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To characterize the splicing machinery (SM) of leukocytes from primary antiphospholipid syndrome (APS), systemic lupus erythematosus (SLE) and antiphospholipid syndrome with lupus (APS + SLE) patients, and to assess its clinical involvement.Monocytes, lymphocytes and neutrophils from 80 patients (22 APS, 35 SLE and 23 APS + SLE) and 50 HD were purified, and 45 selected SM components were evaluated by qPCR-microfluidic array. Relationship with clinical features and underlying regulatory mechanisms were assessed.APS, SLE and APS + SLE leukocytes displayed significant and specific alterations in SM-components (SMC), associated with clinical features [autoimmune profiles, disease activity, lupus nephritis (LN), and CV-risk markers]. A remarkable relationship among dysregulated SMC in monocytes and the presence of LN in SLE was highlighted, revealing a novel pathological mechanism, which was further explored. Immunohistology analysis of renal biopsies highlighted the pathological role of the myeloid compartment in LN. Transcriptomic analysis of monocytes from SLE-LN(+) vs SLE-LN(-) identified 271 genes differentially expressed, mainly involved in inflammation and IFN-signaling. Levels of IFN-related genes correlated with those of SMC in SLE-LN(+). These results were validated in two external SLE-LN(+) datasets of whole-blood and kidney biopsies. In vitro, SLE-LN(+)-serum promoted a concomitant dysregulation of both, the IFN signature and several SMC, further reversed by JAKinibs treatment. Interestingly, IFNs, key inflammatory cytokines in SLE pathology, also altered SMC. Lastly, the over/down-expression of selected SMC in SLE-monocytes reduced the release of inflammatory cytokines and their adhesion capacity.Overall, we have identified, for the first time, a specific alteration of SMC in leukocytes from APS, SLE and APS + SLE patients that would be responsible for the development of distinctive clinical profiles.

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.

Rheumatoid Arthritis (RA), Systemic lupus erythematosus (SLE) and antiphospholipid syndrome (APS) are the systemic autoimmune diseases (SADs) most associated with an increased risk of developing cardiovascular (CV) events. Cardiovascular disease (CVD) in SADs results from a complex interaction between traditional CV-risk factors, immune deregulation and disease activity. Oxidative stress, dyslipidemia, endothelial dysfunction, inflammatory/prothrombotic mediators (cytokines/chemokines, adipokines, proteases, adhesion-receptors, NETosis-derived-products, and intracellular-signaling molecules) have been implicated in these vascular pathologies. Genetic and genomic analyses further allowed the identification of signatures explaining the pro-atherothrombotic profiles in RA, SLE and APS. However, gene modulation has left significant gaps in our understanding of CV co-morbidities in SADs. MicroRNAs (miRNAs) are emerging as key post-transcriptional regulators of a suite of signaling pathways and pathophysiological effects. Abnormalities in high number of miRNA and their associated functions have been described in several SADs, suggesting their involvement in the development of atherosclerosis and thrombosis in the setting of RA, SLE and APS. This review focusses on recent insights into the potential role of miRNAs both, as clinical biomarkers of atherosclerosis and thrombosis in SADs, and as therapeutic targets in the regulation of the most influential processes that govern those disorders, highlighting the potential diagnostic and therapeutic properties of miRNAs in the management of CVD.