Background: The genetic risk associated with rheumatoid arthritis (RA) includes genes regulating DNA methylation, one of the hallmarks of epigenetic re-programing, as well as many T-cell genes, with a strong MHC association, pointing to immunogenetic mechanisms as disease triggers leading to chronicity. The aim of our study was to explore DNA methylation in early, drug-naïve RA patients, towards a better understanding of early events in pathogenesis. Result: Monocytes, naïve and memory CD4 + T-cells were sorted from 6 healthy controls and 10 RA patients. DNA methylation was assessed using a genome-wide Illumina 450K CpG promoter array. Differential methylation was confirmed using bisulfite sequencing for a specific gene promoter, ELISA for several cytokines and flow cytometry for cell surface markers. Differentially methylated (DM) CpGs were observed in 1047 genes in naïve CD4 + T-cells, 913 in memory cells and was minimal in monocytes with only 177 genes. Naive CD4 + T-cells were further investigated as presenting differential methylation in the promoter of > 500 genes associated with several disease-relevant pathways, including many cytokines and their receptors. We confirmed hypomethylation of a region of the TNF-alpha gene in early RA and differential expression of 3 cytokines (IL21, IL34 and RANKL). Using a bioinformatics package (DMRcate) and an in-house analysis based on differences in β values, we established lists of DM genes between health and RA. Publicly available gene expression data were interrogated to confirm differential expression of over 70 DM genes. The lists of DM genes were further investigated based on a functional relationship database analysis, which pointed to an IL6/JAK1/STAT3 node, related to TNF-signalling and engagement in Th17 cell differentiation amongst many pathways. Five DM genes for cell surface markers (CD4, IL6R, IL2RA/CD25, CD62L, CXCR4) were investigated towards identifying subpopulations of CD4 + T-cells undergoing these modifications and pointed to a subset of naïve T-cells, with high levels of CD4, IL2R, and CXCR4, but reduction and loss of IL6R and CD62L, respectively. Conclusion: Our data provided novel conceptual advances in the understanding of early RA pathogenesis, with implications for early treatment and prevention.

Background:The interferon (IFN) pathway is a complex system with multiple proteins and diverse downstream effects on gene and protein expression. IFNs have been implicated in multiple RMDs. Despite significant potential, IFN assays have not progressed into clinical practice.Objectives:To perform a SLR on IFN assays in RMDs and propose a consensus terminology.Methods:OvidMedline, Embase and Web of Science were searched for reports of IFN and RMDs up to October 2019. Information about the properties of assays measuring type I IFN and measures of truth were extracted and summarised. Terminology was agreed through an interactive consensus process with reference to the existing evidence.Results:10037 abstracts were identified. 275 fulfilled eligibility criteria, and were used for data extraction. Some used more than one technique to measure IFN-I pathway activation. Hence, 275 papers generated data on 393 methods. There was great heterogeneity in the methods used and presentation of results. IFN-I pathway activation was measured using: qPCR (n=121), immunoassays (n=101), microarray (n=69), reporter cell assay (n=38), DNA methylation (n=14), flow cytometry (n=14), cytopathic effect assay (n=11), RNA sequencing (n=9), Plaque reduction assay (n=8), Nanostring (n=5), bisulphite sequencing (n=3). All papers fulfilled Face Validity. Due to lack of gold standard for IFN-I pathway activation, evidence of criterion validity was variable. Concurrent validity was presented for n=150 assays. The terminology used to describe aspects of type I IFN pathway activation was not consistent, so a consensus terminology for IFN research (Table 1) was proposed by the taskforce.Table 1.Consensus terminologyTermAbbreviationDefinitionInterferonIFNProteins with anti-viral activity; IFNs are mediators of an anti-viral response. They belong to the Type I, Type II and Type III IFN families.Type I interferonIFN-IThe IFNs alpha, beta, omega, kappa, epsilon, secreted by any nucleated cell, and binding to the IFNAR, which is expressed on any nucleated cell.Type II interferonIFN-IIIFN gamma, mostly secreted by T cells, binding to the IFNGR, which is expressed on most leucocytes.Type III interferonIFN-IIIIFN lambda, which are structurally more similar to IL-10 but share downstream signalling and gene expression with IFN-I.Interferon-stimulated genesISGsGenes whose expression is known to be upregulated by any kind of IFN. Individual ISGs may not exclusively represent Type I IFN pathway activation.Type I Interferon pathway activationAny evidence for function of the components of the Type I IFN pathway. This includes: secretion of a Type I IFN protein, binding to the IFNAR, initiation of JAK/STAT signalling pathways, expression of IFN-stimulated genes, expression of IFN-stimulated proteins.Type I interferon pathway assayAn assay measuring one or more components of the Type I IFN pathway at a molecular or functional level.Interferon stimulated gene expression signatureA qualitative description of coordinated expression of a set of ISGs that is indicative of Type I IFN pathway activation.Interferon stimulated gene expression scoreA quantitative variable derived from expression of a defined set of ISGs that is indicative of Type I IFN pathway activation.Interferon stimulated protein scoreA variable derived from expression of a defined set of soluble biomarkers known to be upregulated by IFN, although not specific for Type I IFN.InterferonopathyMonogenic diseases in which there is constitutive Type I IFN pathway activation with a causal role in pathology. The clinical picture may resemble rheumatic musculoskeletal diseases. However, most diseases with IFN pathway activation are not Interferonopathies.Conclusion:Diverse methods have been reported as IFN assays and these differ in what elements of type IFN-I pathway activation they measure. The taskforce consensus terminology on type I IFN reporting should be considered for research and clinical applications.Disclosure of Interests:Agata Burska: None declared, Javier Rodriguez Carrio: None declared, Philip G Conaghan: None declared, Willem A Dik: None declared, Robert Biesen: None declared, Maija-leena Eloranta: None declared, Giulio Cavalli: None declared, Marianne Visser: None declared, Dimitrios Boumpas: None declared, George Bertsias: None declared, Marie Wahren-Herlenius: None declared, Jan Rehwinkel: None declared, Marie-Louise Frémond: None declared, Mary K. Crow Consultant of: AstraZeneca, Bristol Meyers Squibb, Lilly, Shannon Pharmaceuticals, Grant/research support from: Gilead, Lars Ronnblom Consultant of: AstraZeneca, Edward Vital Speakers bureau: GSK, Consultant of: AURINIA, SANDOZ, GSK, AstraZeneca, Roche, Modus, Grant/research support from: AstraZeneca, Marjan Versnel: None declared

Background and Objectives ACPA+ individuals with non-specific musculoskeletal symptoms are at high risk of developing rheumatoid arthritis (RA). We previously demonstrated dys-regulation of T-cell subsets with loss of naïve and regulatory T-cells (Treg) in early disease. The aim of the current study is to demonstrate the predictive value of T-cell subset analysis for progression towards symptom onset in ACPA+ individuals. Materials and Methods 84 ACPA+ individuals without clinical synovitis at recruitment were followed. 95 healthy controls (HC) provided a reference group. At baseline T-cell subset analyses were performed using 6-colour flowcytometry for naïve T-cells (CD4+ CD45RB + CD45RA+ CD62L+), Treg (CD4+ CD25highFoxp3 + CD127low) and inflammation related cells (IRC: CD4+ CD45RB + CD45RA+ CD62L-). The relationship between naïve cell frequency and age was established in HC and used to age-correct values in ACPA+ . ROC curve analysis was used to identify 2 T-cell cut-offs predicting progression to IA at any time; one which maximised the Youden index (sensitivity + specificity-1), and one which prioritised specificity over sensitivity. Results 42/84 (50%) of patients developed clinical synovitis within a median follow-up of 6.0 months (range 1 week-46 months). For age-corrected naïve T-cells area under the ROC curve (AUC) was 0.67 (95% CI 0.55, 0.79; n = 84, p = 0.007), for IRC 0.70 (0.59, 0.81; n = 81, p = 0.002) and for Treg 0.67 (0.53, 0.80; n = 65, p = 0.021). For each of the three subsets, the Youden index cut-off correctly classified >65% of patients (Table 1). Cut-offs prioritising specificity were identified which did not greatly reduce overall classification success. The confidence intervals for these estimates remain wide and our sample size may still be limited for running such analysis. Youden index cut-off Specificity priority cut-off Subset Cut-off Sensitivity Specificity % Correct Cut-off Sensitivity Specificity % Correct Naïve ≤-6.4 59.5 (43.3, 74.4) 76.2 (60.5, 87.9) 67.9 ≤-14.0 26.2 (13.9, 42.0) 90.5 (77.4, 97.3) 58.3 IRC ≥2.8 56.4 (39.6, 72.2) 76.2 (60.5, 87.9) 66.7 ≥4.5 30.8 (17.0, 47.6) 90.5 (77.4, 97.3) 61.7 Treg ≤4.15 90.6 (75.0, 98.0) 41.2 (24.6, 98.0) 65.1 ≤1.6 34.4 (18.6, 53.2) 91.2 (76.3, 98.1) 63.7 Abstract A1.33 Table 1 Sensitivity and specificity of T-cell subset frequencies for progression to IA, using two different cut-off values for each subset; one where the Youden index was maximised and another that prioritised specificity over sensitivity. Conclusions T-cell dys-regulation in ACPA+ individuals with non-specific musculoskeletal pain may be useful in predicting progression to inflammatory arthritis. Multivariable modelling in larger cohorts is needed to quantify the utility of T-cell subsets in predicting progression to IA.