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Computational Pipeline for Identification of Disease-Causing Variants in Genes of the Cardiac Sarcomere

用于鉴定心脏肌节基因致病变异的计算流程

基本信息

批准号：
10736459
负责人：
STUART G CAMPBELL
金额：
$ 71.33万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-10 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736459
关键词：
Actins Algorithms Amino Acids Behavior Benchmarking Benign Binding Biological Assay Biophysics Cardiac Cardiomyopathies Cells Classification ClinVar Clinical Clinical Management Clinical Research Complex Computer Models Computing Methodologies Data Databases Disease Early Diagnosis Early identification Ensure Evaluation Family Funding Gene Mutation Genes Genetic Screening Genotype Goals Health Heart Heart Diseases Human Human Engineering Hypertrophy In Vitro Individual Induced Mutation Link Maps Measurable Methods Modeling Molecular Muscle Contraction Muscle function Mutation Outcome Pathogenicity Patients Phenotype Population Proteins Publishing Regulation Risk Role Sarcomeres Structural Models Subcellular structure Symptoms Testing Thin Filament Tropomyosin Troponin Troponin I Troponin T Variant Work cardiac tissue engineering cell motility clinical decision support clinical decision-making computational pipelines design experience experimental study genetic testing genetic variant interest link protein molecular dynamics multi-scale modeling novel predictive modeling protein protein interaction rational design risk stratification tool variant of unknown significance

项目摘要

PROJECT SUMMARY / ABSTRACT Contractile force in the heart is generated by densely packed subcellular structures known as sarcomeres. Mutations in sarcomeric genes have been repeatedly linked to potentially lethal conditions known as cardiomyopathies, including hypertrophic (HCM) and dilated (DCM) forms. Optimal clinical management of HCM/DCM requires identification of at-risk individuals before they experience symptoms. Genetic testing can be useful, but results are not always definitive enough to support clinical decision making. This is because genetic variants found in patients are often unique to their family. These so-called variants of unknown significance (VUS) could be pathogenic or benign. Unfortunately, testing each VUS experimentally is prohibitively expensive, and generic pathogenicity algorithms are proving unreliable for prediction of cardiomyopathies. The goal of this proposal is to create an accurate and scalable computational method for classifying sarcomeric variants of unknown significance so that more HCM/DCM families can benefit from genetic screening and early diagnosis. Our long-term approach to solving this critical shortcoming is to create a computational pipeline to predict pathogenicity of novel sarcomeric gene variants, providing cardiologists with a biophysical basis for performing risk stratification in HCM/DCM patient families. Our work during the last funding period was focused specifically on mutations to the protein tropomyosin (Tpm), leading to important milestones in genotype-phenotype predictive modeling. For this renewal, we aim to expand these capabilities to include characterization of highly prevalent VUS in Tpm’s binding partners, troponin I (TnI) and troponin T (TnT). This will widen the impact of our work, encompassing families with VUS in TPM1, TNNI3, or TNNT2. Breakthroughs documented in our recent published work on the actin/Tpm/troponin regulatory complex have allowed us to construct increasingly precise maps of the binding interactions among these proteins at an atomic level, including the specific amino acid sidechains upon which binding and regulatory function depend. Our hypothesis is that these refined structural interaction maps will allow us to make more accurate predictions of thin filament VUS pathogenicity using our computational pipeline. We will test this hypothesis in three aims. Aim 1 experiments will extend our preliminary tests of the interacting pairs hypothesis through the study of 18 additional mutations scattered strategically across our three proteins of interest. In Aim 2, twelve mutations in TPM1, TNNT2, and TNNI3 that are known to produce clinical disease in humans will be analyzed in our dual computational/experimental pipeline. These real-world cases will make it possible to define what constitutes a meaningful mutation-induced change in muscle function. Having established model accuracy (Aim 1) and thresholds of pathogenicity (Aim 2), in Aim 3 we will perform a computational screen of 200+ VUS from the ClinVar database and validate twelve of these HCM/DCM pathogenicity predictions in engineered heart tissues. This project will continue our successful efforts to achieve sarcomeric genotype-phenotype predictions.

项目总结/摘要心脏中的收缩力是由称为肌节的密集亚细胞结构产生的。肌节基因的突变已经被反复与潜在的致命疾病联系起来，心肌病，包括肥厚型（HCM）和扩张型（DCM）。最佳临床管理 HCM/DCM需要在出现症状之前识别风险个体。基因检测可以这些结果可能是有用的，但结果并不总是足以支持临床决策。这是因为在患者体内发现的遗传变异通常是其家族所独有的。这些所谓的未知变量显著性（VUS）可以是致病的或良性的。不幸的是，对每个VUS进行实验测试，昂贵得令人望而却步，通用致病性算法被证明不可靠的预测心肌病该提案的目标是创建一个准确和可扩展的计算方法，对意义未知的肌节变异进行分类，以便更多的HCM/DCM家族可以从以下方面受益：基因筛查和早期诊断。我们解决这一关键缺陷的长期方法是创建一个计算管道来预测新的肌节基因变异的致病性，为心脏病学家提供了一个在HCM/DCM患者家族中进行风险分层的生物物理学基础。我们在过去一年中的工作基金期间特别关注蛋白质原肌球蛋白（Tpm）的突变，导致重要的基因型-表型预测建模的里程碑。对于这次更新，我们的目标是扩大这些能力，包括Tpm结合伴侣、肌钙蛋白I（TnI）和肌钙蛋白T中高度流行的VUS的表征（TnT）。这将扩大我们工作的影响，包括家庭与VUS在TPM 1，TNNI 3，或TNNT 2。在我们最近发表的关于肌动蛋白/Tpm/肌钙蛋白调节复合物的工作中，使我们能够构建这些蛋白质之间的结合相互作用的越来越精确的地图，原子水平，包括结合和调节功能所依赖的特定氨基酸侧链。我们的假设是，这些精细的结构相互作用图将使我们能够做出更准确的预测细丝状病毒的致病性。我们将从三个方面来检验这一假设。 Aim 1实验将通过对18个实验的研究，另外的突变策略性地分散在我们感兴趣的三种蛋白质中。在目标2中，已知在人类中产生临床疾病的TPM 1、TNNT 2和TNNI 3将在我们的双重研究中进行分析。计算/实验流水线。这些真实世界的案例将使我们有可能定义什么构成了一个有意义的突变引起的肌肉功能变化。在建立了模型精度（目标1）之后，致病性阈值（目标2），在目标3中，我们将从 ClinVar数据库，并在工程心脏中验证了其中12个HCM/DCM致病性预测组织中该项目将继续我们成功的努力，实现肌节基因型-表型预测。