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Journal Article

Molecular signatures and new candidates to target the pathogenesis of rheumatoid arthritis.


Ungethuem,  U.
Max Planck Society;

Witt,  H.
Max Planck Society;

Drungowski,  M.
Max Planck Society;


Lehrach,  H.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

Ruiz,  P.
Max Planck Society;

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Ungethuem, U., Haeupl, T., Witt, H., Koczan, D., Krenn, V., Huber, H., et al. (2010). Molecular signatures and new candidates to target the pathogenesis of rheumatoid arthritis. Physiological Genomics, 42A(4), 267-282. doi:10.1152/physiolgenomics.00004.2010.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-7A4E-3
Rheumatoid arthritis (RA) is a chronic, inflammatory joint disease of unknown etiology and pronounced inter-patient heterogeneity. To characterize RA at the molecular level and to uncover pathomechanisms, we performed genome-wide gene expression analysis. We identified a set of 1054 genes significantly deregulated in pair-wise comparisons between RA and osteoarthritis (OA) patients, RA and normal donors (ND), or OA and ND. Correlation analysis revealed gene sets regulated identically in all three groups. As a prominent example secreted phosphoprotein 1 (SPP1) was identified to be significantly upregulated in RA as compared to both OA and ND. SPP1 expression was found to correlate with genes expressed during an inflammatory response, T cell activation and apoptosis, suggesting common underlying regulatory networks. A sub-classification of RA patients was achieved on the basis of proteoglycan 4 (PRG4) expression distinguishing PRG4 high- and low expressors and reflecting the heterogeneity of the disease. In addition, we found that low PRG4 expression was associated with a more aggressive disease stage, which is in accordance with PRG4 loss-of-function mutations causing camptodactyly-arthropathy-coxa vara-pericarditis syndrome. Altogether we provide evidence for molecular signatures of RA and RA subclasses, sets of new candidate genes as well as for candidate gene networks, which extend our understanding of disease mechanisms and may lead to an improved diagnosis.