Systems Genetics of Vascular Smooth Muscle Phenotypes

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PROJECT SUMMARY Coronary artery disease (CAD) remains the leading cause of death in the Western world despite significant advances in early detection and extensive use of lipid-lowering and anti-hypertensive drugs. To date, no single drug has been developed to target the primary disease process in the vessel wall. A complete understanding of the disease susceptibility is urgently needed to develop additional therapies. Common forms of atherosclerosis involve environmental factors, hundreds of genetic variations, and their interactions, each of which exert a relatively small effect on disease susceptibility. The most recent genome-wide association study (GWAS) in nearly six hundred fifty thousand individuals identified 175 independent variants associated with increased risk for CAD. However, most of the underlying genes and the related mechanisms of how these variants contribute to the disease process remain unknown. This proposal outlines an integrative genetics study in a unique resource of human aortic smooth muscle cells (SMCs) isolated from 151 genotyped multi- ethnic heart transplant donors to discover the CAD-associated variants that perturb SMC gene expression and their downstream functional consequences. In recent studies, we measured gene expression in quiescent and proliferative culture conditions representing the transdifferentiation of SMCs from a healthy to an atherogenic phenotype. We identified 84 genes whose expression was associated with CAD variants in GWAS loci. However, the causal genetic variants in these loci remain to be elucidated. Therefore, as part of the proposed studies, we will first perform massively parallel reporter assays to identify the variants that modulate gene expression in SMCs. We will also take advantage of the natural variation in gene expression to construct co- expression and Bayesian networks to understand how the predicted candidate causal genes function in SMCs. We will refine these networks by mapping regulatory elements to nascent RNA transcripts in response to pro- inflammatory cytokines. We will then validate our predictions in gain and loss-of-function experiments in cultured SMCs. We will also validate our predictions in well-phenotyped coronary artery specimens from cases of unexpected sudden death by performing immunohistochemical analysis of proteins encoded by genes that are predicted to play a key role in atherosclerosis-relevant SMC phenotypes. The overall goal of the proposed studies is to integrate systems genetics and computational biology leading to mechanistic predictions of the gene networks that are perturbed by CAD. Besides understanding CAD loci, these integrative genetics studies will provide a useful window into the flow of biological information from genetic variants to SMC gene expression and atherosclerosis-relevant phenotypes.