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The meaning of clinical remission in polyarticular juvenile idiopathic arthritisGene expression profiling in peripheral blood mononuclear cells identifies distinct disease states.

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Vol. 60, No. 3, March 2009, pp 892–900
DOI 10.1002/art.24298
© 2009, American College of Rheumatology
The Meaning of Clinical Remission in
Polyarticular Juvenile Idiopathic Arthritis
Gene Expression Profiling in Peripheral Blood Mononuclear Cells
Identifies Distinct Disease States
Nicholas Knowlton,1 Kaiyu Jiang,2 Mark Barton Frank,1 Amita Aggarwal,3 Carol Wallace,4
Ryan McKee,2 Brad Chaser,2 Catherine Tung,2 Laura Smith,2 Yanmin Chen,2 Jeanette Osban,1
Kathleen O’Neil,2 Michael Centola,1 Julie L. McGhee,5 and James N. Jarvis6
Objective. The development of biomarkers to predict response to therapy in polyarticular juvenile idiopathic arthritis (JIA) is an important issue in pediatric
rheumatology. A critical step in this process is determining whether there is biologic meaning to clinically
derived terms such as “active disease” and “remission.”
The aim of this study was to use a systems biology
approach to address this question.
Methods. We performed gene transcriptional profiling on children who fulfilled the criteria for specific
disease states as defined by the consensus criteria
developed by Wallace and colleagues. The study group
comprised children with active disease (n ⴝ 14), children with clinical remission on medication (CRM; n ⴝ
9), children with clinical remission off medication (CR;
n ⴝ 6), and healthy control children (n ⴝ 13). Transcriptional profiles in peripheral blood mononuclear
cells (PBMCs) 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 active disease and those with disease in CRM. Thus, underlying
immune/inflammatory abnormalities persist despite a
response to therapy. The PBMC transcriptional profiles
of children whose disease was in remission did not
return to normal but revealed networks of proinflammatory and antiinflammatory genes, suggesting that
remission is a state of homeostasis, not a return to a
normal state.
Conclusion. Gene transcriptional profiling of
PBMCs revealed that clinically derived criteria for JIA
disease states reflect underlying biology. We also demonstrated that neither CRM nor CR status results in
resolution of the underlying inflammatory process, but
that these conditions are more likely to be states of
balanced homeostasis between proinflammatory and
antiinflammatory mechanisms.
Supported by the NIH (grants RR-03145, RR-020143, RR16478, RR-15577, AI-062629, AR-061-015, AR-081-006, and HR-07139), and by the Oklahoma Center for the Advancement of Science
and Technology. Dr. Aggarwal’s work was supported by an Overseas
Associateship grant from the Indian government, Department of
Biotechnology. Mr. McKee and Dr. Chaser’s work was supported by
Summer Medical Student Preceptorships from the American College
of Rheumatology; Dr. Chaser also received a summer research stipend
from the University of Oklahoma Health Sciences Center, Native
American Center of Excellence. Dr. Jarvis’ work was supported by an
Innovative Research grant from the Arthritis Foundation.
Nicholas Knowlton, MS, Mark Barton Frank, PhD, Jeanette
Osban, BS, Michael Centola, PhD: Oklahoma Medical Research
Foundation, Oklahoma City; 2Kaiyu Jiang, PhD, Ryan McKee, BS,
Brad Chaser, MD, Catherine Tung, BS, Laura Smith, BSRT, Yanmin
Chen, BS, Kathleen O’Neil, MD: University of Oklahoma, Oklahoma
City; 3Amita Aggarwal, MD, DM: Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India; 4Carol Wallace, MD:
Children’s Hospital and Regional Medical Center, Seattle, Washington; 5Julie L. McGhee, MD: St. Louis Children’s Hospital, St. Louis,
Missouri; 6James N. Jarvis, MD: Children’s Hospital of Oklahoma,
University of Oklahoma, and Oklahoma University Health Services
Center, Oklahoma City.
Address correspondence and reprint requests to James N.
Jarvis, MD, Department of Pediatrics/Rheumatology, Oklahoma University Health Services Center, Basic Sciences Education Building,
Room 235A, Oklahoma City, OK 73104. E-mail: james-jarvis@
Submitted for publication June 19, 2008; accepted in revised
form November 7, 2008.
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. Because the etiology of JIA is unknown,
therapy remains entirely empirical, is sometimes only
marginally effective, and frequently is associated with
unwanted side effects.
The empirical nature of therapy for JIA is one
of the most vexing problems in the field of pediatric
rheumatology. A critical question, which is often asked
by parents as well as physicians, is when and whether
children who are doing well on medication can have
treatment with those medications reduced or discontinued. Answering this question relies on 2 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 of these suppositions is necessarily valid.
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 during treatment with 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 in whom treatment with the
medication can safely be discontinued.
Only recently have investigators arrived at a
consensus regarding the meaning of terms such as
“active disease,” “inactive disease,” and “clinical remission” (3). Although the definitions of these terms have
been validated clinically, it is currently unknown whether
they actually represent distinct biologic 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 states
represent underlying immunobiology in children with
polyarticular, IgM–rheumatoid factor (RF)–negative JIA.
Patient population and definition of disease states. We
studied 14 children with active polyarticular RF-negative JIA,
as defined by the International League of Associations for
Rheumatology criteria (4). Because the long-term intent of this
project is to identify children who can safely stop receiving
medication, all patients studied here, with the exception of
those studied while their disease was in clinical remission, were
receiving medication at the time of study. All patients (except
those whose disease was in clinical remission) were receiving
oral or subcutaneous methotrexate; in addition, 5 children
were receiving subcutaneous etanercept. We studied 9 children
who fit the criteria for clinical remission while receiving
medication. Because this was a cross-sectional study, children
were studied only once as they achieved different disease
states. Finally, we studied 6 children whose disease was in
remission while they were not receiving medication.
Patients ranged in age from 3 years to 18 years and had
had polyarticular 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; 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), as follows:
active disease (AD), which defines children with synovitis
and/or fever, rash, lymphadenopathy, splenomegaly, uveitis, an
elevated erythrocyte sedimentation rate (ESR) or C-reactive
protein (CRP) level, or a physician’s global assessment score
indicating active disease; inactive disease (ID), which defines
children, including those who are and those who are not
receiving medication, with no evidence of synovitis, the absence of fever, rash, lymphadenopathy, and splenomegaly,
no active uveitis, a normal ESR and CRP level, and a
physician’s global assessment score indicating no active disease; clinical remission on medication (CRM), which defines
children who are receiving medication and have inactive
disease and in whom that state has been maintained for 6
continuous months; and clinical remission (CR), which defines
children who are not receiving medication and have inactive
disease and in whom that state has been maintained for 12
continuous months.
Healthy control subjects. The control group comprised
13 healthy children, ages 3–15 years, who were undergoing
elective surgery for noninflammatory conditions (e.g., minor
orthopedic procedures) or were being seen for routine health
maintenance in the Oklahoma University Children’s Physicians general pediatrics clinic. Healthy children were excluded
from the control group if they had experienced fever (temperature ⱖ38°C) in the 48 hours prior to phlebotomy. Topical
anesthesia with 2.5% lidocaine/2.5% prilocaine was applied to
the phlebotomy site in all children for at least 30 minutes
before the procedure. The participation of all human subjects
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 citrated CPT tubes (catalog no. 362760;
Becton Dickinson, Franklin Lakes, NJ). Peripheral blood
mononuclear cells (PBMCs) were separated from granulocytes
and red blood cells by density-gradient centrifugation and then
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 the manufacturer’s directions and were 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, Palo Alto, CA) to determine the ratio of
28S:18S ribosomal RNA in each sample. A ratio greater than
1.0 was used to define samples of sufficient quality, and only
samples with a ratio above this limit were used for microarray
studies. Complementary DNA (cDNA) synthesis, hybridization, and staining were performed as specified by Affymetrix
(Santa Clara, CA) using Affymetrix Human Genome U133
Plus 2.0 Arrays, an Affymetrix automated GeneChip Fluidics
Station 450, and an Affymetrix Scanner 3000 7G.
Statistical analysis. All preprocessing of Affymetrix
array data was performed in the R/Bioconductor package,
Affy. The raw Affymetrix Perfect Match probes were normalized by the robust multichip analysis method combined with
median polish (6). The marginal data distributions were adjusted through quantile normalization. The resulting normalized values were imported into JMP Genomics software version 3.2 (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 2 classes by using Student’s 2-sample t-test (7). We used Student’s t-test to provide
a false discovery rate (FDR) of 5% (8). The FDR is the
proportion of the list of genes claimed to be differentially
expressed that are false-positive identifications.
The data were exported to Excel (Microsoft, Redmond, WA), where averages of the classes were used to
calculate expression ratios. Genes that were simultaneously
differentially expressed (⬍5% FDR), had a ratio 2-fold or
larger, and for which the minimum normalized average intensity was ⬎64 units in at least 1 group were retained for further
analysis. Unsupervised hierarchical clustering was performed
in Spotfire (Tibco, Sommerville, MA), using Ward’s minimum
variance method (9). Differences between cluster groups were
tested using a chi-square test. P values less than 0.05 were
considered significant.
Predictive modeling. To predict group membership
(i.e., disease state), a so-called “one-versus-many” approach
was taken (10). Using this approach, the data first were broken
into 2 groups for every predictive outcome. For example,
subject 1 was either a control or not a control. This process was
repeated for every variable (e.g., subject 1 either had active
disease or did not have active disease). After all variables were
dichotomized, each binary variable created was modeled using
a logistic regression of the 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 2
restrictions. First, all terms in the model were statistically
significant at ␣ ⫽ 0.05. Second, due to the small sample size, a
maximum of 5 terms were 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 might receive 4 scores: 5.2 for
control, 2.3 for CRM, 3.9 for CR, and 2 for AD. Because the
score of 5.2 is the highest, this patient would be classified as a
control. In an attempt to avoid overfitting, 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 software
(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,” which was derived from known
interactions between the products of these genes.
Real-time reverse transcriptionⴚpolymerase chain reaction (RT-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 bp and 150 bp, using
Primer Express 2.0 software (Applied Biosystems, Foster City,
CA). First strand cDNA was generated from 1.8 ␮g of total
RNA per sample, using OmniScript reverse transcriptase
according to manufacturer’s directions (Qiagen). Complementary DNA was diluted in water at a ratio of 1:20. PCRs were
run with 4 ␮l of cDNA template in 20-␮l reactions in duplicate
on an ABI 7000 Sequence Detection System, using ABI SYBR
Green PCR Master Mix (Applied Biosystems) and genespecific primers at a concentration of 0.2 ␮M each. The
temperature profile consisted of an initial step at 95°C for 10
minutes, followed by 40 cycles of 95°C for 15 seconds, 60°C for
1 minute, and then a final melting curve analysis with a ramp
from 60°C to 95°C over 20 minutes. Gene-specific amplification was confirmed by a single peak, using ABI Dissociation
Curves software (Applied Biosystems). Average Ct values for
GAPDH (run in parallel reactions to the genes of interest)
were used to normalize average Ct values for the gene of
interest. These values were used to calculate averages for each
group (normal or patient subsets), and the relative ⌬Ct was
used to calculate fold-change values between the groups.
Corroboration of array results by PCR. Two
genes were chosen from each of the comparisons (i.e.,
AD versus CRM, CRM versus CR, CR versus healthy
controls) for corroboration of the array data. (The
results are summarized in a table that can be viewed
online at
asp.) In all cases, the directional change (JIA versus
controls) identified by the arrays were corroborated
by real-time PCR analysis. These data are a subset of a
larger group of RT-PCR corroborations (28 genes) for
these same patients, comparing other disease states
(e.g., AD versus CR). For all genes tested, those that
were differentially overexpressed or underexpressed on
Table 2.
Cross-validation of disease states*
Poly CR
Poly CRM
* Poly ⫽ polyarticular; CR ⫽ clinical remission off medication;
CRM ⫽ clinical remission on medication; AD ⫽ active disease.
Figure 1. Hierarchical cluster analysis of differentially expressed
genes in peripheral blood mononuclear cells. C ⫽ control samples;
CRM ⫽ clinical remission on medication; CR ⫽ clinical remission off
medication; A ⫽ active disease.
microarrays were similarly overexpressed or underexpressed by quantitative PCR analysis.
Hierarchical cluster and 1 versus many analyses.
Hierarchical cluster analysis between groups, using the
differentially expressed genes in PBMCs from microarray data, demonstrated that each of the different disease
states, as defined by the consensus conference (5), could
be distinguished from each other. As shown in Figure 1,
gene expression profiles largely distinguished control
from patient samples, with control samples clustered
toward the left and samples from children with active
disease clustered toward the right side of the largest
cluster, which contained 37 of the samples (P ⫽ 6.3 ⫻
10⫺4, by Fisher’s exact test). An additional 7 samples
(far right cluster in Figure 1) contained 4 CR, 1 CRM,
and 2 additional AD samples. Most CRM samples
clustered within the control or AD subclusters.
Predictive modeling revealed a unique set of 10
genes across all 4 models, the expression levels of which
accurately predicted the disease state (Table 1). The
Table 1.
concordance analysis between the observed clinical state
and the predicted clinical state by microarray data
revealed that a correct diagnosis could be made in 42 of
52 individuals (80%) (Table 2).
Network modeling. When children with active
disease were compared with children who had achieved
clinical remission while receiving medication, we found
23 genes that were differentially expressed between the
2 groups, 22 of which were overexpressed in children
with active disease. (A table annotating these genes and
relative expression levels can be viewed online at http:// As described above, all of these patients were receiving medication. In silico modeling of the array data was
informative. Analysis of these differentially expressed
genes (Figure 2) revealed a single network of
interferon-␥ (IFN␥)–, interleukin-6 (IL-6)–, and IL-4–
regulated genes that we (11) and other investigators (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 IFN␥-regulated networks. The single gene
that showed decreased expression in children with active
disease was the aldehyde dehydrogenase A1 family
member (ALDH1A1), which is known to regulate sex
Genes discriminating the disease state
probe ID
Gene description
Discs, large homolog 1 (Drosophila)
Trophoblast-derived noncoding RNA
CCAAT/enhancer binding protein ␦
Tissue factor pathway inhibitor (lipoprotein-associated
coagulation inhibitor)
Tumor-associated calcium signal transducer 2
Killer cell lectin-like receptor subfamily C, member 3
Histone cluster 1, H2am
Adrenergic, ␤, receptor kinase 2
Protein phosphatase 1, regulatory (inhibitory) subunit 3D
Guanosine monophosphate reductase
Gene symbol
Figure 2. Single network derived from Ingenuity analysis of differentially expressed genes,
comparing children with polyarticular juvenile idiopathic arthritis (JIA) with active disease and
children with JIA who had achieved clinical remission while receiving medication. This network
consists largely of genes that show increased expression in children with active disease (red).
Figure 3. Overlapping gene networks derived from Ingenuity analysis of the transcriptional profile of peripheral blood mononuclear cells (PBMCs),
comparing children with juvenile idiopathic arthritis (JIA) who had achieved clinical remission while receiving medication (CRM) and those whose
disease was in clinical remission while they were not receiving medication. Genes overexpressed in children with CRM status are shown in red. Note
the clusters of genes regulated by the leukocyte activators Jun and NF-␬B (left) and interferon-␥ and tumor necrosis factor ␣ (right). This suggests
that, even during CRM, there is still an active proinflammatory response in PBMCs from patients with JIA.
Figure 4. Comparison of patients with juvenile idiopathic arthritis (JIA) whose disease was in clinical remission and who were not receiving
medication (CR) and healthy control subjects. A, 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 and healthy control children. Genes associated with leukocyte activation (e.g., Jun and other
MAPKs) are networked with markers of leukocyte activation (e.g., matrix metalloproteinases). These genes, in turn, are counterregulated by genes
known to modulate inflammation (e.g., transforming growth factor ␤). Genes overexpressed in patients with CR are highlighted in red, and genes
underexpressed in patients with CR are shown in green. B and C, Persistence of tumor necrosis factor ␣–regulated hubs (B) and interleukin-4–
regulated hubs (C) when children whose disease was in remission were compared with healthy control subjects. D, A fourth network, consisting of
clusters of genes regulated by ␤-estradiol and dihydrotestosterone.
steroid hormones and to be IL-1 responsive (13). It is
noteworthy that insulin also appears as a central mediator in this network, which is an interesting finding given
the emerging data demonstrating critical “cross-talk”
between tumor necrosis factor ␣ (TNF␣)– and insulinregulated pathways (14).
When children who had achieved CRM status
were compared with children with CR status, we ob-
served the persistence of linked proinflammatory networks in children with CRM, as shown in Figures 3A and
B. In all, 39 genes distinguished these 2 patient groups.
(A table annotating these genes and relative expression
levels can be viewed online at
section_rheumatology.asp.) Although it is impossible to
determine how or whether these expression patterns are
altered by medication (patients with CRM are still
receiving medication, while patients with CR are not), it
is worth noting that these networks consist of genes
regulated by known leukocyte proinflammatory regulators (e.g., Jun, NF-␬B) (Figure 3) as well as IFN␥- and
TNF␣-regulated genes, as we have previously reported
(10). This finding may explain the tendency to misclassify CRM as AD, as shown in Table 2.
The gene expression profile of PBMCs did not
normalize in children whose disease was in remission, as
indicated in the hierarchical cluster analysis (Figure 1).
Genes in PBMCs that were differentially expressed
between children with CR and control subjects included
74 up-regulated and 8 down-regulated genes. (A table
annotating these genes and relative expression levels
can be viewed online at
rheumatology.asp.) Ingenuity analysis revealed 4 interconnected gene networks. The structure of the largest of
these networks (Figure 4A) demonstrated genes that are
known mediators of leukocyte activation (e.g., Jun and
other MAPKs) (15,16) as well as markers of inflammation (e.g., matrix metalloproteinases) (17). These genes
are networked with transforming growth factor ␤1
(TGF␤1), which, depending on its physiologic context, is
generally regarded as a negative regulator of inflammation and an important mediator of immune tolerance
These findings reveal that remission is not a
return to normalcy but, rather, a physiologic state in
which proinflammatory elements are countered or kept
in check. This interpretation is supported by the networks revealed in Figures 4B and C, which show the
persistence of gene networks regulated by TNF␣ (Figure
4B) and IL-4 (Figure 4C), both of which are known to be
involved in the immunopathology of JIA (18,19). The
fourth network (Figure 4D) consisted of genes regulated
by both ␤-estradiol and dihydrotestosterone, which is an
interesting finding in light of the known role of estrogens
in regulating inflammation (20) and the female preponderance among patients with polyarticular JIA. This
same network consists of genes regulated by CCAAT/
enhancer binding protein ␣, a member of a family of
proteins previously implicated in regulating IL-1␤ (21)
and other aspects of inflammation, including regulation
of cytokine expression within the rheumatoid synovium
(22,23). This transcription factor is also known to play a
role in estrogen-mediated regulation of cytokine production (24,25).
The development of clinically useful prognostic
genomic biomarkers in JIA requires the ability to dis-
cern specific disease states (e.g., active disease, remission) as a first step. If clinical remission while receiving
medication is indistinguishable from active disease, for
example, 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 biologic level than at the clinical level (Figure 1).
This suggests that underlying cellular abnormalities persist in PBMCs from patients with JIA, 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 observed in children
with inactive disease who had not received medication
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
antiinflammatory (e.g., TGF␤-driven) mechanisms balance the dysregulatory elements that lead to chronic
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 crossvalidation does provide validation that the model is not
overfit. For example, the major misclassification occurred in the CRM group, in which 3 patients classified
as CRM were predicted to have active disease. It is
possible that our model is correct and that disease in
these children is not in remission on a molecular level.
This hypothesis is supported by Ingenuity modeling,
which suggests that networks of proinflammatory genes
are still active, even in these children who have achieved
a state of remission. Under any circumstances, we will
need to follow up these children over time to determine
whether our cross-sectional model has prognostic capabilities.
Taken together, these findings explain 2 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 in children who seem to be doing well: underlying
abnormalities at the gene expression level are still
present, even if such abnormalities are not reflected in
standard clinical measures such as the ESR, the serum
CRP level, the hemoglobin concentration, or the white
blood cell count. These findings also explain why disease
recurrences are common: remission is still a biologically
abnormal state. Although it is impossible to speculate
on what extrinsic factors might disrupt the complex
homeostatic mechanisms that are reflected during disease remission, it is reasonable to hope that a longitudinal analysis of a large cohort of children will be highly
It is important to point out that many of the
pathologic networks visualized in these studies demonstrate the structure of scale-free systems (26), as we have
previously seen in neutrophils from patients with JIA
(27). That is, the network structures demonstrate areas
of high 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, which
is another feature of cellular–physiologic systems (28).
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 pathologic 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 biologic
processes (e.g., the distinction between self and nonself). 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
(29) (think of what happens to air traffic when inclement
weather disrupts flights into and out of Atlanta or
Chicago). This means that successful new treatments for
JIA will have to focus on 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 clinical remission (both
during treatment with medication and without medical
treatment) is a biologically distinct state. Furthermore,
we have demonstrated that clinical remission in the
absence of treatment with medication is not a normal
state but represents a homeostatic condition in which
proinflammatory and antiinflammatory mechanisms appear to be in balance. Answering the critical questions
about biomarkers will require the study of large groups
of children prospectively, a task that we have already
Dr. Jarvis had full access to all of the data in the study and
takes responsibility for the integrity of the data and the accuracy of the
data analysis.
Study design. Knowlton, Frank, Jarvis.
Acquisition of data. Jiang, Frank, Aggarwal, Wallace, McKee, Chaser,
Tung, Smith, Chen, Osban, O’Neil, Centola, McGhee, Jarvis.
Analysis and interpretation of data. Knowlton, Jiang, Frank, O’Neil,
Centola, Jarvis.
Manuscript preparation. Knowlton, Jiang, Frank, Aggarwal, Wallace,
O’Neil, Jarvis.
Statistical analysis. Knowlton.
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expressions, periphery, distinct, polyarticular, profiling, state, disease, arthritisgene, blood, cells, remission, identifier, idiopathic, clinical, meaning, juvenile, mononuclear
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