From variants to mechanisms for cardiac arrhythmias

Project summary There are hundreds of genomic loci where common genetic variants associate with the risk of cardiac arrhythmias, yet the slow rate of functional assessment severely limits our ability to unlock the unique biology that they identify. Our long-term goal is to systematically link arrhythmia risk loci to their mechanisms, identifying the unexpected mechanisms of arrhythmogenesis, and priming them for therapeutic translation. The key feature of arrhythmia genetic association loci is their non-protein-coding nature, a finding which leads to our overarching hypothesis that transcriptional misregulation underlies much of cardiac arrhythmia risk. To address this hypothesis, we first examine the relationships between known arrhythmia target genes which encode transcription factors and cardiomyocyte electrophysiology using inducible CRISPR-Cas9 modifiers of gene expression. We will seek to understand the transcriptional changes underlying electrophysiological changes by profiling gene expression and protein abundance. At the same time, we recognize that the vast majority of loci remain entirely undefined, a limitation which serves as a great impediment to further translational research. To address this, we will use two orthogonal approaches in human atrial tissue samples. First, our group has led early large-scale implementations of single nucleus RNA sequencing on the human heart, experience which we propose to extend to the goals of this proposal. We aim to link genotype to expression by performing single nucleus RNA sequencing on a large biobank of non-diseased left atrial tissue with available genotypes and clinical metadata. This will provide not only the target gene(s) for the association loci, but also the directionality of effect and the pertinent cell type(s), greatly facilitating downstream validation by our team and others. To complement the direct measurement of genotype to expression, we aim to supplement these analyses with chromatin conformation analysis. While these assays do not resolve the effects of genotype, they measure contact between regions of risk and target promoters to provide putative gene targets. Our preliminary high- resolution contact map from the left atrial lateral wall greatly improved the number of candidate genes for atrial fibrillation association loci. We recognize the importance of anatomically restricted events in the initiation and propagation of arrhythmias, and thus propose to assess the physical proximity between regulatory elements within association loci and their putative gene targets in prospectively sampled atrial tissues using micro-C, a technology which assesses chromatin conformation across the entire genome. Ultimately, accomplishing these aims could prove transformative for facilitating studies of cardiac arrhythmias, unlocking the mechanisms of arrhythmia genetic risk to generate novel therapeutic approaches and guide clinical practices.