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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Arthritis Rheum. Author manuscript; available in PMC Mar 1, 2010.
Published in final edited form as:
PMCID: PMC2758237
NIHMSID: NIHMS87494

The Meaning of Clinical Remission in Polyarticular Juvenile Idiopathic Arthritis: Gene Expression Profiling in Peripheral Blood Mononuclear Cells Identifies Distinct Disease States

Abstract

Objective

The development of biomarkers to predict response to therapy in polyarticular juvenile idiopathic arthritis (JIA) is an important issue in pediatric rheumatology. An critical step in this process is determining whether there is biological meaning to clinically derived terms such as “active disease” and “remission.” We used a systems biology approach to address this question.

Methods

We performed gene transcriptional profiling on children who fit criteria for specific disease states as defined by consensus criteria developed by Wallace et al. (J Rheumatol 2005). Children with active disease (AD, n=14), clinical remission on medication (CRM, n=9) and clinical remission off medication (CR, n=6) were studied, in addition to healthy control children (n=13). Transcriptional profiles in peripheral blood mononuclear cells (PBMC) were obtained using Affymetrix U133 Plus 2.0 Arrays.

Results

Hierarchical cluster analysis and predictive modeling demonstrated that the clinically-derived criteria represent biologically-distinct states. Minimal differences were seen between children with AD and those with CRM. Thus, underlying immune/inflammatory abnormalities persist despite response to therapy. The PBMC transcriptional profiles of children in remission did not return to normal, but revealed networks of pro- and anti-inflammatory genes suggesting that “remission” is a state of homeostasis, not a return to normal.

Conclusions

Gene transcriptional profiling of PBMC reveals that clinically-derived criteria for JIA disease states reflect underlying biology. We also demonstrate that neither CRM nor CR states result in resolution of the underlying inflammatory process, but are more likely to be states of balanced homeostasis between pro- and anti-inflammatory mechanisms.

INTRODUCTION

Juvenile idiopathic arthritis (JIA) is a term used to designate a family of childhood-onset diseases that are characterized by chronic inflammation of synovial membranes. The etiology of JIA is unknown, and, thus, therapy remains entirely empiric, sometimes only marginally effective, and frequently associated with unwanted side-effects.

The empiric nature of therapy for JIA is one of the most vexing problems in the field of pediatric rheumatology. A critical question, often asked by parents as well as physicians, is when and whether children doing well on medication can have those medications reduced or discontinued. Answering this question relies on two suppositions: (1) there is something that can be called “remission” in JIA and (2) remission can be identified on the basis of specific clinical or laboratory features of the disease. Unfortunately, neither is necessarily the case. Studies in the past 10 years have shown that a significant percentage of children with polyarticular JIA experience disease flares when methotrexate is discontinued, even when disease has been stable on that drug for years (1, 2). No reliable biomarker or set of biomarkers accurately separates those children fated to experience disease recurrence as methotrexate is discontinued from those children whose medication can safely be discontinued. Only recently have investigators even arrived at a consensus definition of what terms such as “active disease,” “inactive disease,” and “clinical remission” mean (3). Although these definitions have been validated clinically, it is currently unknown whether they actually represent distinct biological states. The development of predictive biomarkers would certainly be facilitated if these distinct disease states could be identified biologically in children with treated disease.

Because conventional biomarkers have, to date, shown limited capacity to identify “remission,” we elected to use genome-wide transcription profiling to determine whether the clinically-derived criteria for disease state represent underlying immunobiology in children with polyarticular, IgM rheumatoid factor negative JIA

Materials and Methods

Patient population and definition of disease states

We studied 14 children with active polyarticular rheumatoid factor-negative JIA (AD-see definition below), as defined by the International League Against Rheumatism (ILAR) criteria (4). Because the long-term intent of this project is to identify children who can safely come off medication, all patients studied here, with the exception of those studied while in clinical remission (CR, defined below), were on medication at the time of study. All patients (except those in CR) were taking oral or subcutaneous methotrexate, and, in addition 5 children were taking subcutaneous etanercept. We studied 9 children who fit criteria for clinical remission on medication (CRM-see definition below). As this was a cross-sectional study, children were studied only once at not on multiple times as they achieved different disease states. Finally, we studied 6 children in remission off medication.

Subjects ranged in age from 3 to 18 years and had had poly JIA for 6 months to 12 years at the time of sampling. Blood was obtained at the time of routine clinical monitoring under normal sanitary conditions, and topical anesthesia with 2.5 % lidocaine/2.5% prilocaine cream was provided to all children prior to the procedure.

Disease states were defined according to the consensus criteria developed by Wallace and colleagues (5) and are defined as follows:

AD - Active Disease

This term defines children with synovitis and/or fever, rash, lymphadenopathy, splenomegaly, uveitis, elevated ESR or CRP or physician global assessment score indicating active disease.

ID – Inactive disease

This term defines children, on or off therapy, with no evidence of synovitis and with absence of fever, rash, lymphadenopathy, splenomegaly, no active uveitis, normal ESR and CRP and physician global assessment score indicating no active disease.

CRM – Clinical remission on medication

This term defines children with inactive disease on medication who have maintained that state for 6 continuous months.

CR – Clinical remission

This term defines children with inactive disease off medication who have maintained that state for 12 continuous months.

Healthy control subjects

Controls consisted of thirteen healthy children, ages 3 – 15 years. Control subjects consisted of children undergoing elective surgery for non-inflammatory conditions (e.g., minor orthopedic procedures) or children seen for routine health maintenance in the OU Children’s Physicians’ general pediatrics clinic. Healthy children were excluded if they had experienced fever (T ≥ 38°C) in the 48 hr prior to phlebotomy. Topical anesthesia with 2.5 % lidocaine/2.5% prilocaine was applied to the phlebotomy site for all children for at least 30 min before the procedure. All human subjects’ participation was reviewed and approved by the University of Oklahoma Health Sciences Center Institutional Review Board.

Specimens and Specimen Handling

Whole blood was drawn into 10 ml Becton-Dickinson (Franklin Lakes, NJ) citrated CPT tubes (#362760). PBMC were separated from granulocytes and red blood cells by density gradient centrifugation. PBMC were collected and placed immediately in Trizol reagent (Invitrogen, Carlsbad, CA).

RNA Isolation, labeling,hybridization, and scanning

Total RNA extractions from Trizol® reagent were carried out according to manufacturer’s directions and further purified by passage through RNeasy mini-columns (QIAGEN, Valencia, CA) according to manufacturer’s protocols. Final RNA preparations were suspended in RNase-free water. The RNAs were quantified spectrophotometrically. RNA integrity was assessed using capillary gel electrophoresis (Agilent 2100 Bioanalyzer; Agilent Technologies, Inc., Palo Alto, CA) to determine the ratio of 28s:18s rRNA in each sample. A ratio greater than 1.0 was used to define samples of sufficient quality, and only samples above this limit were used for microarray studies. cDNA synthesis, hybridization and staining were performed as specified by Affymetrix (Santa Clara, CA) using Affymetrix human U133 Plus 2.0 Arrays, an Affymetrix automated GeneChip® 450 fluidics station, and an Affymetrix 3000 7G scanner.

Statistical Analysis

All Affymetrix array data pre-processing was performed in the R/Bioconductor Package, “Affy”. The raw Affymetrix perfect match probes were normalized by the RMA method combined with median-polish (6). The marginal data distributions were adjusted through quantile normalization. The resulting normalized values were imported into JMP Genomics v3.2 (Cary, NC) where they were then log transformed. Genes were filtered using the “Log Expression Variation Filter” to screen out genes that are not likely to be informative, based on the variance of each gene across the arrays. In this case, the filter was set to exclude genes that fell below the 50th percentile of gene variance. We identified genes that were differentially expressed between the two classes by using two-sample Student’s T test (7). We used the Student’s T test to provide a False Discovery Rate of 5 % (8). The false discovery rate is the proportion of the list of genes claimed to be differentially expressed that are false positives. Data were exported to Excel (Microsoft, Redmond, WA) where averages of the classes were used to calculate expression ratios. Genes who simultaneously were differentially expressed (<5% FDR), had a ratio 2-fold or larger, and minimum normalized average intensity >64 units in at least one group were retained for further analysis. Unsupervised hierarchical clustering was performed in Spotfire (Sommerville, MA) using Ward’s minimum variance method (9). Differences between cluster groups will be tested through a Chi-Square test. A p-value less than 0.05 was considered statistically significant.

Predictive Modeling

To predict group membership (i.e., disease state) a so called “One-vs-Many” approach was taken (10). First the data are broken into two groups for every predictive outcome. For example, subject 1 is either a control or not a control. This process is repeated for every variable. For example, subject 1 is either active disease or not active disease. After all variables have been dichotomized, each binary variable created is modeled using a logistic regression of differentially expressed genes selected previously. Model terms were selected through a forward stepwise procedure. The concordance statistic was used to select the best model. Additionally, there were two restrictions: (1) all terms in the model were statistically significant at an alpha of 0.05; (2) due to the small sample size, a maximum of 5 terms was allowed in a single model. Once the models were created, individuals were scored and assigned group membership. Every logistic regression was given a propensity score as belonging to a given group. Every individual was scored in all 4 models and the model with the highest score determined classification. For example, a given patient entering the model the t might receive 4 scores: Control-5.2, Poly CR-2.3, PolyCRM-3.9, Active Disease-2. The control score of 5.2 is the largest; therefore, this patient would be classified as a control. In an attempt to avoid over-fitting, we performed a 5 fold cross validation of our model.

Physiologic Pathway Modeling

Pathways of potential interactions between gene products were generated by placing only the statistically significantly differentially expressed genes between groups into Ingenuity Pathways Analysis (Ingenuity Systems®, Redwood City, CA). Each Affymetrix gene identifier was mapped to its corresponding gene object in the Ingenuity knowledge base. These “focus” genes were overlaid onto a global molecular network developed from information contained in the Ingenuity knowledge base. Networks of these focus genes were then algorithmically generated based on their “connectivity” derived from known interactions between products of these genes.

Reverse Transcription - Quantitative Real-time PCR Validation

Total RNA was prepared as described above. Primers were designed with a 60°C melting temperature and a length of 15–28 nucleotides to produce PCR products with lengths between 50–150 bp using Applied Biosystems' Primer Express 2.0 software (Applied Biosystems Inc., Foster City, CA). First strand cDNA was generated from 1.8 ug of total RNA per sample using OmniScript Reverse Transcriptase according to manufacturer’s directions (QIAGEN, Valencia, CA). cDNA was diluted 1:20 in water. PCR was run with 4 µl cDNA template in 20 µl reactions in duplicate on an ABI SDS 7000 using the ABI SYBR Green I Master Mix and gene specific primers at a concentration of 0.2 µM each. The temperature profile consisted of an initial 95°C step for 10 minutes, followed by 40 cycles of 95°C for 15 sec, 60°C for 1 min, and then a final melting curve analysis with a ramp from 60°C to 95°C over 20 min. Gene-specific amplification was confirmed by a single peak using the ABI Dissociation Curve software. Average Ct values for GAPDH (run in parallel reactions to the genes of interest) were used to normalize average Ct values of the gene of interest. These values were used to calculate averages for each group (normal or patient subsets), and the relative ΔCt was used to calculate fold-change values between the groups.

Results

PCR corroboration of array results

Two genes were chosen from each of the comparisons (i.e., AD vs. CRM, CRM vs CR, CR vs. healthy controls) for corroboration of the array data. Results are summarized in a table that can be viewed online at http://peds.ouhsc.edu/section_rheumatology.asp. In all cases, the directional change (JIA compared with controls) identified by the arrays were corroborated by rtPCR. These data are a subset of a larger group of rtPCR corroborations (28 genes) of these same patients comparing other disease states (e.g., AD vs. CR). For all genes tested, those that were differentially over-expressed or under-expressed on microarrays were similarly over- or under-expressed by quantitative PCR.

Hierarchical cluster and one vs. many analyses

Hierarchical cluster analysis using differentially expressed genes in PBMC between groups from microarray data demonstrated that each of the different disease states, as defined by the consensus conference (5), could be distinguished from one-another. As shown in Figure 1, gene expression profiles largely distinguished control from patient samples, where control children clustered toward the left and children with active disease clustered toward the right side of the largest cluster containing 37 of the samples (Fisher’s exact test, p = 6.3 × 10−4). An additional seven samples (far right cluster) contained 4 of 7 CR, 1 CRM and 2 additional AD samples. Most CRM clustered within the control or active disease sub-clusters.

Figure 1
Hierarchical cluster analysis of differentially expressed genes in PBMC. “C” = control samples, “A” = active disease, “CRM” = clinical remission on medicine, “CR” = clinical remission without ...

Predictive modeling revealed a unique set of 10 genes across all 4 models whose expression levels accurately predicted disease state (Table 1). The concordance analysis between observed clinical state and predicted clinical state by microarray data revealed that 42/52 individuals could be correctly diagnosed (80%) (Table 2).

Table 1
Genes Discriminating Disease State
Table 2
Cross-Validation of Disease States

Network Modeling

When children with AD were compared with children who had achieved CRM, we found 23 genes differentially expressed between the two groups, 22 of which were over-expressed in children with AD. A table annotating these genes and relative expression levels can be viewed at: http://peds.ouhsc.edu/section_rheumatology.asp. As described above, all of these patients were taking medication. In silico modeling of the array data was informative. Analysis of these differentially expressed genes (Figure 2) revealed a single network of IFGN, IL-6, and IL-4-regulated genes that we (11) and others (12) have identified as important elements of JIA immunopathology. This physiologic model suggests that reaching CRM status is achieved by suppression of these IL-6, IL-4, and IFNG-regulated networks. The single gene that showed decreased expression in children with AD was the aldehyde dehydrogenase A1 family member (ALDH1A1), which is know to regulate sex steroid hormones and to be IL-1-responsive (13). Note that insulin also appears as a central mediator in this network, an interesting finding given the emerging data demonstrating critical “cross-talk” between TNFA and insulin-regulated pathways (14).

Figure 2
Single network derived from Ingenuity analysis of differentially expressed genes comparing children with polyarticular JIA with AD and children who have achieved CRM. This network consists largely of genes that show increased expression in children with ...

When children who had achieved CRM status were compared with children who were in CR, we found persistence of linked pro-inflammatory networks in children with CRM, as shown in Figures 3A and 3B. In all, 39 genes distinguished these two patient groups.. A table annotating these genes and relative expression levels can be viewed at: http://peds.ouhsc.edu/section_rheumatology.asp. While it is impossible to determine whether/how these expression patterns are altered by medication (CRM patients are still on medication, while CR patients are not) it is worth noting that these networks consist of genes regulated by known leukocyte pro-inflammatory regulators (e.g., Jun, NFkB, Figure 3A) as well as IFNG and TNFA- regulated genes, as we have previously reported (10). This finding may explain the tendency to misclassify CRM as AD, as shown in Table 2.

Figure 3
3A–3B Overlapping gene networks derived from Ingenuity analysis of the transcriptional profile of PBMC comparing children with JIA who had achieved CRM and those in CR. Genes over-expressed in children in CRM status are shown in red. Note clusters ...

The gene expression profile of PBMC did not normalize in children in remission, as indicated in the hierarchical cluster analysis (Figure 1). Differentially expressed genes in PBMC between children in remission off medicine (CR) and controls included 74 up-regulated and 8 down regulated genes (a table annotating these genes and relative expression levels can be viewed at: http://peds.ouhsc.edu/section_rheumatology.asp.). Ingenuity analysis revealed 4 interconnected gene networks. The structure of the largest of these networks, Figure 4A, demonstrates genes that are known mediators of leukocyte activation (e.g., JUN and other mitogen-activated protein kinases [MAPK] [15, 16] as well as markers of inflammation (e.g., matrix metaloproteases [MMPs] [17]). These genes are networked with transforming growth factor beta 1 (TGFB1), which, depending on its physiologic context, is generally regarded as a negative regulator of inflammation and an important mediator of immune tolerance (18, 19). These findings reveal that remission is not a return to normalcy, but, rather, a physiologic state in which pro-inflammatory elements are countered or kept in check. This interpretation is supported by the networks revealed in Figures 4B and 4C, which show the persistence of gene networks regulated by TNFA (Figure 4B) and IL4 (Figure 4C), both known to be involved in the immunopatholopgy of JIA (20, 21). The fourth network (Figure 4D) consisted of genes regulated by both beta-estradiol and dihydrotestosterone, an interesting finding in light of the known role of estrogens in regulating inflammation (22) and the female preponderance of polyarticular JIA patients. This same network consists of genes regulated by CCAAT/enhancer-binding protein (CEBPA), a member of a family of proteins previously implicated in regulating IL-1 beta (23) and other aspects of inflammation, including regulation of cytokine expression within the rheumatoid synovium (24, 25). This transcription factor is also known to play a role in estrogen-mediated regulation of cytokine production (26, 27).

Figure 4
Comparison of children with CR status and healthy controls. “A” (top left) shows the largest of the 4 functional gene networks derived from Ingenuity analysis of the transcriptional profile of children with JIA who had achieved CR status ...

Discussion

The development of clinically-useful genomic prognostic biomarkers in JIA requires our being able to discern specific disease states (e.g., active disease, remission) as a first step. If remission on medication is indistinguishable from active disease, for example, then it is unlikely that this technology will provide much assistance. That is, it is possible that the clinically-derived consensus criteria for disease status do not reflect underlying disease biology. This study tested and confirmed the hypothesis that these disease states are distinguishable at the molecular level using gene expression profiling, and thus provides an important first step in biomarker development.

We report here that gene profiling and hierarchical cluster analysis can distinguish different disease states, although there is more “blurring” of the groups at the biological level than at the clinical level (Figure 1). This suggests that underlying cellular abnormalities persist in JIA PBMC, even when treatment is successful in controlling symptoms. A plausible (but not currently provable) explanation for this observation is that the synovium is a more critical target of drug action than has previously been supposed. This hypothesis is supported by what we found in children off medication with inactive disease for at least 6 months (that is, children who had achieved CR status). Remission, as reflected at the molecular level, is clearly not a return to a normal immune/inflammatory state. Rather, the gene expression profiles suggest that “remission” is a state of homeostasis in which anti-inflammatory (e.g., TGFB-driven) mechanisms balance the dysregulatory elements that lead to chronic inflammation.

It is difficult to determine how accurate or predictive our models of the specific disease states in polyarticular JIA are until we test them in an independent cohort. However, the use of the 5 fold cross validation does provide validation that the model isn’t over-fit. For example, the major misclassification occurred in Poly CRM, with 3 Poly CRM children predicted to have active disease (Table 3). It is possible that our model is correct and these children are not in remission on a molecular level. This hypothesis is supported by Ingenuity modeling, which suggests that there are still networks of pro-inflammatory genes active even in these children who have achieved a state of “remission.” Under any circumstances, we will need to follow these children over time to determine whether our cross-sectional model has prognostic capabilities.

Taken together, these findings explain two observations that have puzzled physicians caring for children with polyarticular JIA for many years. First, our data explain at least conceptually why recurrences or flares are so common when medications are tapered or discontinued on children who seem to be doing well: underlying abnormalities at the gene expression level are still present, even if not reflected in such standard clinical measures such as erythrocyte sedimentation rate, serum CRP level, hemoglobin, or white blood cell count. These findings also explain why disease recurrences are common: “remission” is still a biologically abnormal state. While it is impossible to speculate on what extrinsic factors might disrupt the complex homeostatic mechanisms that are reflected in remission, it is reasonable to hope that a longitudinal analysis of a large cohort of children will be highly informative.

It is important to point out that many of the pathological networks visualized in these studies demonstrate the structure of scale-free systems (28) as we have previously seen in JIA neutrophils (29). That is, the network structures demonstrate high areas of connectivity between some genes (designated “hubs” in systems biology) and other genes showing only limited connectivity to the system (“nodes”). Furthermore, the meta-structure of the collected profiles, especially in neutrophils, demonstrated modularity, another feature of cellular-physiologic systems (30). These findings have interesting implications both for our understanding of pathogenesis and for elucidating new targets of therapy. From the standpoint of pathogenesis, we note that the pathological structures revealed on Ingenuity are organized, and are therefore as likely to represent physiologic adaptation to an externally-applied force as they are an unraveling of basic biological processes (e.g., the distinction between “self” and “non-self”). From the standpoint of therapy, it is useful to mention one of the primary characteristics of scale-free systems: they are highly resistant to perturbation at their peripheral nodes but vulnerable to attack at their “hubs” (31) (think of what happens to air traffic when weather disrupts Atlanta or Chicago). This means that successful new treatments of therapy for JIA will have to focus pathophysiologic structures, not specific genes. A gene that is expressed “20-fold above controls” is not necessarily a promising target if it represents a peripheral node. A gene that shows no differential expression at all might be a promising target if it represents a system hub.

We are still a long way from the ultimate goal of developing gene expression-based disease biomarkers that will direct therapy in polyarticular JIA. What this study has done is confirm that remission (both CRM and CR) is a biologically distinct state. Furthermore, we have demonstrated that CR is not a “normal”, but represents a homeostatic state in which pro- and anti-inflammatory mechanisms appear to be in balance. Answering the critical biomarker questions will require the study of large groups of children prospectively, a task we already have under way.

Acknowledgements

This work was supported by an Innovative Research grant (JNJ) from the Arthritis Foundation and by grants from the National Institutes of Health (RR03145, RR020143, RR16478, RR15577, AI062629), and Oklahoma Center for the Advancement of Science and Technology (AR061-015, AR081-006 and HR07-139). Amita Aggarwal was supported by an overseas associateship grant from Department of Biotechnology, Government of India. Ryan McKee and Brad Chaser were supported by summer medical student preceptorships from the American College of Rheumatology. Brad Chaser was also supported on a summer research stipend from the University of Oklahoma Health Sciences Center Native American Center of Excellence.

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