Gene expression signatures for autoimmune disease in peripheral blood mononuclear cells

Abstract

The relatively new technology of DNA microarrays offers the possibility to probe the human genome for clues to the pathogenesis and treatment of human disease. While early studies using this approach were largely in oncology, many new reports are emerging in other fields including infectious diseases and pharmacology, and applications in autoimmunity have been recently reported by our group and others. Some of these investigations have examined animal models of autoimmune disease, but a number of human studies have also been carried out. Of special interest are those that have used peripheral blood samples because, unlike tissue biopsies, these are readily available from all subjects. Using this approach, patterns of gene expression can be detected that distinguish patients with autoimmune conditions from normal subjects. Furthermore, the genes that are identified provide clues to possible pathogenetic mechanisms and are likely to be useful in developing tests to establish diagnostic categories and predict therapeutic responses.

Figure 1

Kmeans analysis of the normal immune response in studies of Maas et al. [17]. Data are presented as the natural logarithm of the ratio of the experimental group to the control group indicated on the x-axis. Individual lines in the plot represent expression ratios of individual genes over the time course. Clustering patterns correspond to early upregulated genes (a), late upregulated genes (b) and genes that were downregulated throughout the followup period (c).

Figure 2

Cluster analysis using the self-organizing map algorithm of subjects studied by Maas et al. [17] including normal controls before (cont) and after vaccination (imm) compared to four types of autoimmune patients. Data were filtered to exclude genes that did not show significant change (2 S.D.) under any of the conditions. A portion of the total number of genes analyzed is shown.

Figure 3

Classification and prediction of autoimmune disease. The score (y-axis) is shown for each individual sample analyzed form the different patient groups (x-axis). The 35 genes used to derive this score are found in the original reference (Maas et al; [17]).

Figure 4

Schematic presentation of how the autoimmune gene expression signature might be compared to other gene expression patterns. Overlaps of the autoimmune patients with autoimmune families and with early RA patients have been described, while normal immune responses appear to be distinct.