An initial work by Heller and colleagues [4
] introduced a customised array of 96 genes, demonstrating the usefulness of arrays in the analysis of inflammatory diseases such as rheumatoid arthritis (RA). Basing their work on a specific selection of genes, they identified in synovial tissue samples from RA the expression of the matrix metalloproteinases stromelysin 1, collagenase 1, gelatinase A and human matrix metallo-elastase, TIMP (tissue inhibitor of metalloproteinases) 1 and 3, interleukin (IL)-6, vascular cell adhesion molecule and discernible levels of monocyte chemotactic protein (MCP)-1, migration inhibitory factor and RANTES.
More advanced platform technologies with many thousands of genes up to genome-wide arrays have been applied in recent studies, aiming for new candidates, functional mechanisms and diagnostic patterns. Comparing autoimmune diseases with the response to influenza vaccination in healthy donors, Maas and colleagues investigated peripheral blood mononuclear cells (PMBCs) from patients with RA, systemic lupus erythematosus (SLE), type I diabetes and multiple sclerosis [5
]. Genes differentially expressed after vaccination were compared with the profiles of the four autoimmune groups. A panel of genes was extracted that discriminated between normal immune and autoimmune responses. However, the investigators could not identify genes that distinguished between different autoimmune diseases. Their candidates were predominantly genes involved in apoptosis, cell cycle progression, cell differentiation and cell migration, but not necessarily in the immune response. They further developed an algorithm to identify patients with these autoimmune diseases. Because this algorithm also sorted relatives of patients with autoimmune diseases to the disease group, the authors speculated that their gene selection might reflect a genetic trait rather than the disease process.
Gene expression profiling in lupus was reviewed recently in detail by Crow and Wohlgemuth [6
]. Four different groups [6
] have independently identified an interferon signature by analysing PBMCs. One group [7
] confirmed these findings by comparing the patients' profiles with in vitro
-induced interferon (IFN)-α, IFN-β or IFN-γ signatures in PBMCs from healthy donors. This attributed 23 of 161 genes to induction by IFN. In addition to the IFN signature, Bennett and colleagues [8
] found the differential expression of granulopoietic genes. As Ficoll separation usually excludes granulocytes, they became aware of a subpopulation of granular cells, which was co-separated only in SLE. These were identified as cells of the myeloid lineage, ranging from promyelocytes to segmented neutrophils.
Gu and colleagues [10
] investigated PBMCs from spondyloarthropathies, RA and psoriatic arthritis on a 588-gene commercial platform. Their dominant candidates included MNDA, a myeloid nuclear differentiation antigen, two members of the S100 family of proteins, calgranulin A and B (involved in cellular processes such as cycle progression and differentiation), JAK3 and mitogen-activated protein kinase p38, tumour necrosis factor (TNF) receptors, the chemokine receptors CCR1 and CXCR4 and also IL-1β and IL-8. Because stromal cell-derived factor-1 (SDF-1), the ligand of CXCR4, was found increased in the synovial fluids of arthritides, the authors suggested an important role of this chemotactic axis in spondyloarthropathies and RA. In our studies on highly purified separated cells, these genes revealed the highest expression level in neutrophil granulocytes in comparison with cells positive for CD14, CD4 and CD8. In view of the findings by Bennett and colleagues [8
] that granulocytes might be co-separated with PBMCs in inflammatory diseases such as SLE, these data need further confirmation.
Van der Pouw Kraan and colleagues investigated synovial tissue samples from RA and osteoarthritis (OA) [11
]. Basing their decision on molecular profiles, they divided their RA samples into three subgroups: first, immune-related processes; second, complement-related activities with fibroblast dedifferentiation; and third, processes of tissue remodelling. Their analyses also reflect the established histological classification of RA into different subgroups, which is in part based on cellular composition [13
]. Furthermore, the STAT1 pathway was identified as being associated with immune-related processes. Our own data on synovial tissues, which were established on a different technology platform, confirm many of these findings [14
]. We also identified that some of the processes, especially those associated with tissue remodelling, are also active in OA compared with normal tissues [15
A similar tissue-based approach showed various inflammatory genes to be upregulated in chronic inflammation of periprosthetic membranes of RA and OA patients in the process of prosthetic loosening [16
To overcome the problem of unspecific dilution and to allow the histological association of complete profiles, Judex and colleagues [17
] have presented an initial study on gene expression analysis of laser-microdissected areas from synovial tissues. They have been able to extract sufficient RNA from as few as 600 cells to perform subsequent array analysis.
In contrast, in vitro
studies on isolated synovial fibroblasts from RA patients are well established. Pierer and colleagues [18
] have investigated profiles of synoviocytes on a functional basis by stimulation through Toll-like receptor 2 with Staphylococcus aureus
peptidoglycan. Their focus on chemokines revealed a preferential activation of granulocyte chemotactic protein (GCP)-2, RANTES, MCP-2, IL-8 and GRO2. Functional dependence on NF-κB for the induction of MCP-2, RANTES and GCP-2 was confirmed by inhibition experiments. Chemotactic importance for monocyte migration was demonstrated for RANTES and MCP-2, and for T-cell migration only for RANTES. The expression of GCP-2 and MCP-2, which have not yet been investigated in RA, was identified in both synovial tissue and synovial fluid.
Besides the application in human studies, gene expression profiling was also performed in arthritis models. Wester and colleagues [19
] investigated the effect of pristan-induced arthritis in DA rats in comparison with resistant E3 rats. The authors compared two different array platforms for a selected number of genes and also used pooled samples. They demonstrated variable cellular composition of the lymph nodes by fluorescence-activated cell sorting and identified only a relatively small number of genes that were differentially expressed, including mRNA for major histocompatibility complex class II antigen, immunoglobulins, CD28, mast cell protease 1, gelatinase B, carboxylesterase precursor, K-cadherin, cyclin G1, DNA polymerase and the tumour-associated glycoprotein E4.
By expression profiling in experimental SLE of NZB/W mice, Alexander and colleagues [20
] identified endogenous retroviral transcripts in kidney tissue as the highest differentially expressed genes. Results were confirmed by in situ
hybridisation, demonstrating retroviral transcripts in renal tubules and also in brain and lung tissue.
Azuma and colleagues used microarrays for the detection of new candidates in salivary gland tissue from the MLR/MpJ-lpr/lpr (MRL/lpr) mouse as a model of human secondary Sjögren's syndrome [21
]. From nine genes, which were confirmed by reverse transcriptase polymerase chain reaction (PCR), five had been already identified in patients with Sjögren's syndrome.
Firneisz and colleagues [22
] used gene expression profiling in two genetically different arthritis mouse models [23
] to identify genes involved in both models. Subsequently, they computed the spatial autocorrelation function, a statistical technique used in astrophysics, and identified critical clustering of selected genes in the two different genetic backgrounds of these mice.
Aidinis and colleagues [25
] investigated immortalised synovial fibroblasts from human (h)TNF transgenic mice by microarray and differential display technology. Microarrays revealed 372 differentially regulated genes, whereas differential display provided many unknown sequences and a total of 49 different genes and sequences. Only 20% (n
= 11) of these were represented on the mouse array. The significance of regulation was only partly confirmed, and one gene (SPARC) was identified as being regulated in both but in opposite directions. Functional clustering of all differentially regulated genes in either of the two methods revealed genes involved in stress response, energy production, transcription, RNA processing, protein synthesis and degradation, growth control, adhesion, cytoskeletal organisation, Ca2+
binding and antigen presentation.