"Epigenome-wide methylation profile of chronic kidney disease-derived arterial DNA uncovers novel pathways in disease-associated cardiovascular pathology."

Abstract

Chronic kidney disease (CKD) related cardiovascular disease (CVD) is characterized by vascular remodelling with well-established structural and functional changes in the vascular wall such as arterial stiffness, matrix deposition, and calcification. These phenotypic changes resemble pathology seen in ageing, and are likely to be mediated by sustained alterations in gene expression, which may be caused by epigenetic changes such as tissue-specific DNA methylation. We aimed to investigate tissue specific changes in DNA methylation that occur in CKD-related CVD. Genome-wide DNA methylation changes were examined in bisulphite converted genomic DNA isolated from the vascular media of CKD and healthy arteries. Methylation-specific PCR was used to validate the array data, and the association between DNA methylation and gene and protein expression was examined. The DNA methylation age was compared to the chronological age in both cases and controls. Three hundred and nineteen differentially methylated regions (DMR) were identified spread across the genome. Pathway analysis revealed that DMRs associated with genes were involved in embryonic and vascular development, and signalling pathways such as TGFβ and FGF. Expression of top differentially methylated gene HOXA5 showed a significant negative correlation with DNA methylation. Interestingly, DNA methylation age and chronological age were highly correlated, but there was no evidence of accelerated age-related DNA methylation in the arteries of CKD patients. In conclusion, we demonstrated that differential DNA methylation in the arterial tissue of CKD patients represents a potential mediator of arterial pathology and may be used to uncover novel pathways in the genesis of CKD-associated complications.

Keywords: Chronic kidney disease (CKD); DNA methylation; arterial ageing; cardiovascular disease (CVD); epigenetics; vascular remodelling.

Figure 1.

Annotation of differentially methylated probes and regions. (a) Cluster plot for CKD and control samples based on their methylation profiles. (b) Pie chart of 4.5 k DMPs that reached genome-wide significance (p-value <9x10⁻⁸) showing percentage of hypermethylated (52%) and hypomethylated (48%) probes. (c) Bar plot showing the position of the DMRs across the human genome in the autosomal chromosomes. (d) Basic annotation of the DMRs in relation to genomic regions. Y-axis: % enrichment normalized to background. (e) Gene set enrichment and gene ontology analysis showing lead gene ontology categories (biological processes), and representative genes per category. X-axis: -log₁₀ of the adjusted p-values

Figure 2.

Methylation specific PCR (MSP). (a) Primers were designed to specifically amplify methylated and unmethylated DNA. Control 0% (100% u) and 100% (100% m) methylated DNA samples were used individually or at a ratio of 50:50 to assess the dose-response across methylation levels. (b,c) correlation analysis of methylation using MSP (y-axis) and EPIC array methylation (x-axis). r: Spearman correlation coefficient

Figure 3.

DNA methylation associates with HOXA5 gene and protein expression. Correlation of (a) HOXA5 gene expression in VSMC, normalized to housekeeping genes and (b) HOXA5 protein expression, assessed by immunohistochemistry with the EPIC methylation array values of the corresponding DMR 17. r: Spearman correlation coefficient

Figure 4.

DNA methylation age and chronological age are strongly correlated. CKD: chronic kidney disease subjects, Control: healthy donors, r: Spearman correlation coefficient. Chronological age is presented in years