(M-064) Mechanistic Insights Uncover Novel Therapeutic Targets in Cardiac Hypertrophy

Lionel Watkins – Biomedical Engineering – University of Virginia; Jeffrey Saucerman – Biomedical Engineering – University of Virginia

Objectives: Cardiac hypertrophy is a key risk factor in the clinical prediction of heart failure. Signaling mechanisms that drive cardiac hypertrophy are poorly understood from a molecular and cellular perspective. I propose that unrecognized genetic contributors to hypertrophy can be uncovered by integrating large-scale phenotypic data from the publicly available, high-throughput database of the International Mouse Phenotyping Consortium (IMPC) with systems-level network modeling. I aim to use a well-established, literature-validated, large-scale network model of cardiac hypertrophy [1] to assess the IMPC genes’ effects on hypertrophic phenotypes and validate these findings in vitro to identify novel therapeutic targets for cardiac hypertrophy.

Methods: The IMPC is a global initiative that utilizes in vivo single-gene knockout mouse models to generate a high-throughput phenotyping resource encompassing approximately 9,000 genes, with approximately 1,000 of these having potential relevance to cardiac hypertrophy. Using known protein-protein interaction databases, I identified "1st neighbor" genes that directly interact with components of our established hypertrophy network model. I assessed the impact of these genes via in silico network knockdown simulations via our Netflux software [2], and binarization of IMPC phenotype data. When both methods suggested hypertrophic regulation, a gene was considered a candidate regulator. siRNA experiments have been performed in neonatal rat cardiomyocytes for validation of modeling results. High-content imaging analysis and qPCR are used to quantify changes in phenotype and confirm gene knockdown, with statistical analyses performed to establish confidence in experimental results.

Results: Combining the IMPC phenotype binarization with protein-protein interaction data and modeling with Netflux, 37 “1st neighbor genes” (genes with direct interactions to established hypertrophy network genes) were identified. Knockdown simulation identified 2 genes (LRIG1, CBL) as negative regulators (genes whose knockdown increases hypertrophy) of cardiac hypertrophy. 3 genes (VAV2, LEPR, RASA1) were identified as positive regulators (genes whose knockdown decreases hypertrophy). Given the network-wide hypertrophic effects enacted by their knockdowns, these five genes emerged as strong candidates for further experimental testing. siRNA-based gene silencing protocols continue to be refined to produce reliable quantitative experimental results and confirm the roles of these genes in hypertrophic regulation and support them as potential therapeutic targets.

Conclusions: Computational analysis revealed five novel genetic modulators of cardiac hypertrophy. Sensitivity testing confirmed known pathway components and supported the validity of the model. Ongoing knockdown experiments aim to corroborate the computational predictions and deepen our understanding of hypertrophic signaling.

Citations: [1] Ryall, Karen A., et al. "Network Reconstruction and Systems Analysis of Cardiac Myocyte Hypertrophy Signaling." Journal of Biological Chemistry, vol. 287, no. 50, 2012, pp. 42259–42268. https://doi.org/10.1074/jbc.M112.382937.

[2] Clark, Alexander P., et al. "Logic-Based Modeling of Biological Networks with Netflux." PLOS Computational Biology, vol. 21, no. 4, 2025, e1012864. https://doi.org/10.1371/journal.pcbi.1012864.

Keywords: Mechanistic modeling, Systems pharmacology, Cardiac hypertrophy