Cardiac fibrosis is a major contributor to diastolic and systolic dysfunction for millions of heart failure patients. Unfortunately, current prediction and control over cardiac fibrosis are lacking due in part to complexity within collagen regulation networks, and in part to patient-to-patient variabilities in the biochemical and mechanical cues that regulate collagen turnover. Our overarching hypothesis is that computationally integrating multiple biochemical and mechanical signaling pathways (rather than a single biomarker) will enable personalized fibrosis risk predictions and improved therapy selection. In preliminary work, we have developed two unique, large-scale network models spanning critical collagen regulation processes: a cardiac fibroblast intracellular signaling network and an extracellular collagen-MMP-TIMP interaction network. For the proposed work, we will integrate the intracellular and extracellular network models with new cell culture experiments, existing animal experiments, and existing patient datasets in order to test the model?s ability for predicting cardiac fibrosis across patient-specific variabilities. We have assembled a team of investigators with expertise spanning computational modeling, in vitro bioreactors, advanced microscopy, fibroblast and matrix biology, clinical assessment and treatment of heart failure, and biostatistical analysis, in order to accomplish the following aims:
Aim 1 A will test the model-predicted hypothesis that mechanical loading can sensitize, desensitize, and reverse fibroblast signaling responses to biochemical cues;
Aim 1 B will test the hypothesis that mechanical loading can increase and decrease MMP-mediated collagen degradation in an isoform-specific manner;
Aim 2 will integrate the intracellular and extracellular network models and test model-predicted matrix turnover dynamics against cardiac fibrosis time-courses available in the literature;
and Aim 3 will test model-based prognosis across patient- specific chemo-mechano-contexts. Successful completion of this work will (1) uncover fundamental biological understanding of chemo-mechano-interactions regulating collagen remodeling, and (2) produce a publicly available computational model capable of predicting cardiac fibrosis given a personalized chemo-mechano- context. Our follow-up work will utilize this model for computational drug screens to improve current therapy selection for patient-specific conditions and to discover novel therapeutic targets for controlling tissue fibrosis.
Cardiac fibrosis refers to an excessive buildup of extracellular matrix in the heart, which can contribute to the development of heart failure. Controlling cardiac fibrosis is very difficult due to a complex matrix regulation system and large variabilities between different patients. We are developing and testing computational models that can run personalized simulations using patient-specific measurements of biochemical and mechanical conditions, so that we can better predict which patients will develop more fibrosis and predict which patients will respond best to which therapies.