(T-034) In silico model of hypertrophic cardiomyopathy caused by MYBPC3 mutation predicted key drivers of cardiomyocyte morphology
Tuesday, October 21, 2025
7:00 AM - 1:45 PM MDT
Location: Colorado A
Alice Luanpaisanon – Biomedical Engineering – University of Virginia; Matthew Wolf – Cardiovascular medicine – University of Virginia; Jeffrey Saucerman – Biomedical Engineering and Cardiovascular Medicine – University of Virginia
Graduate Student University of Virginia Charlottesville, Virginia, United States
Disclosure(s):
Alice Luanpaisanon: No financial relationships to disclose
Introduction: Hypertrophic cardiomyopathy (HCM) is the most frequent hereditary cardiomyopathy and a leading cause of sudden cardiac death, particularly in adolescent. Despite recent improved prognosis and FDA approved drugs, current cardiac therapeutics cannot efficiently prevent remodeling across all HCM patient populations due to genetic variability. Therefore, we require a systems biology approach to understand how the complex cardiac signaling networks regulate cardiomyocyte shape and size due to an HCM mutation. This study leverages a computational systems approach to explore MYBPC3-induced HCM mutation regulation of cardiomyocyte hypertrophy.
Methods: A logic-based differential equations model (NETFLUX) was employed to construct a signaling network validated using in vitro data from published studies. This model captured signaling dynamics and cross talks between pathways governing cardiomyocyte size and shape. Additionally, a MYBPC3 HCM mutation is integrated in the model as an input providing a comparison between a control and simulated HCM responses. Sensitivity analyses are performed to investigate the most influential signaling pathways that mediate changes in mass. We then integrated drug-target interactions from a publicly available database (DrugBank) to simulate the effect of drugs on cardiomyocyte size and shape in the context of MYBPC3 mutation.
Results: We created a MYBPC3-induced HCM model by integrating an additional cardiac myosin binding protein-C node to a previously validated inherited cardiomyopathy signaling network. This updated model predicted an increase in mass when the HCM mutation is simulated. The simulated inhibition of each node in the model revealed that inhibition of calcium pathway, growth factor receptor, and titin stiffness could mitigate the HCM phenotype by reducing mass. The sensitivity analysis results in combination with DrugBank integration identified PI3K- and mTOR- antagonists as potential therapeutic targets mediating the decrease in mass. Conclusion and Future Work: Our systems approach gave insights on the mechanisms governing the cardiomyocyte response to a MYBPC3-induced HCM mutation. We identified FDA-approved PI3K and mTOR inhibitors that are predicted to reduce cardiac mass in MYBPC3-induced HCM mutation. This will be further validated via a MYBPC3-HCM model using human iPSC-CMs with MYBPC3-induced mutations. This will create a cell line that will provide genetically precise comparisons for identifying new molecular mechanisms of disease, thereby offering a strategy for advancing precision medicine.
Citations: [1] Khalilimeybodi, Ali, et al. “Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy.” Journal of Molecular and Cellular Cardiology, vol. 174, Jan. 2023, pp. 1–14