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: 10358783
• 负责人: STUART G CAMPBELL
• 金额: $ 7.84万
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10358783
• 关键词: Abnormal Cell; Actins; Affect; Behavior; Biophysics; Cardiac; Cardiac Muscle Contraction; Cardiovascular Diseases; Cells; Companions; Complex; Computing Methodologies; Disease; Event; Filament; Gene Mutation; Genetic; Genomics; Genotype; Goals; Heart Diseases; Human; Hybrids; Hypertrophic Cardiomyopathy; Hypertrophy; Investigation; Knowledge; Lead; Life; Link; Measures; Mechanics; Microfilaments; Modeling; Molecular; Muscle; Muscle Proteins; Mutate; Mutation; Myocardium; Pathogenicity; Patients; Pattern; Phenotype; Physiological; Property; Proteins; Sarcomeres; Solid; Statistical Mechanics; Structure; Surface; Techniques; Technology; Testing; Tropomyosin; Variant; Viral; Work; behavior in vitro; cardiac tissue engineering; cell growth; clinical practice; experimental study; flexibility; genetic information; genetic makeup; molecular dynamics; molecular scale; multi-scale modeling; mutant; overexpression; 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.

项目概要/摘要 通过混合计算-实验方法揭示突变 TPM1 的病理机制 该提案的目标是开发和验证可以预测心脏的多尺度计算方法 基于基因组成的肌肉行为。单基因突变已被确定为致病因素 由于基因组测序的出现，导致多种心血管疾病的因素 技术。遗传信息有能力在很多方面改变临床实践，但其潜力 由于将突变与可观察到的事件联系起来的事件链存在重大知识差距，因此仍未实现 疾病状态。我们的目标是通过使用已知突变来解锁丰富的分子信息 新的多尺度模型可以预测分子尺度现象并将其向上投影到 生理相关性。凭借跨学科的研究，我们准备在实现这一目标方面取得关键进展 团队包括多尺度建模、结构生物学、生物物理学、肌肉力学和干细胞方面的专家 细胞生物学。我们将重点关注原肌球蛋白 (TPM1)，这是一种调节心肌收缩和收缩的蛋白质。 当它发生突变时，可能会导致一种危及生命的疾病，称为肥厚性心肌病（HCM）。在 在细胞水平上，HCM 涉及由于肌肉蛋白表达增加而导致的异常细胞生长，但是 原肌球蛋白突变如何触发这种过度表达尚不清楚。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补，我们将追踪五种因素的影响 跨分子、亚细胞和细胞尺度的原肌球蛋白突变。在目标 1 中，我们将进行分子 动力学模拟预测原肌球蛋白灵活性和肌动蛋白表面相互作用引起的变化 突变。统计力学的原理将用于将这些变化嵌入到一个模型中 大分子肌动蛋白丝复合物。这种尺度交叉技术将能够预测突变如何发生 影响体外细丝行为。配套实验将测试模型的预测。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内，以预测动态 每个突变体的肌肉抽搐反应。这些反应将通过病毒表达来检查准确性 人源工程心脏组织中的突变原肌球蛋白。 Aim 3 将使用 Aims 中开发的模型 1 和 2 用于预测患者中发现但从未验证的 20 个 TPM1 变异的肥大致病性 实验性地。将通过将一些分析的变体放入工程心脏中来检查预测 组织并测量其肥大反应。这些目标的可行性很高，因为我们的团队 将突变原肌球蛋白的结构特性与其生理学联系起来所需的独特专业知识 行为。在展示成功的基因型-表型建模方法时，我们的工作将为 用于许多其他具有遗传起源的心血管疾病的机制研究。