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Bioinformatics, Computational and Systems Biology

Bioinformatics, Computational and Systems Biology - Poster Session E

M31 - Network Modeling Identified CHD Gene Variants that Dysregulate Cardiomyocyte Proliferation

Saturday, October 11, 2025
10:00 AM - 11:00 AM PDT
Location: Exhibit Hall F, G & H

Introduction: : Congenital Heart Disease (CHD), the leading cause of infant mortality, is characterized by structural and functional heart defects present at birth due to abnormal heart development. However, many biological mechanisms underlying CHD remain poorly understood because of complex interactions between genetic and environmental factors. Cardiomyocyte (CM) proliferation contributes to the proper formation of cardiac structures, and abnormalities in CM proliferation could lead to CHD phenotypes. Therefore, understanding how CHD gene variants regulate CM signaling pathways has the potential to identify therapeutic targets and advance treatment options. In this study, we test the hypothesis that CHD gene variants dysregulate cardiomyocyte proliferation through established signaling networks.

Materials and

Methods: : Network simulations were run using logic-based ODEs in Netflux. Seventy-five CHD gene variants of the 189 in the CHDgene database [1] were individually integrated into a previously developed CM proliferation network model [2] using Omnipath and PathLinker tools in Cytoscape. Validation accuracy of 94% against independent experiments from the literature was checked with each expanded network model to ensure original model predictions were maintained. Next, in the context of each CHD variant, a knockdown screen was simulated to predict pathways that regulate cardiomyocyte cell cycle activity, and the change in activity was measured for five cell cycle outputs: DNA Replication, Mitosis, Polyploid, Binucleation, and Cytokinesis. Mechanistic subnetworks were built using the subset of network nodes that change in response to the knockdown of a gene and mediate the effect on downstream cell cycle outputs post-knockdown.

Results, Conclusions, and Discussions:: Hierarchical clustering of seventy-five CHD gene knockdown simulations showed distinct patterns of network-wide responses, suggesting that different CHD variants regulate cardiomyocyte signaling through various mechanisms. Knockdown screens of 32 genes predicted anti-proliferative activity with decreased fold changes across all cell cycle outputs, while another 32 genes predicted pro-proliferative activity with increased outputs. The remaining genes showed dysregulation of cell cycle outputs. For example, Activin A Receptor Type 1 (ACVR1) knockdown induced a unique network state, discordantly downregulating cytokinesis but upregulating all the other cell cycle outputs. A mechanistic subnetwork predicted that ACVR1 knockdown inhibits the Rock1 pathway, which mediates cytokinesis, and activates the pro-proliferative YAP/TEAD pathway.

Seventy-five CHD variants were successfully integrated into a previously developed CM proliferation network model, and knockdown simulations were used to mechanistically predict pathways that regulate CM proliferation outputs. The model predicts that ACVR1 knockdown selectively downregulates cytokinesis while enhancing the other cell cycle outputs, potentially explaining the increased number of polyploid and multinucleated cardiomyocytes found in some CHD-affected infants [3]. Ongoing work will use genetic perturbations in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to experimentally validate the predictions of ACVR1 and other CHD genes.

Acknowledgements and/or References (Optional): :

References:
1. Yang et al., Circulation: Genomic and Precision Medicine 2022
2. Harris et al., Development 2025.
3. Liang et al., Pediatric Discovery 2024

Acknowledgements/

Funding: NSF Award #2447802