Journal
SCIENTIFIC REPORTS
Volume 12, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-022-07776-2
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Funding
- Generalitat de Catalunya [2020 FI_B 00680]
- European Commission [MSCA-2020-ITN-ETN GA: 955735]
- Ministerio de Ciencia, Innovacion y Universidades [HOLOA-DPI2016- 80283-C2-1/2-R]
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This study proposes a network-based model at the chondrocyte level to explain the complex mechanisms of osteoarthritis progression. By analyzing literature and enriching the network, we obtained a molecular interaction network that represents chondrocyte activity and accurately predicts the outcomes of OA treatment based on experimental data.
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.
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