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Objective: Fibroblast-like synoviocytes (FLS) contribute to the pathogenesis of rheumatoid arthritis (RA), in part due to activation of the proinflammatory transcription factor NF-κB. Neddylation is modulated by the negative regulator of ubiquitin-like protein (NUB) 1. We determined whether NUB1 and neddylation are aberrant in the models with RA FLS, thereby contributing to their aggressive phenotype., Methods: Models with RA or osteoarthritis (OA) FLS were obtained from arthroplasty synovia. Real-time quantitative polymerase chain reaction and Western blot analysis assessed gene and protein expression, respectively. NUB1 was overexpressed using an expression vector. NF-κB activation was assessed by stimulating FLS with interleukin (IL)-1β. Neddylation inhibitor (MLN4924) and proteasome inhibitor were used in migration and gene expression assays. MLN4924 was used in the model with K/BxN serum-transfer arthritis., Results: Enhanced H3K27ac and H3K27me3 peaks were observed in the NUB1 promoter in the OA FLS compared with the RA FLS. NUB1 was constitutively expressed by FLS, but induction by IL-1β was significantly greater in the OA FLS. The ratio of neddylated cullin (CUL) 1 to nonneddylated CUL1 was lower in the OA FLS than the RA FLS. NUB1 overexpression decreased NF-κB nuclear translocation and IL-6 messenger RNA (mRNA) in IL-1β-stimulated the RA FLS. MLN4924 decreased CUL1 neddylation, NF-κB nuclear translocation, and IL-6 mRNA in IL-1β-stimulated the RA FLS. MLN4924 significantly decreased arthritis severity in the model with K/BxN serum-transfer arthritis., Conclusion: CUL1 neddylation and NUB1 induction is dysregulated in the models with RA, which increases FLS activation. Inhibition of neddylation is an effective therapy in an animal model of arthritis. These data suggest that the neddylation system contributes to the pathogenesis of RA and that regulation of neddylation could be a novel therapeutic approach., (© 2024 The Authors. Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Objective: Rheumatoid arthritis (RA) is an autoimmune disease in which the joint lining or synovium becomes highly inflamed and majorly contributes to disease progression. Understanding pathogenic processes in RA synovium is critical for identifying therapeutic targets. We performed laser capture microscopy (LCM) followed by RNA sequencing (LCM-RNAseq) to study regional transcriptomes throughout RA synovium., Methods: Synovial lining, sublining, and vessel samples were captured by LCM from seven patients with RA and seven patients with osteoarthritis (OA). RNAseq was performed on RNA extracted from captured tissue. Principal component analysis was performed on the sample set by disease state. Differential expression analysis was performed between disease states based on log 2 fold change and q value parameters. Pathway analysis was performed using the Reactome Pathway Database on differentially expressed genes among disease states. Significantly enriched pathways in each synovial region were selected based on the false discovery rate., Results: RA and OA transcriptomes were distinguishable by principal component analysis. Pairwise comparisons of synovial lining, sublining, and vessel samples between RA and OA revealed substantial differences in transcriptional patterns throughout the synovium. Hierarchical clustering of pathways based on significance revealed a pattern of association between biologic function and synovial topology. Analysis of pathways uniquely enriched in each region revealed distinct phenotypic abnormalities. As examples, RA lining samples were marked by anomalous immune cell signaling, RA sublining samples were marked by aberrant cell cycle, and RA vessel samples were marked by alterations in heme scavenging., Conclusion: LCM-RNAseq confirms reported transcriptional differences between the RA synovium and the OA synovium and provides evidence supporting a relationship between synovial topology and molecular anomalies in RA., (© 2024 American College of Rheumatology.)

Objective: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis., Methods: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time., Results: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient., Conclusion: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases., (© 2020, American College of Rheumatology.)