The CH25H-CYP7B1-RORα axis of cholesterol metabolism regulates osteoarthritis.

Osteoarthritis-the most common form of age-related degenerative whole-joint disease1-is primarily characterized by cartilage destruction, as well as by synovial inflammation, osteophyte formation and subchondral bone remodelling2,3. However, the molecular mechanisms that underlie the pathogenesis of osteoarthritis are largely unknown. Although osteoarthritis is currently considered to be associated with metabolic disorders, direct evidence for this is lacking, and the role of cholesterol metabolism in the pathogenesis of osteoarthritis has not been fully investigated4-6. Various types of cholesterol hydroxylases contribute to cholesterol metabolism in extrahepatic tissues by converting cellular cholesterol to circulating oxysterols, which regulate diverse biological processes7,8. Here we show that the CH25H-CYP7B1-RORα axis of cholesterol metabolism in chondrocytes is a crucial catabolic regulator of the pathogenesis of osteoarthritis. Osteoarthritic chondrocytes had increased levels of cholesterol because of enhanced uptake, upregulation of cholesterol hydroxylases (CH25H and CYP7B1) and increased production of oxysterol metabolites. Adenoviral overexpression of CH25H or CYP7B1 in mouse joint tissues caused experimental osteoarthritis, whereas knockout or knockdown of these hydroxylases abrogated the pathogenesis of osteoarthritis. Moreover, retinoic acid-related orphan receptor alpha (RORα) was found to mediate the induction of osteoarthritis by alterations in cholesterol metabolism. These results indicate that osteoarthritis is a disease associated with metabolic disorders and suggest that targeting the CH25H-CYP7B1-RORα axis of cholesterol metabolism may provide a therapeutic avenue for treating osteoarthritis.

HiDRA: Hierarchical Network for Drug Response Prediction with Attention.

Understanding differences in drug responses between patients is crucial for delivering effective cancer treatment. We describe an interpretable AI model for use in predicting drug responses in cancer cells at the gene, molecular pathway, and drug level, which we have called the hierarchical network for drug response prediction with attention. We found that the model shows better accuracy in predicting drugs having efficacy against a given cell line than other state-of-the-art methods, with a root mean squared error of 1.0064, a Pearson's correlation coefficient of 0.9307, and an R2 value of 0.8647. We also confirmed that the model gives high attention to drug-target genes and cancer-related pathways when predicting a response. The validity of predicted results was proven by in vitro cytotoxicity assay. Overall, we propose that our hierarchical and interpretable AI-based model is capable of interpreting intrinsic characteristics of cancer cells and drugs for accurate prediction of cancer-drug responses.