Synovial inflammation in osteoarthritis progression.
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ABSTRACT:
SUBMITTER: Sanchez-Lopez E
PROVIDER: S-EPMC9050956 | biostudies-literature | 2022 May
REPOSITORIES: biostudies-literature
Publications
Synovial inflammation in osteoarthritis progression.
Sanchez-Lopez Elsa E Coras Roxana R Torres Alyssa A Lane Nancy E NE Guma Monica M
Nature reviews. Rheumatology 20220214 5
Osteoarthritis (OA) is a progressive degenerative disease resulting in joint deterioration. Synovial inflammation is present in the OA joint and has been associated with radiographic and pain progression. Several OA risk factors, including ageing, obesity, trauma and mechanical loading, play a role in OA pathogenesis, likely by modifying synovial biology. In addition, other factors, such as mitochondrial dysfunction, damage-associated molecular patterns, cytokines, metabolites and crystals in th ...[more]
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Project description:ObjectiveInflammatory responses are associated with changes in tissue metabolism. Prior studies find altered metabolomic profiles in both the synovial fluid (SF) and serum of osteoarthritis subjects. Our study determined the metabolomic profile of synovial tissue (ST) and SF of individuals with osteoarthritis (OA) and its association with synovial inflammation.Design37 OA ST samples were collected during joint replacement, 21 also had SF. ST samples were fixed in formalin for histological analysis, cultured (explants) for cytokine analysis by enzyme-linked immunosorbent assay, or snap-frozen for metabolomic analysis. ST samples were categorized by Krenn synovitis score and picrosirius red. CD68 and vimentin expression was assessed by immunohistochemistry and semi-quantified using Image J. Proton-nuclear magnetic resonance (1H NMR) was used to acquire a spectrum from ST and SF samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. Metaboanalyst 5.0, SPSS v26, and R (v4.1.2) were used for statistical analysis.Results42 and 29 metabolites were detected in the ST and SF respectively by 1H NMR. Only 3 metabolites, lactate, dimethylamine, and creatine positively correlated between SF and ST. ST concentrations of several metabolites (lactate, alanine, fumarate, glutamine, glycine, leucine, lysine, methionine, trimethylamine N-oxide, tryptophan and valine) were associated with synovitis score, mostly to the lining score. IL-6, acetoacetate, and tyrosine in SF predicted high Krenn synovitis scores in ST.ConclusionMetabolomic profiling of ST identified metabolic changes associated with inflammation. Further studies are needed to determine whether metabolomic profiling of synovial tissue can identify new therapeutic targets in osteoarthritis.
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Project description:Osteoarthritis (OA) is a chronic arthropathy that occurs in the middle‑aged and elderly population. The present study aimed to identify gene signature differences between synovial cells from OA synovial membrane with and without inflammation, and to explain the potential mechanisms involved. The differentially expressed genes (DEGs) between 12 synovial membrane with inflammation and 12 synovial membrane without inflammation from the dataset GSE46750 were identified using the Gene Expression Omnibus 2R. The DEGs were subjected to enrichment analysis, protein‑protein interaction (PPI) analysis and module analysis. The analysis results were compared with text‑mining results. A total of 174 DEGs were identified. Gene Ontology enrichment results demonstrated that functional molecules encoded by the DEGs primarily had extracellular location, molecular functions predominantly involving 'chemokine activity' and 'cytokine activity', and were associated with biological processes, including 'inflammatory response' and 'immune response'. The Kyoto Encyclopedia of Genes and Genomes results demonstrated that DEGS may function through pathways associated with 'rheumatoid arthritis', 'chemokine signaling pathway', 'complement and coagulation cascades', 'TNF signaling pathway', 'intestinal immune networks for IgA production', 'cytokine‑cytokine receptor interaction', 'allograft rejection', 'Toll‑like receptor signaling pathway' and 'antigen processing and presentation'. The top 10 hub genes [interleukin (IL)6, IL8, matrix metallopeptidase (MMP)9, colony stimulating factor 1 receptor, FOS proto‑oncogene, AP1 transcription factor subunit, insulin‑like growth factor 1, TYRO protein tyrosine kinase binding protein, MMP3, cluster of differentiation (CD)14 and CD163] and four gene modules were identified from the PPI network using Cytoscape. In addition, text‑mining was used to identify the commonly used drugs and their targets for the treatment of OA. It was initially verified whether the results of the present study were useful for the study of OA treatment targets and pathways. The present study provided insight for the molecular mechanisms of OA synovitis. The hub genes and associated pathways derived from analysis may be targets for OA treatment. IL8 and MMP9, which were validated by text‑mining, may be used as molecular targets for the OA treatment, while other hub genes require further validation.
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