Subtype-specific peripheral blood gene expression profiles in recent-onset juvenile idiopathic arthritis

Abstract

Objective: To identify differences in peripheral blood gene expression between patients with different subclasses of juvenile idiopathic arthritis (JIA) and healthy controls in a multicenter study of patients with recent-onset JIA prior to treatment with disease-modifying antirheumatic drugs (DMARDs) or biologic agents.

Methods: Peripheral blood mononuclear cells (PBMCs) from 59 healthy children and 136 patients with JIA (28 with enthesitis-related arthritis [ERA], 42 with persistent oligoarthritis, 45 with rheumatoid factor [RF]-negative polyarthritis, and 21 with systemic disease) were isolated from whole blood. Poly(A) RNA was labeled using a commercial RNA amplification and labeling system (NuGEN Ovation), and gene expression profiles were obtained using commercial expression microarrays (Affymetrix HG-U133 Plus 2.0).

Results: A total of 9,501 differentially expressed probe sets were identified among the JIA subtypes and controls (by analysis of variance; false discovery rate 5%). Specifically, 193, 1,036, 873, and 7,595 probe sets were different in PBMCs from the controls compared with those from the ERA, persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA patients, respectively. In patients with persistent oligoarthritis, RF-negative polyarthritis, and systemic JIA subtypes, up-regulation of genes associated with interleukin-10 (IL-10) signaling was prominent. A hemoglobin cluster was identified that was underexpressed in ERA patients but overexpressed in systemic JIA patients. The influence of JAK/STAT, ERK/MAPK, IL-2, and B cell receptor signaling pathways was evident in patients with persistent oligoarthritis. In systemic JIA, up-regulation of innate immune pathways, including IL-6, Toll-like receptor/IL-1 receptor, and peroxisome proliferator-activated receptor signaling, were noted, along with down-regulation of gene networks related to natural killer cells and T cells. Complement and coagulation pathways were up-regulated in systemic JIA, with a subset of these genes being differentially expressed in other subtypes as well.

Conclusion: Expression analysis identified differentially expressed genes in PBMCs obtained early in the disease from patients with different subtypes of JIA and in healthy controls, providing evidence of immunobiologic differences between these forms of childhood arthritis.

Figure 1. Quality control

In a study of this size and duration, quality control of sample collection and processing is essential. The QC plan is split into three general categories indicated on the left side: sample collection, sample processing, and data processing. The percentage of samples removed at each step is indicated. Samples were collected at multiple sites and all RNA was isolated at CCHMC. RNAs were sent to the Affymetrix GeneChip® core where probes were synthesized and hybridized to Affymetrix U133 plus 2.0 GeneChips®. Data was pre-processed and quality controlled. Data from 54,459 probe sets and 195 arrays which passed all QC measures were used in all further analyses.

Figure 2. Hierarchical clustering of differentially expressed genes by JIA subtype

Differentially expressed genes were identified using ANOVA (false discovery rate 5%) followed by Tukey post-hoc testing. The normalized expression level for each gene (rows) in each sample (columns) is indicated by color. Red, yellow, and blue rectangles reflect expression levels that are greater than, equal to, or less than the mean expression level in all samples, respectively. The colored lines below the clusterings indicates diagnoses (ERA: orange; persistent oligoarthritis: light blue; RF- polyarthritis: dark blue; systemic: yellow; controls: green). Bars and letters to the right of each heat map indicate how the gene lists were divided for Ingenuity analysis (Figure 3).

Figure 3. Global analysis of differentially expressed genes

Gene lists based on strong patterns in the heat maps (Figure 2) were evaluated via Ingenuity's “Canonical Pathways” function. To be included in the list of differentially expressed pathways, the pathway had to be over-represented in the list of interest (p≤0.05) as well as being among the top 5 pathways for that list. The negative log of the p-value is indicated on each horizontal axis and the name of the pathway is on the vertical axis. Pathways are separated into broad categories to simplify interpretation. Letters and numbers indicate which portion of the heat map from Figure 2 contributes to the respective pathway. This figure should be used to identify clusters of under- or over-expressed genes. If a pathway was found in more than one cluster, only the minimal p value (maximal negative decadic logarithm) is represented.