From Variants to Mechanisms for Cardiac Arrhythmias

项目总结 有数百个基因组座位的常见基因变异与心脏病的风险有关 心律失常，然而，缓慢的功能评估速度严重限制了我们解锁独特生物学的能力 他们辨认出来的。我们的长期目标是系统地将心律失常风险基因与其机制联系起来，识别 心律失常发生的意想不到的机制，并为治疗翻译做好准备。关键功能 心律失常遗传关联基因的特点是其非蛋白质编码的性质，这一发现导致了我们的 认为转录失调是心律失常风险的主要原因的假说。要解决这个问题 假设，我们首先检查已知的心律失常靶基因之间的关系 使用可诱导的CRISPR-Cas9基因修饰剂的转录因子和心肌细胞电生理学 表情。我们将通过以下方式来了解潜在的电生理变化的转录变化 分析基因表达和蛋白质丰度。与此同时，我们认识到绝大多数基因座 仍然完全没有定义，这一限制对进一步的翻译研究来说是一个很大的障碍。至 为了解决这个问题，我们将在人的心房组织样本中使用两种正交方法。首先，我们团队领先于 早期在人类心脏上大规模实施单核RNA测序，我们的经验是 提议延伸到本提案的目标。我们的目标是通过进行单次试验将基因和表达联系起来 在一个大的生物库上进行了核糖核酸测序，该生物库具有有效的基因和 临床元数据。这不仅为关联基因座提供了靶基因(S)，而且还提供了方向性 效应和相关的细胞类型(S)，极大地方便了我们的团队和其他人进行下游验证。至 作为对基因表达的直接测量的补充，我们的目标是补充这些分析 染色质构象分析。虽然这些检测不能解决基因型的影响，但它们测量了 风险区域和目标启动子之间的联系，以提供假定的基因靶点。我们的初步高潮- 来自左房侧壁的分辨率接触图大大提高了房室候选基因的数量 纤颤相关基因座。我们认识到解剖学上受限的事件在启蒙和 心律失常的传播，因此建议评估调节元件之间的物理接近程度 用microC，a在前瞻性采样的心房组织中的关联基因座及其可能的基因靶点 评估整个基因组染色质构象的技术。最终，实现这些 AIMS可能被证明是促进心律失常研究的变革性的，解锁了 心律失常的遗传风险，以产生新的治疗方法和指导临床实践。

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.