Abstract
Heart Failure (HF) is a complex disease involving numerous environmental and genetic factors. We previously reported a genetic analysis of HF traits in a population of inbred mouse strains treated with isoproterenol, a β-adrenergic agonist used to mimic catecholamine-driven cardiac hypertrophy. We now present a systems genetics analysis in which we have used left ventricular transcript levels from these mice to perform co-expression network modeling. We constructed gene networks composed of 8,126 genes and 20 modules using the wMICA algorithm. In the wMICA network generated from treated hearts, we identified a module with significant correlations to several HF-related phenotypic traits. Further analysis of this module showed significant over-representation of genes known to contribute to the development of HF. Using the causal modeling algorithm NEO, we identified the gene Adamts2 as a putative master regulator of the module. We then validated the role of this gene through siRNA-mediated knockdown in neonatal rat ventricular myocytes (NRVM). Consistent with our model, Adamts2 silencing was able to regulate the expression of the genes residing within the module as well as impairing isoproterenol-induced cell size changes . Our results provide a view of higher order interactions in heart failure with potential to facilitate diagnostic and therapeutic approaches.
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