Dr. J. “Danny” Muehlschlegel is a Co-investigator in the CABG Genomics Program and the Principal Investigator of the TRANSCRIBE study (Transcriptomic Analysis of Left Ventricular Gene Expression). He is working on several projects to examine the response of the human myocardium to ischemia.
Myocardial ischemia and infarction due to coronary artery disease impose an enormous socioeconomic burden through their impact on a majority of Americans at some stage in their life. Identifying genes and pathways responsible for ischemic injury and genetic sources of variation in responses to ischemia may identify potential drug targets or other therapeutic interventions.
With our research, we aim to identify the genes expressed in response to acute myocardial ischemia, and the genetic modifiers of such gene expression using an in-vivo human model. The genetic controllers of gene expression (expression quantitative trait loci – eQTLs) determine an individual’s response to ischemia and allow us to identify the pathways that respond to an ischemic insult.
Our novel methodology uses unbiased state-of-the-art high-coverage methods to completely examine the human heart’s response to ischemia. Using a human model of myocardial ischemia has tremendous value because of its direct applicability, without errors of interpretation from species and tissue variability.
Genetics of Cardiovascular Disease
Coronary artery disease (CAD) and myocardial infarction (MI) are the leading causes of death and disability worldwide. Early linkage studies identified several rare loci with large risk of developing MI. In the last few years, genome-wide association (GWA) studies have detected common variants for MI. The strongest and most replicated genetic risk for MI has been found in a locus on chromosome 9p21. We recently established that the same variants in the 9p21 locus are associated with both perioperative myocardial injury (PMI) and mortality after isolated primary coronary artery bypass graft (CABG) surgery with cardiopulmonary bypass (CPB), independent of CAD severity. Most importantly, this chromosomal region and the identified SNPs are not located within protein coding genes, but are likely markers of the causal variant. To gain full understanding of the biology, one needs to identify the functional variant and that variant’s mechanism. Therefore, we are examining the eQTLs associated with SNPs for one or more nearby genes.
Gene expression in Cardiovascular Disease
Gene expression is an important intermediate phenotype that provides information about environmental and genetic effects on cellular processes. Disease-associated SNPs have been found in loci without nearby protein coding genes – so-called “gene deserts”, such as 9p21. Similarly, disease-associated SNPs have been identified in promoter and enhancer regions of genes, thus affecting gene expression levels, which have been shown in a candidate gene-driven approach. These observations underscore the central rationale for this study – that identification of nearby or distant genes that possess allele-specific expression provide direct access to the fundamental biology of cardiovascular disease.
Expression Quantitative Trait Loci
Some common DNA variants alter the expression of human genes. By analyzing whole genome RNA expression in normal and diseased tissues and treating the expression levels of genes as quantitative traits, gene expression that is highly correlated with nearby (cis) genetic variants can establish the direct biological mechanism of the observed association between SNP and disease. GWA studies using eQTLs have identified widespread regulation of gene expression by cis-acting SNPs. Similarly, several studies have found that SNPs and other genetic variants alter the splicing of mRNA, thus creating either new or different quantities of proteins. Public datasets of GWAS and eQTLs results are becoming increasingly available, including the NIH Genotype-Tissue Expression Project (GTEx). However, these existing datasets do not provide the disease-specific GWAS and eQTL data in individuals with myocardial injury.
Tissue Specificity of the Transcriptome
Gene expression is profoundly specific to each cell-type; even closely related cell types such as lymphoblastoid cells and T-cells share only a small fraction of cis-eQTLs. Up to 80% of regulatory variants are cell-type specific with regulatory variant complexity correlating with transcript complexity. Therefore, for SNPs associated with a particular disease phenotype, eQTL associations need to be performed using the tissue involved in the pathogenesis of the disease. We believe that only by examining left ventricular tissue are we capable of discerning the biological mechanisms of myocardial injury.