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.

shinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

MOTIVATION: The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. RESULTS: We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. AVAILABILITY AND IMPLEMENTATION: The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

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.

Predicting best treatment in rheumatoid arthritis.

BACKGROUND: Although targeted biological treatments have transformed the outlook for patients with rheumatoid arthritis (RA), 40% of patients show poor clinical response, and there is an imperative to unravel the molecular pathways and mechanisms underlying non-response and disease progression. 5-20% of RA individuals do not respond to all current medications including biologic and targeted therapies, which suggests that distinct pathogenic processes underlie multi-drug refractoriness. OBJECTIVES: In this brief review we discuss advances from recent studies in precision medicine in rheumatoid arthritis. METHODS: Bulk RNA-Sequencing of synovial biopsies from RA individuals combined with histology and deep clinical phenotyping has revealed substantial insights into divergent pathogenic pathways which lead to disease progression and illuminated mechanisms underlying failure to response to specific treatments. Biopsy-driven randomised controlled trials, such as R4RA and the forthcoming STRAP trial, have enabled the development of machine learning predictive models for predicting response to different therapies. RESULTS: In the Pathobiology of Early Arthritis Cohort (PEAC), gene expression analysis showed that individuals could be classified into three gene expression subgroups which correlated with histopathotypes defined by histological markers: pauci-immune fibroid pathotype characterised by fibroblasts and an absence of immune inflammatory cells; diffuse-myeloid pathotype characterised by macrophage influx; and the lympho-myeloid pathotype delineated by the presence of B cells, but typically containing a complex inflammatory infiltrate with ectopic lymphoid structure formation. In the R4RA biopsy-driven randomised controlled trial, patients were randomised to either rituximab or tocilizumab. Comprehensive analysis of synovial biopsies pre/post-treatment identified gene signatures of response associated with pathogenic pathways which could be tracked over time. A group of true refractory patients were identified who had failed anti-TNF prior to the study (it was an entry criterion) and then subsequently failed both trial biologics during the trial. RNA-Seq analysis and digital spatial profiling identified specific cell types including DKK3+ fibroblasts as being associated with the refractory state. We identified machine learning predictive models based on specific gene signatures which were able to predict future response to therapy as well as the refractory state. CONCLUSIONS: RNA-sequencing of synovial biopsies has enabled substantial progress in understanding disease endotypes in RA and identifying synovial gene signatures which predict prognosis and future response to treatment.

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.

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.

Identification of autoantigens and their potential post-translational modification in EGPA and severe eosinophilic asthma.

Background: The chronic airway inflammation in severe eosinophilic asthma (SEA) suggests potential autoimmune aetiology with unidentified autoantibodies analogous to myeloperoxidase (MPO) in ANCA-positive EGPA (eosinophilic granulomatosis with polyangiitis). Previous research has shown that oxidative post-translational modification (oxPTM) of proteins is an important mechanism by which autoantibody responses may escape immune tolerance. Autoantibodies to oxPTM autoantigens in SEA have not previously been studied. Methods: Patients with EGPA and SEA were recruited as well as healthy control participants. Autoantigen agnostic approach: Participant serum was incubated with slides of unstimulated and PMA-stimulated neutrophils and eosinophils, and autoantibodies to granulocytes were identified by immunofluorescence with anti-human IgG FITC antibody. Target autoantigen approach: Candidate proteins were identified from previous literature and FANTOM5 gene set analysis for eosinophil expressed proteins. Serum IgG autoantibodies to these proteins, in native and oxPTM form, were detected by indirect ELISA. Results: Immunofluorescence studies showed that serum from patients with known ANCA stained for IgG against neutrophils as expected. In addition, serum from 9 of 17 tested SEA patients stained for IgG to PMA-stimulated neutrophils undergoing NETosis. Immunofluorescent staining of eosinophil slides was evident with serum from all participants (healthy and with eosinophilic disease) with diffuse cytoplasmic staining except for one SEA individual in whom subtle nuclear staining was evident. FANTOM5 gene set analysis identified TREM1 (triggering receptor expressed on myeloid cells 1) and IL-1 receptor 2 (IL1R2) as eosinophil-specific targets to test for autoantibody responses in addition to MPO, eosinophil peroxidase (EPX), and Collagen-V identified from previous literature. Indirect ELISAs found high concentrations of serum autoantibodies to Collagen-V, MPO, and TREM1 in a higher proportion of SEA patients than healthy controls. High concentrations of serum autoantibodies to EPX were evident in serum from both healthy and SEA participants. The proportion of patients with positive autoantibody ELISAs was not increased when examining oxPTM compared to native proteins. Discussion: Although none of the target proteins studied showed high sensitivity for SEA, the high proportion of patients positive for at least one serum autoantibody shows the potential of more research on autoantibody serology to improve diagnostic testing for severe asthma. Clinical trial registration: ClinicalTrials.gov, identifier, NCT04671446.

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.

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.

Syntenin-1-mediated arthritogenicity is advanced by reprogramming RA metabolic macrophages and Th1 cells.

OBJECTIVES: Syntenin-1, a novel endogenous ligand, was discovered to be enriched in rheumatoid arthritis (RA) specimens compared with osteoarthritis synovial fluid and normal synovial tissue (ST). However, the cellular origin, immunoregulation and molecular mechanism of syntenin-1 are undescribed in RA. METHODS: RA patient myeloid and lymphoid cells, as well as preclinical models, were used to investigate the impact of syntenin-1/syndecan-1 on the inflammatory and metabolic landscape. RESULTS: Syntenin-1 and syndecan-1 (SDC-1) co-localise on RA ST macrophages (MΦs) and endothelial cells. Intriguingly, blood syntenin-1 and ST SDC-1 transcriptome are linked to cyclic citrullinated peptide, erythrocyte sedimentation rate, ST thickness and bone erosion. Metabolic CD14+CD86+GLUT1+MΦs reprogrammed by syntenin-1 exhibit a wide range of proinflammatory interferon transcription factors, monokines and glycolytic factors, along with reduced oxidative intermediates that are downregulated by blockade of SDC-1, glucose uptake and/or mTOR signalling. Inversely, IL-5R and PDZ1 inhibition are ineffective on RA MΦs-reprogrammed by syntenin-1. In syntenin-1-induced arthritis, F4/80+iNOS+RAPTOR+MΦs represent glycolytic RA MΦs, by amplifying the inflammatory and glycolytic networks. Those networks are abrogated in SDC-1-/- animals, while joint prorepair monokines are unaffected and the oxidative metabolites are moderately replenished. In RA cells and/or preclinical model, syntenin-1-induced arthritogenicity is dependent on mTOR-activated MΦ remodelling and its ability to cross-regulate Th1 cells via IL-12 and IL-18 induction. Moreover, RA and joint myeloid cells exposed to Syntenin-1 are primed to transform into osteoclasts via SDC-1 ligation and RANK, CTSK and NFATc1 transcriptional upregulation. CONCLUSION: The syntenin-1/SDC-1 pathway plays a critical role in the inflammatory and metabolic landscape of RA through glycolytic MΦ and Th1 cell cross-regulation (graphical abstract).

Stratification of biological therapies by pathobiology in biologic-naive patients with rheumatoid arthritis (STRAP and STRAP-EU): two parallel, open-label, biopsy-driven, randomised trials.

BACKGROUND: Despite highly effective targeted therapies for rheumatoid arthritis, about 40% of patients respond poorly, and predictive biomarkers for treatment choices are lacking. We did a biopsy-driven trial to compare the response to rituximab, etanercept, and tocilizumab in biologic-naive patients with rheumatoid arthritis stratified for synovial B cell status. METHODS: STRAP and STRAP-EU were two parallel, open-label, biopsy-driven, stratified, randomised, phase 3 trials done across 26 university centres in the UK and Europe. Biologic-naive patients aged 18 years or older with rheumatoid arthritis based on American College of Rheumatology (ACR)-European League Against Rheumatism classification criteria and an inadequate response to conventional synthetic disease-modifying antirheumatic drugs (DMARDs) were included. Following ultrasound-guided synovial biopsy, patients were classified as B cell poor or B cell rich according to synovial B cell signatures and randomly assigned (1:1:1) to intravenous rituximab (1000 mg at week 0 and week 2), subcutaneous tocilizumab (162 mg per week), or subcutaneous etanercept (50 mg per week). The primary outcome was the 16-week ACR20 response in the B cell-poor, intention-to-treat population (defined as all randomly assigned patients), with data pooled from the two trials, comparing etanercept and tocilizumab (grouped) versus rituximab. Safety was assessed in all patients who received at least one dose of study drug. These trials are registered with the EU Clinical Trials Register, 2014-003529-16 (STRAP) and 2017-004079-30 (STRAP-EU). FINDINGS: Between June 8, 2015, and July 4, 2019, 226 patients were randomly assigned to etanercept (n=73), tocilizumab (n=74), and rituximab (n=79). Three patients (one in each group) were excluded after randomisation because they received parenteral steroids in the 4 weeks before recruitment. 168 (75%) of 223 patients in the intention-to-treat population were women and 170 (76%) were White. In the B cell-poor population, ACR20 response at 16 weeks (primary endpoint) showed no significant differences between etanercept and tocilizumab grouped together and rituximab (46 [60%] of 77 patients vs 26 [59%] of 44; odds ratio 1·02 [95% CI 0·47-2·17], p=0·97). No differences were observed for adverse events, including serious adverse events, which occurred in six (6%) of 102 patients in the rituximab group, nine (6%) of 108 patients in the etanercept group, and three (4%) of 73 patients in the tocilizumab group (p=0·53). INTERPRETATION: In this biologic-naive population of patients with rheumatoid arthrtitis, the dichotomic classification into synovial B cell poor versus rich did not predict treatment response to B cell depletion with rituximab compared with alternative treatment strategies. However, the lack of response to rituximab in patients with a pauci-immune pathotype and the higher risk of structural damage progression in B cell-rich patients treated with rituximab warrant further investigations into the ability of synovial tissue analyses to inform disease pathogenesis and treatment response. FUNDING: UK Medical Research Council and Versus Arthritis.

Follicular dendritic cell differentiation is associated with distinct synovial pathotype signatures in rheumatoid arthritis.

Follicular dendritic cells (FDCs) fundamentally contribute to the formation of synovial ectopic lymphoid-like structures in rheumatoid arthritis (RA) which is associated with poor clinical prognosis. Despite this critical role, regulation of FDC development in the RA synovium and its correlation with synovial pathotype differentiation remained largely unknown. Here, we demonstrate that CNA.42+ FDCs distinctively express the pericyte/fibroblast-associated markers PDGFR-ß, NG2, and Thy-1 in the synovial perivascular space but not in established follicles. In addition, synovial RNA-Seq analysis revealed that expression of the perivascular FDC markers was strongly correlated with PDGF-BB and fibroid synovitis, whereas TNF-α/LT-ß was significantly associated with lymphoid synovitis and expression of CR1, CR2, and FcγRIIB characteristic of mature FDCs in lymphoid follicles. Moreover, PDGF-BB induced CNA.42+ FDC differentiation and CXCL13 secretion from NG2+ synovial pericytes, and together with TNF-α/LT-ß conversely regulated early and late FDC differentiation genes in unsorted RA synovial fibroblasts (RASF) and this was confirmed in flow sorted stromal cell subsets. Furthermore, RASF TNF-αR expression was upregulated by TNF-α/LT-ß and PDGF-BB; and TNF-α/LT-ß-activated RASF retained ICs and induced B cell activation in in vitro germinal center reactions typical of FDCs. Additionally, FDCs trapped peptidyl citrulline, and strongly correlated with IL-6 expression, and plasma cell, B cell, and T cell infiltration of the RA synovium. Moreover, synovial FDCs were significantly associated with RA disease activity and radiographic features of tissue damage. To the best of our knowledge, this is the first report describing the reciprocal interaction between PDGF-BB and TNF-α/LT-ß in synovial FDC development and evolution of RA histological pathotypes. Selective targeting of this interplay could inhibit FDC differentiation and potentially ameliorate RA in clinically severe and drug-resistant patients.