Correlation analyses of clinical and molecular findings identify candidate biological pathways in systemic juvenile idiopathic arthritis
- Xuefeng B Ling†1,
- Claudia Macaubas†2,
- Heather C Alexander3,
- Qiaojun Wen1,
- Edward Chen1,
- Sihua Peng1,
- Yue Sun2,
- Chetan Deshpande2,
- Kuang-Hung Pan4,
- Richard Lin4,
- Chih-Jian Lih4,
- Sheng-Yung P Chang3,
- Tzielan Lee5,
- Christy Sandborg5,
- Ann B Begovich3,
- Stanley N Cohen4 and
- Elizabeth D Mellins2, 5Email author
© Ling et al; licensee BioMed Central Ltd. 2012
Received: 2 May 2012
Accepted: 23 October 2012
Published: 23 October 2012
Clinicians have long appreciated the distinct phenotype of systemic juvenile idiopathic arthritis (SJIA) compared to polyarticular juvenile idiopathic arthritis (POLY). We hypothesized that gene expression profiles of peripheral blood mononuclear cells (PBMC) from children with each disease would reveal distinct biological pathways when analyzed for significant associations with elevations in two markers of JIA activity, erythrocyte sedimentation rate (ESR) and number of affected joints (joint count, JC).
PBMC RNA from SJIA and POLY patients was profiled by kinetic PCR to analyze expression of 181 genes, selected for relevance to immune response pathways. Pearson correlation and Student's t-test analyses were performed to identify transcripts significantly associated with clinical parameters (ESR and JC) in SJIA or POLY samples. These transcripts were used to find related biological pathways.
Combining Pearson and t-test analyses, we found 91 ESR-related and 92 JC-related genes in SJIA. For POLY, 20 ESR-related and 0 JC-related genes were found. Using Ingenuity Systems Pathways Analysis, we identified SJIA ESR-related and JC-related pathways. The two sets of pathways are strongly correlated. In contrast, there is a weaker correlation between SJIA and POLY ESR-related pathways. Notably, distinct biological processes were found to correlate with JC in samples from the earlier systemic plus arthritic phase (SAF) of SJIA compared to samples from the later arthritis-predominant phase (AF). Within the SJIA SAF group, IL-10 expression was related to JC, whereas lack of IL-4 appeared to characterize the chronic arthritis (AF) subgroup.
The strong correlation between pathways implicated in elevations of both ESR and JC in SJIA argues that the systemic and arthritic components of the disease are related mechanistically. Inflammatory pathways in SJIA are distinct from those in POLY course JIA, consistent with differences in clinically appreciated target organs. The limited number of ESR-related SJIA genes that also are associated with elevations of ESR in POLY implies that the SJIA associations are specific for SJIA, at least to some degree. The distinct pathways associated with arthritis in early and late SJIA raise the possibility that different immunobiology underlies arthritis over the course of SJIA.
KeywordsArthritis Inflammation Juvenile idiopathic arthritis (JIA) Systemic JIA Polyarticular JIA Transcriptional analysis
Systemic juvenile idiopathic arthritis (SJIA) is currently classified as a subtype of juvenile idiopathic arthritis , and is characterized by a combination of arthritis and systemic inflammation, including fever, rash and serositis. SJIA has distinct demographic characteristics compared to other JIA subtypes, including onset throughout childhood and lack of gender preference. At clinical presentation, SJIA may resemble other diseases in children, including viral infection and Kawasaki disease [2–4]. The outcome in SJIA is variable, with close to half of children having a monocyclic course, less than 10% having an intermittent course, and over half having a persistent course [5, 6], the latter often dominated by chronic arthritis. An adult form of SJIA is called Adult Onset Still Disease (AOSD) and occurs rarely .
There are also unique immunophenotypic features in SJIA compared to other JIA subtypes, such as the lack of human leukocyte antigen (HLA) class II allele association, low or absent autoantibodies (specifically, antinuclear antibodies, rheumatoid factor or anti-CCP antibodies ), a tendency toward monocytosis [9, 10], high levels of IL-18 [11, 12] and natural killer cell abnormalities in at least a subset of patients . These immunologic features, together with the therapeutic efficacy of inhibitors of IL-1 or IL-6 in SJIA and AOSD, suggest that these diseases might be best classified as autoinflammatory rather than autoimmune [14–17].
Despite our knowledge of some important immunological characteristics of active SJIA, the pathogenesis of SJIA remains unknown. One of the unanswered questions is whether independent biological processes underlie the systemic symptoms and the arthritis. Evidence from clinical studies shows that earlier in the disease, IL-1 inhibitors (and perhaps also IL-6 blockade) are efficacious, especially against systemic symptoms, but at a later stage, where arthritis may predominate, patients may develop resistance to these therapies [18–20]. These findings suggest that distinct biological processes may be associated with different manifestations and/or different stages of the disease.
Transcriptional profiling of peripheral blood cells has been a useful approach for identifying biological pathways involved in SJIA and other complex diseases, such as polyarticular JIA (POLY), rheumatoid arthritis (RA), systemic lupus erythematosus and Kawasaki disease [21–24]. Previous studies of SJIA using microarray analyses have revealed transcriptional signatures in peripheral blood associated with active disease and with patient subsets [25–29].
We hypothesized that distinct gene expression patterns may be associated with individual clinical parameters used as measures of the systemic inflammation and the arthritis. We analyzed expression in peripheral blood mononuclear cells (PBMC) of a panel of inflammation-associated genes to determine patterns associated with elevations in two markers of disease activity in JIA, erythrocyte sedimentation rate (ESR) and number of active joints (joint count, JC). ESR is a marker of inflammation that is elevated in association with systemic as well as organ-specific inflammation, including arthritis . Active joints are defined as joints with non-bony swelling or limited range of motion, with either tenderness or pain on motion; we chose active joint count as a marker of arthritis.
We asked if common or unique expression profiles are associated with ESR and JC in SJIA. In order to assess the specificity of our results for SJIA, we also asked whether the expression of the panel of tested genes differed in SJIA patients compared to patients with polyarticular course JIA (POLY), which is characterized by chronic polyarthritis. We then analyzed if JC associated genes differ during the early and late phase of SJIA. Based on the gene expression patterns, we identified candidate biological pathways associated with the systemic and arthritis components of SJIA.
Subject population and clinical data collection
Systemic scoring system for SJIA patients.
No active disease
Having any one of the following: rash, fevers < 10 days in the past month, ESR 40 to 90, platelets > 450,000
Having at least three of the following: rash, fever > 10 days in the past month, WBC > 20,000, ESR > 90, platelets > 550,000, d-dimers 250 to 500, elevated fibrinogen
Having any one of the following symptoms: pneumonitis, percarditis, pleural effusion, Macrophage Activation Syndrome (MAS)
Arthritis scoring system for SJIA patients.
No joint involvement
< 5 active joints
5 to 10 active joints
> 10 active joints
Arthritis scoring system for POLY JIA patients*
No joint involvement
1 to 10 active joints
10 to 20 active joints
> 20 active joints
Subjects demographic and clinical characteristics
Median age (yr) at disease onset (range)
5.8 (1.7 to 15.7)
5.8 (1.4 to 15.7)
8.6 (1.2 to 15.1)
8.6 (1.2 to 15.1)
Median age (yr) at sample collection (range)
9.3 (3.5 to 16.6)
11.1 (2.4 to 18.9)
13.4 (5 to 18.7)
14.1 (6.1 to 19.2)
Median WBC (x103/ul)
13.3 (5.3 to 27.4)3
6.9 (3.4 to 11.8)
8.8 (4.9 to 15.1)
7.5 (5.6 to 9.8)
Median platelets (x103/ul)
461 (257 to 814)4
280 (170 to 400)
384 (211 to 512)5
283 (224 to 432)
Median ESR (mm/h)
81 (11 to 121)6
5.5 (0 to 18)
31 (7 to 78)7
9 (2 to 15)
Median joint count (range)
9 (1 to 28)8
0 (0 to 1)
25 (3 to 62)9
Median prednisone dose,
0.01 (0 to 0.53)10
0 (0 to 1.1)
0 (0 to 0.1)
0 (0 to 1)
total no. samples (%)
Anti-TNF/total no. samples (%)
IL-1RA/total no. samples (%)
Blood samples were obtained only when there was a clinical need for blood tests. A total of 3 to 4 ml of blood was collected directly in vacutainer cell preparation tubes (CPT) with sodium citrate (Becton Dickinson, Franklin Lakes, NJ, USA). Peripheral blood mononuclear cells (PBMCs) were isolated within three hours of collection by centrifugation of CPT tubes, per the manufacturer's instructions.
Purified PBMCs were lysed in RLT reagent (Qiagen, Valencia, CA, USA) and lysate was stored at -80°C until RNA extraction. RNA was isolated using the RNeasy mini kit (Qiagen), per the manufacturer's instructions with an additional on-column DNase I (Qiagen) treatment for 40 minutes. The RNA concentration was measured by the Ribogreen assay (Molecular Probes, Grand Island, NY, USA) or by absorbance at 260 nm. The purity of RNA was assessed by the ratio of the absorbance readings at 260 and 280 nm. The integrity of the RNA samples was also checked by either agarose gel electrophoresis or with the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).
Gene panel selection
In a pilot study, paired flare/remission PBMC samples from 14 SJIA patients were processed for RNA as described  and analyzed using Lymphochip cDNA microarrays (Patrick Brown, Stanford University, Stanford, CA, USA) [37, 38]. A large number of genes were identified as differentially expressed in flare versus remission samples by Significance Analysis of Microarrays (SAM) . Hierarchical clustering was performed with the Cluster program and visualized using TreeView  (Eisen Lab, University of California, Berkeley, CA), as illustrated for a subset of genes in Additional file 1, and also in [36, 41]. The full data set, GSE37388, is released to the public on the Gene Expression Omnibus (GEO) database. From the large set, we selected genes (n = 131) representing various ontologic categories, such as signaling, transcription, inflammation and immune function. We then added other immune-related genes (n = 50) that are expressed in PBMC and implicated in JIA or RA by published reports. The genes were selected prior to analysis of any blood samples for this study, and the samples used for the microarray experiment were not re-used here. The 181 selected genes are shown on Additional file 2; we confirmed that many are immune-related using the program PANTHER 7.0 (Protein ANalysis THrough Evolutionary Relationships) Classification System (Thomas Lab, University of Southern California, Los Angeles, CA, USA), which classifies proteins by their functions, using published experimental evidence and evolutionary relationships  (http://www.pantherdb.org/) to categorize their biological functions. This analysis showed that the largest functional category is inflammatory chemokine and cytokine signaling pathways (14.6% of the genes), followed by interleukin signaling pathways (10.8%), apoptosis signaling pathways (9.9%) and toll receptor signaling pathways (6.2%). A full list of categories covered is shown on Additional file 3.
Gene expression detection by kinetic PCR
The kinetic RT-PCR assay was performed as described . Briefly, all reactions were carried out in duplicate as a single-step RT-PCR reaction, using SYBR green chemistry. Data from duplicate reactions for each gene were averaged and normalized based on levels of expression of four housekeeping genes: eukaryotic translation elongation factor 1 alpha1 (EEF1A1), protein phosphatase 1, catalytic subunit, gamma isoform (PPP1CC), ribosomal protein L12 (RPL12), and ribosomal protein L41 (RPL41). The normalized expression level, housekeeping normalized units, of each gene was used to determine the fold change among samples. In a preliminary experiment, we found that a subset (n = 75) of our gene panel showed very limited variation in level (± 2-fold difference from the mean value) in five healthy individuals (two females and three males) over a four-month period (data not shown).
Identification of ESR or JC significantly associated genes in SJIA and POLY
Genes significantly associated with SJIA and POLY were determined using Pearson's correlation and Student's t-test, as explained in the Results section.
Significance analysis of the canonical biological pathways
The biological pathways indicated by the group of genes associated with each clinical parameter/patient cohort subset were determined by pathway analysis with Ingenuity IPA system (Ingenuity Systems, Redwood City, CA, USA; http://www.ingenuity.com). The significance of either ESR or JC related pathways was analyzed using sparse linear discriminant analysis method, as previously described . Correlation between SJIA ESR-related and JC-related pathways was analyzed by Pearson correlation. To determine a threshold to extract pathways that significantly differentiate ESR and JC in SJIA, 500 simulated SJIA ESR-related and 500 simulated JC-related pathway data sets were created by permutation of canonical pathway identifications and their associated pathway P-values for SJIA ESR or JC. For each canonical pathway, the absolute P-value difference in logarithm form between SJIA ESR and JC was computed using one of the 500 simulated SJIA ESR and one of the 500 simulated JC pathway P-value data sets. This led to 500 absolute log P-value differences for each canonical pathway between SJIA ESR and JC, which later were sorted and 20%, 50% and 80% values were computed. Densities of the absolute differences between SJIA ESR and JC-related pathways for the original and the simulated data sets (20%, 50%, and 80%) were plotted using the R package. Comparison of the original data set and the 80th percentile simulated data set determined the threshold to select significantly different pathways between SJIA ESR and JC. A similar approach was applied to the analysis of significantly different pathways between SJIA ESR and POLY ESR.
ESR and JC-associated gene expression in JIA
ESR was chosen as a quantitative measure of systemic inflammation for our analysis, as it typically rises in association with flares of systemic symptoms and was assessed in the largest number of samples. We also considered another measure of systemic inflammation, C-reactive protein (CRP), but few samples were assessed for CRP, precluding the use of this parameter in our analysis. The number of affected counts (joint count, JC), as defined above (Introduction), was used as a quantitative measure of arthritis.
We also analyzed gene expression association using Student's t-test, as shown in Figure 2B. For ESR, based on the analysis from Figure 1A, we initially divided our samples into three groups: flare samples with ESR < 20 (F1), flare samples with ESR > 20 (F2), and quiescence (to ensure that differences between F1 and Quiescence were not overlooked). We identified genes whose mean expression value differed significantly between the F1 (ESR < 20) and F2 (ESR > 20) patient groups, but no differences in genes expressed by the F1 and the quiescence group were found. Subsequently, we grouped the flare F1 and the quiescence groups into one group for ESR analysis. For JC, no other partitioning was necessary, as shown in Figure 1B, and samples were grouped into flare and quiescence groups. As we did previously for Pearson analysis, we calculated local FDR and a value of < 0.05 was considered significant (Figure 2B). This second analysis found genes missed by correlation analysis, as the latter requires a linear relationship and captures genes with more tightly regulated expression (small differences between F and Q samples).
Pearson correlation analysis of expression data from SJIA subjects found 79 genes from our panel to be ESR-correlated and 36 genes to be JC-correlated. Student's t-test found 66 ESR-associated and 79 JC-associated genes in SJIA. This pattern differed from relationships of the expression levels of the same genes with these clinical parameters in POLY-course JIA patients: 20 ESR-correlated and no JC-correlated genes were found in POLY, and none of the genes were ESR-associated or JC-associated by Student's t-test in POLY. Combining both analyses, we found 91 ESR-related and 92 JC-related genes in SJIA, and 20 ESR-related and no JC-related genes in POLY. A list of significantly associated genes is on Additional file 4. Additional file 5 (Supplementary Figure 2A-F) diagrams the fold changes in expression of the selected genes between groups (for example, F2 versus F1 + Q) and between quartiles of ESR or JC. The probability density analysis graphically represents the normalized frequency distribution of the fold ratio of the selected two groups. This result indicates that our selected genes have significant variation between groups (limited variation = fold ratio close to 1) while showing strong association. This analysis further supports our approach (Figure 2) for the identification of significant associations. The reduced number of genes associated with these clinical parameters in the POLY cohort was not surprising given that the gene list was chosen in large part using expression data from SJIA PBMC. Indeed, this finding implies a degree of specificity of the associated genes for SJIA (see discussion). Another likely contribution to this difference might be the extent that disease-related processes are reflected in peripheral blood in the two disease types.
Comparative analysis of SJIA ESR and JC related pathways
Biologic pathways that distinguish or are shared between SJIA ESR and SJIA JC
IPA Canonical Pathways
Gene expression with higher ESR/JC*
Glucocorticoid receptor signaling
Role of PKR in interferon induction and antiviral response
Altered T cell and B cell signaling in rheumatoid arthritis
T helper cell differentiation
iCOS-iCOSL signaling in T helper cells
MIF regulation of innate immunity
LPS-stimulated MAPK signaling
CD27 signaling in lymphocytes
MIF-mediated glucocorticoid regulation
Role of cytokines in mediating communication between immune cells
Crosstalk between dendritic cells and natural killer cells
Docosahexaenoic acid (DHA) signaling
Regulation of IL-2 expression in activated and anergic T lymphocytes
Toll-like receptor signaling
fMLP signaling in neutrophils
RANK signaling in osteoclasts
4-1BB signaling in T lymphocytes
CD28 signaling in T helper cells
Communication between innate and adaptive immune cells
T cell receptor signaling
A number of pathways were significantly related to SJIA ESR and JC to the same degree (Table 5). For several of these pathways, the expression of most of the associated genes was higher in samples with higher ESR or JC. These pathways include, among others, protein kinase receptor (PKR, a pattern-recognition receptor) signaling in interferon induction, T cell and B cell signaling in the pattern of rheumatoid arthritis (RA), and (macrophage) migration inhibition factor (MIF) regulation of innate immunity. Other genes in pathways associated with activating innate responses, such as lipopolysaccharide (LPS) signaling and triggering receptor expressed on myeloid cells (TREM1) signaling are also higher samples with either higher ESR or JC. Genes in other pathways showed lower expression in samples with higher ESR or JC, such as T helper cell differentiation, iCOS-iCOSL (inducible T-cell co-stimulator/ligand) signaling in T helper cells and CD40 (co-stimulatory molecule on antigen presenting cells) signaling. Notably, these down-regulated pathways are associated with adaptive immune responses. Also down-regulated in association with elevations of both SJIA ESR and JC is the pathway for crosstalk between dendritic cells and natural killer cells, which can be involved in restriction of innate responses .
Two genes, the DNA repair enzyme ATM and the transcription factor NFATC2 (also known as NFAT1), are in the pathway for RANK signaling in osteoclasts and are both down-regulated in association with systemic (ESR) and arthritic (JC) disease activity. The Rank/RankL pathway is an important regulator of bone remodeling . An ATM deficiency has been described in CD4+ T cells from rheumatoid arthritis (RA) patients , associated with premature immunosenescence. However, ATM may also be involved in bone formation, and ATM deficient animals show increased numbers of osteoclasts . The transcription factor NFATC2 has been identified as a negative regulator of cartilage cell growth . It is also important in T cell effector function, translocating to the nucleus following T cell receptor activation and regulating expression several cytokines in CD4 T cells (reviewed in ). Thus, its inverse correlation with ESR and JC may be similar to the other T cell-related pathways described above. Interestingly, hyperactivation of NFATC2 in T cells is associated with decreased susceptibility to experimental autoimmune encephalomyelitis, indicating that increased NFATC2 activity may have immunomodulatory effects that down-regulate autoaggressive reactions .
Comparative analysis of SJIA and POLY ESR-related pathways
We next asked whether some biological pathways involved in SJIA ESR elevation are also involved in POLY ESR elevation, by comparing SJIA and POLY ESR-related genes. As shown in Figure 3C, there is reduced correlation (correlation coefficient, 0.59) between SJIA and POLY ESR-related pathways (n = 119), compared to the correlation we observed between SJIA ESR- and SJIA JC-related pathways. Shown in Figure 3D, densities of the absolute logarithm P-value differences of all pathways between SJIA and POLY ESR, for the original and the 20, 50 and 80 percentile of the simulated random data sets, were computed and plotted.
Biologic pathways that distinguish or are shared between SJIA ESR and POLY ESR
IPA Canonical Pathways
Gene expression with higher
Role of macrophages, fibroblasts and endothelial cells in rheumatoid arthritis
Role of osteoblasts, osteoclasts and chondrocytes in rheumatoid arthritis
Glucocorticoid receptor signaling
B cell receptor signaling
Role of PKR in interferon induction and antiviral response
Systemic lupus Erythematosus signaling
altered T cell and B cell signaling in rheumatoid arthritis
A proliferation-inducing ligand mediated signaling
T helper cell differentiation
iCOS-iCOSL signaling in T helper cells
B cell activating factor signaling
Dendritic cell maturation
IL-12 signaling and production in macrophages
p38 MAPK signaling
PPAR alpha/RXR alpha activation
As observed in the previous analysis, pathways associated with T cell responses are significantly associated with ESR in SJIA but the genes in these pathways show lower expression in samples with higher ESR in comparison to samples with lower ESR. In addition, this analysis showed that genes in B cell activating factor (BAFF) signaling, April (A proliferation-inducing ligand, TNFSF13)-mediated signaling and IL-15 signaling pathways show lower expression samples with in elevated ESR in SJIA (Table 6).
Comparative analysis of joint count (JC) correlated genes in systemic and arthritis phase (SAF) and arthritis phase (AF) SJIA patients
In this study, we sought to identify molecular pathways involved in the systemic and arthritic components of SJIA by investigating the gene expression pathways associated with increases in ESR and active joint count. We chose ESR as a marker of systemic inflammation, but we note that SJIA flares associated with elevated ESR may also include arthritis. Further, SJIA flares with macrophage activation syndrome (MAS) may actually lower ESR from fibrinogen consumption as part of the coagulopathy . The latter issue does not confound our analysis, as the three flare samples with low ESR were from patients with mild flares without MAS. Strictly speaking, our approach delineated gene associations with ESR; however, in our group of SJIA samples, ESR typically correlated closely with other evidence of systemic disease.
Several variables that influence transcriptional profiles should be considered in relation to our results. It is possible that some of the observed differences in gene expression are due to differences in cell type composition of PBMC between SJIA and POLY, or between flare and quiescence . Changes in abundance of cell types may be relevant to disease mechanisms. For monocyte-related genes, we  and others  have shown that the differences in transcript abundance are not explained by differences in monocyte numbers alone, but reflect activation state. The use of medication and disease duration at the time of sampling may influence the pattern of gene expression. A larger, likely multi-center, study will be needed to rigorously control for these important variables.
Our analysis revealed overlap in molecular pathways involved in increased ESR and elevated JC in SJIA. This result was not unexpected, given reported correlations between these two parameters [30, 57]. However, the glucocorticoid receptor (GCR) signaling pathway was more significantly related to ESR than JC. Systemic symptoms of SJIA respond to exogenous steroids, suggesting the elevation of GCR signaling may represent an endogenous effort to dampen systemic inflammation. The comparable doses of exogenous steroids in the F and Q groups make it less likely that steroid therapy is inducing this pathway. Notably, polymorphism in the GCR gene is associated with the level of inflammatory activity in JIA . Involvement of GCR signaling in systemic inflammation in SJIA and stronger association of this pathway with inflammation in SJIA versus POLY (at least as reflected in blood cells) is consistent with reduced responses in SJIA patients to non-glucocorticoid drugs that are efficacious in subsets of POLY patients (for example, methotrexate and anti-TNFα [59, 60]).
We also found that the PI3K/Akt signaling pathway is more significantly related to SJIA JC than ESR. This pathway, which is activated by a variety of stimuli, including IL-1β, TNFα and IL-6, is potentially involved in IL-17 production . IL-17 could be an important factor in SJIA arthritis , particularly in the later phase. We did not assess expression of IL-17 in this study, but our preliminary data suggest that CD4+ T cells from SJIA patients secrete higher levels of IL-17 than control cells when cultured in TH17-polarizing conditions [Wong M, Mellins E, unpublished results]. Recently, enrichment of Th17 (and Th1) cells in blood of SJIA patients has been described .
Our findings are consistent with the hypothesis that dysregulation of the innate immune system makes a more prominent contribution to SJIA immunopathology than alterations of the adaptive immune system [17, 63], whereas adaptive responses are thought to drive oligoarticular and polyarticular JIA [64, 65]. However, our results implicate deficiencies in genes associated with T cell-related responses in SJIA pathology, similar to observations in other studies . For example, reduced cytolytic cell activity  and diminished function of T regulatory cells may play roles in SJIA etiology . Down-regulated genes associated with cytolytic function also participate in dendritic cell/NK cell and monocyte/NK cell interaction. Some cytolysis genes are part of the IL-15 signaling pathway, and IL-15 is involved in the development of NK cells .
In the systemic plus arthritic stage of SJIA, we found that expression of IL-10 in PBMC was positively associated with arthritis. In in vitro studies of SJIA monocytes, we and others [26, 29] observe that IL-10 is expressed after TLR stimulation, and IL-10 signaling is intact [Macaubas et al., unpublished]. Given the immunosuppressive effect of IL-10, association of this gene with arthritis in SJIA may represent an attempt by the immune system to reduce inflammation. The level of IL-10 may be inadequate to deal with the inflammatory challenge, as the frequency of a promoter allele associated with low IL-10 expression is increased in SJIA patients [69, 70]. We found that LPS-induced production of IL-10 protein in SJIA monocytes is comparable to controls .
A striking finding of this study is that deficiency in IL-4-related pathways correlates with JC in the arthritic phase of SJIA. IL-4 has been implicated in protection against arthritis. Polymorphism in the IL4Rα gene that confers reduced responsiveness to IL-4 is associated with worse outcome in RA . Low levels of circulating IL-4 are observed in patients with active POLY . IL-4 has been shown to suppress growth factor-induced proliferation of cultured rheumatoid synovial cells by interfering with the cell cycle and by decreasing cell survival . In the murine model of collagen-induced arthritis, IL-4 is protective against cartilage and bone destruction , and neutralization of IL-4 in the same model results in reversal of arthritis suppression . IL-4 is also protective in the model of proteoglycan-induced arthritis . Interestingly, in proteoglycan-induced arthritis, mice deficient in IL-4Rα showed higher IL-1β, IL-6 and MIP1a, whereas levels of IFNγ and autoantibodies were less affected. These results imply that IL-4 suppresses innate immune activity more than the adaptive system in this arthritis model . This might model the arthritis of late stage SJIA.
IL-4 inhibits expression of pro-inflammatory cytokines, such as IL-1β, TNFα [78, 79] and IL-17 . As mentioned, IL-17 is an attractive candidate for a driver of inflammation in the later arthritic phase of SJIA. Th17 cells may become IL-1 independent in SJIA, as seen in an animal model . The IL-1β independence of IL-17 action would be consistent with the decreased efficiency of anti-IL1 therapy in the later arthritic phase of SJIA . The ability of IL-4 to suppress reactivation of committed Th17 cells  may be another mechanism by with IL-4 deficiency could contribute to arthritis in SJIA. Finally, in a small, open label study, oral histone deacetylase inhibitors in patients with mean SJIA duration of five years showed significant therapeutic benefit, specifically for arthritis . This finding is consistent with the idea that distinct biology may be involved in later phase arthritis in SJIA.
We found no gene association or correlation linked with POLY joint count, and a limited number of somewhat different genes were associated with elevated ESR in POLY-JIA subjects. Our gene panel was largely derived from a SJIA-based microarray study and, as such, it has a significant bias towards SJIA-related genes. Further, the systemic nature of SJIA predicts more changes in peripheral blood than for POLY, where pathology is more localized. Our POLY cohort was itself heterogeneous, including RF+ and RF- patients, which were analyzed as one group. Most gene expression studies have analyzed RF- patients only [29, 84, 85]; some have not determined the RF status . Griffin et al. 2009 showed that RF+ and RF- patients can share a similar gene signature .
It will be of interest to determine the cell type within PBMC that is responsible for particular transcripts. Based on correlated expression patterns with more lineage specific genes, it is most likely that IL-4 transcripts derive from CD4 T cells; the IL-4 message expression is correlated with expression of CD40LG and IL2RG (not shown). In contrast, the IL-10 expression correlates with expression of IL-1, IL-1-related genes and IL-6 (not shown), suggesting IL-10 transcripts are expressed in monocytes. Further studies are also needed to determine the specificity of the SJIA gene signature in relation to other acute inflammatory diseases, such as bacterial and viral infections and other rheumatologic pediatric diseases . Nonetheless, our current results add to the growing evidence that different molecular mechanisms distinguish SJIA from other JIA subtypes [10, 11, 26, 29, 32].
This study demonstrates that analysis of individual clinical parameters in a complex disease like SJIA may reveal unique and informative molecular associations. In addition to elucidating disease immunopathology, this approach may help identify therapeutic targets and strategies tailored to the different phases of SJIA.
Adult Onset Still Disease
B cell activating factor
cell preparation tubes
eukaryotic translation elongation factor 1 alpha1
erythrocyte sedimentation rate
false discovery rate
Gene Expression Omnibus
macrophage activation syndrome
Protein ANalysis THrough Evolutionary Relationships) Classification System
peripheral blood mononuclear cells
polymerase chain reaction
protein kinase receptor
polyarticular juvenile idiopathic arthritis
protein phosphatase 1, catalytic subunit, gamma isoform
ribosomal protein L12
ribosomal protein L41
systemic plus arthritic phase
Significance Analysis of Microarrays
systemic onset juvenile idiopathic arthritis
white blood count.
The authors thank colleague scientists at the Stanford University Pediatric Proteomics group for critical discussions, and the Stanford University IT group for excellence in Linux cluster support. XBL was supported by the Stanford NIH/NCRR CTSA award number UL1 RR025744 and by the Lucile Packard Foundation for Children's Health. EDM, CM, YS and CD were supported by funding from the Lucile Packard Foundation for Children's Health (LPFCH) and the Stanford NIH CTSA (UL1 RR025744), The Wasie Foundation, Dana Foundation and the National Institutes of Health (NIH); these funds also supported the manuscript preparation. K-HP, RL and C-JL were supported by the Defense Advanced Research Projects Agency. SNC is the Kwoh-Ting Li Professor in the School of Medicine. Funding bodies played no role in the collection, analysis and interpretation of data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.
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