BackgroundSynovitis is the common feature across all individuals with a diagnosis of rheumatoid arthritis (RA). Yet, cellular and transcriptomic alterations occuring in RA synovium are highly variable amongst patients. So far, most data on clinical-tissue correlations either rely on hypothesis-driven approaches or are potentially biased by heterogeneous clinical characteristics (e.g. disease duration or disease-modifying antirheumatic drugs).ObjectivesWe used transcriptomic profiling of synovial tissue from early, untreated rheumatoid arthritis patients (ERA) to 1/ identify the genes with the most variable expression amongst patients and 2/ explore the ability of unbiased (data-driven) approaches to define clinically relevant ERA subgroups.MethodsSynovial biopsies were harvested from clinically involved joints of ERA patients using needle arthroscopy or ultrasound-guided biopsy. Data on disease activity were collected at inclusion. For each sample, 350ng total RNA was sent for RNAsequencing using a standardized protocol (Macrogen Europe). After quality control (Fast QC) and genome alignement (HiSat2), normalized read counts were analyzed on Qlucore Omics Explorer. To focus on inter-sample heterogeneity, genes were filtered based on variance (σ/σmax). Unbiased approaches (Principal Component Analysis, Unsupervised Clustering) were applied to define patients’ clusters. Pathway enrichment analysis were performed on Metascape. CibersortX was used to extrapolate the immune cell subsets relative composition from gene expression data. All other statistical analyses were performed on GraphPad Prism v9.ResultsTotal RNA was obtained from synovial biopsies from 74 patients. We first applied variance filtering to identify the genes whose expression showed the greatest variation between patients (n = 894 most variable genes). PCA analysis on the level of expression of these genes did not divide samples into distinct groups, instead yielding a continuous distribution broadly associated with baseline disease activity, as measured by DAS28CRP. Consequently, we used unsupervised clustering to allow for unbiased definition of two patient clusters (PtC): PtC1 (n=52) and PtC2 (n=22) based on their expression of these 894 genes. Pathway analysis of these genes revealed significant enrichment of immune system genes, in the Inflammatory response and Rheumatoid Arthritis pathways (gene cluster 1: GC1), B cell & plasma cell-related pathways (GC2) and metabolic processes-related genes (GC3). Interestingly, PtC1 and PtC2 were characterized by very different clinical features. More specifically, patients from the group with a strong B & plasma cell signature (PtC1) displayed higher baseline indices of all disease activity score components (median DAS28CRP: 5.56 vs 4.09; p-value = 0.0003). They also had higher rates of baseline radiological erosions (erosive disease in 34.6 % vs 10%; p-value = 0.0252) but similar rates of seropositive disease. In line with our pathway analyses, we found a higher signature (inferred relative frequency) of B & plasma cells, T cells and M1-like macrophages in PtC1 compared to PtC2 synovia. PtC2 synovia instead had relatively higher M2-like macrophage and resting mast cell signatures.ConclusionIn this large synovial biopsy study, we found that synovial transcriptomic profiles in ERA patients distribute continuously based on the expression of inflammatory and immune cell transcriptomic pathways. These synovial transcriptomic signatures correlate strongly with systemic disease activity.AcknowledgementsThis work was funded in part by unrestricted grants from Cap48 (RTBF), the Fonds de la Recherche Scientifique (FNRS), and the Fund for Scientific Research in Rheumatology (FWRO/FRSR), managed by the King Baudouin Foundation. CT is funded by the FNRS and Fondation Saint-Luc (Cliniques Universitaires Saint-Luc). NL is a chercheur qualifiée of the FNRS.Disclosure of InterestsClément Triaille: None declared, Tatiana Sokolova: None declared, Stéphanie de Montjoye: None declared, Adrien Nzeusseu Toukap: None declared, Laurent Meric de Bellefon: None declared, Axelle Loriot: None declared, Bernard Lauwerys Shareholder of: BL owns shares (<15000€) in DNALytics, Employee of: BL is currently employed at UCB Biopharma, Patrick Durez: None declared, Nisha Limaye: None declared.
Objectives To evaluate the proportion of patients with ERA who have initiated or not GC, to analyse the baseline characteristics, and to assess the clinical benefit and side effects of GC during 5 years of follow-up. Methods We included patients with ERA from the UCLouvain Brussels cohort who met the ACR/EULAR 2010 classification criteria and were naïve to cDMARDs. We retrospectively collected patient characteristics prior to the introduction of cDMARDs with or without GC. Efficiency and serious adverse events were analysed at 6, 12, 36 and 60 months. Results Data from 474 eligible ERA patients were collected. 180 patients initiated GC compared with 294 who did not. At baseline, the increased CRP is the main factor that favors the initiation of GC followed by smoking, absence of ACPA, prescription of methotrexate as a monotherapy and age. 5 years follow-up of DAS28-CRP, HAQ or VAS pain values did not differ between the two groups. We also analysed a subgroup of 139 patients who received >1 g of prednisolone during the 5 years period. We confirmed the same baseline differences and observed in addition more males and higher DAS-28CRP values. During the 5 years follow up, DAS-28CRP, VAS pain and HAQ remained significantly higher in this subgroup. More severe infections were also reported. Conclusion In our ERA cohort, the initiation of GC treatment does not bring additional benefit for the short and long-term control of the disease. GC was more prescribed in seronegative RA patients with a higher level of inflammation. Disclosure statement the authors declare no conflicts of interest. Ethics statement authors declare that the study complies with the Declaration of Helsinki.
ObjectivesTranscriptomic profiling of synovial tissue from patients with early, untreated rheumatoid arthritis (RA) was used to explore the ability of unbiased, data-driven approaches to define clinically relevant subgroups.MethodsRNASeq was performed on 74 samples, with disease activity data collected at inclusion. Principal components analysis (PCA) and unsupervised clustering were used to define patient clusters based on expression of the most variable genes, followed by pathway analysis and inference of relative abundance of immune cell subsets. Histological assessment and multiplex immunofluorescence (for CD45, CD68, CD206) were performed on paraffin sections.ResultsPCA on expression of the (n=894) most variable genes across this series did not divide samples into distinct groups, instead yielding a continuum correlated with baseline disease activity. Two patient clusters (PtC1, n=52; PtC2, n=22) were defined based on expression of these genes. PtC1, with significantly higher disease activity and probability of response to methotrexate therapy, showed upregulation of immune system genes; PtC2 showed upregulation of lipid metabolism genes, described to characterise tissue resident or M2-like macrophages. In keeping with these data, M2-like:M1-like macrophage ratios were inversely correlated with disease activity scores and were associated with lower synovial immune infiltration and the presence of thinner, M2-like macrophage-rich synovial lining layers.ConclusionIn this large series of early, untreated RA, we show that the synovial transcriptome closely mirrors clinical disease activity and correlates with synovial inflammation. Intriguingly, lower inflammation and disease activity are associated with higher ratios of M2:M1 macrophages, particularly striking in the synovial lining layer. This may point to a protective role for tissue resident macrophages in RA.
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