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

• 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
• 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

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.

项目摘要/摘要 用计算与实验相结合的方法揭示突变型TPM1的致病机制 这项提议的目标是开发和验证能够预测心脏疾病的多尺度计算方法 肌肉的行为是基于基因组成的。单基因突变已被确定为致病因素 由于基因组测序的出现，多种心血管疾病的因素 技术。遗传信息在许多方面都有能力改变临床实践，但它的潜力 仍然没有实现，因为在将突变与可观察到的事件链联系起来的重大知识缺口 疾病状态。我们的目标是解锁驻留在已知突变中的丰富分子信息 新的多尺度模型可以预测分子尺度的现象并将其向上投影到 生理上的相关性。我们准备在这一目标方面取得关键进展，这要归功于一项跨学科的 包括多尺度建模、结构生物学、生物物理学、肌肉力学和茎的专家的团队 细胞生物学。我们将关注原肌球蛋白(TPM1)，这是一种调节心肌收缩和 当基因突变时，可能会导致一种名为肥厚型心肌病(HCM)的危及生命的疾病。在… 在细胞水平上，HCM涉及到由于肌肉蛋白表达增加而导致的细胞异常生长，但 这种过度表达是如何由原肌球蛋白突变触发的，目前尚不清楚。为了证明 这种类型的基因型-表型差距可以通过多尺度建模来弥合，我们将跟踪五个因素的影响 跨分子、亚细胞和细胞尺度的原肌球蛋白突变。在目标1中，我们将执行分子 预测原肌球蛋白柔韧性和肌动蛋白表面相互作用变化的动力学模拟 突变。统计力学的原理将被用来将这些变化嵌入到 大分子肌动蛋白细丝复合体。这种比例交叉技术将能够预测突变是如何 影响灯丝的体外行为。配套的实验将检验模型的预测。对于目标2，肌动蛋白 细丝模型将被放置在心脏肌节的表示中，以预测动态 每个突变体的肌肉抽动反应。这些反应将通过病毒表达来检查准确性 人类来源的工程化心脏组织中的突变原肌球蛋白。AIM 3将使用在AIMS中开发的模型 1和2预测患者中发现但从未证实的20种TPM1变异的肥厚性致病作用 试验性的。预测将通过将一些分析的变体放入工程心脏来检验 并测量它们的肥大反应。这些目标的可行性很高，因为我们的团队 将突变的原肌球蛋白的结构特性与其生理特性联系起来所需的独特专业知识 行为。在演示一种成功的基因-表型建模方法时，我们的工作将为 用于许多其他心血管疾病的机制研究，这些疾病源于基因。