Signaling network model of cardiomyocyte morphological changes in familial cardiomyopathy

Ali Khalilimeybodi1, Muhammad Riaz2, Stuart G Campbell3

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America.

Insights

Researchers developed a signaling network model to understand how gene mutations cause hypertrophic (HCM) and dilated (DCM) cardiomyopathies. The model accurately predicts disease phenotypes and suggests potential drug targets for familial cardiomyopathy.

Area of Science:

  • Cardiovascular Research
  • Systems Biology
  • Genetics

Background:

  • Familial cardiomyopathy, a cause of heart failure, arises from gene mutations affecting sarcomeric and cytoskeletal proteins.
  • Existing knowledge gaps hinder understanding of molecular mechanisms linking genetic mutations to hypertrophic (HCM) and dilated (DCM) cardiomyopathy phenotypes.

Purpose of the Study:

  • To develop and validate a cohesive signaling network model for investigating genotype-to-phenotype mechanisms in familial cardiomyopathy.
  • To identify key signaling pathways and potential therapeutic targets for HCM and DCM.

Main Methods:

  • Integrated preclinical data into a logic-based differential equations signaling network model.
  • Evaluated model performance across four contexts: HCM, DCM, pressure overload, and volume overload.
  • Conducted global sensitivity and structural revision analyses, and simulated pharmacotherapy effects.

Main Results:

  • The model achieved an overall prediction accuracy of 83.8% (HCM: 90%, DCM: 75%).
  • Key signaling pathways identified include calcium-mediated force development and calcium-calmodulin kinase signaling.
  • In silico pharmacotherapy simulations predicted effective treatment strategies, with ERK1/2 and PI3K-AKT inhibition rescuing HCM phenotypes in patient-derived cells.

Conclusions:

  • The developed HCM/DCM signaling network model effectively elucidates genotype-to-phenotype mechanisms in familial cardiomyopathy.
  • The model provides a platform for predicting disease progression and evaluating therapeutic interventions.
  • Identified signaling components and pathways offer promising targets for novel combination pharmacotherapies.

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