Reconstruction of Cardiomyocyte Growth and Remodeling Networks

Alterations in cardiomyocyte shape and biology are critical to the heart’s adaptive capability. While many biochemical and mechanical processes influencing cardiac hypertrophy have been isolated, the precise signaling mechanisms separating adaptive and maladaptive responses remain uncertain. Here, we integrate high-content image analysis and computational network modeling to identify novel control structures and pathways underlying cardiomyocyte growth and remodeling. First, we develop and validate a comprehensive literature-based predictive model of the cardiac mechano-signaling network. We use the model to identify key regulators of mechanical cues, to illuminate the mechanism of action of a combination therapy, and to predict further pairs of drug targets with maximum effects on mechano-signaling. Next, we identify clusters correlating with differential forms of hypertrophy from RPPA and phenotypic screens. Finally, we pioneer and implement a workflow for analysis, filtering, normalization, and interpretation of a genome-scale imaging screen, finding hundreds of novel regulators of cardiomyocyte morphology. Overall, this work determines signaling pathways driving variant myocardial outcomes and identifies new biochemical targets for modulating cardiac remodeling.