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Integrative DNA methylome and transcriptome analysis identify potential genes on the influence of dilated cardiomyopathy-associated heart failure

Clinical Epigenetics volume 17, Article number: 64 (2025) Cite this article

Abstract

Objective

Dilated cardiomyopathy (DCM)-associated heart failure (HF) presents a significant clinical challenge, underlying epigenetic mechanisms remaining poorly understood. This study aims to investigate the interplay between DNA methylation and gene expression in the hearts of patients with DCM-associated HF (DCM-HF).

Methods

Atrial tissues were collected from five healthy donors and five heart transplant recipients suffering from heart failure due to DCM. We conducted RNA-sequencing (RNA-seq) to analyze mRNA expression profiles and performed whole-genome bisulfite sequencing (WGBS) to evaluate DNA methylation levels. Correlation analyses between RNA-seq and WGBS data were executed by integrating differentially expressed genes (DEGs) with genes associated with differentially methylated regions (DMRs) located in the promoter regions.

Results

The RNA-seq analysis identified a total of 681 DEGs, comprising 406 significantly downregulated genes and 275 upregulated genes in DCM-HF tissues, which were enriched in pathways related to cardiomyopathy. WGBS revealed 16,158 hypomethylated and 6,857 hypermethylated differentially methylated regions (DMRs), with 3,185 of these located in promoter regions. The integration of promoter-hypomethylated and hypermethylated DMRs-related genes (DMGs) with DEGs resulted in the identification of 46 hub genes associated with cardiac development and function. Protein–protein interaction and disease association analyses highlighted five key genes—NPPA, NPPB, ACTN2, NEBL, and MYO18B-that exhibited promoter hypomethylation and increased expression, potentially linked to the activity of transcription factors such as HIF1A and KLF4.

Conclusions

These findings suggest that the epigenetic dysregulation of cardiac stress-response and structural genes contributes to the pathogenesis of DCM-HF. Furthermore, the detection of promoter methylation levels in these loci may offer new opportunities for developing diagnostic tools and therapeutic strategies for DCM-HF management.

Introduction

Dilated cardiomyopathy (DCM) is a common cause of heart failure characterized by left ventricular dilation and impaired systolic function, which leads to significant morbidity and mortality [1,2,3]. Despite advances in medical and surgical treatments, the underlying molecular mechanisms driving DCM-associated heart failure (DCM-HF) remain inadequately understood. DNA methylation, an important epigenetic modification, involves the addition of a methyl (CH3) group to the cytosine base of DNA, primarily in cytosine-guanine dinucleotides (CpGs) [4]. This modification dynamically regulates gene expression, and its potentially reversible nature in response to biological and environmental factors makes it a valuable indicator for predicting disease risk and prognosis of outcomes, including cancer, obesity, and aging [5,6,7].

Emerging evidence has linked changes in DNA methylation to the risk and outcomes of cardiovascular diseases (CVDs), including congenital heart defects [8], hypertension [9], myocardial infarction (MI) [10], stroke [11], and dilated cardiomyopathy (DCM) [12]. These associations may be attributed to the modulation of gene expression related to cardiac function, metabolism, and inflammation. For instance, differentially methylated regions (DMRs) at the ZBTB12 gene and LINE-1 elements in white blood cells have been associated with an increased risk of myocardial infarction (MI) [13]. Furthermore, aberrant DNA methylation has been shown to significantly alter the mRNA expression of LY75 and ADORA2A, which contribute to the development and progression of DCM [14].

Many studies have demonstrated that DNA methylation patterns can significantly influence the severity and prognosis of heart failure; however, the specific regulatory mechanisms involved remain inadequately understood [15,16,17]. It is widely acknowledged that DNA methylation in promoter regions modulates gene expression through various mechanisms, including the inhibition of transcription factor binding, alterations to chromatin structure, synergistic interactions with histone modifications, and effects on transcription initiation [18]. In this study, we performed RNA-seq and whole-genome bisulfite sequencing (WGBS) on atrial tissues obtained from healthy donors and heart transplant recipients to identify differentially expressed genes regulated by promoter methylation. These analyses aim to uncover potential biomarkers and therapeutic targets for dilated cardiomyopathy-associated heart failure (DCM-HF).

Methods

Human sample collection

Atrial tissue samples were collected from both recipients and donors at the Heart Transplant Center of Zhengzhou Seventh People’s Hospital (Zhengzhou Cardiovascular Hospital). This study received approval from the Medical Ethics Committee of Zhengzhou Seventh People’s Hospital (Approval No: 2020-012). Written informed consent detailing the research objectives involving human material was obtained from all patients or their families. The donors were male individuals who had experienced brain death due to cerebral hemorrhage or traffic accidents. The recipient hearts were sourced from males diagnosed with end-stage heart failure resulting from dilated cardiomyopathy (DCM), in accordance with the guidelines established by the European Society of Cardiology (ESC) [19, 20].

RNA-sequencing

Total RNA was extracted from atrial tissues using Trizol reagent (Invitrogen, USA). The integrity and concentration of the extracted RNA were assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). High-quality complementary DNA (cDNA) libraries were constructed and sequenced on Novaseq X (Illumina) to generate raw sequencing reads. The raw data were filtered using Fastp to remove adaptor sequences, unknown reads containing poly-N sequences, and low-quality reads, resulting in a set of clean reads. These clean reads were subsequently aligned to the human reference genome (hg38) using HISAT2 v2.0.5. Differential expression analysis was performed with DESeq2, applying thresholds of a false discovery rate (FDR) of a false discovery rate (FDR) < 0.05 and |log2 (fold change)|> 1 to identify significantly differentially expressed genes (DEGs). An online platform (https://www.bioinformatics.com.cn) was utilized for data analysis and visualization, encompassing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA).

Whole-genome bisulfite sequencing

DNA was extracted from tissue samples using the FastPure Tissue DNA Isolation Mini Kit (#DC112, Vazyme, China) following the manufacturer’s instruction. 100 ng of genomic DNA was combined with 0.5 ng of unmethylated lambda DNA and subsequently fragmented to a size range of 200–400 bp using an ultrasonic crusher (Covaris S220, USA). Following fragmentation, unmethylated cytosines were converted to uracils using the EZ DNA Methylation-Gold™ kit (Zymo Research). The constructed library, with an effective concentration exceeding 1.5 nM, was assessed using a Bioanalyzer (Agilent 5400, USA), quantified via Q-PCR, and sequenced on the Novaseq X platform (Illumina). The quality of the raw data was evaluated using FastQC (v0.11.8), and the data were further filtered to obtain clean data through fastp software (v0.23.1). This clean data was subsequently aligned to the reference human genome (hg38) using Bismark software (v0.24.0). Binary tests were performed to identify methylation sites by analyzing the number of methylated cytosines (mC), the total number of cytosines (the sum of mC and unmethylated cytosines, umC), and the non-conversion rate (r). A site was classified as methylated when the false discovery rate (FDR) was less than 0.05. Differentially methylated regions (DMRs) were subsequently analyzed using DSS software (v2.12.0).

Protein–protein interaction analysis

The protein–protein interaction network was constructed utilizing the STRING database (version 12.0, https://string-db.org). A gene list was uploaded to the STRING platform, with Homo sapiens designated as the target organism. Interactions were predicted based on a combination of curated databases, experimental evidence, co-expression data, and text mining approaches. Functional enrichment analysis of hub genes was conducted using the STRING database, filtering for significance in Gene Ontology (GO) terms and Disease-gene Associations (DISEASES) with a false discovery rate (FDR) threshold of less than 0.05.

Quantitative real-time PCR (RT-qPCR)

Total RNA was extracted from atrial tissues of various groups using TRIzol (Invitrogen, USA) in accordance with the manufacturer’s instructions. The quality and concentration of the RNA were assessed using a Nanodrop spectrophotometer (Thermo Fisher Scientific, USA), and cDNA was synthesized using the RevertAid RT kit (Thermo Fisher Scientific, USA). Real-time PCR was conducted on the Applied Biosystems StepOnePlus qPCR system utilizing SYBR qPCR Master Mix (#Q712, Vazyme, China). The relative mRNA expression was quantified using the 2 − ΔΔCt method, normalizing to actin-beta (ACTB), with the primers shown in Table S1.

Statistical analysis

Data were presented as the means ± SEM, and two-tailed Student’s t-test was used to analyze statistical differences between two groups. P < 0.05 was considered statistically significant.

Results

Clinical characteristics of recipients and donors

Recipients of heart transplant surgery due to end-stage heart failure, primarily resulting from dilated cardiomyopathy (DCM), were identified based on their medical history, cardiac ultrasound, and serum biomarkers including N-terminal pro-B-type natriuretic peptide (NT-proBNP). Analysis of clinical data revealed no significant differences in age, height, and weight between the recipients and healthy donors (Fig. 1A). However, echocardiographic assessments demonstrated that the left ventricular ejection fraction (LVEF) in recipients was significantly lower than that observed in donors, with all recorded values falling below 40%. Additionally, left ventricular end-diastolic internal dimensions (LVIDd) were significantly increased in recipients (Fig. 1B). Further analysis using Masson’s trichrome staining on atrial tissues revealed pronounced myocardial fibrosis in the hearts of recipients (Fig. 1C). Collectively, these findings suggest that patients with DCM-HF exhibit significant pathological alterations and cardiac dysfunction. In preparation for transcriptomic and DNA methylation analyses, we obtained direct samples of left atrial tissue without appendage, which are expected to provide a more accurate representation of the overall state of the entire atrium. In this study, samples from five recipient hearts and five donor hearts were collected, and the detailed research process is shown in Fig. 2A.

Fig. 1
figure 1

Cardiac function and pathological manifestations in DCM-HF. A Basic clinical information of the patients. B Echocardiographic functional assessment of hearts from healthy donors and heart transplant recipients with dilated cardiomyopathy-associated heart failure (DCM), including ejection fraction (EF) and left ventricular end-diastolic dimension (LVIDd). Data are presented as means ± SEM (n = 5 samples per group), **P < 0.01; ***P < 0.001. C Masson’s trichrome staining of atrial tissues from donor and recipient hearts. Scale bar = 100 μm

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Fig. 2
figure 2

RNA-sequencing analysis of atrial tissues from the Donor and Recipient groups. A General workflow used in this study. B Principal Component Analysis (PCA) of mRNA expression profiles in the hearts of donors and recipients. C Volcano plots depicting the distribution of differentially expressed genes (DEGs). D Heatmaps of 681 DEGs with |log2FC|> 1 and FDR < 0.05. (E) Gene Set Enrichment Analysis (GSEA) of expressed genes in Recipient versus Donor. F-G Kyoto Encyclopedia of Genes and Genomes (KEGG) (F, upper) and Gene Ontology (GO)-Biological Process (BP) enrichment (G, lower) of the significantly up- and downregulated DEGs

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Gene expression profile of DCM-HF by RNA-seq

Principal Component Analysis (PCA) and differential gene expression analysis reveal significant difference in expression patterns between recipients and donors, identifying a total of 681 differentially expressed genes (DEGs) (Fig. 2B and C, Table S2). Among these, 275 genes are significantly upregulated while 406 genes are significantly downregulated (Fig. 2D) in the recipients. To further elucidate the functional implications of these DEGs and their impact on the heart, we conducted enrichment analyses utilizing Gene Set Enrichment Analysis (GSEA), Gene Ontology-Biological Process (GO-BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) methodologies. The results indicate that the upregulated DEGs are predominantly associated with cardiac development, differentiation, structural integrity, and contractile function, and are implicated in conditions such as hypertrophic cardiomyopathy and dilated cardiomyopathy. In contrast, the downregulated DEGs are linked to the proliferation, activation, and interaction of immune cells, with associations to inflammatory pathways including TNF-α signaling and immune responses (Fig. 2E–G). To identify key genes among the candidate DEGs, we employed the String online tool to construct an interaction network (Fig. 3A). Through Disease-gene Association (DISEASES) analysis, we identified 12 pivotal genes associated with various cardiovascular diseases, including dilated cardiomyopathy, heart conduction disease, and artery diseases. These key genes include DMD, TTN, MYH7, SGCD, TNNI3, ACTN2, CDH2, MYPN, FLNC, ANKRD1, RBM20, and MYOM2. Notably, the expression of these genes is markedly elevated in recipients (Fig. 3B and C). These findings indicate that the expression patterns in recipient hearts have been altered, suggesting that the upregulated DEGs may contribute to the remodeling and dysfunction of cardiomyocytes associated with DCM-HF.

Fig. 3
figure 3

Screening of candidate differentially expressed genes (DEGs) implicated in the pathogenesis of heart failure. A and B The Protein–protein Interaction (PPI) network (A, upper) and the Disease-gene Associations (DISEASES) prediction (B, lower) among the DEGs constructed using the STRING online tool. C Heatmaps of 12 DEGs associated with heart diseases

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DNA methylation landscape of DCM-HF by WGBS

Whole-genome bisulfite sequencing (WGBS) was employed to construct a comprehensive genome-wide DNA methylation landscape [21]. The results indicate that the average DNA methylation level in the recipient group is lower than that observed in the donor group (Fig. 4A). A comparative analysis of methylation levels across various genomic functional regions revealed an increase in DNA methylation within the promoter region of the recipient group, while methylation levels in the 5’ untranslated region (UTR) and 3’ UTR showed a decrease (Fig. 4B). Furthermore, while the methylation levels within the gene body remained largely unchanged, those located 2 kb upstream of the transcription start site (TSS) and 2 kb downstream of the transcription termination site (TES) exhibited an increase (Fig. 4C). In total, 23,015 differentially methylated regions (DMRs) were identified, comprising 16,158 hypomethylated DMRs and 6,857 hypermethylated DMRs (Fig. 4D). These DMRs are distributed across various genomic functional regions, including promoter regions, exon regions, intron regions, CpG islands (CGIs), CGI shores, and repeat regions (Fig. 4E). Notably, these DMRs encompass 3,630 genes and are distributed across all chromosomes (Fig. 4F). Consistent with transcriptome results, GO-BP and KEGG enrichment analyses revealed that the differentially methylated region-related genes (DMGs) predominantly influence biological processes associated with cardiac development and function (Fig. 4G). Additionally, these genes are implicated in conditions such as hypertrophic cardiomyopathy, dilated cardiomyopathy, and cell adhesion, as well as in pathways related to TGF-β and other signaling mechanisms (Fig. 4H). Collectively, these findings suggest that DNA methylation levels in the hearts of recipients are altered, with hypomethylated DMGs potentially linked to DCM-HF.

Fig. 4
figure 4

Whole-Genome Bisulfite Sequencing (WGBS) analysis of atrial tissues from the Donor and Recipient groups. A Violin plots showing the global DNA methylation levels of CpG sites in recipient hearts compared to donors. (n = 5 samples per group, ***P < 0.001). B Methylation levels across various genomic functional regions in Recipient versus Donor. C Methylation patterns of the gene body, 2 kb upstream of the transcription start site (TSS) and 2 kb downstream of the transcription end site (TES) in each group. D The ratio of hypermethylated and hypomethylated Differentially Methylated Regions (DMRs) in Recipient versus Donor. E Proportions of DMRs located in different genomic features. F The distribution of DMRs across the whole-genome. G and H GO-BP enrichment (G, left) and KEGG analysis (H, right) of DMR-related Genes (DMGs)

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Identification of candidate DEGs regulated by DNA methylation in the promoter region

Considering the negative regulatory effects of DNA methylation in the promoter region on gene expression, we conducted an integrative analysis of genes associated with DMRs in this region and DEGs. Among the identified DMRs, 3,185 were located in the promoter region (Table S3), influencing 1,614 genes involved in various pathways, including the Wnt signaling pathway, Notch signaling pathway, cGMP-PKG signaling pathway, regulation of the actin cytoskeleton, and axon guidance (Fig. 5A and B). We subsequently combined hypermethylated DMGs in the promoter region with downregulated DEGs, identifying 13 intersecting genes. Additionally, we combined hypomethylated DMGs in the promoter region with upregulated DEGs, resulting in the identification of 33 intersecting genes (Fig. 5C and D). By analyzing the frequency of occurrence of these genes, we ranked them and found that the top ten genes appeared most frequently and were most closely related to cardiac-related biological processes, including NPPA, NPPB, ACTN2, NEBL, MYO18B, EGR1, RORA, CYBA, SHOX2, and SCX (Fig. 5E and F). Through STRING protein–protein interaction and Gene Ontology biological process (GO-BP) analyses of 46 genes, we identified seven interacting genes: MYO18B, ACTN2, NPPA, NPPB, NEBL, SHOX2, and IRX3. Among these, MYO18B, ACTN2, NPPA, NPPB, and NEBL are closely associated with cardiac-related biological processes, such as striated muscle cell differentiation, myofibril assembly, and cGMP-mediated signaling. Additionally, transcriptome analysis indicated that the expression levels of SHOX2 and IRX3 were low (FPKM < 10), leading to their exclusion from further analysis. Notably, the retained five genes exhibit reduced DNA methylation in the promoter region, which correlates with increased gene expression (Fig. 5G and H). This observation implies that DEGs associated with DMRs in the promoter region may play a regulatory role in the pathological processes associated with DCM-HF.

Fig. 5
figure 5

Correlation studies of DMRs-related Genes (DMGs) and Differentially Expressed Genes (DEGs). A Heatmaps showing DNA methylation levels of DMRs in promoter regions of Recipient and Donor group. B KEGG enrichment of DMGs in promoter regions. C Venn diagrams displaying the overlap between DEGs and DMGs in promoter regions. D Heatmap depicting the mRNA expression levels of 46 overlapped genes. E GO-BP enrichment of the overlapped genes. (F) 10 screened candidate key genes involved in enriched GO-BP terms. G and H Protein–protein Interaction (PPI) network (G, left) and the GO-BP enrichment (H, right) for the overlapped genes

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Validation of 5 key genes’ expression in the hearts

To verify the expression of screened key genes in both the receptor and donor hearts, we initially utilized a human heart single cell online analysis tool (Heart Cell Atlas v2, https://www.heartcellatlas.org/)[22] to evaluate the expression of five specific genes across various heart subsets. Notably, these genes are predominantly expressed in cardiomyocytes, and the methylation levels of CpG sites in the promoter region of the recipient group are significantly lower than those in the donor group (Fig. 6A and B). Correspondingly, the mRNA expression levels in the recipient group are significantly higher than those in the donor group (Fig. 6C). To investigate transcription factors that may influence the methylation of the promoter region regulating gene expression pathways, we conducted transcription factor enrichment analyses of the promoter regions of these five genes using HOMER (v5.1.7), identifying the top 20 transcription factors (Fig. 6D). We subsequently refined this list to four candidate transcription factors based on their expression in the heart and the presence of binding motifs containing CpG sequences in the genomic DNA: PROX1, MXI1, KLF4, and HIF1A (Fig. 6E). These findings suggest that the reduction in DNA methylation levels in the promoter region may activate the expression of genes such as NPPA and NPPB by modulating the interactions of transcription factors like KLF4 and HIF1A, thereby contributing to the regulatory network associated with DCM-HF.

Fig. 6
figure 6

Validation of candidate genes associated with heart failure. A Feature plots of expression distribution for selected genes through Single cell RNA-sequencing (ScRNA-seq) analysis of the human heart cells. B Methylation levels in the promoter regions of the candidate genes. C RT-qPCR validation of the mRNA expression levels in Recipient and Donor groups. D Top20 identified transcription factors within the promoter regions of candidate genes using HOMER. E Candidate transcription factors associated with DCM-HF

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Disscusion

Multi-omics analysis provides a comprehensive perspective on biological information, significantly enhancing our understanding of the complexity and dynamics of diseases from various angles. This approach elucidates the biomarker and molecular foundations of human diseases [23,24,25,26]. Typically, this analytical process involves genomic sequencing techniques, such as whole-genome sequencing, whole exome sequencing, and copy number variation sequencing, which clarify the origins of genetic diseases, including single nucleotide polymorphisms (SNPs), segmental deletions, and duplications. Additionally, epigenomic sequencing—which encompasses DNA methylation, RNA methylation, and chromatin accessibility—is employed to elucidate the pathogenesis of these diseases. Furthermore, expression profiling, including transcriptomics, proteomics, and metabolomics, is utilized to analyze downstream therapeutic targets associated with these conditions. This study integrates DNA methylation and RNA-sequencing to provide a comprehensive investigation of the epigenetic and transcriptomic changes related to dilated cardiomyopathy-related heart failure (DCM-HF). It offers essential insights into the relationship between DNA methylation in promoter regions and the resulting alterations in gene expression. By combining RNA-sequencing and whole-genome bisulfite sequencing analyses of atrial tissues from both DCM-HF patients and healthy donors, we identified 681 differentially expressed genes (DEGs) and 23,015 differentially methylated regions (DMRs). Among these, a subset of 46 hub genes exhibited coordinated changes in both methylation and expression. Notably, five genes—NPPA, NPPB, ACTN2, NEBL, and MYO18B—were identified as key contributors, demonstrating promoter hypomethylation alongside increased expression. These results underscore the significance of epigenetic dysregulation in cardiac stress-response pathways and structural remodeling, thereby providing new insights into the molecular mechanisms that drive the progression of DCM-HF.

Changes in transcriptome and DNA methylation associated with DCM-HF

Among the 681 genes identified as significantly DEGs through transcriptome analysis, 406 were found to be upregulated and closely associated with pathways related to dilated cardiomyopathy. Conversely, 275 downregulated genes were linked to immune status, which plays a crucial regulatory role in heart failure (HF). Throughout the progression of heart failure, multiple immune cells within the heart-including macrophages, B cells, T cells, regulatory T cells, dendritic cells, natural killer cells, neutrophils, and mast cells-become activated. The activation of these immune cells may lead to an exacerbated inflammatory response, worsening myocardial injury and deteriorating cardiac function [27]. In contrast, specific immune markers, such as lymphocytes and the lymphocyte-to-monocyte ratio (LMR), are negatively correlated with the incidence of heart failure [28]. Moreover, lymphatic endothelial cells are activated following myocardial infarction, effectively creating an immunosuppressive microenvironment in the infarcted area by enhancing immunosuppressive functions, which suppress the proliferation of autoreactive CD8+ T cells and promote the expansion of reparative macrophages [29]. This immunosuppressive microenvironment helps mitigate further cardiac damage from autoimmune responses and facilitates cardiac repair. In our transcriptomic expression profiles, we did not observe an increase in the expression of classic pro-inflammatory factors such as TNF, IL1B, IL6, CXCL8, and IL17. This lack of increase can primarily be attributed to the FPKM values of these genes, which were all below 1, resulting in their exclusion from the analysis. Conversely, we noted a significant downregulation in the expression of genes associated with the proliferation and function of lymphocytes, including T cells, neutrophils, and monocytes, suggesting a potential presence of immune suppression. All patients in our study were in the late stages of chronic heart failure, leading us to speculate that the immune status in the hearts of heart failure patients may be influenced by the acute or chronic nature and duration of their condition. Furthermore, variations in omics technologies and donor characteristics may also impact the results of our analyses. The most significantly altered DEGs include transcription factors such as HAND1 and FOSB, inflammatory regulatory proteins like S100A9 and SAA1, as well as the muscle contraction protein NEB. These factors may play a crucial role in the pathological changes associated with DCM-HF. For instance, cardiomyocyte-specific overexpression of HAND1 can drive the pathogenesis of DCM through the genome-wide enhancer-promoter connectome, which is linked to decrease cardiomyocyte contractility and impaired calcium handling capacity, ultimately compromising cardiac function [30]. Moreover, the reduced expression of SAA1 may pose certain risks, including a weakened immune response and diminished cardiovascular protection [31]. Additionally, we compared the marker genes identified in the transcriptome with the plasma markers of heart failure documented in the literature [32, 33]. Our findings indicate that markers such as ANP, BNP, TNNT2, TNNI3, LTBP4, and FSTL3 align with existing reports. Notably, our examination of classic intracellular signaling pathways [34] revealed a significant activation of key genes in the PI3K/AKT pathway within the recipients. Although the fold changes in the expression of these genes were not pronounced, this finding suggests that intracellular signaling pathways, such as PI3K/AKT, may play a potential role in the pathogenesis of DCM-HF.

In studies of DNA methylation, DMRs-related genes (DMGs) are implicated in various biological processes and signaling pathways associated with DCM-HF. The DMGs with most significant alterations in promoter region include the transcription factors PBX2 and HOXA3, the RNA-binding protein RBFOX1, the cell adhesion protein PKP3, the protein kinase PHLDB1, the GTPase-activating protein AGAP2, and the metabolism and immune regulatory protein METRNL. These alterations have been reported to potentially contribute to the progression of heart failure through mechanisms such as extracellular matrix remodeling, immune regulation, and transcriptional regulation. Furthermore, the identification of overlapping genes between hyper-DMGs and hypo-DMGs in the promoter region has revealed a total of 73 genes. The expression of these genes may be regulated by various transcription factors, and maintaining their expression homeostasis could be implicated in the pathological mechanisms underlying dilated cardiomyopathy (DCM). KEGG enrichment analysis indicates that these genes are associated with platelet activation and the relaxin signaling pathway, including COL1A1, GNAS, and LCK, among others. For instance, the overexpression of COL1A1 can lead to increased collagen deposition within myocardial tissue, resulting in myocardial stiffness and thickening, which adversely affects the heart’s normal function [35]. The GNAS protein plays a critical role in regulating cardiomyocyte contractility and heart rate, thereby influencing the heart’s pumping function and electrophysiological properties; its aberrant expression increases the risk of atherosclerosis and arrhythmia [36]. Additionally, LCK can interact with PKCε and undergo phosphorylation by it, enhancing the myocardium’s tolerance to ischemic injury [37].

Integration of epigenetic and transcriptomic dysregulation

Our observation of widespread DNA hypomethylation in hearts affected by dilated cardiomyopathy-associated heart failure (DCM-HF) aligns with previous studies linking global methylation loss to various cardiovascular pathologies, including heart failure and myocardial infarction. The preferential hypomethylation of promoter regions in critical cardiac genes elucidates the mechanisms underlying the well-documented alterations associated with heart failure. Natriuretic peptides, particularly NPPA (translated to ANP) and NPPB (translated to BNP), have emerged as valuable predictors of stroke and heart failure, including NT-proBNP [38,39,40]. As a specific biomarker for heart failure, the elevated expression of NT-proBNP may correlate with reduced levels of DNA methylation in promoter regions. ACTN2 encodes an essential Z-disc protein critical for cross-linking actin filaments, and its dysregulation has been implicated in arrhythmias and contractile dysfunction [41]. The hypomethylation-driven overexpression of ACTN2 may initially stabilize sarcomeres but could eventually contribute to maladaptive hypertrophy. Similarly, NEBL, which anchors titin to actin, plays a pivotal role in diastolic function, and its altered expression may exacerbate ventricular stiffness [42]. MYO18B is crucial for maintaining the structural integrity of the myocardium in cardiomyocytes by regulating higher-order tissue organization and actin cross-linking within the sarcomere [43]. Dysregulation of MYO18B expression can lead to damage to the sarcomeric structure of the myocardium, subsequently impairing cardiac function and increasing the risk of cardiovascular disease. The prominence of these genes in protein–protein interaction networks further underscores their centrality in maintaining cardiomyocyte architecture, suggesting that their epigenetic dysregulation disrupts the delicate balance between compensatory remodeling and pathological decompensation.

The enrichment of transcription factors, including HIF1A and KLF4, in the promoter regions of hypomethylated genes indicates that hypoxia and metabolic stress are significant drivers of epigenetic reprogramming. HIF1A, a master regulator of hypoxic adaptation, is recognized for its ability to recruit DNA demethylases to specific genomic loci, which may explain the observed hypomethylation in stress-response genes [44]. Similarly, KLF4, which is essential in regulating cardiomyocyte differentiation and survival, may orchestrate methylation changes that influence the expression of target genes [45]. These interactions highlight the importance of investigating how environmental stressors, such as chronic ischemia and hemodynamic overload, dynamically affect the cardiac methylome in the context of dilated cardiomyopathy-associated heart failure (DCM-HF).

Smoking is recognized as one of the primary risk factors for cardiovascular diseases (CVD). Research indicates that smoking induces changes in DNA methylation and leads to abnormal expression of immune and inflammation-related genes, thereby increasing the prevalence of smoking-related diseases. Among these genes is the G-protein coupled receptor 15 (GPR15), which plays a critical role in regulating T-cell migration and immunity. Given that the male heart failure patients involved in our research exhibit varying degrees of smoking history, we assessed the expression and methylation levels of GPR15 alongside other reported smoking-related differentially expressed genes [46, 47]. Our findings indicate that these genes are largely not expressed in the heart. Future combined analysis of DNA methylation and transcriptome in the hearts of both smoking and non-smoking heart failure patients may provide more insights into the direct impact of smoking on DCM-HF.

Limitations and future directions

While this study provides valuable insights, several limitations merit consideration. Firstly, the reliance on atrial tissues, rather than ventricular tissues where dilated cardiomyopathy-associated failure (DCM-HF) primarily occur, may limit the generalizability of the findings. Secondly, the small cohort size (n = 5 per group) restricts statistical power and highlights the necessity for validation in larger, multi-center cohorts. Thirdly, the correlative nature of our analysis prevents the establishment of causality between methylation changes and gene expression; therefore, functional studies utilizing CRISPR-based methylation editing or demethylating agents are crucial to confirm these regulatory relationships. Lastly, longitudinal analyses are needed to determine whether these methylation signatures serve as drivers of disease progression or are merely secondary consequences of heart failure.

Translational perspectives

The reversible nature of DNA methylation positions it as an attractive therapeutic target. Hypomethylation of the promoters of NPPA, NPPB, ACTN2, NEBL, and MYO18B may serve as biomarkers for the early detection of heart failure (HF). Additionally, demethylation inhibitors and editing technologies that target maladaptive pathways have the potential to mitigate adverse remodeling [48]. Furthermore, the identification of KLF4 and HIF1A as upstream regulators opens avenues for therapies modulating transcription factor activity to restore epigenetic homeostasis.

Conclusion

In summary, our integrative multi-omics approach elucidates the crucial role of promoter methylation in regulating cardiac stress-response and structural genes in dilated cardiomyopathy -associated heart failure (DCM-HF). By establishing a link between epigenetic dysregulation and subsequent transcriptional and functional alterations, this study deepens our understanding of the pathogenesis of heart failure and identifies novel targets for diagnostic and therapeutic innovation. Future research should prioritize the functional validation of these epigenetic drivers and their translation into clinical applications for managing DCM-HF.

Data availability

The datasets used in the current study are available from the corresponding author on reasonable request.

References

  1. Giri P, Mukhopadhyay A, Gupta M, Mohapatra B. Dilated cardiomyopathy: a new insight into the rare but common cause of heart failure. Heart Fail Rev. 2022;27:431–54.

    Article  PubMed  Google Scholar 

  2. Heymans S, Lakdawala NK, Tschope C, Klingel K. Dilated cardiomyopathy: causes, mechanisms, and current and future treatment approaches. Lancet. 2023;402:998–1011.

    Article  CAS  PubMed  Google Scholar 

  3. Rosenbaum AN, Agre KE, Pereira NL. Genetics of dilated cardiomyopathy: practical implications for heart failure management. Nat Rev Cardiol. 2020;17:286–97.

    Article  PubMed  Google Scholar 

  4. Yousefi PD, Suderman M, Langdon R, Whitehurst O, Davey Smith G, Relton CL. DNA methylation-based predictors of health: applications and statistical considerations. Nat Rev Genet. 2022;23:369–83.

    Article  CAS  PubMed  Google Scholar 

  5. Heikkinen A, Bollepalli S, Ollikainen M. The potential of DNA methylation as a biomarker for obesity and smoking. J Intern Med. 2022;292:390–408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Noroozi R, Ghafouri-Fard S, Pisarek A, Rudnicka J, Spolnicka M, Branicki W, Taheri M, Pospiech E. DNA methylation-based age clocks: From age prediction to age reversion. Ageing Res Rev. 2021;68: 101314.

    Article  CAS  PubMed  Google Scholar 

  7. Ortiz-Barahona V, Joshi RS, Esteller M. Use of DNA methylation profiling in translational oncology. Semin Cancer Biol. 2022;83:523–35.

    Article  CAS  PubMed  Google Scholar 

  8. Cao J, Wu Q, Huang Y, Wang L, Su Z, Ye H. The role of DNA methylation in syndromic and non-syndromic congenital heart disease. Clin Epigenetics. 2021;13:93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ulrich A, Wu Y, Draisma H, Wharton J, Swietlik EM, Cebola I, Vasilaki E, Balkhiyarova Z, Jarvelin MR, Auvinen J, Herzig KH, Coghlan JG, Lordan J, Church C, Howard LS, Pepke-Zaba J, Toshner M, Wort SJ, Kiely DG, Condliffe R, Lawrie A, Graf S, Morrell NW, Wilkins MR, Prokopenko I, Rhodes CJ. Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension. Nat Commun. 2024;15:330.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ren J, Jiang L, Liu X, Liao Y, Zhao X, Tang F, Yu H, Shao Y, Wang J, Wen L, Song L. Heart-specific DNA methylation analysis in plasma for the investigation of myocardial damage. J Transl Med. 2022;20:36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cullell N, Soriano-Tarraga C, Gallego-Fabrega C, Carcel-Marquez J, Torres-Aguila NP, Muino E, Lledos M, Llucia-Carol L, Esteller M, de Castro Moura M, Montaner J, Fernandez-Sanles A, Elosua R, Delgado P, Marti-Fabregas J, Krupinski J, Roquer J, Jimenez-Conde J, Fernandez-Cadenas I. DNA Methylation and Ischemic Stroke Risk: An Epigenome-Wide Association Study. Thromb Haemost. 2022;122:1767–78.

    Article  PubMed  Google Scholar 

  12. Wada K, Misaka T, Yokokawa T, Kimishima Y, Kaneshiro T, Oikawa M, Yoshihisa A, Takeishi Y. Blood-Based Epigenetic Markers of FKBP5 Gene Methylation in Patients With Dilated Cardiomyopathy. J Am Heart Assoc. 2021;10: e021101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Guarrera S, Fiorito G, Onland-Moret NC, Russo A, Agnoli C, Allione A, Di Gaetano C, Mattiello A, Ricceri F, Chiodini P, Polidoro S, Frasca G, Verschuren MWM, Boer JMA, Iacoviello L, van der Schouw YT, Tumino R, Vineis P, Krogh V, Panico S, Sacerdote C, Matullo G. Gene-specific DNA methylation profiles and LINE-1 hypomethylation are associated with myocardial infarction risk. Clin Epigenetics. 2015;7:133.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Haas J, Frese KS, Park YJ, Keller A, Vogel B, Lindroth AM, Weichenhan D, Franke J, Fischer S, Bauer A, Marquart S, Sedaghat-Hamedani F, Kayvanpour E, Kohler D, Wolf NM, Hassel S, Nietsch R, Wieland T, Ehlermann P, Schultz JH, Dosch A, Mereles D, Hardt S, Backs J, Hoheisel JD, Plass C, Katus HA, Meder B. Alterations in cardiac DNA methylation in human dilated cardiomyopathy. EMBO Mol Med. 2013;5:413–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Benincasa G, Pepin ME, Russo V, Cacciatore F, D'Alto M, Argiento P, Romeo E, Chiappetti R, Laezza N, Wende AR, Schiattarella GG, Coscioni E, La Montagna A, Amarelli C, Maiello C, Golino P, Condorelli G and Napoli C. High-resolution DNA methylation changes reveal biomarkers of heart failure with preserved ejection fraction versus reduced ejection fraction. Basic Res Cardiol. 2024.

  16. Oeing CU, Pepin ME, Saul KB, Agircan AS, Assenov Y, Merkel TS, Sedaghat-Hamedani F, Weis T, Meder B, Guan K, Plass C, Weichenhan D, Siede D, Backs J. Indirect epigenetic testing identifies a diagnostic signature of cardiomyocyte DNA methylation in heart failure. Basic Res Cardiol. 2023;118:9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sopic M, Robinson EL, Emanueli C, Srivastava P, Angione C, Gaetano C, Condorelli G, Martelli F, Pedrazzini T, Devaux Y. Integration of epigenetic regulatory mechanisms in heart failure. Basic Res Cardiol. 2023. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00395-023-00986-3.

    Article  PubMed  PubMed Central  Google Scholar 

  18. de Mendoza A, Nguyen TV, Ford E, Poppe D, Buckberry S, Pflueger J, Grimmer MR, Stolzenburg S, Bogdanovic O, Oshlack A, Farnham PJ, Blancafort P, Lister R. Large-scale manipulation of promoter DNA methylation reveals context-specific transcriptional responses and stability. Genome Biol. 2022;23:163.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Authors/Task Force M, McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, Burri H, Butler J, Celutkiene J, Chioncel O, Cleland JGF, Coats AJS, Crespo-Leiro MG, Farmakis D, Gilard M, Heymans S, Hoes AW, Jaarsma T, Jankowska EA, Lainscak M, Lam CSP, Lyon AR, McMurray JJV, Mebazaa A, Mindham R, Muneretto C, Francesco Piepoli M, Price S, Rosano GMC, Ruschitzka F, Kathrine Skibelund A and Group ESCSD. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2022; 24:4–131.

  20. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, Burri H, Butler J, Celutkiene J, Chioncel O, Cleland JGF, Crespo-Leiro MG, Farmakis D, Gilard M, Heymans S, Hoes AW, Jaarsma T, Jankowska EA, Lainscak M, Lam CSP, Lyon AR, McMurray JJV, Mebazaa A, Mindham R, Muneretto C, Francesco Piepoli M, Price S, Rosano GMC, Ruschitzka F, Skibelund AK and Group ESCSD. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2023; 44:3627-3639

  21. Gong T, Borgard H, Zhang Z, Chen S, Gao Z, Deng Y. Analysis and performance assessment of the whole genome bisulfite sequencing data workflow: currently available tools and a practical guide to advance DNA methylation studies. Small Methods. 2022;6: e2101251.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Litvinukova M, Talavera-Lopez C, Maatz H, Reichart D, Worth CL, Lindberg EL, Kanda M, Polanski K, Heinig M, Lee M, Nadelmann ER, Roberts K, Tuck L, Fasouli ES, DeLaughter DM, McDonough B, Wakimoto H, Gorham JM, Samari S, Mahbubani KT, Saeb-Parsy K, Patone G, Boyle JJ, Zhang H, Zhang H, Viveiros A, Oudit GY, Bayraktar OA, Seidman JG, Seidman CE, Noseda M, Hubner N, Teichmann SA. Cells of the adult human heart. Nature. 2020;588:466–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Chatzikyriakou P, Brempou D, Quinn M, Fishbein L, Noberini R, Anastopoulos IN, Tufton N, Lim ES, Obholzer R, Hubbard JG, Moonim M, Bonaldi T, Nathanson KL, Izatt L, Oakey RJ. A comprehensive characterisation of phaeochromocytoma and paraganglioma tumours through histone protein profiling, DNA methylation and transcriptomic analysis genome wide. Clin Epigenetics. 2023;15:196.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Koks S, Soomets U, Paya-Cano JL, Fernandes C, Luuk H, Plaas M, Terasmaa A, Tillmann V, Noormets K, Vasar E, Schalkwyk LC. Wfs1 gene deletion causes growth retardation in mice and interferes with the growth hormone pathway. Physiol Genomics. 2009;37:249–59.

    Article  CAS  PubMed  Google Scholar 

  25. Liu X, Zhou S, Huang M, Zhao M, Zhang W, Liu Q, Song K, Wang X, Liu J, OuYang Q, Dong Z, Yang M, Li Z, Lin L, Liu Y, Yu Y, Liao S, Zhu J, Liu L, Li W, Jia L, Zhang A, Guo C, Yang L, Li QG, Bai X, Li P, Cai G, Lu Q, Chen X. DNA methylation and whole-genome transcription analysis in CD4(+) T cells from systemic lupus erythematosus patients with or without renal damage. Clin Epigenetics. 2024;16:98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Reimann E, Koks S, Ho XD, Maasalu K, Martson A. Whole exome sequencing of a single osteosarcoma case–integrative analysis with whole transcriptome RNA-seq data. Hum Genomics. 2014;8:20.

    PubMed  PubMed Central  Google Scholar 

  27. Martini E, Kunderfranco P, Peano C, Carullo P, Cremonesi M, Schorn T, Carriero R, Termanini A, Colombo FS, Jachetti E, Panico C, Faggian G, Fumero A, Torracca L, Molgora M, Cibella J, Pagiatakis C, Brummelman J, Alvisi G, Mazza EMC, Colombo MP, Lugli E, Condorelli G, Kallikourdis M. Single-cell sequencing of mouse heart immune infiltrate in pressure overload-driven heart failure reveals extent of immune activation. Circulation. 2019;140:2089–107.

    Article  CAS  PubMed  Google Scholar 

  28. Wang J, Zhang Z, Sun Y, Yu B, Wang Y, Lu Y, Yu J, Wang N, Xia F. Association of innate versus specific immunity with heart failure incidence: a prospective study. Heart. 2024;111:76–82.

    Article  PubMed  Google Scholar 

  29. Wang W, Li X, Ding X, Xiong S, Hu Z, Lu X, Zhang K, Zhang H, Hu Q, Lai KS, Chen Z, Yang J, Song H, Wang Y, Wei L, Xia Z, Zhou B, He Y, Pu J, Liu X, Ke R, Wu T, Huang C, Baldini A, Zhang M, Zhang Z. Lymphatic endothelial transcription factor Tbx1 promotes an immunosuppressive microenvironment to facilitate post-myocardial infarction repair. Immunity. 2023;56(2342–2357): e10.

    Google Scholar 

  30. Feng Y, Cai L, Hong W, Zhang C, Tan N, Wang M, Wang C, Liu F, Wang X, Ma J, Gao C, Kumar M, Mo Y, Geng Q, Luo C, Lin Y, Chen H, Wang SY, Watson MJ, Jegga AG, Pedersen RA, Fu JD, Wang ZV, Fan GC, Sadayappan S, Wang Y, Pauklin S, Huang F, Huang W, Jiang L. Rewiring of 3D chromatin topology orchestrates transcriptional reprogramming and the development of human dilated cardiomyopathy. Circulation. 2022;145:1663–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu Y, Hong J, Peng R. SAA1 as a key mediator of immune inflammatory pathways in fungal keratitis through FOXO3a phosphorylation regulation. Cytokine. 2025;189: 156898.

    Article  CAS  PubMed  Google Scholar 

  32. Chan MY, Efthymios M, Tan SH, Pickering JW, Troughton R, Pemberton C, Ho HH, Prabath JF, Drum CL, Ling LH, Soo WM, Chai SC, Fong A, Oon YY, Loh JP, Lee CH, Foo RSY, Ackers-Johnson MA, Pilbrow A, Richards AM. Prioritizing candidates of post-myocardial infarction heart failure using plasma proteomics and single-cell transcriptomics. Circulation. 2020;142:1408–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dieden A, Girerd N, Ottosson F, Molvin J, Pareek M, Melander O, Bachus E, Råstam L, Lindblad U, Daka B, Leósdóttir M, Nilsson PM, Olsen MH, Clark AL, Cleland JGF, Delles C, González A, Lamiral Z, Duarte K, Rossignol P, Zannad F, Gudmundsson P, Jujić A, Magnusson M. Proteomic biomarkers and pathway analysis for progression to heart failure in three epidemiological representative cohorts. Euro J Heart Failure. 2024. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/ejhf.3502.

    Article  Google Scholar 

  34. Kingo K, Aunin E, Karelson M, Ratsep R, Silm H, Vasar E, Koks S. Expressional changes in the intracellular melanogenesis pathways and their possible role in the pathogenesis of vitiligo. J Dermatol Sci. 2008;52:39–46.

    Article  CAS  PubMed  Google Scholar 

  35. Perco P, Wilflingseder J, Bernthaler A, Wiesinger M, Rudnicki M, Wimmer B, Mayer B, Oberbauer R. Biomarker candidates for cardiovascular disease and bone metabolism disorders in chronic kidney disease: a systems biology perspective. J Cell Mol Med. 2008;12:1177–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Shelkowitz E, Chan CM, Jones T, Nakano SJ, Meeks NJL. A novel GNAS variant presents with disorders of GNAS inactivation and cardiomyopathy. Am J Med Genet A. 2022;188:2147–52.

    Article  CAS  PubMed  Google Scholar 

  37. Ping P, Song C, Zhang J, Guo Y, Cao X, Li RC, Wu W, Vondriska TM, Pass JM, Tang XL, Pierce WM, Bolli R. Formation of protein kinase C(epsilon)-Lck signaling modules confers cardioprotection. J Clin Invest. 2002;109:499–507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kuwahara K. The natriuretic peptide system in heart failure: Diagnostic and therapeutic implications. Pharmacol Ther. 2021;227: 107863.

    Article  CAS  PubMed  Google Scholar 

  39. Rubattu S, Stanzione R, Cotugno M, Bianchi F, Marchitti S, Forte M. Epigenetic control of natriuretic peptides: implications for health and disease. Cell Mol Life Sci. 2020;77:5121–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tsutsui H, Albert NM, Coats AJS, Anker SD, Bayes-Genis A, Butler J, Chioncel O, Defilippi CR, Drazner MH, Felker GM, Filippatos G, Fiuzat M, Ide T, Januzzi JL Jr, Kinugawa K, Kuwahara K, Matsue Y, Mentz RJ, Metra M, Pandey A, Rosano G, Saito Y, Sakata Y, Sato N, Seferovic PM, Teerlink J, Yamamoto K, Yoshimura M. Natriuretic peptides: role in the diagnosis and management of heart failure: a scientific statement from the Heart Failure Association of the European Society of Cardiology, Heart Failure Society of America and Japanese Heart Failure Society. Eur J Heart Fail. 2023;25:616–31.

    Article  CAS  PubMed  Google Scholar 

  41. Noureddine M, Mikolajek H, Morgan NV, Denning C, Loughna S, Gehmlich K, Mohammed F. Structural and functional insights into α-actinin isoforms and their implications in cardiovascular disease. J Gen Physiol. 2025. https://doiorg.publicaciones.saludcastillayleon.es/10.1085/jgp.202413684.

    Article  PubMed  Google Scholar 

  42. Vejandla RM, Orgil BO, Alberson NR, Li N, Munkhsaikhan U, Khuchua Z, Martherus R, Azeloglu EU, Xu F, Lu L, Towbin JA, Purevjav E. Deficiency in nebulin repeats of sarcomeric nebulette is detrimental for cardiomyocyte tolerance to exercise and biomechanical stress. Am J Physiol Heart Circ Physiol. 2021;320:H2130–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Latham SL, Weiss N, Schwanke K, Thiel C, Croucher DR, Zweigerdt R, Manstein DJ, Taft MH. Myosin-18B Regulates Higher-Order Organization of the Cardiac Sarcomere through Thin Filament Cross-Linking and Thick Filament Dynamics. Cell Rep. 2020;32: 108090.

    Article  CAS  PubMed  Google Scholar 

  44. Knutson AK, Williams AL, Boisvert WA, Shohet RV. HIF in the heart: development, metabolism, ischemia, and atherosclerosis. J Clin Invest. 2021. https://doiorg.publicaciones.saludcastillayleon.es/10.1172/JCI137557.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zhang D, Zhu L, Li C, Mu J, Fu Y, Zhu Q, Zhou Z, Liu P, Han C. Sialyltransferase7A, a Klf4-responsive gene, promotes cardiomyocyte apoptosis during myocardial infarction. Basic Res Cardiol. 2015;110:28.

    Article  PubMed  Google Scholar 

  46. Koks G, Uudelepp ML, Limbach M, Peterson P, Reimann E, Koks S. Smoking-induced expression of the GPR15 gene indicates its potential role in chronic inflammatory pathologies. Am J Pathol. 2015;185:2898–906.

    Article  CAS  PubMed  Google Scholar 

  47. Mao Y, Huang P, Wang Y, Wang M, Li MD, Yang Z. Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking. Clin Epigenetics. 2021;13:215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tremblay F, Xiong Q, Shah SS, Ko C-W, Kelly K, Morrison MS, Giancarlo C, Ramirez RN, Hildebrand EM, Voytek SB, El Sebae GK, Wright SH, Lofgren L, Clarkson S, Waters C, Linder SJ, Liu S, Eom T, Parikh S, Weber Y, Martinez S, Malyala P, Abubucker S, Friedland AE, Maeder ML, Lombardo A, Myer VE, Jaffe AB. A potent epigenetic editor targeting human PCSK9 for durable reduction of low-density lipoprotein cholesterol levels. Nature Med. 2025;31(4):1329–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41591-025-03508-x.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This work is supported by National Natural Science Foundation of China (Grant No. 32300627 to Z.G., 82371864 to S.L.), Natural Science Foundation of Henan province (Grant No. 232300421281 to Z.G., and 232300421121 to S.L.), Henan Provincial Science and Technology Research Project (Grant No. 242102310224 to B.Y.), Young Talents of Health Science and Technology Innovation in Henan Province (Grant No. YQRC2023010 to Z.G. and YXKC2021002 to B.H.) and Major Projects Jointly Constructed by Henan Province and Ministry of Science and Technology (Grant No. SBGJ202101003 to S.L.).

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Author notes

  1. Zhenglong Guo and Yunfei Liu have contributed equally to this work.

Authors and Affiliations

  1. Henan Provincial Key Laboratory of Genetic Diseases and Functional Genomics and Medical, Genetics Institute of Henan Province, Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University, Zhengzhou, China

    Zhenglong Guo, Yibin Hao & Shixiu Liao

  2. School of Medicine, People’s Hospital of Henan University, Henan University, Zhengzhou, China

    Zhenglong Guo, Yibin Hao & Shixiu Liao

  3. Henan Key Laboratory of Cardiac Remodeling and Transplantation, Zhengzhou Seventh People’s Hospital, Zhengzhou, China

    Yunfei Liu, Zhiming Zhou, Jianchao Chen, Lin Guo, Keke Liang & Bin Yang

  4. Department of Critical Care Medicine, Zhengzhou Seventh People’s Hospital, Zhengzhou, China

    Yunfei Liu, Lin Guo & Keke Liang

  5. Department of Immunology, School of Basic Medical, Zhengzhou University, Zhengzhou, China

    Bingtao Hao

  6. Eye Institute, Henan Academy of Innovations in Medical Science, Zhengzhou, Henan, China

    Bingtao Hao

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  1. Zhenglong Guo
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Contributions

G.Z., B.H., B.Y., Y.H., and S.L. conceived and designed the project. G.Z. and Y.L. contributed to methodology, software, validation, data curation, and draw the figures. Z.Z., J.C., L.G., and K.L. collected the human samples and clinical information. G.Z., and S.L. wrote the paper with the critical review of B.H., B.Y., and Y.H. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Yibin Hao, Bingtao Hao, Bin Yang or Shixiu Liao.

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Ethics approval and consent to participate

This study received approval from the Medical Ethics Committee of Zhengzhou Seventh People’s Hospital (Approval No: 2020–012). Written informed consent detailing the research objectives involving human material was obtained from all patients or their families.

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Guo, Z., Liu, Y., Zhou, Z. et al. Integrative DNA methylome and transcriptome analysis identify potential genes on the influence of dilated cardiomyopathy-associated heart failure. Clin Epigenet 17, 64 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13148-025-01876-2

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Keywords

Clinical Epigenetics

ISSN: 1868-7083

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