Time-dependent computational model of post-traumatic osteoarthritis to estimate how mechanoinflammatory mechanisms impact cartilage aggrecan content

TL;DR

Researchers created a computational model to understand how joint injuries lead to cartilage degradation by tracking aggrecan loss over time.

Contribution

A novel computational model integrating mechanoinflammatory mechanisms to simulate cartilage adaptation after injury.

Findings

The model showed 14%-points greater aggrecan loss near lesions after 12 days of physiological loading.

Fluid flow and proteolytic activity drive near-lesion aggrecan loss, while deeper tissue shows increased biosynthesis.

The model aligns with experimental Safranin-O-stained bovine cartilage data.

Abstract

Degenerative musculoskeletal diseases like osteoarthritis can be initiated by joint injury. Injurious overloading-induced mechanical straining of articular cartilage and subsequent biological responses may trigger cartilage degradation. One early sign of degradation is loss of aggrecan content which is potentially accelerated near chondral lesions under physiological loading. Yet, the mechanoinflammatory mechanisms explaining time-dependent degradation in regions with disparate mechanical loading are unclear and challenging to assess with experiments alone. Here, we developed computational models unraveling potential mechanisms behind aggrecan content adaptation in fibril-reinforced porohyperelastic cartilage after single injurious overloading (50% compressive strain magnitude, 100%/s strain rate) followed by physiological cyclic loading (15% strain, 1 Hz, haversine waveform). The simulated adaptation of aggrecan content was compared spatially and at several time points to tissue composition found in Safranin-O-stained sections of young bovine knee cartilage subjected to the same loading protocols. Incorporating mechanical strain-driven cell damage and downstream proteolytic enzyme release, fluid flow-driven aggrecan depletion, and fluid pressure-stimulated regulation of aggrecan biosynthesis, the models agreed with experiments and exhibited 14%-points greater near-lesion aggrecan loss after 12 days of physiological loading compared to without loading. The near-lesion aggrecan loss was driven by fluid flow and proteolytic aggrecanase activity, while chondroprotective pro-anabolic responses (increased aggrecan biosynthesis) were prominent in the deeper tissue despite damaged superficial layer. This significant advancement in mechanistic understanding incorporated into cartilage adaptation model can help in development and guidance of personalized therapies, such as rehabilitation protocols and tissue-engineered constructs.

Post-traumatic osteoarthritis is an incurable musculoskeletal disease in which articular cartilage degenerates over time. One of the earliest signs of cartilage tissue degeneration is loss of its aggrecan content. Although both immobilization and excessive cartilage loading are known to cause aggrecan loss, the underlying cell-mediated inflammatory responses to loading — referred to as “mechanoinflammation” — remain unclear. Understanding mechanoinflammation necessitates methodological development which complements carefully designed experiments with state-of-the-art computational models incorporating cell-mediated mechanisms. Such biotechnological development presented here can be used to not only investigate cellular damage and tissue adaptation over time in differently loaded cartilage regions, but also to estimate the underlying, challenging-to-measure mechanical shear strains and fluid pressurization. Therefore, the presented computational framework of mechanoinflammation contributes to the development of models and computer-aided tools aimed at estimating and limiting osteoarthritis progression, while also being adoptable to research of other diseases.

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