Abstract Body: Background: Heart failure (HF), manifested as compromised cardiac function and output, is a leading cause of death in the United States. HF -omics studies in humans face challenges from late-staged disease detection, primary sample acquisition, and significant environmental effects on gene expression. To better understand HF molecular etiology, we initiated an HF study in the Collaborative Cross (CC), a genetically diverse mouse population.
Methods and Results: HF was modeled using synthetic β-agonist isoproterenol (ISO), administered to CC mice (from 8 founder and 63 derived strains, 2 mice per sex) at 30 mg/kg/day for 21 days via implantable osmotic minipumps, with saline as control. HF progression was tracked via echocardiography and organ weights, and left ventricles were collected for bulk RNA sequencing.
Gene co-expression networks were constructed using Weighted Gene Correlation Network Analysis (WGCNA), a correlation-based networks approach that treats genes as nodes, gene clusters as modules, and edges derived from Pearson correlation on gene expression. Transcriptomes from control and ISO-treated mice were analyzed separately and together. Modules eigengenes were correlated with HF traits (i.e. ejection fraction, ventricular size, heart weight) to uncover modules with significant correlation. Modules were analyzed for enriched GO terms using the GeneAnalytics suite to identify enriched gene modules. We then used a curated list of cell-type specific genes to construct cell-type specific and inter-cell-type networks to infer local or paracrine signaling in the transcriptional modules. Using the Quantitative Endocrine Network Interaction Estimation (QENIE) framework, we generated a ranked list of secreted factors from one cell type based on their expression correlation with module genes of another cell type, illustrating an effect of intercellular crosstalk and transcriptional landscape modification.
Conclusion: By modeling biological gene networks accompanied by context-specific evidence, we highlight novel HF-associated genes with the goal of identifying major regulators of HF pathophysiology for future mechanistic study. Future works will focus on experimental validation of these findings.