Regulatory network-based model to simulate the biochemical regulation of chondrocytes in healthy and osteoarthritic environments

• Published in: Scientific reports 2022-03, Vol.12 (1), p.3856-3856, Article 3856
• Main Authors: Segarra-Queralt, Maria, Neidlin, Michael, Tio, Laura, Monfort, Jordi, Monllau, Joan Carles, González Ballester, Miguel Á., Alexopoulos, Leonidas G., Piella, Gemma, Noailly, Jérôme
• Format: Article
• Language: English
• Subjects: 631/114; 631/45; 631/553; Biodegradation; Blood; Cartilage diseases; Cartilage, Articular - metabolism; Chondrocytes; Chondrocytes - metabolism; Computer applications; Drug therapy; Humanities and Social Sciences; Humans; Inflammation; Mechanical stimuli; Metabolism; multidisciplinary; Osteoarthritis; Osteoarthritis - metabolism; Pain perception; Science; Science (multidisciplinary); Therapeutic targets
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-07776-2

In osteoarthritis (OA), chondrocyte metabolism dysregulation increases relative catabolic activity, which leads to cartilage degradation. To enable the semiquantitative interpretation of the intricate mechanisms of OA progression, we propose a network-based model at the chondrocyte level that incorporates the complex ways in which inflammatory factors affect structural protein and protease expression and nociceptive signals. Understanding such interactions will leverage the identification of new potential therapeutic targets that could improve current pharmacological treatments. Our computational model arises from a combination of knowledge-based and data-driven approaches that includes in-depth analyses of evidence reported in the specialized literature and targeted network enrichment. We achieved a mechanistic network of molecular interactions that represent both biosynthetic, inflammatory and degradative chondrocyte activity. The network is calibrated against experimental data through a genetic algorithm, and 81% of the responses tested have a normalized root squared error lower than 0.15. The model captures chondrocyte-reported behaviors with 95% accuracy, and it correctly predicts the main outcomes of OA treatment based on blood-derived biologics. The proposed methodology allows us to model an optimal regulatory network that controls chondrocyte metabolism based on measurable soluble molecules. Further research should target the incorporation of mechanical signals.