A Computational Model of Traction Force-Induced Fibronectin Fibril Growth

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 DESCRIPTION (provided by applicant): Fibrosis is the aberrant assembly of extracellular matrix. It is seen in nearly all organ systems, and is responsible for organ failure in heart, live, lung, and kidney disease, while also playing a prominent role in malignant tumor growth and implanted biomaterial failure. Despite its pathological prevalence, there are scant effective therapeutics for treating fibrosis. Fibrosis is initiated by an inflammatory response that drives resident cells to differentiate into a myofibroblast phenotype. Under a normal tissue healing scenario, this process ceases upon dissipation of the original inflammatory signal. In fibrosis, this process continues in a self- sustaining manner even after inflammatory signals have ceased. Understanding of these dynamics is crucial to understanding fibrosis. A key component of fibrosis initiation is the cell-derived assembly of the extracellular matrix protein fibronectin Fibronectin (FN) is a soluble protein that is assembled into elastic, insoluble fibrils by cells. Despite over 40 years of research into FN fibril formation, the process is still not completely understood. This much we do know: cells stretch soluble FN, exposing a cryptic binding site that allows for the binding of a second FN. This process continues to form a rope-like fibril. Assembled FN fibrils contain a growth factor binding site that localizes pro-fibrotic growth factor at the cell surface. We hypothesize a positive feedback loop in which soluble growth factors at the site of damage initiate increased contractile forces and expression of FN, which drives FN assembly. This assembly clusters the pro-fibrotic growth factors at the cell surface, which in turn drives further FN assembly, pro-fibrotic growth factor expression, and a self-sustaining fibrosis system. To investigate this, we will develop a computational biophysical model that predicts the assembly of FN fibrils in response to actomyosin-driven forces. This model is a hybrid stochastic-deterministic model that builds on previously published models of the cell-substrate interface. The model will be validated using novel microfabricated substrates that allow for quantification of FN fibril growth and cell-generated traction forces along with a family of recombinant FN proteins that allow for visualization of FN domain opening during stretch. Development of the model will allow us to investigate molecular-scale FN assembly processes (including a novel hypothesis in which each Type III domain in FN is able to bind another FN) by predicting measurable, macro-scale events. We will then model the subsequent clustering and signaling of the pro-fibrotic growth factor TGF-b1 to predict parameter spaces that lead to sustainable signaling following the removal of initial insult. These model outcomes will be compared to in vitro fibrosis assays. We envision that this model will create a new paradigm of systems biology modeling in which the biophysics of the extracellular matrix are coupled to soluble growth factor signaling pathways. We believe that this line of modeling could lead to dramatic improvements in our understanding of fibrosis and thus impact a wide array of pathologies.