Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension

Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension Na Liu, Yunhong Zeng, Ting Huang, Wanyun Zuo, Yunbin Xiao, Biao Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-64337/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Despite its functional importance in various fundamental bioprocesses, studies of N6-methyladenosine (m 6 A) in the pulmonary arterial hypertension (PAH) are lacking. Here we studied the potential relevance of m 6 A RNA methylation and immune response in PAH development. Methods We constructed a monocrotaline (MCT) induced PAH rat model and performed Methylated RNA immunoprecipitation sequencing (MeRIP-Seq). The 18 idiopathic PAH (IPAH) microarray data obtained from the GEO database was used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). CIBERSORT was used to investigate the effect of m 6 A methylation on immune cell infiltration during PAH. Results A differential pattern of m 6 A abundance, mainly up-methylation, was observed in the lung tissues of rats with MCT induced PAH. By WGCNA, multi-list pathway enrichment analysis and protein-protein interaction (PPI) analysis, we found that m 6 A methylation modification may play important roles in mediating immune response during PAH. CYBERSORT algorithm indicated that the m 6 A methylation can drive monocyte to form M1 macrophage, which may be mediated by CCR5 and CXCL9. Conclusion Collectively, m 6 A landscape is altered in PAH. We summarize newly discovered m 6 A in controlling immune response, which caused activation of M1 macrophage during PAH. It’s provided a novel insight into the therapeutic mechanisms of PAH. Cardiac & Cardiovascular Systems N6-methyladenosine (m6A) methylation pulmonary arterial hypertension (PAH) weighted gene co-expression network analysis (WGCNA) immune response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Pulmonary arterial hypertension (PAH) is a condition characterized by increased pulmonary arterial pressure and vascular remodeling of the small pulmonary arteries. PAH develops either as an idiopathic condition or in association with various underlying diseases such as collagen vascular disease, portal hypertension, or HIV infection( 1 , 2 ). The incidence of PAH ranges from 2.0 to 7.6 cases per million adults per year, and its prevalence varies from 11 to 26 cases per million adults. The incidence of PAH is fourfold higher in women than in men, but survival is paradoxically worse in men with PAH( 3 , 4 ). Despite striking progress in the development of diagnosis and therapeutics in the last three decades, PAH remains relatively incurable and the limited efficacy of current treatment options possibly results from an incomplete understanding of the pathophysiology of this cardiopulmonary disorder( 5 ). Epigenetic modifications,such as DNA methylation, histone modifications, and noncoding RNAs, may play important roles in regulating the pathogenesis of PAH( 1 ). As a bridge to pass the genetic information from DNA to proteins RNA, RNA is an important part of the central dogma, and its various chemical modifications are involved in the regulation of various biological processes( 6 ). N6-methyladenosine (m 6 A) of RNA transcripts is the most prevalent modification found in many classes of RNA( 7 , 8 ). The abundance of m 6 A on RNA is determined by the dynamic interplay between its methyltransferases (“writers”, such as METTL3-METTL14 complex( 9 )), and demethylases (“erasers”, including FTO( 10 ) and ALKBH5( 11 )). The binding proteins (“readers”, such as YTH domain–containing proteins( 12 , 13 )) then mediate the effect of m 6 A modifications on RNA processing or metabolisms, including alternative splicing( 14 , 15 ), export( 16 , 17 ), stability( 18 , 19 ), and translation( 20 , 21 ). Recent studies have suggested the involvement of the immune response during PAH. Inflammatory cell infiltrations can be observed around the pulmonary artery, such as macrophages dendritic cells, mast cells, T lymphocytes, and B lymphocytes( 22 ). These immune cells play an important role in the vascular remodeling characteristic of PAH and might be important targets for PAH therapy. Nevertheless, how the immune system is activated and regulated remains poorly understood. m 6 A RNA modifications might be a novel immunoregulatory factor( 23 ). Weighted gene co-expression network analysis (WGCNA) is a system biology algorithm for describing the correlation patterns among genes across samples( 24 ). WGCNA can identify and cluster highly correlated genes into the same module. Furthermore, this method can also be used for relating modules to external clinical traits. By far, WGCNA has been validated as a valuable method to identify underlying mechanisms, potential biomarkers, or therapeutic targets in different types of diseases. In our study, we first constructed a PAH rat model and conducted the m 6 A methylation sequencing analysis. We then analyzed 18 idiopathic PAH (IPAH) patients microarray data from the GEO database to identify m 6 A methylation correlated modules by using the method of WGCNA. Afterward, we applied the CIBERSORT algorithm to investigate the effect of m 6 A methylation on immune cell infiltration during PAH. Findings from our study may contribute to novel regulators and therapeutic targets in PAH from an epigenomic perspective. 2. Material And Methods 2.1.1 Establishment and verification of rat model of PAH induced by MCT The study was approved by the Institutional Animal Care and Use Committee of the Second Xiangya Hospital of Central South University and complied with the standards in the Guide for the Care and Use of Laboratory Animals. Sprague-Dawley rats (specific pathogen-free (SPF), male, 180–200 g, 6 weeks old, n = 5) were obtained from Changsha Tianqin Biotechnology Company (China). The rats were randomized to the control (n = 2) and PAH groups (n = 3). The rats in the PAH group were intraperitoneally injected with monocrotaline (MCT) (60 mg·kg − 1 , Sigma, C2401), while the rats in the control group were injected intraperitoneally with the same volume of saline. All rats were housed on a 12 h light/dark cycle with free access to food and water. After 4 weeks of feeding, rats were anesthetized with 1% sodium pentobarbital (130 mg·kg − 1 ) for echocardiography and right heart catheterization. Echocardiography was used to record the tricuspid regurgitation velocity, tricuspid annulus contraction rate, tricuspid annular plane systolic excursion (TAPSE) and pulmonary artery blood flow acceleration time (PAAT). After echocardiographic examination, We performed a right heart catheter to measure pulmonary artery pressure. Rats were sacrificed by cervical dislocation after deep anesthesia. Then, heart tissues were removed and segregated. The ratio of right ventricle to left ventricle plus ventricular septum [RV/ (LV + S)] was used as an index of right ventricular hypertrophy. Lung tissues were excised and immediately frozen at liquid nitrogen or fixed in 4% buffered paraformaldehyde solution. 2.1.2 Histological analysis The lung tissues obtained in each group were placed in 4% buffered paraformaldehyde solution overnight, and then dehydrated and embedded in paraffin. Then, all lung tissues were sliced into 5 µm-thick sections, fixed on a glass slide and baked dry. The staining procedures were performed according to the instructions. Briefly, the sections were soaked in xylene, ethanol in gradient concentration and hematoxylin, respectively, and sealed with resin. After dryness, pulmonary vascular morphology was observed and photographed under optical microscope. Last, the ratio of pulmonary small artery wall thickness and muscularization was calculated. 2.1.3 RNA preparation For each group, four biological replicates were selected, of which every two were combined into one. Then, total RNA of tissue was extracted using TRIzol reagent (Invitrogen Corporation, CA, USA) following the manufacturer’s instructions. The Ribo-Zero rRNA Removal Kit (Illumina, Inc., CA, USA) was used to reduce the ribosomal RNA content of total RNAs. Then, the RNA was chemically fragmented into fragments of about 100 nucleotides in length using fragmentation buffer (Illumina, Inc.). 2.1.4 Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and library construction MeRIP-Seq was performed following a previously reported procedure with slight modifications. Briefly, m 6 A RNA immunoprecipitation was performed with the GenSeqTM m 6 A RNA IP Kit (GenSeq Inc., China) by following the manufacturer’s instructions. Both the input sample without immunoprecipitation and the m 6 A IP samples were used for RNA-seq library generation with NEBNext® Ultra II Directional RNA Library Prep Kit (New England Biolabs, Inc., USA). The library quality was evaluated with BioAnalyzer 2100 system (Agilent Technologies, Inc., USA). Library sequencing was performed on an illumina Hiseq instrument with 150 bp paired-end reads. 2.2 Microarray data collection and processing Materials of GSE15197 were downloaded from the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) database. The dataset contains lung tissue transcriptomic profiles from 18 IPAH patients. The median expression values among all multiple probe IDs were selected to represent the corresponding gene symbol, leading to the identification of 18612 unique genes across 18 samples. Human gene annotation file (GRCh38 release 99 gene transfer format, ensembl.org/index.html) was applied to annotate genes and the 15934 protein-coding genes were selected for further analysis. 2.3 Estimation of m 6 A methylation score and implementation of weight correlation network analysis (WGCNA) We constructed gene signatures of m 6 A writer (METTL3, METTL14, METTL16, WTAP, KIAA1429, ZC3H13, RBM15/RBM15B), and m 6 A eraser (ALKBH5, FTO) as suggested by Yang et.al ( 25 ). We then computed the Gene Set Variation Analysis (GSVA) enrichment score of m 6 A writer and m 6 A eraser across the 18 samples using the ‘GSVA’ package( 26 ) in R software. The estimated m 6 A methylation score was calculated by substrating m 6 A eraser score from m 6 A writer score. WGCNA was accomplished with the R package ‘WGCNA’( 24 ). According to the standard variation value of gene, we ranked them from largest to smallest and only selected the top 2000 as input for WGCNA. A power of β value was introduced so that it could transform the similarity matrix into an adjacency matrix. In this study, β = 5 was used as a soft threshold parameter to ensure a scale-free network. The Dynamic Tree Cut method was applied to generate modules with the following major parameters to avoid the generation of too many modules: deepSplit of 2, minModuleSize of 30, and the height cut-off was set as 0.25 (modules were merged if their similarity was > 0.75). Module eigengenes (MEs) referred to the first principal component of all gene expression levels in the module, and therefore, it was reasonable to consider that MEs represented all genes within a specific module. According to Pearson's correlation tests, we further identified the association between MEs and m 6 A methylation score. Within the most relavant module, those positively correlative with m 6 A methylation score were made subjected to further analysis. 2.4 Pathway enrichment analysis Metascape ( https://Metascape.org/ ) is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for biologists( 27 ). To gain insights into biological roles of m 6 A methylation correlated genes identified from WGCNA and upmethylated genes identified from m 6 A-seq, we conducted pathway enrichment analysis in Metascape tools using Gene Ontology biological process (GO BP), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome( 27 ). By inputting the lists of m 6 A methylation correlated genes and upmethylated genes simultaneously, Metascape can identify commonly-enriched and selectively-enriched pathways from two levels, which enable a comprehensive assessment of the molecular features of the biological process. For Multi-list Enrichment Analysis, Metascape first applies enrichment analysis to each gene list individually and identifies terms that are statistically enriched. All gene lists are then combined into one new list, and enrichment analysis is conducted on this combined list. Distinguishing it from many existing portals, Metascape automatically clusters enriched terms into non-redundant groups. Briefly, pairwise similarities between any two enriched terms are computed based on a Kappa-test score. The similarity matrix is then hierarchically clustered and a 0.3 similarity threshold is applied to trim the resultant tree into separate clusters. We selected the top 20 clusters and chose the most significant (lowest P-value) term within each of them to represent it. 2.5 Protein-protein interaction (PPI) network construction of m 6 A methylation correlated genes To find out the functional associations among the m 6 A methylation correlated genes, we used the online Search Tool for the Retrieval of Interacting Genes (STRING database; http://string-db.org/ ) to construct a PPI network based on uniquely comprehensive coverage and predictive function of genome-wide data( 28 ). A stringent threshold of a combined score of > 0.7 was used to construct the PPI network and Cytoscape software was used to visualize and analyze the biological networks. Plugin Molecular Complex Detection (MCODE) was applied to identify significant clusters with strong protein-protein linkages with default parameters. 2.6 Estimation of immunocyte infiltration The CIBERSORT algorithm ( https://cibersort.stanford.edu/ ) was applied to estimate the abundance of infiltrated immune cell subtypes in the lung tissues of 18 samples, based on a deconvolution algorithm in the R software( 29 ). The perm parameter was set as 1000. The 18 samples were grouped to ‘High m 6 A methylation’ and ‘Low m 6 A methylation’ by the median value of their m 6 A methylation score. Comparisons between two groups were tested by the Wilcox rank test. P-value < 0.05 was considered significant. 2.7 Statistical analysis Briefly, Paired-end reads were harvested from Illumina HiSeq 4000 sequencer, and were quality controlled by Q30. After 3’ adaptor-trimming and low quality reads removing by cutadapt software (v1.9.3). First, clean reads of all libraries were aligned to the reference genome (UCSC RN5) by Hisat2 software (v2.0.4). Methylated sites on RNAs (peaks) were identified by MACS software. Differentially methylated sites with a fold change cutoff of ≥ 2 and false discovery rate cutoff of ≤ 0.0001 were identified with the diffReps differential analysis package. These peaks identified by both softwares overlapping with exons of mRNA were figured out and choosed by home-made scripts. 3. Results 3.1 Construction of rats PAH model The pulmonary artery velocity diagram appeared as a dagger in PAH group, and PAAT consumedly shortened compared to control group (20.33 ± 2.62 vs 29.50 ± 0.50 ms, p < 0.0001). Then we observed ventricular septum significantly shift to the left, simultaneously, right atrium and right ventricle enlarged compared to control group (4.97 ± 0.30 vs 3.42 ± 0.09 mm, p < 0.0001). The range of TAPSE was greatly reduced in PAH (1.33 ± 0.05 vs 1.58 ± 0.04 mm, p < 0.0001) (Fig. 1 .A). Four weeks after the MCT injection, the right ventricular systolic pressure (RVSP) of the PAH group was elevated to 49.87 ± 1.17 mmHg compared with 24.84 ± 0.34 mmHg in the control (p < 0.001), and at the same time, we found that RV/ (LV + S) increased in PAH group (0.51 ± 0.03 vs 0.25 ± 0.04 g, p < 0.0001) (Fig. 1 .B). The results of HE staining showed that the pulmonary artery smooth muscle was significantly thickened in PAH group compared with control group (71.13 ± 4.02% vs. 17.95 ± 1.35%, p < 0.001) (Fig. 1 .C). These results indicate that the rat model of PAH has been successfully established. [Insert Fig. 1 ] 3.2 Methylations profile of rat lung tissue MeRIP-Seq analysis identified 922 non-overlapping m 6 A sites in control group and 9059 non-overlapping m 6 A sites in PAH group, while 18655 m 6 A sites were overlapped in two groups (Fig. 2 .A). Next, differentially methylated m 6 A sites (DMMSs) were identified by diffReps with the following default screening criteria: p -value ≤ 0.0001 and fold change ≥ 2 between the groups. We selected 3298 DMMSs in the two groups. A total of 777 m 6 A sites exhibited up-methylation, and 2521 exhibited down-methylation. (Fig. 2 .B). These results demonstrated an increased m 6 A modification process during PAH and the 2521 up-methylated m 6 A sites representing 1261 unique genes were selected for further analysis. [Insert Fig. 2 ] 3.3 Construction of weighted gene co-expression network and identification of key modules The top 2000 genes in 18 samples of PAH lung tissue were used to construct the co‑expression network. β values of 5 were confirmed to obtain the approximate scale‑free topology with a scale‑free topology fit index > 0.85 and the lowest power (Fig. 3 .A). Next, the method of dynamic tree cutting was employed to produce co-expression modules, which led to the identification of 11 modules (Fig. 3 .B). We then calculated and plotted the relation of each module with the m 6 A methylation score. Among these modules, the yellow module depicting the highest correlation (module-trait weighted correlation = 0.46, p = 0.055) with m 6 A methylation score (Figrue 4). Within the yellow module, a total of 87 genes were positively correlative with m 6 A methylation score and were made subjected to further analysis. [Insert Fig. 3 and Fig. 4 ] 3.4 Multi-list pathway enrichment analysis and visualization We conducted a multi-list pathway enrichment analysis in Metascape by inputting the list of 87 m 6 A methylation correlated genes identified from WGCNA and 1261 m 6 A upmethylated genes identified from MeRIP-SEq. As Fig. 5 shown, both m 6 A correlated genes and upmethylated genes are involved in immune response-related pathways such as ‘cytokine-mediated signaling pathway’, ‘leukocyte migration’, and ‘activation of immune response’. Besides, the upmethylated genes also participate in some biological processes already known to be associate with PAH such as ‘ECM organization’ and ‘supermolecular fiber organization’. In sum, the results demonstrated that m 6 A methylation may play an important role in PAH pathogenesis, especially in mediating immune response during PAH. [Insert Fig. 5 ] 3.5 Identification of hub genes in the yellow module through PPI analysis To identifiy hub genes, we submitted the gene list of 87 m 6 A correlated genes in the yellow module to STRING for protein-protein interaction analysis, and the cut-off confidence interval was set to 0.7. After MCODE algorithm, the most significant cluster containing 10 hub genes was identified and enrichment analysis shows that genes in this cluster were mainly involved in ‘Chemokine receptors bind chemokines’ pathway (Fig. 6 ). By comparing the results of MeRIP-Seq, CCR5 and CXCL9 were found to be significantly upmethylated with a fold change of 2.55 and 6.93, respectively. [Insert Fig. 6 ] 3.6 Correlation analysis between m 6 A methylation and immunocyte proportion We have concluded that m 6 A methylation can mediate immune response during PAH process. We then aimed to investigate how it regulates immune cell infiltration. After the Wilcox rank test, monocyte and M1 macrophage were detected differentially infiltrated between the high methylation group and low methylation group (p = 0.03, and p = 0.001, respectively) (Fig. 7 ). Figure 8 displayed the scale histogram of immune cell fraction among 18 samples. These results indicated that during PAH, the m 6 A methylation can drive monocyte to form M1 macrophage, which plays vital roles in the immune response. [Insert Fig. 7 and Fig. 8 ] 4. Discussion m 6 A is the most prevalent modification in the mRNA of many eukaryotic species. There is growing evidence that m 6 A dysregulation has a profound impact on the pathogenesis of various human diseases( 30 – 33 ). Su et al. firstly identified the transcriptome-wide map of m 6 A circRNAs in hypoxic PAH rat model and confirmed that m 6 A methylation can affect the circRNA-miRNA-mRNA network( 34 ). However, the mechanism of mRNA m 6 A methylation modification to regulate IPAH has not been reported. In this study, we took advantage of MeRIP-seq to map the transcriptomic landscape of PAH rat lung tissue and thus quantitatively compared transcriptome-wide changes between PAH and control groups. The results found that the m 6 A level was significantly up-regulated in MCT induced PAH lung tissue, which highlighted the role of m 6 A methylation in the pathogenesis of PAH. WGCNA is a method for constructing gene co-expression networks based on gene expression data. We applied this method to identified key modules associated with the estimated m 6 A methylation score. Through multi-list enrichment analysis, the 87 m 6 A methylation correlated genes identified from WGCNA and 1261 m 6 A up-methylated genes identified from MeRIP-seq analysis are both significantly involved in immune response-related pathways such as ‘cytokine-mediated signaling pathway’, ‘leukocyte migration’, and ‘activation of immune response’. These results highly suggested the immunoregulatory role of m 6 A methylation during PAH. Through PPI analysis of the 87 m 6 A methylation correlated genes, a cluster containing 10 hub genes were identified, including CXCL9, CXCL10, CXCR3, PMCH, CXCL11, CXCR6, CCR7, CCL5, CCR5, and HCAR3. Among them, CCR5 and CXCL9 were significantly up-methylated. CCR5 is activated on stimulation by the CCR5 ligands CCL3 (macrophage inflammatory protein-1α), CCL4 (macrophage inflammatory protein-1β), and CCL5 (RANTES) and is strongly expressed on the principal cell types implicated in PAH progression, including endothelial cells, SMCs, T cells, and macrophages( 35 – 38 ). Amsellem et al. studied the effect of CCR5 receptor antagonists on PASMC and inflammatory response in PAH mouse model and found that the activation of the CCL5-CCR5 axis directly leads to PASMC proliferation and macrophage recruitment( 39 ). CXCL9, a chemokine, is a T-cell chemoattractant that is induced by IFN-γ. CXCL9 is closely related to two other CXC chemokines called CXCL10 and CXCL11, whose genes are located near the gene for CXCL9 on human chromosome 4( 40 , 41 ). CXCL9, CXCL10, and CXCL11 are commonly produced by local cells in inflammatory lesions and can attract Th1 cells( 42 ). A high level of CXCL9 in peripheral liquids can be considered as a marker of host immune response, especially of that involving Th1 cells( 43 , 44 ). By applying the CYBERSORT algorithm, we found that the m 6 A methylation can drive monocyte to form M1 macrophage. Circulating monocytes migrate into the majority of tissues in the body, where they differentiate into functionally distinct mature macrophages( 45 , 46 ). Macrophages undergo classical M1 activation induced by IFN-γ, which mediates Th1 cell-type activation of macrophages( 47 ). The activated macrophage then causes vasoconstriction, increases vascular permeability, and induce PASMCs proliferation( 48 ). Our research sheld light on the regulatory role of m 6 A methylation in macrophage activation during PAH. As we discussed above, The hub 10 m 6 A methylation correlated genes,especially CCR5 and CXCL9༌may mediate m 6 A methylation caused M1 macrophage activation. 5. Conclusions In conclusion, our research revealed that m 6 A methylation modification may play important roles in mediating immune response during PAH. It also caused activation of M1macrophage, which may be mediated by CCR5 and CXCL9. These results will help us to better understand the mechanisms of PAH, and provide candidate therapeutic targets. 6. Abbreviations DMMSs differentially methylated m6A sites m6A N6-methyladenosine MCT monocrotaline MeRIP-Seq Methylated RNA immunoprecipitation sequencing PAH pulmonary arterial hypertension PAAT pulmonary artery blood flow acceleration time PPI protein-protein interaction RV/ (LV + S) right ventricle to left ventricle plus ventricular septum RVSP right ventricular systolic pressure TAPSE tricuspid annular plane systolic excursion WGCNA weighted gene co-expression network analysis 7. Declarations Ethics approval and consent to participate The study was approved by the Research Ethics Committee of Second Xiangya Hospital of Central South University. The research was conducted according to all ethical standards. Consent for publication Consent to publish was obtained from all authors. Availability of data and materials All data in our study are available upon request. Competing interests The authors declare that they have no competing interests. Funding This project was supported by grants from the National Natural Science Foundation of China (No. 81770337). This project was supported by grants from the Hunan Provincial Natural Science Youth Foundation of China (No. 2019JJ50881). Hunan Provincial Health Commission Scientific Research Project (NO. 20201302). Hunan Province Traditional Chinese Medicine Research Program Project (No. 201914). Author’s contributions QML and YBX conceived and designed this study; NL and YZL performed experiments, analyzed and interpreted data, and wrote the manuscript; YHZ and HT designed the microarray and analyzed the microarray data; BL and ZHY collected clinical samples, interpreted data and clinical information; QML improved and revised the manuscript; NL and YZL analyzed and interpreted data, supervised experiments, and wrote the manuscript. All authors read and approved the final manuscript. Ackonwledgements We are grateful to CloudSeq Biotech Inc. for scientific support. 8. 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Transcriptome-wide map of m(6)A circRNAs identified in a rat model of hypoxia mediated pulmonary hypertension. BMC Genomics. 2020;21(1):39. Schober A. Chemokines in vascular dysfunction and remodeling. Arteriosclerosis, thrombosis, and vascular biology. 2008;28(11):1950–9. Charo IF, Ransohoff RM. The many roles of chemokines and chemokine receptors in inflammation. N Engl J Med. 2006;354(6):610–21. Schecter AD, Calderon TM, Berman AB, McManus CM, Fallon JT, Rossikhina M, et al. Human vascular smooth muscle cells possess functional CCR5. J Biol Chem. 2000;275(8):5466–71. Ishida Y, Kimura A, Kuninaka Y, Inui M, Matsushima K, Mukaida N, et al. Pivotal role of the CCL5/CCR5 interaction for recruitment of endothelial progenitor cells in mouse wound healing. J Clin Investig. 2012;122(2):711–21. Amsellem V, Lipskaia L, Abid S, Poupel L, Houssaini A, Quarck R, et al. CCR5 as a treatment target in pulmonary arterial hypertension. Circulation. 2014;130(11):880–91. 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Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme. 2008;40(6):361–8. Chaudhuri A. Regulation of Macrophage Polarization by RON Receptor Tyrosine Kinase Signaling. Frontiers in immunology. 2014;5:546. Schultze JL, Schmieder A, Goerdt S. Macrophage activation in human diseases. Seminars in immunology. 2015;27(4):249–56. Wang N, Liang H, Zen K. Molecular mechanisms that influence the macrophage m1-m2 polarization balance. Frontiers in immunology. 2014;5:614. Colvin KL, Cripe PJ, Ivy DD, Stenmark KR, Yeager ME. Bronchus-associated lymphoid tissue in pulmonary hypertension produces pathologic autoantibodies. Am J Respir Crit Care Med. 2013;188(9):1126–36. Supplementary Files Q30.xlsx readstatistics.xlsx samplegroups.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-64337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":2435168,"identity":"4a4473c6-6367-4526-9431-48dea105baba","order_by":0,"name":"Na Liu","email":"","orcid":"","institution":"Department of Cardiology/Cardiac Catheterization Lab, Second Xiangya Hospital, Central South University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Liu","suffix":""},{"id":2435169,"identity":"9f4474da-c79b-4694-b74c-ca86eff47aa1","order_by":1,"name":"Yunhong Zeng","email":"","orcid":"","institution":"Department of Cardiology, Hunan Children's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yunhong","middleName":"","lastName":"Zeng","suffix":""},{"id":2435170,"identity":"40a759e6-b6f9-4db7-91e8-398b4cb6b976","order_by":2,"name":"Ting Huang","email":"","orcid":"","institution":"Department of Cardiology, Hunan Children's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Huang","suffix":""},{"id":2435171,"identity":"9cb1d27e-fb9b-496f-b7d4-dbd681357832","order_by":3,"name":"Wanyun Zuo","email":"","orcid":"","institution":"Department of Cardiovascular Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wanyun","middleName":"","lastName":"Zuo","suffix":""},{"id":2435172,"identity":"fbd71e58-8331-4366-a555-70fa258565a1","order_by":4,"name":"Yunbin Xiao","email":"","orcid":"","institution":"Department of Cardiology, Hunan Children's Hospital, Changsha, Hunan, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yunbin","middleName":"","lastName":"Xiao","suffix":""},{"id":2435173,"identity":"d6815b47-ce39-41a8-96fa-d6dc8cfd001e","order_by":5,"name":"Biao Li","email":"","orcid":"","institution":"Department of Cardiovascular Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Biao","middleName":"","lastName":"Li","suffix":""},{"id":2435174,"identity":"6789456f-798a-436e-a26e-90fbca8f3815","order_by":6,"name":"Zhenghui Xiao","email":"","orcid":"","institution":"Department of Cardiology, Hunan Children's Hospital, Changsha, Hunan, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenghui","middleName":"","lastName":"Xiao","suffix":""},{"id":2435175,"identity":"c7c27d9d-774b-401c-bf3a-3c67dcdc4091","order_by":7,"name":"Yaozhong Liu","email":"","orcid":"","institution":"Department of Cardiovascular Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaozhong","middleName":"","lastName":"Liu","suffix":""},{"id":2435176,"identity":"50255421-0e7e-4750-83d9-7c11ecb06029","order_by":8,"name":"Qiming Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZgaGAwwMFjwM7A1gPmMDkVokeBh4DhCrBQIkgCiBSC18x7kTDxf8kpAxuPn48WceBhvZDQeYnz3Ap0XyMO+GwzP7JHgMbqeZSfMwpBlvOMBmboBPiwFIC28PSEsOGzMPw+HEDQd42CSI03LzDDPQYf+J1MLzA6jlBg8D0GEHCGsB+4W3QYJH8kyameQcg2TjmYfZzPBq4Tt/dvNnnj829nzHDz/+8KbCTrbvePMzvFpA8cjA2AZ3JwM4cvEDkBaGP4RUjYJRMApGwYgGAEajSBDffhdIAAAAAElFTkSuQmCC","orcid":"","institution":"Second Xiangya Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Qiming","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2020-08-23 10:36:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-64337/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-64337/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2477348,"identity":"5f5387b9-f35b-4a6f-a88b-01854ab07127","added_by":"auto","created_at":"2020-09-18 15:04:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240113,"visible":true,"origin":"","legend":"Construction of rats PAH model. (A) Echocardiography data, (B) Hemodynamic data, (C) Pulmonary artery HE staining images of the control group and PAH group were obtained under microscopy. * represents statistical significance.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure1.jpg"},{"id":2477349,"identity":"f40ef5ab-530a-450f-a5a3-5be5297a0b63","added_by":"auto","created_at":"2020-09-18 15:04:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":316217,"visible":true,"origin":"","legend":"Methylations profile of rat lung tissue. (A) The differential m6A methylation and the expression of mRNA methylation statistical volcano map. Green represents m6A modification and the expression of mRNA methylation is decreased, red is the opposite. (B) Differential m6A enrichment peaks and related genes statistical graph.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure2.jpg"},{"id":2477350,"identity":"d768b2d3-dc66-4b55-879d-e3f8c4faca48","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62594,"visible":true,"origin":"","legend":"Methylations profile of rat lung tissue. (A) The differential m6A methylation and the expression of mRNA methylation statistical volcano map. Green represents m6A modification and the expression of mRNA methylation is decreased, red is the opposite. (B) Differential m6A enrichment peaks and related genes statistical graph.","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure3.png"},{"id":2477351,"identity":"011840f7-b9d6-4a1c-bd6f-cc5ae0c441b3","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40840,"visible":true,"origin":"","legend":"Heatmap of the correlation between m6A methylation score and module eigengenes. Each row corresponds to a module eigengene, and each column corresponds to a trait. Each cell contains the corresponding correlation (first line) and p-value (second line). The table is color-coded by correlation according to the color legend. ","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure4.png"},{"id":2477352,"identity":"a0cb1558-ad89-4bd3-a128-87d6ea5b4a02","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21352,"visible":true,"origin":"","legend":"Multi-list enrichment pathway analysis of m6A methylation correlated genes and upmethylated genes. The diagram depicts the top 20 clusters with the smallest p-value.","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure5.png"},{"id":2477353,"identity":"86aed6da-4b42-43d4-8ee9-416cb69ff1c3","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24210,"visible":true,"origin":"","legend":"Key cluster of 87 methylation correlated genes identified from PPI analysis. Yellow represents upmethylated genes.","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure6.png"},{"id":2477354,"identity":"815dc4f9-b8f0-4528-99e8-765dfdd6ff7a","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33748,"visible":true,"origin":"","legend":"Immune cells composition between high methylation and low methylation group. * represents statistical significance. Black dots represent outliers.","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure7.png"},{"id":2477355,"identity":"81fe1e31-9559-4959-b2b4-ff1790a27473","added_by":"auto","created_at":"2020-09-18 15:04:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":24125,"visible":true,"origin":"","legend":"Scale histogram of immune cell fraction among 18 IPAH samples.","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Figure8.png"},{"id":13594117,"identity":"0414c796-7db1-4686-9955-6e2ec2992a5e","added_by":"auto","created_at":"2021-09-17 05:19:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1430307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/bc32d539-cefe-4394-8115-f1959d21df99.pdf"},{"id":2477357,"identity":"a22fd5a3-85ed-451b-8f97-7b1d64c93ffe","added_by":"auto","created_at":"2020-09-18 15:04:42","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10684,"visible":true,"origin":"","legend":"","description":"","filename":"Q30.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/Q30.xlsx"},{"id":2477358,"identity":"80e80369-808a-4f23-ae23-fc730a319db1","added_by":"auto","created_at":"2020-09-18 15:04:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9223,"visible":true,"origin":"","legend":"","description":"","filename":"readstatistics.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/readstatistics.xlsx"},{"id":2477359,"identity":"2efb36c5-3778-4a24-9d7e-9cc88d9d0953","added_by":"auto","created_at":"2020-09-18 15:04:42","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6007,"visible":true,"origin":"","legend":"","description":"","filename":"samplegroups.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-64337/v1/samplegroups.xlsx"}],"financialInterests":"","formattedTitle":"Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension","fulltext":[{"header":"1. Background","content":" \u003cp\u003ePulmonary arterial hypertension (PAH) is a condition characterized by increased pulmonary arterial pressure and vascular remodeling of the small pulmonary arteries. PAH develops either as an idiopathic condition or in association with various underlying diseases such as collagen vascular disease, portal hypertension, or HIV infection(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The incidence of PAH ranges from 2.0 to 7.6 cases per million adults per year, and its prevalence varies from 11 to 26 cases per million adults. The incidence of PAH is fourfold higher in women than in men, but survival is paradoxically worse in men with PAH(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Despite striking progress in the development of diagnosis and therapeutics in the last three decades, PAH remains relatively incurable and the limited efficacy of current treatment options possibly results from an incomplete understanding of the pathophysiology of this cardiopulmonary disorder(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpigenetic modifications,such as DNA methylation, histone modifications, and noncoding RNAs, may play important roles in regulating the pathogenesis of PAH(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As a bridge to pass the genetic information from DNA to proteins RNA, RNA is an important part of the central dogma, and its various chemical modifications are involved in the regulation of various biological processes(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) of RNA transcripts is the most prevalent modification found in many classes of RNA(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The abundance of m\u003csup\u003e6\u003c/sup\u003eA on RNA is determined by the dynamic interplay between its methyltransferases (\u0026ldquo;writers\u0026rdquo;, such as METTL3-METTL14 complex(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)), and demethylases (\u0026ldquo;erasers\u0026rdquo;, including FTO(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and ALKBH5(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)). The binding proteins (\u0026ldquo;readers\u0026rdquo;, such as YTH domain\u0026ndash;containing proteins(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)) then mediate the effect of m\u003csup\u003e6\u003c/sup\u003eA modifications on RNA processing or metabolisms, including alternative splicing(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), export(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), stability(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), and translation(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies have suggested the involvement of the immune response during PAH. Inflammatory cell infiltrations can be observed around the pulmonary artery, such as macrophages dendritic cells, mast cells, T lymphocytes, and B lymphocytes(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These immune cells play an important role in the vascular remodeling characteristic of PAH and might be important targets for PAH therapy. Nevertheless, how the immune system is activated and regulated remains poorly understood. m\u003csup\u003e6\u003c/sup\u003eA RNA modifications might be a novel immunoregulatory factor(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeighted gene co-expression network analysis (WGCNA) is a system biology algorithm for describing the correlation patterns among genes across samples(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). WGCNA can identify and cluster highly correlated genes into the same module. Furthermore, this method can also be used for relating modules to external clinical traits. By far, WGCNA has been validated as a valuable method to identify underlying mechanisms, potential biomarkers, or therapeutic targets in different types of diseases.\u003c/p\u003e \u003cp\u003eIn our study, we first constructed a PAH rat model and conducted the m\u003csup\u003e6\u003c/sup\u003eA methylation sequencing analysis. We then analyzed 18 idiopathic PAH (IPAH) patients microarray data from the GEO database to identify m\u003csup\u003e6\u003c/sup\u003eA methylation correlated modules by using the method of WGCNA. Afterward, we applied the CIBERSORT algorithm to investigate the effect of m\u003csup\u003e6\u003c/sup\u003eA methylation on immune cell infiltration during PAH. Findings from our study may contribute to novel regulators and therapeutic targets in PAH from an epigenomic perspective.\u003c/p\u003e "},{"header":"2. Material And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.1.1 Establishment and verification of rat model of PAH induced by MCT\u003c/div\u003e \u003cp\u003eThe study was approved by the Institutional Animal Care and Use Committee of the Second Xiangya Hospital of Central South University and complied with the standards in the Guide for the Care and Use of Laboratory Animals. Sprague-Dawley rats (specific pathogen-free (SPF), male, 180\u0026ndash;200\u0026nbsp;g, 6 weeks old, n\u0026thinsp;=\u0026thinsp;5) were obtained from Changsha Tianqin Biotechnology Company (China). The rats were randomized to the control (n\u0026thinsp;=\u0026thinsp;2) and PAH groups (n\u0026thinsp;=\u0026thinsp;3). The rats in the PAH group were intraperitoneally injected with monocrotaline (MCT) (60 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Sigma, C2401), while the rats in the control group were injected intraperitoneally with the same volume of saline. All rats were housed on a 12\u0026nbsp;h light/dark cycle with free access to food and water. After 4 weeks of feeding, rats were anesthetized with 1% sodium pentobarbital (130 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for echocardiography and right heart catheterization. Echocardiography was used to record the tricuspid regurgitation velocity, tricuspid annulus contraction rate, tricuspid annular plane systolic excursion (TAPSE) and pulmonary artery blood flow acceleration time (PAAT). After echocardiographic examination, We performed a right heart catheter to measure pulmonary artery pressure.\u003c/p\u003e \u003cp\u003eRats were sacrificed by cervical dislocation after deep anesthesia. Then, heart tissues were removed and segregated. The ratio of right ventricle to left ventricle plus ventricular septum [RV/ (LV\u0026thinsp;+\u0026thinsp;S)] was used as an index of right ventricular hypertrophy. Lung tissues were excised and immediately frozen at liquid nitrogen or fixed in 4% buffered paraformaldehyde solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.1.2 Histological analysis\u003c/div\u003e \u003cp\u003eThe lung tissues obtained in each group were placed in 4% buffered paraformaldehyde solution overnight, and then dehydrated and embedded in paraffin. Then, all lung tissues were sliced into 5\u0026nbsp;\u0026micro;m-thick sections, fixed on a glass slide and baked dry. The staining procedures were performed according to the instructions. Briefly, the sections were soaked in xylene, ethanol in gradient concentration and hematoxylin, respectively, and sealed with resin. After dryness, pulmonary vascular morphology was observed and photographed under optical microscope. Last, the ratio of pulmonary small artery wall thickness and muscularization was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.1.3 RNA preparation\u003c/div\u003e \u003cp\u003eFor each group, four biological replicates were selected, of which every two were combined into one. Then, total RNA of tissue was extracted using TRIzol reagent (Invitrogen Corporation, CA, USA) following the manufacturer\u0026rsquo;s instructions. The Ribo-Zero rRNA Removal Kit (Illumina, Inc., CA, USA) was used to reduce the ribosomal RNA content of total RNAs. Then, the RNA was chemically fragmented into fragments of about 100 nucleotides in length using fragmentation buffer (Illumina, Inc.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.1.4 Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and library construction\u003c/div\u003e \u003cp\u003eMeRIP-Seq was performed following a previously reported procedure with slight modifications. Briefly, m\u003csup\u003e6\u003c/sup\u003eA RNA immunoprecipitation was performed with the GenSeqTM m\u003csup\u003e6\u003c/sup\u003eA RNA IP Kit (GenSeq Inc., China) by following the manufacturer\u0026rsquo;s instructions. Both the input sample without immunoprecipitation and the m\u003csup\u003e6\u003c/sup\u003eA IP samples were used for RNA-seq library generation with NEBNext\u0026reg; Ultra II Directional RNA Library Prep Kit (New England Biolabs, Inc., USA). The library quality was evaluated with BioAnalyzer 2100 system (Agilent Technologies, Inc., USA). Library sequencing was performed on an illumina Hiseq instrument with 150\u0026nbsp;bp paired-end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Microarray data collection and processing\u003c/h2\u003e \u003cp\u003eMaterials of GSE15197 were downloaded from the NCBI Gene Expression Omnibus (GEO; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e) database. The dataset contains lung tissue transcriptomic profiles from 18 IPAH patients. The median expression values among all multiple probe IDs were selected to represent the corresponding gene symbol, leading to the identification of 18612 unique genes across 18 samples. Human gene annotation file (GRCh38 release 99 gene transfer format, ensembl.org/index.html) was applied to annotate genes and the 15934 protein-coding genes were selected for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Estimation of m\u003csup\u003e6\u003c/sup\u003eA methylation score and implementation of weight correlation network analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWe constructed gene signatures of m\u003csup\u003e6\u003c/sup\u003eA writer (METTL3, METTL14, METTL16, WTAP, KIAA1429, ZC3H13, RBM15/RBM15B), and m\u003csup\u003e6\u003c/sup\u003eA eraser (ALKBH5, FTO) as suggested by Yang et.al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We then computed the Gene Set Variation Analysis (GSVA) enrichment score of m\u003csup\u003e6\u003c/sup\u003eA writer and m\u003csup\u003e6\u003c/sup\u003eA eraser across the 18 samples using the \u0026lsquo;GSVA\u0026rsquo; package(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) in R software. The estimated m\u003csup\u003e6\u003c/sup\u003eA methylation score was calculated by substrating m\u003csup\u003e6\u003c/sup\u003eA eraser score from m\u003csup\u003e6\u003c/sup\u003eA writer score. WGCNA was accomplished with the R package \u0026lsquo;WGCNA\u0026rsquo;(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). According to the standard variation value of gene, we ranked them from largest to smallest and only selected the top 2000 as input for WGCNA. A power of β value was introduced so that it could transform the similarity matrix into an adjacency matrix. In this study, β\u0026thinsp;=\u0026thinsp;5 was used as a soft threshold parameter to ensure a scale-free network. The Dynamic Tree Cut method was applied to generate modules with the following major parameters to avoid the generation of too many modules: deepSplit of 2, minModuleSize of 30, and the height cut-off was set as 0.25 (modules were merged if their similarity was \u0026gt;\u0026thinsp;0.75). Module eigengenes (MEs) referred to the first principal component of all gene expression levels in the module, and therefore, it was reasonable to consider that MEs represented all genes within a specific module. According to Pearson's correlation tests, we further identified the association between MEs and m\u003csup\u003e6\u003c/sup\u003eA methylation score. Within the most relavant module, those positively correlative with m\u003csup\u003e6\u003c/sup\u003eA methylation score were made subjected to further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eMetascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://Metascape.org/\u003c/span\u003e\u003c/span\u003e) is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for biologists(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). To gain insights into biological roles of m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes identified from WGCNA and upmethylated genes identified from m\u003csup\u003e6\u003c/sup\u003eA-seq, we conducted pathway enrichment analysis in Metascape tools using Gene Ontology biological process (GO BP), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). By inputting the lists of m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes and upmethylated genes simultaneously, Metascape can identify commonly-enriched and selectively-enriched pathways from two levels, which enable a comprehensive assessment of the molecular features of the biological process. For Multi-list Enrichment Analysis, Metascape first applies enrichment analysis to each gene list individually and identifies terms that are statistically enriched. All gene lists are then combined into one new list, and enrichment analysis is conducted on this combined list. Distinguishing it from many existing portals, Metascape automatically clusters enriched terms into non-redundant groups. Briefly, pairwise similarities between any two enriched terms are computed based on a Kappa-test score. The similarity matrix is then hierarchically clustered and a 0.3 similarity threshold is applied to trim the resultant tree into separate clusters. We selected the top 20 clusters and chose the most significant (lowest P-value) term within each of them to represent it.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein-protein interaction (PPI) network construction of m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes\u003c/h2\u003e \u003cp\u003eTo find out the functional associations among the m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes, we used the online Search Tool for the Retrieval of Interacting Genes (STRING database; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003c/span\u003e) to construct a PPI network based on uniquely comprehensive coverage and predictive function of genome-wide data(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). A stringent threshold of a combined score of \u0026gt;\u0026thinsp;0.7 was used to construct the PPI network and Cytoscape software was used to visualize and analyze the biological networks. Plugin Molecular Complex Detection (MCODE) was applied to identify significant clusters with strong protein-protein linkages with default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Estimation of immunocyte infiltration\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003c/span\u003e) was applied to estimate the abundance of infiltrated immune cell subtypes in the lung tissues of 18 samples, based on a deconvolution algorithm in the R software(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The perm parameter was set as 1000. The 18 samples were grouped to \u0026lsquo;High m\u003csup\u003e6\u003c/sup\u003eA methylation\u0026rsquo; and \u0026lsquo;Low m\u003csup\u003e6\u003c/sup\u003eA methylation\u0026rsquo; by the median value of their m\u003csup\u003e6\u003c/sup\u003eA methylation score. Comparisons between two groups were tested by the Wilcox rank test. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eBriefly, Paired-end reads were harvested from Illumina HiSeq 4000 sequencer, and were quality controlled by Q30. After 3\u0026rsquo; adaptor-trimming and low quality reads removing by cutadapt software (v1.9.3). First, clean reads of all libraries were aligned to the reference genome (UCSC RN5) by Hisat2 software (v2.0.4). Methylated sites on RNAs (peaks) were identified by MACS software. Differentially methylated sites with a fold change cutoff of \u0026ge;\u0026thinsp;2 and false discovery rate cutoff of \u0026le;\u0026thinsp;0.0001 were identified with the diffReps differential analysis package. These peaks identified by both softwares overlapping with exons of mRNA were figured out and choosed by home-made scripts.\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":" \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Construction of rats PAH model\u003c/h2\u003e \u003cp\u003eThe pulmonary artery velocity diagram appeared as a dagger in PAH group, and PAAT consumedly shortened compared to control group (20.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62 vs 29.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u0026nbsp;ms, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Then we observed ventricular septum significantly shift to the left, simultaneously, right atrium and right ventricle enlarged compared to control group (4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 vs 3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u0026nbsp;mm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The range of TAPSE was greatly reduced in PAH (1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 vs 1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u0026nbsp;mm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A). Four weeks after the MCT injection, the right ventricular systolic pressure (RVSP) of the PAH group was elevated to 49.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u0026nbsp;mmHg compared with 24.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u0026nbsp;mmHg in the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and at the same time, we found that RV/ (LV\u0026thinsp;+\u0026thinsp;S) increased in PAH group (0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 vs 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u0026nbsp;g, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.B). The results of HE staining showed that the pulmonary artery smooth muscle was significantly thickened in PAH group compared with control group (71.13\u0026thinsp;\u0026plusmn;\u0026thinsp;4.02% vs. 17.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.C). These results indicate that the rat model of PAH has been successfully established.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Methylations profile of rat lung tissue\u003c/h2\u003e \u003cp\u003eMeRIP-Seq analysis identified 922 non-overlapping m\u003csup\u003e6\u003c/sup\u003eA sites in control group and 9059 non-overlapping m\u003csup\u003e6\u003c/sup\u003eA sites in PAH group, while 18655\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA sites were overlapped in two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.A). Next, differentially methylated m\u003csup\u003e6\u003c/sup\u003eA sites (DMMSs) were identified by diffReps with the following default screening criteria: \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.0001 and fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 between the groups. We selected 3298 DMMSs in the two groups. A total of 777\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA sites exhibited up-methylation, and 2521 exhibited down-methylation. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.B). These results demonstrated an increased m\u003csup\u003e6\u003c/sup\u003eA modification process during PAH and the 2521 up-methylated m\u003csup\u003e6\u003c/sup\u003eA sites representing 1261 unique genes were selected for further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction of weighted gene co-expression network and identification of key modules\u003c/h2\u003e \u003cp\u003eThe top 2000 genes in 18 samples of PAH lung tissue were used to construct the co‑expression network. β values of 5 were confirmed to obtain the approximate scale‑free topology with a scale‑free topology fit index\u0026thinsp;\u0026gt;\u0026thinsp;0.85 and the lowest power (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.A). Next, the method of dynamic tree cutting was employed to produce co-expression modules, which led to the identification of 11 modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.B). We then calculated and plotted the relation of each module with the m\u003csup\u003e6\u003c/sup\u003eA methylation score. Among these modules, the yellow module depicting the highest correlation (module-trait weighted correlation\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;0.055) with m\u003csup\u003e6\u003c/sup\u003eA methylation score (Figrue 4). Within the yellow module, a total of 87 genes were positively correlative with m\u003csup\u003e6\u003c/sup\u003eA methylation score and were made subjected to further analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multi-list pathway enrichment analysis and visualization\u003c/h2\u003e \u003cp\u003eWe conducted a multi-list pathway enrichment analysis in Metascape by inputting the list of 87\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes identified from WGCNA and 1261\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA upmethylated genes identified from MeRIP-SEq.\u0026nbsp;As Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shown, both m\u003csup\u003e6\u003c/sup\u003eA correlated genes and upmethylated genes are involved in immune response-related pathways such as \u0026lsquo;cytokine-mediated signaling pathway\u0026rsquo;, \u0026lsquo;leukocyte migration\u0026rsquo;, and \u0026lsquo;activation of immune response\u0026rsquo;. Besides, the upmethylated genes also participate in some biological processes already known to be associate with PAH such as \u0026lsquo;ECM organization\u0026rsquo; and \u0026lsquo;supermolecular fiber organization\u0026rsquo;. In sum, the results demonstrated that m\u003csup\u003e6\u003c/sup\u003eA methylation may play an important role in PAH pathogenesis, especially in mediating immune response during PAH.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Identification of hub genes in the yellow module through PPI analysis\u003c/h2\u003e \u003cp\u003eTo identifiy hub genes, we submitted the gene list of 87\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA correlated genes in the yellow module to STRING for protein-protein interaction analysis, and the cut-off confidence interval was set to 0.7. After MCODE algorithm, the most significant cluster containing 10 hub genes was identified and enrichment analysis shows that genes in this cluster were mainly involved in \u0026lsquo;Chemokine receptors bind chemokines\u0026rsquo; pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). By comparing the results of MeRIP-Seq, CCR5 and CXCL9 were found to be significantly upmethylated with a fold change of 2.55 and 6.93, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation analysis between m\u003csup\u003e6\u003c/sup\u003eA methylation and immunocyte proportion\u003c/h2\u003e \u003cp\u003eWe have concluded that m\u003csup\u003e6\u003c/sup\u003eA methylation can mediate immune response during PAH process. We then aimed to investigate how it regulates immune cell infiltration. After the Wilcox rank test, monocyte and M1 macrophage were detected differentially infiltrated between the high methylation group and low methylation group (p\u0026thinsp;=\u0026thinsp;0.03, and p\u0026thinsp;=\u0026thinsp;0.001, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e displayed the scale histogram of immune cell fraction among 18 samples. These results indicated that during PAH, the m\u003csup\u003e6\u003c/sup\u003eA methylation can drive monocyte to form M1 macrophage, which plays vital roles in the immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e "},{"header":"4. Discussion","content":" \u003cp\u003em\u003csup\u003e6\u003c/sup\u003eA is the most prevalent modification in the mRNA of many eukaryotic species. There is growing evidence that m\u003csup\u003e6\u003c/sup\u003eA dysregulation has a profound impact on the pathogenesis of various human diseases(\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Su et al. firstly identified the transcriptome-wide map of m\u003csup\u003e6\u003c/sup\u003eA circRNAs in hypoxic PAH rat model and confirmed that m\u003csup\u003e6\u003c/sup\u003eA methylation can affect the circRNA-miRNA-mRNA network(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). However, the mechanism of mRNA m\u003csup\u003e6\u003c/sup\u003eA methylation modification to regulate IPAH has not been reported. In this study, we took advantage of MeRIP-seq to map the transcriptomic landscape of PAH rat lung tissue and thus quantitatively compared transcriptome-wide changes between PAH and control groups. The results found that the m\u003csup\u003e6\u003c/sup\u003eA level was significantly up-regulated in MCT induced PAH lung tissue, which highlighted the role of m\u003csup\u003e6\u003c/sup\u003eA methylation in the pathogenesis of PAH.\u003c/p\u003e \u003cp\u003eWGCNA is a method for constructing gene co-expression networks based on gene expression data. We applied this method to identified key modules associated with the estimated m\u003csup\u003e6\u003c/sup\u003eA methylation score. Through multi-list enrichment analysis, the 87\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes identified from WGCNA and 1261\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA up-methylated genes identified from MeRIP-seq analysis are both significantly involved in immune response-related pathways such as \u0026lsquo;cytokine-mediated signaling pathway\u0026rsquo;, \u0026lsquo;leukocyte migration\u0026rsquo;, and \u0026lsquo;activation of immune response\u0026rsquo;. These results highly suggested the immunoregulatory role of m\u003csup\u003e6\u003c/sup\u003eA methylation during PAH.\u003c/p\u003e \u003cp\u003eThrough PPI analysis of the 87\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes, a cluster containing 10 hub genes were identified, including CXCL9, CXCL10, CXCR3, PMCH, CXCL11, CXCR6, CCR7, CCL5, CCR5, and HCAR3. Among them, CCR5 and CXCL9 were significantly up-methylated. CCR5 is activated on stimulation by the CCR5 ligands CCL3 (macrophage inflammatory protein-1α), CCL4 (macrophage inflammatory protein-1β), and CCL5 (RANTES) and is strongly expressed on the principal cell types implicated in PAH progression, including endothelial cells, SMCs, T cells, and macrophages(\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Amsellem et al. studied the effect of CCR5 receptor antagonists on PASMC and inflammatory response in PAH mouse model and found that the activation of the CCL5-CCR5 axis directly leads to PASMC proliferation and macrophage recruitment(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). CXCL9, a chemokine, is a T-cell chemoattractant that is induced by IFN-γ. CXCL9 is closely related to two other CXC chemokines called CXCL10 and CXCL11, whose genes are located near the gene for CXCL9 on human chromosome 4(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). CXCL9, CXCL10, and CXCL11 are commonly produced by local cells in inflammatory lesions and can attract Th1 cells(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). A high level of CXCL9 in peripheral liquids can be considered as a marker of host immune response, especially of that involving Th1 cells(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy applying the CYBERSORT algorithm, we found that the m\u003csup\u003e6\u003c/sup\u003eA methylation can drive monocyte to form M1 macrophage. Circulating monocytes migrate into the majority of tissues in the body, where they differentiate into functionally distinct mature macrophages(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Macrophages undergo classical M1 activation induced by IFN-γ, which mediates Th1 cell-type activation of macrophages(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). The activated macrophage then causes vasoconstriction, increases vascular permeability, and induce PASMCs proliferation(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Our research sheld light on the regulatory role of m\u003csup\u003e6\u003c/sup\u003eA methylation in macrophage activation during PAH. As we discussed above, The hub 10\u0026nbsp;m\u003csup\u003e6\u003c/sup\u003eA methylation correlated genes,especially CCR5 and CXCL9༌may mediate m\u003csup\u003e6\u003c/sup\u003eA methylation caused M1 macrophage activation.\u003c/p\u003e "},{"header":"5. Conclusions","content":" \u003cp\u003eIn conclusion, our research revealed that m\u003csup\u003e6\u003c/sup\u003eA methylation modification may play important roles in mediating immune response during PAH. It also caused activation of M1macrophage, which may be mediated by CCR5 and CXCL9. These results will help us to better understand the mechanisms of PAH, and provide candidate therapeutic targets.\u003c/p\u003e "},{"header":"6. Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDMMSs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially methylated m6A sites\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003em6A\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN6-methyladenosine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonocrotaline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMeRIP-Seq\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethylated RNA immunoprecipitation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePAH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulmonary arterial hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePAAT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epulmonary artery blood flow acceleration time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRV/ (LV\u0026thinsp;+\u0026thinsp;S)\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eright ventricle to left ventricle plus ventricular septum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRVSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eright ventricular systolic pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTAPSE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etricuspid annular plane systolic excursion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWGCNA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eweighted gene co-expression network analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"7. Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Research Ethics Committee of Second Xiangya Hospital of Central South University. The research was conducted according to all ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent to publish was obtained from all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in our study are available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was supported by grants from the National Natural Science Foundation of China (No. 81770337). This project was supported by grants from the Hunan Provincial Natural Science Youth Foundation of China (No. 2019JJ50881). Hunan Provincial Health Commission Scientific Research Project (NO. 20201302). Hunan Province Traditional Chinese Medicine Research Program Project (No. 201914).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor\u0026rsquo;s contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQML and YBX conceived and designed this study; NL and YZL performed experiments, analyzed and interpreted data, and wrote the manuscript; YHZ and HT designed the microarray and analyzed the microarray data; BL and ZHY collected clinical samples, interpreted data and clinical information; QML improved and revised the manuscript; NL and YZL analyzed and interpreted data, supervised experiments, and wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAckonwledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to CloudSeq Biotech Inc. for scientific support.\u003c/p\u003e"},{"header":"8. References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eThenappan T, Ormiston ML, Ryan JJ, Archer SL. Pulmonary arterial hypertension: pathogenesis and clinical management. BMJ. 2018;360:j5492.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSimonneau G, Gatzoulis MA, Adatia I, Celermajer D, Denton C, Ghofrani A, et al. Updated clinical classification of pulmonary hypertension. J Am Coll Cardiol. 2013;62(25 Suppl):D34\u0026ndash;41.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJacobs W, van de Veerdonk MC, Trip P, de Man F, Heymans MW, Marcus JT, et al. The right ventricle explains sex differences in survival in idiopathic pulmonary arterial hypertension. 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Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme. 2008;40(6):361\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChaudhuri A. Regulation of Macrophage Polarization by RON Receptor Tyrosine Kinase Signaling. Frontiers in immunology. 2014;5:546.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSchultze JL, Schmieder A, Goerdt S. Macrophage activation in human diseases. Seminars in immunology. 2015;27(4):249\u0026ndash;56.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang N, Liang H, Zen K. Molecular mechanisms that influence the macrophage m1-m2 polarization balance. Frontiers in immunology. 2014;5:614.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eColvin KL, Cripe PJ, Ivy DD, Stenmark KR, Yeager ME. Bronchus-associated lymphoid tissue in pulmonary hypertension produces pathologic autoantibodies. Am J Respir Crit Care Med. 2013;188(9):1126\u0026ndash;36.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"N6-methyladenosine (m6A) methylation, pulmonary arterial hypertension (PAH), weighted gene co-expression network analysis (WGCNA), immune response","lastPublishedDoi":"10.21203/rs.3.rs-64337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-64337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eDespite its functional importance in various fundamental bioprocesses, studies of N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) in the pulmonary arterial hypertension (PAH) are lacking. Here we studied the potential relevance of m\u003csup\u003e6\u003c/sup\u003eA RNA methylation and immune response in PAH development.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eWe constructed a monocrotaline (MCT) induced PAH rat model and performed Methylated RNA immunoprecipitation sequencing (MeRIP-Seq). The 18 idiopathic PAH (IPAH) microarray data obtained from the GEO database was used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). CIBERSORT was used to investigate the effect of m\u003csup\u003e6\u003c/sup\u003eA methylation on immune cell infiltration during PAH.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eA differential pattern of m\u003csup\u003e6\u003c/sup\u003eA abundance, mainly up-methylation, was observed in the lung tissues of rats with MCT induced PAH. By WGCNA, multi-list pathway enrichment analysis and protein-protein interaction (PPI) analysis, we found that m\u003csup\u003e6\u003c/sup\u003eA methylation modification may play important roles in mediating immune response during PAH. CYBERSORT algorithm indicated that the m\u003csup\u003e6\u003c/sup\u003eA methylation can drive monocyte to form M1 macrophage, which may be mediated by CCR5 and CXCL9.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eCollectively, m\u003csup\u003e6\u003c/sup\u003eA landscape is altered in PAH. We summarize newly discovered m\u003csup\u003e6\u003c/sup\u003eA in controlling immune response, which caused activation of M1 macrophage during PAH. It’s provided a novel insight into the therapeutic mechanisms of PAH.\u003c/p\u003e","manuscriptTitle":"Comprehensive bioinformatic analysis identified m6A methylation as an important regulator in mediating immune response during idiopathic pulmonary arterial hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-18 15:04:39","doi":"10.21203/rs.3.rs-64337/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"408ea2f1-35fa-460a-a6ad-6768a62db47d","owner":[],"postedDate":"September 18th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":545717,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2020-09-24T17:03:41+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-18 15:04:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-64337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-64337","identity":"rs-64337","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}