Splicing machinery is impaired in rheumatoid arthritis, associated with disease activity and modulated by anti-TNF therapy.

OBJECTIVES: To characterise splicing machinery (SM) alterations in leucocytes of patients with rheumatoid arthritis (RA), and to assess its influence on their clinical profile and therapeutic response. METHODS: Leucocyte subtypes from 129 patients with RA and 29 healthy donors (HD) were purified, and 45 selected SM elements (SME) were evaluated by quantitative PCR-array based on microfluidic technology (Fluidigm). Modulation by anti-tumour necrosis factor (TNF) therapy and underlying regulatory mechanisms were assessed. RESULTS: An altered expression of several SME was found in RA leucocytes. Eight elements (SNRNP70, SNRNP200, U2AF2, RNU4ATAC, RBM3, RBM17, KHDRBS1 and SRSF10) were equally altered in all leucocytes subtypes. Logistic regressions revealed that this signature might: discriminate RA and HD, and anti-citrullinated protein antibodies (ACPAs) positivity; classify high-disease activity (disease activity score-28 (DAS28) >5.1); recognise radiological involvement; and identify patients showing atheroma plaques. Furthermore, this signature was altered in RA synovial fluid and ankle joints of K/BxN-arthritic mice. An available RNA-seq data set enabled to validate data and identified distinctive splicing events and splicing variants among patients with RA expressing high and low SME levels. 3 and 6 months anti-TNF therapy reversed their expression in parallel to the reduction of the inflammatory profile. In vitro, ACPAs modulated SME, at least partially, by Fc Receptor (FcR)-dependent mechanisms. Key inflammatory cytokines further altered SME. Lastly, induced SNRNP70-overexpression and KHDRBS1-overexpression reversed inflammation in lymphocytes, NETosis in neutrophils and adhesion in RA monocytes and influenced activity of RA synovial fibroblasts. CONCLUSIONS: Overall, we have characterised for the first time a signature comprising eight dysregulated SME in RA leucocytes from both peripheral blood and synovial fluid, linked to disease pathophysiology, modulated by ACPAs and reversed by anti-TNF therapy.

Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis.

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients. Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions. Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort. Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.