Network Modeling Approach to Predict Myofibroblast Differentiation

Fibrotic disease is a major cause of morbidity and mortality and is characterized by the transition of resident fibroblast cells into active myofibroblasts, identified by their expression of alpha smooth muscle actin. Myofibroblast differentiation is regulated by growth factor signaling and mechanical signals transduced through integrins, which converge at focal adhesion proteins (Src and FAK) and MAPK signaling, but lead to divergent outcomes. While details are known about individual pathways, little is known about their interactions. To this end, an ODE-based model of this cell signaling network was developed in parallel with experiments to analyze potential mechanisms of crosstalk and regulation of SMA production. We found that cells lacking Src or FAK produce significantly less or more SMA than wild type cells, respectively. Transforming growth factor beta 1 and fibroblast growth factor signal through ERK and MAPK p38 with different dynamic profiles to increase or decrease SMA expression, respectively. Our model effectively recreated SMA expression levels across a set of 22 experimental conditions and matched some features of transient phosphorylation of ERK and p38. These results support a potential mechanism for regulation of fibroblast differentiation: SMA production is promoted by active p38 and Src and opposed by ERK.