Expression analysis in rheumatic diseases

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The first report on array hybridisation in rheumatic diseases was published by Heller et al. [2]. A customised array of 96 immunologically relevant genes was applied to demonstrate that multiparameter screens may be useful in the analysis of inflammatory diseases such as rheumatoid arthritis. Several matrix metalloproteines (MMPs), MMP inhibitors, cytokines and chemokines were detectable in rheumatoid synovium.

In subsequent studies with increasing numbers of genes beyond 10,000 and technical improvement in terms of reliability, arrays were more and more applied for gene discovery and diagnostic pattern identification. Maas et al. [3] suggested an algorithm for discriminating autoimmune from normal immune responses by profiling peripheral blood mononuclear cells (PMBC) from patients with rheumatoid arthritis, systemic lupus erythematosus (SLE), type I diabetes and multiple sclerosis (MS) compared to PBMC from healthy donors after influenza vaccination. Their candidates were involved in apoptosis, cell cycle progression, cell differentiation and cell migration. Relatives of the patients with autoimmune diseases were also sorted to the disease group. Thus, the authors speculate that their gene selection may reflect a genetic trait rather than the different disease processes, which could not be distinguished from each other.

Studies on rheumatoid synovium by van der Pouw Kraan and co-workers [4, 5] which will be further discussed in another chapter, were aiming for functionally relevant genes and for the identification of RA subgroups to improve diagnostic and therapeutic stratification. Devauchelle and co-workers [6] demonstrated that a set of 48 genes was able to classify rheumatoid arthritis (RA) from non-RA. Aiming for discovery of new candidates, they selected six genes for further confirmation by polymerase chain reaction (PCR) in the tissue and three in synovial fibroblast cultures.

Also tissue-based, the approach by Morawietz et al. [7] showed various inflammatory genes to be upregulated in chronic inflammation of periprosthetic membranes of RA and osteoarthritis (OA) patients in the process of prosthetic loosening.

PBMC were again the target of investigation in studies by Gu et al. [8] in spondy-loarthropathies, rheumatoid arthritis, and psoriatic arthritis. Their dominant candidates included several genes which were inflammation related including CXCR4. As SDF-1, the ligand of CXCR4, was found increased in the synovial fluids of arthritides, an important role of this chemotactic axis in SpA and RA was suggested.

Interesting studies on functional signatures were performed by several groups in systemic lupus on PBMC [9-12]. One group [10] confirmed these findings by the experimental proof that 23 of their 161 candidates were induced by IFN-a, or -y in PBMC from healthy donors. Bennett et al. [9] also identified genes involved in granulopoiesis, which belonged to cells of the myeloic lineage that were co-separated only in SLE.

In an initial attempt to address the problems of mixed cell populations and unspecific dilution of low expressed candidates in tissues as well as to allow histo-logical association of complete profiles, Judex et al. [13] demonstrated the feasibility of microarray analysis from laser microdissected areas of synovial tissues with a minimum of 600 cells.

Pierer et al. [14] circumvented the problem by performing functional analyses on purified synovial fibroblasts in vitro. They stimulated via toll-like receptor 2 and investigated the chemokine response. On the other hand, Schmutz et al. [15] directly focused on the investigation of chemokine receptor expression by profiling whole tissue and suggested CXCR5 as relevant in RA.

The studies summarised above were selected as a representation of typical array experiments performed from patient material either ex vivo as unsorted complex tissues, as blood cells composed of different populations or as in vitro stimulation experiment. All these studies have identified genes, which could be possible candidates for further analysis, diagnostic application or therapeutic targeting.

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Arthritis Relief and Prevention

Arthritis Relief and Prevention

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