Deep Mutational Scanning and Functional Analysis of Repolarization Determinants

复极化决定因素的深度突变扫描和功能分析

• 批准号: 10467096
• 负责人: Lee Lochbaum Eckhardt
• 金额: $ 40.67万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10467096
• 关键词: Address; Arrhythmia; Biological Assay; Candidate Disease Gene; Cardiac; Cardiac Myocytes; Catalogs; Cessation of life; Classification; Classification Scheme; Clinical; Clinical Data; Code; Computer Analysis; Computer Models; Correlative Study; Coupled; Data; Data Set; Databases; Decision Making; Disease; Frequencies; Genes; Genetic; Genomics; Genotype; Heart; Heart Abnormalities; Individual; Ion Channel; Knowledge; Link; Long QT Syndrome; Membrane; Methodology; Methods; Modeling; Molecular Computations; Nucleic Acid Regulatory Sequences; Other Genetics; Outcome Study; Pathogenicity; Phenotype; Physiological; Play; Potassium Channel; Proteins; Reporting; Reproducibility; Research; Resources; Rest; Risk; Role; Running; Speed; Structure; Sudden Death; Syndrome; Techniques; Time; Trans-Omics for Precision Medicine; Variant; Work; base; biobank; bioinformatics tool; clinical care; clinical phenotype; cost; exome; fitness; functional genomics; genetic testing; genetic variant; genome wide association study; genomic data; induced pluripotent stem cell; innovation; insight; interdisciplinary approach; loss of function; molecular dynamics; molecular modeling; mortality risk; multiplex assay; mutation screening; novel; phenomics; predictive modeling; screening; sex; sudden cardiac death; variant of unknown significance

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.

项目摘要/摘要 猝死的基础与遗传和获得的离子通道异常有关， 许多与钾通道变体有关。表型基因型相关研究的收益已有 彻底改变了我们对一系列突然心律不齐综合症的理解，但目前是识别 编码变体已经超过了我们正确分类变体的能力，对于大多数基因而言，还有更多 未分类的变体（未知意义的变体，VUS）比分类。这为临床造成了障碍 护理，家族性级联筛查以及与疾病的功能联系。生理学和 已经强调了变体分类的功能分析，但现代方法很麻烦 （时间和资源）降低与遗传变异相关的心律失常风险的效率。 我们的实验室工作着重于异常心脏重复和心律失常突然的功能基因组学 死亡综合症，我们已经开发了大量测定，以了解变异的致病性。最多 变体表征以反应性方式进行（临床变异鉴定，然后进行功能研究） 临床关联通常缺乏（孤立的研究）；这会在最佳有效的变体中造成差距 分类。我们旨在通过创建积极主动的数据驱动和 机械变体分类方案与临床数据交叉验证。在AIM 1中，深度突变扫描 Kir2.1的（DMS），一种重脱极化必不可少的K+通道和MAVE（变异效应的多重测定） 创建将揭示所有可能变体的功能注释，只是为了创建全面的健身 景观。在AIM 2中，Mave将应用于TopMed和UK Biobank的所有K+通道变体 对复制作用的影响，以验证遗传变异的现象基因组功能数据 分类。最后，在AIM 3中，我们将遗传变异和MAVE结果与传统的蜂窝标记集成在一起 使用IPS-毛肌细胞模型和分子计算建模的异常复极。我们的中心 假设是，DMS将发现Kir2.1调节区域的功能变异损失，低的MAVE 来自顶部和英国生物库的频率K+通道编码变体将揭示常见的主题和 机械读数，可以在IPS-CMS和计算分子建模中进行验证。这 这项研究的结果将使功能基因组学领域能够随着快速发展而开始步伐 通过高完整性，高吞吐量和高度可重现和无偏见的技术来发现遗传发现。 将创建一个方法学模板来分类所有其他高影响库相关的变体 从反应性到主动分类过渡的步骤。此外，我们将帮助建立方法 使用平行机械技术将临床发现与变异表征相关。这是一个 创新的积极主动，数据驱动的方法，可供临床医生和研究团队确定 给定变体的可行性并为预测模型提供信息，以揭示新的结构功能见解。