To our knowledge this is the first study to comprehensively evaluate PBMC gene expression patterns in several subtypes of JIA early in disease and prior to initiation of DMARDs or biologics. Extensive QC measures were observed during sample collection and processing, and data analysis to ensure validity of results. The results demonstrate that JIA subtypes can be distinguished from healthy controls using PBMC gene expression patterns. The most striking differences are found in systemic JIA, and a number of pathways differentially affected in each JIA subtype provide a framework for future investigations of pathogenesis.
An important consideration in the interpretation of gene expression profiles obtained from complex mixtures of cells is the influence of cellular composition. Genes that are referred to as `up- or down-regulated' may actually be `over- or under-represented' due to differences in abundance of cell populations. This was nicely demonstrated by Bennett et al., who found a granulopoiesis signature in SLE (5
), and is likely responsible for some of the striking differences seen in systemic JIA (6
In the current study, there are several examples of disease-specific pathways that are altered. For example, in persistent oligoarthritis IL-2, B cell receptor, JAK/STAT and ERK/MAPK signaling pathways are over-represented. In RF- polyarthritis pathways representing G-protein coupled receptor and cAMP-mediated signaling, and NRF-2-mediated oxidative stress response signaling were prominent. IL-10 signaling was over-represented in several subtypes including persistent oligoarthritis, RF- polyarthritis, and systemic JIA, perhaps reflecting activation of anti-inflammatory mechanisms. In systemic JIA, IL-6, TLR/IL1R and PPAR pathways were affected, consistent with results recently reported by Ogilvie et al. (8
). Additionally, networks related to NK cells and T cells were down-regulated in systemic JIA, a pattern that is remarkably similar to what has been seen in septic shock (30
). These observations support the concept that innate immune activation plays a prominent role in systemic JIA pathogenesis, and provide evidence for immunobiological differences between persistent oligoarthritis and RF- polyarthritis.
While ERA has not been extensively examined previously, several studies have reported gene expression differences in systemic JIA (6
), a limited number in RF- polyarthritis (7
), and a single study of persistent oligoarthritis(7
), although these JIA subtypes were not generally examined in combination. In systemic JIA, Pascual et al. emphasized the importance of IL-1β by noting its increased production by patient-derived PBMCs and dramatic responses to IL-1β inhibition in 7/9 patients (10
). Conversely, Ogilvie et al. did not see a prominent IL-1β gene expression signature (8
), which may be consistent with recent work of Gattorno et al., who identified two subsets of systemic JIA based on differential responsiveness to IL-1 blockade(31
). In the present study, we did not see prominent over-expression of IL-1-responsive genes, but we found evidence for over-representation of TLR/IL1R pathway genes, raising the possibility of excessive TLR stimulation. Taken together, these observations are consistent with systemic JIA being a heterogeneous disease and further exploration of disease subgroups is warranted.
More recently, Allantaz et al. defined a set of 12 differentially expressed genes that distinguish systemic JIA from other systemic illnesses such as viral or bacterial infection and SLE (10
). In our data set, seven of these 12 genes were differentially expressed in systemic JIA, with direction and magnitude of changes similar to that reported (10
). Five genes exhibit increased expression (CLIC-2, TLOC1, WNK1, C18orf10, and UBB3'UTR), while two are reduced (WHDC1 and C18orf17). Differential expression of these genes was specific to systemic JIA, and not seen in other subtypes. There are several possible explanations for the lack of differential expression of the additional 5 genes noted by Allantaz et al., including exposure of their subjects to medications such as corticosteroids and methotrexate, disease duration, and disease heterogeneity. Note that comparisons between our results and those of Ogilvie et al. are limited by study design (8
). They examined active and inactive systemic JIA patients with well-established disease who were treated with combinations of corticosteroids, methotrexate and TNF-α inhibitors. They found evidence of IL-6 and IL-10 over-expression, which is consistent with our Ingenuity analysis.
Jarvis et al. have reported peripheral blood leukocyte gene expression changes in subjects classified by ACR criteria as having polyarticular juvenile rheumatoid arthritis (JRA) compared to healthy controls (9
). Comparisons with this study are limited because different cell populations were studied and the microarrays were different. Notably, buffy coat-derived leukocytes contain neutrophils, which are generally absent from Ficoll-purified PBMC.
Barnes et al. (7
) previously reported gene expression differences using PBMC from pauciarticular and polyarticular course JRA. The main finding was up-regulation of pro-angiogenic CXCL chemokines in polyarticular JRA compared to pauciarticular or healthy controls. The current study does not identify these genes as being over-expressed. This is not unexpected since the most prominent findings of the earlier study were based on comparisons of PBMC to synovial fluid mononuclear cells, which was not part of the current study. In addition, the previous samples were obtained from patients with long-standing disease (average 9.3 years), were not collected with rigorous attention to QC, and had been stored in many instances for several years. Consequently, extensive comparisons with the current study are not possible.
A traditional way to visualize microarray data is supervised hierarchical clustering. This method returns clusters of the patient subgroups used to derive the gene lists. In the current study, visual inspection of clustering trees suggests that gene expression patterns identify distinct subgroups within each of the JIA subtypes. Thus, current JIA subtypes may include more heterogeneity than previously appreciated. Studies are ongoing to assess this heterogeneity and its impact on classification and prognosis.
The decreased adult hemoglobin gene expression in patients with ERA was unexpected. Patients with ankylosing spondylitis and other types of spondyloarthritis rarely are anemic (32
). In fact, in our study, patients with ERA had the highest hemoglobin levels and systemic patients had the lowest (data not shown). We hypothesize that the decreased expression of hemoglobin in ERA might be a response to TGF-β, which has been reported to be over-expressed in ankylosing spondylitis (33
) and can act through an AP-1 binding site near the hemoglobin gene.
One of the most highly correlated gene groups distinguishing systemic JIA from controls is designated `o' in . Examination of this cluster revealed a large group of genes encoding red blood cell structural proteins and enzymes, similar to the erythropoietic signature described previously (6
) where it was suggested that the signature was due to the presence of increased immature erythrocyte precursors in peripheral circulation. This cluster also contains various hemoglobins including those normally expressed during embryonic and fetal stages of human development (hemoglobins γ, δ, ε, μ, and θ). Under normal conditions, genes encoding fetal hemoglobins undergo silencing soon after birth (34
) although these proteins may sometimes be present in adults (35
). We hypothesize that the increased expression of adult and fetal hemoglobins in patients with systemic JIA may be explained by the presence of immature erythrocyte precursors.
The “Coagulation System Pathway” was identified as over-represented in systemic JIA although many of the genes are modulated in other subtypes. These findings are consistent with the clinical observations of mildly increased levels of D-dimers in a majority of JIA patients with the largest increase seen in systemic JIA, which suggests a coagulopathy that may correlate with disease activity (36
). In innate inflammatory responses, activation of coagulation and fibrin deposition is an important mechanism that helps contain inflammatory activity to the site of injury or infection; however, when not localized, coagulation can have a deleterious effect in patients suffering from systemic inflammatory disorders such as septic shock or macrophage activation syndrome, a well-known complication of systemic JIA. We hypothesize that the over-representation of several anti-coagulant proteins such as TFPI in RF-polyarthritis and systemic JIA indicates an attempt to down-regulate systemic inflammation-induced coagulation and fibrin deposition.
Many factors from the complement cascade were over-expressed in JIA patients (). While a large proportion of complement synthesis occurs in the liver, production also occurs locally in areas of inflammation by circulating cells (macrophages, dendritic cells and monocytes (23
)). Three subtypes of JIA had increased expression of some complement inhibitory proteins (CR1, CR2, CD55, CD59). CR1 is present on blood cells including erythrocytes, neutrophils, monocytes, eosinophils and T and B cells and is involved in immune clearance and inhibition of the complement cascade by acting as a cofactor for cleavage of C3b. CR2 is found mainly on B cells and is involved in B cell activation and immune clearance. CD55 and CD59 are markedly up-regulated on activated macrophages and less so on CD4 and CD8 positive lymphocytes. This up-regulation could indicate the release of complement factors during poorly controlled inflammation in the joint.
High-throughput technologies such as Affymetrix GeneChips® produce vast amounts of data that can be analyzed and interpreted in many different ways. Making large datasets publicly available allows investigators to apply alternative methods of analysis and increase data utilization. High-throughput technologies are by their nature hypothesis generating, and the hypotheses presented in this study must be validated in other cohorts and the biological relevance of gene expression differences identified here remain to be determined at protein and organism levels.
In conclusion, this study has identified gene expression differences in PBMC between JIA subtypes and controls related to immunity and inflammation. Several differentially expressed genes replicate findings in other studies providing further confidence in our results which have the potential to greatly expand our understanding of JIA. Expression patterns indicate that defined JIA subtypes have strong internal similarities although clustering also indicates some degree of heterogeneity within subtypes. These findings will likely provide important mechanistic information with respect to JIA subtypes and lead to an improved molecular definition of JIA.