- Open Access
Dynamic tracking of functional gene modules in treated juvenile idiopathic arthritis
- Nan Du†1,
- Kaiyu Jiang†2,
- Ashley D. Sawle3,
- Mark Barton Frank4,
- Carol A. Wallace5,
- Aidong Zhang1 and
- James N. Jarvis2, 6, 7Email author
© Du et al. 2015
Received: 18 February 2015
Accepted: 1 October 2015
Published: 24 October 2015
We have previously shown that childhood-onset rheumatic diseases show aberrant patterns of gene expression that reflect pathology-associated co-expression networks. In this study, we used novel computational approaches to examine how disease-associated networks are altered in one of the most common rheumatic diseases of childhood, juvenile idiopathic arthritis (JIA).
Using whole blood gene expression profiles derived from children in a pediatric rheumatology clinical trial, we used a network approach to understanding the impact of therapy and the underlying biology of response/non-response to therapy.
We demonstrate that therapy for JIA is associated with extensive re-ordering of gene expression networks, even in children who respond inadequately to therapy. Furthermore, we observe distinct differences in the evolution of specific network properties when we compare children who have been treated successfully with those who have inadequate treatment response.
Despite the inherent noisiness of whole blood gene expression data, our findings demonstrate how therapeutic response might be mapped and understood in pathologically informative cells in a broad range of human inflammatory diseases.
While they are typically described and studied discretely and in isolation, the multiple components of a cell (genes, proteins, metabolites, RNA molecules and their splice variants, and so on) are highly inter-connected and interactive. One of the most interesting recent discoveries in modern biology, and one that has significant implications for the understanding of human disease, is the fact that the hundreds of thousands of individual cellular components can be described and visualized as interactive networks (for example, [1–4]). Furthermore, these networks share structural characteristics that frequently include ‘scale-free’ hub and node structures [5, 6] and specific, functionally related modules [7–9]. We  and others [11, 12] have proposed that human illnesses emerge as a consequence of perturbation of these networks, whether from genetic variation, direct external stimuli (for example, toxins, infectious agents), or via epigenetic changes that accumulate over generations; these three categories, of course, are not mutually exclusive. There is ample evidence for this viewpoint in model organisms; physiologic perturbation of yeast, for example, results in extensive remodeling of interaction networks in such a way that the vast majority of interactions seen in the resting state are no longer seen after perturbation .
Juvenile idiopathic arthritis (JIA) is a complex trait characterized by known genetic susceptibility  and presumed gene-environment interactions . The hallmark pathology of JIA is the presence of inflamed and hypertrophied synovium in one or more joints, characteristically accompanied by morning stiffness and limited range of motion . The illnesses classified under the nosologic entity ‘JIA’ have several different categories, each of which is considered to be distinct both phenotypically and immunogenetically. Two of the major categories, polyarticular JIA (rheumatoid factor negative and rheumatoid factor positive), resemble adult rheumatoid arthritis . As with adult rheumatoid disease, the causes(s) of polyarticular JIA are unknown and therapy remains largely empiric. However, effective agents are available and prolonged periods of normal function without disease activity are now possible for many children with this disease .
Previous work by our group has demonstrated the presence of complex gene co-expression networks in JIA and other pediatric rheumatic diseases . These networks involve cells of both the innate  and adaptive  immune systems. More recently, Stevens et al.  used genetic association and publicly available gene expression data to elucidate complex network structures in JIA. However, these analyses, including our own, have not attempted to examine the complex, dynamic changes to network properties and structure that likely underlie disease progression or therapeutic response.
The Trial of Early Aggressive Therapy in JIA (TREAT) study represents a once-in-a-generation opportunity to observe therapeutic response in polyarticular JIA in a controlled setting using agents of known efficacy. The TREAT study was an NIH-funded clinical trial  that compared two aggressive therapeutic regimens for treatment of newly diagnosed, polyarticular JIA. One arm of the study used subcutaneous methotrexate (MTX) at 0.5 mg/kg/week as an initial therapy, while the other used a combined regimen of MTX, the TNF inhibitor, etanercept (ET), in addition to brief oral prednisone. As part of the TREAT trial, whole blood was collected for RNA expression studies at specific time points during the course of the first year of therapy. The TREAT study therefore represents an unprecedented opportunity to observe and describe the dynamics of therapeutic response in a chronic inflammatory disease of humans at the molecular level.
The study undertaken here was directed at determining whether mathematical methods used in social network analysis may assist in characterizing the pathologic gene expression networks that may underlie JIA, and to determine whether and how effective therapy perturbs those networks. At the same time, and equally important, we aimed to describe the alterations in a network structure that represent a failed ‘re-wiring’, that is, treatment failure. We report here the results of these analyses from longitudinal samples obtained from the TREAT study subjects.
A summary of patient characteristics
Age range, 2–7 years
Age range, 7–12 years
Age range, 12–17 years
Age, mean ± SD
10.99 ± 3.998
RF positive (n)
ANAs positive (n)
Disease activity was assessed using criteria developed by Wallace et al. . Children were assessed to have active disease (AD) if they had synovitis in at least one joint at the time the sample was taken. For children to be assessed to have inactive disease (ID), they were required to have: zero joints with active arthritis, no fever, rash, serositis, splenomegaly or generalized lymphadenopathy attributable to JIA, no active uveitis, a normal ESR in the laboratory where tested, and a physician’s global assessment of disease activity score of 0 (0 being best possible score).
Healthy control samples
Controls consisted of eight healthy girls and 11 healthy boys between the ages of 7 and 13 years that were recruited from the OU Children’s Physicians General Pediatrics clinic. The protocol for obtaining these specimens was approved by the University of Oklahoma IRB (#13205). Anesthesia for the phlebotomy was provided using topical lidocaine/prilocaine solution. These samples are hereafter referred to as HC.
RNA was purified from whole blood PAXgene specimens using a PAXgene Blood RNA kit (Qiagen, Valencia, CA, USA) as recommended by the manufacturer, including a DNAse (Qiagen) step to remove genomic DNA. Globin transcripts, which reduce labeling efficiency of whole blood cell RNA and decrease signal-to-noise ratios on microarrays  were reduced using GLOBINclear-Human (Ambion, Austin, TX, USA). Final RNA preparations were suspended in RNase-free water, quantified spectrophotometrically, and analyzed for RNA integrity by capillary gel electrophoresis (Agilent 2100 Bioanalyzer; Agilent Technologies, Palo Alto, CA, USA).
Data analysis was performed on microarray data whose preliminary results we have previously reported from the standpoint of biomarker development . cRNA was produced from reverse transcribed cDNA using the Illumina® TotalPrep RNA Amplification Kit (Ambion, Inc., Austin, TX, USA), hybridized to Illumina WG-6 v3 or Illumina HT-12 v4 human whole genome microarrays, and stained according to the manufacturer’s directions. Array hybridizations were undertaken in three separate batches. The first batch consisted of the 19 healthy controls, 26 m0 samples, two m4 samples, and one m12 sample hybridized on Illumina WG-6 v3 arrays. The second batch consisted of the remaining 147 patient samples from the main study hybridized to Illumina HT-12 v4 arrays. The final independent cohort of OK samples was hybridized on Illumina WG-6 v3 slides. cRNA preparation and hybridizations of the second and third batches were carried 12 months subsequent to the analysis of the first batch. Microarray data were validated by quantitative rtPCR on an independent cohort of untreated JIA patients, as previously reported in .
Analysis of differential gene expression
All statistical analyses were carried out in R . To facilitate statistical analyses relative to healthy controls, it was necessary to combine data from different hybridization batches. Due to the difference in the microarrays it was necessary to create combined datasets using only those probes that were present on both array formats. Illumina probe IDs were used to identify 39,426 common probes. Datasets were variance stabilized and normalized using robust spline normalization via the lumi package [28, 29]. Raw and normalized data were submitted to the Gene Expression Omnibus (Series GSE55319). Batch effects were removed using the ComBat algorithm in the sva package . Briefly, ComBat employs a parametric empirical Bayes approach to estimate scaling parameters for mean and variance of expression for each gene to compensate for systematic batch effects. The method is designed to be effective for relatively small studies and robust to outliers and has been shown to be more effective than other commonly used algorithms such as distance-weighted discrimination or surrogate variable analysis . Prior to statistical analysis, non-responding probes were filtered out of the datasets using the detection P value provided by the Illumina quality control metrics to eliminate probes not responding at higher than background levels. Analysis of differential gene expression between patients and controls was performed by fitting a linear model to the expression data using the limma package [32, 33]. False discovery rate (FDR) was estimated using the method described by Benjamini and Hochberg . Statistical significance of gene expression was determined at FDR ≤0.05. Validation of gene array analysis was accomplished from an independent cohort of children with JIA using real-time, quantitative rtPCR for selected genes. These data have been reported elsewhere .
Dynamic gene co-expression network construction
From more than 39,000 measured genes in our JIA microarray gene expression set, we selected 2,000 genes that had the smallest P values (via t-test, where the P value is 1.75 e-47) across the patient and control groups. While this cutoff is arbitrary, it specifically selects those genes whose expression values best distinguish children with disease from healthy children. The process for constructing the gene co-expression network was as follows: (1) calculation of the correlation between each gene pair via Pearson correlation coefficients; (2) after pair-wise correlation was calculated, we defined a threshold to establish the pair-wise gene relationships. Only gene pairs that had correlation values larger than the threshold were considered as having an interaction. We chose the top 10 % of gene pairs having the highest correlation coefficients. This approach allows us to transform the continuous matrix data into discrete network data.
The rule for selecting the interactions was as follows: on the one hand, we wanted the constructed gene co-expression networks to be able to display characteristics of scale-free networks, as demonstrated by the previous work [34, 35]; on the other hand, the constructed networks were required to be connected (that is, demonstrate a path connecting each node pair). Note that this is the general method for constructing gene co-expression networks [36, 37].
where V x i is the set of proteins of C x i , and the overlap threshold α defines whether two modules are matched in a given overlap ratio, which is also used in the definitions of evolutionary events below. So this module similarity measures the optimal matching module for C x i at (i + 1)-th timestamp. If none of the modules in C i+1 has an overlap ratio larger than α, then return ∅ (∅ denotes an empty matching result).
Functional module strength progression analysis
Besides detecting the evolutionary events of functional modules, tracking the temporal progression of these functional modules may also provide significant insights into the disease or mechanisms of therapeutic response. In the field of data mining, community (in our case, module) analysis in dynamic networks has recently attracted attention [38–40]. However, the module information provided by current approaches is limited; these existing methods cannot provide a complete view of how gene modules evolve through the entirety of a specific observation period.
In the biological domain, interactions between genes change gradually in dynamic gene co-expression networks. Thus the strength of gene modules also changes. For example, it has been reported that the expression of key genes change  as the cancer progresses. In such cases, the corresponding gene modules’ strength also changes. Discovering the strengths of gene modules throughout a specific disease progression (or therapeutic response, as in our case) may provide useful clues to the underlying biology. For our specific disease, JIA, if a gene module is strong at diagnosis, such a module may be monitored through treatment to determine whether module strengthening or weakening is associated with disease response or refractoriness.
To precisely estimate each functional module’s strength, we considered the following: (1) when calculating the strength of a specific module corresponding to a particular time point, we should also consider the historical networks, that is, those in the previous time stamp. Biological networks usually evolve gradually and may be influenced by the fact that biological samples (including those used here) may contain mixed populations of cells. At the same time, experimental design or measurement errors also contribute to the ‘noisiness’ of gene expression data generated from hybridization-based gene microarrays . Thus, community strength determined from only a single source, sample, or time point is difficult to assess precisely. In addition, we believe that, in the setting of chronic illness, community strength will change gradually instead of dramatically. Because response to therapy in juvenile arthritis occurs gradually (over weeks or months), we expect a certain level of temporal smoothness between module strengths in successive snapshots; such an assumption is both biologically plausible and allows us to filter out ‘noise’ that is inherent in whole blood microarray data. Therefore, we used both the current and previous biological networks to calculate the temporal community strength. (2) Since it is hard to determine whether a module is strong without comparing it with other modules, we normalized the strengths of all modules. Finally (3), the overall strength of all modules at each timestamp was also estimated.
In our previous work on social networks , we proposed an integrated optimization framework that conducts community (module) strength detection across snapshots by taking all the requirements mentioned above into consideration. To be more specific, we first identify the temporal functional modules at each timestamp via a clustering method, and all functional modules ascertained by this method are then collected into a candidate set. Next, the strength of each detected module corresponding to each specific snapshot is calculated through solving an objective function. Using this approach, we estimated each functional module’s strength over the time course of treatment response in JIA.
Analysis of dynamic network changes
From the whole blood gene expression profiles from children with JIA enrolled in the NIH-funded study, we undertook two separate analyses, one of which was strategy-based and the other of which was time-based.
Unsatisfactory initial response – Patients switched to open label drug (MTX, etanercept, and oral corticosteroids). Patients who were on this arm of the protocol had treatment re-initiated with the same drugs and tapering oral corticosteroids.
Satisfactory response – These patients were maintained on blinded study drug.
Achieved inactive disease status.
In addition to the strategy-based analysis, we also undertook a time-based analysis, comparing treatment phenotypes (active disease vs. inactive disease) at each of the time points at which samples were available. In this case, the first stage still denotes the baseline, that is, newly diagnosed, untreated disease. The second stage describes patients at 4 months, while the third and fourth stages describe patients at 6 and 12 months, respectively. Figure 4b shows the strategy for the time-based analysis.
Temporal characteristic of the dynamic networks
Evolutionary events analysis of functional modules
Based on different stages’ gene networks, we were able to elucidate corresponding temporal functional modules in treated JIA. We next developed a module-status-based framework for characterizing the evolution of these functional gene modules. We characterized the transformation of these modules by defining and identifying specific critical module evolutionary events using our previously published method for detecting such events . The evolutionary pattern of functional modules can be represented as a sequence of key evolutionary events (changes) in consecutive timestamps. We used these evolutionary events to compute and characterize novel behavior-oriented measures, which offer insights into the characterization of dynamic behavior of functional modules.
First, we detected evolutionary events based on the strategy-based analysis. We used the Non-negative Matrix Factorization (NMF)  clustering method to detect functional modules at each stage of treatment response. In order to assess clustering at each timestamp, it is necessary to determine a module number into which the genes are partitioned. We preset this module number at each stage as 50, and using this setting, the gene number of each gene module contains 30 to 180 genes, within the range of previous studies . Thus, at each specific stage, we selected 50 functional modules and tracked the evolutionary events among them.
Result of module strength progression
To gather a better biological understanding of these patterns, we randomly sampled 15 modules (approximately 15 % to 20 % of each pattern) from each pattern, and submitted them to functional analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID) Software (v6.7), a National Institute of Allergy and Infectious Disease-supported analysis tool that allows functional analysis of large genomic datasets . By using random sampling, the analysis results are more likely to be representative and free from bias and clustering errors, as this approach gives each cluster of a population an equal chance of being selected.
As Fig. 11 shows, two functional annotations are depressed early in therapy but strengthen between 6 and 12 months in patients with persistently active disease. For Pattern 2, we also found two functional annotations which show significant differences different across the pattern. These are the modules that show significant strengthening between 0 and 4 months only in those patients with persistently active disease. These functional groups are GO:0006811 (ion transport) and GO:0045165 (cell fate commitment). These processes are important in both leukocyte activation and terminal differentiation to effector cells.
As Fig. 11 shows, these two functional annotations are depressed in the first three stages, but after stage 4 (inactive stage) is reached they start to be active.
For Pattern 2, we also found two functional annotations which show significant differences different across the pattern. These functional groups are GO:0006811 (ion transport) and GO:0045165 (cell fate commitment); the frequencies are shown in Fig. 12b.
For Pattern 3, we found that the gene annotation GO: 0007166 (cell surface receptor linked signal transduction), which shows remarkable different across patterns, where the frequencies are shown in Fig. 12c. As the result shows, this annotation is only active at the first stage, but after that it begins to depress. Thus, it is possible that the drug has an influence on this biological function.
In the time-based analysis, we have detected 50 functional modules from each stage of AD group, and there are 188 modules are inserted into our observed module set after removing the duplicate modules.
For these detected modules, we have also made the gene cluster strength analysis. First of all, we have learned three cluster strength evolutionary patterns from AD group which look similarly to the patterns we found from the strategy-based analysis. The reason why the detected patterns are similar is that the patients used for constructing the strategy-based and the AD group in the time-based analysis are actually highly overlapping.
Annotation frequency analysis for patterns derived from the active disease (AD) group in time-based analysis
GO:0007186 – G-protein coupled receptor signaling pathway
GO:0050890 – cognition
GO:0006811 – ion transport
GO:0045165 – cell fate commitment
GO:0007166 – cell surface receptor linked signal transduction
One of the fundamental findings of modern biology has been the discovery that gene expression is tightly coordinated across the genome . Furthermore, gene expression occurs in such a way as to form complex networks  that display a high degree of cohesiveness and robustness . These networks permit cells to function properly in the setting of multiple perturbations in the surrounding milieu [4, 6] and thus are likely to be essential to survival of both single and multi-cellular organisms. The findings from basic biology have led to the hypothesis that human illnesses emerge because of disturbances in these complex cellular networks [11, 12], and there is clinical and experimental evidence to support this hypothesis [54, 55]. Indeed, even ‘simple’ Mendelian traits appear to demonstrate complex alterations and network ‘rewiring’ that was previously unsuspected . In this paper, we demonstrate that medical intervention itself is associated with complex alterations in gene expression networks, and that different patterns of rewiring are associated with efficacy and degree of treatment response.
Juvenile idiopathic arthritis (JIA), the illness studied here, has long been assumed to be a complex trait characterized by gene-environment interactions , and is one of the most common chronic illnesses in children [57, 58]. The illness is characterized by inflammation and synovial hypertrophy in affected joints, and, prior to the availability of effective therapies, frequently resulted in permanent functional impairment or disability. Therapy with methotrexate and biological inhibitors of tumor necrosis factor (TNF) is now common practice and provides most children with prolonged periods without disease symptoms [59, 60]. Long categorized as an autoimmune disease, it is now known that this illness probably emerges from complex interactions between the adaptive and innate immune systems and includes specific aberrations in neutrophil function [19, 61].
In this study, we used mathematical approaches previously used to analyze social networks  to probe the biological basis of treatment response/non-response in JIA. In this paper, we identified disease-associated networks, as we have previously described . Treatment was associated with rapid re-ordering of these networks, even in patients whose therapeutic response was inadequate. Regardless of the stringency of the selection process used map network evolution events (as described in Fig. 1), very few networks persisted unchanged after the first 4 months of therapy. This finding is consistent with the clinical data from the TREAT study subjects, which demonstrated that the first 4 months of treatment are crucial in determining therapeutic response (57), and is also consistent with additional analyses we have undertaken with the TREAT study gene expression data (unpublished data). Furthermore, non-responders demonstrated merge, split, and continue events more than responders. Finally, when we examined network cohesiveness through the lens of specific network properties, we found distinct differences between children with favorable and unfavorable treatment responses (Fig. 11). Once again, many of these differences could be observed through network strength evolution over the first 4 months of therapy (Fig. 11, Pattern 2).
While there have been many efforts to define and categorize the composition of gene co-expression networks in human disease, including JIA , our previous work with social networks has shown that such networks also have specific properties (for example, module strength) that may change over time even when the specific components of the network remain little altered. By examining both network composition and network properties, we demonstrate the plausibility of gaining additional insights into therapeutic response that would not be available by limiting the examination to individual components of any given network. Furthermore, the networks identified via these approaches also reflected biological plausibility, as shown in functional analysis of randomly-selected networks (Fig. 12). For example, GO:0007166 (cell surface receptor linked signal transduction) ontologies link critical leukocyte activation processes into defined networks whose properties changed over the course of therapy in this study.
There are several limits to these data that must be acknowledged. The first in the unique nature of the TREAT study subjects, which also means the absence of a comparable patient group to independently corroborate our specific findings. The TREAT study was a once-in-a-generation clinical trial, with a design quite different from any treatment approach that might be used in typical clinical practice. In addition, the TREAT study subjects differed a bit from what might be seen in a routine cross-section of children with JIA, with a skewing of the TREAT subjects toward children with more severe disease. It would be impossible, even within well-developed pediatric research networks like the Children’s Arthritis and Rheumatology Research Alliance, to find comparable patients on whom to validate these findings. We should point out, however, that we have corroborated findings from the TREAT whole blood gene expression data on an independent cohort of children with untreated JIA, as reported in . In the absence of a validation study for the TREAT trial itself, this this the highest level of independent corroboration available. Given that the analysis performed here relied on the statistical methods and approaches used by Jiang and Sawle  in the earlier paper and was corroborated independently in at least the untreated patients, there is reason to have confidence that these analyses reflect actual biologic events. This confidence is increased insofar as the data reported here are corroborated by recently published clinical findings from the TREAT study .
The other major limitation of these data is the inherent noisiness of whole blood gene expression data. Peripheral blood is composed of multiple different cell types and subtypes. Although Roadmap Epigenomics has shown that there are overlaps in the transcriptomes of peripheral blood cells, each of the specific cells and cell subtypes has characteristic transcripts that define the functional differences between those cells. Furthermore, many potentially important cells may circulate in low abundance, and network rewiring associated with transcriptional changes in such low abundance cells would be lost in the background ‘noise’ that would emerge from cells of higher abundance. We have found, for example, that about 56 % of the differentially expressed transcripts in the TREAT study subjects are expressed by neutrophils, the most abundant cell in the peripheral blood (unpublished observation). While we note that there were no significant changes in the composition of peripheral blood cells as measured in routine clinical analyses in the TREAT study subjects over time, we have avoided over-emphasis on specific gene networks or modules and roles they might play in determining therapeutic response, other than the random selection of specific modules for functional analysis as shown in Figs. 12. We have demonstrate that many of the interesting patterns (for example, module cohesion, Fig. 11) are associated with plausible physiologic processes as assessed by gene ontology analysis (for example, GO:0007186, G-protein signaling pathways).
Treatment response in JIA can be analyzed through the lens of evolving gene expression networks. We demonstrate that treatment is associated with significant re-ordering of gene expression networks and with multiple different patterns of network/module evolution. We believe that these preliminary studies provide a framework for similar approaches and analyses which, when applied to data from purified, pathologically-relevant cells, will provide unprecedented insight into the biology of therapeutic response in this common childhood disease.
This study was supported by NIH grants R01 AI-084200, R01 AR-060604 (JNJ) and R01-AR 049762 (CAW). The authors would like to thank Dr. Laiping Wong for her thoughtful review and comments on this manuscript.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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