3/323935999H1N1-infected vs. mock-infected (6 hours)HAEC3/323935999H1N1-infected vs. mock-infected (12 hours)HAEC3/323935999H1N1-infected vs. mock-infected (18 hours)HAEC3/323935999H1N1-infected vs. mock-infected (24 hours)HAEC3/323935999H1N1-infected vs. mock-infected (36 hours)HAEC3/323935999H1N1-infected vs. mock-infected (48 hours)HAEC4/323935999GSE49840H7N9-infected vs. mock-infected (3 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (3 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (3 hours)Calu-3 cells3/424496798H5N1-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (3 hours)Calu-3 cells3424496798H3N2-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798Abbreviations: No. of samples:numberofsamples=numberofdiseases/numberofcontrols; MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome; H1N1: influenza A, one type of avian influenza virus.Table 3
Eight microarray datasets about endothelial cells in different conditions in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected to analyze the changes of m6A methylation regulators.
GEO IDComparisonCell/tissueNo. of samplesPMIDGSE59226Influenza virus-infected vs. inactivate virus-infectedHuman umbilical vein ECs2/225863179GSE1377Kaposi sarcoma-associated herpes virus-infected for 7 days vs. uninfected controlPrimary human dermal ECs2/215220917GSE588310 ng LPS stimulation for 4 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NA10 ng LPS stimulation for 8 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NA10 ng LPS stimulation for 24 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NAGSE39201000 IU IFNα treated for 5 hours vs. untreated controlHuman umbilical vein ECs5/517202376, 195530031000 IU IFNβ treated for 5 hours vs. untreated controlHuman umbilical vein ECs2/517202376, 195530031000 IU IFNγ treated for 5 hours vs. untreated controlHuman umbilical vein ECs3/517202376, 19553003GSE85987NOTCH1 siRNA vs. scrambled control siRNAHuman umbilical vein ECs3/329449332NOTCH1 siRNA+IL-1β treated 24 hours vs. scrambled siRNAHuman umbilical vein ECs3/329449332GSE72633NOTCH1 siRNA vs. scrambled control siRNAHuman aortic ECs3/326552708oxPAPC treated vs. scrambled control siRNAHuman aortic ECs3/326552708GSE26953Oscillatory shear vs. laminar shear (fibrosa)Human aortic valvular ECs6/621705672Oscillatory shear vs. laminar shear (ventricularis)Human aortic valvular ECs6/621705672GSE39264apoE KO vs. WTMouse aortic ECs4/623990205LPS treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205oxLDL treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205oxPAPC treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205Abbreviations: LPS: lipopolysaccharide; IFN: interferon; NOTCH1: notch receptor 1; ox-PAPC: oxidized-1-palmitoyl-2-arachidonyl-sn-glycerol-3-phosphocholine; siRNA: small interfering RNA; apoE: apolipoprotein E; KO: knockout; WT: wild type; ox-LDL: oxidized low-density lipoprotein; DMEM: Dulbecco’s modified eagle medium; No. of samples:numberofsamples=numberofdiseases/numberofcontrols.


### 2.2. Expression Profile of m6A-RMRs in Single-Cell RNA Sequencing (scRNA-Seq) Datasets from Studies of Sepsis, Atherosclerosis, Tumors, and Endothelial Cell

Five scRNA-Seq datasets were collected from the Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell), including one study about sepsis, one study about atherosclerosis, two studies about tumors (astrocytoma and melanoma), and one study about endothelial cell populations (Supplementary Table 1). The expressions of 29 m6A-RMRs were online analyzed.


### 2.3. Expression Regulation Analysis of m6A-RMRs from Deficiency of Folate Cycle and Metabolism-Related Enzymes, m6A-RMRs, H3K4 Methylase, DNA Methyltransferase, Regulatory T Cells’ Signature Genes, Proinflammatory Cytokines, Oncogene, Tumor Suppressors, and Immune Checkpoint Receptors

The 68 microarray datasets in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected in analyzing the regulatory mechanisms of m6A-RMRs (Supplementary Table 2). There are six microarrays about deficiencies of the folate cycle and metabolism-related enzymes and m6A-RMRs, four datasets of deficiencies of H3K4 methylase, six datasets of deficiencies of DNA methyltransferase, three datasets of Treg versus conventional T cells, 11 datasets of deficiencies of Treg cells’ signature genes, one dataset of macrophage polarization, eight datasets of deficiencies of proinflammatory cytokines, nine datasets of deficiencies of oncogene, ten datasets of deficiencies of tumor suppressors, and ten datasets of deficiencies of immune checkpoint receptors.


### 2.4. Statistical Analysis of Microarray Data

The expression changes of m6A-RMRs are not big enough in diseases; we may miss some important information if the expression change fold thresholds are set high. Thus, the expression changes were listed in the results with p<0.05 (statistical significance), m6A-RMRs with expression changes more than 1-fold (red) were defined as the upregulated genes, while genes with expression decreased more than 1-fold (blue) were defined as downregulated genes. The missing value was marked with “NA,” and “NA” was excluded in the total count number.


## 2.1. Expression Levels of m6A-RMRs in Patients with Various Inflammatory Disorders and Tumors

We collected 23 microarray datasets of acute inflammation, metabolic and cardiovascular diseases, autoimmune diseases, and organ failures (Table1); one microarray of Middle-East respiratory syndrome (MERS) coronavirus-infected human microvascular endothelial cells; one microarray dataset (nine comparisons) of subacute respiratory syndrome coronavirus- (SARS-CoV-) infected human airway epithelial cells; one microarray dataset (23 comparisons) of influenza virus-infected lung epithelial cells (Table 2); and eight microarray datasets of endothelial cells (Table 3) from National Institutes of Health- (NIH-) National Center for Biotechnology Information- (NCBI-) Gene Expression Omnibus (GEO) databases (https://www.ncbi.nlm.nih.gov/gds/). These datasets were analyzed with GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/). Some datasets were overlapped with our previous studies [44]. In addition, the Oncomine database (https://www.oncomine.org) was used to analyze the gene expression profile from 19 tumors [45], with threshold parameters of foldchange>2, p<0.05, and gene rank in the top 10%. Because these microarray studies employed diverse cell types, we were unable to compare the effects of illness circumstances on m6A-RMR regulation in the same cell types. It is worth noting that our strategy was well justified. For example, we and others frequently investigated gene expression in nonideal heterogeneous peripheral blood mononuclear cell populations (PBMCs) in pathophysiological conditions, which are made up of a variety of cell types (also see Discussion).Table 1
List of microarray datasets (23) of acute inflammations, metabolic diseases, autoimmune diseases, and organ failures in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) we collected in this study.
GEO IDDisease/phenotypeTissueComparisonNo. of samplesPMIDAcute inflammationGSE32707Lung injuryWhole bloodSepsis day 0 vs. no sepsis30/3422461369Lung injuryWhole bloodSepsis day 7 vs. no sepsis28/3422461369Lung injuryWhole bloodSepsis/ARDS day 0 vs. no sepsis ARDS18/3422461369Lung injuryWhole bloodSepsis/ARDS day 7 vs. no sepsis ARDS13/3422461369GSE13904Septic shockWhole bloodSepsis day 1 vs. normal32/1819325468Septic shockWhole bloodSepsis day 3 vs. normal20/1819325468Septic shockWhole bloodSepsis/shock day 1 vs. normal67/1819325468Septic shockWhole bloodSepsis/shock day 3 vs. normal39/1819325468GSE5580Severe traumaMonocytesSevere trauma vs. health7/717032758Severe traumaLeukocytesSevere trauma vs. health7/717032758Severe traumaT cellsSevere trauma vs. health7/717032758Metabolic diseaseGSE48964ObeseAdipose stem cellsMorbidly obese vs. nonobese3/324040759GSE55200MHOSubcutaneous adiposeMHO vs. LH8/724933025MUOSubcutaneous adiposeMUO vs. LH8/724933025GSE94752Obese IRAdipocytesObese IR vs. lean18/928570579Obese ISAdipocytesObese IS vs. lean21/928570579GSE23343T2DLiverT2D vs. normal glucose tolerance10/721035759GSE29221T2DSkeletal muscleT2D vs. nondiabetes12/1223308243GSE29226T2DSubcutaneous adiposeT2D vs. nondiabetes12/1223308243GSE29231T2DVisceral adiposeT2D vs. nondiabetes12/1223308243GSE28829AtherosclerosisCarotid arteryAdvanced plaque vs. early plaque16/1322388324GSE41571AtherosclerosisPlaque macrophagesRuptured plaques vs. stable plaque5/623122912GSE1010FCHLymphoblastic cellsFCH vs. normal12/1215388524GSE6054FHC and atherosclerosisMonocytesFHC homozygote vs. control4/1319040724GSE6088FHC and atherosclerosisT cellFHC homozygote vs. control3/1319040724Autoimmune diseaseGSE97779RAMacrophages∗RA vs. normal9/528813657GSE109248ACLESkinACL vs. normal7/1429889098CCLESkinCCL vs. normal6/1429889098PsoriasisSkinPsoriasis vs. normal17/1429889098SCLESkinSCL vs. normal12/1429889098GSE38713UCSigmoid colon or rectumUC active involved vs. normal22/1323135761GSE3365UCPBMCUC vs. normal26/4216436634CDPBMCCD vs. normal59/4216436634Organ failureGSE76701Heart failureLeft ventricleFailing heart vs. nonfailing heart4/426756417GSE38941HBV-ALFLiverHBV-ALF vs. normal17/1023185381GSE37171ESRFWhole bloodChronic renal failure vs. healthy controls75/4023809614GSE15072CKD hemodialysisPBMCHemodialysis vs. healthy controls17/819698090Abbreviations: No. of samples:numberofsamples=numberofdiseases/numberofcontrols; ARDS: acute respiratory distress syndrome; ALI: acute liver injury; ALF: acute liver failure; PBMC: peripheral blood mononuclear cell; IS: insulin sensitive; IR: insulin resistance; MHO: metabolically healthy obese; MUO: metabolically unhealthy obese; LH: lean health; T2D: type 2 diabetes; FCH: familial combined hyperlipidemia; FHC: familial hypercholesterolemia; RA: rheumatoid arthritis; ACLE: acute cutaneous lupus; CCLE: chronic cutaneous lupus; SCLE: subacute cutaneous lupus; UC: ulcerative colitis; CD: Crohn’s disease; HBV-ALF: hepatitis B virus-associated acute liver failure; ESRF: end-stage renal failure; CKD: chronic kidney disease; PBMC: peripheral blood mononuclear cell, macrophages; ∗macrophages from synovial fluids for RA and blood-derived macrophages for control. All the studies and samples are from human.Table 2
Three microarray datasets of respiratory virus infections with time course including Middle-East respiratory syndrome (MERS) coronavirus, severe acute respiratory syndrome (SARS) coronavirus, and avian influenza virus infection in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected to analyze the expression changes of m6A-RNA methylation regulators.
GEO IDComparisonCell/tissueNo. of samplesPMIDGSE79218icMERS-inoculated vs. mock-inoculated (0 hour)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (12 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (24 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (36 hours)HMEC5/528830941icMERS-inoculated vs. mock-inoculated (48 hours)HMEC5/428830941GSE47960SARS-CoV-infected vs. mock-infected (0 hour)HAEC4/323935999SARS-CoV-infected vs. mock-infected (12 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (24 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (36 hours)HAEC2/323935999SARS-CoV-infected vs. mock-infected (48 hours)HAEC4/323935999SARS-CoV-infected vs. mock-infected (60 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (72 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (84 hours)HAEC4/423935999SARS-CoV-infected vs. mock-infected (96 hours)HAEC4/423935999H1N1-infected vs. mock-infected (0 hour)HAEC3/323935999H1N1-infected vs. mock-infected (6 hours)HAEC3/323935999H1N1-infected vs. mock-infected (12 hours)HAEC3/323935999H1N1-infected vs. mock-infected (18 hours)HAEC3/323935999H1N1-infected vs. mock-infected (24 hours)HAEC3/323935999H1N1-infected vs. mock-infected (36 hours)HAEC3/323935999H1N1-infected vs. mock-infected (48 hours)HAEC4/323935999GSE49840H7N9-infected vs. mock-infected (3 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H7N9-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (3 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H7N7-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (3 hours)Calu-3 cells3/424496798H5N1-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H5N1-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (3 hours)Calu-3 cells3424496798H3N2-infected vs. mock-infected (7 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (12 hours)Calu-3 cells4/424496798H3N2-infected vs. mock-infected (24 hours)Calu-3 cells4/424496798Abbreviations: No. of samples:numberofsamples=numberofdiseases/numberofcontrols; MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome; H1N1: influenza A, one type of avian influenza virus.Table 3
Eight microarray datasets about endothelial cells in different conditions in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected to analyze the changes of m6A methylation regulators.
GEO IDComparisonCell/tissueNo. of samplesPMIDGSE59226Influenza virus-infected vs. inactivate virus-infectedHuman umbilical vein ECs2/225863179GSE1377Kaposi sarcoma-associated herpes virus-infected for 7 days vs. uninfected controlPrimary human dermal ECs2/215220917GSE588310 ng LPS stimulation for 4 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NA10 ng LPS stimulation for 8 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NA10 ng LPS stimulation for 24 hours vs. no LPS stimulationHuman lung microvascular ECs4/4NAGSE39201000 IU IFNα treated for 5 hours vs. untreated controlHuman umbilical vein ECs5/517202376, 195530031000 IU IFNβ treated for 5 hours vs. untreated controlHuman umbilical vein ECs2/517202376, 195530031000 IU IFNγ treated for 5 hours vs. untreated controlHuman umbilical vein ECs3/517202376, 19553003GSE85987NOTCH1 siRNA vs. scrambled control siRNAHuman umbilical vein ECs3/329449332NOTCH1 siRNA+IL-1β treated 24 hours vs. scrambled siRNAHuman umbilical vein ECs3/329449332GSE72633NOTCH1 siRNA vs. scrambled control siRNAHuman aortic ECs3/326552708oxPAPC treated vs. scrambled control siRNAHuman aortic ECs3/326552708GSE26953Oscillatory shear vs. laminar shear (fibrosa)Human aortic valvular ECs6/621705672Oscillatory shear vs. laminar shear (ventricularis)Human aortic valvular ECs6/621705672GSE39264apoE KO vs. WTMouse aortic ECs4/623990205LPS treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205oxLDL treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205oxPAPC treated for 4 hours vs. DMEM treated for 4 hoursMouse aortic ECs3/323990205Abbreviations: LPS: lipopolysaccharide; IFN: interferon; NOTCH1: notch receptor 1; ox-PAPC: oxidized-1-palmitoyl-2-arachidonyl-sn-glycerol-3-phosphocholine; siRNA: small interfering RNA; apoE: apolipoprotein E; KO: knockout; WT: wild type; ox-LDL: oxidized low-density lipoprotein; DMEM: Dulbecco’s modified eagle medium; No. of samples:numberofsamples=numberofdiseases/numberofcontrols.


## 2.2. Expression Profile of m6A-RMRs in Single-Cell RNA Sequencing (scRNA-Seq) Datasets from Studies of Sepsis, Atherosclerosis, Tumors, and Endothelial Cell

Five scRNA-Seq datasets were collected from the Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell), including one study about sepsis, one study about atherosclerosis, two studies about tumors (astrocytoma and melanoma), and one study about endothelial cell populations (Supplementary Table 1). The expressions of 29 m6A-RMRs were online analyzed.


## 2.3. Expression Regulation Analysis of m6A-RMRs from Deficiency of Folate Cycle and Metabolism-Related Enzymes, m6A-RMRs, H3K4 Methylase, DNA Methyltransferase, Regulatory T Cells’ Signature Genes, Proinflammatory Cytokines, Oncogene, Tumor Suppressors, and Immune Checkpoint Receptors

The 68 microarray datasets in the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds/) were collected in analyzing the regulatory mechanisms of m6A-RMRs (Supplementary Table 2). There are six microarrays about deficiencies of the folate cycle and metabolism-related enzymes and m6A-RMRs, four datasets of deficiencies of H3K4 methylase, six datasets of deficiencies of DNA methyltransferase, three datasets of Treg versus conventional T cells, 11 datasets of deficiencies of Treg cells’ signature genes, one dataset of macrophage polarization, eight datasets of deficiencies of proinflammatory cytokines, nine datasets of deficiencies of oncogene, ten datasets of deficiencies of tumor suppressors, and ten datasets of deficiencies of immune checkpoint receptors.


## 2.4. Statistical Analysis of Microarray Data

The expression changes of m6A-RMRs are not big enough in diseases; we may miss some important information if the expression change fold thresholds are set high. Thus, the expression changes were listed in the results with p<0.05 (statistical significance), m6A-RMRs with expression changes more than 1-fold (red) were defined as the upregulated genes, while genes with expression decreased more than 1-fold (blue) were defined as downregulated genes. The missing value was marked with “NA,” and “NA” was excluded in the total count number.


## 3. Results

### 3.1. A Few m6A-RNA Methylation Regulators (m6A-RMRs) Are Upregulated, and Most m6A-RMRs Are Downregulated in Sepsis, Sepsis plus Acute Respiratory Distress Syndrome, Sepsis plus Shock, and Trauma, and Upregulated Two RNA Methyltransferases WTAP and PCIF1 and Three RNA Binding Proteins YTHDF3, IGF2BP2, and IGF2BP3 May Promote Acute Inflammations

We hypothesized that pathological conditions significantly modulate the expressions of m6A-RMRs in cell type-specific and disease-specific manners. To test this hypothesis, we collected 29 m6A-RMRs in five groups from PubMed database (https://pubmed.ncbi.nlm.nih.gov/) (Figure 2 and Supplementary Table 3). The 29 m6A-RMRs included (1) 10 m6A-RNA methylation writers (methyltransferases) including METTL14, METTL3, WTAP, KIAA1429, ZC3H13,CBLL1, RBM15, METTL16,RBM15B, and PCIF1; (2) two m6A-RNA demethylases, FTO and ALKBH5; (3) 11 RNA binding proteins (readers) including YTHDC1, YTHDF1, YTHDF2, YTHDF3, YTHDC2, HNRNPA2B1, EIF3A, IGF2BP1, IGF2BP2, IGF2BP3, and FMR1; (4) three m6A-dependent RNA binding proteins including HNRNPC, RMMX, and PRRC2A; and (5) three m6A-repelled RNA binding proteins including ELAVL1, G3BP1, and G3BP2. As shown in Figure 3(a), one to four out of 28 m6A-RMRs were upregulated; and 14 to 15 out of 28 m6A-RMRs were downregulated in 0 day and 7 days in patients with sepsis and sepsis plus acute respiratory distress syndrome (ARDS) [46], respectively. In the second datasets with sepsis and sepsis plus shock, two to four out of 26 m6A-RMRs were upregulated; and 10 to 17 out of 26 m6A-RMRs were downregulated in 1 day and 3 days in patients with sepsis and sepsis plus shock [47, 48], respectively. In the third datasets with leukocytes, monocytes, and T cells from trauma, zero to one out of 22 m6A-RMRs was upregulated; and 5 to 11 out of 22 m6A-RMRs were downregulated in leukocytes, monocytes, and T cells from trauma [49], respectively.Figure 2
An overview of RNA methylation processes, which include (a) homocysteine-methionine-folate cycles for methyl generation and donation; (b) 29 m6A-RNA methylation regulators (m6A-RMRs) are classified into five groups: (i) RNA methylation writers, (ii) RNA methylation erasers, (iii) RNA methylation readers, (iv) m6A-dependent RNA binding proteins (RBPs), and (v) m6A repelled RNAs; (c) the hypothesis we proposed. (a) The methyl group is transferred to proteins, nucleic acids, and other biochemicals from the biochemical reaction of S-adenosylmethione (SAM) to S-adenosylhomocysteine (SAH) in homocysteine-methionine metabolic cycle. For RNA methylation, mRNA, lncRNA, tRNA, SnRNA, pre-miRNA, and CirRNA can be methylated in many different ways. In addition to the basic methylated forms, the common methylated forms include N6-methyladenosine (m6A), N1-methyladenosine, N1-methyladenosine, 2′-O-methyls at mRNA, N6-methyladenosine at lncRNA, pre-miRNA, circRNA [50–55], N1-methyladenosine at tRNA, 2′-O-methyls at rRNA, and snRNA [56]. (b) The reported 29 m6A-RMRs were chosen for analysis, including 10 m6A methyltransferase enzymes (writers), two m6A demethylases (erasers), 11 m6A binding proteins (readers), three m6A dependent on RNA binding proteins, and three RNA binding proteins repelled by m6A. The detailed information including the reference articles of the 29 m6A-RMRs is listed in Supplementary Table 3. (c) The methyl donor is derived from folate cycle and coupled homocysteine-methionine cycle. When the donor source is impaired, the DNA, RNA, and protein methylations will be in disorder. We proposed the hypothesis: mRNA methylation disorders will affect the expressions of m6A-RMRs; then, the m6A-RMRs will be involved in inflammation, metabolic diseases, and tumors by methylating or demethylating target genes including RNAs.
(a)(b)(c)Figure 3
Most m6A-RNA methylation regulators (m6A-RMRs) were downregulated in acute inflammatory diseases, suggesting that most m6A-RMRs are not required for the pathogenesis of acute inflammatory diseases. (a) The results showed that m6A-RMRs were downregulated (marked in blue) in sepsis and acute respiratory distress syndrome (ARDS), sepsis and shock, and severe trauma. (b) Venn diagram showed that in the same base disease, there were the shared upregulated or downregulated m6A regulators. There were five upregulated m6A-RMRs including shared writers WTAP and 19 downregulated regulators including nine shared genes in sepsis and ARDS of lung injury. There were six upregulated regulators including common writer PCIF1 and reader IGF2BP3 and 17 downregulated m6A methylation regulators (10 common genes) in sepsis and combined shock. WTAP was upregulated on the first day of sepsis and shock. In severe trauma, there were two genes upregulated and 15 genes downregulated including one common writer METTL3. In the three acute inflammatory diseases, nine downregulated m6A regulators (METTL3, RBM15, FTO, YTHDC2, HNRNPA2B1, EIF3A, HNRNPC, G3BP1, and CBLL1) were shared by three acute inflammation diseases. Note: the red marked genes are those that are up- or downregulated in different diseases. (c) 29 m6A-RMRs were examined in single-cell sequencing dataset online (https://singlecell.broadinstitute.org/single_cell) in an immune-cell signature of bacterial sepsis study (PMID: 32066974). The 11 out of 29 m6A methylation regulators including WTAP, ZC3H13, YTHDC1, YTHDF2, HNRNPA2B1, EIF3A, HNRNPC, RBMX, ELAVL1, G3BP1, and G3BP2 expressed differentially in different cell populations of bone-marrow under stimulation. (d) The expressions of most m6A methylation regulators except HNRNPA2B1 and HNRNPC were lower than those of others in PBMC subset cells (p<0.05). Abbreviations: ARDS: acute respiratory distress syndrome; FC: fold change; NA: not available (missing value); NK: natural killer cell; GMP: granulocyte-macrophage progenitor; MEP: megakaryocyte-erythroid progenitors; MYL: myeloblasts; DC: dendritic cell; CMP: common myeloid progenitors; HSC/MPP: hematopoietic stem cells and multipotent progenitors; RBC: red blood cells; MS1: CD14+ cells with high expression of resistin (RETN), arachidonate 5-lipoxygenase activating protein (ALOX5AP), and interleukin-1 receptor type 2 (IL1R2); MS2: with high expression of class II major histocompatibility complex (MHC); MS3: similar to nonclassical CD16hi monocytes; MS4: which is composed of the remaining CD14+ cells that express low levels of both class II MHC and inflammatory cytokines; NHP: nonhuman primate; mTB: Mycobacterium tuberculosis; SHIV: simian-human immunodeficiency virus; CAF: cancer-associated fibroblasts.
(a)(b)(c)(d)We then analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. As shown in Figure 3(b), four diseases including sepsis at 0 day and 7 days and sepsis plus ARDS at 0 day and 7 days shared one RNA methyltransferase WTAP. In the second sepsis datasets, four diseases including sepsis at 1 day and 3 days and sepsis plus shock at 0 day and 7 days shared one RNA methyltransferase PCIF1 and one RNA methylation reader IGF2BP3. In the trauma datasets, three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated m6A-RMRs. When comparing three groups of acute inflammations, sepsis plus ARDS shared three upregulated m6A-RMRs such as writers WTAP, PCIF1, and reader YTHDF3 with sepsis plus shock; sepsis plus ARDS shared upregulation of one RNA binding protein IGF2BP2 with trauma, and sepsis plus shock shared one RNA binding protein IGF2BP3 with trauma.We noticed that in the trauma datasets (Figure3(b)), three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated RNA methylation regulators, suggesting that upregulation of m6A-RMRs is in a cell-specific manner. To further examine this issue, we collected single-cell RNA-sequencing (scRNA-Seq) datasets in sepsis [57] in a comprehensive single-cell sequencing database (https://singlecell.broadinstitute.org/single_cell). As shown in Figure 3(c), the 11 out of 29 m6A-RMRs (38%) including WTAP, ZC3H13, YTHDC1, YTHDF2, HNRNPA2B1, EIF3A, HNRNPC, RBMX, ELAVL1, G3BP1, and G3BP2 were expressed differentially in 13 different immune cell types (natural killer cell (NK), granulomyeloid progenitor (GMP), megakaryocyte-erythrocyte progenitor (MEP), common lymphoid progenitor (CLP), myeloblasts (MYL), T cells, dendritic cell (DC), common myeloid progenitor (CMP), monocyte (mono), hematopoietic stem cell/multipotent progenitor (HSC/MPP), B cells, CD14+RETN+ALOX5AP+IL1R2+ monocyte (iMS1), and red blood cells (RBC)) in bacterial sepsis. The expressions of most m6A-RMRs except HNRNPA2B1 and HNRNPC were lower in monocyte subsets than those in other peripheral mononuclear cell (PBMC) types (Figure 3(d)).These results have demonstrated that first, a few m6A-RMRs are upregulated and most m6A-RMRs are downregulated in sepsis, sepsis plus acute respiratory distress syndrome, sepsis plus shock, and trauma; second, two RNA methyltransferases WTAP and PCIF1 and three RNA binding proteins such as YTHDF3, IGF2BP2, and IGF2BP3 are commonly upregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that these five m6A-RMRs are the emergency m6A-RMRs for promoting acute inflammatory diseases; third, nine m6A-RMRs including METTL3, RBM15, FTO, YTHDC2, HNRNPA2B1, EIF3A, HNRNPC, G3BP1, and CBLL1 are commonly downregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that those m6A-RMRs play more important roles in maintaining homeostasis and suppressing inflammation than emergency roles for acute inflammations; and fourth, the expressions of 38% m6A-RMRs in immune cell types in response to sepsis stimulation are different.


### 3.2. Type 2 Diabetes Has More Modulation of m6A-RMR Expressions Than Atherogenic Diseases and Obesity; Nearly Half of 29 m6A-RMRs Are Downregulated as Atherosclerosis Progression Compared with That of Atherosclerosis Regression

We hypothesized that major metabolic cardiovascular disease groups such as obesity [58, 59], type 2 diabetes, and atherogenic diseases differentially modulate the expressions of m6A-RMRs. To test this hypothesis, we collected five datasets of obesity including obese, metabolically unhealthy obesity (MUO), metabolically healthy obesity (MHO), obese with insulin resistance (ob IR), and obese with insulin sensitivity (ob IS); four datasets of type 2 diabetes; and five datasets of atherosclerosis, familial hypercholesterolemia (FHC) plus atherosclerosis, and familial combined hyperlipidemia (FCH) from the NCBI-GeoDataset (https://www.ncbi.nlm.nih.gov/gds/). As shown in Figure 4(a), in five diseases in the obesity group, zero to three m6A-RMRs were upregulated and zero to 10 m6A-RMRs were downregulated. However, in the four type 2 diabetes datasets, zero to 11 m6A-RMRs were upregulated and one to seven m6A-RMRs were downregulated. The five datasets of atherosclerotic diseases had modulations of m6A-RMRs higher than those in the obesity group but lower than those of type 2 diabetes group: zero to seven m6A-RMRs were upregulated, and zero to seven m6A-RMRs were downregulated.Figure 4
More m6A-RNA methylation regulators (m6A-RMRs) upregulated in type 2 diabetes and abnormal lipid metabolism than in obesity and obesity-related diseases. (a) The result showed that the majority m6A-RMRs were downregulated (marked in blue) in obesity and obesity-related diseases, and only the expression fold change of IGF2BP1 and IGF2BP3 are above two. The expression pattern of m6A-RMRs in different tissues of T2D and atherosclerosis disease is different. (b) Venn diagram showed that in obesity, the decreased genes are more than the increased genes (six and 15, respectively); in T2D, few genes are shared in different tissues; in atherosclerosis disease, PCIF and PRRC2A are commonly upregulated and METTL14 is commonly downregulated in macrophages and monocytes. In the three metabolic diseases, the majority m6A-RMRs showed the heterogeneity expression (genes marked in red). METTL14 is the common decreased m6A methylation regulator among obesity, T2D, and atherosclerosis diseases. Note: the red marked genes are those that are up- or downregulated in different diseases. Expression of m6A-RMRs is different in cell populations of atherosclerosis study. 29 RNA m6A-RMRs were examined in single-cell sequencing dataset online (https://singlecell.broadinstitute.org/single_cell) in atherosclerotic mice (PMID: 30830865). The results showed that expressions of Wtap, Pcif1, Alkbh5, Ythdc1, Ythdf1, Ythdf2, Ythdf3, Hnrnpa2b1, Eif3a, Fmr1, Hnrnpc, Prrc2a, G3bp1, and G3bp2 were decreased in progressive atherosclerosis compared with regression atherosclerosis. The expression of Elavl1 was increased in progressive atherosclerosis compared with regression atherosclerosis (Virma, Igf2bp1 not found) (p<0.05). Abbreviations: FC: fold change; T2D: type 2 diabetes; MUO: metabolically unhealthy obese; FCH: familial combined hyperlipidemia; FHC: familial hypercholesterolemia; NA: not available (missing value).
(a)(b)(c)We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis (Figure 4(b)). In six m6A-RMRs upregulated in the obesity group, two regulators IGF2BP3 (RNA methyltransferase) and G3BP1 (m6A repelled RNA binding protein) were shared by obesity IS and obesity IR. Among the 15 m6A-RMRs upregulated in four different tissues in type 2 diabetes, one regulator HNRNPA2B1 (reader) was shared by liver, subcutaneous adipose, and visceral adipose, and one regulator G3BP1 was shared by liver and visceral adipose. In 10 m6A-RMRs upregulated in atherosclerotic diseases, one regulator WTAP (RNA methyltransferase) was shared by atherosclerosis (athero) carotid artery and atheromacrophages, and two regulators PCIF1 (RNA methyltransferase) and PRRC2A (m6A dependent RNA binding protein) were shared by atheromacrophages and FHC and atheromonocytes. Among 21 m6A-RMRs upregulated in three major metabolic cardiovascular disease groups, one regulator WTAP (RNA methyltransferase) was shared by three major groups; one regulator IGF2BP3 was shared by obesity and athero; three regulators FTO (demethylase), YTHDF2 (RNA binding protein), and G3BP1 were shared by obesity and type 2 diabetes; and four regulators such as PCIF1 (RNA methyltransferase), PRRC2A (m6A-dependent RNA binding protein), YTHDC2 (RNA binding protein), and HNRNPC (m6A-dependent RNA binding protein) were shared by type 2 diabetes and atherosclerotic diseases.We then examined a hypothesis that atherosclerosis progression and regression differentially modulate the expressions of m6A-RMRs. We collected a dataset of scRNA-Seq in the scRNA-Seq database in the Broad Institute at MIT. As shown in Figure 4(c), the expressions of 14 out of 27 m6A-RMRs including Wtap, Pcif1, Alkbh5, Ythdc1, Ythdf1, Ythdf2, Ythdf3, Hnrnpa2b1, Eif3a, Fmr1, Hnrnpc, Prrc2a, G3bp1, and G3bp2 were decreased in the progressive atherosclerosis compared with those in the regressive atherosclerosis. The expression of Elavl1 was increased in the progressive atherosclerosis compared with that in the regressive atherosclerosis. Of note, the expressions of Virma and Igf2bp1 were not found. These results have illustrated that (1) atherosclerotic macrophages have higher upregulation of m6A-RMRs than atherosclerotic carotid artery, and FHC and atherosclerotic monocytes have more modulation of m6A-RMRs than FHC and atherosclerotic T cells; (2) type 2 diabetes adipose tissues have the highest modulation of m6A-RMRs (11 upregulated and three downregulated) among the 14 metabolic disease datasets; (3) obesity diseases have more downregulation than upregulation of m6A-RMRs, and (4) nearly half of 29 m6A-RMRs are downregulated as atherosclerosis progression compared with that of atherosclerosis regression.Additionally, for further understanding the m6A-RMRs’s modulation in cardiovascular diseases, our study of m6A-RMRs in atherosclerosis and other three studies of m6A-RMRs in cardiovascular disease (CVDs) [60–62] were compared. From Table 4, METTL14 is upregulated in our study and other two studies; FTO is downregulated in our study and other two studies. At least, these data revealed that in atherosclerosis and other CVDs, m6A writer METTL14 is upregulated and eraser FTO is downregulated.Table 4
Comparison of our study with other three studies about cardiovascular disease.
m6A-RMRsResults in our study (Figure4(a))Table1 in PMID: 34489709Table1 in PMID:34221238Table1 in PMID: 32910911M6A-RMRs in atherosclerosisRNA methylation-related enzymes in CVDsRoles and mechanisms of RNA m6A effectors in CVDsThe association between m6A methylation and CVDsUpregulated genesPCIF1, YTHDC2, IGF2BP2, IGF2BP3, HNRNPC, PRRC2A, ELAVL1METTL3, YTHDF2, METTL14, KIAA1429,N/AMETTL14Downregulated genesMETTL14, KIAA1429,ZC3H13, FTO, YTHDF3, HNRNPA2B1, FMR1, G3BP1, G3BP2FTOALKBH5, YTHDF2FTOGenes without expression changesMETTL3, CBLL1, RBM15B, YTHDF1, YTHDF2, EIF3A, IGF2BP1N/AN/AN/AExpression of genes not knownMETTL16, YTHDC1, RBMXN/AN/AN/AUncertain (paradox)WTAP, RBM15, ALKBH5YTHDF1METTL3, FTO, WTAPMETTL3Abbreviations: CVDs: cardiovascular diseases; N/A: not available.


### 3.3. Inflammatory Bowel Diseases and Rheumatoid Arthritis Modulate m6A-RMRs More Than Autoimmune Lupus and Psoriasis; and Some Autoimmune Diseases Share Five Upregulated m6A-RMRs such as PCIF1, G3BP2, G3BP1, WTAP, and FTO

We hypothesized that major autoimmune diseases including rheumatoid arthritis (RA), psoriasis (PS), acute cutaneous lupus (ACLE), chronic cutaneous lupus (CCLE), subacute cutaneous lupus (SCLE), Crohn’s disease (CD), and ulcerative colitis (UC) differentially modulate the expressions of m6A-RMRs. To examine this hypothesis, we collected four microarray datasets (eight comparisons) from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/). In a RA dataset, four out of 26 m6A-RMRs were upregulated; and 10 out of 26 m6A-RMRs were downregulated. In four lupus datasets, two to six out of 28 m6A-RMRs were upregulated; and one to four out of 28 m6A-RMRs were downregulated. In three colitis datasets, five to eight m6A-RMRs were upregulated; and eight to nine m6A-RMRs were downregulated (Figure 5).Figure 5
The m6A-RNA methylation regulators (m6A-RMRs) were more differentially expressed in rheumatoid arthritis (RA) and autoimmune skin diseases than those in other autoimmune diseases. (a) The expression changes of m6A-RMRs showed the higher expression fold changes in RA and skin autoimmune diseases than those in inflammatory bowel disease. KIAA1429 and PCIF1 were highly increased; and METTL14, CBLL1, and RBM15 were highly decreased in RA; WTAP, PRRC2A, and G3BP2 were highly increased genes in skin autoimmune disease psoriasis. (b) Venn diagram showed that there were seven upregulated m6A-RMRs and five downregulated m6A-RMRs in autoimmune skin diseases. The writer WTAP and reader HNRNPA2B1 were the common increased and decreased genes in autoimmune skin diseases. There were 13 upregulated m6A-RMRs and 16 downregulated m6A-RMRs in inflammatory bowel diseases. YTHDC2 and G3BP2 were the common downregulated m6A-RMRs. PCIF1 was the shared upregulated m6A-RMR by RA and inflammatory bowel disease. CBLL1, RBM15, YTHDF3, and EIF3A were the shared downregulated m6A-RMRs by RA and inflammatory bowel disease. There were 19 upregulated m6A-RMRs and 21 downregulated m6A-RMRs in these three types of autoimmune diseases. Note: the red marked genes are those that are up- or downregulated in different diseases (p<0.05). Abbreviations: RA: rheumatoid arthritis; ACLE: acute cutaneous lupus; CCLE: chronic cutaneous lupus; PS: psoriasis; SCLE: subacute cutaneous lupus; UC: ulcerative colitis; CD: Crohn’s disease. FC: fold change; NA: not available (missing value).
(a)(b)We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. In seven m6A-RMRs upregulated in the autoimmune skin disease group, one m6A-RMR WTAP was shared by four autoimmune skin diseases; one m6A-RMR G3BP1 was shared by three autoimmune skin diseases including ACLE, CCLE, and psoriasis; one m6A-RMR PRRC2A was shared by three autoimmune skin diseases including CCLE, psoriasis, and SCLE; and one m6A-RMR G3BP2 was shared by two autoimmune skin diseases including CCLE and psoriasis. In 13 m6A-RMRs upregulated in the inflammatory bowel disease group, one m6A-RMR ELAVL1 was shared by UC sigmoid colon or rectum and UC PBMCs; one m6A-RMR WTAP was shared by UC sigmoid colon or rectum and CD PBMCs; and four m6A-RMRs including ZC3H13, RBM15B, IGF2BP2, and IGF2BP3 were shared by CD PBMCs and UC PBMCs. In 19 m6A-RMRs upregulated in all eight autoimmune diseases, one m6A-RMR PCIF1 was shared by RA and inflammatory bowel disease; one m6A-RMR G3BP2 was shared by RA and autoimmune skin diseases; and three m6A-RMRs including G3BP1, WTAP, and FTO were shared by autoimmune skin diseases and inflammatory bowel diseases. Taken together, our results have shown that first, inflammatory bowel diseases and RA modulate m6A-RMRs more than autoimmune lupus and psoriasis; second, autoimmune skin diseases share four upregulated m6A-RMRs including G3BP1, G3BP2, PRRC2A, and WTAP; third, inflammatory bowel diseases share six upregulated m6A-RMRs including ELAVL1, WTAP, ZC3H13, RBM15B, IGF2BP2, and IGF2BP3; and fourth, autoimmune diseases have no any commonly shared m6A-RMRs but share five upregulated m6A-RMRs between groups including PCIF1, G3BP2, G3BP1, WTAP, and FTO.


### 3.4. Some Organ Failures Share Eight Upregulated m6A-RMRs between Groups including Four Writers WTAP, PCIF1, RBM15, and RBM15B; Two m6A-Dependent RNA Binding Proteins PRRC2A and HNRNPC; and Two m6A-Repelled RNA Binding Proteins IGF2BP2 and IGF2BP3; End-Stage Renal Failure (ESRF) Downregulates 85% of 26 m6A-RMRs, and Upregulation of 45% m6A-RMRs in Hemodialysis from ESRF (15%) May Be Associated with Clinical Benefits

We hypothesized that major organ failures including heart failure, hepatitis B virus-associated acute liver failure (HBV-ALF), end-stage renal failure (ESRF), and hemodialysis modulate the expressions of m6A-RMRs. We collected four microarray datasets from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/) to examine this hypothesis. As shown in Figure 6, in the heart failure dataset, six out of 26 m6A-RMRs were upregulated; and seven out of 26 m6A-RMRs were downregulated. In the HBV-ALF dataset, eight out of 26 m6A-RMRs were upregulated; and 17 out of 26 m6A-RMRs were downregulated. In the ESRF dataset, four out of 26 m6A-RMRs were upregulated; and 22 out of 26 m6A-RMRs were downregulated. In the hemodialysis dataset, 10 out of 22 m6A-RMRs were upregulated; and five out of 22 m6A-RMRs were downregulated. We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. In 20 m6A-RMRs upregulated in four organ failure groups, one upregulated m6A-RMR (RBM15B, RNA methyltransferase) was shared by HF and HBV-ALF; one upregulated m6A-RMR (PRRC2A) was shared by HF and ESRF; two upregulated m6A-RMRs (IGF2BP2 and IGF2BP3, RNA binding proteins) were shared by HBV-ALF and ESRF; and four m6A-RMRs including WTAP (RNA methyltransferase), RBM15 (RNA methyltransferase), PCIF1 (RNA methyltransferase), and HNRNPC (m6A-dependent RNA binding protein) were shared by HBV-ALF and hemodialysis.Figure 6
The m6A-RNA methylation regulators (m6A-RMRs) were differentially expressed in several different organ failure diseases. (a) More m6A-RMRs were significantly increased and decreased in hepatitis B virus- (HBV-) associated acute liver failure (HBV-ALF) and hemodialysis diseases. The expression fold changes of IGF2BP1 and IGF2BP2 were higher, and the expression fold changes of ZC3H13 and YTHDF3 were lower in HBV-ALF. The expression fold changes of most m6A-RMRs were lower in whole blood of end-stage renal failure (ESRF) while the expression fold changes of some m6A-RMRs (WTAP, PCIF1, and EIF3A) were higher in PBMC of hemodialysis compared with healthy control. (b) Venn diagram showed that there were no shared upregulated or downregulated m6A-RMRs among these four studies. Some m6A-RMRs were decreased between organ failure diseases: YTHDC2 and FMR1 were shared between HF and HBV-ALF; METTL14, FTO, and YTHDF1 were shared between HBV-ALF and ESRF; G3BP1 was shared between HBV-ALF and ESRF. Note: the red marked genes are those that are up- or downregulated in different diseases (p<0.05). Abbreviations: HBV-ALF: hepatitis B virus- (HBV-) associated acute liver failure; CKD: chronic kidney disease; ESRF: end-stage renal failure; FC: fold change; NA: not available (missing value).
(a)(b)These results demonstrated that first, four major organ failures have no commonly shared upregulated m6A-RMRs but share eight m6A-RMRs between groups including RBM15B, IGF2BP2, IGF2BP3, PRRC2A, WTAP, RBM15, PCIF1, and HNRNPC; second, hemodialysis is the only organ failure that upregulates more m6A-RMRs than downregulates m6A-RMRs; other organ failures have less upregulation of m6A-RMRs than downregulation of m6A-RMRs; third, surprisingly, ESRF and hemodialysis have no any shared upregulated m6A-RMRs, suggesting that the clinical improvements of hemodialysis from ESRF are benefited from upregulation of m6A-RMRs by hemodialysis; and fourth, ESRF modulates 100% of 26 m6A-RMRs, which is the highest modulation rate found among all the diseases examined.


### 3.5. MERS-CoV Infections at 36 Hours in Human Microvascular Endothelial Cells Modulate the Highest Numbers of m6A-RMRs (Upregulated 20 and Downregulated Four) among Three Types of Viral Infections (MERS-CoV, SARS-CoV, and Influenza Virus); and the Differences in Modulating the Expressions of m6A-RMRs May Be Related to the Virulence of Virus and Virus Replication

We hypothesized that viral infections with Middle East respiratory syndrome (MERS) coronavirus infection [63], severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV) [64], and influenza virus infections [65] modulate the expressions of m6A-RMRs. To test this hypothesis, we collected three groups of microarray datasets from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/). We found that MERS-CoV infection of human microvascular endothelial cells at the time course of 0, 12, 24, 36, and 48 hours (h) [63] after infection resulted in upregulation of two to 20 out of 28 m6A-RMRs and downregulation of one to 10 out of 28 m6A-RMRs (Figure 7). In addition, SARS-CoV infection of human airway epithelial cells at the time course of 0, 24, 36, 48, 60, 72, 84, and 96 hours (h) [64] after infection resulted in upregulation of two to nine out of 27 m6A-RMRs and downregulation of zero to eight out of 27 m6A-RMRs. H1N1 influenza virus infection of human airway epithelial cells at the time course of 0, 6, 12, 18, 24, 36, and 48 hours after infection resulted in upregulation of one to eight out of 27 m6A-RMRs and downregulation of five to 11 out of 27 m6A-RMRs. Moreover, infections of Calu-3 cells (a non-small-cell lung cancer cell line) with four different strains of influenza viruses (H7N9, H7N7, H5N1, and H3N2) [65] resulted in upregulation of four to 16, one to nine, two to 12, and one to eight out of 28 m6A-RMRs, respectively, and downregulation of three to 11, four to 17, three to 14, and four to 16 out of 28 m6A-RMRs, respectively. Taken together, these results have demonstrated that (1) MERS-CoV infections in human microvascular endothelial cells modulate the highest numbers of m6A-RMRs (upregulated 20 and downregulated four at the time course of 36 hours) among the three types of viral infections examined; (2) similar to MERS-CoV infection, SARS-CoV infections in human airway epithelial cells upregulated more m6A-RMRs than downregulated but significantly less than that in MERS-CoV infections; (3) influenza virus (H1N1) infection in human airway epithelial cells downregulated more m6A-RMRs than upregulated; and (4) novel avian-origin influenza virus (IAV) H7N9 infections at 24-hour postinfection upregulated 16 and downregulated six out of 28 m6A-RMRs, which are significantly different from that of upregulation of nine, 12, and eight and downregulation of 17, 14, and 13 out of 28 m6A-RMRs by highly pathogenic avian-origin influenza viruses H7N7, H5N1, and human seasonal H3N2 IAV, respectively. The differences in modulating the expressions of m6A-RMRs may be related to the virulence of the virus, virus replication, and other aspects [65].Figure 7
The m6A-RNA methylation regulators (m6A-RMRs) were upregulated in virus-infected cells as the infection time was extended. (a) In general, upregulated m6A-RMRs were more than downregulated m6A-RMRs in the condition of MERS and SARS infections. The expressions of m6A methylation writers such as WTAP, KIAA1429, CBLL1, and RBM15 and readers YTHDF1, YTHDF2, YTHDF3, and RBP dependent on m6A PRRC2A were increased; and the expression of eraser FTO was decreased when endothelial cells were infected with MERS coronavirus (MERS-CoV) and SARS coronavirus (SARS-CoV). (b) In avian influenza virus studies, it was obvious that the expressions of writer WTAP and PCIF1 were increased, and the expression of eraser FTO was decreased in all avian influenza virus-infected cells; the longer the infected time was, the higher the numbers of differentially expressed m6A methylation regulators there were. The results suggest that MERS coronavirus, SARS coronavirus, and avian influenza virus infections upregulated the expressions of m6A-RMRs (p<0.05). Abbreviations: MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome; H1N1, H7N9, H7N7, H5N1, and H3N2 are the cell populations of avian influenza virus. FC: fold change; NA: not available (missing value).
(a)(b)


### 3.6. Proinflammatory Lipid oxPAPC and NOTCH1 Knockdown Modulates m6A-RMRs More Than Other DAMP Stimulation of ECs including LPS, oxLDL, and IFNs; and Two m6A-RMRs Such as Hnrnpa2b1 and Eif3a Were Differentially Expressed in Three Aortic EC Clusters

We hypothesized that pathogen-associated molecular patterns (PAMPs)/danger-associated molecular patterns (DAMPs) modulate the expressions of m6A-RMRs in various endothelial cells. To examine this hypothesis, we collected eight microarray datasets from various endothelial cells stimulated by PAMPs/DAMPs. As shown in Figure 8(a), influenza virus infection of human umbilical vein endothelial cells (HUVEC) resulted in upregulation of nine out of 26 m6A-RMRs (with the highest upregulation of writer KIAA1429 for 10 folds) and downregulation of 16 out of 26 m6A-RMRs. By comparison, Kaposi’s sarcoma-associated herpesvirus (KSHV) infection of human dermal endothelial cells modulated m6A-RMR expressions significantly less than that of influenza virus infection, upregulating zero and downregulating two out of 22 m6A-RMRs. Toll-like receptor 4 (TLR4) ligand lipopolysaccharide (LPS) stimulation of human lung microvascular endothelial cells for 4, 8, and 24 hours resulted in upregulation of two to three m6A-RMRs (with the high upregulation of writer WTAP) and downregulation of one to two m6A-RMRs, respectively. In addition, cytokine interferon-α (IFNα), IFNβ, and IFNγ treatments of HUVEC led to upregulation of zero to one and downregulation of one to five out of 22 m6A-RMRs, respectively. The knockdown (KD) of proinflammatory NOTCH1 signaling and NOTCH1 KD plus proinflammatory cytokine interleukin-1β (IL-1β) in HUVEC led to upregulation of seven and five out of 25 m6A-RMRs and downregulation of five and six out of 25 m6A-RMRs. The differences of m6A-RMR expression modulation between NOTCH1 KD and NOTCH1 KD plus IL-1β may contribute to the inflammatory status of treated HUVEC [66]. In contrast, another report found that NOTCH1 is antiatherogenic; and proinflammatory lipids oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (Ox-PAPC) decrease NOTCH1 expression in human aortic endothelial cells (HAEC) [67]. In this experimental setting, NOTCH1 KD in HAEC resulted in upregulation of five and downregulation of 10 out of 25 m6A-RMRs; proinflammatory lipid oxPAPC stimulation in HAEC led to upregulation of 10 and downregulation of seven out of 25 m6A-RMRs. It was reported that oscillatory shear (OS) present on the fibrosa stimulates fibrosa human aortic valve endothelial cells (HAVEC), which may contribute to aortic valve disease [68]. In this experimental setting, oscillatory shear on fibrosa HAVEC and ventricularis HAVEC resulted in upregulation of four and six and downregulation of two and three out of 28 m6A-RMRs, respectively. Moreover, mouse aortic endothelial cells (MAEC) isolated from atherogenic apolipoprotein E-deficient (ApoE-/-) mice [3], TLR4 ligand LPS-treated MAEC, another TLR4 ligand oxidized low-density lipoprotein- (oxLDL-) stimulated MAEC [69, 70], and oxPAPC-stimulated MAEC [71] upregulated two, two, five, and eight out of 21 m6A-RMRs and downregulated five, six, three, and five out of 21 m6A-RMRs, respectively.Figure 8
The expressions of m6A-RNA methylation regulators (m6A-RMRs) were modulated in endothelial cells with different stimulations. (a) The expression changes of m6A-RMRs were the most obvious in influenza virus infections with the highest expression FC values. The expressions of writer KIAA1429 and readers YTHDF1, YTHDF2, IGF2BP1, IGF2BP2, HNRNPC, and ELAVL1 were upregulated with the FC>2; the expressions of writers METTL3, METTL14, WTAP, ZC3H13, RBM5, RBM15B; eraser FTO; and readers YTHDF3, YTHDC2, HNRNPA2B1, IGF2BP3, G3BP1, and G3BP2 were downregulated with the FC<0.5. In KSHV-infected ECs, IGF2BP3 was downregulated with low FC values. In LPS-treated microvascular ECs, writer WTAP was upregulated in four and eight hours. In IFNa- and IFNb-treated cells, FMR1 was increased. In the cells with NOTCH1 knockdown combined with IL-1β treatment, the expression changes of m6A-RMRs were not obvious. ECs activated with ox-PAPC showed that the expressions of WTAP and CBLL1 were upregulated. There were gentle expression changes of m6A-RMRs for ECs exposed to shear or for mouse aortic ECs from apolipoprotein E- (ApoE-) deficient (ApoE knock out) or treated with LPS, oxLDL, and oxPAPC, respectively. (b) The m6A-RMRs were differentially expressed in aortic EC populations. The 12 out of 29 m6A-RMRs including Wtap, Zc3h13, Pcif1, FTO, Alkbh5, Ythdc1, YthDf2, Ythdf3, Fmr1, Prrc2a, Elavl1, and G3bp1 were in medium expression levels in almost all aortic cell populations of normal mouse aorta; and m6A-RMRs such as Hnrnpa2b1, Eif3a, Hnmpc, and G3bp2 were differentially expressed in aortic cell populations. (Mettl14, Igf2bp1, and Igf2bp2 not found) (p<0.05). Abbreviations: KSHV: Kaposi sarcoma-associated herpes virus; oxPAPC: oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (inflammatory lipids); oxLDL: oxidized LDL; FC: fold change; NA: not available (missing value); VSMC: vascular smooth muscle cells; EC: endothelial cells.
(a)(b)We then examined the expressions of m6A-RMRs in three mouse aortic endothelial cell (EC) clusters identified recently with single-cell RNA sequencing (scRNA-Seq) [72]. As shown in Figure 8(b), 12 out of 26 m6A-RMRs (46%) including Wtap, Zc3h13, Pcif1, FTO, Alkbh5, Ythdc1, YthDf2, Ythdf3, Fmr1, Prrc2a, Elavl1, and G3bp1 were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m6A-RMRs such as Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters, which were Cytl1+Gkn3+ endothelial cell (EC) cluster 1, Fabp4+Gphbp1+Rgcc+ EC cluster 2, and Ccl21a+Lrg1+ EC cluster 3 [72]. Of note, the expressions of Mettl14, Igf2bp1, and Igf2bp2 were not found.These results conclude that (1) influenza virus infection of HUVEC results in expression modulation of most m6A-RMRs (25 out of 26), which were similar to that found in influenza virus infections of human airway epithelial cells and Calu-3 lung cells for 24 hours in Figure 7; (2) proinflammatory lipids oxPAPC and NOTCH1 knockdown modulate m6A-RMRs more than other DAMP stimulation of ECs including LPS, oxLDL, and IFNs; (3) ten m6A methyltransferases were not significantly modulated in MAEC from ApoE KO aorta with or without further stimulations of LPS, oxLDL, and oxPAPC, respectively (Figure 8, GSE39264); and (4) 12 out of 26 m6A-RMRs (46%) were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m6A-RMRs including Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters.


### 3.7. Upregulated m6A-RMRs Were More Than Downregulated m6A-RMRs in Various Cancer Types; Head and Neck Cancer, Cervical Cancer, Brain and CNS Cancer, Other Cancer, and Kidney Cancer Upregulated ≥10 m6A-RMRs; and IGF2BP3, G3BP1, IGF2BP2, and HNRNPC Were Upregulated in ≥10 Cancer Types

We hypothesized that the expressions of m6A-RMRs are modulated in various cancer cell populations. To test this hypothesis, we collected 19 cancer datasets in a comprehensive cancer gene expression database Oncomine (https://www.oncomine.org) [45] including brain and CNS cancer, head and neck cancer, esophageal cancer, gastric cancer, bladder cancer, breast cancer, cervical cancer, ovarian cancer, colorectal cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, prostate cancer, leukemia, lymphoma, melanoma, myeloma, sarcoma, and other cancer (Figure 9(a)). As shown in Figure 9, the numbers in red indicated the numbers of studies with upregulated m6A-RMRs; and the numbers in blue indicated the numbers of studies with downregulated m6A-RMRs. The results showed that upregulated m6A-RMRs were more than downregulated m6A-RMRs in various cancer types (number of red cells is more than that of blue cells). Of note, the “Significant Unique Analyses” indicated the numbers of studies in which the analyzed genes were significantly upregulated (red) or downregulated (blue). The “Total Unique Analysis” indicated the numbers of studies in which the analyzed genes were included (p<0.05, foldchange>2). Three m6A-RMRs such as METTL16, YTHDC1, and PRRC2A were not found.Figure 9
The m6A-RNA methylation regulators (m6A-RMRs) were differentially expressed in 18 cell populations of cancers in the Oncomine database (https://www.oncomine.org/). (a) The numbers in red were the numbers of m6A-RMRs upregulated, and the numbers in blue were the numbers of m6A-RMRs downregulated. The results showed that m6A-RMRs upregulated were more than m6A methylation regulators downregulated. The “Significant Unique Analyses” indicated the numbers of studies in which the analyzed genes were significantly upregulated (red) or downregulated (blue). The “Total Unique Analysis” indicated the numbers of studies in which the analyzed genes were included (p<0.05, foldchange>2) (METTL16, YTHDC1, and PRRC2A not found). (b) Different expression patterns of m6A-RMRs in different cell populations of cancers. The bar graph showed differential expression m6A regulators IGF2BP2 (upregulated in ten cancers, downregulated in three cancers, and uncertain in three cancers), IGF2BP3, and G3BP1 are the top 3 genes involved in different cancers. (c) IGF2BP3, G3BP1, and IGF2BP2/HNRNPC were upregulated in 12, 11, and 10 cancers, respectively. (d) ZC3H13, IGF2BP1, and WTAP/FTO/YTHDC2 were downregulated in six, five, and four cancers, respectively. (e) The expression of G3BP2 in five cancers was uncertain; WTAP, PCIF1, HNRNPA2B1, EIF3A, and IGF2BP2 in three cancers were uncertain.
(a)(b)(c)(d)(e)We then determined the top cancer types that upregulated or downregulated m6A-RMRs the most. As shown in Figures 9(b) and 9(c), head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m6A-RMRs. In contrast, breast cancer, ovarian cancer, kidney cancer, esophageal cancer, leukemia, and lymphoma downregulated ≥four m6A-RMRs. In addition, lymphoma, leukemia, other cancer, sarcoma, and brain and CNS cancer modulated ≥five m6A-RMRs in uncertain manners. We also determine the top m6A-RMRs that upregulated or downregulated in cancer types the most. As shown in Figures 9(d) and 9(e), IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types. In contrast, ZC3H13, IGF2BP1, WTAP, FTO, and YTHDC2 were downregulated in ≥four cancer types. In addition, G3BP2, WTAP, PCIF1, HNRNPA2B1, EIF3A, and IGF2BP2 were modulated in ≥three cancer types in uncertain manners.The results indicated that most m6A-RMRs were downregulated in acute inflammatory diseases, while in cancer, the upregulated m6A-RMRs were more than downregulated m6A-RMRs. This situation is because acute inflammation is a part of an innate immune system reaction that can be produced by various factors, such as signaling pathways of the receptors for danger-associated molecule patterns (DAMPs/PAMPs) derived from pathogens, viruses, bacteria, and toxic substances as well as cytokine signals and stress hormone signal. These factors may activate the interactions and cross-talks among the receptors of cytokines, viruses, DAMPs, or PAMPs and promote the migration of macrophages or neutrophils to the area of inflammation [73]. However, during cancer occurs, tumor cells will generate tumor antigens that activate adaptive immune responses and induce T cells and B cells. T cell activation could cause a variety of immune signaling, including T cell antigen receptor signaling, costimulation signaling, immune checkpoint coinhibition signaling, and cytokine signaling. The composition of signaling receptors and pathways is different between acute inflammation and cancer. Therefore, RNA methylation and its transcription, splicing, and mRNA stability are also different under these two situations.We further hypothesized that m6A-RMRs were differentially expressed as different cancer progression. To examine this hypothesis, the expression matrix GSE114783 was downloaded from the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds); and the expressions of m6A-RMRs were screened. The heat map was generated from Cluster web tool [45]. As shown in Supplementary figure 1A, the results showed that the expressions of m6A-RMRs were different among healthy controls, hepatitis B virus carriers, and the patients with liver cirrhosis and hepatocellular carcinoma, especially genes in the red box; compared with healthy controls, there were more upregulated m6A-RMRs in patients with chronic hepatitis B (CHB) group than those in healthy controls. Potentially, due to the tumor heterogeneity or the small sample sizes, there were no differentially expressed m6A-RMRs in the hepatocellular carcinoma group compared with healthy controls. In addition, as shown in Supplementary figure 1B, using the same method as Supplementary figure 1A, the expressions of m6A-RMRs were analyzed in human preinvasive and invasive cervical squamous cell carcinomas and normal cervical epithelia. The differential expressions of m6A-RMRs were shown in the red box. The 22 m6A-RMRs were examined in these three groups. The results showed that the expressions of WTAP were downregulated; and the expressions of four m6A-RMRs such as HNRNPA2B1, IGF2BP2, FMR1, and HNRNPC were upregulated in patients with preinvasive and invasive cervical squamous cell carcinomas compared with normal controls. Moreover, as shown in Supplementary figure 1C, the expressions of m6A-RMRs were analyzed in prostate cancer from benign prostatic hyperplasia to metastatic prostate cancer. The expressions of RBMX were gradually increased from prostatic intraepithelial neoplasia to hormone-naïve prostate cancer compared with normal samples. There were more obvious differential expressions of m6A-RMRs between adjacent prostate cancer samples and prostate carcinoma samples. The expressions of two m6A-RMRs such as EIF3A and RBMX were upregulated in prostate carcinoma samples compared with normal prostate epithelium-adjacent samples.Finally, we examined the expressions of 29 m6A-RMRs [74] in single-cell RNA-Seq data. As shown in Supplementary figure 2A, in a single-cell RNA-seq analysis of astrocytoma dataset [75], 23 out of 28 m6A-RMRs (82%) (except ALKBH5, IGF2BP1, IGF2BP2, IGF2BP3, and G3BP2) were in the medium to high expression levels in malignant cells of astrocytoma. In addition, as shown in Supplementary figure 2B, in a melanoma intratumor heterogeneity dataset [76], 24 out of 29 m6A-RMRs (83%) (except RBM15B, IGF2BP1, IGF2BP3, HNRNPA2B1, and HNRNPC) were in the medium to high expression levels in cancer-associated fibroblasts (CAF) of melanoma.Taken together, these results have demonstrated that first, upregulated m6A-RMRs were more than downregulated m6A-RMRs in various cancer types; second, head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m6A-RMRs; third, IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types; fourth, four m6A-RMRs such as HNRNPA2B1, IGF2BP2, FMR1, and HNRNPC were upregulated in patients with preinvasive and invasive cervical squamous cell carcinomas; and the expressions of two m6A-RMRs such as EIF3A and RBMX were upregulated in prostate carcinoma samples; and fifth, 82-83% of 29 m6A-RMRs were in the medium to high expression levels in astrocytoma and melanoma.


### 3.8. M1 Macrophage Polarization Upregulates Seven m6A-RMRs including METTL14, WTAP, PCIF1, HNRNPA2B1, IGF2BP2, FMR1, and G3BP1, among Which WTAP and IGF2BP2 May Not Only Promote M1 Polarization but Also Inhibit M2 Polarization

We hypothesized that macrophage polarization from M0 into type 1 proinflammatory macrophages (M1) or M2 anti-inflammatory macrophages modulate the expressions of m6A-RMRs. To examine this hypothesis, we collected a microarray dataset GSE85346 from the NCBI-GeoDataset database. As shown in Figure 10, M1 macrophage polarization upregulated seven m6A-RMRs including METTL14, WTAP, PCIF1, HNRNPA2B1, IGF2BP2, FMR1, and G3BP1 whereas M2 polarization into M2a, M2b, or M2c downregulated three m6A-RMRs including WTAP, IGF2BP2, and IGF2BP3. These results suggest that these M1 polarization upregulated seven m6A-RMRs may promote proinflammatory M1 polarization. In contrast, two M1 polarization-upregulated and M2 polarization-downregulated m6A-RMRs such as WTAP and IGF2BP2 may not only promote M1 polarization but also inhibit M2 polarization.Figure 10
Macrophage polarization regulated the expression of m6A-RNA methylation regulators (m6A-RMRs). Macrophage polarization microarray analysis result showed that expression of WTAP is significantly upregulated in M1 with a fold change of 10.578 and in M2b with a fold change of 2.415 compared with M0 cells; expression of IGF2BP2 is upregulated in M1, M2a, and M2c cells; RBMX is the common downregulated m6A-RMRs in M1, M2a, M2b, and M2c macrophages.Macrophages are diverse immune cells polarized by numerous stimuli, resulting in a wide range of traits and functions. The polarized process from M0 to M1 is induced by the stimulations of TLR ligands or Th1 cytokines, like TNF-a, IFN-γ, and CSF2 signaling. Under inflammation, these macrophage polarization signals are specific and robust [77], whereas, during tumorigenesis, so many factors can be involved. Moreover, different cancers are related to different cell types. A great amount of different signaling cross-talks may cause chaos signaling [78]. Consequently, the signaling that modulates the upregulation of WTAP could be diffused by the chaos cross-talking signaling.


### 3.9. The 86% of m6A-RMRs Were Differentially Expressed in the Six Spleen Treg Clusters; and 8 Out of 12 Treg Signature Genes including FOXO1, HDAC9, Dicer, BLIMP1, GATA3, EP300, BCL6, and PPAR Significantly Regulated the Expressions of m6A-RMRs

Our and others’ reports demonstrated that CD4+Foxp3+ regulatory T cells play significant roles in suppressing immune responses, inflammations, atherosclerosis [13, 25–27, 70, 79–83], and antitumor immune reactions [84] and promoting tissue regeneration. We hypothesized that m6A-RMRs are modulated in Treg differentiation and Treg responses to tissue microenvironment. As shown in Figure 11(a), Treg upregulated six m6A-RMRs such as ZC3H13, YTHDC1, YTHDC2, IGF2BP1, IGF2BP3, and HNRNPC in three Treg datasets compared to that of CD4+Foxp3- effector T cells.Figure 11
Regulatory T cells regulate the expression of m6A-RNA methylation regulators (m6A-RMRs). (a) Compared with conventional T cells, expression of IGF2BP3 is upregulated and RBMX is downregulated in regulatory T cells. (b) The expression data matrix was obtained from scRNA study (PMID: 29434354), and the expression heat map using Cluster web tool (PMID: 25969447) was generated. The result showed that the expression of m6A-RMRs in six types of Treg clusters was different. Expression of Ythdf3 and Rbm15b have the same pattern as Treg signature gene Foxp3. (c) Most m6A-RMRs are downregulated in deficiencies of GATA3, HDAC6, HDAC9, IL2RB, and FOXO1. The numbers of upregulated regulators are more than those of downregulators in deficiencies of BCL6, DICER, BLIMP1, PPARG, EOS, and EP300.
(a)(b)(c)In addition, a recent report with single-cell RNA sequencing (scRNA-Seq) characterizes spleen Treg into six clusters including S100a4highS100a6high cluster 1 (activated), Itgb1high cluster 2 (activated), Dusp2highNr4a1highFoxp3highIL2rahigh cluster 3 (activated), Ikzf2highFoxp3high cluster 4 (resting), Bach2high cluster 5 (resting), and Satb1highSellhigh cluster 6 (resting) [85]. We hypothesized that the m6A-RMRs are differentially expressed in these six Treg clusters. As shown in Figure 11(b), four m6A-RMRs including PCIF1, METTL3, HNRNPA2B1, and EIF3A were upregulated in the cluster 1 Treg; two m6A-RMRs including RBM15 and YTHDC2 were upregulated in the cluster 2 Treg; seven m6A-RMRs including ALKBH5, YTHDF3, RBM15B, HNRNPC, ZC3H13, FTO, Wand TAP were upregulated in cluster 3 Treg; four m6A-RMRs including IGF2BP2, RBMX, G3BP2, and PRRC2A were upregulated in cluster 4 Treg; three m6A-RMRs including METTL14, YTHDF2, and IGF2BP3 were upregulated in cluster 5 Treg; and five m6A-RMRs including G3BP1, ELAVL1, IGF2BP1, FMR1, and METTL16 were upregulated in cluster 6 Treg. Therefore, 25 out of 29 m6A-RMRs (86%) were differentially expressed in the six spleen Treg clusters.Moreover, we hypothesized that Treg signature genes play significant roles in regulating the expressions of m6A-RMRs. To examine this hypothesis, we collected the microarray datasets of 12 Treg signature gene deficiencies. As shown in Figure 11(c), the deficiencies of type 2 T helper (Th2) transcription factor (TF) GATA binding protein 3 (GATA3), follicular Th cell (Tfh) TF B cell lymphoma 6 protein (BCL6), histone deacetylase 6 (HDAC6), HDAC9, microRNA maturation enzyme DICER, interleukin-2 receptor b chain (IL2rb), B-lymphocyte-induced maturation protein 1 (BLIMP1, PR domain Zinc finger protein 1), peroxisome proliferator activated receptor gamma (PPARg) (visceral adipose tissue, VAT), PPARg (lymph nodes, LN), IKAROS family zinc finger 4 (EOS), Foxhead box 1 (FOXO1), and histone acetyltransferase P300 (EP300) modulated and upregulated 1, 6, 0, 3, 5, 0, 6, 3, 1, 1, 6, and 5 m6A-RMRs, respectively, and downregulated 6, 0, 2, 5, 3, 1, 3, 2, 0, 0, 11, and 2 m6A-RMRs, respectively. The 12 Treg signature genes were ranked based on the total modulation numbers when they were deficient, FOXO117>HDAC98=Dicer8=BLIMP18>GATA37=EP3007>BCL66>PPARgVAT5>HDAC62>IL−2RB1=PPARGLN1=EOS1. It has been reported that mechanistic target of rapamycin kinase (mTOR)/interferon regulatory factor 4 (IRF4)/GATA3 promotes IL-4+ Th2-like Treg; phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/AKT serine/threonine kinase 1 (Akt)/Foxo, Th1 TF T-box transcription factor 21 (T-bet), hypoxia inducible factor 1 subunit alpha (HIF-1a), glycolysis promote interferon-gamma (IFNg)+ Th1-like Treg, microbiota/MAF BZIP transcription factor (c-Maf)/signal transducer and activator of transcription 3 (Stat3)/RAR-related orphan receptor C (RORgt), aryl hydrocarbon receptor (AhR) promote IL-17+ Th17 like Treg, and mTOR/Stat3/hepatocyte nuclear factor 1-alpha (TCF1) promote Bcl6+ follicular Treg (Tfr).Our results have shown that (1) Treg upregulated six m6A-RMRs including ZC3H13, YTHDC1, YTHDC2, IGF2BP1, and HNRNPC; (2) 25 out of 29 m6A-RMRs (86%) were differentially expressed in the six spleen Treg clusters, suggesting that m6A-RMRs play significant roles in reprogramming Treg activation status (clusters 1-3) and resting status (clusters 4-6); and (3) the transcription data from 12 Treg signature gene deficiencies suggest that eight out of 12 Treg signature genes including FOXO1, HDAC9, Dicer, BLIMP1, GATA3, EP300, BCL6, and PPARγ regulate the expressions of m6A-RMRs significantly; and m6A-RMRs play important roles in reshaping Treg stability and plasticity.


### 3.10. Immune Checkpoint Receptors TIM3, TIGIT, PD-L2, and CTLA4 Play Significant Roles in Modulating the Expressions of m6A-RMRs; and Inhibition of Costimulation Receptor CD40 with Anti-CD40 Antibody Significantly Upregulates the Expressions of m6A-RMRs

We recently reported that cosignaling receptors localized on cell membrane including costimulation receptors and immune checkpoint receptors (coinhibition receptors) serve as a prototypic cell-cell contact interaction and regulate T cell plasticity, immune tolerance, cellular physiology, and sterile inflammatory disorders [86]. We hypothesized that cosignaling receptors regulate the expressions of m6A-RMRs. We collected 10 cosignaling receptor deficiency/suppression microarray datasets from the NIH-NCBI GeoDataset database. As shown in Figure 12, cytotoxic T-lymphocyte-associated protein 4 (CTLA4) KO and anti-CTLA4 upregulated 2-3 m6A-RMRs and downregulated one m6A-RMR. CD80+ programmed cell death 1 ligand 2 (PD-L2)+ (memory B cells) [87] upregulated three and downregulated five m6A-RMRs. PD-L1 and anti-PD-L1 upregulated one m6A-RMRs and downregulated zero m6A-RMRs. T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) KO, CD3+CD8+TIM-3- cells [88], and CD8+PD1+TIM3- cells [89] upregulated 0-5 m6A-RMRs and downregulated 2-11 m6A-RMRs. T cell immunoreceptor with Ig and ITIM domain- (TIGIT-) Treg upregulated 3 m6A-RMRs and downregulated 13 m6A-RMRs. In addition to the modulation of m6A-RMR expressions by immune checkpoint receptors, the modulation with anti-CD40 (tumor necrosis factor receptor superfamily member 5), a costimulation receptor, resulted in upregulation of 5-12 m6A-RMRs and downregulation of 1-2 m6A-RMRs. Taken together, these results have demonstrated that immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 play significant roles in modulating the expressions of m6A-RMRs; and inhibition of costimulation receptor CD40 with anti-CD40 antibody significantly upregulates the expressions of m6A-RMRs, which is associated with suppression of autoimmune responses by anti-CD40 antibody therapy [90].Figure 12
Immune checkpoint receptors regulate the expression of m6A-RNA methylation regulators (m6A-RMRs). T cell activation can regulate the expression of m6A-RMRs. Microarrays about immune checkpoint knockout (KO), knockdown (KD), anticheckpoint, or sorted negative and positive checkpoint were searched from the GEO database to analyze the regulation relationship between immune checkpoint and m6A-RMRs. The result showed that when the coinhibitory immune checkpoints PD-L2, TIM-3, and TIGIT were inhibited (KO or KD vs. control, anti- vs. control) or reduced (negative vs. positive), the expressions of m6A-RMRs were decreased. However, the costimulatory immune checkpoint CD40L was inhibited, and more m6A-RMRs’ expressions were increased. That is, maybe, T cell activation negatively regulates the expression of m6A-RMRs.


### 3.11. Proinflammatory Cytokine Signaling Pathways Significantly Modulate the Expressions of m6A-RMRs, and Suppression of Proinflammatory Cytokine Pathways Upregulate More Than Downregulate the Expressions of m6A-RMRs

We recently reported that cytokines including IL-17 [91–93], IL-2 [26], and IL-35 [15, 94, 95] play significant roles in inflammation regulation, Treg maintenance, and various pathologies. We hypothesized that signal pathways of cytokines and cytokine receptors regulate the expressions of m6A-RMRs. We collected eight cytokine and cytokine receptor microarray datasets from the NIH-NCBI GeoDataset database. As shown in Figure 13, deficiencies of tumor necrosis factor-α (TNFα, TNF receptor 1-2 (TNFR1-2), interferon-γ (IFNγ), IFNγ receptor-1 (IFNγR1), IL6, IL-17 receptor (IL17R), IL18, and IL18 upregulated 3, 7, 1, 7, 2, 3, 5, and 2 m6A-RMRs, respectively, and downregulated 0, 3, 0, 1, 2, 2, 1, and 3 m6A-RMRs, respectively. These results have demonstrated that (1) proinflammatory cytokine signaling pathways significantly modulate the expressions of m6A-RMRs, and (2) suppression of proinflammatory cytokine pathways upregulates more than downregulates the expressions of m6A-RMRs.Figure 13
Proinflammatory cytokines can regulate m6A-RNA methylation regulators (m6A-RMRs). In deficiency of proinflammatory cytokine microarrays, the number of upregulated m6A-RMRs is more than that of downregulated m6A-RMRs. That is, maybe, proinflammatory cytokines negatively regulate m6A methylation. The result is consistent with the result of the m6A-RMRs’ expression in acute inflammation.


### 3.12. NF-κB Components and JAK/STAT Signaling (Except STAT1) Play Important Roles in Upregulating More Than Downregulating m6A-RMRs; and Tumor Suppressors TP53, PTEN, and APC Play Significant Roles in Downregulating More Than Upregulating m6A-RMRs

We and others reported that proinflammatory/immune regulatory transcription factors (TFs), Janus kinase (JAK)/signal transducer and activator of transcription (STAT) [94], nuclear factor kappa B (NF-κB) [96], tumor protein P53 (TP53), phosphatase and tensin homolog (PTEN), and adenomatous polyposis coli (APC regulator of WNT signaling pathway, APC) [97] play significant roles in regulating inflammation, Treg maintenance, cytokine responses, tumorigenesis, and development. We hypothesized that proinflammatory and immune regulatory TFs, NF-κB, and JAK/STAT pathways and three tumor suppressors TP53, PTEN, and APC pathways regulate the expressions of m6A-RMRs. We collected five NF-κB subunit deficiency datasets (seven comparisons), one JAK2 deficiency dataset, one STAT1 deficiency dataset, two STAT3 deficiency datasets, six TP53 deficiency datasets (eight comparisons), five PTEN datasets, and one APC dataset from the NIH-NCBI GeoDataset database. As shown in Figure 14, deficiencies of IKK2 (cell line), IKK2 (tumor), IKK complex, IKK complex, IKK2, RELA, and Rela upregulated 4, 2, 1, 1, 5, 3, and 2 m6A-RMRs, respectively, and downregulated 7, 3, 7, 6, 6, 13, and 13, respectively. Of note, deficiencies of NF-κB signaling components resulted in more downregulation than upregulation of m6A-RMRs. Deficiencies of JAK2, STAT1, STAT3, and STA3 resulted in upregulating 1, 10, 2, and 4 m6A-RMR, respectively, and downregulating 5, 2, 4, and 8 m6A-RMRs, respectively. STAT1 deficiency upregulated more than downregulated m6A-RMRs, but deficiencies of JAK2 and STAT3 downregulated more than upregulated m6A-RMRs. Deficiencies of TP53 in eight datasets upregulated 8, 9, 9, 15, 4, 10, 3, and 4 m6A-RMRs, respectively, and downregulated 7, 5, 0, 0, 5, 7, 3, and 1 m6A-RMRs, respectively. Of note, six out of 8 TP53-deficient datasets had more upregulation than downregulation of m6A-RMR, suggesting that tumor progression triggered by TP53 deficiencies may require more upregulation than downregulation of m6A-RMRs. Deficiencies of PTEN in five datasets upregulated 10, 2, 5, 4, and 4 m6A-RMRs, respectively, and downregulated 0, 14, 2, 2, and 11 m6A-RMRs, respectively. Of note, three out of five PTEN deficient datasets had more upregulation than downregulation of m6A-RMR, suggesting that tumor progression triggered by PTEN deficiencies may require more upregulation than downregulation of m6A-RMRs. Deficiencies of APC upregulated 14 and downregulated 4 m6A-RMRs, respectively. These results have exhibited that (1) NF-κB signaling components play important roles in upregulating more than downregulating m6A-RMRs; (2) JAK/STAT signaling pathways play important roles in upregulating more than downregulating m6A-RMRs except STAT1; and (3) tumor suppressors TP53, PTEN, and APC play significant roles in downregulating more than upregulating m6A-RMRs, suggesting that upregulation of m6A-RMRs may favor more than inhibit tumor progression.Figure 14
Oncogenes and tumor suppressors can regulate m6A-RNA methylation regulators (m6A-RMRs). (a) In deficiency of oncogenes, the number of upregulated m6A-RMRs is less than that of downregulated m6A-RMRs in addition to individual research (GSE44652). (b) In deficiency of tumor suppressors, the number of upregulated m6A-RMRs is more than that of downregulated m6A-RMRs in addition to individual research. That is, maybe, tumor suppressors negatively regulate m6A methylation. These results are consistent with the result of the m6A-RMRs’ expression in cancers.
(a)(b)


### 3.13. Methionine-Homocysteine Cycle Enzyme Mthfd1 Downregulates More Than Upregulates m6A-RMRs; One m6A Writer RBM15 and One m6A Eraser FTO Significantly Modulate the Expressions of m6A-RMRs; and H3K4-Specific Methyltransferase MLL1 and DNA Methyltransferase, DNMT1, Significantly Regulate the Expressions of m6A-RMRs

We reported that hyperhomocysteinemia promotes differentiation of the inflammatory Ly6C+ monocyte [5, 98, 99], decreased high-density lipoprotein (HDL) [100, 101], endothelial cell activation, and programmed cell death [6] and atherosclerosis [102]. We hypothesized that m6A-RMRs, histone lysine methylases, DNA methyltransferases, and methyl donating homocysteine-methionine metabolism cycle play significant roles in regulating the expressions of m6A-RMRs as a feedback mechanism. To examine this hypothesis, we collected numerous microarray datasets including a methionine diet-fed model, Mthfd1 KO, Mthfr KO, eight histone 3 lysine 4 (H3K4) methylase KOs, and eight DNA methyltransferase KOs from the NIH-NCBI-GeoDataset database. As shown in Figure 15(a), the methionine diet-fed model upregulated three m6A-RMRs and downregulated one m6A-RMR; deficiencies of methylenetetrahydrofolate dehydrogenase (Mthfd1) and methylenetetrahydrofolate reductase (Mthfr) resulted in upregulation of one and zero m6A-RMR, respectively, and downregulation of two and one m6A-RMRs, respectively. Deficiencies of m6A-RMRs RBM15 and FTO upregulated 1 to 11 m6A-RMRs and downregulated 3-5 m6A-RMRs. As shown in Figure 15(b), deficiencies of histone lysine 4 methylase in eight datasets upregulated one to 13 m6A-RMRs and downregulated zero to 11 m6A-RMRs. Among the eight comparison datasets, three datasets of deficiencies of H3K4-specific methyltransferase MLL1 [103] modulated m6A-RMRs the most, with 10 out of 29, 20 out of 29, and 16 out of 29 m6A-RMRs, respectively. Moreover, deficiencies of DNA methyltransferase 1 (DNMT1) in GSE54841, GSE27434, GSE44277, and GSE86147; DNMT3A at GSE54841 and GSE42304; and DNMT3B at GSE54841 and GSE75401 upregulated 0, 1, 0, 3, 0, 1, 0, and 0 m6A-RMRs, respectively, and downregulated 2, 8, 12, 15, 1, 3, 1, and 1 m6A-RMRs, respectively. Among the eight datasets of DNA methyltransferase deficiencies, DNMT1 deficiencies modulated the most with 9, 12, and 18 m6A-RMRs except GSE54841 (Figure 15(c)). Taken together, these results have demonstrated that (1) methionine diet-fed model and methionine-homocysteine cycle enzyme Mthfr do not significantly regulate the expressions of m6A-RMRs but methionine-homocysteine cycle enzyme Mthfd1 downregulates more than upregulates m6A-RMRs; (2) one m6A writer RBM15 and one eraser FTO significantly modulate the expressions of m6A-RMRs, suggesting a feedback mechanism for regulation of m6A-RMR expressions; (3) H3K4-specific methyltransferase MLL1 significantly regulates the expressions of m6A-RMRs; and (4) DNA methyltransferase, DNMT1, modulates the expressions of m6A-RMRs.Figure 15
Hyperhomocysteinemia, folate cycle-related enzymes, m6A-RNA methylation regulators (m6A-RMRs), H3K4 methylases, and DNA methyltransferase regulate the expression of m6A-RMRs, suggesting a methylation hierarchy. (a) Hyperhomocysteinemia regulated fewer numbers of m6A-RMRs but more m6A-RMRs were changed after antihyperhomocysteinemia treatment in hyperhomocysteinemia animal model- (Rattus norvegicus) related microarray (GSE30308). The deficiencies of the folate cycle and metabolism related enzymes and m6A-RMRs affect the expression of m6A-RMRs. IGF2BP3 was upregulated with fold changes of 4.486, and FMR1 was downregulated with fold changes of 0.327 in the deficiency of methylenetetrahydrofolate Mthfd1. In the deficiencies of m6A-RMRs, writer RBM15 and WTAP were downregulated, and CBLL1 and YTHDF1 were upregulated. WTAP, HNRNPA2B1, IGF2BP2, ELAVL1, and RBMX were upregulated in the deficiency of FTO in thoracic aortae. (b) In mammals, SET1a and SET1b and MLL1–MLL4 are the main six enzymes that catalyze strimethylation of lysine 4 in histone H3 (H3K4). The deficiencies of MLL1, MLL2, and MLL4 can regulate the expression change of m6A-RMRs; regulation of m6A-RMRs by MLL1 is different in a circadian study (GSE24964); upregulated m6A-RMRs were increased in 30 hours compared to 18 hours in deficiency of MLL1; regulation of m6A-RMRs by the heterozygote of MLL2 is similar to the deficiency of homozygous (GSE67388); deficiency of homozygous MLL4 only regulates one of the 29 m6A-RMRs compared with heterozygous MLL4 (GSE30971). (c) In deficiency of DNA methyltransferases microarrays, the number of downregulated m6A-RMRs is more than that of upregulated m6A-RMRs. That is, maybe, DNA methyltransferases positively regulate m6A methylation. More differentially expressed m6A-RMRs in microarrays with deficiencies of DNMT1 are more than those in microarrays with DNMT3A or DNMT3B. Most m6A-RMRs (18/26) are differentially expressed, and the downregulated genes (n=15) are more than the upregulated genes (n=3) in DNMT1 knockdown myeloma cells (GSE86147).
(a)(b)(c)


### 3.14. The 18 Out of 165 ROS Regulators (11%) Were Modulated by Four Human m6A Writers KIAA1429, METTL14, METTL3, and WTAP, and Gene KO Data Showed That 40 Out of 165 ROS Regulators (24%) Were Modulated by m6A Eraser FTO and Two m6A Writers METTL3 and WTAP

Our and others’ reports showed that reactive oxygen species (ROS) play significant roles not only in mediating endothelial cell activation and vascular inflammation [104] but also in sensing metabolic homeostasis and stress in organelle metabolic process as an integrated system. An important question remained whether m6A-RMRs regulate ROS regulators. We hypothesized that m6A-RMRs regulate the expressions of ROS regulators. To examine this hypothesis, the ROS regulators were the targets of RNA methylation regulators in human cell studies as shown in Table 5. The TREW data (Target of m6A Readers, Erasers, and Writers) was download from Met-DB v2.0 (MeT-DB V2.0: the MethylTranscriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php) [105]. The 165 ROS regulators were examined as we reported. The result showed that 18 out of 165 ROS regulators (11%) (F2RL1, PDK4, TIGAR, BCL2, SESN2, GNAI2, DDIT4, SH3PXD2A, FOXM1, AATF, TGFB1, TSPO, G6PD, GNAI3, and CYP1B1) were modulated by four m6A writers KIAA1429, METTL14, METTL3, and WTAP (several ROS regulators were modulated in more than one position in chromosome or by more than one RNA methylation regulators) (Table 5). The deficiencies of four m6A writers (KIAA1429, METTL14, METTL3, and WTAP) downregulated the m6A modification of ROS regulators. As shown in Supplementary Table 4, the expressions of ROS regulators were also the targets of RNA methylation regulators in mouse cell studies. TREW (Target of m6A Readers, Erasers and Writers) was download from Met-DB v2.0 (MeT-DB V2.0: the MethylTranscriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php) [105]. The 165 ROS regulators were examined; and the result showed that 40 out of 165 ROS regulators (24%) were modulated by m6A eraser FTO and two m6A writers METTL3 AND WTAP. The deficiency of m6A eraser FTO upregulated the m6A modification of ROS regulators, and the deficiencies of two m6A writers METTL3 or WTAP downregulated the m6A modification of ROS regulators.Table 5
The reactive oxygen species (ROS) regulators were the targets of RNA methylation regulators in human cell studies. TREW (Target of m6A Readers, Erasers, and Writers) was downloaded from Met-DB v2.0 (MeT-DB V2.0: the Methyl Transcriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php PMID: 29126312). 165 ROS regulators were examined, and the result showed 18 ROS regulators (F2RL1, PDK4, TIGAR, BCL2, SESN2, GNAI2, DDIT4, SH3PXD2A, FOXM1, AATF, TGFB1, TSPO, G6PD, GNAI3, and CYP1B1) were modulated by writers KIAA1429, METTL14, METTL3, and WTAP (several ROS regulators were modulated in more than one position in the chromosome or by more than one RNA methylation regulator). The deficiencies of writers (KIAA1429, METTL14, METTL3, and WTAP) can downregulate the m6A modification of ROS regulators (padj<0.05).
Targeted-ROS regulatorTypem6A methyl-regulatorp adjp_treatedp_controlLog2_ORLog2_RRqExperiment (modification-RNA methylation regulators-cell)PMIDF2RL1WriterKIAA14290.010.350.59-1.39-0.74893.65m6A-KIAA1429-si-A54924981863F2RL1WriterKIAA14290.020.460.75-1.86-0.721959.11m6A-KIAA1429-si-A54924981863PDK4WriterKIAA14290.020.110.69-4.12-2.6091.22m6A-KIAA1429-si-A54924981863TIGARWriterKIAA14290.040.190.73-3.56-1.95309.32m6A-KIAA1429-si-A54924981863BCL2WriterMETTL140.030.630.95-3.37-0.58130.91m6A-METTL14-sh2-A54924981863SESN2WriterMETTL30.030.250.46-1.35-0.8880.55m6A-METTL3-kd-Hela24316715GNAI2WriterMETTL3≤0.010.260.41-0.99-0.66474.67m6A-METTL3-kd-Hela24316715GNAI2WriterMETTL3≤0.010.260.41-0.99-0.66474.67m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715FOXM1WriterMETTL30.020.330.40-0.43-0.27427.19m6A-METTL3-kd-Hela24316715AATFWriterMETTL3≤0.010.190.39-1.40-1.00132.56m6A-METTL3-kd-Hela24316715TGFB1WriterMETTL3≤0.010.460.60-0.79-0.371205.15m6A-METTL3-kd-Hela24316715TSPOWriterMETTL30.010.250.37-0.78-0.54186.38m6A-METTL3-kd-Hela24316715G6PDWriterMETTL3≤0.010.360.40-0.28-0.172992.01m6A-METTL3-kd-Hela24316715GNAI3WriterWTAP0.020.170.46-2.01-1.41154.65m6A-WTAP-kd-Hela24316715GNAI3WriterWTAP0.020.170.46-2.01-1.41154.65m6A-WTAP-kd-Hela24316715TIGARWriterWTAP0.010.190.56-2.42-1.54104.56m6A-WTAP-kd-Hela24316715CYP1B1WriterWTAP0.020.500.95-4.33-0.93583.55m6A-WTAP-si-A54924981863DDIT4WriterWTAP≤0.010.460.73-1.66-0.66825.45m6A-WTAP-si-Hek293T24981863Met-DB v2.0 contains a significant increase in context-specific m6A peaks and single-base sites predicted from 185 samples from 26 separate studies for 7 species. It has also been updated to include a new database for targets of m6A readers, erasers, and writers, as well as additional functional data gathering. The abbreviation TREW stands for Target of m6A Readers, Erasers, and Writers. To discover their target sits, we collected ParCLIP-seq and MeRIP-seq data for 8 regulator/reader proteins (including FTO, KIAA1429, METTL14, METTL3, WTAP, HNRNPC, YTHDC1, and YTHDF1) from 10 independent studies. Then, the differential m6A peaks that showed significant hypermethylation (hypomethylation) after knocking down of a demethylase (methylase) were determined to the target peaks. p_treat: peak of treated group; p_control: peak of control group; OR: odds ratio; RR: relative risk or risk ratio.


## 3.1. A Few m6A-RNA Methylation Regulators (m6A-RMRs) Are Upregulated, and Most m6A-RMRs Are Downregulated in Sepsis, Sepsis plus Acute Respiratory Distress Syndrome, Sepsis plus Shock, and Trauma, and Upregulated Two RNA Methyltransferases WTAP and PCIF1 and Three RNA Binding Proteins YTHDF3, IGF2BP2, and IGF2BP3 May Promote Acute Inflammations

We hypothesized that pathological conditions significantly modulate the expressions of m6A-RMRs in cell type-specific and disease-specific manners. To test this hypothesis, we collected 29 m6A-RMRs in five groups from PubMed database (https://pubmed.ncbi.nlm.nih.gov/) (Figure 2 and Supplementary Table 3). The 29 m6A-RMRs included (1) 10 m6A-RNA methylation writers (methyltransferases) including METTL14, METTL3, WTAP, KIAA1429, ZC3H13,CBLL1, RBM15, METTL16,RBM15B, and PCIF1; (2) two m6A-RNA demethylases, FTO and ALKBH5; (3) 11 RNA binding proteins (readers) including YTHDC1, YTHDF1, YTHDF2, YTHDF3, YTHDC2, HNRNPA2B1, EIF3A, IGF2BP1, IGF2BP2, IGF2BP3, and FMR1; (4) three m6A-dependent RNA binding proteins including HNRNPC, RMMX, and PRRC2A; and (5) three m6A-repelled RNA binding proteins including ELAVL1, G3BP1, and G3BP2. As shown in Figure 3(a), one to four out of 28 m6A-RMRs were upregulated; and 14 to 15 out of 28 m6A-RMRs were downregulated in 0 day and 7 days in patients with sepsis and sepsis plus acute respiratory distress syndrome (ARDS) [46], respectively. In the second datasets with sepsis and sepsis plus shock, two to four out of 26 m6A-RMRs were upregulated; and 10 to 17 out of 26 m6A-RMRs were downregulated in 1 day and 3 days in patients with sepsis and sepsis plus shock [47, 48], respectively. In the third datasets with leukocytes, monocytes, and T cells from trauma, zero to one out of 22 m6A-RMRs was upregulated; and 5 to 11 out of 22 m6A-RMRs were downregulated in leukocytes, monocytes, and T cells from trauma [49], respectively.Figure 2
An overview of RNA methylation processes, which include (a) homocysteine-methionine-folate cycles for methyl generation and donation; (b) 29 m6A-RNA methylation regulators (m6A-RMRs) are classified into five groups: (i) RNA methylation writers, (ii) RNA methylation erasers, (iii) RNA methylation readers, (iv) m6A-dependent RNA binding proteins (RBPs), and (v) m6A repelled RNAs; (c) the hypothesis we proposed. (a) The methyl group is transferred to proteins, nucleic acids, and other biochemicals from the biochemical reaction of S-adenosylmethione (SAM) to S-adenosylhomocysteine (SAH) in homocysteine-methionine metabolic cycle. For RNA methylation, mRNA, lncRNA, tRNA, SnRNA, pre-miRNA, and CirRNA can be methylated in many different ways. In addition to the basic methylated forms, the common methylated forms include N6-methyladenosine (m6A), N1-methyladenosine, N1-methyladenosine, 2′-O-methyls at mRNA, N6-methyladenosine at lncRNA, pre-miRNA, circRNA [50–55], N1-methyladenosine at tRNA, 2′-O-methyls at rRNA, and snRNA [56]. (b) The reported 29 m6A-RMRs were chosen for analysis, including 10 m6A methyltransferase enzymes (writers), two m6A demethylases (erasers), 11 m6A binding proteins (readers), three m6A dependent on RNA binding proteins, and three RNA binding proteins repelled by m6A. The detailed information including the reference articles of the 29 m6A-RMRs is listed in Supplementary Table 3. (c) The methyl donor is derived from folate cycle and coupled homocysteine-methionine cycle. When the donor source is impaired, the DNA, RNA, and protein methylations will be in disorder. We proposed the hypothesis: mRNA methylation disorders will affect the expressions of m6A-RMRs; then, the m6A-RMRs will be involved in inflammation, metabolic diseases, and tumors by methylating or demethylating target genes including RNAs.
(a)(b)(c)Figure 3
Most m6A-RNA methylation regulators (m6A-RMRs) were downregulated in acute inflammatory diseases, suggesting that most m6A-RMRs are not required for the pathogenesis of acute inflammatory diseases. (a) The results showed that m6A-RMRs were downregulated (marked in blue) in sepsis and acute respiratory distress syndrome (ARDS), sepsis and shock, and severe trauma. (b) Venn diagram showed that in the same base disease, there were the shared upregulated or downregulated m6A regulators. There were five upregulated m6A-RMRs including shared writers WTAP and 19 downregulated regulators including nine shared genes in sepsis and ARDS of lung injury. There were six upregulated regulators including common writer PCIF1 and reader IGF2BP3 and 17 downregulated m6A methylation regulators (10 common genes) in sepsis and combined shock. WTAP was upregulated on the first day of sepsis and shock. In severe trauma, there were two genes upregulated and 15 genes downregulated including one common writer METTL3. In the three acute inflammatory diseases, nine downregulated m6A regulators (METTL3, RBM15, FTO, YTHDC2, HNRNPA2B1, EIF3A, HNRNPC, G3BP1, and CBLL1) were shared by three acute inflammation diseases. Note: the red marked genes are those that are up- or downregulated in different diseases. (c) 29 m6A-RMRs were examined in single-cell sequencing dataset online (https://singlecell.broadinstitute.org/single_cell) in an immune-cell signature of bacterial sepsis study (PMID: 32066974). The 11 out of 29 m6A methylation regulators including WTAP, ZC3H13, YTHDC1, YTHDF2, HNRNPA2B1, EIF3A, HNRNPC, RBMX, ELAVL1, G3BP1, and G3BP2 expressed differentially in different cell populations of bone-marrow under stimulation. (d) The expressions of most m6A methylation regulators except HNRNPA2B1 and HNRNPC were lower than those of others in PBMC subset cells (p<0.05). Abbreviations: ARDS: acute respiratory distress syndrome; FC: fold change; NA: not available (missing value); NK: natural killer cell; GMP: granulocyte-macrophage progenitor; MEP: megakaryocyte-erythroid progenitors; MYL: myeloblasts; DC: dendritic cell; CMP: common myeloid progenitors; HSC/MPP: hematopoietic stem cells and multipotent progenitors; RBC: red blood cells; MS1: CD14+ cells with high expression of resistin (RETN), arachidonate 5-lipoxygenase activating protein (ALOX5AP), and interleukin-1 receptor type 2 (IL1R2); MS2: with high expression of class II major histocompatibility complex (MHC); MS3: similar to nonclassical CD16hi monocytes; MS4: which is composed of the remaining CD14+ cells that express low levels of both class II MHC and inflammatory cytokines; NHP: nonhuman primate; mTB: Mycobacterium tuberculosis; SHIV: simian-human immunodeficiency virus; CAF: cancer-associated fibroblasts.
(a)(b)(c)(d)We then analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. As shown in Figure 3(b), four diseases including sepsis at 0 day and 7 days and sepsis plus ARDS at 0 day and 7 days shared one RNA methyltransferase WTAP. In the second sepsis datasets, four diseases including sepsis at 1 day and 3 days and sepsis plus shock at 0 day and 7 days shared one RNA methyltransferase PCIF1 and one RNA methylation reader IGF2BP3. In the trauma datasets, three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated m6A-RMRs. When comparing three groups of acute inflammations, sepsis plus ARDS shared three upregulated m6A-RMRs such as writers WTAP, PCIF1, and reader YTHDF3 with sepsis plus shock; sepsis plus ARDS shared upregulation of one RNA binding protein IGF2BP2 with trauma, and sepsis plus shock shared one RNA binding protein IGF2BP3 with trauma.We noticed that in the trauma datasets (Figure3(b)), three immune cell types, leukocytes, monocytes, and T cells did not share any upregulated RNA methylation regulators, suggesting that upregulation of m6A-RMRs is in a cell-specific manner. To further examine this issue, we collected single-cell RNA-sequencing (scRNA-Seq) datasets in sepsis [57] in a comprehensive single-cell sequencing database (https://singlecell.broadinstitute.org/single_cell). As shown in Figure 3(c), the 11 out of 29 m6A-RMRs (38%) including WTAP, ZC3H13, YTHDC1, YTHDF2, HNRNPA2B1, EIF3A, HNRNPC, RBMX, ELAVL1, G3BP1, and G3BP2 were expressed differentially in 13 different immune cell types (natural killer cell (NK), granulomyeloid progenitor (GMP), megakaryocyte-erythrocyte progenitor (MEP), common lymphoid progenitor (CLP), myeloblasts (MYL), T cells, dendritic cell (DC), common myeloid progenitor (CMP), monocyte (mono), hematopoietic stem cell/multipotent progenitor (HSC/MPP), B cells, CD14+RETN+ALOX5AP+IL1R2+ monocyte (iMS1), and red blood cells (RBC)) in bacterial sepsis. The expressions of most m6A-RMRs except HNRNPA2B1 and HNRNPC were lower in monocyte subsets than those in other peripheral mononuclear cell (PBMC) types (Figure 3(d)).These results have demonstrated that first, a few m6A-RMRs are upregulated and most m6A-RMRs are downregulated in sepsis, sepsis plus acute respiratory distress syndrome, sepsis plus shock, and trauma; second, two RNA methyltransferases WTAP and PCIF1 and three RNA binding proteins such as YTHDF3, IGF2BP2, and IGF2BP3 are commonly upregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that these five m6A-RMRs are the emergency m6A-RMRs for promoting acute inflammatory diseases; third, nine m6A-RMRs including METTL3, RBM15, FTO, YTHDC2, HNRNPA2B1, EIF3A, HNRNPC, G3BP1, and CBLL1 are commonly downregulated in sepsis, sepsis plus ARDS, sepsis plus shock, and trauma, suggesting that those m6A-RMRs play more important roles in maintaining homeostasis and suppressing inflammation than emergency roles for acute inflammations; and fourth, the expressions of 38% m6A-RMRs in immune cell types in response to sepsis stimulation are different.


## 3.2. Type 2 Diabetes Has More Modulation of m6A-RMR Expressions Than Atherogenic Diseases and Obesity; Nearly Half of 29 m6A-RMRs Are Downregulated as Atherosclerosis Progression Compared with That of Atherosclerosis Regression

We hypothesized that major metabolic cardiovascular disease groups such as obesity [58, 59], type 2 diabetes, and atherogenic diseases differentially modulate the expressions of m6A-RMRs. To test this hypothesis, we collected five datasets of obesity including obese, metabolically unhealthy obesity (MUO), metabolically healthy obesity (MHO), obese with insulin resistance (ob IR), and obese with insulin sensitivity (ob IS); four datasets of type 2 diabetes; and five datasets of atherosclerosis, familial hypercholesterolemia (FHC) plus atherosclerosis, and familial combined hyperlipidemia (FCH) from the NCBI-GeoDataset (https://www.ncbi.nlm.nih.gov/gds/). As shown in Figure 4(a), in five diseases in the obesity group, zero to three m6A-RMRs were upregulated and zero to 10 m6A-RMRs were downregulated. However, in the four type 2 diabetes datasets, zero to 11 m6A-RMRs were upregulated and one to seven m6A-RMRs were downregulated. The five datasets of atherosclerotic diseases had modulations of m6A-RMRs higher than those in the obesity group but lower than those of type 2 diabetes group: zero to seven m6A-RMRs were upregulated, and zero to seven m6A-RMRs were downregulated.Figure 4
More m6A-RNA methylation regulators (m6A-RMRs) upregulated in type 2 diabetes and abnormal lipid metabolism than in obesity and obesity-related diseases. (a) The result showed that the majority m6A-RMRs were downregulated (marked in blue) in obesity and obesity-related diseases, and only the expression fold change of IGF2BP1 and IGF2BP3 are above two. The expression pattern of m6A-RMRs in different tissues of T2D and atherosclerosis disease is different. (b) Venn diagram showed that in obesity, the decreased genes are more than the increased genes (six and 15, respectively); in T2D, few genes are shared in different tissues; in atherosclerosis disease, PCIF and PRRC2A are commonly upregulated and METTL14 is commonly downregulated in macrophages and monocytes. In the three metabolic diseases, the majority m6A-RMRs showed the heterogeneity expression (genes marked in red). METTL14 is the common decreased m6A methylation regulator among obesity, T2D, and atherosclerosis diseases. Note: the red marked genes are those that are up- or downregulated in different diseases. Expression of m6A-RMRs is different in cell populations of atherosclerosis study. 29 RNA m6A-RMRs were examined in single-cell sequencing dataset online (https://singlecell.broadinstitute.org/single_cell) in atherosclerotic mice (PMID: 30830865). The results showed that expressions of Wtap, Pcif1, Alkbh5, Ythdc1, Ythdf1, Ythdf2, Ythdf3, Hnrnpa2b1, Eif3a, Fmr1, Hnrnpc, Prrc2a, G3bp1, and G3bp2 were decreased in progressive atherosclerosis compared with regression atherosclerosis. The expression of Elavl1 was increased in progressive atherosclerosis compared with regression atherosclerosis (Virma, Igf2bp1 not found) (p<0.05). Abbreviations: FC: fold change; T2D: type 2 diabetes; MUO: metabolically unhealthy obese; FCH: familial combined hyperlipidemia; FHC: familial hypercholesterolemia; NA: not available (missing value).
(a)(b)(c)We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis (Figure 4(b)). In six m6A-RMRs upregulated in the obesity group, two regulators IGF2BP3 (RNA methyltransferase) and G3BP1 (m6A repelled RNA binding protein) were shared by obesity IS and obesity IR. Among the 15 m6A-RMRs upregulated in four different tissues in type 2 diabetes, one regulator HNRNPA2B1 (reader) was shared by liver, subcutaneous adipose, and visceral adipose, and one regulator G3BP1 was shared by liver and visceral adipose. In 10 m6A-RMRs upregulated in atherosclerotic diseases, one regulator WTAP (RNA methyltransferase) was shared by atherosclerosis (athero) carotid artery and atheromacrophages, and two regulators PCIF1 (RNA methyltransferase) and PRRC2A (m6A dependent RNA binding protein) were shared by atheromacrophages and FHC and atheromonocytes. Among 21 m6A-RMRs upregulated in three major metabolic cardiovascular disease groups, one regulator WTAP (RNA methyltransferase) was shared by three major groups; one regulator IGF2BP3 was shared by obesity and athero; three regulators FTO (demethylase), YTHDF2 (RNA binding protein), and G3BP1 were shared by obesity and type 2 diabetes; and four regulators such as PCIF1 (RNA methyltransferase), PRRC2A (m6A-dependent RNA binding protein), YTHDC2 (RNA binding protein), and HNRNPC (m6A-dependent RNA binding protein) were shared by type 2 diabetes and atherosclerotic diseases.We then examined a hypothesis that atherosclerosis progression and regression differentially modulate the expressions of m6A-RMRs. We collected a dataset of scRNA-Seq in the scRNA-Seq database in the Broad Institute at MIT. As shown in Figure 4(c), the expressions of 14 out of 27 m6A-RMRs including Wtap, Pcif1, Alkbh5, Ythdc1, Ythdf1, Ythdf2, Ythdf3, Hnrnpa2b1, Eif3a, Fmr1, Hnrnpc, Prrc2a, G3bp1, and G3bp2 were decreased in the progressive atherosclerosis compared with those in the regressive atherosclerosis. The expression of Elavl1 was increased in the progressive atherosclerosis compared with that in the regressive atherosclerosis. Of note, the expressions of Virma and Igf2bp1 were not found. These results have illustrated that (1) atherosclerotic macrophages have higher upregulation of m6A-RMRs than atherosclerotic carotid artery, and FHC and atherosclerotic monocytes have more modulation of m6A-RMRs than FHC and atherosclerotic T cells; (2) type 2 diabetes adipose tissues have the highest modulation of m6A-RMRs (11 upregulated and three downregulated) among the 14 metabolic disease datasets; (3) obesity diseases have more downregulation than upregulation of m6A-RMRs, and (4) nearly half of 29 m6A-RMRs are downregulated as atherosclerosis progression compared with that of atherosclerosis regression.Additionally, for further understanding the m6A-RMRs’s modulation in cardiovascular diseases, our study of m6A-RMRs in atherosclerosis and other three studies of m6A-RMRs in cardiovascular disease (CVDs) [60–62] were compared. From Table 4, METTL14 is upregulated in our study and other two studies; FTO is downregulated in our study and other two studies. At least, these data revealed that in atherosclerosis and other CVDs, m6A writer METTL14 is upregulated and eraser FTO is downregulated.Table 4
Comparison of our study with other three studies about cardiovascular disease.
m6A-RMRsResults in our study (Figure4(a))Table1 in PMID: 34489709Table1 in PMID:34221238Table1 in PMID: 32910911M6A-RMRs in atherosclerosisRNA methylation-related enzymes in CVDsRoles and mechanisms of RNA m6A effectors in CVDsThe association between m6A methylation and CVDsUpregulated genesPCIF1, YTHDC2, IGF2BP2, IGF2BP3, HNRNPC, PRRC2A, ELAVL1METTL3, YTHDF2, METTL14, KIAA1429,N/AMETTL14Downregulated genesMETTL14, KIAA1429,ZC3H13, FTO, YTHDF3, HNRNPA2B1, FMR1, G3BP1, G3BP2FTOALKBH5, YTHDF2FTOGenes without expression changesMETTL3, CBLL1, RBM15B, YTHDF1, YTHDF2, EIF3A, IGF2BP1N/AN/AN/AExpression of genes not knownMETTL16, YTHDC1, RBMXN/AN/AN/AUncertain (paradox)WTAP, RBM15, ALKBH5YTHDF1METTL3, FTO, WTAPMETTL3Abbreviations: CVDs: cardiovascular diseases; N/A: not available.


## 3.3. Inflammatory Bowel Diseases and Rheumatoid Arthritis Modulate m6A-RMRs More Than Autoimmune Lupus and Psoriasis; and Some Autoimmune Diseases Share Five Upregulated m6A-RMRs such as PCIF1, G3BP2, G3BP1, WTAP, and FTO

We hypothesized that major autoimmune diseases including rheumatoid arthritis (RA), psoriasis (PS), acute cutaneous lupus (ACLE), chronic cutaneous lupus (CCLE), subacute cutaneous lupus (SCLE), Crohn’s disease (CD), and ulcerative colitis (UC) differentially modulate the expressions of m6A-RMRs. To examine this hypothesis, we collected four microarray datasets (eight comparisons) from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/). In a RA dataset, four out of 26 m6A-RMRs were upregulated; and 10 out of 26 m6A-RMRs were downregulated. In four lupus datasets, two to six out of 28 m6A-RMRs were upregulated; and one to four out of 28 m6A-RMRs were downregulated. In three colitis datasets, five to eight m6A-RMRs were upregulated; and eight to nine m6A-RMRs were downregulated (Figure 5).Figure 5
The m6A-RNA methylation regulators (m6A-RMRs) were more differentially expressed in rheumatoid arthritis (RA) and autoimmune skin diseases than those in other autoimmune diseases. (a) The expression changes of m6A-RMRs showed the higher expression fold changes in RA and skin autoimmune diseases than those in inflammatory bowel disease. KIAA1429 and PCIF1 were highly increased; and METTL14, CBLL1, and RBM15 were highly decreased in RA; WTAP, PRRC2A, and G3BP2 were highly increased genes in skin autoimmune disease psoriasis. (b) Venn diagram showed that there were seven upregulated m6A-RMRs and five downregulated m6A-RMRs in autoimmune skin diseases. The writer WTAP and reader HNRNPA2B1 were the common increased and decreased genes in autoimmune skin diseases. There were 13 upregulated m6A-RMRs and 16 downregulated m6A-RMRs in inflammatory bowel diseases. YTHDC2 and G3BP2 were the common downregulated m6A-RMRs. PCIF1 was the shared upregulated m6A-RMR by RA and inflammatory bowel disease. CBLL1, RBM15, YTHDF3, and EIF3A were the shared downregulated m6A-RMRs by RA and inflammatory bowel disease. There were 19 upregulated m6A-RMRs and 21 downregulated m6A-RMRs in these three types of autoimmune diseases. Note: the red marked genes are those that are up- or downregulated in different diseases (p<0.05). Abbreviations: RA: rheumatoid arthritis; ACLE: acute cutaneous lupus; CCLE: chronic cutaneous lupus; PS: psoriasis; SCLE: subacute cutaneous lupus; UC: ulcerative colitis; CD: Crohn’s disease. FC: fold change; NA: not available (missing value).
(a)(b)We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. In seven m6A-RMRs upregulated in the autoimmune skin disease group, one m6A-RMR WTAP was shared by four autoimmune skin diseases; one m6A-RMR G3BP1 was shared by three autoimmune skin diseases including ACLE, CCLE, and psoriasis; one m6A-RMR PRRC2A was shared by three autoimmune skin diseases including CCLE, psoriasis, and SCLE; and one m6A-RMR G3BP2 was shared by two autoimmune skin diseases including CCLE and psoriasis. In 13 m6A-RMRs upregulated in the inflammatory bowel disease group, one m6A-RMR ELAVL1 was shared by UC sigmoid colon or rectum and UC PBMCs; one m6A-RMR WTAP was shared by UC sigmoid colon or rectum and CD PBMCs; and four m6A-RMRs including ZC3H13, RBM15B, IGF2BP2, and IGF2BP3 were shared by CD PBMCs and UC PBMCs. In 19 m6A-RMRs upregulated in all eight autoimmune diseases, one m6A-RMR PCIF1 was shared by RA and inflammatory bowel disease; one m6A-RMR G3BP2 was shared by RA and autoimmune skin diseases; and three m6A-RMRs including G3BP1, WTAP, and FTO were shared by autoimmune skin diseases and inflammatory bowel diseases. Taken together, our results have shown that first, inflammatory bowel diseases and RA modulate m6A-RMRs more than autoimmune lupus and psoriasis; second, autoimmune skin diseases share four upregulated m6A-RMRs including G3BP1, G3BP2, PRRC2A, and WTAP; third, inflammatory bowel diseases share six upregulated m6A-RMRs including ELAVL1, WTAP, ZC3H13, RBM15B, IGF2BP2, and IGF2BP3; and fourth, autoimmune diseases have no any commonly shared m6A-RMRs but share five upregulated m6A-RMRs between groups including PCIF1, G3BP2, G3BP1, WTAP, and FTO.


## 3.4. Some Organ Failures Share Eight Upregulated m6A-RMRs between Groups including Four Writers WTAP, PCIF1, RBM15, and RBM15B; Two m6A-Dependent RNA Binding Proteins PRRC2A and HNRNPC; and Two m6A-Repelled RNA Binding Proteins IGF2BP2 and IGF2BP3; End-Stage Renal Failure (ESRF) Downregulates 85% of 26 m6A-RMRs, and Upregulation of 45% m6A-RMRs in Hemodialysis from ESRF (15%) May Be Associated with Clinical Benefits

We hypothesized that major organ failures including heart failure, hepatitis B virus-associated acute liver failure (HBV-ALF), end-stage renal failure (ESRF), and hemodialysis modulate the expressions of m6A-RMRs. We collected four microarray datasets from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/) to examine this hypothesis. As shown in Figure 6, in the heart failure dataset, six out of 26 m6A-RMRs were upregulated; and seven out of 26 m6A-RMRs were downregulated. In the HBV-ALF dataset, eight out of 26 m6A-RMRs were upregulated; and 17 out of 26 m6A-RMRs were downregulated. In the ESRF dataset, four out of 26 m6A-RMRs were upregulated; and 22 out of 26 m6A-RMRs were downregulated. In the hemodialysis dataset, 10 out of 22 m6A-RMRs were upregulated; and five out of 22 m6A-RMRs were downregulated. We also analyzed the shared and disease-specific m6A-RMRs using Venn diagram analysis. In 20 m6A-RMRs upregulated in four organ failure groups, one upregulated m6A-RMR (RBM15B, RNA methyltransferase) was shared by HF and HBV-ALF; one upregulated m6A-RMR (PRRC2A) was shared by HF and ESRF; two upregulated m6A-RMRs (IGF2BP2 and IGF2BP3, RNA binding proteins) were shared by HBV-ALF and ESRF; and four m6A-RMRs including WTAP (RNA methyltransferase), RBM15 (RNA methyltransferase), PCIF1 (RNA methyltransferase), and HNRNPC (m6A-dependent RNA binding protein) were shared by HBV-ALF and hemodialysis.Figure 6
The m6A-RNA methylation regulators (m6A-RMRs) were differentially expressed in several different organ failure diseases. (a) More m6A-RMRs were significantly increased and decreased in hepatitis B virus- (HBV-) associated acute liver failure (HBV-ALF) and hemodialysis diseases. The expression fold changes of IGF2BP1 and IGF2BP2 were higher, and the expression fold changes of ZC3H13 and YTHDF3 were lower in HBV-ALF. The expression fold changes of most m6A-RMRs were lower in whole blood of end-stage renal failure (ESRF) while the expression fold changes of some m6A-RMRs (WTAP, PCIF1, and EIF3A) were higher in PBMC of hemodialysis compared with healthy control. (b) Venn diagram showed that there were no shared upregulated or downregulated m6A-RMRs among these four studies. Some m6A-RMRs were decreased between organ failure diseases: YTHDC2 and FMR1 were shared between HF and HBV-ALF; METTL14, FTO, and YTHDF1 were shared between HBV-ALF and ESRF; G3BP1 was shared between HBV-ALF and ESRF. Note: the red marked genes are those that are up- or downregulated in different diseases (p<0.05). Abbreviations: HBV-ALF: hepatitis B virus- (HBV-) associated acute liver failure; CKD: chronic kidney disease; ESRF: end-stage renal failure; FC: fold change; NA: not available (missing value).
(a)(b)These results demonstrated that first, four major organ failures have no commonly shared upregulated m6A-RMRs but share eight m6A-RMRs between groups including RBM15B, IGF2BP2, IGF2BP3, PRRC2A, WTAP, RBM15, PCIF1, and HNRNPC; second, hemodialysis is the only organ failure that upregulates more m6A-RMRs than downregulates m6A-RMRs; other organ failures have less upregulation of m6A-RMRs than downregulation of m6A-RMRs; third, surprisingly, ESRF and hemodialysis have no any shared upregulated m6A-RMRs, suggesting that the clinical improvements of hemodialysis from ESRF are benefited from upregulation of m6A-RMRs by hemodialysis; and fourth, ESRF modulates 100% of 26 m6A-RMRs, which is the highest modulation rate found among all the diseases examined.


## 3.5. MERS-CoV Infections at 36 Hours in Human Microvascular Endothelial Cells Modulate the Highest Numbers of m6A-RMRs (Upregulated 20 and Downregulated Four) among Three Types of Viral Infections (MERS-CoV, SARS-CoV, and Influenza Virus); and the Differences in Modulating the Expressions of m6A-RMRs May Be Related to the Virulence of Virus and Virus Replication

We hypothesized that viral infections with Middle East respiratory syndrome (MERS) coronavirus infection [63], severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV) [64], and influenza virus infections [65] modulate the expressions of m6A-RMRs. To test this hypothesis, we collected three groups of microarray datasets from the NCBI-GeoDatasets (https://www.ncbi.nlm.nih.gov/gds/). We found that MERS-CoV infection of human microvascular endothelial cells at the time course of 0, 12, 24, 36, and 48 hours (h) [63] after infection resulted in upregulation of two to 20 out of 28 m6A-RMRs and downregulation of one to 10 out of 28 m6A-RMRs (Figure 7). In addition, SARS-CoV infection of human airway epithelial cells at the time course of 0, 24, 36, 48, 60, 72, 84, and 96 hours (h) [64] after infection resulted in upregulation of two to nine out of 27 m6A-RMRs and downregulation of zero to eight out of 27 m6A-RMRs. H1N1 influenza virus infection of human airway epithelial cells at the time course of 0, 6, 12, 18, 24, 36, and 48 hours after infection resulted in upregulation of one to eight out of 27 m6A-RMRs and downregulation of five to 11 out of 27 m6A-RMRs. Moreover, infections of Calu-3 cells (a non-small-cell lung cancer cell line) with four different strains of influenza viruses (H7N9, H7N7, H5N1, and H3N2) [65] resulted in upregulation of four to 16, one to nine, two to 12, and one to eight out of 28 m6A-RMRs, respectively, and downregulation of three to 11, four to 17, three to 14, and four to 16 out of 28 m6A-RMRs, respectively. Taken together, these results have demonstrated that (1) MERS-CoV infections in human microvascular endothelial cells modulate the highest numbers of m6A-RMRs (upregulated 20 and downregulated four at the time course of 36 hours) among the three types of viral infections examined; (2) similar to MERS-CoV infection, SARS-CoV infections in human airway epithelial cells upregulated more m6A-RMRs than downregulated but significantly less than that in MERS-CoV infections; (3) influenza virus (H1N1) infection in human airway epithelial cells downregulated more m6A-RMRs than upregulated; and (4) novel avian-origin influenza virus (IAV) H7N9 infections at 24-hour postinfection upregulated 16 and downregulated six out of 28 m6A-RMRs, which are significantly different from that of upregulation of nine, 12, and eight and downregulation of 17, 14, and 13 out of 28 m6A-RMRs by highly pathogenic avian-origin influenza viruses H7N7, H5N1, and human seasonal H3N2 IAV, respectively. The differences in modulating the expressions of m6A-RMRs may be related to the virulence of the virus, virus replication, and other aspects [65].Figure 7
The m6A-RNA methylation regulators (m6A-RMRs) were upregulated in virus-infected cells as the infection time was extended. (a) In general, upregulated m6A-RMRs were more than downregulated m6A-RMRs in the condition of MERS and SARS infections. The expressions of m6A methylation writers such as WTAP, KIAA1429, CBLL1, and RBM15 and readers YTHDF1, YTHDF2, YTHDF3, and RBP dependent on m6A PRRC2A were increased; and the expression of eraser FTO was decreased when endothelial cells were infected with MERS coronavirus (MERS-CoV) and SARS coronavirus (SARS-CoV). (b) In avian influenza virus studies, it was obvious that the expressions of writer WTAP and PCIF1 were increased, and the expression of eraser FTO was decreased in all avian influenza virus-infected cells; the longer the infected time was, the higher the numbers of differentially expressed m6A methylation regulators there were. The results suggest that MERS coronavirus, SARS coronavirus, and avian influenza virus infections upregulated the expressions of m6A-RMRs (p<0.05). Abbreviations: MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome; H1N1, H7N9, H7N7, H5N1, and H3N2 are the cell populations of avian influenza virus. FC: fold change; NA: not available (missing value).
(a)(b)


## 3.6. Proinflammatory Lipid oxPAPC and NOTCH1 Knockdown Modulates m6A-RMRs More Than Other DAMP Stimulation of ECs including LPS, oxLDL, and IFNs; and Two m6A-RMRs Such as Hnrnpa2b1 and Eif3a Were Differentially Expressed in Three Aortic EC Clusters

We hypothesized that pathogen-associated molecular patterns (PAMPs)/danger-associated molecular patterns (DAMPs) modulate the expressions of m6A-RMRs in various endothelial cells. To examine this hypothesis, we collected eight microarray datasets from various endothelial cells stimulated by PAMPs/DAMPs. As shown in Figure 8(a), influenza virus infection of human umbilical vein endothelial cells (HUVEC) resulted in upregulation of nine out of 26 m6A-RMRs (with the highest upregulation of writer KIAA1429 for 10 folds) and downregulation of 16 out of 26 m6A-RMRs. By comparison, Kaposi’s sarcoma-associated herpesvirus (KSHV) infection of human dermal endothelial cells modulated m6A-RMR expressions significantly less than that of influenza virus infection, upregulating zero and downregulating two out of 22 m6A-RMRs. Toll-like receptor 4 (TLR4) ligand lipopolysaccharide (LPS) stimulation of human lung microvascular endothelial cells for 4, 8, and 24 hours resulted in upregulation of two to three m6A-RMRs (with the high upregulation of writer WTAP) and downregulation of one to two m6A-RMRs, respectively. In addition, cytokine interferon-α (IFNα), IFNβ, and IFNγ treatments of HUVEC led to upregulation of zero to one and downregulation of one to five out of 22 m6A-RMRs, respectively. The knockdown (KD) of proinflammatory NOTCH1 signaling and NOTCH1 KD plus proinflammatory cytokine interleukin-1β (IL-1β) in HUVEC led to upregulation of seven and five out of 25 m6A-RMRs and downregulation of five and six out of 25 m6A-RMRs. The differences of m6A-RMR expression modulation between NOTCH1 KD and NOTCH1 KD plus IL-1β may contribute to the inflammatory status of treated HUVEC [66]. In contrast, another report found that NOTCH1 is antiatherogenic; and proinflammatory lipids oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (Ox-PAPC) decrease NOTCH1 expression in human aortic endothelial cells (HAEC) [67]. In this experimental setting, NOTCH1 KD in HAEC resulted in upregulation of five and downregulation of 10 out of 25 m6A-RMRs; proinflammatory lipid oxPAPC stimulation in HAEC led to upregulation of 10 and downregulation of seven out of 25 m6A-RMRs. It was reported that oscillatory shear (OS) present on the fibrosa stimulates fibrosa human aortic valve endothelial cells (HAVEC), which may contribute to aortic valve disease [68]. In this experimental setting, oscillatory shear on fibrosa HAVEC and ventricularis HAVEC resulted in upregulation of four and six and downregulation of two and three out of 28 m6A-RMRs, respectively. Moreover, mouse aortic endothelial cells (MAEC) isolated from atherogenic apolipoprotein E-deficient (ApoE-/-) mice [3], TLR4 ligand LPS-treated MAEC, another TLR4 ligand oxidized low-density lipoprotein- (oxLDL-) stimulated MAEC [69, 70], and oxPAPC-stimulated MAEC [71] upregulated two, two, five, and eight out of 21 m6A-RMRs and downregulated five, six, three, and five out of 21 m6A-RMRs, respectively.Figure 8
The expressions of m6A-RNA methylation regulators (m6A-RMRs) were modulated in endothelial cells with different stimulations. (a) The expression changes of m6A-RMRs were the most obvious in influenza virus infections with the highest expression FC values. The expressions of writer KIAA1429 and readers YTHDF1, YTHDF2, IGF2BP1, IGF2BP2, HNRNPC, and ELAVL1 were upregulated with the FC>2; the expressions of writers METTL3, METTL14, WTAP, ZC3H13, RBM5, RBM15B; eraser FTO; and readers YTHDF3, YTHDC2, HNRNPA2B1, IGF2BP3, G3BP1, and G3BP2 were downregulated with the FC<0.5. In KSHV-infected ECs, IGF2BP3 was downregulated with low FC values. In LPS-treated microvascular ECs, writer WTAP was upregulated in four and eight hours. In IFNa- and IFNb-treated cells, FMR1 was increased. In the cells with NOTCH1 knockdown combined with IL-1β treatment, the expression changes of m6A-RMRs were not obvious. ECs activated with ox-PAPC showed that the expressions of WTAP and CBLL1 were upregulated. There were gentle expression changes of m6A-RMRs for ECs exposed to shear or for mouse aortic ECs from apolipoprotein E- (ApoE-) deficient (ApoE knock out) or treated with LPS, oxLDL, and oxPAPC, respectively. (b) The m6A-RMRs were differentially expressed in aortic EC populations. The 12 out of 29 m6A-RMRs including Wtap, Zc3h13, Pcif1, FTO, Alkbh5, Ythdc1, YthDf2, Ythdf3, Fmr1, Prrc2a, Elavl1, and G3bp1 were in medium expression levels in almost all aortic cell populations of normal mouse aorta; and m6A-RMRs such as Hnrnpa2b1, Eif3a, Hnmpc, and G3bp2 were differentially expressed in aortic cell populations. (Mettl14, Igf2bp1, and Igf2bp2 not found) (p<0.05). Abbreviations: KSHV: Kaposi sarcoma-associated herpes virus; oxPAPC: oxidized 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (inflammatory lipids); oxLDL: oxidized LDL; FC: fold change; NA: not available (missing value); VSMC: vascular smooth muscle cells; EC: endothelial cells.
(a)(b)We then examined the expressions of m6A-RMRs in three mouse aortic endothelial cell (EC) clusters identified recently with single-cell RNA sequencing (scRNA-Seq) [72]. As shown in Figure 8(b), 12 out of 26 m6A-RMRs (46%) including Wtap, Zc3h13, Pcif1, FTO, Alkbh5, Ythdc1, YthDf2, Ythdf3, Fmr1, Prrc2a, Elavl1, and G3bp1 were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m6A-RMRs such as Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters, which were Cytl1+Gkn3+ endothelial cell (EC) cluster 1, Fabp4+Gphbp1+Rgcc+ EC cluster 2, and Ccl21a+Lrg1+ EC cluster 3 [72]. Of note, the expressions of Mettl14, Igf2bp1, and Igf2bp2 were not found.These results conclude that (1) influenza virus infection of HUVEC results in expression modulation of most m6A-RMRs (25 out of 26), which were similar to that found in influenza virus infections of human airway epithelial cells and Calu-3 lung cells for 24 hours in Figure 7; (2) proinflammatory lipids oxPAPC and NOTCH1 knockdown modulate m6A-RMRs more than other DAMP stimulation of ECs including LPS, oxLDL, and IFNs; (3) ten m6A methyltransferases were not significantly modulated in MAEC from ApoE KO aorta with or without further stimulations of LPS, oxLDL, and oxPAPC, respectively (Figure 8, GSE39264); and (4) 12 out of 26 m6A-RMRs (46%) were in medium expression levels in almost all aortic cell populations of normal mouse aortas; and two m6A-RMRs including Hnrnpa2b1 and Eif3a were differentially expressed in three aortic EC clusters.


## 3.7. Upregulated m6A-RMRs Were More Than Downregulated m6A-RMRs in Various Cancer Types; Head and Neck Cancer, Cervical Cancer, Brain and CNS Cancer, Other Cancer, and Kidney Cancer Upregulated ≥10 m6A-RMRs; and IGF2BP3, G3BP1, IGF2BP2, and HNRNPC Were Upregulated in ≥10 Cancer Types

We hypothesized that the expressions of m6A-RMRs are modulated in various cancer cell populations. To test this hypothesis, we collected 19 cancer datasets in a comprehensive cancer gene expression database Oncomine (https://www.oncomine.org) [45] including brain and CNS cancer, head and neck cancer, esophageal cancer, gastric cancer, bladder cancer, breast cancer, cervical cancer, ovarian cancer, colorectal cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, prostate cancer, leukemia, lymphoma, melanoma, myeloma, sarcoma, and other cancer (Figure 9(a)). As shown in Figure 9, the numbers in red indicated the numbers of studies with upregulated m6A-RMRs; and the numbers in blue indicated the numbers of studies with downregulated m6A-RMRs. The results showed that upregulated m6A-RMRs were more than downregulated m6A-RMRs in various cancer types (number of red cells is more than that of blue cells). Of note, the “Significant Unique Analyses” indicated the numbers of studies in which the analyzed genes were significantly upregulated (red) or downregulated (blue). The “Total Unique Analysis” indicated the numbers of studies in which the analyzed genes were included (p<0.05, foldchange>2). Three m6A-RMRs such as METTL16, YTHDC1, and PRRC2A were not found.Figure 9
The m6A-RNA methylation regulators (m6A-RMRs) were differentially expressed in 18 cell populations of cancers in the Oncomine database (https://www.oncomine.org/). (a) The numbers in red were the numbers of m6A-RMRs upregulated, and the numbers in blue were the numbers of m6A-RMRs downregulated. The results showed that m6A-RMRs upregulated were more than m6A methylation regulators downregulated. The “Significant Unique Analyses” indicated the numbers of studies in which the analyzed genes were significantly upregulated (red) or downregulated (blue). The “Total Unique Analysis” indicated the numbers of studies in which the analyzed genes were included (p<0.05, foldchange>2) (METTL16, YTHDC1, and PRRC2A not found). (b) Different expression patterns of m6A-RMRs in different cell populations of cancers. The bar graph showed differential expression m6A regulators IGF2BP2 (upregulated in ten cancers, downregulated in three cancers, and uncertain in three cancers), IGF2BP3, and G3BP1 are the top 3 genes involved in different cancers. (c) IGF2BP3, G3BP1, and IGF2BP2/HNRNPC were upregulated in 12, 11, and 10 cancers, respectively. (d) ZC3H13, IGF2BP1, and WTAP/FTO/YTHDC2 were downregulated in six, five, and four cancers, respectively. (e) The expression of G3BP2 in five cancers was uncertain; WTAP, PCIF1, HNRNPA2B1, EIF3A, and IGF2BP2 in three cancers were uncertain.
(a)(b)(c)(d)(e)We then determined the top cancer types that upregulated or downregulated m6A-RMRs the most. As shown in Figures 9(b) and 9(c), head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m6A-RMRs. In contrast, breast cancer, ovarian cancer, kidney cancer, esophageal cancer, leukemia, and lymphoma downregulated ≥four m6A-RMRs. In addition, lymphoma, leukemia, other cancer, sarcoma, and brain and CNS cancer modulated ≥five m6A-RMRs in uncertain manners. We also determine the top m6A-RMRs that upregulated or downregulated in cancer types the most. As shown in Figures 9(d) and 9(e), IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types. In contrast, ZC3H13, IGF2BP1, WTAP, FTO, and YTHDC2 were downregulated in ≥four cancer types. In addition, G3BP2, WTAP, PCIF1, HNRNPA2B1, EIF3A, and IGF2BP2 were modulated in ≥three cancer types in uncertain manners.The results indicated that most m6A-RMRs were downregulated in acute inflammatory diseases, while in cancer, the upregulated m6A-RMRs were more than downregulated m6A-RMRs. This situation is because acute inflammation is a part of an innate immune system reaction that can be produced by various factors, such as signaling pathways of the receptors for danger-associated molecule patterns (DAMPs/PAMPs) derived from pathogens, viruses, bacteria, and toxic substances as well as cytokine signals and stress hormone signal. These factors may activate the interactions and cross-talks among the receptors of cytokines, viruses, DAMPs, or PAMPs and promote the migration of macrophages or neutrophils to the area of inflammation [73]. However, during cancer occurs, tumor cells will generate tumor antigens that activate adaptive immune responses and induce T cells and B cells. T cell activation could cause a variety of immune signaling, including T cell antigen receptor signaling, costimulation signaling, immune checkpoint coinhibition signaling, and cytokine signaling. The composition of signaling receptors and pathways is different between acute inflammation and cancer. Therefore, RNA methylation and its transcription, splicing, and mRNA stability are also different under these two situations.We further hypothesized that m6A-RMRs were differentially expressed as different cancer progression. To examine this hypothesis, the expression matrix GSE114783 was downloaded from the NIH-NCBI-GeoDataset database (https://www.ncbi.nlm.nih.gov/gds); and the expressions of m6A-RMRs were screened. The heat map was generated from Cluster web tool [45]. As shown in Supplementary figure 1A, the results showed that the expressions of m6A-RMRs were different among healthy controls, hepatitis B virus carriers, and the patients with liver cirrhosis and hepatocellular carcinoma, especially genes in the red box; compared with healthy controls, there were more upregulated m6A-RMRs in patients with chronic hepatitis B (CHB) group than those in healthy controls. Potentially, due to the tumor heterogeneity or the small sample sizes, there were no differentially expressed m6A-RMRs in the hepatocellular carcinoma group compared with healthy controls. In addition, as shown in Supplementary figure 1B, using the same method as Supplementary figure 1A, the expressions of m6A-RMRs were analyzed in human preinvasive and invasive cervical squamous cell carcinomas and normal cervical epithelia. The differential expressions of m6A-RMRs were shown in the red box. The 22 m6A-RMRs were examined in these three groups. The results showed that the expressions of WTAP were downregulated; and the expressions of four m6A-RMRs such as HNRNPA2B1, IGF2BP2, FMR1, and HNRNPC were upregulated in patients with preinvasive and invasive cervical squamous cell carcinomas compared with normal controls. Moreover, as shown in Supplementary figure 1C, the expressions of m6A-RMRs were analyzed in prostate cancer from benign prostatic hyperplasia to metastatic prostate cancer. The expressions of RBMX were gradually increased from prostatic intraepithelial neoplasia to hormone-naïve prostate cancer compared with normal samples. There were more obvious differential expressions of m6A-RMRs between adjacent prostate cancer samples and prostate carcinoma samples. The expressions of two m6A-RMRs such as EIF3A and RBMX were upregulated in prostate carcinoma samples compared with normal prostate epithelium-adjacent samples.Finally, we examined the expressions of 29 m6A-RMRs [74] in single-cell RNA-Seq data. As shown in Supplementary figure 2A, in a single-cell RNA-seq analysis of astrocytoma dataset [75], 23 out of 28 m6A-RMRs (82%) (except ALKBH5, IGF2BP1, IGF2BP2, IGF2BP3, and G3BP2) were in the medium to high expression levels in malignant cells of astrocytoma. In addition, as shown in Supplementary figure 2B, in a melanoma intratumor heterogeneity dataset [76], 24 out of 29 m6A-RMRs (83%) (except RBM15B, IGF2BP1, IGF2BP3, HNRNPA2B1, and HNRNPC) were in the medium to high expression levels in cancer-associated fibroblasts (CAF) of melanoma.Taken together, these results have demonstrated that first, upregulated m6A-RMRs were more than downregulated m6A-RMRs in various cancer types; second, head and neck cancer, cervical cancer, brain and CNS cancer, other cancer, and kidney cancer upregulated ≥10 m6A-RMRs; third, IGF2BP3, G3BP1, IGF2BP2, and HNRNPC were upregulated in ≥10 cancer types; fourth, four m6A-RMRs such as HNRNPA2B1, IGF2BP2, FMR1, and HNRNPC were upregulated in patients with preinvasive and invasive cervical squamous cell carcinomas; and the expressions of two m6A-RMRs such as EIF3A and RBMX were upregulated in prostate carcinoma samples; and fifth, 82-83% of 29 m6A-RMRs were in the medium to high expression levels in astrocytoma and melanoma.


## 3.8. M1 Macrophage Polarization Upregulates Seven m6A-RMRs including METTL14, WTAP, PCIF1, HNRNPA2B1, IGF2BP2, FMR1, and G3BP1, among Which WTAP and IGF2BP2 May Not Only Promote M1 Polarization but Also Inhibit M2 Polarization

We hypothesized that macrophage polarization from M0 into type 1 proinflammatory macrophages (M1) or M2 anti-inflammatory macrophages modulate the expressions of m6A-RMRs. To examine this hypothesis, we collected a microarray dataset GSE85346 from the NCBI-GeoDataset database. As shown in Figure 10, M1 macrophage polarization upregulated seven m6A-RMRs including METTL14, WTAP, PCIF1, HNRNPA2B1, IGF2BP2, FMR1, and G3BP1 whereas M2 polarization into M2a, M2b, or M2c downregulated three m6A-RMRs including WTAP, IGF2BP2, and IGF2BP3. These results suggest that these M1 polarization upregulated seven m6A-RMRs may promote proinflammatory M1 polarization. In contrast, two M1 polarization-upregulated and M2 polarization-downregulated m6A-RMRs such as WTAP and IGF2BP2 may not only promote M1 polarization but also inhibit M2 polarization.Figure 10
Macrophage polarization regulated the expression of m6A-RNA methylation regulators (m6A-RMRs). Macrophage polarization microarray analysis result showed that expression of WTAP is significantly upregulated in M1 with a fold change of 10.578 and in M2b with a fold change of 2.415 compared with M0 cells; expression of IGF2BP2 is upregulated in M1, M2a, and M2c cells; RBMX is the common downregulated m6A-RMRs in M1, M2a, M2b, and M2c macrophages.Macrophages are diverse immune cells polarized by numerous stimuli, resulting in a wide range of traits and functions. The polarized process from M0 to M1 is induced by the stimulations of TLR ligands or Th1 cytokines, like TNF-a, IFN-γ, and CSF2 signaling. Under inflammation, these macrophage polarization signals are specific and robust [77], whereas, during tumorigenesis, so many factors can be involved. Moreover, different cancers are related to different cell types. A great amount of different signaling cross-talks may cause chaos signaling [78]. Consequently, the signaling that modulates the upregulation of WTAP could be diffused by the chaos cross-talking signaling.


## 3.9. The 86% of m6A-RMRs Were Differentially Expressed in the Six Spleen Treg Clusters; and 8 Out of 12 Treg Signature Genes including FOXO1, HDAC9, Dicer, BLIMP1, GATA3, EP300, BCL6, and PPAR Significantly Regulated the Expressions of m6A-RMRs

Our and others’ reports demonstrated that CD4+Foxp3+ regulatory T cells play significant roles in suppressing immune responses, inflammations, atherosclerosis [13, 25–27, 70, 79–83], and antitumor immune reactions [84] and promoting tissue regeneration. We hypothesized that m6A-RMRs are modulated in Treg differentiation and Treg responses to tissue microenvironment. As shown in Figure 11(a), Treg upregulated six m6A-RMRs such as ZC3H13, YTHDC1, YTHDC2, IGF2BP1, IGF2BP3, and HNRNPC in three Treg datasets compared to that of CD4+Foxp3- effector T cells.Figure 11
Regulatory T cells regulate the expression of m6A-RNA methylation regulators (m6A-RMRs). (a) Compared with conventional T cells, expression of IGF2BP3 is upregulated and RBMX is downregulated in regulatory T cells. (b) The expression data matrix was obtained from scRNA study (PMID: 29434354), and the expression heat map using Cluster web tool (PMID: 25969447) was generated. The result showed that the expression of m6A-RMRs in six types of Treg clusters was different. Expression of Ythdf3 and Rbm15b have the same pattern as Treg signature gene Foxp3. (c) Most m6A-RMRs are downregulated in deficiencies of GATA3, HDAC6, HDAC9, IL2RB, and FOXO1. The numbers of upregulated regulators are more than those of downregulators in deficiencies of BCL6, DICER, BLIMP1, PPARG, EOS, and EP300.
(a)(b)(c)In addition, a recent report with single-cell RNA sequencing (scRNA-Seq) characterizes spleen Treg into six clusters including S100a4highS100a6high cluster 1 (activated), Itgb1high cluster 2 (activated), Dusp2highNr4a1highFoxp3highIL2rahigh cluster 3 (activated), Ikzf2highFoxp3high cluster 4 (resting), Bach2high cluster 5 (resting), and Satb1highSellhigh cluster 6 (resting) [85]. We hypothesized that the m6A-RMRs are differentially expressed in these six Treg clusters. As shown in Figure 11(b), four m6A-RMRs including PCIF1, METTL3, HNRNPA2B1, and EIF3A were upregulated in the cluster 1 Treg; two m6A-RMRs including RBM15 and YTHDC2 were upregulated in the cluster 2 Treg; seven m6A-RMRs including ALKBH5, YTHDF3, RBM15B, HNRNPC, ZC3H13, FTO, Wand TAP were upregulated in cluster 3 Treg; four m6A-RMRs including IGF2BP2, RBMX, G3BP2, and PRRC2A were upregulated in cluster 4 Treg; three m6A-RMRs including METTL14, YTHDF2, and IGF2BP3 were upregulated in cluster 5 Treg; and five m6A-RMRs including G3BP1, ELAVL1, IGF2BP1, FMR1, and METTL16 were upregulated in cluster 6 Treg. Therefore, 25 out of 29 m6A-RMRs (86%) were differentially expressed in the six spleen Treg clusters.Moreover, we hypothesized that Treg signature genes play significant roles in regulating the expressions of m6A-RMRs. To examine this hypothesis, we collected the microarray datasets of 12 Treg signature gene deficiencies. As shown in Figure 11(c), the deficiencies of type 2 T helper (Th2) transcription factor (TF) GATA binding protein 3 (GATA3), follicular Th cell (Tfh) TF B cell lymphoma 6 protein (BCL6), histone deacetylase 6 (HDAC6), HDAC9, microRNA maturation enzyme DICER, interleukin-2 receptor b chain (IL2rb), B-lymphocyte-induced maturation protein 1 (BLIMP1, PR domain Zinc finger protein 1), peroxisome proliferator activated receptor gamma (PPARg) (visceral adipose tissue, VAT), PPARg (lymph nodes, LN), IKAROS family zinc finger 4 (EOS), Foxhead box 1 (FOXO1), and histone acetyltransferase P300 (EP300) modulated and upregulated 1, 6, 0, 3, 5, 0, 6, 3, 1, 1, 6, and 5 m6A-RMRs, respectively, and downregulated 6, 0, 2, 5, 3, 1, 3, 2, 0, 0, 11, and 2 m6A-RMRs, respectively. The 12 Treg signature genes were ranked based on the total modulation numbers when they were deficient, FOXO117>HDAC98=Dicer8=BLIMP18>GATA37=EP3007>BCL66>PPARgVAT5>HDAC62>IL−2RB1=PPARGLN1=EOS1. It has been reported that mechanistic target of rapamycin kinase (mTOR)/interferon regulatory factor 4 (IRF4)/GATA3 promotes IL-4+ Th2-like Treg; phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/AKT serine/threonine kinase 1 (Akt)/Foxo, Th1 TF T-box transcription factor 21 (T-bet), hypoxia inducible factor 1 subunit alpha (HIF-1a), glycolysis promote interferon-gamma (IFNg)+ Th1-like Treg, microbiota/MAF BZIP transcription factor (c-Maf)/signal transducer and activator of transcription 3 (Stat3)/RAR-related orphan receptor C (RORgt), aryl hydrocarbon receptor (AhR) promote IL-17+ Th17 like Treg, and mTOR/Stat3/hepatocyte nuclear factor 1-alpha (TCF1) promote Bcl6+ follicular Treg (Tfr).Our results have shown that (1) Treg upregulated six m6A-RMRs including ZC3H13, YTHDC1, YTHDC2, IGF2BP1, and HNRNPC; (2) 25 out of 29 m6A-RMRs (86%) were differentially expressed in the six spleen Treg clusters, suggesting that m6A-RMRs play significant roles in reprogramming Treg activation status (clusters 1-3) and resting status (clusters 4-6); and (3) the transcription data from 12 Treg signature gene deficiencies suggest that eight out of 12 Treg signature genes including FOXO1, HDAC9, Dicer, BLIMP1, GATA3, EP300, BCL6, and PPARγ regulate the expressions of m6A-RMRs significantly; and m6A-RMRs play important roles in reshaping Treg stability and plasticity.


## 3.10. Immune Checkpoint Receptors TIM3, TIGIT, PD-L2, and CTLA4 Play Significant Roles in Modulating the Expressions of m6A-RMRs; and Inhibition of Costimulation Receptor CD40 with Anti-CD40 Antibody Significantly Upregulates the Expressions of m6A-RMRs

We recently reported that cosignaling receptors localized on cell membrane including costimulation receptors and immune checkpoint receptors (coinhibition receptors) serve as a prototypic cell-cell contact interaction and regulate T cell plasticity, immune tolerance, cellular physiology, and sterile inflammatory disorders [86]. We hypothesized that cosignaling receptors regulate the expressions of m6A-RMRs. We collected 10 cosignaling receptor deficiency/suppression microarray datasets from the NIH-NCBI GeoDataset database. As shown in Figure 12, cytotoxic T-lymphocyte-associated protein 4 (CTLA4) KO and anti-CTLA4 upregulated 2-3 m6A-RMRs and downregulated one m6A-RMR. CD80+ programmed cell death 1 ligand 2 (PD-L2)+ (memory B cells) [87] upregulated three and downregulated five m6A-RMRs. PD-L1 and anti-PD-L1 upregulated one m6A-RMRs and downregulated zero m6A-RMRs. T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) KO, CD3+CD8+TIM-3- cells [88], and CD8+PD1+TIM3- cells [89] upregulated 0-5 m6A-RMRs and downregulated 2-11 m6A-RMRs. T cell immunoreceptor with Ig and ITIM domain- (TIGIT-) Treg upregulated 3 m6A-RMRs and downregulated 13 m6A-RMRs. In addition to the modulation of m6A-RMR expressions by immune checkpoint receptors, the modulation with anti-CD40 (tumor necrosis factor receptor superfamily member 5), a costimulation receptor, resulted in upregulation of 5-12 m6A-RMRs and downregulation of 1-2 m6A-RMRs. Taken together, these results have demonstrated that immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 play significant roles in modulating the expressions of m6A-RMRs; and inhibition of costimulation receptor CD40 with anti-CD40 antibody significantly upregulates the expressions of m6A-RMRs, which is associated with suppression of autoimmune responses by anti-CD40 antibody therapy [90].Figure 12
Immune checkpoint receptors regulate the expression of m6A-RNA methylation regulators (m6A-RMRs). T cell activation can regulate the expression of m6A-RMRs. Microarrays about immune checkpoint knockout (KO), knockdown (KD), anticheckpoint, or sorted negative and positive checkpoint were searched from the GEO database to analyze the regulation relationship between immune checkpoint and m6A-RMRs. The result showed that when the coinhibitory immune checkpoints PD-L2, TIM-3, and TIGIT were inhibited (KO or KD vs. control, anti- vs. control) or reduced (negative vs. positive), the expressions of m6A-RMRs were decreased. However, the costimulatory immune checkpoint CD40L was inhibited, and more m6A-RMRs’ expressions were increased. That is, maybe, T cell activation negatively regulates the expression of m6A-RMRs.


## 3.11. Proinflammatory Cytokine Signaling Pathways Significantly Modulate the Expressions of m6A-RMRs, and Suppression of Proinflammatory Cytokine Pathways Upregulate More Than Downregulate the Expressions of m6A-RMRs

We recently reported that cytokines including IL-17 [91–93], IL-2 [26], and IL-35 [15, 94, 95] play significant roles in inflammation regulation, Treg maintenance, and various pathologies. We hypothesized that signal pathways of cytokines and cytokine receptors regulate the expressions of m6A-RMRs. We collected eight cytokine and cytokine receptor microarray datasets from the NIH-NCBI GeoDataset database. As shown in Figure 13, deficiencies of tumor necrosis factor-α (TNFα, TNF receptor 1-2 (TNFR1-2), interferon-γ (IFNγ), IFNγ receptor-1 (IFNγR1), IL6, IL-17 receptor (IL17R), IL18, and IL18 upregulated 3, 7, 1, 7, 2, 3, 5, and 2 m6A-RMRs, respectively, and downregulated 0, 3, 0, 1, 2, 2, 1, and 3 m6A-RMRs, respectively. These results have demonstrated that (1) proinflammatory cytokine signaling pathways significantly modulate the expressions of m6A-RMRs, and (2) suppression of proinflammatory cytokine pathways upregulates more than downregulates the expressions of m6A-RMRs.Figure 13
Proinflammatory cytokines can regulate m6A-RNA methylation regulators (m6A-RMRs). In deficiency of proinflammatory cytokine microarrays, the number of upregulated m6A-RMRs is more than that of downregulated m6A-RMRs. That is, maybe, proinflammatory cytokines negatively regulate m6A methylation. The result is consistent with the result of the m6A-RMRs’ expression in acute inflammation.


## 3.12. NF-κB Components and JAK/STAT Signaling (Except STAT1) Play Important Roles in Upregulating More Than Downregulating m6A-RMRs; and Tumor Suppressors TP53, PTEN, and APC Play Significant Roles in Downregulating More Than Upregulating m6A-RMRs

We and others reported that proinflammatory/immune regulatory transcription factors (TFs), Janus kinase (JAK)/signal transducer and activator of transcription (STAT) [94], nuclear factor kappa B (NF-κB) [96], tumor protein P53 (TP53), phosphatase and tensin homolog (PTEN), and adenomatous polyposis coli (APC regulator of WNT signaling pathway, APC) [97] play significant roles in regulating inflammation, Treg maintenance, cytokine responses, tumorigenesis, and development. We hypothesized that proinflammatory and immune regulatory TFs, NF-κB, and JAK/STAT pathways and three tumor suppressors TP53, PTEN, and APC pathways regulate the expressions of m6A-RMRs. We collected five NF-κB subunit deficiency datasets (seven comparisons), one JAK2 deficiency dataset, one STAT1 deficiency dataset, two STAT3 deficiency datasets, six TP53 deficiency datasets (eight comparisons), five PTEN datasets, and one APC dataset from the NIH-NCBI GeoDataset database. As shown in Figure 14, deficiencies of IKK2 (cell line), IKK2 (tumor), IKK complex, IKK complex, IKK2, RELA, and Rela upregulated 4, 2, 1, 1, 5, 3, and 2 m6A-RMRs, respectively, and downregulated 7, 3, 7, 6, 6, 13, and 13, respectively. Of note, deficiencies of NF-κB signaling components resulted in more downregulation than upregulation of m6A-RMRs. Deficiencies of JAK2, STAT1, STAT3, and STA3 resulted in upregulating 1, 10, 2, and 4 m6A-RMR, respectively, and downregulating 5, 2, 4, and 8 m6A-RMRs, respectively. STAT1 deficiency upregulated more than downregulated m6A-RMRs, but deficiencies of JAK2 and STAT3 downregulated more than upregulated m6A-RMRs. Deficiencies of TP53 in eight datasets upregulated 8, 9, 9, 15, 4, 10, 3, and 4 m6A-RMRs, respectively, and downregulated 7, 5, 0, 0, 5, 7, 3, and 1 m6A-RMRs, respectively. Of note, six out of 8 TP53-deficient datasets had more upregulation than downregulation of m6A-RMR, suggesting that tumor progression triggered by TP53 deficiencies may require more upregulation than downregulation of m6A-RMRs. Deficiencies of PTEN in five datasets upregulated 10, 2, 5, 4, and 4 m6A-RMRs, respectively, and downregulated 0, 14, 2, 2, and 11 m6A-RMRs, respectively. Of note, three out of five PTEN deficient datasets had more upregulation than downregulation of m6A-RMR, suggesting that tumor progression triggered by PTEN deficiencies may require more upregulation than downregulation of m6A-RMRs. Deficiencies of APC upregulated 14 and downregulated 4 m6A-RMRs, respectively. These results have exhibited that (1) NF-κB signaling components play important roles in upregulating more than downregulating m6A-RMRs; (2) JAK/STAT signaling pathways play important roles in upregulating more than downregulating m6A-RMRs except STAT1; and (3) tumor suppressors TP53, PTEN, and APC play significant roles in downregulating more than upregulating m6A-RMRs, suggesting that upregulation of m6A-RMRs may favor more than inhibit tumor progression.Figure 14
Oncogenes and tumor suppressors can regulate m6A-RNA methylation regulators (m6A-RMRs). (a) In deficiency of oncogenes, the number of upregulated m6A-RMRs is less than that of downregulated m6A-RMRs in addition to individual research (GSE44652). (b) In deficiency of tumor suppressors, the number of upregulated m6A-RMRs is more than that of downregulated m6A-RMRs in addition to individual research. That is, maybe, tumor suppressors negatively regulate m6A methylation. These results are consistent with the result of the m6A-RMRs’ expression in cancers.
(a)(b)


## 3.13. Methionine-Homocysteine Cycle Enzyme Mthfd1 Downregulates More Than Upregulates m6A-RMRs; One m6A Writer RBM15 and One m6A Eraser FTO Significantly Modulate the Expressions of m6A-RMRs; and H3K4-Specific Methyltransferase MLL1 and DNA Methyltransferase, DNMT1, Significantly Regulate the Expressions of m6A-RMRs

We reported that hyperhomocysteinemia promotes differentiation of the inflammatory Ly6C+ monocyte [5, 98, 99], decreased high-density lipoprotein (HDL) [100, 101], endothelial cell activation, and programmed cell death [6] and atherosclerosis [102]. We hypothesized that m6A-RMRs, histone lysine methylases, DNA methyltransferases, and methyl donating homocysteine-methionine metabolism cycle play significant roles in regulating the expressions of m6A-RMRs as a feedback mechanism. To examine this hypothesis, we collected numerous microarray datasets including a methionine diet-fed model, Mthfd1 KO, Mthfr KO, eight histone 3 lysine 4 (H3K4) methylase KOs, and eight DNA methyltransferase KOs from the NIH-NCBI-GeoDataset database. As shown in Figure 15(a), the methionine diet-fed model upregulated three m6A-RMRs and downregulated one m6A-RMR; deficiencies of methylenetetrahydrofolate dehydrogenase (Mthfd1) and methylenetetrahydrofolate reductase (Mthfr) resulted in upregulation of one and zero m6A-RMR, respectively, and downregulation of two and one m6A-RMRs, respectively. Deficiencies of m6A-RMRs RBM15 and FTO upregulated 1 to 11 m6A-RMRs and downregulated 3-5 m6A-RMRs. As shown in Figure 15(b), deficiencies of histone lysine 4 methylase in eight datasets upregulated one to 13 m6A-RMRs and downregulated zero to 11 m6A-RMRs. Among the eight comparison datasets, three datasets of deficiencies of H3K4-specific methyltransferase MLL1 [103] modulated m6A-RMRs the most, with 10 out of 29, 20 out of 29, and 16 out of 29 m6A-RMRs, respectively. Moreover, deficiencies of DNA methyltransferase 1 (DNMT1) in GSE54841, GSE27434, GSE44277, and GSE86147; DNMT3A at GSE54841 and GSE42304; and DNMT3B at GSE54841 and GSE75401 upregulated 0, 1, 0, 3, 0, 1, 0, and 0 m6A-RMRs, respectively, and downregulated 2, 8, 12, 15, 1, 3, 1, and 1 m6A-RMRs, respectively. Among the eight datasets of DNA methyltransferase deficiencies, DNMT1 deficiencies modulated the most with 9, 12, and 18 m6A-RMRs except GSE54841 (Figure 15(c)). Taken together, these results have demonstrated that (1) methionine diet-fed model and methionine-homocysteine cycle enzyme Mthfr do not significantly regulate the expressions of m6A-RMRs but methionine-homocysteine cycle enzyme Mthfd1 downregulates more than upregulates m6A-RMRs; (2) one m6A writer RBM15 and one eraser FTO significantly modulate the expressions of m6A-RMRs, suggesting a feedback mechanism for regulation of m6A-RMR expressions; (3) H3K4-specific methyltransferase MLL1 significantly regulates the expressions of m6A-RMRs; and (4) DNA methyltransferase, DNMT1, modulates the expressions of m6A-RMRs.Figure 15
Hyperhomocysteinemia, folate cycle-related enzymes, m6A-RNA methylation regulators (m6A-RMRs), H3K4 methylases, and DNA methyltransferase regulate the expression of m6A-RMRs, suggesting a methylation hierarchy. (a) Hyperhomocysteinemia regulated fewer numbers of m6A-RMRs but more m6A-RMRs were changed after antihyperhomocysteinemia treatment in hyperhomocysteinemia animal model- (Rattus norvegicus) related microarray (GSE30308). The deficiencies of the folate cycle and metabolism related enzymes and m6A-RMRs affect the expression of m6A-RMRs. IGF2BP3 was upregulated with fold changes of 4.486, and FMR1 was downregulated with fold changes of 0.327 in the deficiency of methylenetetrahydrofolate Mthfd1. In the deficiencies of m6A-RMRs, writer RBM15 and WTAP were downregulated, and CBLL1 and YTHDF1 were upregulated. WTAP, HNRNPA2B1, IGF2BP2, ELAVL1, and RBMX were upregulated in the deficiency of FTO in thoracic aortae. (b) In mammals, SET1a and SET1b and MLL1–MLL4 are the main six enzymes that catalyze strimethylation of lysine 4 in histone H3 (H3K4). The deficiencies of MLL1, MLL2, and MLL4 can regulate the expression change of m6A-RMRs; regulation of m6A-RMRs by MLL1 is different in a circadian study (GSE24964); upregulated m6A-RMRs were increased in 30 hours compared to 18 hours in deficiency of MLL1; regulation of m6A-RMRs by the heterozygote of MLL2 is similar to the deficiency of homozygous (GSE67388); deficiency of homozygous MLL4 only regulates one of the 29 m6A-RMRs compared with heterozygous MLL4 (GSE30971). (c) In deficiency of DNA methyltransferases microarrays, the number of downregulated m6A-RMRs is more than that of upregulated m6A-RMRs. That is, maybe, DNA methyltransferases positively regulate m6A methylation. More differentially expressed m6A-RMRs in microarrays with deficiencies of DNMT1 are more than those in microarrays with DNMT3A or DNMT3B. Most m6A-RMRs (18/26) are differentially expressed, and the downregulated genes (n=15) are more than the upregulated genes (n=3) in DNMT1 knockdown myeloma cells (GSE86147).
(a)(b)(c)


## 3.14. The 18 Out of 165 ROS Regulators (11%) Were Modulated by Four Human m6A Writers KIAA1429, METTL14, METTL3, and WTAP, and Gene KO Data Showed That 40 Out of 165 ROS Regulators (24%) Were Modulated by m6A Eraser FTO and Two m6A Writers METTL3 and WTAP

Our and others’ reports showed that reactive oxygen species (ROS) play significant roles not only in mediating endothelial cell activation and vascular inflammation [104] but also in sensing metabolic homeostasis and stress in organelle metabolic process as an integrated system. An important question remained whether m6A-RMRs regulate ROS regulators. We hypothesized that m6A-RMRs regulate the expressions of ROS regulators. To examine this hypothesis, the ROS regulators were the targets of RNA methylation regulators in human cell studies as shown in Table 5. The TREW data (Target of m6A Readers, Erasers, and Writers) was download from Met-DB v2.0 (MeT-DB V2.0: the MethylTranscriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php) [105]. The 165 ROS regulators were examined as we reported. The result showed that 18 out of 165 ROS regulators (11%) (F2RL1, PDK4, TIGAR, BCL2, SESN2, GNAI2, DDIT4, SH3PXD2A, FOXM1, AATF, TGFB1, TSPO, G6PD, GNAI3, and CYP1B1) were modulated by four m6A writers KIAA1429, METTL14, METTL3, and WTAP (several ROS regulators were modulated in more than one position in chromosome or by more than one RNA methylation regulators) (Table 5). The deficiencies of four m6A writers (KIAA1429, METTL14, METTL3, and WTAP) downregulated the m6A modification of ROS regulators. As shown in Supplementary Table 4, the expressions of ROS regulators were also the targets of RNA methylation regulators in mouse cell studies. TREW (Target of m6A Readers, Erasers and Writers) was download from Met-DB v2.0 (MeT-DB V2.0: the MethylTranscriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php) [105]. The 165 ROS regulators were examined; and the result showed that 40 out of 165 ROS regulators (24%) were modulated by m6A eraser FTO and two m6A writers METTL3 AND WTAP. The deficiency of m6A eraser FTO upregulated the m6A modification of ROS regulators, and the deficiencies of two m6A writers METTL3 or WTAP downregulated the m6A modification of ROS regulators.Table 5
The reactive oxygen species (ROS) regulators were the targets of RNA methylation regulators in human cell studies. TREW (Target of m6A Readers, Erasers, and Writers) was downloaded from Met-DB v2.0 (MeT-DB V2.0: the Methyl Transcriptome DataBase Version 2.0 http://180.208.58.19/metdb_v2/html/index.php PMID: 29126312). 165 ROS regulators were examined, and the result showed 18 ROS regulators (F2RL1, PDK4, TIGAR, BCL2, SESN2, GNAI2, DDIT4, SH3PXD2A, FOXM1, AATF, TGFB1, TSPO, G6PD, GNAI3, and CYP1B1) were modulated by writers KIAA1429, METTL14, METTL3, and WTAP (several ROS regulators were modulated in more than one position in the chromosome or by more than one RNA methylation regulator). The deficiencies of writers (KIAA1429, METTL14, METTL3, and WTAP) can downregulate the m6A modification of ROS regulators (padj<0.05).
Targeted-ROS regulatorTypem6A methyl-regulatorp adjp_treatedp_controlLog2_ORLog2_RRqExperiment (modification-RNA methylation regulators-cell)PMIDF2RL1WriterKIAA14290.010.350.59-1.39-0.74893.65m6A-KIAA1429-si-A54924981863F2RL1WriterKIAA14290.020.460.75-1.86-0.721959.11m6A-KIAA1429-si-A54924981863PDK4WriterKIAA14290.020.110.69-4.12-2.6091.22m6A-KIAA1429-si-A54924981863TIGARWriterKIAA14290.040.190.73-3.56-1.95309.32m6A-KIAA1429-si-A54924981863BCL2WriterMETTL140.030.630.95-3.37-0.58130.91m6A-METTL14-sh2-A54924981863SESN2WriterMETTL30.030.250.46-1.35-0.8880.55m6A-METTL3-kd-Hela24316715GNAI2WriterMETTL3≤0.010.260.41-0.99-0.66474.67m6A-METTL3-kd-Hela24316715GNAI2WriterMETTL3≤0.010.260.41-0.99-0.66474.67m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715DDIT4WriterMETTL3≤0.010.270.44-1.05-0.681549.26m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715SH3PXD2AWriterMETTL30.030.550.60-0.29-0.132889.11m6A-METTL3-kd-Hela24316715FOXM1WriterMETTL30.020.330.40-0.43-0.27427.19m6A-METTL3-kd-Hela24316715AATFWriterMETTL3≤0.010.190.39-1.40-1.00132.56m6A-METTL3-kd-Hela24316715TGFB1WriterMETTL3≤0.010.460.60-0.79-0.371205.15m6A-METTL3-kd-Hela24316715TSPOWriterMETTL30.010.250.37-0.78-0.54186.38m6A-METTL3-kd-Hela24316715G6PDWriterMETTL3≤0.010.360.40-0.28-0.172992.01m6A-METTL3-kd-Hela24316715GNAI3WriterWTAP0.020.170.46-2.01-1.41154.65m6A-WTAP-kd-Hela24316715GNAI3WriterWTAP0.020.170.46-2.01-1.41154.65m6A-WTAP-kd-Hela24316715TIGARWriterWTAP0.010.190.56-2.42-1.54104.56m6A-WTAP-kd-Hela24316715CYP1B1WriterWTAP0.020.500.95-4.33-0.93583.55m6A-WTAP-si-A54924981863DDIT4WriterWTAP≤0.010.460.73-1.66-0.66825.45m6A-WTAP-si-Hek293T24981863Met-DB v2.0 contains a significant increase in context-specific m6A peaks and single-base sites predicted from 185 samples from 26 separate studies for 7 species. It has also been updated to include a new database for targets of m6A readers, erasers, and writers, as well as additional functional data gathering. The abbreviation TREW stands for Target of m6A Readers, Erasers, and Writers. To discover their target sits, we collected ParCLIP-seq and MeRIP-seq data for 8 regulator/reader proteins (including FTO, KIAA1429, METTL14, METTL3, WTAP, HNRNPC, YTHDC1, and YTHDF1) from 10 independent studies. Then, the differential m6A peaks that showed significant hypermethylation (hypomethylation) after knocking down of a demethylase (methylase) were determined to the target peaks. p_treat: peak of treated group; p_control: peak of control group; OR: odds ratio; RR: relative risk or risk ratio.


## 4. Discussion

Recent progress has reported that m6A-RNA methylation [50] plays a significant role in regulating cardiovascular diseases (CVD) and CVD-related diseases and complications such as cardiac remodeling, atherosclerosis, heart failure, inflammation adipogenesis, obesity, insulin resistance, hypertension, and type 2 diabetes mellitus [41]. In addition, m6A-RNA methylation plays critical roles in the pathogenesis of cancers and tumors [106, 107], aging [107], immune responses and autoimmunity, and viral infections [108, 109]. However, two major issues remain unknown: first, transcriptomic regulation of a complete list of m6A-RNA methylation regulators in various diseases and second, cellular mechanisms and molecular mechanisms underlying transcriptomic changes of m6A-RNA methylation regulators in pathophysiological conditions. To solve these problems, we performed a transcriptomic data mining of the expressions of 29 m6A-RNA methylation regulators in diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis progression compared with those of regression; (3) IBD and RA modulated m6A-RMRs more than lupus and psoriasis; and some autoimmune diseases share five upregulated m6A-RMRs; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; and upregulation of m6A-RMRs in hemodialysis more than in ESRF may have clinical benefits; (5) MERS-CoV infections modulated m6A-RMRs the most among viral infections; (6) oxPAPC and NOTCH1 knockdown modulated m6A-RMRs more than other DAMs stimulation of endothelial cells including LPS, oxLDL, and IFNs; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) M1 macrophages upregulated seven m6A-RMRs; WTAP and IGF2BP2 may not only promote M1 but also inhibit M2 polarization; (9) 86% of m6A-RMRs were differentially expressed in the six spleen Treg clusters; and 8 out of 12 Treg signatures significantly regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 significantly modulated m6A-RMRs; and inhibition of costimulation receptor CD40 with anti-CD40 significantly upregulated m6A-RMRs; (11) proinflammatory cytokines significantly modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways (except STAT1) upregulated more than downregulated m6A-RMRs; and TP53, PTEN, and APC downregulated more than upregulated m6A-RMRs; (13) methionine-homocysteine cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; m6A writer RBM15 and one m6A eraser FTO significantly modulated m6A-RMRs; and H3K4 methyltransferase MLL1 and DNA methyltransferase, DNMT1, significantly regulated m6A-RMRs; and (14) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP.For some diseases, both eraser and writing enzymes of m6A-RMRs have changed or have the same expression trend. In order to explain this phenotype, we checked the m6A-RMRs changes in Met-DB v2.0 and the result supports m6A-RMRs regulate expression of m6A-RMRs themselves (Supplementary Tables 5 and 6). When the writer METTL3 or WTAP was knocked down, the expression of eraser ALKBH5 also was down regulated (Supplementary Table 5). Then, Protein-Protein Interaction (PPI) analysis was performed by using STRING (https://string-db.org/) and the result also suggest the interactions among m6A-RMRs are complex (Supplementary Figure 3). Additionally, the expression of two kinds of m6A-RMRs such as writer WTAP and eraser FTO is positively correlated in some tumors (Supplementary Figure 4). So, in some diseases, writers and erasers both are upregulated or downregulated, which is reasonable and the main function of m6A-RMRs can be confirmed by using some well-designed experiments.One of the potential issues related to database mining is that we were unable to compare the impact of different regulators in controlling the expressions of m6A-RMRs in the same cell types since the original microarray studies we looked at employed different cells. Although our database mining strategy was not optimal, our approach was justified in filling up a critical knowledge gap. This is, in fact, a common practice that we and others [110] often use in studying gene expression in nonideal, heterogeneous peripheral blood mononuclear cell populations (PBMCs) in disease conditions versus healthy conditions, and PBMCs are actually made up of a variety of cell types, including B cells (~15%), T cells (~70%), monocytes (~5%), and natural killer (NK) cells (~10%) among others [111]. Another limitation of the current study is that, due to the low-throughput nature of verification techniques in every laboratory, including ours, we were unable to confirm every result we uncovered using high-throughput data analyses. We recognize that in the future, carefully designed in vitro and in vivo experimental models will be required to confirm regulator gene deficiency-upregulated m6A-RMRs further and the underlying mechanisms we disclose here.Based on our findings, we proposed a novel working model in Figure16. First, we recently proposed a new theory that because of their connections with three metabolic pathways including folate cycle, transsulfuration pathway, glutathione synthesis, polyamine metabolism, and methionine salvage pathway, homocysteine-methionine cycle serves as a sensor-receptor system to sense the intracellular metabolic homeostasis and stresses of four amino acids such as methionine, homocysteine, serine, and arginine as well as vitamin B12 and folate; second, similar to protein phosphorylation/dephosphorylation-based signaling pathways, the metabolic homeostasis and stress signals relay the metabolic reprogramming signals into cellular methylation processes via various methyltransferases to methylate DNAs, proteins, histones, RNAs, and other molecules. The methylations of those important molecules regulate their biological functions; third, m6A-RNA methylation is a dominant RNA methylation for various RNA types including mRNAs, tRNAs, rRNAs, and noncoding RNAs. Throughout transcriptomic data analyses of 102 microarrays, RNA-Seq, and single-cell RNA-Seq related to 41 diseases in six categories organ failures, viral infections, metabolic diseases, acute inflammations, cancers, and autoimmune diseases, our data have demonstrated that several layers of regulatory systems regulate the transcriptomic changes of m6A-RMRs in diseases as well as pathophysiological conditions in various cell types, which include (1) cell surface receptors such as cytokine receptors, viral receptors, danger-associated molecular pattern (DAMP) receptor, pathogen-associated molecular pattern (PAMP) receptors, immune checkpoint receptors, and cosignaling receptors. Of note, immune checkpoint receptors and cosignaling receptors are the prototypic membrane protein interactions between cells; (2) cellular mechanisms such as macrophage polarization, endothelial cell activation, CD4+Foxp3+ regulatory T cell activation, resting status, and pathophysiological changes of other cells; and (3) nuclear transcription factors (TFs) including proinflammatory TFs NF-κB, Jak-STATs, and tumor suppressors TP53, PTEN, and APC. In summary, our results have demonstrated that transcriptional regulations of m6A-RMRs are highly significant mechanisms in regulating m6A-RNA methylations related to various pathophysiological processes and diseases. Our findings provide novel insights on the roles of m6A-RMRs in the development of inflammatory disorders and malignancies as well as novel pathways for future therapeutic strategies for inflammatory diseases, sepsis, trauma, organ failures, autoimmune diseases, metabolic CVDs, and cancers.Figure 16
A novel working model was proposed in our study. (1) M6A-RMRs differently expressed in acute inflammations, metabolic diseases, autoimmune diseases, organ failures, viral infections, and tumors. (2) Cellular mechanisms such as macrophage polarization, endothelial cell activation, CD4+Foxp3+ regulatory T cell activation, and resting status, and pathophysiological changes of other cells regulate the transcriptomic changes of m6A-RMRs. (3) In folate cycle, transsulfuration pathway, glutathione synthesis, polyamine metabolism, and methionine salvage pathway, homocysteine-methionine cycle serves as a sensor-receptor system to sense the intracellular metabolic homeostasis and stresses of four amino acids such as methionine, homocysteine, serine, and arginine as well as vitamin B12 and folate. The metabolic homeostasis and stress signals relay the metabolic reprogramming signals into cellular methylation processes via various methyltransferases to methylate DNAs, proteins, histones, RNAs, and other molecules. (4) Cell surface receptors such as cytokine receptors, viral receptors, danger-associated molecular pattern (DAMPs) receptors/pathogen-associated molecular pattern (PAMPs) receptors, immune checkpoint receptors, and cosignaling receptors regulate the transcriptomic changes of m6A-RMRs. Nuclear transcription factors (TFs) including proinflammatory TFs NF-κB, Jak-STATs, tumor suppressors TP53, PTEN, and APC regulate the transcriptomic changes of m6A-RMRs. The figure was created with http://BioRender.com.



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*Source: 1433323-2022-02-15.xml*