To assess the alterations in cell-specific DNA methylation associated with chondroitin sulphate response using peripheral blood collected from Kashin-Beck disease (KBD) patients before initiation of chondroitin sulphate treatment. Peripheral blood samples were collected from KBD patients at baseline of chondroitin sulphate treatment. Methylation profiles were generated using reduced representation bisulphite sequencing (RRBS) from peripheral blood. Differentially methylated regions (DMRs) were identified using MethylKit, while DMR-related genes were defined as those annotated to the gene body or 2.2-kilobase upstream regions of DMRs. Selected DMR-related genes were further validated by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) to assess expression levels. Tensor composition analysis was performed to identify cell-specific differential DNA methylation from bulk tissue.Aims
Methods
This study aimed to investigate the role and mechanism of meniscal cell lysate (MCL) in fibroblast-like synoviocytes (FLSs) and osteoarthritis (OA). Meniscus and synovial tissue were collected from 14 patients with and without OA. MCL and FLS proteins were extracted and analyzed by liquid chromatography‒mass spectrometry (LC‒MS). The roles of MCL and adenine nucleotide translocase 3 (ANT3) in FLSs were examined by enzyme-linked immunosorbent assay (ELISA), flow cytometry, immunofluorescence, and transmission electron microscopy. Histological analysis was performed to determine ANT3 expression levels in a male mouse model.Aims
Methods
This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed.Aims
Methods
To explore the synovial expression of mucin 1 (MUC1) and its role in rheumatoid arthritis (RA), as well as the possible downstream mechanisms. Patients with qualified synovium samples were recruited from a RA cohort. Synovium from patients diagnosed as non-inflammatory orthopaedic arthropathies was obtained as control. The expression and localization of MUC1 in synovium and fibroblast-like synoviocytes were assessed by immunohistochemistry and immunofluorescence. Small interfering RNA and MUC1 inhibitor GO-203 were adopted for inhibition of MUC1. Lysophosphatidic acid (LPA) was used as an activator of Rho-associated pathway. Expression of inflammatory cytokines, cell migration, and invasion were evaluated using quantitative real-time polymerase chain reaction (PCR) and Transwell chamber assay.Aims
Methods
The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
Methods
