Delayed skeletal muscle repair following inflammatory damage in simulated agent-based models of muscle regeneration

Abstract

Healthy skeletal muscle undergoes repair in response to mechanically localised strains during activities such as exercise. The ability of cells to transduce the external stimuli into a cascade of cell signalling responses is important to the process of muscle repair and regeneration. In chronic myopathies such as Duchenne muscular dystrophy and inflammatory myopathies, muscle is often subject to chronic necrosis and inflammation that perturbs tissue homeostasis and leads to non-localised, widespread damage across the tissue. Here we present an agent-based model that simulates muscle repair in response to both localised eccentric contractions similar to what would be experienced during exercise, and non-localised widespread inflammatory damage that is present in chronic disease. Computational modelling of muscle repair allows for in silico exploration of phenomena related to muscle disease. In our model, widespread inflammation led to delayed clearance of tissue damage, and delayed repair for recovery of initial fibril counts at all damage levels. Macrophage recruitment was delayed and significantly higher in widespread compared to localised damage. At higher damage percentages of 10%, widespread damage led to impaired muscle regeneration and changes in muscle geometry that represented alterations commonly observed in chronic myopathies, such as fibrosis. This computational work offers insight into the progression and aetiology of inflammatory muscle diseases, and suggests a focus on the muscle regeneration cascade in understanding the progression of muscle damage in inflammatory myopathies.

Fig 1. Methods of strain input for the agent-based model.

(A) Mechanically localised strain values from FEM of a muscle bundle were used to seed an ABM of muscle damage. This was compared to (B) widespread damage seeded by selecting random muscle fibrils in the ABM and labelling them as damaged in the simulation. (C) Visualisation of the damage in localised vs widespread agent-based simulations, zoom inset displays the agents used in this ABM.

Fig 2. Flowchart of ABM agent behaviour.

Green arrows indicate where data was imported to seed the ABM. Strain values from the FEM were taken in the case of localised damage, while random fibril objects were set to ‘damaged’ for the widespread damage simulations.

Fig 3. Simulation fibroblast count compared to reference fibroblast count at 0, 48, 168 and 672 hours post exercise induced injury.

Fig 4. Cell counts (mean ± SD) over 672 h, from 50 simulations in localised and widespread damage conditions.

(A-E) are control cell counts where no damage was imposed. (F-J) show fibril, neutrophil, macrophage, SC and ECM counts simulated over 672 h following 1% damage. (K-O) show 2% damage, (P-T) show 5% damage, and (U-Y) show 10% damage. Fibril repair is delayed and does not meet pre-injury levels at 10%. Neutrophils are increased in localised simulations compared to widespread. Macrophage recruitment is delayed and increased in widespread damage simulations. Satellite cell recruitment increases with damage level. The ECM count is recovered under both simulation conditions.

Fig 5. ECM changes during regeneration simulations.

(A) Change in percentage ECM at endpoint from initial, for 10% localised and 10% widespread damage, and (B) change in endpoint ABM geometry from starting geometry for 10% localised and 10% widespread damage. Widespread damage leads to gaps in muscle fibres and changes in fibre shape.

Fig 6. Cytokine and growth factor levels for muscle regeneration over 672 h.

(A-G) no damage levels, (H-N) cytokine response following 1% damage, (O-U) 2% damage cytokine response, (V-BB) 5% damage cytokine response, and (CC-II) 10% damage cytokine levels over 672 hours, mean ± SD. In these simulations, 0 represents a starting value or steady state that has been optimised for but is not absolute. The subsequent changes to these values then represent increases or decreases relative to the optimised value.