A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells - PubMed (original) (raw)

Comparative Study

A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells

N Olsen et al. Ann Rheum Dis. 2004 Nov.

Abstract

Background: In previous studies the presence of a distinct gene expression pattern has been shown in peripheral blood cells from patients with autoimmune disease.

Objective: To determine whether other specific signatures might be used to identify subsets of these autoimmune diseases and whether gene expression patterns in early disease might identify pathogenetic factors.

Methods: Peripheral blood mononuclear cells were acquired from patients with rheumatoid arthritis (RA) and analysed by microarrays containing over 4300 named human genes. Patients with RA for <2 years were compared with subjects with longstanding RA (average duration 10 years) and with patients with other immune or autoimmune diagnoses.

Results: Cluster analyses permitted separation of the patients with early RA (ERA) from those with longstanding disease. Comparison with other patient groups suggested that the ERA signature showed some overlap with that seen in the normal immune response to viral antigen as well as with a subset of patients with systemic lupus erythematosus.

Conclusions: The ERA signature may reflect, in part, a response to an unknown infectious agent. Furthermore, shared features with some lupus patients suggest that common aetiological factors and pathogenetic pathways may be involved in these two autoimmune disorders.

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Figures

Figure 1

Figure 1

Relative levels of expression of genes in PBMCs from 8 patients with RA, 11 patients with ERA, and 9 normal control subjects (C). Genes that did not vary for any of the conditions (3 SD) were removed from the analyses. Data are shown as the ratio, ln 2, for each group compared with controls. Individual lines show expression levels of individual genes.

Figure 2

Figure 2

Clustering of patients with ERA and established RA using the self organising map algorithm with two different input vectors (top) and the hierarchical clustering algorithm with complete linkage clustering (bottom). Gene expression data were filtered to include only those that displayed at least three standard deviations of variability.

Figure 3

Figure 3

Real time PCR confirmation of differential expression for the genes CHI3 and CHES1, both of which are up regulated on the gene arrays by eightfold or greater in RA compared with ERA. Values represent means from 5 subjects with RA and 11 with ERA. Normalisation to corresponding values for JUND were carried out for each subject. Relative increases are indicated as log2.

Figure 4

Figure 4

Gene expression levels for eight genes significantly up regulated in ERA. Results from individual patients are shown for eight patients with established RA (dark grey bars) and eight patients with ERA (light grey bars).

Figure 5

Figure 5

Score derived from eight genes that distinguish ERA from RA. The eight genes that were up regulated in ERA used to generate the equation were: TGFBR2, CYP3A4, TNNI2, HSD11B2, SNTA1, TNNT2, CSF3R, ZNF74. The ERA and RA groups with eight subjects each were used to generate the score. The equation was retested in a second group of patients with ERA (ERA2) who were not used to derive the score and in patients with SLE, and allergic disease (ALL) as well as in normal control subjects (CON).

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