Clinical Phenotypes, Serological Biomarkers, and Synovial Features Defining Seropositive and Seronegative Rheumatoid Arthritis: A Literature Review.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder which can lead to long-term joint damage and significantly reduced quality of life if not promptly diagnosed and adequately treated. Despite significant advances in treatment, about 40% of patients with RA do not respond to individual pharmacological agents and up to 20% do not respond to any of the available medications. To address this large unmet clinical need, several recent studies have focussed on an in-depth histological and molecular characterisation of the synovial tissue to drive the application of precision medicine to RA. Currently, RA patients are clinically divided into "seropositive" or "seronegative" RA, depending on the presence of routinely checked antibodies. Recent work has suggested that over the last two decades, long-term outcomes have improved significantly in seropositive RA but not in seronegative RA. Here, we present up-to-date differences in epidemiology, clinical features, and serological biomarkers in seronegative versus seropositive RA and discuss how histological and molecular synovial signatures, revealed by recent large synovial biopsy-based clinical trials, may be exploited to refine the classification of RA patients, especially in the seronegative group.

Synovial fibroblast gene expression is associated with sensory nerve growth and pain in rheumatoid arthritis.

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We developed a machine-learning approach (graph-based gene expression module identification or GbGMI) to identify an 815-gene expression module associated with pain in synovial biopsy samples from patients with established RA who had limited synovial inflammation at arthroplasty. We then validated this finding in an independent cohort of synovial biopsy samples from patients who had early untreated RA with little inflammation. Single-cell RNA sequencing analyses indicated that most of these 815 genes were most robustly expressed by lining layer synovial fibroblasts. Receptor-ligand interaction analysis predicted cross-talk between human lining layer fibroblasts and human dorsal root ganglion neurons expressing calcitonin gene-related peptide (CGRP+). Both RA synovial fibroblast culture supernatant and netrin-4, which is abundantly expressed by lining fibroblasts and was within the GbGMI-identified pain-associated gene module, increased the branching of pain-sensitive murine CGRP+ dorsal root ganglion neurons in vitro. Imaging of solvent-cleared synovial tissue with little inflammation from humans with RA revealed CGRP+ pain-sensing neurons encasing blood vessels growing into synovial hypertrophic papilla. Together, these findings support a model whereby synovial lining fibroblasts express genes associated with pain that enhance the growth of pain-sensing neurons into regions of synovial hypertrophy in RA.

Axl and MerTK regulate synovial inflammation and are modulated by IL-6 inhibition in rheumatoid arthritis.

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.

Comparative analysis of late-stage rheumatoid arthritis and osteoarthritis reveals shared histopathological features.

OBJECTIVES: Osteoarthritis (OA) is a debilitating and heterogeneous condition, characterized by various levels of articular cartilage degradation, osteophytes formation, and synovial inflammation. Multiple evidences suggest that synovitis may appear early in the disease development and correlates with disease severity and pain, therefore representing a relevant therapeutic target. In a typical synovitis-driven joint disease, namely rheumatoid arthritis (RA), several pathotypes have been described by our group and associated with clinical phenotypes, disease progression, and response to therapy. However, whether these pathotypes can be also observed in the OA synovium is currently unknown. METHODS: Here, using histological approaches combined with semi-quantitative scoring and quantitative digital image analyses, we comparatively characterize the immune cell infiltration in a large cohort of OA and RA synovial tissue samples collected at the time of total joint replacement. RESULTS: We demonstrate that OA synovium can be categorized also into three pathotypes and characterized by disease- and stage-specific features. Moreover, we revealed that pathotypes specifically reflect distinct levels of peripheral inflammation. CONCLUSIONS: In this study, we provide a novel and relevant pathological classification of OA synovial inflammation. Further studies investigating synovial molecular pathology in OA may contribute to the development of disease-modifying therapies.

Notch ligands are biomarkers of anti-TNF response in RA patients.

Notch and its ligands play a critical role in rheumatoid arthritis (RA) pathogenesis. Hence, studies were conducted to delineate the functional significance of the Notch pathway in RA synovial tissue (ST) cells and the influence of RA therapies on their expression. Morphological studies reveal that JAG1, DLL4, and Notch1 are highly enriched in RA ST lining and sublining CD68+CD14+ MΦs. JAG1 and DLL4 transcription is jointly upregulated in RA MΦs reprogrammed by TLR4/5 ligation and TNF, whereas Syntenin-1 exposure expands JAG1, DLL4, and Notch1 expression levels in these cells. Single-cell RNA-seq data exhibit that JAG1 and Notch3 are overexpressed on all fibroblast-like synoviocyte (FLS) subpopulations, in parallel, JAG2, DLL1, and Notch1 expression levels are modest on RA FLS and are predominately potentiated by TLR4 ligation. Intriguingly, JAG1, DLL1/4, and Notch1/3 are presented on RA endothelial cells, and their expression is mutually reconfigured by TLR4/5 ligation in the endothelium. Synovial JAG1/JAG2/DLL1 or Notch1/3 transcriptomes were unchanged in patients who received disease-modifying anti-rheumatic drugs (DMARDs) or IL-6R Ab therapy regardless of disease activity score. Uniquely, RA MΦs and endothelial cells rewired by IL-6 displayed DLL4 transcriptional upregulation, and IL-6R antibody treatment disrupted RA ST DLL4 transcription in good responders compared to non-responders or moderate responders. Nevertheless, the JAG1/JAG2/DLL1/DLL4 transcriptome was diminished in anti-TNF good responders with myeloid pathotype and was unaltered in the fibroid pathotype except for DLL4. Taken together, our findings suggest that RA myeloid Notch ligands can serve as markers for anti-TNF responsiveness and trans-activate Notch receptors expressed on RA FLS and/or endothelial cells.

Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome.

OBJECTIVE: Genome-wide association studies have successfully identified more than 100 loci associated with susceptibility to rheumatoid arthritis (RA). However, our understanding of the functional effects of genetic variants in causing RA and their effects on disease severity and response to treatment remains limited. METHODS: In this study, we conducted expression quantitative trait locus (eQTL) analysis to dissect the link between genetic variants and gene expression comparing the disease tissue against blood using RNA-Sequencing of synovial biopsies (n=85) and blood samples (n=51) from treatment-naïve patients with RA from the Pathobiology of Early Arthritis Cohort. RESULTS: This identified 898 eQTL genes in synovium and genes loci in blood, with 232 genes in common to both synovium and blood, although notably many eQTL were tissue specific. Examining the HLA region, we uncovered a specific eQTL at HLA-DPB2 with the critical triad of single-nucleotide polymorphisms (SNPs) rs3128921 driving synovial HLA-DPB2 expression, and both rs3128921 and HLA-DPB2 gene expression correlating with clinical severity and increasing probability of the lympho-myeloid pathotype. CONCLUSIONS: This analysis highlights the need to explore functional consequences of genetic associations in disease tissue. HLA-DPB2 SNP rs3128921 could potentially be used to stratify patients to more aggressive treatment immediately at diagnosis.

Metabolic reprogramming by Syntenin-1 directs RA FLS and endothelial cell-mediated inflammation and angiogenesis.

A novel rheumatoid arthritis (RA) synovial fluid protein, Syntenin-1, and its receptor, Syndecan-1 (SDC-1), are colocalized on RA synovial tissue endothelial cells and fibroblast-like synoviocytes (FLS). Syntenin-1 exacerbates the inflammatory landscape of endothelial cells and RA FLS by upregulating transcription of IRF1/5/7/9, IL-1ß, IL-6, and CCL2 through SDC-1 ligation and HIF1α, or mTOR activation. Mechanistically, Syntenin-1 orchestrates RA FLS and endothelial cell invasion via SDC-1 and/or mTOR signaling. In Syntenin-1 reprogrammed endothelial cells, the dynamic expression of metabolic intermediates coincides with escalated glycolysis along with unchanged oxidative factors, AMPK, PGC-1α, citrate, and inactive oxidative phosphorylation. Conversely, RA FLS rewired by Syntenin-1 displayed a modest glycolytic-ATP accompanied by a robust mitochondrial-ATP capacity. The enriched mitochondrial-ATP detected in Syntenin-1 reprogrammed RA FLS was coupled with mitochondrial fusion and fission recapitulated by escalated Mitofusin-2 and DRP1 expression. We found that VEGFR1/2 and Notch1 networks are responsible for the crosstalk between Syntenin-1 rewired endothelial cells and RA FLS, which are also represented in RA explants. Similar to RA explants, morphological and transcriptome studies authenticated the importance of VEGFR1/2, Notch1, RAPTOR, and HIF1α pathways in Syntenin-1 arthritic mice and their obstruction in SDC-1 deficient animals. Consistently, dysregulation of SDC-1, mTOR, and HIF1α negated Syntenin-1 inflammatory phenotype in RA explants, while inhibition of HIF1α impaired synovial angiogenic imprint amplified by Syntenin-1. In conclusion, since the current therapies are ineffective on Syntenin-1 and SDC-1 expression in RA synovial tissue and blood, targeting this pathway and its interconnected metabolic intermediates may provide a novel therapeutic strategy.

Attitudes towards technology supported rheumatoid arthritis care: investigating patient- and clinician-perceived opportunities and barriers.

Objectives: Globally, demand outstrips capacity in rheumatology services, making Mobile Health (mHealth) attractive, with the potential to improve access, empower patient self-management and save costs. Existing mHealth interventions have poor uptake by end users. This study was designed to understand existing challenges, opportunities and barriers for computer technology in the RA care pathway. Methods: People with RA were recruited from Barts Health NHS Trust rheumatology clinics to complete paper questionnaires and clinicians were recruited from a variety of centres in the UK to complete an online questionnaire. Data collected included demographics, current technology use, challenges managing RA, RA medications and monitoring, clinic appointments, opportunities for technology and barriers to technology. Results: A total of 109 patient and 41 clinician questionnaires were completed. A total of 83.5% of patients and 93.5% of clinicians use smartphones daily. However, only 25% had ever used an arthritis app and only 5% had persisted with one. Both groups identified managing pain, flares and RA medications as areas of existing need. Access to care, medication support and disease education were mutually agreeable opportunities; however, discrepancies existed between groups with clinicians prioritizing education over access, likely due to concerns of data overwhelm (80.6% considered this a barrier). Conclusions: In spite of high technology use and willingness from both sides, our cohort did not utilize technology to support care, suggesting inadequacies in the existing software. The lack of an objective biomarker for RA disease activity, existing challenges in the healthcare system and the need for integration with existing technical systems were identified as the greatest barriers. Trial registration: Registered on the Clinical Research Network registry (IRAS ID: 264690).

Synovial biopsies for molecular definition of rheumatoid arthritis and treatment response phenotyping: where can we improve?

INTRODUCTION: The extensive knowledge gained in the cellular and molecular mechanisms underlying Rheumatoid Arthritis (RA) pathogenesis has led to therapeutic advances. However, up to 10-20% of patients fail to respond to multiple therapeutic agents being classified as multi-drugresistant. A key challenge moving forward will be the implementation of synovial biopsies in clinical practice to facilitate the shift from the current trial-and-error strategy toward new forms of clinical trials. Biomarker-driven trials have the potential to improve drug selection and patient stratification, reduce economic costs and unnecessary drug-related toxicity. AREAS COVERED: This special report explores the clinical and research applications of synovial biopsy, the advancement in the molecular pathobiology of RA to better understand disease pathogenesis and treatment response, and the way forward for the paradigm shift needed. EXPERT OPINION: In the current era of highly targeted biologic drugs which have dramatically transformed the outlook of RA patients, the use of synovial biopsy represents a valuable practical tool to dissect disease pathogenesis and, consequently, treatment response. In the near future, it is hoped that technological advances will allow for speeding up synovial molecular analysis and that the design of new biomarker-driven trials will enable the allocation of patients to more effective treatment.

Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes.

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction1. There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2. Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments.

Machine Learning Reveals Synovial Fibroblast Genes Associated with Pain Affect Sensory Nerve Growth in Rheumatoid Arthritis.

It has been presumed that rheumatoid arthritis (RA) joint pain is related to inflammation in the synovium; however, recent studies reveal that pain scores in patients do not correlate with synovial inflammation. We identified a module of 815 genes associated with pain, using a novel machine learning approach, Graph-based Gene expression Module Identification (GbGMI), in samples from patients with longstanding RA, but limited synovial inflammation at arthroplasty, and validated this finding in an independent cohort of synovial biopsy samples from early, untreated RA patients. Single-cell RNA-seq analyses indicated these genes were most robustly expressed by lining layer fibroblasts and receptor-ligand interaction analysis predicted robust lining layer fibroblast crosstalk with pain sensitive CGRP+ dorsal root ganglion sensory neurons. Netrin-4, which is abundantly expressed by lining fibroblasts and associated with pain, significantly increased the branching of pain-sensitive CGRP+ neurons in vitro . We conclude GbGMI is a useful method for identifying a module of genes that associate with a clinical feature of interest. Using this approach, we find that Netrin-4 is produced by synovial fibroblasts in the absence of inflammation and can enhance the outgrowth of CGRP+ pain sensitive nerve fibers. One Sentence Summary: Machine Learning reveals synovial fibroblast genes related to pain affect sensory nerve growth in Rheumatoid Arthritis addresses unmet clinical need.

DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis.

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.

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis.

Rheumatoid arthritis (RA) is a systemic autoimmune disease with currently no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in 'At-Risk' populations prior to clinical disease onset is crucial to establishing effective prevention strategies. Here, we applied mass cytometry to deeply characterize the immunophenotypes in blood from At-Risk individuals identified through the presence of serum antibodies to citrullinated protein antigens (ACPA) and/or first-degree relative (FDR) status (n=52), as compared to established RA (n=67), and healthy controls (n=48). We identified significant cell expansions in At-Risk individuals compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5 low naïve B cell population was expanded in ACPA-positive FDRs. Further, we developed an "RA immunophenotype score" classification method based on the degree of enrichment of cell states relevant to established RA patients. This score significantly distinguished At-Risk individuals from controls. In all, we systematically identified activated lymphocyte phenotypes in At-Risk individuals, along with immunophenotypic differences among both ACPA+ and ACPA-FDR At-Risk subpopulations. Our classification model provides a promising approach for understanding RA pathogenesis with the goal to further improve prevention strategies and identify novel therapeutic targets.

Cytokine-directed cellular cross-talk imprints synovial pathotypes in rheumatoid arthritis.

INTRODUCTION: Structural reorganisation of the synovium with expansion of fibroblast-like synoviocytes (FLS) and influx of immune cells is a hallmark of rheumatoid arthritis (RA). Activated FLS are increasingly recognised as a critical component driving synovial tissue remodelling by interacting with immune cells resulting in distinct synovial pathotypes of RA. METHODS: Automated high-content fluorescence microscopy of co-cultured cytokine-activated FLS and autologous peripheral CD4+ T cells from patients with RA was established to quantify cell-cell interactions. Phenotypic profiling of cytokine-treated FLS and co-cultured T cells was done by flow cytometry and RNA-Seq, which were integrated with publicly available transcriptomic data from patients with different histological synovial pathotypes. Computational prediction and knock-down experiments were performed in FLS to identify adhesion molecules for cell-cell interaction. RESULTS: Cytokine stimulation, especially with TNF-α, led to enhanced FLS-T cell interaction resulting in cell-cell contact-dependent activation, proliferation and differentiation of T cells. Signatures of cytokine-activated FLS were significantly enriched in RA synovial tissues defined as lymphoid-rich or leucocyte-rich pathotypes, with the most prominent effects for TNF-α. FLS cytokine signatures correlated with the number of infiltrating CD4+ T cells in synovial tissue of patients with RA. Ligand-receptor pair interaction analysis identified ICAM1 on FLS as an important mediator in TNF-mediated FLS-T cell interaction. Both, ICAM1 and its receptors were overexpressed in TNF-treated FLS and co-cultured T cells. Knock-down of ICAM1 in FLS resulted in reduced TNF-mediated FLS-T cell interaction. CONCLUSION: Our study highlights the role of cytokine-activated FLS in orchestrating inflammation-associated synovial pathotypes providing novel insights into disease mechanisms of RA.

BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.

MOTIVATION: While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE). RESULTS: Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples. AVAILABILITY AND IMPLEMENTATION: We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.

SUMMARY: The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term. AVAILABILITY AND IMPLEMENTATION: DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.

nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high-dimensional data.

Motivation: Although machine learning models are commonly used in medical research, many analyses implement a simple partition into training data and hold-out test data, with cross-validation (CV) for tuning of model hyperparameters. Nested CV with embedded feature selection is especially suited to biomedical data where the sample size is frequently limited, but the number of predictors may be significantly larger (P ≫ n). Results: The nestedcv R package implements fully nested k × l-fold CV for lasso and elastic-net regularized linear models via the glmnet package and supports a large array of other machine learning models via the caret framework. Inner CV is used to tune models and outer CV is used to determine model performance without bias. Fast filter functions for feature selection are provided and the package ensures that filters are nested within the outer CV loop to avoid information leakage from performance test sets. Measurement of performance by outer CV is also used to implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model and determine unbiased model accuracy. Availability and implementation: The R package nestedcv is available from CRAN: https://CRAN.R-project.org/package=nestedcv.

Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium.

Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.

UBE2L3 regulates TLR7-induced B cell autoreactivity in Systemic Lupus Erythematosus.

Both TLR7 and NF-κB hyperactivity are known to contribute to pathogenesis in Systemic Lupus Erythematosus (SLE), driving a pro-interferon response, autoreactive B cell expansion and autoantibody production. UBE2L3 is an SLE susceptibility gene which drives plasmablast/plasma cell expansion in SLE, but its role in TLR7 signalling has not been elucidated. We aimed to investigate the role of UBE2L3 in TLR7-mediated NF-κB activation, and the effect of UBE2L3 inhibition by Dimethyl Fumarate (DMF) on SLE B cell differentiation in vitro. Our data demonstrate that UBE2L3 is critical for activation of NF-κB downstream of TLR7 stimulation, via interaction with LUBAC. DMF, which directly inhibits UBE2L3, significantly inhibited TLR7-induced NF-κB activation, differentiation of memory B cells and plasmablasts, and autoantibody secretion in SLE. DMF also downregulated interferon signature genes and plasma cell transcriptional programmes. These results demonstrate that UBE2L3 inhibition could potentially be used as a therapy in SLE through repurposing of DMF, thus preventing TLR7-driven autoreactive B cell maturation.