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Revealing Pathomechanisms of Mutant TPM1 Through a Hybrid Computational-Experimental Approach

通过混合计算-实验方法揭示突变 TPM1 的病理机制

基本信息

批准号：
9398261
负责人：
STUART G CAMPBELL
金额：
$ 57.84万
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 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9398261
关键词：
Abnormal Cell Actins Affect Algorithms Behavior Benign Biological Assay Biophysics Cardiac Cardiac Muscle Contraction Cardiac Myocytes Cardiovascular Diseases Cell Line Cells Cereals Classification Clinical Clinical Management Companions Complex Computer Simulation Computing Methodologies Data Databases Disease Early identification Electron Microscopy Engineering Evaluation Event Family Filament Gene Mutation Generic Drugs Genes Genetic Genetic screening method Genomics Genotype Goals Heart Heart Diseases Human Human Engineering Hybrids Hypertrophic Cardiomyopathy Hypertrophy In Vitro Inborn Genetic Diseases Individual Investigation Isometric Exercise Knowledge Lead Life Link Measures Mechanics Methodology Methods Microfilaments Modeling Molecular Molecular Structure Muscle Muscle Contraction Muscle Proteins Mutate Mutation Myocardium Outcome Pathogenicity Patients Pattern Phenotype Physiological Property Proteins Regulation Resources Risk Sarcomeres Savings Slide Solid Statistical Mechanics Structure-Activity Relationship Surface System Techniques Technology Test Result Testing Time Tissues Transfection Tropomyosin Validation Variant Viral Work base cell growth cell motility clinical practice experience experimental study flexibility genetic information genetic makeup genetic variant induced pluripotent stem cell mechanical load molecular dynamics molecular scale multi-scale modeling mutant novel overexpression prediction algorithm predictive modeling response stem cell biology structural biology

项目摘要

PROJECT SUMMARY/ABSTRACT Revealing Pathomechanisms of Mutant TPM1 Through a Hybrid Computational-Experimental Approach The goal of this proposal is to develop and validate multiscale computational methods that can predict cardiac muscle behavior on the basis of genetic makeup. Single gene mutations have been identified as causative factors in a multitude of cardiovascular disorders, thanks to the emergence of genomic sequencing technologies. Genetic information has the power to transform clinical practice in many ways, but its potential remains unrealized because of major knowledge gaps in the chain of events linking mutations to observable disease states. Our goal is to unlock the rich molecular information that resides in known mutations by using new multiscale models that can predict molecular-scale phenomena and project them upward to scales of physiological relevance. We are poised to make key progress toward this goal thanks to an interdisciplinary team that includes experts in multiscale modeling, structural biology, biophysics, muscle mechanics, and stem cell biology. We will focus on tropomyosin (TPM1), a protein that regulates cardiac muscle contraction and which, when mutated, can lead to a life-threatening disease known as hypertrophic cardiomyopathy (HCM). At the cellular level, HCM involves abnormal cell growth due to increased expression of muscle proteins, but exactly how this overexpression is triggered by tropomyosin mutations is not known. In order to demonstrate that this type of genotype-phenotype gap can be closed by multiscale modeling, we will trace the effects of five tropomyosin mutations across molecular, sub-cellular, and cellular scales. In Aim 1, we will perform molecular dynamics simulations to predict changes in tropomyosin flexibility and actin surface interactions caused by mutations. Principles of statistical mechanics will be used to embed these changes within a model of the macromolecular actin filament complex. This scale-crossing technique will enable prediction of how mutations affect filament behavior in vitro. Companion experiments will test the model predictions. For Aim 2, the actin filament model will be placed within a representation of the cardiac sarcomere in order to predict dynamic muscle twitch responses for each mutant. These responses will be checked for accuracy by viral expression of mutant tropomyosins in human-derived engineered heart tissues. Aim 3 will use the models developed in Aims 1 & 2 to predict hypertrophic pathogenicity for 20 TPM1 variants identified in patients but never validated experimentally. Predictions will be checked by placing some of the analyzed variants into engineered heart tissues and measuring their hypertrophic responses. Feasibility of these aims is high because our team has the unique expertise required to relate the structural properties of mutant tropomyosins to their physiological behavior. In demonstrating a successful genotype-phenotype modeling approach, our work will pave the way for mechanistic investigation of many other cardiovascular disorders with genetic origins.

项目总结/文摘