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.