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

项目概要/摘要 通过计算-实验混合方法揭示突变型TPM 1的致病机制 该提案的目标是开发和验证多尺度计算方法， 肌肉行为的基因组成。单基因突变已被确定为病因 由于基因组测序的出现， 技术.遗传信息有能力在许多方面改变临床实践，但其潜力 仍然没有实现，因为在将突变与可观察到的 疾病状态。我们的目标是通过使用 新的多尺度模型，可以预测分子尺度的现象，并将其投射到 生理相关性由于跨学科的合作，我们准备朝着这一目标取得关键进展。 该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（TPM 1），一种调节心肌收缩的蛋白质， 一旦发生突变，就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，HCM涉及由于肌肉蛋白表达增加引起的异常细胞生长，但 原肌球蛋白突变究竟如何触发这种过表达尚不清楚。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合，我们将跟踪五种影响 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 动力学模拟预测原肌球蛋白的灵活性和肌动蛋白表面相互作用的变化引起的 突变统计力学的原理将被用来嵌入这些变化的模型， 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 影响体外细丝行为。伴随实验将测试模型的预测。对于目标2， 细丝模型将被放置在心脏肌节的代表内，以预测动态 每种突变体的肌肉抽搐反应将通过以下病毒表达检查这些应答的准确性： 人源性工程心脏组织中的突变原肌球蛋白。Aim 3将使用Aims开发的模型 1和2预测在患者中鉴定但从未验证的20种TPM 1变体的肥大致病性 实验性的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。

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.