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 (MI) 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 the reparative phase required paired stimuli (e.g. TGFβ and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFβ positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based 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.

Figure 1:. Computational model of post-MI fibroblast dynamics.

A) Schematic of coupled model of post-MI fibroblast dynamics, incorporating dynamic paracrine stimuli, a fibroblast signaling network, and tissue-level collagen metabolism. B) Dynamic paracrine stimuli were modeled by fitting idealized bi-exponential curves to post-MI timecourse data from the literature[, –59]. These time-dependent signals provide inputs to the signaling network model. Experimental data were digitized from the indicated sources.

Figure 2:. Modeling of post-MI fibroblast signaling reproduces dynamics of post-MI collagen expression and deposition.

A) Validation of the predicted timing of collagen expression post-MI against experimental data from rat infarcts. B) Validation of predicted collagen accumulation (area fraction) post-MI from the tissue-level model. Experimental data were digitized from [37, 51].Model predictions are shown for the default model which has a uniform paracrine input peak height, along with an ensemble of 500 simulations in which the paracrine peak heights were randomly varied.

Figure 3:. Simplified paracrine stimuli that mimic distinct phases of the post-MI fibroblast phenotype.

A) Principal component analysis (PCA) to visualize the fibroblast phenotype at specific times post-MI (red circles) or at steady state with 45 static paracrine conditions (representative singles in dark blue, pairs in light blue). B) PCA node loadings show the contribution of each node towards the overall predicted fibroblast phenotype in the first two principal components. C) Activity profile of fibroblast phenotype nodes at selected timepoints from dynamic post-MI simulations (left), or at steady-state with the best matching single (center) or paired (right) paracrine stimuli.

Figure 4:. Modulators of collagen mRNA in the context of static paracrine stimuli that mimic inflammatory (IL1+NP), reparative (TGFβ+ET1) and maturation (NE+AngII) phases.

Network nodes were each overexpressed 10-fold (normalized expression parameter ymax = 10) in the context of the indicated static paracrine stimuli (set to 0.6 normalized activity), predicting the change in collagen I and III mRNA compared to no overexpression. Nodes were rank-ordered by their predicted effect on collagen I and III mRNA expression with no paracrine stimulus (Control). Smad3, PKG, NOX, IL1, ETAR and B1int are emphasized for comparison with subsequent simulations of post-MI dynamics. Overexpressed nodes that did not affect collagen mRNA in any condition are not shown.

Figure 5:. Post-MI overexpression screen to identify phase-specific regulators of collagen mRNA expression.

Each row shows the effect of 10-fold overexpression of the indicated node. The predicted change in collagen expression is calculated as [Collagen I mRNA + Collagen III mRNA]_{Overexpressed}.-[Collagen I RNA + Collagen III mRNA]_control. overexpressed nodes that did not affect collagen mRNA in any condition are not shown.

Figure 6:. Mechanisms contributing to context-dependent regulators of collagen expression post-MI.

Overexpression or knockdown were simulated by increasing or decreasing the normalized expression parameter (ymax) for Smad7 (panel A), PKG (B), NOX (C), IL1 (D). The resulting post-MI dynamics of that node’s activity, collagen mRNA expression, and collagen area fraction are shown. Simplified schematics indicate the network mechanisms by which these nodes regulate collagen expression.