Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis

Abstract

Psoriatic arthritis (PsA) is a chronic and erosive form of arthritis of unknown cause. We aimed to characterize the PsA phenotype using gene expression profiling and comparing it with healthy control subjects and patients rheumatoid arthritis (RA). Peripheral blood cells (PBCs) of 19 patients with active PsA and 19 age- and sex-matched control subjects were used in the analyses of PsA, with blood samples collected in PaxGene tubes. A significant alteration in the pattern of expression of 313 genes was noted in the PBCs of PsA patients on Affymetrix U133A arrays: 257 genes were expressed at reduced levels in PsA, and 56 genes were expressed at increased levels, compared with controls. Downregulated genes tended to cluster to certain chromosomal regions, including those containing the psoriasis susceptibility loci PSORS1 and PSORS2. Among the genes with the most significantly reduced expression were those involved in downregulation or suppression of innate and acquired immune responses, such as SIGIRR, STAT3, SHP1, IKBKB, IL-11RA, and TCF7, suggesting inappropriate control that favors proin-flammatory responses. Several members of the MAPK signaling pathway and tumor suppressor genes showed reduced expression. Three proinflammatory genes--S100A8, S100A12, and thioredoxin--showed increased expression. Logistic regression and recursive partitioning analysis determined that one gene, nucleoporin 62 kDa, could correctly classify all controls and 94.7% of the PsA patients. Using a dataset of 48 RA samples for comparison, the combination of two genes, MAP3K3 followed by CACNA1S, was enough to correctly classify all RA and PsA patients. Thus, PBC gene expression profiling identified a gene expression signature that differentiated PsA from RA, and PsA from controls. Several novel genes were differentially expressed in PsA and may prove to be diagnostic biomarkers or serve as new targets for the development of therapies.

Figure 1

Gene expression profiles of PBCs from 19 controls and 19 PsA patients. Unsupervised hierarchical clustering of 313 genes that distinguish PsA patients (red dendrogram) from healthy controls (blue dendrogram). Each row represents a gene; each column shows the expression for 313 genes expressed by each individual. Red indicates genes that are expressed at higher levels compared with the control mean. Green indicates genes that are expressed at lower levels relative to the control mean. PsA patients cluster to the left, and control samples cluster to the right of the figure (see supplemental Tables I and II for complete list of genes and individual expression data).

Figure 2

Logistic regression analysis showing the three best discriminators between PsA and controls. Nucleoporin 62 kDa was expressed in reduced levels and had the best classifying performance (P = 1.2 × 10⁻¹⁰), followed by MAP3K3 (P = 4 ×10⁻¹⁰), which was also expressed in reduced levels, and by ASXL2 (P = 1.4 ×10⁻¹⁰), which was expressed at increased levels in PsA compared with controls.

Figure 3

Logistic regression analysis showing the three best discriminators between PsA and RA. MAP3K3 was expressed in reduced levels in PsA and had the best discriminating performance between PsA and RA (P = 2.3 ×10⁻¹⁰), whereas KIF5B (P = 1.4 ×10⁻¹¹) and SFRS2IP (P = 1.4 ×10⁻⁸) were expressed in increased levels and were the second- and third-ranked genes.

Figure 4

Recursive partitioning analysis tree. (A) PsA versus controls: levels of expression of nucleoporin 62 kDa (NUP62) expression correctly classified all controls (≥ 817 SU) and 18 of the 19 (94.7%) PsA patients (< 817 SU). (B) PsA versus RA: levels of MAP3K3 expression correctly classified all PsA patients (< 4,042 SU) and 42 of the 48 RA patients (87.5%). The stepwise addition of CACNA1S expression data correctly classified the remaining 6 RA patients.