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Transcriptome dynamics during cholesterol-induced transdifferentiation of human coronary artery smooth muscle cells: A Gene Ontology-centric clustering approach.

ABSTRACT: Macrophage-like cells derived from vascular smooth muscle cells (SMCs) play critical roles in atherogenesis, and DNA hydroxymethylation was implicated in transdifferentiation. We examined transcriptomes and (hydroxy)methylomes of human coronary artery SMCs during cholesterol-induced transdifferentiation. A unique approach of exhaustive identification of differentially expressed genes followed by Gene Ontology-centric clustering facilitated deeper understanding of multifaceted modulations of genes involved in extracellular matrix organization, angiogenesis, cell migration, hypoxia response, and cholesterol biosynthesis. Intriguingly, type I interferon response was transiently activated, presumably forming an immuno-metabolic circuit with cholesterol metabolism. Neither global nor DEG-proximal changes were evident in (hydroxy)methylation. These results would not only provide a unique data resource for atherosclerosis research but present a potentially useful approach in transcriptome data interpretation.

SUBMITTER: Inoue K

PROVIDER: S-EPMC8254084 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGenome-wide association studies have identified many genetic loci that are robustly associated with coronary artery disease (CAD). However, the underlying biological mechanisms are still unknown for most of these loci, hindering the progress to medical translation. Evidence suggests that the genetic influence on CAD susceptibility may act partly through vascular smooth muscle cells (VSMCs).MethodsWe undertook genotyping, RNA sequencing, and cell behavior assays on a large bank of VSMCs (n>1499). Expression quantitative trait locus and splicing quantitative trait locus analyses were performed to identify genes with an expression that was influenced by CAD-associated variants. To identify candidate causal genes for CAD, we ascertained colocalizations of VSMC expression quantitative trait locus signals with CAD association signals by performing causal variants identification in associated regions analysis and the summary data-based mendelian randomization test. Druggability analysis was then performed on the candidate causal genes. CAD risk variants were tested for associations with VSMC proliferation, migration, and apoptosis. Collective effects of multiple CAD-associated variants on VSMC behavior were estimated by polygenic scores.ResultsApproximately 60% of the known CAD-associated variants showed statistically significant expression quantitative trait locus or splicing quantitative trait locus effects in VSMCs. Colocalization analyses identified 84 genes with expression quantitative trait locus signals that significantly colocalized with CAD association signals, identifying them as candidate causal genes. Druggability analysis indicated that 38 of the candidate causal genes were druggable, and 13 had evidence of drug-gene interactions. Of the CAD-associated variants tested, 139 showed suggestive associations with VSMC proliferation, migration, or apoptosis. A polygenic score model explained up to 5.94% of variation in several VSMC behavior parameters, consistent with polygenic influences on VSMC behavior.ConclusionsThis comprehensive analysis shows that a large percentage of CAD loci can modulate gene expression in VSMCs and influence VSMC behavior. Several candidate causal genes identified are likely to be druggable and thus represent potential therapeutic targets.

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Transcriptome dynamics during cholesterol-induced transdifferentiation of human coronary artery smooth muscle cells: A Gene Ontology-centric clustering approach.

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