Endocrine Abstracts

G-protein coupled receptors (GPCRs) are transmembrane proteins whose surface expression and extracellular activation make them desirable drug targets. Approximately 35% of approved drugs target GPCRs, including osteoporosis treatments such as Teriparatide. Despite the ageing population resulting in increased osteoporosis diagnoses, current treatments lack long-term efficacy, highlighting the need to identify new drug targets. Using RNA-sequencing we identified multiple GPCR genes to be highly expressed in human primary osteoclasts and these could provide novel drug targets for osteoporosis. To assess how these GPCRs affect osteoclast differentiation and activity, we designed a pipeline of assays amenable to high-throughput, automated analyses. These comprise: nuclei staining to assess osteoclast differentiation; TRAP enzyme activity; and high-content imaging of NFAT nuclear translocation to measure osteoclast signalling. These were compared to bone resorption assays that are the current gold standard technique to assess osteoclast activity, despite their low efficiency and potential for bias in data interpretation. We first demonstrated the utility of these methods using GIPR as a positive control, and showed GIPR activation reduced bone resorption, TRAP activity, mature osteoclast number and NFAT nuclear translocation, all of which were prevented by pre-treatment with GIPR antagonist. We then chose six GPCRs to assess in detail. Using our assay pipeline with receptor-specific agonists and antagonists we showed that no receptors affected osteoclast differentiation, and SUCNR1 and GPR84 did not affect activity. In contrast, FFAR2 reduces bone resorption, TRAP and NFAT nuclear translocation, while FFAR4, FPRs and GPR35 reduced bone resorption and TRAP. Thus, we identified four GPCRs that reduce osteoclast activity and could represent novel targets for osteoporosis. Moreover, we demonstrated that high-throughput assays, which measure multiple compounds in hundreds of cells in a single experiment using automated analyses, can accurately assess osteoclast activity, reducing observer bias and increasing efficiency of target detection for future osteoporosis therapies.