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 i s 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.
At 40 years old the lifetime risk of developing heart failure is 1 in 5, and one of the characteristics of a failing heart is fibrosis (excess collagen and other matrix proteins) caused by an imbalance in cardiac fibroblast activity. We have developed a computational model that can predict fibroblast activity in a variety of signaling environments associated with heart failure, such as myocardial infarction (heart attack). This study will be useful for more efficiently identifying mechanisms of heart failure development and screening drugs that prevent or reverse cardiac fibrosis.
