Osteoarthritis (OA) affects more than 500 million people worldwide and is the leading cause of disability in the elderly. Joint tissue crosstalk is a central element of OA progression. The knee joint has previously been considered as a whole-organ system instead of as isolated tissues that are analyzed independently. However, joint tissue crosstalk during OA progression remains poorly characterized. Determining the transcriptional crosstalk among joint and immune tissues throughout OA development is key to better understand the multigene etiology of the disease and target potential treatments.

Firstly, we examined the roles of subchondral bone mass, stiffness, and remodeling in OA development in mice. Parathyroid hormone (PTH), an anabolic treatment, was used to increase subchondral bone mass and stiffness in vivo prior to OA initiation. Alendronate (ALN), an anticatabolic treatment, was used to reduce bone remodeling during OA progression. We found that the additive effect of PTH pretreatment prior to cyclic loading combined with ALN treatment during disease progression most effectively attenuated load-induced OA pathology across the whole joint. PTH pretreatment improved cartilage health and may have shifted chondrocyte phenotypes, whereas ALN treatment attenuated osteophyte formation and subchondral bone changes associated with OA progression.

Secondly, we leveraged a systems biology approach to integrate information from multiple tissue transcriptomes and better characterize joint tissue crosstalk during OA progression. Our objective was to understand the multi-directional transcriptomic crosstalk shared among tissues during OA development and the effects of PTH and ALN treatments. We identified an immune-related network of genes conserved across the tibial cartilage, metaphyseal cortical bone, cancellous bone and bone marrow, and inguinal lymph node that was upregulated with load-induced OA. Leveraging the top 10 percent of hub genes in this gene network, we also identified three potential therapeutic targets for further investigation in load-induced OA. Additionally, this systems approach provided a better understanding of the combined multi-tissue effects of PTH pretreatment and ALN treatment with cyclic tibial compression loading.

Finally, we leveraged transcriptomic profiles of osteoarthritic knee cartilage from mice and humans to perform a comprehensive cross-species cartilage co-expression network analysis. We sought to establish an understanding of relevant gene modules preserved between mice and humans. This analysis identified similarities and differences in disease mechanisms among our noninvasive in-vivo loading model, destabilization of the medial meniscus (DMM) surgical model, and human clinical datasets of OA progression. We found that a gene module enriched for pre-hypertrophic chondrocytes was highly correlated to OA progression across both mouse models and clinical samples. Hub genes in the module were able to differentiate between OA and healthy subjects in a blood human transcriptomic dataset with high sensitivity and specificity.

In summary, this thesis improved our molecular level understanding of key unanswered research questions in the OA community. By leveraging computational biology tools to perform transcriptomic analyses of multiple tissues in a mouse model of the disease progression to a network analysis across tissues in the same model to a systems-level comparison of mouse models with clinical samples, we improved our understanding of joint tissue crosstalk during OA progression.