Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction

Abstract

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual overexpression screens with static cytokine pairs and after myocardial infarction predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the proliferative phase. NADPH oxidase overexpression sustained collagen expression from proliferative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later sustained collagen expression due to the TGFβ positive feedback loop. These modelbased predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction..

Network model‐based screen for FDA‐approved drugs affecting cardiac fibrosis

“…We used a previously published computational model of cardiac fibroblast signaling 27,28 . Node activity (y) is simulated by logic-based differential equations (LDEs) 29 with Hill formalism and default parameters for the weight (w), Hill coefficient (n) and EC50.…”

“…To more accurately replicate in vivo conditions post-MI, we incorporated time-varying input curves for the 9 paracrine inputs in the model using post-MI data in the literature. The development of these idealized paracrine signaling curves was described previously 28 .…”

“…We next modeled the effects of these drugs within a dynamic post-MI signaling environment, which was previously described 28 . For collagen, proliferation, and EDAFN, the predicted effect of each drug at the different phases of infarct healing ( Figure 4A) we used the dynamic model to predict percent collagen area fraction post-MI.…”

“…The dynamic paracrine signaling profile that defines the post-MI simulations described here, allows for phase-specific modeling 28 which could predict effective timing of…”

“…In this study, we employed a systems biology approach to screen for drugs effective against heart failure in the post-MI environment. We build on our previous literaturevalidated model of cardiac fibroblast signaling 27 , which has predicted network mechanisms underlying fibroblast phenotype in vitro and during post-MI wound healing 28 . In this study, we…”