Poster Presentation Annual Meetings of the Endocrine Society of Australia and Society for Reproductive Biology and Australia and New Zealand Bone and Mineral Society 2016

Identification of genes regulating osteoarthritis development using a mouse phenotype library (#315)

Ramesh Ram , Grant Morahan , Jennifer Tickner , Jiake Xu , Jacob Kenny

Osteoarthritis is a degenerative joint disease with a slow progression. Treatment is limited to pain management and physiotherapy to maintain mobility, a total joint replacement is the only treatment for cases of severe OA. Investigation of OA affected cartilage is conducted via arthroscopy and/or post joint replacement surgery; therefore observing disease progression in humans is difficult. Hence we have utilised a mouse phenotype library, the collaborative cross, to assess affected cartilage microscopically across a spread of ages. Mice >12 months were selected for the initial cohort. These strains were screened using histology to assess and score the severity of OA in knee joints. The strains were ranked from lowest to highest and the data used to map genetic variation that was consistently associated with the highest OA scores. Forty strains have been screened so far with results showing a range of OA incidence across the strains as is observed in human populations. Two strains have been identified as having consistently high OA scores irrespective of gender. Based on gene mapping data, variation in the Zfhx4 gene was identified to be uniquely associated with the identified strains. It was revealed that Zfhx4 is highly upregulated during chondrocyte maturation in a rabbit cell based microarray study. This association with chondrocyte differentiation indicates that Zfhx4 is a candidate gene for the regulation of OA development. There are currently no known genes associated with spontaneous OA in animal models. The availability of mouse models to investigate spontaneous OA will allow better understanding of the mechanism of disease inheritance and progression and might be of great value to identify biomarkers for OA diagnosis and treatment.