Deep Mutational Scanning and Functional Analysis of Repolarization Determinants

项目概要/摘要 心源性猝死的基础与遗传性和后天性离子通道异常有关 许多与钾通道变异有关。表型-基因型相关研究的进展 彻底改变了我们对一系列心律失常性猝死综合征的理解，但目前，识别 编码变异的数量远远超过了我们正确分类变异的能力，并且对于大多数基因来说，有更多 未分类的变体（意义未知的变体，VUS）比分类的变体要多。这给临床治疗造成了障碍 护理、家族级联筛查以及与疾病的功能性联系。生理和心理的重要性 强调变异分类的功能分析，但当代方法很麻烦 （时间和资源）降低揭示与遗传变异相关的心律失常风险的效率。 我们实验室的工作重点是异常心脏复极和心律失常突发的功能基因组学 死亡综合症，我们已经开发出大量检测方法来了解变异的致病性。然而大多数 变异表征以反应方式进行（临床变异鉴定，然后进行功能研究） 往往缺乏临床关联（孤立的研究）；这造成了最佳和高效变体的差距 分类。我们的目标是通过创建一个积极主动、数据驱动和 机械变异分类方案与临床数据交叉验证。目标 1，深度突变扫描 Kir2.1（复极化所必需的 K 通道）和 MAVE（变异效应的多重检测）的 (DMS) 创建将同时揭示所有可能变体的功能注释，以创建全面的适应性 景观。 Aim 2 MAVE 将应用于 TOPMed 和英国生物银行鉴定的所有 K 通道变体 对复极化有影响，以三角验证遗传变异的表型-基因组-功能数据 分类。最后，在目标 3 中，我们将遗传变异和 MAVE 结果与传统的细胞标记相结合 使用 iPS 心肌细胞模型和分子计算模型进行异常复极化。我们的中央 假设 DMS 将发现 Kir2.1 调节区的功能缺失变异，MAVE 的低 来自 TOPMed 和 UK Biobank 的频率 K 通道编码变体将揭示常见的主题和 机械读数，这些可以在 iPS-CM 和计算分子模型中得到验证。的 这项研究的成果将使功能基因组学领域开始跟上快速发展的步伐 通过高完整性、高通量、高度可重复和公正的技术进行基因发现。我们 将创建一个方法模板，将所有其他高影响力的复极相关变异作为重要的变异进行分类 从被动分类转变为主动分类的步骤。此外，我们将帮助建立方法论 使用并行机械技术将临床发现与变异特征相关联。这是一个 创新的主动、数据驱动的方法，可供临床医生和研究团队等用来确定 给定变体的可操作性并为预测模型提供信息以揭示新的结构功能见解。

PROJECT SUMMARY/ABSTRACT The underpinnings of sudden cardiac death are related to genetic and acquired ion channel abnormalities and many are related to potassium channel variants. Gains in phenotype-genotype correlative studies have revolutionized our understanding of a range of sudden arrhythmic death syndromes, yet currently, identification of coding variants has far outpaced our ability to correctly classify the variant, and for most genes there are more unclassified variants (variants of unknown significance, VUS) than classified. This creates barriers for clinical care, familial cascade screening and, moreover, a functional link to disease. The importance of physiologic and functional analysis for variant classification has been emphasized, yet contemporary methods are cumbersome (time and resources) decreasing efficiency in unraveling the arrhythmic risk associated with genetic variants. Our lab’s work focuses on functional genomics of abnormal cardiac repolarization and cardiac arrhythmic sudden death syndromes, and we have developed high volume assays to understand variant pathogenicity. Yet most variant characterization proceeds in a reactive manner (clinical variant identification followed by functional study) and clinical association is often lacking (siloed research); this creates gaps in optimal and efficient variant classification. We aim to address these major gaps in knowledge by creating a pro-active, data driven and mechanistic variant classification scheme cross-validated with clinical data. In Aim 1, Deep Mutational Scanning (DMS) of Kir2.1, a K+ channel essential for repolarization, and MAVE (multiplexed assay of variant effects) creation will unveil functional annotation of all possible variants simultaneously to create a comprehensive fitness landscape. In Aim 2 MAVE will be applied to all K+ channel variants identified from TOPMed and the UK Biobank that have effects on repolarization to triangularly validate phenomic-genomic-functional data for genetic variant classification. Lastly, in Aim 3 we integrate genetic variant and MAVE results with traditional cellular markers of abnormal repolarization using an iPS-cardiomyocyte model and molecular computational modeling. Our central hypothesis is that DMS will uncover loss of function variants in regulatory regions of Kir2.1, MAVE of low frequency K+ channel coding variants from the TOPMed and UK Biobank will reveal common thematic and mechanistic readouts, and these can be validated in iPS-CMs and computational molecular modeling. The outcomes of this study will allow the field of functional genomics to begin to keep pace with rapidly evolving genetic discovery through high integrity, high throughput, and highly reproducible and unbiased techniques. We will create a methodologic template to catalog all other high-impact repolarization associated variants as a vital step to transition from reactive to proactive classification. Moreover, we will help establish the methodology to correlate clinical findings with variant characterization using parallel mechanistic techniques. This is an innovative proactive, data-driven approach, usable by clinicians and research teams alike to determine actionability of a given variant and to inform predictive models to reveal new structural-functional insights.