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This article is part of the supplement: 25th European Workshop for Rheumatology Research

Poster presentation

Microarray analysis for molecular characterization of disease activity and measuring outcomes of anti-tumour necrosis factor therapy in rheumatoid arthritis

B Stuhlmüller1, T Häupl1, N Tandon1, M Hernandez1, C Hultschig2, RJ Kuban1, J Salfeld3 and GR Burmester1

Author Affiliations

1 Charité, Department of Rheumatology, Berlin, Germany

2 MPI-MG, Berlin, Germany

3 Abbott Bioresearch Center, Worcester, Massachusetts, USA

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Arthritis Research & Therapy 2005, 7(Suppl 1):P159  doi:10.1186/ar1680

The electronic version of this article is the complete one and can be found online at:


Received:11 January 2005
Published:17 February 2005

© 2005 BioMed Central Ltd

Introduction

In the pathogenesis of rheumatoid arthritis (RA), macrophages (Mφ) play a pivotal role in synovitis, especially at the cartilage–pannus junction. Both Mφ and peripheral blood monocytes (MO) are activated and release proinflammatory cytokines (IL-1β, tumour necrosis factor [TNF] alpha). Neutralization of IL1-β and TNF-α leads to clinical improvement in RA.

Objective

To characterize MO expression profiles of patients with active RA before and after treatment with adalimumab, a fully human, anti-TNF monoclonal antibody, using microarray analysis. To identify differences in expression of signature genes between patients with uncontrolled, active RA and those in remission induced by anti-TNF therapy.

Methods

Genome-wide microarray analyses (HG-U133A/B) were performed to identify signature genes and RA-MO pathways and to establish a customized cDNA array suitable for monitoring anti-TNF therapy. Untouched MO were negatively selected by magnetic cell sorting from 40 ml peripheral blood samples. Total RNA was extracted for cRNA synthesis, and was hybridized with whole genome oligonucleotide and customized cDNA microarrays. The customized cDNA array consisted of 313 cDNAs derived from gene subtraction analysis and from comparative genome-wide U133A analysis. The array includes RA-relevant signature genes, and genes triggered or repressed during anti-TNF treatment. To identify significant gene expressions, bioinformatical MAS 5.0, self-organizing map clustering and predictive analysis for microrrays (PAM) analyses were performed.

Results

Genome-wide analysis of MO mRNA expression in patients with active RA before and after receiving anti-TNF-α (n = 7) revealed significant differences in upregulated and downregulated genes. Self-organizing map analysis revealed six different gene expression clusters. MAS 5.0 and PAM analyses identified 103 differentially expressed genes and permitted the separation of RA patients into two subgroups of 'responders' and 'non-responders', correlating with clinical data (DAS28 and ACR response criteria). Results were confirmed by a customized array using most of the PAM-selected genes. Twenty-four genes were further evaluated by real-time PCR using MO from normal donors and from patients with RA before and during therapy. Signature genes identified were characterized as: disease-relevant genes differentially transcribed in activated RA-MO compared with normal donor MO; genes reversed to 'normal levels' by anti-TNF treatment; or pharmacodynamic marker genes probably indicative of anti-TNF action. Selected genes, which may be indicative for the response to therapy, are currently being further evaluated in extended collections of samples.

Conclusions

Signatures are important to define MO activation to characterize disease activity and to support therapeutic stratification. The current gene selection could contribute to the investigation of the role of MO in a wide range of rheumatic diseases and therapeutic intervention, improving rheumatologists' understanding of regulated MO pathways.