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：Farid Moussavi-Harami 抽象的心肌病，包括肥厚性心肌病（HCM），并扩张心肌病（DCM）是实施精密医学策略的理想场所。这是由于更多常规使用基因检测以及有关的大量知识肉瘤变异的潜在生物物理机制，这既有助于DCM和DCM HCM。虽然肉瘤变体如何引起心肌病是一种活跃的机制调查领域，很明显，它们破坏心肌细胞。许多研究人员使用了各种生物物理和生化测定研究肉类变体的机制，然后将这些研究扩展到细胞，组织和动物模型。这些方法是有益的，但是渐进，无法评估许多一次变体。在这一领域的成功需要强大的高通量测定法一次分析数千个不同变体。我们的建议将直接克服限制在现场通过将数据分析应用于生物物理模拟和实验性心脏抽搐。基本假设是心脏抽搐的主要特征总结肉瘤变体的复杂内部和丝间相互作用。而且，我们可以利用这些用于变异分类的功能，预测了治疗反应和新的识别治疗靶标。测试这些假设需要1）大型变体数据集，2）模型这是肉瘤中的变化位置和丰富性的变化，3）开发和验证数据分析方法。肉瘤变体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。我们将使用空间上的明确可以模拟肌动扰动的肌罩的计算模型机械化学改变了心肌力的产生。将生成模拟的抽搐，经过验证并用于预测靶向治疗剂。