Intercellular model predicts mechanisms of inflammation-fibrosis coupling after myocardial infarction

Abstract

After myocardial infarction (MI), cardiac cells work together to regulate wound healing of the infarct. The pathological response to MI yields cardiac remodelling comprising inflammatory and fibrosis phases, and the interplay of cellular dynamics that underlies these phases has not been elucidated. This study developed a computational model to identify cytokine and cellular dynamics post-MI to predict mechanisms driving post-MI inflammation, resolution of inflammation, and scar formation. Additionally, this study evaluated the interdependence between inflammation and fibrosis. Our model bypassed limitations of in vivo approaches in achieving cellular specificity and performing specific perturbations such as global knockouts of chemical factors. The model predicted that inflammation is a graded response to initial infarct size that is amplified by a positive feedback loop between neutrophils and interleukin 1β (IL-1β). Resolution of inflammation was driven by degradation of IL-1β, matrix metalloproteinase 9, and transforming growth factor β (TGF-β), as well as apoptosis of neutrophils. Inflammation regulated TGFβ secretion directly through immune cell recruitment and indirectly through upregulation of macrophage phagocytosis. Lastly, we found that mature collagen deposition was an ultrasensitive switch in response to inflammation, which was amplified primarily by cardiac fibroblast proliferation. These findings describe the relationship between inflammation and fibrosis and highlight how the two responses work together post-MI. This model revealed that post-MI inflammation and fibrosis are dynamically coupled, which provides rationale for designing novel anti-inflammatory, pro-resolving or anti-fibrotic therapies that may improve the response to MI. KEY POINTS: Inflammation and matrix remodelling are two processes involved in wound healing after a heart attack. Cardiac cells work together to facilitate these processes; this is done by secreting cytokines that then regulate the cells themselves or other cells surrounding them. This study developed a computational model of the dynamics of cardiac cells and cytokines to predict mechanisms through which inflammation and matrix remodelling is regulated. We show the roles of various cytokines and signalling motifs in driving inflammation, resolution of inflammation and fibrosis. The novel concept of inflammation-fibrosis coupling, based on the model prediction that inflammation and fibrosis are dynamically coupled, provides rationale for future studies and for designing therapeutics to improve the response after a heart attack.

Keywords: inflammation-fibrosis coupling; intercellular dynamics; myocardial infarction.

Figure 1:

A model of cardiac intercellular dynamics post myocardial infarction. (A) Cellular components of the intercellular model. (B) Network representation of chemical factors secreted by cells, and how the secretion is regulated. (C) Network representation of cell populations regulated by chemical factors, cellular sources, and other model components.

Figure 2:

Intercellular model predicted dynamics measured in mice post myocardial infarction (MI). (A) Predicted dynamics of selected outputs were extended for 30 days after MI. The independent datasets used to calibrate (red cross, x) and validate (black circle, o) the simulated trends are shown for comparison. (B) The predicted dynamics of all the model outputs for 30 days post-MI are shown. All data are normalized to their respective maximum values.

Figure 3:

Intercellular model predicted qualitative outcomes of perturbations post myocardial infarction. (A-D) Predicted qualitative response of outputs is shown in the left columns in response to their respective perturbations. Qualitative experimental outcomes are in the right columns. Red indicates an increase from baseline output, blue indicates a decrease, and white indicates no change from baseline. Overall, the model validates 61 of 84 comparisons (72.6%). (E) Robustness of experimental validation comparing model predictions with experimental literature to varying validation thresholds.

Figure 4:

Inflammation was a graded response to infarction. (A) Predicted IL-1β time courses for select initial infarct sizes. (B) Peak values plotted against initial infarct size indicate a graded increase in the inflammatory response. (C) Cell sources for overall IL-1β in the given time periods post-MI. (D) Neutrophil time courses for select initial infarct sizes. (E) Neutrophil peak values plotted against initial infarct size. (F) Perturbations of neutrophil removal rate or IL-1β degradation rate and their effects on the neutrophil-IL-1β positive feedback loop.

Figure 5:

Inflammation resolution was regulated by removal of inflammatory and enhancement of fibrotic processes. (A) IL-1β time courses for varied IL-1β degradation rates (left) show the changes in peak values. The T50s (right) for the perturbations quantify change in inflammation resolution. (B) IL-1β curves for different neutrophil removal rates show the change in peak values and T50s show changes in inflammation resolution. (C) IL-1β time courses for varied MMP-9 degradation rates (left) and inflammation resolution quantified as T90s (right) (D) Effect of TGFβ inhibition on IL-1β duration (left), quantified by the difference in T90 (middle). Cell sources for overall IL-1β after TGFβ inhibition in the given time periods post-MI are shown on the right.

Figure 6:

Multiple inflammatory mechanisms drive inflammation-fibrosis coupling post-myocardial infarction. (A) Cell sources of TGFβ post MI, followed by fibroblasts. (B) Roles of inflammatory cytokines IL-1β, GM-CSF, and TNFa in TGFβ secretion post MI. (C) Effect of inflammatory cytokines on cell counts, debris, and phagocytosis. (D) Effects of various phagocytosis mechanisms on overall and macrophage TGFβ secretion. The relative area under the curve represents a cumulative sum of the secreted factors in simulated time course.

Figure 7:

Mature collagen deposition was an ultrasensitive switch in response to initial infarct size. (A) Peak values of collagen deposition plotted against infarct size indicated an ultrasensitive switch, with a Hill coefficient of 9.56. (B) Roles of inflammatory cytokines IL-1β, GM-CSF, and TNFa in collagen deposition post MI for different initial infarct sizes. (C) Peak values of model components upstream of collagen indicated ultrasensitivity, amplified by fibroblasts. (D) Bifurcation analysis of a reduced model with TGFβ concentration, as the bifurcation parameter revealed a transcritical bifurcation. Filled circles: stable fixed points; open circles: unstable fixed points. Inset: bifurcation analysis with low concentrations of TGFβ. (E and F) Bifurcation analysis of the reduced model with either (E) fibroblasts or (F) crowding term removed from the proliferation term.