Fibroblast mechanotransduction network predicts targets for mechano-adaptive infarct therapies
Abstract
Regional control of fibrosis after myocardial infarction is critical for maintaining structural integrity in the infarct while preventing collagen accumulation in non-infarcted areas. Cardiac fibroblasts modulate matrix turnover in response to biochemical and biomechanical cues, but the complex interactions between signaling pathways confounds efforts to develop therapies for regional scar formation. We employed a logic-based ordinary differential equation model of fibroblast mechano-chemo signal transduction to predict matrix protein expression in response to canonical biochemical stimuli and mechanical tension. Functional analysis of mechano-chemo interactions showed extensive pathway crosstalk with tension amplifying, dampening, or reversing responses to biochemical stimuli. Comprehensive drug target screens identified 13 mechano-adaptive therapies that promote matrix accumulation in regions where it is needed and reduce matrix levels in regions where it is not needed. Our predictions suggest that mechano-chemo interactions likely mediate cell behavior across many tissues and demonstrate the utility of multi-pathway signaling networks in discovering therapies for context-specific disease states.
Data availability
Our model, datasets used for analysis, and scripts necessary to reproduce all analysis and figures are freely available on GitHub ... https://github.com/SysMechBioLab/Fibroblast_Signaling_Network_Model.
Article and author information
Author details
Funding
National Institute of General Medical Sciences (GM121342)
- William James Richardson
National Heart, Lung, and Blood Institute (HL144927)
- William James Richardson
American Heart Association (17SDG33410658)
- William James Richardson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Rogers & Richardson
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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