Transcriptome-wide identification of N6-methyladenosine modifications for aortic dissection

Transcriptome-wide identification of N6-methyladenosine modifications for aortic dissection | 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 Article Transcriptome-wide identification of N6-methyladenosine modifications for aortic dissection Tianci Chai, Likang Ma, Jiakang Li, Rumei Xie, Lele Tang, Jian He, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972169/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 : N6-methyladenosine (m 6 A) plays important roles in many biological processes such as gene expression control and may have functional roles in aortic dissection (AD). The aim of this study was to identify N6-methyladenosine (m 6 A) modification and the expressions of the m 6 A regulatory genes related to AD. Methods : Aortic tissue samples were obtained from AD and controls and MeRIP-seq and RNA-seq experiments were performed to detect m 6 A methylation and mRNA expression profiles, respectively. The differentially RNA methylation peaks were validated by MeRIP-PCR in AD cases and controls. Results: Compared with the control samples, 3,318 up methylated and 1,573 down methylated coding genes in AD were detected. These genes were mainly enriched in focal adhesion, ECM-receptor interaction and regulating the transcription such as splicing. Significant differentially methylated m 6 A sites in some well-known susceptibility genes for AD were identified, including FBN1 , TGFB1 , TGFBR1/2 , LOXL3 , COL3A1 , SMAD3 , VEGFA and MAPK1/3 . A total of 651 differentially expressed genes, including 594 protein-coding genes (96 upregulated and 498 downregulated), and 57 lncRNAs (20 upregulated and37 downregulated) were identified. Integrated analysis of the data from MeRIP-seq and RNA-Seq identified 74 genes that changed significantly in both m 6 A level and mRNA abundance in AD cases compared with the controls. We observed the same m 6 A-level changes in 14 out of the 16 selected m 6 A methylated transcripts in the independent sample. Conclusions : This study identified m 6 A changes in critical AD susceptibility genes. The identified m 6 A modification may play a role in critical AD-related pathways, thereby regulating the pathogenesis of AD. Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Vascular diseases/Aortic diseases Health sciences/Biomarkers/Predictive markers Aortic dissection N6-methyladenosine modification Focal adhesion Extracellular matrix Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aortic dissections (ADs) are extremely dangerous cardiovascular diseases that affecting the aorta. AD is a life-threatening disease begins with a tear in the aortic intima or bleeding within the aortic wall, which resulting a false lumen. AD is associated with a high mortality rate, with about 2.78 per 100 000 people each year 1, 2 . The disease is most commonly seen in people aged 65–75 years old, and is more common in men than women 2 . AD can be inherited and genetic factors play roles in its development. Family studies have demonstrated the familial aggregation of AD, and genetic association studies have uncovered many susceptibility genes 3–6 . The pathogenic variants were found in FBN1 , PHACTR1 , EDN1 7 , COL3A1 8 , SMAD3 8, 9 , TGFBR1/2 , TGFB2/3 10 , TSR1 11, 12 , TLN1 13 and others 5, 13, 14 . Recent studies have demonstrated that epigenetic factors (e.g., DNA methylation, histone modification and non-coding RNA) were key regulators in AD 15, 16 . Although many susceptibility genes for AD have been identified and the role of epigenetic regulation in AD has been studied, it is still not comprehensively and deeply analyzed. Therefore, how epigenetic factors regulated these genes are in AD has not been known. RNA modifications are modifiable chemical modifications that are involved in diverse biological regulations 17 . In recent years, various types (more than 170) of chemical modifications present in RNA molecules have been identified. N6-methyladenosine (m 6 A) is the most prevalent modification in eukaryotic RNAs. This type of modification can occur within almost all types of RNA molecules such as mRNA, miRNA, circRNA and lncRNA and has been confirmed to be very important in the post-transcriptional regulation of gene expression, RNA stability and homeostasis 18–21 . It also has been showed to be critical in the etiology of various diseases 22 . Recently, m 6 A has been shown to regulate cardiac gene expression and cellular growth and therefore was associated with cardiac function 23–25 . It has also been shown to be associated with blood pressure 26 , the most important risk factor of AD, and coronary artery disease 27–29 . This evidence indicated an etiological role of m 6 A in AD. Although some differential m 6 A methylation peaks have been found in AD 30 , the roles have not been studied still in a deepgoing way by now. To comprehensively and deeply investigate the transcriptome-wide m 6 A methylome of AD, this study performed the m 6 A specific RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq in 9 AD and 9 normal aorta samples. The differential methylation peaks were validated in 20 independent samples by MeRIP-qPCR. Results Transcriptome-wide m 6 A methylation The mean total MeRIP-seq reads for the 9 AD cases and 9 controls was 12,819,119,767 bp. The cut-adapt software remove about 8% of the reads. The mean mapped rate was 80%. The analysis demonstrated that there were 33,792 nonoverlapping m 6 A peaks within 11,817 mRNA transcripts in controls and 36,517 nonoverlapping m6A peaks within 11,458 mRNA transcripts in AD cases. There were 1,259 nonoverlapping m 6 A peaks within 763 lncRNA transcripts in AD group, 1195 nonoverlapping m6A peaks within 758 lncRNA transcripts in control group. Nearly 20% of the m6A-methylated mRNAs in the two groups contained one or two m 6 A peaks, about 18% and 12% contained three or four m 6 A peaks, respectively, and about 20% contained ≥ 5 m 6 A peaks (Figure 1A). For lncRNAs, more than 60% of the m 6 A-methylated lncRNAs had only one m 6 A modification peak and about 18% contained two m 6 A peaks (Figure 1B). All m 6 A peaks were classified based on their locations in mRNA transcripts: the 5'-UTR, start codon, stop codon, coding DNA sequence (CDS) and 3'-UTR. As the pie chart displayed, m 6 A peaks were most often harbored in the CDS and UTR region in both case and control groups (Figure 1C and 1D, respectively). As shown in Metagene plot, the identified m 6 A peaks were mainly enriched in the CDS, especially in the immediate vicinity of the stop codon (Figure 1E). The m 6 A peaks in lncRNAs were located in the exons (Figure 1F). Differentially methylated m 6 A sites In total, 4,988 differentially methylated m 6 A sites (DMMSs) within 3,768 mRNA transcripts were identified (FC > 2, FDR < 0.05) (Figure 2A, Online Table 1). Compared with control group, 3,318 upmethylated m 6 A sites within 2,642 mRNA transcripts and 1,573 downmethylated m 6 A sites within 1,355 mRNA transcripts were found in AD group (Table 2). We also identified 97 DMMSs within 79 lncRNA transcripts between the two groups, among which there were 63 upmethylated and 34 downmethylated m 6 A sites (Online Figure 1, Online Table 2). Tables 3 showed the up (5) and down (11) methylated m 6 A sites within the mRNAs that have >100-fold change between AD cases and controls. The DMMSs were mapped to chromosomes, and chromosome 1 (517), 2 (349) and 3 (308) harbored most DMMSs in mRNA (Online Figure 2A), and chromosome 15 (10), 6 (8) and 19 (8) harbored most DMMSs in lncRNA (Online Figure 2B). We further analyzed the m 6 A distribution patterns of mRNAs and found that most of the DMMSs were located in the CDS and UTR region. Significant DMMSs in some well-known susceptibility genes for AD were identified (Table 4), including FBN1 , TGFB1 , TGFBR1/2 , LOXL3 , COL3A1 , SMAD3 , VEGFA and MAPK1/3 . Three significant DMMSs, including chr15:48474328-48483880, chr15:48499022-48505066 and chr15:48537655-48537806 in FBN1 were found. These three sites were all upmethylated (FC = 2.81, 5.13 and 2.51, respectively). Two significant upmethylated DMMSs in COL3A1 , two significant downmethylated DMMSs in TGFBR1 and one significant DMMS in other genes were found. Differentially methylated mRNAs are involved in specific biological functions and pathways To uncover the functions of m 6 A in AD, the protein-coding genes containing DMMSs were selected for GO enrichment analysis and KEGG pathway analysis. The analysis showed that genes with up or down methylated m 6 A sites significant (FDR < 0.05) enriched in 1,434 BP (Online Table 3), 292 CC (Online Table 4) and 201 MF (Online Table 5) GO terms. For the CC and MF categories, both up or down methylation of m 6 A sites showed notable enrichment in the focal adhesion. For the BP category, genes with up methylation of m 6 A sites were enriched in splicing (Online Figure 3A), while genes with down methylation of m 6 A sites showed high enrichment in extracellular matrix organization (Online Figure 3B). These results suggested that m 6 A might play an important role in the occurrence and development of AD. KEGG pathway analysis of DMMS-contained mRNA associated genes was performed and 47 pathways were identified (Online Table 6). The top pathway for both genes with up or down methylation of m 6 A sites was focal adhesion. In addition, genes with up methylation of m 6 A sites were enriched in regulating the transcription (Figure 2B), while genes with down methylation of m 6 A sites were mainly enriched in ECM-receptor interaction, TGF-beta signaling pathway and cardiomyopathy (Figure 2C). These pathways highlighted some genes related to AD, including MAP4K4 , DSP , GADD45B , PYGL , COL1A1 , VEGFA , TNNC1 , STMN1 , MYBPC3 , TPM1 , ACTC1 , TNXB , F2R and RYR2 . The expression of m 6 A methylated genes RNA-seq dataset was used to discover the differentially expressed lncRNAs/mRNAs between the two groups. A total of 651 DEGs were identified (FC > 2, FDR < 0.05), including 594 protein-coding genes (96 upregulated and 498 downregulated) (Figure 3A, Online Table 7) and 57 lncRNAs (20 upregulated and 37 downregulated) (Figure 3B, Online Table 8). Some of the known AD susceptibility genes such as VEGFA (FC = 8.44, FDR = 2.42×10 -6 ) and IL6 (FC = 15.22, FDR = 1.26×10 -6 ) were differentially expressed between Ads and controls (Figure 3C). The lncRNAs H19 (FC = 31.20, FDR = 3.57×10 -5 ) and NEAT1 (FC = 2.15, FDR = 0.04) were differentially expressed between cases and controls (Figure 3C). DEGs such as COL1A1 , VEGFA , TNNC1 , SLC2A1 , COX4I1 , MYBPC3 , TPM1 , CREB3L1 , ACTC1 , TNXB and RYR2 were enriched in specific pathways such as focal adhesion, ECM-receptor interaction, insulin secretion, cardiac muscle contraction, dilated cardiomyopathy, oxidative phosphorylation, adrenergic signaling in cardiomyocytes and calcium signaling pathway. Integration of DMMSs and DEGs Integrated analysis of the data from MeRIP-seq and RNA-Seq identified 74 genes that changed significantly (FC > 2, FDR < 0.05) in both m 6 A level and mRNA abundance in AD cases compared with the controls (Online Table 9). These genes were classified into four groups, including 10 hypermethylated and upregulated genes (hyper-up), 21 hypermethylated but downregulated genes (hyper-down), 22 hypomethylated but upregulated genes (hypo-up) and 21 hypomethylated and downregulated genes (hypo-down) (Figure 4A). Based on the above data, we deduced that m 6 A modification tended to have a positive correlation with mRNA expression in AD. DSP (DMMSs FC = 25.81, FDR = 1.45×10 -6 ; DEG FC = 91.92, FDR = 2.29×10 -7 ), TNNC1 (DMMSs FC = 113.77, FDR = 1.00×10 -7 ; DEG FC = 9.83, FDR = 2.64×10 -3 ) and PCDH1 (DMMSs FC = 448.82, FDR = 2.95×10 -7 ; DEG FC = 4.68, FDR = 1.30×10 -2 ) were representative hypo-down genes in AD group. MAP4K4 (DMMSs FC = 28.44, FDR = 7.94×10 -15 ; DEG FC = 2.11, FDR = 2.13×10 -2 ) and VEGFA (DMMSs FC = 7.89, FDR = 1.45×10 -5 ; DEG FC = 8.44, FDR = 5.80×10 -4 ) and lncRNA NEAT1 (DMMSs FC = 2.77, FDR = 4.57×10 -10 ; DEG FC = 2.15, FDR = 4.12×10 -2 ) were one of hyper-up genes in AD group. Furthermore, gene set enrichment analysis was performed to deduce the functional pathway for genes identified in the integrative analysis. These 74 genes were mainly enriched in cardiac muscle contraction, adrenergic signaling in cardiomyocytes, hypertrophic cardiomyopathy and dilated cardiomyopathy KEGG pathways (Figure 4B). The GO enrichment analysis of the 74 genes showed the most enriched terms of biological processes were mainly included in the cardiac muscle tissue development and morphogenesis (Figure 4C). MYBPC3 , RYR2 , ACTC1 , CREB3L1 , TNNC1 , COX4I1 , TPM1 , UQCRC1 , DSP , FHOD3 and VEGFA were representative genes involved in these pathways. MeRIP-qPCR of differentially methylated transcripts To further confirm the results of MeRIP-seq, we conducted MeRIP-qPCR assays for differentially methylated transcripts of representative DMGs, including 15 protein-coding genes ( COL1A1 , VEGFA , F2R , PKD1 , PKD2 , SLC2A1 , RYR2 , ACTC1 , CREB3L1 , TNNC1 , COX4I1 , TNXB , CP , PCOLCE2 and TM4SF1 ) and one lncRNA NEAT1 (Online Table 10). These 16 genes were identified by the above integration and enrichment analyses. We observed the same m 6 A-level changes in 13 out of the 16 m 6 A methylated transcripts (Table 5), including 13 ( COL1A1 , VEGFA , F2R , SLC2A1 , RYR2 , ACTC1 , CREB3L1 , TNNC1 , COX4I1 , PKD2 , CP , PCOLCE2 and TM4SF1 ) protein-coding genes and the lncRNA NEAT1. The changes of six of these genes were nominally significant, including COL1A1 , VEGFA , F2R , ACTC1 , TNNC1 and PKD2 (Figure 5). After further immunohistochemical validation of paraffin sections of aortic tissue, the results showed a similar protein expression trend. ACTC1 and TNNC1 were found to be downregulated in AD patients, while COL1A1, VEGFA, F2R and PKD2 were found to be upregulated (Figure 6). Discussion In this study, we describe the m 6 A RNA methylation landscape and reveal the potential functions of this methylation in the regulation of RNA metabolism in AD. We identified that abnormal methylation in FBN1 , TGFB1 , TGFBR1/2 , LOXL3 , COL3A1 , SMAD3 , VEGFA and MAPK1/3 might be involved in the pathophysiology of AD. Conjoint analysis of m 6 A-RIP-seq and RNA-seq data resulted in the identification of differentially expressed hyper-methylated or hypo-methylated mRNA m 6 A peaks. Differential m 6 A methylation in COL1A1 , VEGFA , F2R , ACTC1 , TNNC1 and PKD2 was validated in independent sample. AD is currently a serious public health problem worldwide and patients with AD are associated with extremely high mortality rates. Convincing evidence has suggested that VSMCs play a key role in the development and progression of AD. As shown in previous study, mRNA and protein expression level of METTL14 was up-regulated, while the mRNA level of FTO was down-regulated in AD tissue samples compared with normal samples 30 . The differences among the expressions of METTL14 and FTO were found in the other types of inflammatory cells that exist in the AD tissues 32 and m 6 A mRNA modification can regulate T cell homeostasis 33 . Our study identified many genes that were abnormally methylated in AD aortic tissues, including many well-known AD susceptibility genes such as FBN1 , TGFB1 , VEGFA , LOXL3 , COL3A1 and SMAD3 . Abnormal methylation TGFB2 , TGFBR1 , TGFBR1 , TGFBR2 , TGFB1I1 , VEGFB , VEGFC and MAPK1/3 was also found. Therefore, m 6 A modification may have an impaction on the expression of genes in aortic tissues, which was consistent with previous findings. It has been shown that m 6 A plays a role in the extracellular matrix 30, 34 . The results of GO and KEGG analyses in previous study indicated that AD-related methylated genes are mainly enriched in the processes and pathways related to cellular matrix, such as the extracellular matrix organization, and ECM-receptor interaction signaling pathways. Our findings also supported the importance of m 6 A in the ECM-receptor interaction signaling pathway. But the outstanding pathway identified in this study was focal adhesion. In addition, differentially expressed hyper-methylated or hypo-methylated genes identified in the present study are showed enrichment in cardiomyopathy and cardiac muscle tissue development and morphogenesis. The canonical TGF-β signaling pathway promotes aortic development and maintains aortic wall homeostasis 35 . Although TGF-β signaling pathway was not significant in functional enrichment analysis, TGFB1 , TGFB2 , TGFBR1 , TGFBR1 , TGFBR2 and TGFB1I1 were abnormally methylated in aorta of AD patients. Genes in these pathways are critical molecules that contribute to the development and progression of AD. The findings provides further evidence that AD is a heterogeneous and multifactorial disease and that a variety of pathogenic mechanisms are implicated in its occurrence and development and m 6 A modification might be involved in. It is known that m 6 A methylation plays a critical role in the regulation of coordinate transcriptional and post-transcriptional gene expression, including mRNA splicing, export, localisation and stability. In this study, many abnormal m 6 A methylated genes were also found to be enriched in biological processes of RNA splicing, RNA processing and RNA localization, which uncovers emerging links between m 6 A mRNA methylation and genome transcription. Thus, RNA modification by m 6 A promotes the interaction of the methylated genes with nuclear transcription factors. The distortion of RNA splicing and localization induced by abnormal m 6 A modification in these gene may be a cause of AD. In addition to the genes that were previously associated with AD, we also identified a number of novel genes. Some of the most essential and crucial pathological changes in the AD process include focal adhesion, ECM-receptor interaction, actin cytoskeleton, TGF-beta signaling pathway and Hypertrophic cardiomyopathy. These pathways can regulate several biological processes such as proliferation, migration, invasion, and apoptosis. Conjoint analysis of m 6 A-RIP-seq and RNA-seq data identified m6A-modified mRNA transcripts that were hyper-methylated or hypo-methylated and significantly differentially expressed. This may shed light on a new layer of gene regulation at the RNA level, ultimately giving rise to the field of m 6 A epitranscriptomics. Studies of the human aorta not only enrich our understanding of the mechanisms underlying AD but also provide a theoretical basis for the prevention and treatment. To provide further insight, it will be necessary, in a future study, to elucidate the biological function of the identified genes in the context of AD and verify if m 6 A methylation in these genes may provide a prospective therapeutic target for the prevention and treatment of AD. Conclusion In summary, we reveal a region-specific m 6 A methylation map and identified numerous m 6 A changes in critical AD susceptibility genes (e.g., FBN1 , TGFB1 , VEGFA , LOXL3 , COL3A1 and SMAD3 ) in AD patients compared to healthy controls. The identified m 6 A modification might regulate critical AD-related pathways such as focal adhesion, ECM-receptor interaction signaling pathway, cardiomyopathy and cardiac muscle tissue development and morphogenesis. The findings provided new insights into the pathogenesis of AD by m 6 A methylation. Further research on m 6 A target genes in AD might contribute to the clinical application of AD molecular targeted therapy. Materials and Methods Study participants All aorta samples used in this study were collected from the department of Cardiac Surgery, Fujian Medical University Union Hospital. A total of 142 participants have been enrolled in this study. Diseased aortic tissue samples were obtained from AD patients, and control aortic tissue samples were obtained from recipients of heart transplants or lung donors. The aorta samples were obtained safely during surgery. Tissues were immediately snap-frozen and kept in liquid nitrogen. The cause of the donor’s death was cerebrovascular or traffic accident. All donors had no history of cardiovascular diseases or active infection at the time of transplantation. Participants with hereditary aortic diseases, connective tissue disorders, cancers, drug history, infections, or any other immune-related diseases were excluded. 9 AD cases and nine controls for MeRIP-seq and RNA-seq (The clinical characteristics of the two groups were shown in Table 1), and 10 AD cases and 10 controls for MeRIP-qPCR validation were selected from the 142 participants. This study has been approved by the Ethics Committee of Fujian Medical University Union Hospital. Written informed consent was collected from the participant or their families, conforming to the Declaration of Helsinki. RNA-seq Total RNA from the aorta was extracted using TRIzol reagent (Invitrogen Corporation, CA, USA) following the manufacturer’s instructions. After removing the cytoplasmic ribosomal RNA and mitochondrial rRNA from the total RNA by Ribo-Zero rRNA Removal Kit (MRZG12324; Illumina, CA, USA), mRNAs and lncRNAs were fragmented to 100-200 nt fragments using fragmentation buffer (Illumina). For RNA-seq, 10 ng RNA fragment was used to constructed strand-specific RNA library in accordance with a dUTP method instead of dTTP to label the second-strand cDNA during synthesis. The quality of the library was done using a Bioptic Qsep100 analyzer. RNA-seq was performed on Illumina HiSeq 4000 sequencer (Illumina). The cut-adapt software (v1.9.3) was used to remove the 3’ adaptor trimming and low-quality reads, and then the clean high-quality trimmed reads were aligned to the reference genome (hg38) with HISAT2 software (v2.0.4) 31 . The expression profiles were obtained with the cuffdiff software (v2.2.1) and differentially expressed genes (DEGs) were assessed. The DEGs were determined with the threshold of |log2FC| > 1 and FDR < 0.05. The genomic assembly version used in this study was GRCh38. MeRIP-seq RNA fragments were incubated with anti-m6A polyclonal antibody (202003; Synaptic Systems, Goettingen, Germany) in immunoprecipitation (IP) buffer for 2 h at 4◦C, and subsequently, the mixture was immunoprecipitated by incubation with protein-A beads (Thermo Fisher Scientific, MA, USA) at 4◦C for an additional 2 h. The purified immunoprecipitated RNA and input RNA were collected for the generation of an RNA-seq library with NEBNext R UltraTM RNA Library Prep Kit (New England Biolabs, MA, USA). The quality of the library was done using a Bioptic Qsep100 analyzer. Then, paired-end library sequencing was performed on Illumina HiSeq 4000 sequencer (Illumina). The quality of the paired-end reads was analyzed using fastQC (v0.11.8). After the 3’ adaptor trimming and low-quality reads removing by cut-adapt software (v1.9.3), the clean high-quality trimmed reads were aligned to the reference genome (hg38) with HISAT2 software (v2.0.4) 31 . Methylated sites on RNAs (peaks) were detected by MACS software and diffReps software was used to detect differentially methylated lncRNAs/mRNAs. Overlapped exons of mRNA and lncRNA were identified and chosen by home-made scripts. For differentially methylated m6A peaks, HOMER was used for de novo motif prediction. The input RNA-seq and MeRIP-seq data were analyzed jointly to screen genes with significant differences in both RNA m6A methylation and abundance between the two groups (|log2FC| > 1, FDR < 0.05). Differential methylation sites were identified using the exomePeak R package (v2.13.2) with default parameters. The DEGs were assessed using the DESeq2 R package (v1.10.1) between the AD group and the normal individuals. m6A peak visualization of representative genes was performed by IGV software. Gene set enrichment analysis Next, molecular pathways of differentially methylated genes (DMGs) with differential expression were determined by gene set enrichment analysis. Gene symbol and log2(FC) were sorted in descending order to create the ranking list. Then, the ranking list was used to calculate normalized enrichment scores and p-value. The C2 gene set ‘c2.cp.v7.1.symbols.gmt’ from the Molecular Signature Database version 7.0 was selected as the reference gene set. The number of permutations was set to 1000, and a gene set with nominal p-value < 0.05 was treated as significantly enriched. The data supporting the findings of the present study are available from corresponding author upon reasonable request. MeRIP-qPCR validation Transcripts revealed by the integration analysis of DEGs and DMGs were tested by reverse transcription-qPCR in 10 AD cases and 10 controls. The RNA sample was fragmented (300 nt) after incubation with fragmentation buffer at 94◦C for 4 min. A total of 5% of the fragmented RNA was saved as the input control. The procedure of m6A-IP sample preparation was the same as that of MeRIP-seq. Both input control and m6A-IP samples were subjected to reverse transcription-qPCR using Universal SYBR qPCR Master Mix (Q711-02/03, Vazyme Bio). Immunohistochemistry Staining validation The obtained AD and control aortic tissues were paraffin-embedded and sectioned into 3µm slices. After baking the slices for 2 hours (60 °C), they were then subjected to gradient immersion in xylene and ethanol. The slices were processed using an Immunohistochemistry Kit (UItraSensitive SP mouse/rabbit; MXB) and incubated overnight with corresponding primary antibodies (4°C). The next day, DAB staining (DAB-0031; MXB) was performed, and the results were observed under the Nikon fluorescence microscope (Nikon ECLIPSE Ni). Image-J (Version.1.53a) was used for result analysis. Statistical analysis The comparison between the two groups was performed using the GraphPad software with an unpaired Student’s t-test. ∗P < 0.05 was considered statistically significant. Abbreviations ADs Aortic dissections m6A N6-methyladenosine MeRIP-seq m 6 A specific RNA immunoprecipitation sequencing DEGs differentially expressed genes DMGs differentially methylated genes DMMSs differentially methylated m 6 A sites Declarations Ethical approval: The studies involving human participants were reviewed and approved by the Ethics Committee of the Fujian Medical University Union Hospital (IACUC FJMU 2023-0053). This study complied with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. This study did not involve any animal experiments. Consent for publication: Not applicable. Data availability statement: The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors. Competing Interests Statement: The authors declare that they have no competing interests. Funding ： This work was supported by the National Natural Science Foundation of China [U2005202], [82370470] and [82241209], and the Fujian Provincial Special Reserve Talents Fund [2021-25]. Author contributions: TC.Chai and LK.Ma contributed to the conceptualization. TC.Chai and LK.Ma contributed to data curation and writing of the original draft. LK.Ma and JK.Li contributed to the formal analysis. RM.Xie and J.He contributed to the investigation. TC.Chai, LK.Ma, LL.Tang contributed to the methodology and the visualization. ZH.Qiu and LW.Chen contributed to the writing, review, and editing. All authors contributed to the article and approved the submitted version. Acknowledgments: Not applicable. References Sampson UK, Norman PE, Fowkes FG, Aboyans V, Yanna S, Harrell FE, Jr., Forouzanfar MH, Naghavi M, Denenberg JO, McDermott MM, Criqui MH, Mensah GA, Ezzati M, Murray C. 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Characteristics AD (n = 9, % / mean±SD) Con (n = 9, % / mean±SD) X 2 / t P value Age, y 53.78±15.13 53.78±15.13 <0.001 1 Male 6 (66.7) 6 (66.7) <0.001 1 BMI, kg/m 2 25.6±1.75 23.05±2.37 2.598 0.019 ≥25 7 (77.8) 1 (11.1) 8.1 0.015 Standford A 8 (88.9) -- -- -- Diameter of ascending aorta, cm (tracheal carina level) 4.57±0.75 3.28±0.32 4.73 <0.001 Diameter of descending aorta , cm (tracheal carina level) 3.93±1.02 2.58±0.19 3.9 0.001 EF, % 68.2±6.43 71.9±1.98 -1.66 0.13 Risk factors Hypertension 8 (88.9) 1 (11.1) 10.889 0.003 Diabetes 1 (11.1) 1 (11.1) <0.001 1 Smoking 4 (44.4) 3 (33.3) 0.234 1 Drinking 5 (55.6) 3 (33.3) 0.9 0.637 Blood lipids TG, mmol/L 1.41±0.73 1.14±0.47 0.942 0.36 TC, mmol/L 3.97±0.93 4.85±0.67 -2.29 0.036 HDL—C, mmol/L 1.07±0.25 1.51±0.3 -3.34 0.004 LDL—C, mmol/L 2.52±0.89 3.09±0.63 -1.56 0.139 APOA, g/L 1.18±0.22 1.52±0.19 -3.53 0.003 APOB, g/L 0.86±0.19 0.89±0.17 -0.413 0.685 Dyslipidemia 7 (77.8) 4 (44.4) 2.104 0.335 Patient history Marfan syndrome 1 (11.1) 0 (0) 1.059 1 CAD 1 (11.1) 1 (11.1) <0.001 1 HbA1c 5.97±0.89 4.84±0.79 2.825 0.012 HbA1c disorder 5 (55.6) 1 (11.1) 4 0.131 Table 2 General numbers of differentially methylated m 6 A peaks and associated genes Item Upmethylated Downmethylated Total Peak Gene Peak Gene Peak Gene* mRNA 3,318 2,642 1,573 1,355 4,891 3,689 lncRNA 63 51 34 32 97 79 Total 3,381 2,693 1,607 1,387 4,988 3,768 *Some genes contain both upmethylated and downmethylated peaks, therefore the total number of genes do not equals to the number of upmethylated genes plus the number of downmethylated genes. Table 3 Top m 6 A peaks associated with AD Gene name CHR Start End Position FC P value FDR Regulation PCDH1 5 141853769 141853950 3'-UTR 448.82 9.55E-09 2.95E-07 DOWN CNOT3 19 54154034 54154244 CDS 424.61 1.95E-04 2.40E-03 UP CHI3L1 1 203178990 203179230 CDS 349.71 1.00E-14 6.31E-13 DOWN RORC 1 151811347 151811527 CDS 317.37 6.76E-04 7.08E-03 DOWN WWC2 4 183317005 183317186 3'-UTR 224.41 6.31E-14 3.16E-12 UP TMEM268 9 114645732 114645883 3'-UTR 221.32 2.00E-04 2.45E-03 UP LRRC8C 1 89714913 89715094 3'-UTR 186.11 1.58E-28 2.51E-26 DOWN FZD1 7 91270608 91270788 3'-UTR 144.01 2.45E-07 6.03E-06 UP PI16 6 36963097 36963397 CDS 143.01 8.51E-08 2.29E-06 DOWN ARHGAP11A 15 32615678 32616038 5'-UTR 134.36 1.00E-24 1.26E-22 DOWN KDM2A 11 67256196 67256347 3'-UTR 133.44 7.94E-33 1.58E-30 DOWN BLNK 10 96223857 96227457 CDS 133.44 3.16E-05 4.90E-04 DOWN STK32A 5 147387253 147387433 3'-UTR 129.79 2.24E-05 3.63E-04 UP C10orf128 10 49166612 49166792 3'-UTR 113.77 1.26E-03 1.20E-02 DOWN TNNC1 3 52452143 52452674 5'-UTR 113.77 2.95E-09 1.00E-07 DOWN SOCS6 18 70327207 70327388 3'-UTR 110.66 2.24E-10 8.91E-09 DOWN Table 4 Significant differentially methylated m 6 A peaks in known AD susceptibility genes Gene name CHR Start End Position FC P value FDR Regulation TGFB2 1 218434278 218442044 3'-UTR 5.13 1.26E-20 1.26E-18 DOWN LOXL3 2 74532652 74533549 3'-UTR 4.03 1.20E-04 1.62E-03 UP IL1R1 2 102164924 102165270 CDS 2.03 1.10E-05 1.91E-04 UP COL3A1 2 188988619 189003441 CDS 2.38 7.94E-93 5.01E-90 UP COL3A1 2 189004012 189008425 CDS 2.17 2.00E-25 2.51E-23 UP TGFBR2 3 30671744 30674131 CDS 2.36 2.00E-35 3.98E-33 UP VEGFA 6 43779990 43780320 CDS 7.89 6.31E-07 1.45E-05 UP TGFBR1 9 99128892 99132558 CDS 3.10 6.31E-05 8.91E-04 DOWN TGFBR1 9 99152189 99152578 3'-UTR 2.41 1.00E-12 5.01E-11 DOWN FBN1 15 48474328 48483880 CDS 2.81 1.45E-10 5.89E-09 UP FBN1 15 48499022 48505066 CDS 5.13 1.26E-30 2.51E-28 UP FBN1 15 48537655 48537806 CDS 2.51 2.88E-06 5.62E-05 UP SMAD3 15 67065992 67078037 CDS 2.45 3.47E-04 3.98E-03 DOWN MAPK3 16 30118150 30122158 5'-UTR 2.50 5.01E-04 5.37E-03 UP TGFB1 19 41332258 41342100 CDS 3.53 1.82E-04 2.29E-03 UP MAPK1 22 21769164 21788787 CDS 3.68 9.33E-08 2.45E-06 UP Table 5 Validation of differentially methylated transcripts Gene Discovery stage Validation stage Log 2 (FC) P value Log 2 (FC) P value ACTC1 -2.12 2.19E-04 -3.15 1.87E-02 COL1A1 3.03 1.58E-15 3.90 3.97E-02 COX4I1 2.62 2.95E-05 4.04 7.26E-02 CP -3.87 2.00E-91 -3.19 5.23E-01 CREB3L1 -2.46 2.19E-04 -3.93 2.18E-01 F2R 2.06 4.17E-03 4.95 2.56E-02 NEAT1 1.47 1.00E-11 2.82 4.90E-02 PCOLCE2 3.38 3.98E-11 5.09 9.03E-02 PKD2 1.24 2.51E-16 3.97 3.48E-02 RYR2 2.90 2.51E-11 4.00 9.52E-01 SLC2A1 -1.89 1.78E-05 -3.71 3.29E-01 TM4SF1 -3.80 1.00E-78 -4.53 6.07E-01 TNNC1 -6.38 2.95E-09 -5.88 4.44E-03 VEGFA 2.98 6.31E-07 2.55 3.94E-02 Fisher's exact test, t-test for continuous variables. Age and gender were used as matching for 1:1 case-control matching study. P values are for comparisons between controls (Con) and patients with aortic dissection（AD). SD, standard deviation; BMI, body mass index; TC, total cholesterol; TG, triglyceride; HDL—C, high density lipoprotein cholesterol; LDL—C, low density lipoprotein cholesterol; APOA, apolipoprotein A; and APOB, apolipoprotein B; EF, ejection fraction; CAD, coronary artery disease; HbA1c, hemoglobin A1c. Additional Declarations No competing interests reported. Supplementary Files Onlinefigures.pdf Onlinetables.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. 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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-3972169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276339290,"identity":"4dcda50c-e626-4c98-a8a1-e79eb5334c8b","order_by":0,"name":"Tianci Chai","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianci","middleName":"","lastName":"Chai","suffix":""},{"id":276339291,"identity":"1f31f9a3-f6d3-48b5-b36f-1e74375f9ceb","order_by":1,"name":"Likang Ma","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Likang","middleName":"","lastName":"Ma","suffix":""},{"id":276339292,"identity":"a57a237a-6bcf-4602-a675-1436437f13f3","order_by":2,"name":"Jiakang Li","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiakang","middleName":"","lastName":"Li","suffix":""},{"id":276339293,"identity":"05bbbf7f-de7f-4413-b135-b43f3da4903a","order_by":3,"name":"Rumei Xie","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rumei","middleName":"","lastName":"Xie","suffix":""},{"id":276339294,"identity":"b47b3f19-5ce4-44a1-93c5-b67d77100996","order_by":4,"name":"Lele Tang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lele","middleName":"","lastName":"Tang","suffix":""},{"id":276339296,"identity":"d3e998e8-470d-4361-9eac-100d116532c7","order_by":5,"name":"Jian He","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"He","suffix":""},{"id":276339297,"identity":"67ee8d81-61ac-4275-a956-aeadf86da053","order_by":6,"name":"Zhihuang Qiu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhihuang","middleName":"","lastName":"Qiu","suffix":""},{"id":276339298,"identity":"1ed20e2a-b68e-4def-bc14-623ca272d4e5","order_by":7,"name":"Liangwan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDACCSB+YADlfCBaSwJUC+OMBKK1QNnMPMRo4Z/dfOxBQoFdnnxE8rPHtj9sohnYDx/dgNeSO8fSDRIMkosNb6SZG+ckpOU28KSl3cCnxUAix0wiwYA5cePsBDPpnITDuQ0SPGYEtOR/A2qpB2pJ/yZtQZyWHDaglsOJ86VzzKQZiNEicSMN5LDjiRvk35RJ9qSl5bYR8gv/jORnEh/+VCfO7zm+TeKHjU1uP/vhY3i1IFx4AMpgI0o5CMg3EK10FIyCUTAKRhoAADl1SOpadv8VAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Liangwan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-02-20 08:20:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3972169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3972169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52185408,"identity":"343fa019-18f1-4f5c-895f-5ffa0524ea46","added_by":"auto","created_at":"2024-03-07 18:22:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA methylation in AD cases and controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Proportion of genes harboring different numbers of m\u003csup\u003e6\u003c/sup\u003eA peaks within mRNAs in the two group; (B) Proportion of genes harboring different numbers of m\u003csup\u003e6\u003c/sup\u003eA peaks within lncRNAs in the two group; (C) Pie chart showing the percentage of m\u003csup\u003e6\u003c/sup\u003eA peaks in genomic segments of mRNA transcript in AD cases; (D) Pie chart showing the percentage of m\u003csup\u003e6\u003c/sup\u003eA peaks in genomic segments of mRNA transcript in controls; (E) Metagene plot showing the enrichment of m\u003csup\u003e6\u003c/sup\u003eA peaks in mRNA transcripts in two groups; (F) Metagene plot showing the enrichment of m\u003csup\u003e6\u003c/sup\u003eA peaks in lncRNA transcripts in two groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/f54deab9c912657842786a9e.png"},{"id":52183706,"identity":"90971d0d-554c-400c-a019-f579c97c44e5","added_by":"auto","created_at":"2024-03-07 18:14:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially methylated genes between AD cases and controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The volcano plot of DMGs. The vertical axis represents -log 10 False Discovery Rate (FDR) and the horizontal axis represents log 2 fold change (log2FC). The DMGs were selected according to FDR \u0026lt; 0.05 and |log2FC| \u0026gt; 1. The red dots represent the 3,318 upmethylated m\u003csup\u003e6\u003c/sup\u003eA sites within 2,642 mRNA transcripts. The green dots represent the 1,573 downmethylated m\u003csup\u003e6\u003c/sup\u003eA sites within 1,355 mRNA transcripts; (B) The top 10 enriched KEGG pathways of the genes with up methylation of m\u003csup\u003e6\u003c/sup\u003eA sites; (C) The top 10 enriched KEGG pathways of the genes with down methylation of m\u003csup\u003e6\u003c/sup\u003eA sites.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/39764406ca5f7b72a3897d29.png"},{"id":52185409,"identity":"2ed582ca-a3b8-4aed-a013-b0e67739be69","added_by":"auto","created_at":"2024-03-07 18:22:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":684171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes between AD cases and controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The volcano plot of DEGs. The vertical axis represents -log 10 False Discovery Rate (FDR) and the horizontal axis represents log 2 fold change (log2FC). The DEGs were selected according to FDR \u0026lt; 0.05 and |log2FC| \u0026gt; 1. The red dots represent the 96 upregulated genes. The green dots represent the 498 downregulated genes; (B) The volcano plot of differentially expressed lncRNAs. The vertical axis represents -log 10 False Discovery Rate (FDR) and the horizontal axis represents log 2 fold change (log2FC). The differentially expressed lncRNAs were selected according to FDR \u0026lt; 0.05 and |log2FC| \u0026gt; 1. The red dots represent the 20 upregulated lncRNAs. The green dots represent the 37 downregulated lncRNAs; (C) Differential expression of known AD susceptibility genes between AD cases and controls.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/19e17aeffc5a391126f2e57f.png"},{"id":52183709,"identity":"752420e4-463b-4bb2-9e9e-42a9075c3ede","added_by":"auto","created_at":"2024-03-07 18:14:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1365652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConjoint analysis of m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eA-RIP-seq and RNA-sequencing data for AD\u003c/strong\u003e \u003cstrong\u003ecases and controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Four quadrant graph showing the distribution of transcripts with a significant change in both m6A level and expression in AD; (B) The significantly enriched KEGG pathways and (C) GO terms for the 74 genes that changed significantly in both m\u003csup\u003e6\u003c/sup\u003eA methylation and mRNA expression.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/798fa26e925b840346a3d9fb.png"},{"id":52183708,"identity":"073d9f20-baae-4191-86bc-a9d528c05918","added_by":"auto","created_at":"2024-03-07 18:14:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":843933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeRIP-qPCR results of genes with significant differences (N=10).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/550641e48dd7dd2bd2846324.png"},{"id":52183710,"identity":"c6cde0d9-65a4-4ac1-87e3-ea493992eadc","added_by":"auto","created_at":"2024-03-07 18:14:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4783019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemistry Staining results of genes with significant differences (N=4).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/3d3531306954014026137307.png"},{"id":55483257,"identity":"f14a7bd3-2776-4fa0-af6a-5ac14553ae98","added_by":"auto","created_at":"2024-04-29 05:13:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3854628,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/71b7ad43-7e3c-4283-8fe4-0ac57f508cde.pdf"},{"id":52183712,"identity":"e02297d5-7109-487a-97f7-c4e36390f167","added_by":"auto","created_at":"2024-03-07 18:14:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":261218,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/830c62a430b6508d7002c97a.pdf"},{"id":52183713,"identity":"39e155b8-0f8a-4c33-bf22-8019f275c2c4","added_by":"auto","created_at":"2024-03-07 18:14:23","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":926222,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinetables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3972169/v1/2d69431a6db91cab28a5ddc8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome-wide identification of N6-methyladenosine modifications for aortic dissection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAortic dissections (ADs) are extremely dangerous cardiovascular diseases that affecting the aorta. AD is a life-threatening disease begins with a tear in the aortic intima or bleeding within the aortic wall, which resulting a false lumen. AD is associated with a high mortality rate, with about 2.78 per 100 000 people each year\u003csup\u003e1, 2\u003c/sup\u003e. The disease is most commonly seen in people aged 65\u0026ndash;75 years old, and is more common in men than women\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAD can be inherited and genetic factors play roles in its development. Family studies have demonstrated the familial aggregation of AD, and genetic association studies have uncovered many susceptibility genes\u003csup\u003e3\u0026ndash;6\u003c/sup\u003e. The pathogenic variants were found in \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003ePHACTR1\u003c/em\u003e, \u003cem\u003eEDN1\u003c/em\u003e\u003csup\u003e7\u003c/sup\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e\u003csup\u003e8\u003c/sup\u003e, \u003cem\u003eSMAD3\u003c/em\u003e\u003csup\u003e8, 9\u003c/sup\u003e, \u003cem\u003eTGFBR1/2\u003c/em\u003e, \u003cem\u003eTGFB2/3\u003c/em\u003e\u003csup\u003e10\u003c/sup\u003e, \u003cem\u003eTSR1\u003c/em\u003e\u003csup\u003e11, 12\u003c/sup\u003e, \u003cem\u003eTLN1\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003e and others\u003csup\u003e5, 13, 14\u003c/sup\u003e. Recent studies have demonstrated that epigenetic factors (e.g., DNA methylation, histone modification and non-coding RNA) were key regulators in AD\u003csup\u003e15, 16\u003c/sup\u003e. Although many susceptibility genes for AD have been identified and the role of epigenetic regulation in AD has been studied, it is still not comprehensively and deeply analyzed. Therefore, how epigenetic factors regulated these genes are in AD has not been known.\u003c/p\u003e \u003cp\u003eRNA modifications are modifiable chemical modifications that are involved in diverse biological regulations\u003csup\u003e17\u003c/sup\u003e. In recent years, various types (more than 170) of chemical modifications present in RNA molecules have been identified. N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) is the most prevalent modification in eukaryotic RNAs. This type of modification can occur within almost all types of RNA molecules such as mRNA, miRNA, circRNA and lncRNA and has been confirmed to be very important in the post-transcriptional regulation of gene expression, RNA stability and homeostasis\u003csup\u003e18\u0026ndash;21\u003c/sup\u003e. It also has been showed to be critical in the etiology of various diseases\u003csup\u003e22\u003c/sup\u003e. Recently, m\u003csup\u003e6\u003c/sup\u003eA has been shown to regulate cardiac gene expression and cellular growth and therefore was associated with cardiac function\u003csup\u003e23\u0026ndash;25\u003c/sup\u003e. It has also been shown to be associated with blood pressure\u003csup\u003e26\u003c/sup\u003e, the most important risk factor of AD, and coronary artery disease\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. This evidence indicated an etiological role of m\u003csup\u003e6\u003c/sup\u003eA in AD. Although some differential m\u003csup\u003e6\u003c/sup\u003eA methylation peaks have been found in AD\u003csup\u003e30\u003c/sup\u003e, the roles have not been studied still in a deepgoing way by now.\u003c/p\u003e \u003cp\u003eTo comprehensively and deeply investigate the transcriptome-wide m\u003csup\u003e6\u003c/sup\u003eA methylome of AD, this study performed the m\u003csup\u003e6\u003c/sup\u003eA specific RNA immunoprecipitation sequencing (MeRIP-seq) and RNA-seq in 9 AD and 9 normal aorta samples. The differential methylation peaks were validated in 20 independent samples by MeRIP-qPCR.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTranscriptome-wide m\u003csup\u003e6\u003c/sup\u003eA methylation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean total MeRIP-seq reads for the 9 AD cases and 9 controls was 12,819,119,767 bp. The cut-adapt software remove about 8% of the reads. The mean mapped rate was 80%. The analysis demonstrated that there were 33,792 nonoverlapping m\u003csup\u003e6\u003c/sup\u003eA peaks within 11,817 mRNA transcripts in controls and 36,517 nonoverlapping m6A peaks within 11,458 mRNA transcripts in AD cases. There were 1,259 nonoverlapping m\u003csup\u003e6\u003c/sup\u003eA peaks within 763 lncRNA transcripts in AD group, 1195 nonoverlapping m6A peaks within 758 lncRNA transcripts in control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNearly 20% of the m6A-methylated mRNAs in the two groups contained one or two m\u003csup\u003e6\u003c/sup\u003eA peaks, about 18% and 12% contained three or four m\u003csup\u003e6\u003c/sup\u003eA peaks, respectively, and about 20% contained ≥ 5 m\u003csup\u003e6\u003c/sup\u003eA peaks (Figure 1A). For lncRNAs, more than 60% of the m\u003csup\u003e6\u003c/sup\u003eA-methylated lncRNAs had only one m\u003csup\u003e6\u003c/sup\u003eA modification peak and about 18% contained two m\u003csup\u003e6\u003c/sup\u003eA peaks (Figure 1B). All m\u003csup\u003e6\u003c/sup\u003eA peaks were classified based on their locations in mRNA transcripts: the 5'-UTR, start codon, stop codon, coding DNA sequence (CDS) and 3'-UTR. As the pie chart displayed, m\u003csup\u003e6\u003c/sup\u003eA peaks were most often harbored in the CDS and UTR region in both case and control groups (Figure 1C and 1D, respectively). As shown in Metagene plot, the identified m\u003csup\u003e6\u003c/sup\u003eA peaks were mainly enriched in the CDS, especially in the immediate vicinity of the stop codon (Figure 1E). The m\u003csup\u003e6\u003c/sup\u003eA peaks in lncRNAs were located in the exons (Figure 1F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially methylated m\u003csup\u003e6\u003c/sup\u003eA sites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 4,988 differentially methylated m\u003csup\u003e6\u003c/sup\u003eA sites (DMMSs) within 3,768 mRNA transcripts were identified (FC \u0026gt; 2, FDR \u0026lt; 0.05) (Figure 2A, Online Table 1). Compared with control group, 3,318 upmethylated m\u003csup\u003e6\u003c/sup\u003eA sites within 2,642 mRNA transcripts and 1,573 downmethylated m\u003csup\u003e6\u003c/sup\u003eA sites within 1,355 mRNA transcripts were found in AD group (Table 2). We also identified 97 DMMSs within 79 lncRNA transcripts between the two groups, among which there were 63 upmethylated and 34 downmethylated m\u003csup\u003e6\u003c/sup\u003eA sites (Online Figure 1, Online Table 2). Tables 3 showed the up (5) and down (11) methylated m\u003csup\u003e6\u003c/sup\u003eA sites within the mRNAs that have \u0026gt;100-fold change between AD cases and controls. The DMMSs were mapped to chromosomes, and chromosome 1 (517), 2 (349) and 3 (308) harbored most DMMSs in mRNA (Online Figure 2A), and chromosome 15 (10), 6 (8) and 19 (8) harbored most DMMSs in lncRNA (Online Figure 2B). We further analyzed the m\u003csup\u003e6\u003c/sup\u003eA distribution patterns of mRNAs and found that most of the DMMSs were located in the CDS and UTR region.\u003c/p\u003e\n\u003cp\u003eSignificant DMMSs in some well-known susceptibility genes for AD were identified (Table 4), including \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eTGFBR1/2\u003c/em\u003e, \u003cem\u003eLOXL3\u003c/em\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e, \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e and \u003cem\u003eMAPK1/3\u003c/em\u003e. Three significant DMMSs, including chr15:48474328-48483880, chr15:48499022-48505066 and chr15:48537655-48537806 in \u003cem\u003eFBN1\u003c/em\u003e were found. These three sites were all upmethylated (FC = 2.81, 5.13 and 2.51, respectively). Two significant upmethylated DMMSs in \u003cem\u003eCOL3A1\u003c/em\u003e, two significant downmethylated DMMSs in \u003cem\u003eTGFBR1\u003c/em\u003e and one significant DMMS in other genes were found.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially methylated mRNAs are involved in specific biological functions and pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo uncover the functions of m\u003csup\u003e6\u003c/sup\u003eA in AD, the protein-coding genes containing DMMSs were selected for GO enrichment analysis and KEGG pathway analysis. The analysis showed that genes with up or down methylated m\u003csup\u003e6\u003c/sup\u003eA sites significant (FDR \u0026lt; 0.05) enriched in 1,434 BP (Online Table 3), 292 CC (Online Table 4) and 201 MF (Online Table 5) GO terms. For the CC and MF categories, both up or down methylation of m\u003csup\u003e6\u003c/sup\u003eA sites showed notable enrichment in the focal adhesion. For the BP category, genes with up methylation of m\u003csup\u003e6\u003c/sup\u003eA sites were enriched in splicing (Online Figure 3A), while genes with down methylation of m\u003csup\u003e6\u003c/sup\u003eA sites showed high enrichment in extracellular matrix organization (Online Figure 3B). These results suggested that m\u003csup\u003e6\u003c/sup\u003eA might play an important role in the occurrence and development of AD.\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis of DMMS-contained mRNA associated genes was performed and 47 pathways were identified (Online Table 6). The top pathway for both genes with up or down methylation of m\u003csup\u003e6\u003c/sup\u003eA sites was focal adhesion. In addition, genes with up methylation of m\u003csup\u003e6\u003c/sup\u003eA sites were enriched in regulating the transcription (Figure 2B), while genes with down methylation of m\u003csup\u003e6\u003c/sup\u003eA sites were mainly enriched in ECM-receptor interaction, TGF-beta signaling pathway and cardiomyopathy (Figure 2C). These pathways highlighted some genes related to AD, including \u003cem\u003eMAP4K4\u003c/em\u003e, \u003cem\u003eDSP\u003c/em\u003e, \u003cem\u003eGADD45B\u003c/em\u003e, \u003cem\u003ePYGL\u003c/em\u003e, \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, \u003cem\u003eSTMN1\u003c/em\u003e, \u003cem\u003eMYBPC3\u003c/em\u003e, \u003cem\u003eTPM1\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eTNXB\u003c/em\u003e, \u003cem\u003eF2R\u003c/em\u003e and \u003cem\u003eRYR2\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression of m\u003csup\u003e6\u003c/sup\u003eA methylated genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq dataset was used to discover the differentially expressed lncRNAs/mRNAs between the two groups. A total of 651 DEGs were identified (FC \u0026gt; 2, FDR \u0026lt; 0.05), including 594 protein-coding genes (96 upregulated and 498 downregulated) (Figure 3A, Online Table 7) and 57 lncRNAs (20 upregulated and 37 downregulated) (Figure 3B, Online Table 8). Some of the known AD susceptibility genes such as \u003cem\u003eVEGFA\u003c/em\u003e (FC = 8.44, FDR = 2.42×10\u003csup\u003e-6\u003c/sup\u003e) and \u003cem\u003eIL6\u003c/em\u003e (FC = 15.22, FDR = 1.26×10\u003csup\u003e-6\u003c/sup\u003e) were differentially expressed between Ads and controls (Figure 3C). The lncRNAs H19 (FC = 31.20, FDR = 3.57×10\u003csup\u003e-5\u003c/sup\u003e) and NEAT1 (FC = 2.15, FDR = 0.04) were differentially expressed between cases and controls (Figure 3C). DEGs such as\u003cem\u003e\u0026nbsp;COL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, \u003cem\u003eSLC2A1\u003c/em\u003e, \u003cem\u003eCOX4I1\u003c/em\u003e, \u003cem\u003eMYBPC3\u003c/em\u003e, \u003cem\u003eTPM1\u003c/em\u003e, \u003cem\u003eCREB3L1\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eTNXB\u003c/em\u003e and \u003cem\u003eRYR2\u003c/em\u003e were enriched in specific pathways such as focal adhesion, ECM-receptor interaction, insulin secretion, cardiac muscle contraction, dilated cardiomyopathy, oxidative phosphorylation, adrenergic signaling in cardiomyocytes and calcium signaling pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of DMMSs and DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntegrated analysis of the data from MeRIP-seq and RNA-Seq identified 74 genes that changed significantly (FC \u0026gt; 2, FDR \u0026lt; 0.05) in both m\u003csup\u003e6\u003c/sup\u003eA level and mRNA abundance in AD cases compared with the controls (Online Table 9). These genes were classified into four groups, including 10 hypermethylated and upregulated genes (hyper-up), 21 hypermethylated but downregulated genes (hyper-down), 22 hypomethylated but upregulated genes (hypo-up) and 21 hypomethylated and downregulated genes (hypo-down) (Figure 4A). Based on the above data, we deduced that m\u003csup\u003e6\u003c/sup\u003eA modification tended to have a positive correlation with mRNA expression in AD. \u003cem\u003eDSP\u003c/em\u003e (DMMSs FC = 25.81, FDR = 1.45×10\u003csup\u003e-6\u003c/sup\u003e; DEG FC = 91.92, FDR = 2.29×10\u003csup\u003e-7\u003c/sup\u003e), \u003cem\u003eTNNC1\u003c/em\u003e (DMMSs FC = 113.77, FDR = 1.00×10\u003csup\u003e-7\u003c/sup\u003e; DEG FC = 9.83, FDR = 2.64×10\u003csup\u003e-3\u003c/sup\u003e) and \u003cem\u003ePCDH1\u003c/em\u003e (DMMSs FC = 448.82, FDR = 2.95×10\u003csup\u003e-7\u003c/sup\u003e; DEG FC = 4.68, FDR = 1.30×10\u003csup\u003e-2\u003c/sup\u003e) were representative hypo-down genes in AD group. \u003cem\u003eMAP4K4\u003c/em\u003e (DMMSs FC = 28.44, FDR = 7.94×10\u003csup\u003e-15\u003c/sup\u003e; DEG FC = 2.11, FDR = 2.13×10\u003csup\u003e-2\u003c/sup\u003e) and \u003cem\u003eVEGFA\u003c/em\u003e (DMMSs FC = 7.89, FDR = 1.45×10\u003csup\u003e-5\u003c/sup\u003e; DEG FC = 8.44, FDR = 5.80×10\u003csup\u003e-4\u003c/sup\u003e) and lncRNA NEAT1 (DMMSs FC = 2.77, FDR = 4.57×10\u003csup\u003e-10\u003c/sup\u003e; DEG FC = 2.15, FDR = 4.12×10\u003csup\u003e-2\u003c/sup\u003e) were one of hyper-up genes in AD group.\u003c/p\u003e\n\u003cp\u003eFurthermore, gene set enrichment analysis was performed to deduce the functional pathway for genes identified in the integrative analysis. These 74 genes were mainly enriched in cardiac muscle contraction, adrenergic signaling in cardiomyocytes, hypertrophic cardiomyopathy and dilated cardiomyopathy KEGG pathways (Figure 4B). The GO enrichment analysis of the 74 genes showed the most enriched terms of biological processes were mainly included in the cardiac muscle tissue development and morphogenesis (Figure 4C). \u003cem\u003eMYBPC3\u003c/em\u003e, \u003cem\u003eRYR2\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eCREB3L1\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, \u003cem\u003eCOX4I1\u003c/em\u003e, \u003cem\u003eTPM1\u003c/em\u003e, \u003cem\u003eUQCRC1\u003c/em\u003e, \u003cem\u003eDSP\u003c/em\u003e, \u003cem\u003eFHOD3\u003c/em\u003e and \u003cem\u003eVEGFA\u003c/em\u003e were representative genes involved in these pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeRIP-qPCR of differentially methylated transcripts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further confirm the results of MeRIP-seq, we conducted MeRIP-qPCR assays for differentially methylated transcripts of representative DMGs, including 15 protein-coding genes (\u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eF2R\u003c/em\u003e, \u003cem\u003ePKD1\u003c/em\u003e, \u003cem\u003ePKD2\u003c/em\u003e, \u003cem\u003eSLC2A1\u003c/em\u003e, \u003cem\u003eRYR2\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eCREB3L1\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, \u003cem\u003eCOX4I1\u003c/em\u003e, \u003cem\u003eTNXB\u003c/em\u003e, \u003cem\u003eCP\u003c/em\u003e, \u003cem\u003ePCOLCE2\u003c/em\u003e and \u003cem\u003eTM4SF1\u003c/em\u003e) and one lncRNA NEAT1 (Online Table 10). These 16 genes were identified by the above integration and enrichment analyses. We observed the same m\u003csup\u003e6\u003c/sup\u003eA-level changes in 13 out of the 16 m\u003csup\u003e6\u003c/sup\u003eA methylated transcripts (Table 5), including 13 (\u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eF2R\u003c/em\u003e, \u003cem\u003eSLC2A1\u003c/em\u003e, \u003cem\u003eRYR2\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eCREB3L1\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e, \u003cem\u003eCOX4I1\u003c/em\u003e, \u003cem\u003ePKD2\u003c/em\u003e, \u003cem\u003eCP\u003c/em\u003e, \u003cem\u003ePCOLCE2\u003c/em\u003e and \u003cem\u003eTM4SF1\u003c/em\u003e) protein-coding genes and the lncRNA NEAT1. The changes of six of these genes were nominally significant, including \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eF2R\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e and \u003cem\u003ePKD2\u0026nbsp;\u003c/em\u003e(Figure 5). After further immunohistochemical validation of paraffin sections of aortic tissue, the results showed a similar protein expression trend. \u003cem\u003eACTC1\u003c/em\u003e and \u003cem\u003eTNNC1\u003c/em\u003e were found to be downregulated in AD patients, while \u003cem\u003eCOL1A1, VEGFA, F2R\u003c/em\u003e and \u003cem\u003ePKD2\u003c/em\u003e were found to be upregulated (Figure 6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we describe the m\u003csup\u003e6\u003c/sup\u003eA RNA methylation landscape and reveal the potential functions of this methylation in the regulation of RNA metabolism in AD. We identified that abnormal methylation in \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eTGFBR1/2\u003c/em\u003e, \u003cem\u003eLOXL3\u003c/em\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e, \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e and \u003cem\u003eMAPK1/3\u003c/em\u003e might be involved in the pathophysiology of AD. Conjoint analysis of m\u003csup\u003e6\u003c/sup\u003eA-RIP-seq and RNA-seq data resulted in the identification of differentially expressed hyper-methylated or hypo-methylated mRNA m\u003csup\u003e6\u003c/sup\u003eA peaks. Differential m\u003csup\u003e6\u003c/sup\u003eA methylation in \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eF2R\u003c/em\u003e, \u003cem\u003eACTC1\u003c/em\u003e, \u003cem\u003eTNNC1\u003c/em\u003e and \u003cem\u003ePKD2\u003c/em\u003e was validated in independent sample.\u003c/p\u003e \u003cp\u003eAD is currently a serious public health problem worldwide and patients with AD are associated with extremely high mortality rates. Convincing evidence has suggested that VSMCs play a key role in the development and progression of AD. As shown in previous study, mRNA and protein expression level of METTL14 was up-regulated, while the mRNA level of FTO was down-regulated in AD tissue samples compared with normal samples\u003csup\u003e30\u003c/sup\u003e. The differences among the expressions of METTL14 and FTO were found in the other types of inflammatory cells that exist in the AD tissues\u003csup\u003e32\u003c/sup\u003e and m\u003csup\u003e6\u003c/sup\u003eA mRNA modification can regulate T cell homeostasis\u003csup\u003e33\u003c/sup\u003e. Our study identified many genes that were abnormally methylated in AD aortic tissues, including many well-known AD susceptibility genes such as \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eLOXL3\u003c/em\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e and \u003cem\u003eSMAD3\u003c/em\u003e. Abnormal methylation \u003cem\u003eTGFB2\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e, \u003cem\u003eTGFBR2\u003c/em\u003e, \u003cem\u003eTGFB1I1\u003c/em\u003e, \u003cem\u003eVEGFB\u003c/em\u003e, \u003cem\u003eVEGFC\u003c/em\u003e and \u003cem\u003eMAPK1/3\u003c/em\u003e was also found. Therefore, m\u003csup\u003e6\u003c/sup\u003eA modification may have an impaction on the expression of genes in aortic tissues, which was consistent with previous findings.\u003c/p\u003e \u003cp\u003eIt has been shown that m\u003csup\u003e6\u003c/sup\u003eA plays a role in the extracellular matrix\u003csup\u003e30, 34\u003c/sup\u003e. The results of GO and KEGG analyses in previous study indicated that AD-related methylated genes are mainly enriched in the processes and pathways related to cellular matrix, such as the extracellular matrix organization, and ECM-receptor interaction signaling pathways. Our findings also supported the importance of m\u003csup\u003e6\u003c/sup\u003eA in the ECM-receptor interaction signaling pathway. But the outstanding pathway identified in this study was focal adhesion. In addition, differentially expressed hyper-methylated or hypo-methylated genes identified in the present study are showed enrichment in cardiomyopathy and cardiac muscle tissue development and morphogenesis. The canonical TGF-β signaling pathway promotes aortic development and maintains aortic wall homeostasis\u003csup\u003e35\u003c/sup\u003e. Although TGF-β signaling pathway was not significant in functional enrichment analysis, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eTGFB2\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e, \u003cem\u003eTGFBR1\u003c/em\u003e, \u003cem\u003eTGFBR2\u003c/em\u003e and \u003cem\u003eTGFB1I1\u003c/em\u003e were abnormally methylated in aorta of AD patients. Genes in these pathways are critical molecules that contribute to the development and progression of AD. The findings provides further evidence that AD is a heterogeneous and multifactorial disease and that a variety of pathogenic mechanisms are implicated in its occurrence and development and m\u003csup\u003e6\u003c/sup\u003eA modification might be involved in.\u003c/p\u003e \u003cp\u003eIt is known that m\u003csup\u003e6\u003c/sup\u003eA methylation plays a critical role in the regulation of coordinate transcriptional and post-transcriptional gene expression, including mRNA splicing, export, localisation and stability. In this study, many abnormal m\u003csup\u003e6\u003c/sup\u003eA methylated genes were also found to be enriched in biological processes of RNA splicing, RNA processing and RNA localization, which uncovers emerging links between m\u003csup\u003e6\u003c/sup\u003eA mRNA methylation and genome transcription. Thus, RNA modification by m\u003csup\u003e6\u003c/sup\u003eA promotes the interaction of the methylated genes with nuclear transcription factors. The distortion of RNA splicing and localization induced by abnormal m\u003csup\u003e6\u003c/sup\u003eA modification in these gene may be a cause of AD.\u003c/p\u003e \u003cp\u003eIn addition to the genes that were previously associated with AD, we also identified a number of novel genes. Some of the most essential and crucial pathological changes in the AD process include focal adhesion, ECM-receptor interaction, actin cytoskeleton, TGF-beta signaling pathway and Hypertrophic cardiomyopathy. These pathways can regulate several biological processes such as proliferation, migration, invasion, and apoptosis. Conjoint analysis of m\u003csup\u003e6\u003c/sup\u003eA-RIP-seq and RNA-seq data identified m6A-modified mRNA transcripts that were hyper-methylated or hypo-methylated and significantly differentially expressed. This may shed light on a new layer of gene regulation at the RNA level, ultimately giving rise to the field of m\u003csup\u003e6\u003c/sup\u003eA epitranscriptomics. Studies of the human aorta not only enrich our understanding of the mechanisms underlying AD but also provide a theoretical basis for the prevention and treatment. To provide further insight, it will be necessary, in a future study, to elucidate the biological function of the identified genes in the context of AD and verify if m\u003csup\u003e6\u003c/sup\u003eA methylation in these genes may provide a prospective therapeutic target for the prevention and treatment of AD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we reveal a region-specific m\u003csup\u003e6\u003c/sup\u003eA methylation map and identified numerous m\u003csup\u003e6\u003c/sup\u003eA changes in critical AD susceptibility genes (e.g., \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eLOXL3\u003c/em\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e and \u003cem\u003eSMAD3\u003c/em\u003e) in AD patients compared to healthy controls. The identified m\u003csup\u003e6\u003c/sup\u003eA modification might regulate critical AD-related pathways such as focal adhesion, ECM-receptor interaction signaling pathway, cardiomyopathy and cardiac muscle tissue development and morphogenesis. The findings provided new insights into the pathogenesis of AD by m\u003csup\u003e6\u003c/sup\u003eA methylation. Further research on m\u003csup\u003e6\u003c/sup\u003eA target genes in AD might contribute to the clinical application of AD molecular targeted therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll aorta samples used in this study were collected from the department of Cardiac Surgery, Fujian Medical University Union Hospital. A total of 142 participants have been enrolled in this study. Diseased aortic tissue samples were obtained from AD patients, and control aortic tissue samples were obtained from recipients of heart transplants or lung donors. The aorta samples were obtained safely during surgery. Tissues were immediately snap-frozen and kept in liquid nitrogen. The cause of the donor’s death was cerebrovascular or traffic accident. All donors had no history of cardiovascular diseases or active infection at the time of transplantation. Participants with hereditary aortic diseases, connective tissue disorders, cancers, drug history, infections, or any other immune-related diseases were excluded. 9 AD cases and nine controls for MeRIP-seq and RNA-seq (The clinical characteristics of the two groups were shown in Table 1), and 10 AD cases and 10 controls for MeRIP-qPCR validation were selected from the 142 participants. This study has been approved by the Ethics Committee of Fujian Medical University Union Hospital. Written informed consent was collected from the participant or their families, conforming to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA from the aorta was extracted using TRIzol reagent (Invitrogen Corporation, CA, USA) following the manufacturer’s instructions. After removing the cytoplasmic ribosomal RNA and mitochondrial rRNA from the total RNA by Ribo-Zero rRNA Removal Kit (MRZG12324; Illumina, CA, USA), mRNAs and lncRNAs were fragmented to 100-200 nt fragments using fragmentation buffer (Illumina). For RNA-seq, 10 ng RNA fragment was used to constructed strand-specific RNA library in accordance with a dUTP method instead of dTTP to label the second-strand cDNA during synthesis. The quality of the library was done using a Bioptic Qsep100 analyzer. RNA-seq was performed on Illumina HiSeq 4000 sequencer (Illumina). The cut-adapt software (v1.9.3) was used to remove the 3’ adaptor trimming and low-quality reads, and then the clean high-quality trimmed reads were aligned to the reference genome (hg38) with HISAT2 software (v2.0.4)\u003ca href=\"#_ENREF_31\" title=\"Kim, 2015 #137\"\u003e\u003csup\u003e31\u003c/sup\u003e\u003c/a\u003e. The expression profiles were obtained with the cuffdiff software (v2.2.1) and differentially expressed genes (DEGs) were assessed. The DEGs were determined with the threshold of |log2FC| \u0026gt; 1 and FDR \u0026lt; 0.05. The genomic assembly version used in this study was GRCh38.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeRIP-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA fragments were incubated with anti-m6A polyclonal antibody (202003; Synaptic Systems, Goettingen, Germany) in immunoprecipitation (IP) buffer for 2 h at 4◦C, and subsequently, the mixture was immunoprecipitated by incubation with protein-A beads (Thermo Fisher Scientific, MA, USA) at 4◦C for an additional 2 h. The purified immunoprecipitated RNA and input RNA were collected for the generation of an RNA-seq library with NEBNext R UltraTM RNA Library Prep Kit (New England Biolabs, MA, USA). The quality of the library was done using a Bioptic Qsep100 analyzer. Then, paired-end library sequencing was performed on Illumina HiSeq 4000 sequencer (Illumina). The quality of the paired-end reads was analyzed using fastQC (v0.11.8).\u003c/p\u003e\n\u003cp\u003eAfter the 3’ adaptor trimming and low-quality reads removing by cut-adapt software (v1.9.3), the clean high-quality trimmed reads were aligned to the reference genome (hg38) with HISAT2 software (v2.0.4)\u003ca href=\"#_ENREF_31\" title=\"Kim, 2015 #137\"\u003e\u003csup\u003e31\u003c/sup\u003e\u003c/a\u003e. Methylated sites on RNAs (peaks) were detected by MACS software and diffReps software was used to detect differentially methylated lncRNAs/mRNAs. Overlapped exons of mRNA and lncRNA were identified and chosen by home-made scripts. For differentially methylated m6A peaks, HOMER was used for de novo motif prediction. The input RNA-seq and MeRIP-seq data were analyzed jointly to screen genes with significant differences in both RNA m6A methylation and abundance between the two groups (|log2FC| \u0026gt; 1, FDR \u0026lt; 0.05). Differential methylation sites were identified using the exomePeak R package (v2.13.2) with default parameters. The DEGs were assessed using the DESeq2 R package (v1.10.1) between the AD group and the normal individuals. m6A peak visualization of representative genes was performed by IGV software.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, molecular pathways of differentially methylated genes (DMGs) with differential expression were determined by gene set enrichment analysis. Gene symbol and log2(FC) were sorted in descending order to create the ranking list. Then, the ranking list was used to calculate normalized enrichment scores and p-value. The C2 gene set ‘c2.cp.v7.1.symbols.gmt’ from the Molecular Signature Database version 7.0 was selected as the reference gene set. The number of permutations was set to 1000, and a gene set with nominal p-value \u0026lt; 0.05 was treated as significantly enriched. The data supporting the findings of the present study are available from corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeRIP-qPCR validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscripts revealed by the integration analysis of DEGs and DMGs were tested by reverse transcription-qPCR in 10 AD cases and 10 controls. The RNA sample was fragmented (300 nt) after incubation with fragmentation buffer at 94◦C for 4 min. A total of 5% of the fragmented RNA was saved as the input control. The procedure of m6A-IP sample preparation was the same as that of MeRIP-seq. Both input control and m6A-IP samples were subjected to reverse transcription-qPCR using Universal SYBR qPCR Master Mix (Q711-02/03, Vazyme Bio).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry Staining validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe obtained AD and control aortic tissues were paraffin-embedded and sectioned into 3µm slices. After baking the slices for 2 hours (60 °C), they were then subjected to gradient immersion in xylene and ethanol. The slices were processed using an Immunohistochemistry Kit (UItraSensitive SP mouse/rabbit; MXB) and incubated overnight with corresponding primary antibodies (4°C). The next day, DAB staining (DAB-0031; MXB) was performed, and the results were observed under the Nikon fluorescence microscope (Nikon ECLIPSE Ni). Image-J (Version.1.53a) was used for result analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison between the two groups was performed using the GraphPad software with an unpaired Student’s t-test.\u0026nbsp;∗P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eADs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAortic dissections\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\u003eMeRIP-seq\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003em\u003csup\u003e6\u003c/sup\u003eA specific RNA immunoprecipitation sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDEGs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDMGs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially methylated genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDMMSs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifferentially methylated m\u003csup\u003e6\u003c/sup\u003eA sites\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThe studies involving human participants were reviewed and approved by the Ethics Committee of the Fujian Medical University Union Hospital (IACUC FJMU 2023-0053). This study complied with The Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent to participate in this study was provided by the participants\u0026rsquo; legal guardian/next of kin. This study did not involve any animal experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China [U2005202], \u0026nbsp;[82370470] and [82241209], and the Fujian Provincial Special Reserve Talents Fund [2021-25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eTC.Chai and LK.Ma contributed to the conceptualization. TC.Chai and LK.Ma contributed to data curation and writing of the original draft. LK.Ma and JK.Li contributed to the formal analysis. RM.Xie and J.He contributed to the investigation. TC.Chai, LK.Ma, LL.Tang contributed to the methodology and the visualization. ZH.Qiu and LW.Chen contributed to the writing, review, and editing. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSampson UK, Norman PE, Fowkes FG, Aboyans V, Yanna S, Harrell FE, Jr., Forouzanfar MH, Naghavi M, Denenberg JO, McDermott MM, Criqui MH, Mensah GA, Ezzati M, Murray C. 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Aortic aneurysms and dissections series: Part ii: Dynamic signaling responses in aortic aneurysms and dissections. \u003cem\u003eArterioscler Thromb Vasc Biol\u003c/em\u003e. 2020;40:e78-e86\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.\u0026nbsp;Clinical Characteristics of\u0026nbsp;Matched\u0026nbsp;Patients With Aortic Dissection and Controls.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAD (n = 9,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e% / mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCon (n = 9,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e% / mean\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e / t\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.78\u0026plusmn;15.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.78\u0026plusmn;15.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.6\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.05\u0026plusmn;2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandford\u0026nbsp;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiameter of ascending aorta, cm (tracheal carina level)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.57\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.28\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiameter of descending aorta , cm (tracheal carina level)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.93\u0026plusmn;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.58\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEF, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.2\u0026plusmn;6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.9\u0026plusmn;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eBlood\u0026nbsp;lipids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.97\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.85\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL\u0026mdash;C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.07\u0026plusmn;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.51\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL\u0026mdash;C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.09\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPOA, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.52\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAPOB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.89\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePatient history\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarfan syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCAD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.97\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.84\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 General numbers of differentially methylated m\u003csup\u003e6\u003c/sup\u003eA peaks and associated genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.589928057553957%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.158273381294965%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUpmethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.697841726618705%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.51798561151079%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDownmethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.8776978417266186%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.158273381294965%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.2217659137577%\" valign=\"top\"\u003e\n \u003cp\u003ePeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.784394250513348%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.080082135523614%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.659137577002053%\" valign=\"top\"\u003e\n \u003cp\u003ePeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.757700205338809%\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.2854209445585214%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.605749486652977%\" valign=\"top\"\u003e\n \u003cp\u003ePeak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.605749486652977%\" valign=\"top\"\u003e\n \u003cp\u003eGene*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.567324955116696%\" valign=\"top\"\u003e\n \u003cp\u003emRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.183123877917415%\" valign=\"top\"\u003e\n \u003cp\u003e3,318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.926391382405745%\" valign=\"top\"\u003e\n \u003cp\u003e2,642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6929982046678638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.439856373429084%\" valign=\"top\"\u003e\n \u003cp\u003e1,573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.028725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e1,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.872531418312388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e4,891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e3,689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.567324955116696%\" valign=\"top\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.183123877917415%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.926391382405745%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6929982046678638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.439856373429084%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.028725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.872531418312388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.567324955116696%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.183123877917415%\" valign=\"top\"\u003e\n \u003cp\u003e3,381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.926391382405745%\" valign=\"top\"\u003e\n \u003cp\u003e2,693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.6929982046678638%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.439856373429084%\" valign=\"top\"\u003e\n \u003cp\u003e1,607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.028725314183124%\" valign=\"top\"\u003e\n \u003cp\u003e1,387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.872531418312388%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e4,988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.644524236983843%\" valign=\"top\"\u003e\n \u003cp\u003e3,768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Some genes contain both upmethylated and downmethylated peaks, therefore the total number of genes do not equals to the number of upmethylated genes plus the number of downmethylated genes.\u003c/p\u003e\n\u003cp\u003eTable 3 Top m\u003csup\u003e6\u003c/sup\u003eA peaks associated with AD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003eCHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003eStart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003eEnd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eRegulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePCDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e141853769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e141853950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e448.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e9.55E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.95E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCNOT3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e54154034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e54154244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e424.61\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.95E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.40E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCHI3L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e203178990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e203179230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e349.71\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.00E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e6.31E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRORC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e151811347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e151811527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e317.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e6.76E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e7.08E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eWWC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e183317005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e183317186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e224.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e6.31E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e3.16E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTMEM268\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e114645732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e114645883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e221.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.00E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.45E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLRRC8C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e89714913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e89715094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e186.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.58E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.51E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFZD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e91270608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e91270788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e144.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.45E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e6.03E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePI16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e36963097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e36963397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e143.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e8.51E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.29E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eARHGAP11A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e32615678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e32616038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e134.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.00E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eKDM2A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e67256196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e67256347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e133.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e7.94E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.58E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eBLNK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e96223857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e96227457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e133.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e3.16E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e4.90E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSTK32A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e147387253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e147387433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e129.79\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.24E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e3.63E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eC10orf128\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e49166612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e49166792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e113.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.20E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e52452143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e52452674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e5\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e113.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.95E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e1.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.051146384479718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSOCS6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.701940035273369%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e70327207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.403880070546737%\" valign=\"top\"\u003e\n \u003cp\u003e70327388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e110.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e2.24E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.46384479717813%\" valign=\"top\"\u003e\n \u003cp\u003e8.91E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.405643738977073%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4 Significant differentially methylated m\u003csup\u003e6\u003c/sup\u003eA peaks in known AD susceptibility genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003eCHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003eStart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003eEnd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eRegulation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e218434278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e218442044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e5.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLOXL3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e74532652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e74533549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026apos;-UTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e4.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.20E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.62E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIL1R1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e102164924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e102165270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e2.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.10E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.91E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCOL3A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e188988619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e189003441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e2.38\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e7.94E-93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e5.01E-90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e48474328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e48483880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e2.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.45E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e5.89E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFBN1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e48499022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e48505066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e5.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.26E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e2.51E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n 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\u003cp\u003e5.01E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e5.37E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGFB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e41332258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e41342100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e3.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e1.82E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e2.29E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.456140350877194%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMAPK1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.017543859649122%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e21769164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\" valign=\"top\"\u003e\n \u003cp\u003e21788787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eCDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e9.33E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.75438596491228%\" valign=\"top\"\u003e\n \u003cp\u003e2.45E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.701754385964913%\" valign=\"top\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 Validation of differentially methylated transcripts\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.57142857142857%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDiscovery stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.23809523809524%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eValidation stage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.74712643678161%\" valign=\"top\"\u003e\n \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e(FC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.885057471264368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\" valign=\"top\"\u003e\n \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e(FC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.839080459770116%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eACTC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.19E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e1.87E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCOL1A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e1.58E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e3.97E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCOX4I1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.95E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e7.26E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.00E-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e5.23E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCREB3L1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.19E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.18E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eF2R\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e4.17E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.56E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNEAT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e1.00E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e4.90E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePCOLCE2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e3.98E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e9.03E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePKD2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.51E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e3.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e3.48E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRYR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.51E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e9.52E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSLC2A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e1.78E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e3.29E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTM4SF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e1.00E-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e6.07E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTNNC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e-6.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e2.95E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e-5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e4.44E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eVEGFA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e6.31E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0476190476190474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.095238095238095%\" valign=\"top\"\u003e\n \u003cp\u003e3.94E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFisher\u0026apos;s exact test, t-test for continuous variables. Age and gender were used as matching for 1:1 case-control matching study. \u003cem\u003eP\u003c/em\u003e values are for comparisons between controls (Con) and patients with aortic dissection（AD). SD, standard deviation; BMI, body mass index; TC, total cholesterol; TG, triglyceride; HDL\u0026mdash;C, high density lipoprotein cholesterol; LDL\u0026mdash;C, low density lipoprotein cholesterol; APOA, apolipoprotein A; and APOB, apolipoprotein B; EF, ejection fraction; CAD, coronary artery disease; HbA1c, hemoglobin A1c.\u003c/p\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":"Aortic dissection, N6-methyladenosine modification, Focal adhesion, Extracellular matrix","lastPublishedDoi":"10.21203/rs.3.rs-3972169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3972169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) plays important roles in many biological processes such as gene expression control and may have functional roles in aortic dissection (AD). The aim of this study was to identify N6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) modification and the expressions of the m\u003csup\u003e6\u003c/sup\u003eA regulatory genes related to AD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Aortic tissue samples were obtained from AD and controls and MeRIP-seq and RNA-seq experiments were performed to detect m\u003csup\u003e6\u003c/sup\u003eA methylation and mRNA expression profiles, respectively. The differentially RNA methylation peaks were validated by MeRIP-PCR in AD cases and controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCompared with the control samples, 3,318 up methylated and 1,573 down methylated coding genes in AD were detected. These genes were mainly enriched in focal adhesion, ECM-receptor interaction and regulating the transcription such as splicing. Significant differentially methylated m\u003csup\u003e6\u003c/sup\u003eA sites in some well-known susceptibility genes for AD were identified, including \u003cem\u003eFBN1\u003c/em\u003e, \u003cem\u003eTGFB1\u003c/em\u003e, \u003cem\u003eTGFBR1/2\u003c/em\u003e, \u003cem\u003eLOXL3\u003c/em\u003e, \u003cem\u003eCOL3A1\u003c/em\u003e, \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e and \u003cem\u003eMAPK1/3\u003c/em\u003e. A total of 651 differentially expressed genes, including 594 protein-coding genes (96 upregulated and 498 downregulated), and 57 lncRNAs (20 upregulated and37 downregulated) were identified. Integrated analysis of the data from MeRIP-seq and RNA-Seq identified 74 genes that changed significantly in both m\u003csup\u003e6\u003c/sup\u003eA level and mRNA abundance in AD cases compared with the controls. We observed the same m\u003csup\u003e6\u003c/sup\u003eA-level changes in 14 out of the 16 selected m\u003csup\u003e6\u003c/sup\u003eA methylated transcripts in the independent sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study identified m\u003csup\u003e6\u003c/sup\u003eA changes in critical AD susceptibility genes. The identified m\u003csup\u003e6\u003c/sup\u003eA modification may play a role in critical AD-related pathways, thereby regulating the pathogenesis of AD.\u003c/p\u003e","manuscriptTitle":"Transcriptome-wide identification of N6-methyladenosine modifications for aortic dissection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 18:14:17","doi":"10.21203/rs.3.rs-3972169/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":"353a5aae-ed77-4ba7-937d-47992b224d3a","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29127052,"name":"Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases/Vascular diseases/Aortic diseases"},{"id":29127053,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2024-04-29T05:13:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 18:14:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3972169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3972169","identity":"rs-3972169","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}