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AB0508 Glucocorticoid-Responsiveness Correlates with an Interferon Signature in CD4 T-Cells and Monocytes in Rheumatoid Arthritis Patients
  1. R. Fritsch-Stork1,
  2. J. Broen2,
  3. S. Cardoso2,
  4. M. Groot-Koerkamp3,
  5. K. Wurff-Jacobs1,
  6. J. van Roon2,
  7. T. Radstake1,2,
  8. F. Lafeber1,
  9. J. Bijlsma1
  1. 1Dept. of Rheumatology and Clinical Immunology
  2. 2Dept of Translational Immunology
  3. 3Department of Molecular Cancer Research, UMCUtrecht, University Hospital Utrecht, Utrecht, Netherlands


Background Rheumatoid arthritis (RA) is driven by monocytes and T-cells, which are both influenced by glucocorticoids (GC). Although a cornerstone of therapy, one third of RA-patients do not respond adequately to GC and the mechanisms of resistance remain unclear.

Objectives To identify molecular pathways associated with responsiveness to GC in RA-patients.

Methods Patients fulfilling the revised American College of Rheumatology (ACR) criteria for rheumatoid arthritis experiencing an exacerbation of disease on stable medication were scheduled by their treating rheumatologist for GC-pulse therapy. Before and 24 hours after administration of the first of 3 infusions with 1000mg methylprednisolone, monocytes and CD4 T-cells were MACS-isolated. At day 5, response was determined by DAS28. Clinical response was defined according to the European League against Rheumatism (EULAR) response criteria using the absolute DAS28 value and the ΔDAS28. Good responders were classified by a DAS28<3.2 (equivalent to low disease activity) and a ΔDAS28 of ≥1.2. Patients not fulfilling these criteria were considered non-responders. Labeled cRNA of monocytes and CD4 T-cells from 5 responders and 5 non-responders was hybridized to Agilent 4x44K microarray chips. Differentially expressed genes between baseline and after 24 hours were identified via fixed-model analysis of variance based on permutation-based false discovery rates and a >1.5 ratio cut-off. Gene Ontology was used for pathway analysis using the Functional Annotation tool of the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7.; transcription-factors were identified via TRANSFAC. Relevant genes were validated by qPCR.

Results After 24 hours, 48 genes were exclusively changed in responders' monocytes, 253 in non-responders' and 104 genes in both. Apart from genes regulated by GC in both groups (e.g.: VSIG4, CXCL10, IL1R2, SERPING1), most pathways also contained several genes exclusively regulated in non-responders. The majority of these non-responder–exclusive genes were interferon related. In CD4 T-cells, 19 genes were exclusively changed in responders, 104 in non-responders and 18 genes in both. Again, a more pronounced down-regulation of interferon related genes (e.g. IFIH1, IRF7, ISG15 and IFI44L) was seen in non-responders, and validated by qPCR in CD4 T-cells. Additionally, several relevant transcription-factors were identified, including LEF1.

Conclusions Clinical response to high dose GC-treatment is associated with different degrees of suppression of the interferon signature. In view of the pivotal role of IFNs in many autoimmune diseases, this warrants further investigation into the role of GC-induced changes of these molecular pathways in auto-immunity.

Acknowledgements We are thankful for the use of the microarray facilities within the department of Molecular Cancer Research.

Disclosure of Interest None declared

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