Modeling Cardiac Fibroblast Signaling to Predict Context-Dependent Modulators of Cardiac Fibrosis

University Of Virginia, Charlottesville VA

Investigators

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Abstract

Heart failure is a growing health burden in the United States, and ischemic heart failure, specifically, is a common occurrence following acute myocardial infarction (MI) despite improvements in acute MI survival. Fibroblasts play a prominent role during the different phases of wound healing post-MI through up-regulation of proteinases during the inflammatory phase and of extracellular matrix (ECM) components during the proliferative phase. Optimizing fibroblast activity during the inflammatory and proliferative phases post-MI could help prevent the development of ischemic heart failure. This study utilizes an innovative computational modeling approach with experimental validation to determine the drivers of ECM remodeling by cardiac fibroblasts in the inflammatory and proliferative phases. In this proposal, the first aim is to develop a large-scale model of fibroblast signaling and experimentally validate the predicted context-dependent signaling drivers in cardiac fibroblast culture. The second aim is to use this model with known drug-protein interactions to predict context-dependent therapeutics that can modulate expression of ECM proteins. These investigations are expected to identify promising therapies against cardiac fibrosis and outline a new approach for solving complex therapeutic problems such as the pathogenesis of heart failure post-MI.