Cardiac Fibroblast Modeling to Predict and Control Fibrosis in HFrEF Patients

About This Grant

PROJECT SUMMARY 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 improve therapy selection for heart failure patients. In preliminary work, we have developed a unique, large-scale systems mechanobiology model of cardiac fibrosis as well as a unique in vitro cell culture platform with dynamic control over mechanical stimulation of tissue constructs. For the proposed work, we will integrate these computational and experimental platforms with heart failure patient clinical data and primary cells isolated directly from heart failure patients. Aim 1 will identify cardiac fibroblast signaling feedback loops in dynamic mechanical environments; Aim 2 will test model- predicted personalized treatment responses with patient-specific in vitro cultures; Aim 3 will test the prognostic value of serum, imaging, and tissue biomarkers for predicting HFrEF fibrosis and patient outcomes. Successful completion of these aims will (1) uncover fundamental biological understanding of signaling feedback loops regulating collagen turnover in human disease (2) identify new potential therapeutic targets for controlling cardiac fibrosis in HFrEF patients, and (3) produce a model-based personalized risk tool for informing clinical decisions.