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.

项目摘要/摘要 通过混合计算实验方法揭示突变体TPM1的病理机制 该建议的目的是开发和验证可以预测心脏的多尺度计算方法 肌肉行为基于基因组成。单基因突变已被确定为病因 由于基因组测序的出现，多种心血管疾病的因素 技术。遗传信息有能力在许多方面改变临床实践，但其潜力 由于将突变与可观察的事件链中的主要知识差距保持不变，因此仍然没有实现 疾病状态。我们的目标是解锁通过使用的丰富分子信息 可以预测分子规模现象并将其向上投射到量表的新的多尺度模型 生理相关性。通过跨学科，我们准备取得关键的进步 包括多尺度建模，结构生物学，生物物理学，肌肉力学和STEM的专家 细胞生物学。我们将重点介绍肌球蛋白（TPM1），该蛋白质可调节心肌收缩和 突变后，可能导致威胁生命的疾病，称为肥厚性心肌病（HCM）。在 细胞水平HCM涉及由于肌肉蛋白表达增加而导致的异常细胞生长，但 尚不清楚如何触发这种过表达是如何触发的。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封闭，我们将追踪五个的影响 跨分子，亚细胞和细胞尺度跨越肌球蛋白突变。在AIM 1中，我们将执行分子 动态模拟以预测肌球蛋白柔韧性的变化和肌动蛋白表面相互作用。 突变。统计力学原理将用于将这些变化嵌入 大分子肌动蛋白丝复合物。这种规模交叉技术将使突变如何预测 在体外影响细丝行为。伴侣实验将测试模型预测。对于AIM 2，肌动蛋白 细丝模型将放置在心脏肌膜的表示之内，以预测动态 每个突变体的肌肉抽搐反应。这些响应将通过病毒表达的准确性检查 人类衍生的工程心组织中的突变型肌动蛋白。 AIM 3将使用目标中开发的模型 1＆2预测20个TPM1变体的肥厚性致病性，但从未验证 实验。将通过将一些分析的变体放入工程心脏来检查预测 组织和测量其肥厚反应。这些目标的可行性很高，因为我们的团队有 将突变体肌动物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，我们的工作将为方式铺平 用于对许多其他具有遗传起源的心血管疾病的机械研究。