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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Zeigler, AC; Richardson, WJ; Holmes, JW; Saucerman, JJ

Published Date

  • May 2016

Published In

Volume / Issue

  • 94 /

Start / End Page

  • 72 - 81

PubMed ID

  • 27017945

Pubmed Central ID

  • 27017945

Electronic International Standard Serial Number (EISSN)

  • 1095-8584

Digital Object Identifier (DOI)

  • 10.1016/j.yjmcc.2016.03.008

Language

  • eng

Conference Location

  • England