Novel approaches to gene expression analysis of active polyarticular juvenile rheumatoid arthritis

Abstract

Juvenile rheumatoid arthritis (JRA) has a complex, poorly characterized pathophysiology. Modeling of transcriptosome behavior in pathologic specimens using microarrays allows molecular dissection of complex autoimmune diseases. However, conventional analyses rely on identifying statistically significant differences in gene expression distributions between patients and controls. Since the principal aspects of disease pathophysiology vary significantly among patients, these analyses are biased. Genes with highly variable expression, those most likely to regulate and affect pathologic processes, are excluded from selection, as their distribution among healthy and affected individuals may overlap significantly. Here we describe a novel method for analyzing microarray data that assesses statistically significant changes in gene behavior at the population level. This method was applied to expression profiles of peripheral blood leukocytes from a group of children with polyarticular JRA and healthy control subjects. Results from this method are compared with those from a conventional analysis of differential gene expression and shown to identify discrete subsets of functionally related genes relevant to disease pathophysiology. These results reveal the complex action of the innate and adaptive immune responses in patients and specifically underscore the role of IFN-gamma in disease pathophysiology. Discriminant function analysis of data from a cohort of patients treated with conventional therapy identified additional subsets of functionally related genes; the results may predict treatment outcomes. While data from only 9 patients and 12 healthy controls was used, this preliminary investigation of the inflammatory genomics of JRA illustrates the significant potential of utilizing complementary sets of bioinformatics tools to maximize the clinical relevance of microarray data from patients with autoimmune disease, even in small cohorts.

Figure 1

Graphical representation of hypervariable (HV) gene analysis in patients with juvenile rheumatoid arthritis (JRA) (n = 9) and a reference group (n = 12). A reference group of genes from the control group whose expression levels do not vary significantly on a population basis was identified as described in Materials and methods. Expression levels in this reference group, denoted the averaged profile, have a normal distribution. This group is represented by black lines on a plot of residuals (values representing expression level variance in the control population) vs average gene expression levels (log₁₀-transformed). Red lines represent genes whose variation in expression in healthy controls or untreated patients with acute disease was significantly greater than that of the reference group. These genes are defined as hypervariable (HV) genes.

Figure 2

Serum IFN-γ levels in untreated patients with active juvenile rheumatoid arthritis (JRA) and healthy controls (HC). A scatter plot of serum IFN-γ concentrations in 4 patients with active disease (AD) and 13 HC is shown. The values for 11 HC that were < 1.4 pg/ml (the limit of detection of the assay) are represented by triangular symbols that appear as the lowest value in the distribution. Average values in a given population are represented as a horizontal line. Concentrations are shown in pg/ml on a log scale.

Figure 3

Functional associations of genes selected as differentially expressed in patients with juvenile rheumatoid arthritis (JRA) and normal controls. Tabular data from differential expression analysis were analyzed using Pathway Assist software. The graphical output delineating a functionally related network of genes is shown. Genes that were expressed at higher levels in JRA patients are represented as red ovals. Genes expressed at higher levels in controls are represented as blue ovals. Major biologic processes related to these genes are represented as yellow rectangles. White ovals represent genes that are functionally related to the genes used for analysis. Upon addition of these genes, several functional connections among the genes being analyzed can be observed. Green squares signify that a defined regulatory relationship exits between genes. Blue squares signify that a putative regulatory relationship between genes has been identified but not biochemically defined. +, positive regulation; -, negative regulation.

Figure 4

Functional associations of hypervariable (HV) genes in patients with juvenile rheumatoid arthritis (JRA). Tabular data from the HV gene analysis was analyzed using Pathway Assist software. The graphical output delineating a functionally related network of genes is shown. Genes that displayed increased variation in expression in JRA patients are represented as red ovals. Major biologic processes related to these genes are represented as yellow rectangles. JRA is represented as a yellow square to delineate genes directly associated with pathology. Intracellular objects upon which a given set of genes acts are represented as yellow ovals. White ovals represent genes that are functionally related to the genes used for analysis. White hexagons represent organelles functionally associated with the genes analyzed. Orange hexagons represent classes of small molecules associated with the genes analyzed. Green squares signify that a defined regulatory relationship exits between genes. Blue squares signify that a putative regulatory relationship between genes has been identified but not biochemically defined. +, positive regulation; -, negative regulation.

Figure 5

Functional associations of genes identified by discriminant function analysis (DFA) in patients with juvenile rheumatoid arthritis (JRA) who were undergoing treatment. Tabular data from the this analysis was analyzed using Pathway Assist software. The graphical output delineating a functionally related network of genes is shown. Genes that were associated with disease or treatment response are represented as red ovals. Major biologic processes related to these genes are represented as yellow rectangles. White ovals represent genes that are functionally related to the genes used for analysis. Orange hexagons represent classes of small molecules associated with the genes analyzed, and orange circles represent specific small molecules associated with the genes analyzed. Green squares signify that a defined regulatory relationship exits between genes. Blue squares signify that a putative regulatory relationship between genes has been identified but not biochemically defined. +, positive regulation; -, negative regulation.

Figure 6

An overview of the results from the three analytical methods. The numbers of genes that were identified by differential expression analysis, hypervariable (HV) gene analysis, and discriminant function analysis (DFA) are represented in a Venn diagram. The numbers of genes identified uniquely in a given analysis as relevant to patients with juvenile rheumatoid arthritis (JRA) are shown in nonoverlapping regions. The numbers of genes identified in more than one analysis are shown in overlapping regions. HC, genes expressed at higher levels in healthy controls; JRA, genes expressed at higher levels in patients.

Figure 7

A graphical representation of the discriminatory potential of discriminant function analysis (DFA). DFA was used to identify, in a cohort of treated patients with juvenile rheumatoid arthritis (JRA) and healthy controls, a subset of genes whose expression values can be linearly combined in an equation, denoted a root, whose overall value is distinct for a given characterized group. The expression values of the individuals in the cohort were plotted in three dimensions to visually represent the relative differences in gene expression among the distinct populations. Gene expression values were plotted on this graph for nine untreated patients with active disease. Five of these nine patients were followed up prospectively during treatment. Four patients responded to treatment and one patient was nonresponsive. The values obtained for the four responsive patients at the time of partial response (after 2–4 weeks of treatment) and full response (6–8 weeks) are shown, as are four independent values obtained from a nonresponsive patient (taken during an 8-week interval). Values for healthy controls are also represented. Values obtained for individuals from each of these groups tended to cluster. Response to therapy was reflected in the spatial relationships among groups.