AI Article Synopsis

  • Gene-environment interactions causing epigenetic changes, particularly altered DNA methylation, are crucial in the development of Coronary Artery Disease (CAD), especially in young patients.
  • The study analyzed 42 healthy controls and 33 young CAD patients using targeted bisulfite sequencing, identifying methylation patterns in various genes linked to CAD.
  • Findings revealed hypermethylation in genes like GALNT2 and HMGCR and hypomethylation in LDLR, indicating altered lipid metabolism, and a combined analysis showed 14 specific CpG sites in 11 genes that can effectively distinguish CAD patients from healthy individuals with over 93% accuracy.

Article Abstract

Background: Gene environment interactions leading to epigenetic alterations play pivotal role in the pathogenesis of Coronary Artery Disease (CAD). Altered DNA methylation is one such epigenetic factor that could lead to altered disease etiology. In this study, we comprehensively identified methylation sites in several genes that have been previously associated with young CAD patients.

Methods: The study population consisted of 42 healthy controls and 33 young CAD patients (age group <50 years). We performed targeted bisulfite sequencing of promoter as well as gene body regions of several genes in various pathways like cholesterol synthesis and metabolism, endothelial dysfunction, apoptosis, which are implicated in the development of CAD.

Results: We observed that the genes like GALNT2, HMGCR were hypermethylated in the promoter whereas LDLR gene promoter was hypomethylated indicating that intracellular LDL uptake was higher in CAD patients. Although APOA1 did not show significant change in methylation but APOC3 and APOA5 showed variation in methylation in promoter and exonic regions. Glucokinase (GCK) and endothelial nitric oxide synthase 3 (NOS3) were hyper methylated in the promoter. Genes involved in apoptosis (BAX/BCL2/AKT2) and inflammation (PHACTR1/LCK) also showed differential methylation between controls and CAD patients. A combined analysis of the methylated CpG sites using machine learning tool revealed 14 CpGs in 11 genes that could discriminate CAD cases from controls with over 93% accuracy.

Conclusions: This study is unique because it highlights important gene methylation alterations which might predict the risk of young CAD in Indian population. Large scale studies in different populations would be important for validating our findings and understanding the epigenetic events associated with CAD.

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Source
http://dx.doi.org/10.1016/j.gene.2019.144107DOI Listing

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