Integrated Multi-Omics Analyses Reveal Lipid Metabolic Signature in Osteoarthritis

Osteoarthritis (OA) is the most common degenerative joint disease and the second leading cause of disability worldwide. Single-omics analyses are far from elucidating the complex mechanisms of lipid metabolic dysfunction in OA. This study identified a shared lipid metabolic signature of OA by integrating metabolomics, single-cell and bulk RNA-seq, as well as metagenomics. Compared to the normal counterparts, cartilagesin OA patients exhibited significant depletion of homeostatic chondrocytes (HomCs) (P = 0.03) and showed lipid metabolic disorders in linoleic acid metabolism and glycerophospholipid metabolism which was consistent with our findings obtained from plasma metabolomics. Through high- dimensional weighted gene co-expression network analysis (hdWGCNA), weidentified PLA2G2A as a hub gene associated with lipid metabolic disorders in HomCs. And an OA-associated subtype of HomCs, namely HomC1 (marked by PLA2G2A, MT-CO1, MT-CO2, and MT-CO3) was identified, which also exhibited abnormal activation of lipid metabolic pathways. This suggests the involvement of HomC1 in OA progression through the shared lipid metabolism aberrancies, which were further validated via bulk RNA-Seq analysis. Metagenomic profiling identified specific gut microbial species significantly associated with the key lipid metabolism disorders, including Bacteroides uniformis (P < 0.001, R = 0.52), Klebsiella pneumonia (P = 0.003, R = 0.42), Intestinibacter_bartlettii (P = 0.009, R = 0.38), and Streptococcus anginosus (P = 0.009, R = 0.38). By integrating the multi-omics features, a random forest diagnostic model with outstanding performance was developed (AUC = 0.97). In summary, this study deciphered the crucial role of a integrated lipid metabolic signature in OA pathogenesis, and established a regulatory axis of gut microbiota-metabolites-cell-gene, providing new insights into the gut-joint axis and precision therapy for OA. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.