Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic

A Khalilimeybodi1, Jeffrey J Saucerman2, P Rangamani1

  • 1Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla CA 92093, United States of America.

PubMed

Insights

Familial hypertrophic cardiomyopathy (HCM) computational models reveal metabolic shifts and identify key regulators of cardiac hypertrophy and apoptosis. This work offers new therapeutic targets for preventing or reversing HCM phenotypes.

Area of Science:

  • Cardiovascular Biology
  • Computational Biology
  • Systems Biology

Background:

  • Familial hypertrophic cardiomyopathy (HCM) is a genetic heart disease leading to heart failure and sudden death.
  • Current understanding of genotype-to-phenotype links in HCM is limited by the complexity of signaling and metabolic pathways.
  • Therapeutic advancements for HCM patients are hindered by these knowledge gaps.

Purpose of the Study:

  • To develop a computational model of HCM integrating cardiomyocyte signaling and metabolic networks.
  • To elucidate the mechanisms linking HCM genotype to phenotype.
  • To identify potential therapeutic targets for HCM.

Main Methods:

  • Developed a stochastic logic-based ODE computational model of cardiomyocyte signaling and metabolism.
  • Integrated gene regulatory networks and post-translational modifications.
  • Validated the model against published data and transcriptomes from HCM mouse models.

Main Results:

  • Predicted HCM-induced metabolic shifts, including ATP synthase deficiency and a switch from fatty acid to carbohydrate metabolism.
  • Identified key transcription factors (STAT, SRF, GATA4, TP53, FoxO) regulating hypertrophy and apoptosis.
  • Discovered shared and context-specific mechanisms driving genotype-to-phenotype transition and proposed combination drug targets (e.g., mavacamten plus ROS inhibitors).

Conclusions:

  • The computational model provides novel insights into HCM pathogenesis.
  • Identified critical regulators and pathways involved in HCM progression.
  • Offers a framework for evaluating novel therapeutic strategies and understanding HCM variations.

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