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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.

项目摘要/摘要