Genetic Insights Into Smooth Muscle Cell Contributions to Coronary Artery Disease

Abstract

Coronary artery disease is a complex cardiovascular disease involving an interplay of genetic and environmental influences over a lifetime. Although considerable progress has been made in understanding lifestyle risk factors, genetic factors identified from genome-wide association studies may capture additional hidden risk undetected by traditional clinical tests. These genetic discoveries have highlighted many candidate genes and pathways dysregulated in the vessel wall, including those involving smooth muscle cell phenotypic modulation and injury responses. Here, we summarize experimental evidence for a few genome-wide significant loci supporting their roles in smooth muscle cell biology and disease. We also discuss molecular quantitative trait locus mapping as a powerful discovery and fine-mapping approach applied to smooth muscle cell and coronary artery disease-relevant tissues. We emphasize the critical need for alternative genetic strategies, including cis/trans-regulatory network analysis, genome editing, and perturbations, as well as single-cell sequencing in smooth muscle cell tissues and model organisms, under both normal and disease states. By integrating multiple experimental and analytical modalities, these multidimensional datasets should improve the interpretation of coronary artery disease genome-wide association studies and molecular quantitative trait locus signals and inform candidate targets for therapeutic intervention or risk prediction.

Keywords: coronary artery disease; gene editing; genome-wide association study; quantitative trait loci; risk factors; vascular smooth muscle cells.

Figure 1.

Summary of coronary artery disease (CAD) genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) targeting genes with evidence of smooth muscle functions. Circular Manhattan plot depicting genome-wide significant loci associated with CAD from meta-analyses of CARDIoGRAMplusC4D (Coronary Artery Disease Genome Wide and Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics Consortium) and UK Biobank data. Inner circle shows −log₁₀ P values for CAD GWAS loci (maximum set to 50), and dashed lines and text highlight candidate causal genes with evidence of smooth muscle cell functions.^– Middle circle shows −log₁₀ P values for eQTLs (maximum set to 100) identified from tibial artery in Genotype-Tissue Expression (GTEx; largest sample size for artery samples in this database) and outer circle shows −log₁₀ P values for liver eQTLs (maximum set to 30) in GTEx. Note: these candidate target genes are based on existing experimental studies in the literature and may be subject to change with additional statistical and/or functional fine-mapping efforts.

Figure 2.

Overview of genetic approaches to investigate smooth muscle disease mechanisms. A, Example locus association plot showing a genome-wide association study (GWAS) signal for a cardiometabolic trait. Circles indicate individual single nucleotide polymorphisms (SNPs) associated at a given P value and location across the genome. SNPs are color-coded for degree of linkage disequilibrium (r²) in a European population. Blue lines indicate recombination rate. B, Example molecular quantitative trait locus (molQTL) box plot showing the levels of a given molecular trait (eg, gene expression) that are correlated with genotype at a specific GWAS locus. Black line indicates linear regression for molecular trait-genotype association. C, Schematic of shared and tissue-specific gene regulatory networks derived from molQTLs related to cardiometabolic traits (eg, STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]). Yellow, orange, and green colored nodes indicate liver, adipose, and aorta-enriched regulatory signals, respectively, while blue colored nodes indicate shared or nontissue enriched signals. D, Clustered regularly interspaced short palindromic repeats (CRISPR) approaches to perturb candidate gene expression levels using site-specific single guide RNA (sgRNA) and nuclease dead Cas9 (dCas9) fusion proteins to either repress/silence, activate, or epigenetically modify a particular regulatory region. E, Schematic of single-cell analysis (eg, single-cell RNA sequencing), showing single-cell gene matrix generated from bulk population of cells after single-cell capture, reverse transcription, library preparation and next-generation sequencing. Different colors represent uniquely labeled cell type based on cell-specific gene expression levels. Clustering shown for principal components (PC) 1 and 2 which can be used to resolve smooth muscle cell (SMC) heterogeneity and various subpopulations potentially altered by regulatory genetic variation. DNMT3a indicates DNA methyltransferase 3 alpha; KRAB, Kruppel-associated box; LSD1, lysine-specific histone demethylase 1A; p300, histone acetyltransferase p300; QTL, quantitative trait locus; SAM, synergistic activation mediator; TET1, ten-eleven translocation methylcytosine dioxygenase 1; VP64, viral protein 64; and VPR, viral protein R.