Computational Models Provide Insight into In Vivo Studies and Reveal the Complex Role of Fibrosis in mdx Muscle Regeneration.

Duchenne muscular dystrophy is a pro-fibrotic, muscle wasting disease. Reducing fibrosis is a potential therapeutic target; however, its effect on muscle regeneration is not fully understood. This study (1) used an agent-based model to predict the effect of increased fibrosis in mdx muscle on regeneration from injury, and (2) experimentally tested the resulting model-derived hypothesis. The model predicted that increasing the area fraction of fibrosis decreased regeneration 28 days post injury due to limited growth factor diffusion and impaired cell migration. WT, mdx, and TGFß-treated mdx mice were used to test this experimentally. TGFß injections increased the extracellular matrix (ECM) area fraction; however, the passive stiffness of the treated muscle, which was assumed to correlate with ECM protein density, decreased following injections, suggesting that ECM protein density was lower. Further, there was no cross-sectional area (CSA) difference during recovery between the groups. Additional simulations revealed that decreasing the ECM protein density resulted in no difference in CSA, similar to the experiment. These results suggest that increases in ECM area fraction alone are not sufficient to reduce the regenerative capacity of mdx muscle, and that fibrosis is a complex pathological condition requiring further understanding.

Agent-based model illustrates the role of the microenvironment in regeneration in healthy and mdx skeletal muscle.

Duchenne muscular dystrophy (DMD) is a progressive muscle-wasting disease with no effective treatment. Multiple mechanisms are thought to contribute to muscle wasting, including increased susceptibility to contraction-induced damage, chronic inflammation, fibrosis, altered satellite stem cell (SSC) dynamics, and impaired regenerative capacity. The goals of this project were to 1) develop an agent-based model of skeletal muscle that predicts the dynamic regenerative response of muscle cells, fibroblasts, SSCs, and inflammatory cells as a result of contraction-induced injury, 2) calibrate and validate the model parameters based on comparisons with published experimental measurements, and 3) use the model to investigate how changing isolated and combined factors known to be associated with DMD (e.g., altered fibroblast or SSC behaviors) influence muscle regeneration. Our predictions revealed that the percent of injured muscle that recovered 28 days after injury was dependent on the peak SSC counts following injury. In simulations with near-full cross-sectional area recovery (healthy, 4-wk mdx, 3-mo mdx), the SSC counts correlated with the extent of initial injury; however, in simulations with impaired regeneration (9-mo mdx), the peak SSC counts were suppressed relative to initial injury. The differences in SSC counts between these groups were emergent predictions dependent on altered microenvironment factors known to be associated with DMD. Multiple cell types influenced the peak number of SSCs, but no individual parameter predicted the differences in SSC counts. This finding suggests that interventions to target the microenvironment rather than SSCs directly could be an effective method for improving regeneration in impaired muscle. NEW & NOTEWORTHY A computational model predicted that satellite stem cell (SSC) counts are correlated with muscle cross-sectional area (CSA) recovery following injury. In simulations with impaired CSA recovery, SSC counts are suppressed relative to healthy muscle. The suppressed SSC counts were an emergent model prediction, because all simulations had equal initial SSC counts. Fibroblast and anti-inflammatory macrophage counts influenced SSC counts, but no single factor was able to predict the pathological differences in SSC counts that lead to impaired regeneration.

In Silico and In Vivo Experiments Reveal M-CSF Injections Accelerate Regeneration Following Muscle Laceration.

Numerous studies have pharmacologically modulated the muscle milieu in the hopes of promoting muscle regeneration; however, the timing and duration of these interventions are difficult to determine. This study utilized a combination of in silico and in vivo experiments to investigate how inflammation manipulation improves muscle recovery following injury. First, we measured macrophage populations following laceration injury in the rat tibialis anterior (TA). Then we calibrated an agent-based model (ABM) of muscle injury to mimic the observed inflammation profiles. The calibrated ABM was used to simulate macrophage and satellite stem cell (SC) dynamics, and suggested that delivering macrophage colony stimulating factor (M-CSF) prior to injury would promote SC-mediated injury recovery. Next, we performed an experiment wherein 1 day prior to injury, we injected M-CSF into the rat TA muscle. M-CSF increased the number of macrophages during the first 4 days post-injury. Furthermore, treated muscles experienced a swifter increase in the appearance of PAX7+ SCs and regenerating muscle fibers. Our study suggests that computational models of muscle injury provide novel insights into cellular dynamics during regeneration, and further, that pharmacologically altering inflammation dynamics prior to injury can accelerate the muscle regeneration process.

Computational Modeling of Muscle Regeneration and Adaptation to Advance Muscle Tissue Regeneration Strategies.

Skeletal muscle has an exceptional ability to regenerate and adapt following injury. Tissue engineering approaches (e.g. cell therapy, scaffolds, and pharmaceutics) aimed at enhancing or promoting muscle regeneration from severe injuries are a promising and active field of research. Computational models are beginning to advance the field by providing insight into regeneration mechanisms and therapies. In this paper, we summarize the contributions computational models have made to understanding muscle remodeling and the functional implications thereof. Next, we describe a new agent-based computational model of skeletal muscle inflammation and regeneration following acute muscle injury. Our computational model simulates the recruitment and cellular behaviors of key inflammatory cells (e.g. neutrophils and M1 and M2 macrophages) and their interactions with native muscle cells (muscle fibers, satellite stem cells, and fibroblasts) that result in the clearance of necrotic tissue and muscle fiber regeneration. We demonstrate the ability of the model to track key regeneration metrics during both unencumbered regeneration and in the case of impaired macrophage function. We also use the model to simulate regeneration enhancement when muscle is primed with inflammatory cells prior to injury, which is a putative therapeutic intervention that has not yet been investigated experimentally. Computational modeling of muscle regeneration, pursued in combination with experimental analyses, provides a quantitative framework for evaluating and predicting muscle regeneration and enables the rational design of therapeutic strategies for muscle recovery.

Multiscale models of skeletal muscle reveal the complex effects of muscular dystrophy on tissue mechanics and damage susceptibility.

Computational models have been increasingly used to study the tissue-level constitutive properties of muscle microstructure; however, these models were not created to study or incorporate the influence of disease-associated modifications in muscle. The purpose of this paper was to develop a novel multiscale muscle modelling framework to elucidate the relationship between microstructural disease adaptations and modifications in both mechanical properties of muscle and strain in the cell membrane. We used an agent-based model to randomly generate new muscle fibre geometries and mapped them into a finite-element model representing a cross section of a muscle fascicle. The framework enabled us to explore variability in the shape and arrangement of fibres, as well as to incorporate disease-related changes. We applied this method to reveal the trade-offs between mechanical properties and damage susceptibility in Duchenne muscular dystrophy (DMD). DMD is a fatal genetic disease caused by a lack of the transmembrane protein dystrophin, leading to muscle wasting and death due to cardiac or pulmonary complications. The most prevalent microstructural variations in DMD include: lack of transmembrane proteins, fibrosis, fatty infiltration and variation in fibre cross-sectional area. A parameter analysis of these variations and case study of DMD revealed that the nature of fibrosis and density of transmembrane proteins strongly affected the stiffness of the muscle and susceptibility to membrane damage.