Computational translation of mouse models of osteoarthritis predicts human disease.

OBJECTIVE

Translation of biological insights from preclinical studies to human disease is a pressing challenge in biomedical research, including in osteoarthritis. Translatable Components Regression (TransComp-R) is a computational framework previously used to identify biological pathways predictive of human disease conditions. We aimed to evaluate the translatability of two common murine models of post-traumatic osteoarthritis - surgical destabilization of the medial meniscus (DMM) and noninvasive anterior cruciate ligament rupture (ACLR) - to transcriptomics cartilage data from human osteoarthritis studies.

METHOD: Publicly available transcriptomics cartilage data from mouse models and human osteoarthritis were analyzed. TransComp-R was used to project human osteoarthritis data into either DMM or ACLR mouse model principal component analysis space. The principal components (PCs) were regressed against human osteoarthritis using increasing complexity of linear regression models incorporating human covariates of sex and age. Biological pathways of the mouse PCs that significantly stratified human osteoarthritis and control groups were then interpreted using Gene Set Enrichment Analysis.

RESULTS

Using TransComp-R, we identified different enriched biological pathways across DMM and ACLR models. Both murine models predicted at least one human study with greater than 50% cumulative variance explained. Translatable DMM PCs revealed pathways associated with inflammation, cell signaling, and metabolism, and translatable ACLR PCs represented immune function and other cellular pathways associated with osteoarthritis.

CONCLUSIONS

Both mouse model more successfully predicted osteoarthritis in human studies with controls without a history of joint pathology. Cross-species, covariate-aware translational approaches support the selection of preclinical models intended for therapeutic discovery and pathway analysis in humans.