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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：
10614628
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/10614628
关键词：
Address Animal Model Animals Area Biochemical Biological Assay Biophysical Process Biophysics Calibration 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 classification algorithm 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 simulation small molecule success targeted treatment therapeutic target 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 摘要心肌病，包括肥厚型心肌病（HCM）和扩张型心肌病心肌病（DCM）是实施精准医疗战略的理想场所。这是由于更常规的基因检测和大量的知识，肌节变异的潜在生物物理机制，这有助于DCM和 HCM。虽然肌节变异如何引起心肌病的机制是一个积极的调查领域，很明显，他们破坏了微调的力产生性能，心肌细胞许多研究人员使用了各种生物物理和生物化学测定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是信息丰富的，但增量和无法评估许多立即变体。这一领域的成功需要强大的高通量测定，同时分析数千种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总结为肌节变异体的复杂的丝内和丝间相互作用。此外，我们可以利用这些特征用于变体分类、预测治疗反应和鉴定新的治疗靶点。测试这些假设需要1）大量变体数据集，2）模型解释了肌节中不同的位置和丰度，以及3）开发和验证数据分析方法。肌节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验证。我们将使用一个空间上明确的肌节的计算模型，可以模拟肌节中的扰动如何机械化学改变肌细胞力的产生。会产生模拟抽搐，验证并用于预测靶向治疗。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Danicamtiv increases myosin recruitment and alters the chemomechanical cross bridge cycle in cardiac muscle.

Danicamtiv 增加肌球蛋白募集并改变心肌中的化学机械跨桥循环。

DOI：
10.1101/2023.01.31.526380
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Kooiker,KristinaB;Mohran,Saffie;Turner,KyrahL;Ma,Weikang;Flint,Galina;Qi,Lin;Gao,Chengqian;Zheng,Yahan;McMillen,TimothyS;Mandrycky,Christian;Martinson,Amy;Mahoney-Schaefer,Max;Freeman,JeremyC;CostalesArenas,ElijahGabriela;
通讯作者：