Diagnostic and prognostic potential of gene microarrays in rheumatoid arthritis

Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment ("personalized medicine"). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative.

Functional genomics and rheumatoid arthritis: where have we been and where should we go?

Studies in model organisms and humans have begun to reveal the complexity of the transcriptome. In addition to serving as passive templates from which genes are translated, RNA molecules are active, functional elements of the cell whose products can detect, interact with, and modify other transcripts. Gene expression profiling is the method most commonly used thus far to enrich our understanding of the molecular basis of rheumatoid arthritis in adults and juvenile idiopathic arthritis in children. The feasibility of this approach for patient classification (for example, active versus inactive disease, disease subsets) and improving prognosis (for example, response to therapy) has been demonstrated over the past 7 years. Mechanistic understanding of disease-related differences in gene expression must be interpreted in the context of interactions with transcriptional regulatory molecules and epigenetic alterations of the genome. Ongoing work regarding such functional complexities in the human genome will likely bring both insight and surprise to our understanding of rheumatoid arthritis.

From transcriptome to cytome: integrating cytometric profiling, multivariate cluster, and prediction analyses for a phenotypical classification of inflammatory diseases

Gene expression studies of peripheral blood cells in inflammatory diseases revealed a large array of new antigens as potential biomarkers useful for diagnosis, prognosis, and therapy stratification. Generally, their validation on the protein level remains mainly restricted to a more hypothesis-driven manner. State-of-the-art multicolor flow cytometry make it attractive to validate candidate genes at the protein and single cell level combined with a detailed immunophenotyping of blood cell subsets. We developed multicolor staining panels including up to 50 different monoclonal antibodies that allowed the assessment of several hundreds of phenotypical parameters in a few milliliters of peripheral blood. Up to 10 different surface antigens were measured simultaneously by the combination of seven different fluorescence colors. In a pilot study blood samples of ankylosing spondylitis (AS) patients were compared with normal donors (ND). A special focus was set on the establishment of suitable bioinformatic strategy for storing and analyzing hundreds of phenotypical parameters obtained from a single blood sample. We could establish a set of multicolor stainings that allowed monitoring of all major leukocyte populations and their corresponding subtypes in peripheral blood. In addition, antigens involved in complement and antibody binding, cell migration, and activation were acquired. The feasibility of our cytometric profiling approach was demonstrated by a successful classification of AS samples with a reduced subset of 80 statistically significant parameters, which are partially involved in antigen presentation and cell migration. Furthermore, these parameters allowed an error-free prediction of independent AS and ND samples originally not included for parameter selection. This study demonstrates a new level of multiparametric analysis in the post-transcriptomic era. The integration of an appropriate bioinformatic solution as presented here by the combination of a custom-made Access database along with cluster- and prediction-analysis tools predestine our approach to promote the human cytome project.

Effect of infliximab on mRNA expression profiles in synovial tissue of rheumatoid arthritis patients

We examined the gene expression profiles in arthroscopic biopsies retrieved from 10 rheumatoid arthritis patients before and after anti-TNF treatment with infliximab to investigate whether such profiles can be used to predict responses to the therapy, and to study effects of the therapy on the profiles. Responses to treatment were assessed using European League Against Rheumatism response criteria. Three patients were found to be good responders, five patients to be moderate responders and two patients to be nonresponders. The TNF-α status of the biopsies from each of the patients before treatment was also investigated immunohistochemically, and it was detected in biopsies from four of the patients, including all three of the good responders. The gene expression data demonstrate that all patients had unique gene expression signatures, with low intrapatient variability between biopsies. The data also revealed significant differences between the good responding and nonresponding patients (279 differentially expressed genes were detected, with a false discovery rate < 0.025). Among the identified genes we found that MMP-3 was significantly upregulated in good responders (log₂ fold change, 2.95) compared with nonresponders, providing further support for the potential of MMP-3 as a marker for good responses to therapy. An even more extensive list of 685 significantly differentially expressed genes was found between patients in whom TNF-α was found and nonresponders, indicating that TNF-α could be an important biomarker for successful infliximab treatment. Significant differences were also observed between biopsies taken before and after anti-TNF treatment, including 115 differentially expressed genes in the good responding group. Interestingly, the effect was even stronger in the group in which TNF-α was immunohistochemically detected before therapy. Here, 1,058 genes were differentially expressed, including many that were novel in this context (for example, CXCL3 and CXCL14). Subsequent Gene Ontology analysis revealed that several 'themes' were significantly over-represented that are known to be affected by anti-TNF treatment in inflammatory tissue; for example, immune response (GO:0006955), cell communication (GO:0007154), signal transduction (GO:0007165) and chemotaxis (GO:0006935). No genes reached statistical significance in the moderately responding or nonresponding groups. In conclusion, this pilot study suggests that further investigation is warranted on the usefulness of gene expression profiling of synovial tissue to predict and monitor the outcome of rheumatoid arthritis therapies.

Variability in synovial inflammation in rheumatoid arthritis investigated by microarray technology

In recent years microarray technology has been used increasingly to acquire knowledge about the pathogenic processes involved in rheumatoid arthritis. The present study investigated variations in gene expression in synovial tissues within and between patients with rheumatoid arthritis. This was done by applying microarray technology on multiple synovial biopsies obtained from the same knee joints. In this way the relative levels of intra-patient and inter-patient variation could be assessed. The biopsies were obtained from 13 different patients: 7 by orthopedic surgery and 6 by rheumatic arthroscopy. The data show that levels of heterogeneity varied substantially between the biopsies, because the number of genes found to be differentially expressed between pairs of biopsies from the same knee ranged from 6 to 2,133. Both arthroscopic and orthopedic biopsies were examined, allowing us to compare the two sampling methods. We found that the average number of differentially expressed genes between biopsies from the same patient was about three times larger in orthopedic than in arthroscopic biopsies. Using a parallel analysis of the tissues by immunohistochemistry, we also identified orthopedic biopsies that were unsuitable for gene expression analysis of synovial inflammation due to sampling of non-inflamed parts of the tissue. Removing these biopsies reduced the average number of differentially expressed genes between the orthopedic biopsies from 455 to 171, in comparison with 143 for the arthroscopic biopsies. Hierarchical clustering analysis showed that the remaining orthopedic and arthroscopic biopsies had gene expression signatures that were unique for each patient, apparently reflecting patient variation rather than tissue heterogeneity. Subsets of genes found to vary between biopsies were investigated for overrepresentation of biological processes by using gene ontology. This revealed representative 'themes' likely to vary between synovial biopsies affected by inflammatory disease.