ABCA1, TCF7, NFATC1, PRKCZ, and PDGFA DNA methylation as potential epigenetic-sensitive targets in acute coronary syndrome via network analysis

Abstract

Acute coronary syndrome (ACS) is the most severe clinical manifestation of coronary heart disease.We performed an epigenome-wide analysis of circulating CD4+ and CD8+ T cells isolated from ACS patients and healthy subjects (HS), enrolled in the DIANA clinical trial, by reduced-representation bisulphite sequencing (RRBS). In CD4+ T cells, we identified 61 differentially methylated regions (DMRs) associated with 57 annotated genes (53% hyper- and 47% hypo-methylated) by comparing ACS patients vs HS. In CD8+ T cells, we identified 613 DMRs associated with 569 annotated genes (28% hyper- and 72% hypo-methylated) in ACS patients as compared to HS. In CD4+vs CD8+ T cells of ACS patients we identified 175 statistically significant DMRs associated with 157 annotated genes (41% hyper- and 59% hypo-methylated). From pathway analyses, we selected six differentially methylated hub genes (NFATC1, TCF7, PDGFA, PRKCB, PRKCZ, ABCA1) and assessed their expression levels by q-RT-PCR. We found an up-regulation of selected genes in ACS patients vs HS (P < 0.001). ABCA1, TCF7, PDGFA, and PRKCZ gene expression was positively associated with CK-MB serum concentrations (r = 0.75, P = 0.03; r = 0.760, P = 0.029; r = 0.72, P = 0.044; r = 0.74, P = 0.035, respectively).This pilot study is the first single-base resolution map of DNA methylome by RRBS in CD4+ and CD8+ T cells and provides specific methylation signatures to clarify the role of aberrant methylation in ACS pathogenesis, thus supporting future research for novel epigenetic-sensitive biomarkers in the prevention and early diagnosis of this pathology.

Keywords: Acute coronary syndrome; DNA methylation; T lymphocytes; epigenetics.

Figures

Figure1.

DIANA clinical study pipeline. DIANA clinical study (NCT04371809) aimed to investigate specific DNA methylation changes in circulating CD4+ and CD8+ T cells in patients with ACS as compared to HS. Genomic DNA, extracted from the two cellular subsets, was analysed through RRBS. Then, significant DMRs were identified and annotated. Functional pathway analysis was used to identify the most prevalent pathways subtending ACS. Gene expression validation of the DMGs most prevalent in disease pathways was performed by qRT-PCR. Molecular data were correlated with clinical and biochemical parameters. A biological network was built with the aim to visualize protein–protein, co-expression and functional interactions.

Figure 2.

GO molecular function analysis of DMGs in CD8+T cells of ACS patientsvsHS. (a) Bar graph depicts the relative abundance of DMGs for each category. (b) The table shows the specific DMGs for each GO term. Binding, catalytic activity and transcription regulator activity are the most prevalent function of genes in the analysis.

Figure 3.

GO functional pathway analysis of overall DMGs in CD8+ T cells of ACS patients as compared to HS. The Wnt, integrin, and cadherin signalling pathways, angiogenesis, inflammation, T cell activation showed the major number of DMGs. In addition, several genes are shared by different pathways.

Figure 4.

GO molecular function analysis of DMGs in CD4+vs CD8+ T cells of ACS patients. (a) Bar graph depicts the relative abundance of DMGs for each category. (b) The table shows the specific DMGs for each GO term. Binding and catalytic activity classes are the most represented.

Figure 5.

GO enrichment pathway analysis of overall DMGs in CD4+vs CD8+ T cells of ACS patients. The Wnt signalling pathway, angiogenesis, inflammation, B cell activation, EGFR, VEGF, endothelin and FGF pathways are the most enriched and several genes are common to different pathways.

Figure 6.

Differential of methylation in significant DMGs of ACS patients. (a) Clustering heatmap showing the mean methylation levels of the DMR associated genes in CD4+vs CD8+ T cells in ACS patients. The colour indicates DNA methylation levels where red represents hypomethylation and yellow represents hypermethylation. (b) RRBS results show five hypermethylated DMRs in NFATC1 gene while in TCF7 gene 2 hypermethylated. In addition, PDGFA shows  the highest hypomethylation value.

Figure 7.

Gene expression profile evaluation. Bar graphs illustrate the relative expression of ABCA1, NFATC1, TCF7, PDGFA, PRKCB, and PRKCZ genes in PBMNCs of ACS patients as compared to HS (**P < 0.01).

Figure 8.

Correlation plots. Pearson’s correlation between (a) CD4+ T and (b) CD8+ T cell promoter methylation and total PBMNCs gene expression levels of the selected DMGs with clinical and biochemical parameters of ACS patients. The circle size is scaled by the correlation coefficient. Blue and red colours designate positive and negative correlations, respectively (*P < 0.05; **P < 0.01; ***P < 0.001).

Figure 9.

Gene interaction network constructed with GeneMANIA for the six selected DMGs. GeneMANIA PPI and co-expression network of NFATC1, TCF7, ABCA1, PRKCB, PRKCZ, PDGFA, and ABCA1 genes predicts 26 nodes and a total of 138 links representing interactions mainly to immune response regulating cell surface receptor signalling pathway, inflammatory response, regulation of lipid metabolic process, regulation of cell adhesion, blood coagulation, protein kinase signalling, regulation of cell activation and leukocyte migration The colour of the line that connects the genes depicts the interaction type (see legend) and the edge thickness depicts the interaction strength.