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Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n  = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients. Biomarker analysis of the phase 4 R4RA trial identifies pretreatment synovial biopsy features selectively associated with response to rituximab or tocilizumab, and leads to the development of models that might predict treatment benefit in patients with rheumatoid arthritis

Biomarkers are needed for predicting the effectiveness of disease modifying antirheumatic drugs (DMARDs). Here, using functional lipid mediator profiling and deeply phenotyped patients with early rheumatoid arthritis (RA), we observe that peripheral blood  specialized pro-resolving mediator (SPM) concentrations are linked with both DMARD responsiveness and disease pathotype. Machine learning analysis demonstrates that baseline plasma concentrations of resolvin D4, 10S, 17S-dihydroxy-docosapentaenoic acid, 15R-Lipoxin (LX)A 4 and n-3 docosapentaenoic-derived Maresin 1 are predictive of DMARD responsiveness at 6 months. Assessment of circulating SPM concentrations 6-months after treatment initiation establishes that differences between responders and non-responders are maintained, with a decrease in SPM concentrations in patients resistant to DMARD therapy. These findings elucidate the potential utility of  plasma SPM concentrations as biomarkers of DMARD responsiveness in RA. Being able to predict the therapeutic benefit of disease modifying anti-rheumatic drugs (DMARDs) would be of great benefit and a stepping stone towards personalized medicine. Here the authors use machine learning and lipid mediator mass spectrometry to show specialized pro-resolving mediators are indicative of DMARD responsiveness among rheumatoid arthritis patients.

The TAM tyrosine kinases, Axl and MerTK, play an important role in rheumatoid arthritis (RA). Here, using a unique synovial tissue bioresource of patients with RA matched for disease stage and treatment exposure, we assessed how Axl and MerTK relate to synovial histopathology and disease activity, and their topographical expression and longitudinal modulation by targeted treatments. We show that in treatment-naive patients, high AXL levels are associated with pauci-immune histology and low disease activity and inversely correlate with the expression levels of pro-inflammatory genes. We define the location of Axl/MerTK in rheumatoid synovium using immunohistochemistry/fluorescence and digital spatial profiling and show that Axl is preferentially expressed in the lining layer. Moreover, its ectodomain, released in the synovial fluid, is associated with synovial histopathology. We also show that Toll-like-receptor 4-stimulated synovial fibroblasts from patients with RA modulate MerTK shedding by macrophages. Lastly, Axl/MerTK synovial expression is influenced by disease stage and therapeutic intervention, notably by IL-6 inhibition. These findings suggest that Axl/MerTK are a dynamic axis modulated by synovial cellular features, disease stage and treatment. The TAM tyrosine kinases, Axl and MerTK, have been implicated in rheumatoid arthritis (RA). Here, using a synovial tissue bioresource of patients with RA, the authors describe how Axl and MerTK expression and function are linked to synovial histopathology, disease activity, and therapeutic intervention with IL-6 inhibitors.

Ectopic lymphoid structures (ELSs) in the rheumatoid synovial joints sustain autoreactivity against locally expressed autoantigens. We recently identified recombinant monoclonal antibodies (RA-rmAbs) derived from single, locally differentiated rheumatoid arthritis (RA) synovial B cells, which specifically recognize fibroblast-like synoviocytes (FLSs). Here, we aimed to identify the specificity of FLS-derived autoantigens fueling local autoimmunity and the functional role of anti-FLS antibodies in promoting chronic inflammation. A subset of anti-FLS RA-rmAbs reacting with a 60 kDa band from FLS extracts demonstrated specificity for HSP60 and partial cross-reactivity to other stromal autoantigens (i.e., calreticulin/vimentin) but not to citrullinated fibrinogen. Anti-FLS RA-rmAbs, but not anti-neutrophil extracellular traps rmAbs, exhibited pathogenic properties in a mouse model of collagen-induced arthritis. In patients, anti-HSP60 antibodies were preferentially detected in RA versus osteoarthritis (OA) synovial fluid. Synovial HSPD1 and CALR gene expression analyzed using bulk RNA-Seq and GeoMx-DSP closely correlated with the lympho-myeloid RA pathotype, and HSP60 protein expression was predominantly observed around ELS. Moreover, we observed a significant reduction in synovial HSP60 gene expression followed B cell depletion with rituximab that was strongly associated with the treatment response. Overall, we report that synovial stromal-derived autoantigens are targeted by pathogenic autoantibodies and are associated with specific RA pathotypes, with potential value for patient stratification and as predictors of the response to B cell-depleting therapies.

Several studies have reported an up-regulation of interleukin (IL)-36 in the serum of patients with systemic lupus erythematosus (SLE). Here, we sought to define the mechanisms whereby IL-36 may contribute to the over-activation of type I Interferon (IFN) responses observed in SLE. We carried out single-cell (sc)RNA-seq in healthy peripheral blood mononuclear cells treated with IL-36 (n=5 donors). We compared the genes and transcriptional networks that were induced by IL-36 with those that were upregulated in a published SLE scRNA-seq dataset (n=33 cases and 11 controls). In follow-up studies, we validated the effects of IL-36 on monocytes by real-time PCR (n=9 donors) and flow-cytometry (n=6). Classical monocytes were the immune population most affected by IL-36 treatment (n=203 Differentially Expressed Genes). In these cells, IL-36 upregulated transcriptional networks (regulons) driven by IRF7, a key activator of type I IFN responses. A similar upregulation of IRF7 regulons was observed in the monocytes of SLE cases, where measurements of IL-36 and IRF7 activity were significantly correlated (r=0.35, P = 0.02). Experimental follow-up studies in human monocytes showed that IL-36 downregulates multiple RNAse genes ( ). IL-36 treatment of monocytes also increased the percentage of apoptotic cells (45% vs 37% in untreated cells; P = 0.001), which are a critical source of self-nucleic acids. We find that IL-36 promotes monocyte apoptosis while downregulating self-nucleic acid clearance. Thus, IL-36 contributes to the accumulation of self-nucleic acids, a key driver of type I IFN responses in SLE.

Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the biopsy-based, precision-medicine STRAP trial ( n  = 208), to identify gene response signatures to the randomised therapies: etanercept (TNF-inhibitor), tocilizumab (interleukin-6 receptor inhibitor) and rituximab (anti-CD20 B-cell depleting antibody). Machine learning models applied to RNA-Seq predict clinical response to etanercept, tocilizumab and rituximab at the 16-week primary endpoint with area under receiver operating characteristic curve (AUC) values of 0.763, 0.748 and 0.754 respectively ( n  = 67-72) as determined by repeated nested cross-validation. Prediction models for tocilizumab and rituximab are validated in an independent cohort (R4RA): AUC 0.713 and 0.786 respectively ( n  = 65-68). Predictive signatures are converted for use with a custom synovium-specific 524-gene nCounter panel and retested on synovial biopsy RNA from STRAP patients, demonstrating accurate prediction of treatment response (AUC 0.82-0.87). The converted models are combined into a unified clinical decision algorithm that has the potential to transform future clinical practice by assisting the selection of biologic therapies. The heterogenous nature of rheumatoid arthritis renders the prediction of responsiveness to biological treatments difficult. Here the authors analyze bulk RNA-seq data from the STRAP trial ( n  = 208) to build a machine-learning model for predicting responses to etanercept, tocilizumab and rituximab with AUCs around 0.75 to potentially assist in therapy planning.

Background Kinases are intracellular signalling mediators and key to sustaining the inflammatory process in rheumatoid arthritis (RA). Oral inhibitors of Janus Kinase family (JAKs) are widely used in RA, while inhibitors of other kinase families e.g. phosphoinositide 3-kinase (PI3K) are under development. Most current biomarker platforms quantify mRNA/protein levels, but give no direct information on whether proteins are active/inactive. Phosphoproteome analysis has the potential to measure specific enzyme activation status at tissue level. Methods We validated the feasibility of phosphoproteome and total proteome analysis on 8 pre-treatment synovial biopsies from treatment-naive RA patients using label-free mass spectrometry, to identify active cell signalling pathways in synovial tissue which might explain failure to respond to RA therapeutics. Results Differential expression analysis and functional enrichment revealed clear separation of phosphoproteome and proteome profiles between lymphoid and myeloid RA pathotypes. Abundance of specific phosphosites was associated with the degree of inflammatory state. The lymphoid pathotype was enriched with lymphoproliferative signalling phosphosites, including Mammalian Target Of Rapamycin (MTOR) signalling, whereas the myeloid pathotype was associated with Mitogen-Activated Protein Kinase (MAPK) and CDK mediated signalling. This analysis also highlighted novel kinases not previously linked to RA, such as Protein Kinase, DNA-Activated, Catalytic Subunit (PRKDC) in the myeloid pathotype. Several phosphosites correlated with clinical features, such as Disease-Activity-Score (DAS)-28, suggesting that phosphosite analysis has potential for identifying novel biomarkers at tissue-level of disease severity and prognosis. Conclusions Specific phosphoproteome/proteome signatures delineate RA pathotypes and may have clinical utility for stratifying patients for personalised medicine in RA.

Background To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models. Methods We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis. Results Analysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73. Conclusions We demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.

Since the first report of SARS-CoV-2 in China in 2019, there has been a huge debate about the origin. In this work, using a different method we aimed to strengthen the observation that no evidence of genetic manipulation has been found by (1) detecting classical restriction site (RS) sequence in human SARS-CoV-2 genomes and (2) comparing them with other recombinant SARS-CoV-like virus created for experimental purposes. Finally, we propose a novel approach consisting in the generation of a restriction endonucleases site map of SARS-CoV-2 and other related coronavirus genomes to be used as a fingerprint to trace the virus evolution.