doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Objective. The effector T cell and B cell cytokine networks have been implicated in the pathogenesis of systemic autoimmune diseases, but the association of these cytokine networks with the heterogeneity of clinical manifestations and immune profiles has not been carefully examined. This study was undertaken to examine whether cytokine profiles can delineate distinct groups of patients in 4 systemic autoimmune diseases (systemic lupus erythematosus, Sjögren's syndrome, rheumatoid arthritis, and systemic sclerosis).Methods. A total of 179 patients and 48 healthy volunteers were enrolled in the multicenter cross-sectional PRECISE Systemic Autoimmune Diseases (PRECISESADS) study. Multi-low-dimensional omics data (cytokines, autoantibodies, circulating immune cells) were examined. Coculture experiments were performed to test the impact of the cytokine microenvironment on T cell/B cell cross-talk.Results. A proinflammatory cytokine profile defined by high levels of CXCL10, interleukin-6 (IL-6), IL-2, and tumor necrosis factor characterized a distinct group of patients in the 4 systemic autoimmune diseases. In each disease, this proinflammatory cluster was associated with a specific circulating immune cell signature, more severe disease, and higher levels of autoantibodies, suggesting an uncontrolled proinflammatory Th1 immune response. We observed in vitro that B cells reinforce Th1 differentiation and naive T cell proliferation, leading to the induction of type 1 effector B cells and IgG production. This process was associated with an increase in CXCL10, IL-6, IL-2, and interferon-γ production.Conclusion. This composite analysis brings new insights into human B cell functional heterogeneity based on T cell/B cell cross-talk, and proposes a better stratification of patients with systemic autoimmune diseases, suggesting that combined biomarkers would be of great value for the design of personalized treatments.

Primary Sjögren’s syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population.

Objective: Systemic lupus erythematosus (SLE) is associated to boosted atherosclerosis development and a higher cardiovascular disease risk. This study aimed to delineate the role of anti-double stranded DNA (anti-dsDNA) antibodies on the molecular profile and the activity of immune and vascular cells, as well as on their enhanced cardiovascular risk. Approach and Results: Eighty SLE patients were included. Extensive clinical/analytical evaluation was performed, including cardiovascular disease parameters (endothelial function, proatherogenic dyslipidemia, and carotid intima-media thickness). Gene and protein expression profiles were evaluated in monocytes from patients diagnosed positive or negative for anti-dsDNA antibodies by using NanoString and cytokine arrays, respectively. NETosis and circulating inflammatory profile was assessed in both neutrophils and plasma. Positivity and persistence of anti-dsDNA antibodies in SLE patients were associated to endothelial dysfunction, proatherogenic dyslipidemia, and accelerated atherosclerosis. In parallel, anti-dsDNA antibodies were linked to the aberrant activation of innate immune cells, so that anti-dsDNA(+) SLE monocytes showed distinctive gene and protein expression/activity profiles, and neutrophils were more prone to suffer NETosis in comparison with anti-dsDNA(−) patients. Anti-dsDNA(+) patients further displayed altered levels of numerous circulating mediators related to inflammation, NETosis, and cardiovascular risk. In vitro, Ig-dsDNA promoted NETosis on neutrophils, apoptosis on monocytes, modulated the expression of inflammation and thrombosis-related molecules, and induced endothelial activation, at least partially, by FcR (Fc receptor)-binding mechanisms. Conclusions: Anti-dsDNA antibodies increase the cardiovascular risk of SLE patients by altering key molecular processes that drive a distinctive and coordinated immune and vascular activation, representing a potential tool in the management of this comorbidity.

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.