OP0337 DIFFERENTIAL METHYLATION OF PERIPHERAL BLOOD ADAPTIVE IMMUNE CELLS IN INDIVIDUALS AT HIGH RISK FOR RA AND WITH EARLY RA COMPARED WITH CONTROLS IDENTIFIES PATHWAYS IMPORTANT IN TRANSITION TO ARTHRITIS. (2nd June 2020)

• Record Type: Journal Article
• Authors: Ai, R.; Boyle, D.; Hammaker, D.; Deane, K.; Holers, V. M.; Matti, A.; Robinson, W.; Buckner, J.; Rao, N.; Baribaud, F.; Johnsen, A.; Nagpal, S.; Wang, W.; Firestein, G.
• Is Part Of: Annals of the rheumatic diseases. Volume 79(2020)Supplement 1
• Page Start: 207
• Page End: 207
• Publication Date: 2020-06-02
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-eular.2989
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: The "Targeting Immune Responses for Prevention of RA" (TIP-RA) collaboration studies individuals at high risk for developing RA because of serum anti-citrullinated protein antibody positivity in absence of arthritis, and is focused on defining how they transition from at-risk to classifiable disease. One potential mechanism is through alterations in epigenetics patterns in adaptive immune cells. Objectives: Previous studies showed that DNA methylation patterns of early RA (ERA) synoviocytes differ from long-standing RA, suggesting that abnormal methylation occurs early in synovium and evolves over time. To extend these observations, we performed a cross-sectional analysis in TIP-RA of DNA methylation signatures in peripheral blood cells in ERA, at-risk anti-CCP3+ individuals and demographically matched CCP- controls. Methods: Genomic DNA was isolated from two independent cohorts of CCP- (cohorts 1 and 2, respectively: B cell: n = 17/34; memory T cell: n = 21/34; and naïve T cell: n = 21/33), CCP3+ (B cell: n = 18/37; memory T cell: n = 20/36; and naïve T cell: n = 20/35), and CCP3+ ERA (B cell: n = 4/18; memory T cell: n = 5/18; and naïve T cell: n = 5/18) after separating PBMCs using antibodies and magnetic beads. Methylation was measured by Illumina Infinium MethylationEPIC chip. Differentially methylated loci (DMLs) were identified using Welch's t-test and mapped to gene promoter regions to define DM genes (DMGs). Principal component analysis (PCA) was used to represent relationship among groups. Pathway analysis was applied by Reactome. Results: For the initial cohort, 1494, 1097 and 1330 DMLs were identified among CCP+, CCP- and ERA in B cells, memory T cells and naïve T cells, respectively. For the confirmatory cohort, 523, 793 and 548 DMLs were found in corresponding cell populations. The DML overlap between the 2 cohorts was highly significant ( p = 2.48E-77). The DMLs were combined for both groups and corresponded to 411, 412, and 351 DMGs in B cells, memory T cells and naïve T cells. Of these, we found 246, 198 and 195 DMGs between CCP3+ and ERA in each peripheral blood cell population, respectively. PCA showed separation of CCP+, CCP- and ERA in each of the three blood cell types by DMLs (Fig. 1 ). DMGs were mapped to biological pathways to identify DM pathways. Although most were not significant, there were several highly significant differences comparing CCP+, ERA and CCP- in memory T cells involving pathways, including "Interferon gamma signaling" (FDR 7.48E-14), "PD-1 signaling" (FDR 8.71E-10), "Translocation of ZAP-70 to Immunological synapse" (FDR 4.75E-10), and "Phosphorylation of CD3 and TCR zeta chains" (FDR 8.71E-10). Conclusion: We identified reproducible methylation signatures of CCP-, CCP+, and ERA in peripheral blood B cells, memory T cells and naïve T cells in initial and confirmatory cohorts. The methylome of ERA also demonstrated a distinctive pattern from CCP+, indicating that progression to RA is accompanied by epigenetic remodeling, especially in T cell signaling and interferon responses. These signatures identify critical pathways in CCP positivity and classifiable RA and could provide the basis of novel interventions to prevent disease. Disclosure of Interests: Rizi Ai: None declared, David Boyle: None declared, Deepa Hammaker: None declared, Kevin Deane Grant/research support from: Janssen, Consultant of: Inova, ThermoFisher, Janseen, BMS and Microdrop, V. Michael Holers Grant/research support from: Janssen, Celgene, and BMS, Andre Matti: None declared, William Robinson: None declared, Jane Buckner Grant/research support from: Bristol-Myers Squibb, Janssen, Navin Rao Shareholder of: Janssen Pharmaceuticals, Employee of: Janssen Pharmaceuticals, Frederic Baribaud Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Alyssa Johnsen Employee of: Janssen, Sunil Nagpal Shareholder of: Janssen Pharmaceuticals, Employee of: Janssen Pharmaceuticals, Wei Wang: None declared, Gary Firestein Grant/research support from: Lilly, Janssen, Abbvie