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J Mol Cell Cardiol. 2016 May;94:72-81. doi: 10.1016/j.yjmcc.2016.03.008. Epub 2016 Mar 23.

A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation.

Author information

1
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
2
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA. Electronic address: jsaucerman@virginia.edu.

Abstract

Cardiac fibroblasts support heart function, and aberrant fibroblast signaling can lead to fibrosis and cardiac dysfunction. Yet how signaling molecules drive myofibroblast differentiation and fibrosis in the complex signaling environment of cardiac injury remains unclear. We developed a large-scale computational model of cardiac fibroblast signaling in order to identify regulators of fibrosis under diverse signaling contexts. The model network integrates 10 signaling pathways, including 91 nodes and 134 reactions, and it correctly predicted 80% of independent previous experiments. The model predicted key fibrotic signaling regulators (e.g. reactive oxygen species, tissue growth factor β (TGFβ) receptor), whose function varied depending on the extracellular environment. We characterized how network structure relates to function, identified functional modules, and predicted cross-talk between TGFβ and mechanical signaling, which was validated experimentally in adult cardiac fibroblasts. This study provides a systems framework for predicting key regulators of fibroblast signaling across diverse signaling contexts.

KEYWORDS:

Cardiac fibrosis; Fibroblast; Signaling networks; Systems biology

PMID:
27017945
PMCID:
PMC4861657
DOI:
10.1016/j.yjmcc.2016.03.008
[Indexed for MEDLINE]
Free PMC Article

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