Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction
Introduction
Wound healing is a complex process that involves a dynamic interplay between inflammatory and reparative signaling. This process is especially important following injury to the heart, where cardiomyocytes are unable to regenerate. Scar formation and the preservation of viable heart muscle are important for continued cardiac function [1]. After myocardial infarction (MI), ACE inhibitors and beta blockers are prescribed to prevent adverse cardiac remodeling and heart failure [2], but the risk of heart failure and cardiac-related death post-MI remains high [3], [4], [5]. This is partly because wound healing is a balancing act between clearance of debris and formation of new scar, and the regulators of this dynamic process are not fully understood. Therefore, attempts to find therapeutic targets that allow for adequate collagen expression while avoiding excessive fibrosis have been largely unsuccessful. For example, although increased levels of interleukin-1 (IL1) have been linked to fibrosis and diminished cardiac index post-MI [6], blocking IL1 post-MI does not consistently improve healing and is actually associated with an increased risk of secondary MI [7,8].
Myocardial infarcts follow a healing process that is similar to that in other organs [9,10]. There is first an inflammatory phase characterized by extracellular matrix (ECM) breakdown and myocyte necrosis, which lasts around 2 days in rats and 5 days in larger mammals [11]. Then, the reparative phase lasts around 2–5 days in rats (2 weeks in large mammals), during which fibroblasts proliferate, migrate into the wound, differentiate into myofibroblasts, and generate large amounts of collagen I and III and other ECM proteins [11,12]. Ultimately, the wound matures into a stable scar with balanced ECM production and degradation. In the adult heart, cardiomyocytes do not proliferate sufficiently to re-populate the wound, so the ultimate fate of cardiac tissue depends on the behavior of cardiac fibroblasts. Excessive degradation can lead to ventricular dilation and wall rupture due to the loss of structural integrity in the heart wall [13]. Conversely, excessive ECM deposition, particularly in myocardium remote from the infarct, can lead to diastolic dysfunction [14,15]. Many patients with heart failure post-MI have both dilation and fibrosis [1].
A beneficial infarct healing process likely involves a transient burst of collagen deposition that replaces lost cardiomyocytes with strong ECM without a sustained increase in ECM synthesis that leads to adverse remodeling [16]. This “transient fibrosis” is likely facilitated by many different factors including inflammatory cell phenotype and number, the pre-infarct signaling state, the size of the infarct, and the health of the remaining cardiac vessels [17,18]. Fibroblasts play a prominent role throughout the entire wound healing process, and therefore present a good system for studying how cells respond to the dynamic signaling environment of wound healing [19]. Additionally, understanding how fibroblasts respond during the different phases of wound healing could identify mechanisms by which fibrosis develops in other organs [“Cardiac Fibroblast Diversity in Health and Disease” Rossi, Matrix Biology Special Issue on Fibroblasts].
Myocardial infarct healing is notoriously difficult to investigate because it involves many dynamic and interacting signaling processes [“Cardiac Fibroblast Heterogeneity after Myocardial Infarction” Lindsey, Matrix Biology Special Issue on Fibroblasts]. Fibroblasts are particularly difficult to study in situ during wound healing because they can differentiate from many different cell types and there is no clear consensus on fibroblast markers [20]. Computational modeling has been useful for investigating complex dynamic processes in many areas of biology. Although models have been constructed to study the wound healing process post-MI [21], no such model has yet been applied to study fibroblast intracellular signaling and phenotypic changes during myocardial wound healing [20,22]. This study integrates a large-scale computational model of cardiac fibroblast signaling [23] together with a tissue-level model that can predict collagen accumulation post-MI. To simulate post-MI wound healing, we used post-MI experimental data for nine time-dependent paracrine stimuli to identify key paracrine and intracellular drivers of fibroblast phenotype after MI. The simulation of post-MI fibroblast dynamics was validated against experimental in vivo time courses of collagen mRNA and collagen area fraction throughout infarct healing. Next, we applied the computational model to identify simpler conditions with static single or paired paracrine stimuli that induce fibroblast phenotypes that mimic specific phases of post-MI healing as predicted by the dynamic simulations. Virtual overexpression screens predicted both context-independent drivers of collagen synthesis as well as regulators that differentially affect collagen expression in the context of specific paracrine stimuli or phases of post-MI wound healing. Further model simulations generated experimentally testable mechanistic hypotheses for the signaling underlying such context-dependent responses, highlighting the utility of this computational model for interrogating the complex roles of fibroblasts in the post-MI wound healing environment.
Section snippets
Updates to network model of fibroblast signaling
We extended a large-scale computational model of fibroblast signaling [23] to be more relevant for subsequent experimental study of fibroblast phenotype dynamics in vivo. As described in previous studies [22,24], the signaling model was implemented as a system of logic-based differential equations with default normalized reaction and node parameters. Here, the input nodes were separated from their associated ligand, and outputs associated with fibroblast phenotype collagen maturation (e.g. LOX)
Expanding a fibroblast signaling network model to predict post-MI fibroblast phenotype dynamics
A published computational model of cardiac fibroblast signaling network [23] was extended to make it more suited to predict the dynamics of fibroblast phenotype in vivo (see Methods and Fig. S1). While the signaling model was originally developed and validated using a wealth of in vitro experimental data [23], we hypothesized that this model could be extended to predict post-MI fibroblast dynamics because it is capable of predicting semi-quantitative time-dependent behavior and it incorporates
Relationship between fibroblast phenotypes induced by dynamic post-MI vs. static paracrine stimuli
In vitro experiments can provide precise control of simplified environmental conditions, but it is unclear to what extent such conditions can reproduce the phenotype of fibroblasts during the more dynamic and complex process of infarct healing [64,65]. Therefore, we used the model to identify individual or pairs of static paracrine stimuli that drive phenotypes that best mimic distinct phases of the post-MI fibroblast phenotype. In contrast to the dynamic simulations described above, the
Mechanisms contributing to post-MI phase-specific regulation of collagen
The screen above identified pro-fibrotic pathways where the mechanism by which they up-regulate collagen expression has been well-studied. Both moderate and strong overexpression of β1-integrin induced substantial fibrosis even before MI, consistent with its effect across all simplified paracrine contexts (Fig. 4, Fig. S8) and the continual mechanical stress in the beating heart. Moderate ET1AR overexpression induced strong and sustained collagen expression beginning in the inflammatory phase
Discussion
Cardiac fibroblasts are central mediators of wound healing and cardiac function after myocardial infarction [16,50,67], yet the complexity of the dynamic in vivo paracrine environment and the fibroblast intracellular signaling network hinders therapeutic targeting [68]. Here, we extended a large-scale computational model of the fibroblast signaling network to identify paracrine and intracellular drivers of extracellular matrix synthesis in specific phases of post-infarct healing. By integrating
Declarations of Competing Interest
The authors have declared that no conflict of interest exists.
Acknowledgments
We thank Dr. William Richardson for valuable discussion of this work. This work was supported by the National Institutes of Health [grant numbers HL127944, HL137755, HL116449, HL007284]; the National Science Foundation [grant numbers 1560282, 1252854] and the University of Virginia Center for Engineering in Medicine. The funding sources had no involvement in the conduct of the research or decision to publish.
References (107)
- et al.
Predictors of congestive heart failure in the elderly: the cardiovascular health study
J. Am. Coll. Cardiol.
(2000) - et al.
Tet2-Mediated clonal hematopoiesis accelerates heart failure through a mechanism involving the IL-1β/NLRP3 inflammasome
J. Am. Coll. Cardiol.
(2018) - et al.
Maroko.The histopathologic evolution of myocardial infarction
Chest
(1978) - et al.
Translational lessons from scarless healing of cutaneous wounds and regenerative repair of the myocardium
J. Mol. Cell. Cardiol.
(2010) Myofibroblast and endothelial cell proliferation during murine myocardial infarct repair
Am. J. Pathol.
(2003)- et al.
Bobryshev. The role of cardiac fibroblasts in post-myocardial heart tissue repair
Exp. Mol. Pathol.
(2016) - et al.
et al.Neutralization of interleukin-1beta in the acute phase of myocardial infarction promotes the progression of left ventricular remodeling
J. Am. Coll. Cardiol.
(2001) - et al.
Interstitial collagen is increased in the non-infarcted human myocardium after myocardial infarction
J. Mol. Cell. Cardiol.
(1993) - et al.
Cardiac fibroblast activation post-myocardial infarction: current knowledge gaps
Trends Pharmacol. Sci.
(2017) - et al.
Computational modeling of cardiac fibroblasts and fibrosis
J. Mol. Cell. Cardiol.
(2016)