Deep Mutational Scanning and Functional Analysis of Repolarization Determinants

项目摘要/摘要 心脏性猝死的基础与遗传和获得性离子通道异常有关 许多都与钾通道变体有关。表型-基因相关研究的进展 彻底改变了我们对一系列心律失常猝死综合征的理解，但目前， 编码变异的数量远远超过了我们对变异进行正确分类的能力，而且对于大多数基因来说，还有更多 未分类的变种(意义未知的变种，VU)比分类的变种要好。这为临床应用制造了障碍 护理、家族级联筛查以及与疾病的功能性联系。生理学和生理学的重要性 对变体分类的功能分析一直受到重视，但现代方法很繁琐 (时间和资源)在揭示与遗传变异相关的心律失常风险方面效率下降。 我们实验室的工作重点是心脏复极异常和心律失常突发的功能基因组学 死亡综合征，我们已经开发了大量的检测方法来了解不同的致病性。然而，大多数 变异特征以反应性方式进行(临床变异鉴定，随后进行功能研究) 临床关联性往往缺乏(孤立的研究)；这在最佳和有效的变种方面造成了差距 分类。我们的目标是通过创建一个主动、数据驱动和 机械变异分类方案与临床数据交叉验证。在目标1中，深度突变扫描 (Dms)Kir2.1，一种复极化所必需的K通道，以及MAVE(多路不同效应分析) Creation将同时推出所有可能变体的功能注释，以创建全面的适应性 风景。在Aim 2中，MAVE将应用于从TOPMed和UK Biobank中确定的所有K通道变体 对复极化有影响，以三角方式验证遗传变异的表观基因组功能数据 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的异常复极。我们的中央 假设DMS将在Kir2.1、MAve和Low的调节区发现功能变体的丢失 来自TOPMed和UK Biobank的频率K通道编码变体将揭示共同的主题和 机械读数，这些可以在iPS-CMS和计算分子模拟中得到验证。这个 这项研究的结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、高重复性和无偏见的技术进行基因发现。我们 我将创建一个方法模板，将所有其他高影响复极相关的变体归类为至关重要的 从被动分类过渡到主动分类的步骤。此外，我们将帮助建立方法，以 使用平行机械技术将临床结果与变异特征相关联。这是一个 创新的主动式、数据驱动型方法，可供临床医生和研究团队使用，以确定 对给定变种的可操作性，并为预测模型提供信息，以揭示新的结构-功能洞察力。

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.