Background: Rheumatoid arthritis (RA) is a heterogeneous disease with unknown cause.
Aim: To identify peripheral blood (PB) gene expression profiles that may distinguish RA subtypes.
Methods: Large-scale expression profiling by cDNA microarrays was performed on PB from 35 patients and 15 healthy individuals. Differential gene expression was analysed by significance analysis of microarrays (SAM), followed by gene ontology analysis of the significant genes. Gene set enrichment analysis was applied to identify pathways relevant to disease.
Results: A substantially raised expression of a spectrum of genes involved in immune defence was found in the PB of patients with RA compared with healthy individuals. SAM analysis revealed a highly significant elevated expression of interferon (IFN) type I regulated genes in patients with RA compared with healthy individuals, which was confirmed by gene ontology and pathway analysis, suggesting that this pathway was activated systemically in RA. A quantitative analysis revealed that increased expression of IFN-response genes was characteristic of approximately half of the patients (IFNhigh patients). Application of pathway analysis revealed that the IFNhigh group was largely different from the controls, with evidence for upregulated pathways involved in coagulation and complement cascades, and fatty acid metabolism, while the IFNlow group was similar to the controls.
Conclusion: The IFN type I signature defines a subgroup of patients with RA, with a distinct biomolecular phenotype, characterised by increased activity of the innate defence system, coagulation and complement cascades, and fatty acid metabolism.
- DC, dendritic cell
- FLS, fibroblast-like synoviocytes
- GSEA, gene set enrichment analysis
- IFN, interferon
- MTX, methotrexate
- PB, peripheral blood
- RA, rheumatoid arthritis
- SAM, significance analysis of microarrays
- SLE, systemic lupus erythematosus
- SS, Sjögren’s syndrome
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↵* These authors contributed equally to this work.
Published Online First 18 January 2007
This work was supported in part by the EURO-RA Marie Curie Trainings network, the European Community’s FP6 integrated program funding (AUTOCURE), the Innovation Oriented research Program (IOP) on Genomics and the Centre for Medical Systems Biology (a centre of excellence approved by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research). This publication reflects only the author’s views. The European Community is not liable for any use that may be made of the information herein. These sponsors had no involvement in the study design, analysis or interpretation of the data and publications.
Competing interests: None.
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