Experimental and Computational Studies in Genetic Cardiomyopathies

遗传性心肌病的实验和计算研究

基本信息

批准号：
10443421
负责人：
Farid Moussavi-Harami
金额：
$ 66.98万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10443421
关键词：
Address Animal Model Animals Area Biochemical Biological Assay Biophysical Process Biophysics Cardiac Cardiac Myocytes Cardiomyopathies Categories Cell model Classification Complex Computer Models Data Data Analytics Data Set Development Dilated Cardiomyopathy Dimensions Disease Filament Functional disorder Generations Genetic Goals Graph Grouping Health Professional Human Hypertrophic Cardiomyopathy In Vitro Investigation Knowledge Location Measurement Mechanics Methods Microfilaments Modeling Molecular Muscle Muscle Cells Pathogenesis Patients Phenotype Precision therapeutics Prediction of Response to Therapy Property Proteins Research Personnel Rodent Rodent Model Sarcomeres System Testing Thick Filament Thin Filament Time Tissue Model Training Troponin Troponin C Troponin I Validation Variant Viral analytical method base computer studies experimental study genetic testing genetic variant heart function high throughput screening human tissue in vivo indexing individual patient individualized medicine inherited cardiomyopathy large datasets machine learning algorithm machine learning method machine learning model molecular phenotype new therapeutic target novel overexpression palliative precision medicine predicting response simulation small molecule success targeted treatment therapeutic target treatment response treatment strategy

项目摘要

Experimental and Computational Studies in Genetic Cardiomyopathies PI: Farid Moussavi-Harami Abstract Cardiomyopathies, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), are an ideal venue for implementing precision medicine strategies. This is due to more routine use of genetic testing and the vast amount of knowledge regarding underlying biophysical mechanisms of sarcomeric variants, which contribute to both DCM and HCM. While the mechanisms of how sarcomeric variants cause cardiomyopathies is an active area of investigation, it is clear that they disrupt the finely tuned force-generation properties of cardiomyocytes. Many investigators have used a variety of biophysical and biochemical assays to study mechanism of sarcomeric variants and then scale these studies up to cells, tissues and animal models. These approaches are informative, but incremental and unable to asses many variants at once. Success in this area requires robust high-throughput assays with the ability for analysis of thousands of divergent variants at once. Our proposal will directly overcome limitations in the field by applying data analytics to biophysical simulations and experimental cardiac twitches. The fundamental hypothesis is that the principal features of cardiac twitches summarize the complex intra and inter-filament interactions of sarcomeric variants. Moreover, we can utilize these features for variant classification, predicting therapeutic response and identification of new therapeutics targets. Testing these hypotheses requires 1) large datasets of variants, 2) models that account for variant location and abundance in sarcomeres and 3) development and validation of data analytic methods. Biophysical simulations of sarcomeric variants can provide such datasets, but require validation in experimental systems. We will use a spatially explicit computational model of the sarcomere that can simulate how perturbations in sarcomere mechanochemistry change myocyte force generation. Simulated twitches will be generated, validated and used for predicting targeted therapeutics.

遗传性心肌病的实验和计算研究 PI：法里德·穆萨维-哈拉米抽象的心肌病，包括肥厚型心肌病 (HCM) 和扩张型心肌病心肌病（DCM）是实施精准医疗策略的理想场所。这是由于基因检测的更常规使用以及关于基因检测的大量知识肌节变异的潜在生物物理机制，有助于 DCM 和肥厚型心肌病。虽然肌节变异如何引起心肌病的机制是一个活跃的研究领域。在研究领域，很明显它们破坏了精细调整的力生成特性心肌细胞。许多研究人员使用了各种生物物理和生化检测研究肌节变异的机制，然后将这些研究扩展到细胞、组织和动物模型。这些方法信息丰富，但是渐进式的，无法评估许多立即变体。该领域的成功需要强大的高通量检测，并且能够同时分析数千个不同的变体。我们的建议将直接克服限制通过将数据分析应用于生物物理模拟和实验心脏抽搐。基本假设是心脏抽搐的主要特征概括为肌节变体的复杂的肌丝内和肌丝间相互作用。此外，我们可以利用这些特征可用于变异分类、预测治疗反应和识别新的变异治疗目标。测试这些假设需要 1) 大型变体数据集，2) 模型解释了肌节的变异位置和丰度以及 3) 开发和验证的数据分析方法。肌节变异的生物物理模拟可以提供这样的数据集，但需要在实验系统中进行验证。我们将使用空间明确的肌节的计算模型，可以模拟肌节的扰动机械化学改变心肌细胞力的产生。将生成模拟抽搐，经验证并用于预测靶向治疗。