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Gene expression signatures in polyarticular juvenile idiopathic arthritis demonstrate disease heterogeneity and offer a molecular classification of disease subsets.

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Vol. 60, No. 7, July 2009, pp 2113–2123
DOI 10.1002/art.24534
© 2009, American College of Rheumatology
Gene Expression Signatures in
Polyarticular Juvenile Idiopathic Arthritis
Demonstrate Disease Heterogeneity and Offer a
Molecular Classification of Disease Subsets
Thomas A. Griffin,1 Michael G. Barnes,1 Norman T. Ilowite,2 Judyann C. Olson,3
David D. Sherry,4 Beth S. Gottlieb,5 Bruce J. Aronow,1 Paul Pavlidis,6
Claas H. Hinze,1 Sherry Thornton,1 Susan D. Thompson,1
Alexei A. Grom,1 Robert A. Colbert,1 and David N. Glass1
Objective. To determine whether peripheral blood
mononuclear cells (PBMCs) from children with recentonset polyarticular juvenile idiopathic arthritis (JIA)
exhibit biologically or clinically informative gene expression signatures.
Methods. Peripheral blood samples were obtained
from 59 healthy children and 61 children with poly-
articular JIA prior to treatment with second-line medications, such as methotrexate or biologic agents. RNA
was extracted from isolated mononuclear cells, fluorescence labeled, and hybridized to commercial gene expression microarrays (Affymetrix HG-U133 Plus 2.0).
Data were analyzed using analysis of variance at a 5%
false discovery rate threshold after robust multichip
analysis preprocessing and distance-weighted discrimination normalization.
Results. Initial analysis revealed 873 probe sets
for genes that were differentially expressed between
polyarticular JIA patients and healthy controls. Hierarchical clustering of these probe sets distinguished 3
subgroups within the polyarticular JIA group. Prototypical patients within each subgroup were identified
and used to define subgroup-specific gene expression
signatures. One of these signatures was associated with
monocyte markers, another with transforming growth
factor ␤–inducible genes, and a third with immediate
early genes. Correlation of gene expression signatures
with clinical and biologic features of JIA subgroups
suggested relevance to aspects of disease activity and
supported the division of polyarticular JIA into distinct
Conclusion. Gene expression signatures in PBMCs
from patients with recent-onset polyarticular JIA reflect
discrete disease processes and offer a molecular classification of disease.
Supported by the NIH/National Institute of Arthritis and
Musculoskeletal and Skin Diseases (grants P01-AR-048929, P30-AR47363, and P60-AR-47784), the Cincinnati Children’s Hospital Research Foundation, and the Ohio Valley Chapter of the Arthritis
Foundation. Dr. Ilowite’s work was supported by Amgen. Dr. Colbert’s
work was supported by a Cincinnati Children’s Hospital Medical
Center Translational grant.
Thomas A. Griffin, MD, PhD, Michael G. Barnes, PhD,
Bruce J. Aronow, PhD, Claas H. Hinze, MD, Sherry Thornton, PhD,
Susan D. Thompson, PhD, Alexei A. Grom, MD, Robert A. Colbert,
MD, PhD (current address: National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH, Bethesda, Maryland), David N.
Glass, MD: Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio; 2Norman
T. Ilowite, MD: Albert Einstein College of Medicine, Bronx, New
York; 3Judyann C. Olson, MD: Medical College of Wisconsin and
Children’s Research Institute, Milwaukee, Wisconsin; 4David D.
Sherry, MD: Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania; 5Beth S. Gottlieb, MD: Schneider Children’s Hospital, New
Hyde Park, New York; 6Paul Pavlidis, PhD: University of British
Columbia, Vancouver, British Columbia, Canada.
Dr. Ilowite has received consulting fees, speaking fees, and/or
honoraria from Abbott, Novartis, and Bristol-Myers Squibb (less than
$10,000 each). Dr. Colbert has received honoraria from the Spondyloarthritis Research and Treatment Network (SPARTAN), the University of Rochester Medical Center, and Grand Rounds; Carolinas
(2007) (less than $10,000 each).
Address correspondence and reprint requests to Thomas A.
Griffin, MD, PhD, William S. Rowe Division of Pediatric Rheumatology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet
Avenue, ML 4010, Cincinnati, OH 45229. E-mail:
Submitted for publication September 30, 2008; accepted in
revised form February 20, 2009.
Polyarticular juvenile idiopathic arthritis (JIA) is
chronic arthritis affecting more than 4 joints for more
than 6 weeks, with onset before the sixteenth birthday in
a child without other known causes of arthritis (1–3).
Polyarticular JIA is divided into subtypes according to
the presence or absence of rheumatoid factor (RF), with
the RF⫹ subtype having positive results on tests for
serum IgM-RF on 2 occasions at least 3 months apart
within the first 6 months of disease. Ravelli and Martini
(3) have recently proposed that RF⫺ polyarticular JIA
be divided into 3 subsets: one similar to adult rheumatoid arthritis (RA), another with “dry synovitis,” and a
third similar to antinuclear antibody (ANA)–positive
early-onset oligoarticular JIA (3). Given this heterogeneity, it is not surprising that children with polyarticular
JIA have a wide variety of disease courses and outcomes, ranging from self-limited arthritis with no longterm disabilities to relentless and destructive arthritis
with severe disabilities (4). Unfortunately, our present
ability to predict the disease course and outcome is
limited, and treatment is typically tailored to the patient’s current disease activity, the assessment of which is
also imperfect.
Global gene expression profiling is a molecular
technique that measures in parallel the genome-wide
expression of thousands of genes in a sample of cells.
This technology holds promise for dramatically advancing our knowledge of many diseases, including JIA. This
approach has already provided important information
regarding the classification and pathogenesis of several
JIA subtypes in studies that generally used small numbers of patients with various degrees of clinical diversity
In the present study, global gene expression profiling of peripheral blood mononuclear cells (PBMCs) was
used to characterize a relatively large population of
children with recent-onset polyarticular JIA (both RF⫺
and RF⫹ patients) who had not been treated with
methotrexate, biologic agents, or other disease-modifying
antirheumatic drugs (DMARDs). The goals of applying
this technology to JIA are to advance the understanding
of disease pathogenesis, improve the assessment of
disease activity, predict the response to medications, and
foresee the long-term outcomes. The present study takes
a step toward these goals by defining gene expression
signatures that appear to be associated with distinct
disease processes in subgroups of children with polyarticular JIA.
Patients and collection of clinical data. Sixty-one
children with polyarticular JIA classified according to the
criteria of the International League of Associations for Rheu-
matology (2) were recruited at 5 clinical sites: 24 from Cincinnati Children’s Hospital Medical Center (CCHMC), 16 from
Schneider Children’s Hospital, 9 from Children’s Hospital of
Philadelphia, 6 from Toledo Children’s Hospital (Toledo,
OH), and 6 from Children’s Hospital of Wisconsin. Of these 61
patients, 46 were taking scheduled nonsteroidal antiinflammatory drugs, 3 were taking prednisone, and none had ever
been treated with methotrexate, other DMARDs, or biologic
agents. Informed consent was obtained, and clinical data were
The following disease activity measures were determined: erythrocyte sedimentation rate (ESR), count of joints
with active disease (tender and limited range of motion, and/or
swollen), Childhood Health Assessment Questionnaire
(C-HAQ), physician’s global assessment of disease activity,
and patient’s/parent’s global assessment of well-being. All JIA
patients were tested for RF, and if the first test showed positive
results, a second test was performed at least 3 months later.
Most JIA patients were also tested for anti–cyclic citrullinated
peptide (anti-CCP) antibodies and ANAs (Table 1). If frozen
serum was available and RF or anti-CCP testing was not performed at the collecting center, these tests were performed at
CCHMC. The specific joints involved with arthritis at the time
of sampling were documented.
Fifty-nine healthy control subjects were recruited at
CCHMC. Twenty-nine controls provided blood samples as
part of a demographically representative population of Cincinnati that was recruited and screened to serve as healthy
controls. Thirty controls provided blood samples and limited
clinical data when blood was drawn at the CCHMC Test
Referral Center for clinical purposes. These 30 subjects were
screened by questionnaire and were deemed to be free of
inflammatory disease.
Sample processing. PBMCs were isolated by Ficoll
gradient centrifugation and then frozen at ⫺80°C in TRIzol
reagent (Invitrogen, Carlsbad, CA) at the collecting centers,
with documentation of both the time of phlebotomy and the
time of freezing. The time to freezing, which was defined as the
interval between these 2 times (expressed in minutes), was
recorded for each sample. Frozen samples were batched and
shipped on dry ice to CCHMC, where they were stored at
⫺80°C until RNA was extracted and purified on RNeasy
columns (Qiagen, Germantown, MD).
RNA quality was assessed using an Agilent 2100
Bioanalyzer (Agilent Technologies, Palo Alto, CA) according
to CCHMC Affymetrix GeneChip Core protocols. NuGEN
Ovation Version 1 (NuGEN Technologies, San Carlos, CA)
1-round amplification was used, with 20 ng of starting RNA, to
produce fluorescence labeled complementary DNA that was
assayed by 54,675 probe sets on Affymetrix Human Genome
U133 Plus 2.0 GeneChips (Affymetrix, Santa Clara, CA) and
scanned with an Agilent G2500A GeneArray Scanner. A
universal standard was prepared from a pooled mixture of
RNA from 35 healthy adult volunteer donors (6) and was
included in each batch of 12 samples to provide technical
replicates for assessing batch-to-batch variations.
Flow cytometry. Aliquots of PBMCs from 143 JIA
patients and healthy controls (45 with polyarthritis, 21 with
oligoarthritis, 25 with enthesitis-related arthritis, 6 with psoriatic arthritis, 12 with systemic arthritis, and 34 healthy controls) were frozen in 10% DMSO at the collecting centers,
Table 1. Characteristics of the polyarticular JIA subgroups*
Age, mean ⫾ SD years
No. (%) female
No. (%) RF positive
No. (%) anti-CCP positive§
No. (%) ANA positive¶
ESR, median (IQR) mm/hour**
No. of joints with active disease, median (IQR)
C-HAQ score, median (IQR)††
Physician’s global assessment of disease activity,
by 10-point Likert scale, median (IQR)
Patient’s/parent’s global assessment of well-being,
by VAS, mean ⫾ SD mm‡‡
No. (%) with involvement of small joints
Group A
(n ⫽ 9)
Group B
(n ⫽ 17)
Group C
(n ⫽ 11)
Other polyarticular JIA
(n ⫽ 24)
11.3 ⫾ 3.6
7 (78)
5 (56)‡
5 (56)
3 (33)
26 (10–30)
9 (9–14)
1.063 (0.375–1.875)
6 (5–7)
9.8 ⫾ 4.4
16 (94)
5 (29)
5 (29)
7 (41)
22 (18–49)
12 (8–21)
0.813 (0.375–1.375)
7 (5–9)
11.3 ⫾ 4.6
8 (73)
2 (20)
4 (36)
15 (5–28)
9 (8–11)
0.500 (0.125–1.000)
4 (4–7)
7.0 ⫾ 4.5†
21 (88)
4 (17)
6 (30)
15 (65)#
17 (11–28)
8 (5–11)
0.375 (0.000–1.250)
5 (4–7)
65 ⫾ 33
62 ⫾ 26
57 ⫾ 26
67 ⫾ 23
8 (89)
16 (94)
10 (91)
21 (88)
* Hierarchical clustering of probe sets distinguished 3 subgroups of polyarticular juvenile idiopathic arthritis (JIA) patients (groups A–C), as well
as patients who did not cluster into any subgroup (other polyarticular JIA). RF ⫽ rheumatoid factor (positive results on 2 occasions measured at
least 3 months apart within the first 6 months of disease); IQR ⫽ interquartile range; VAS ⫽ visual analog scale (0–100 mm [poor to well]).
† P ⫽ 0.018 versus groups A, B, and C combined, by one-way analysis of variance.
‡ P ⫽ 0.008 versus group C, by Fisher’s exact test.
§ Anti–cyclic citrullinated peptide (anti-CCP) was not tested in 1 patient in group C and 4 patients in the other polyarticular JIA group.
¶ Antinuclear antibody (ANA) was not tested in 1 patient in the other polyarticular JIA group.
# P ⫽ 0.026 versus groups A, B, and C combined, by Fisher’s exact test.
** Erythrocyte sedimentation rate (ESR) was not tested in 2 patients in group A, 1 in group C, and 6 in the other polyarticular JIA group.
†† The Childhood Health Assessment Questionnaire (C-HAQ) was not administered to 1 patient each in group A, group B, and the other
polyarticular JIA group. The range of possible scores is 0–3.
‡‡ Patient’s/parent’s global assessment of well-being was not assessed in 1 patient each in groups A and B, and in 2 patients in the other polyarticular
JIA group.
shipped to CCHMC on dry ice, and stored in liquid nitrogen.
These samples represented a subset of patients involved in a
larger study we conducted, in which we compared gene expression profiles in PBMCs from patients with various JIA subtypes with those in healthy controls (10), and included 45 of the
61 patients with polyarticular JIA studied here. Sixteen polyarticular JIA patients were not sampled for flow cytometry due
to phlebotomy volume limits relative to age.
After thawing and washing PBMCs in fluorescenceactivated cell sorter buffer (phosphate buffered saline with
0.2% bovine serum albumin), cells were stained with the following monoclonal antibodies in appropriate combinations
(all from BD Biosciences, San Jose, CA): peridinin chlorophyll
A protein (PerCP)–conjugated CD3, fluorescein isothiocyanate
(FITC)–conjugated CD8a, allophycocyanin (APC)–conjugated
CD4, phycoerythrin (PE)–conjugated CD15, FITC-conjugated
CD16, APC-conjugated CD33, FITC-conjugated CD34, and
PerCP-conjugated CD45. Stained cells were analyzed on a
FACSCalibur flow cytometer using CellQuest software (both
from BD Biosciences). PBMC subpopulations of interest were
captured by standardized polygonal gates. Linear correlation
analysis was performed between the average normalized expression of the 50 probe sets of signatures I, II, and III (see
Results), with the proportions of PBMC subpopulations determined by flow cytometry.
Statistical analysis. Data were imported into GeneSpring GX 7.3.1 software (Agilent Technologies) with robust
multichip analysis (11) preprocessing, referenced to the median of each gene’s adjusted value across all samples, followed
by distance-weighted discrimination normalization to adjust
for batch-to-batch variations (12). Probe sets for differentially expressed genes were identified by analysis of variance
(ANOVA) at a 5% false discovery rate (13). Tukey’s post hoc
testing identified probe sets for genes that were differentially
expressed between JIA patients and healthy controls. The
complete microarray dataset has been deposited in the Gene
Expression Omnibus at the National Center for Biotechnology
Information (NCBI) and is accessible through GEO Series
accession number GSE13849.
General approach to data analysis. In a preceding study of gene expression differences between various
subtypes of JIA and controls, which is published elsewhere in this issue of Arthritis & Rheumatism (10), we
used ANOVA to identify 873 probe sets for genes that
were differentially expressed in patients with RF⫺ polyarticular JIA (n ⫽ 45) as compared with healthy control
subjects (n ⫽ 59). That comparison was the starting
point for the present study, in which we used an iterative
approach, with several rounds of identifying probe sets
for genes that were differentially expressed between
comparison groups, each time refining the analysis based
on hierarchical clustering of differentially expressed
genes, ultimately defining prototypical gene expression
signatures. The first iteration included RF⫹ polyarticular JIA patients, the next involved subgroups of polyarticular JIA, and the final comparisons involved small
cohorts of prototypical patients within each subgroup.
Prototypical gene expression signatures were then applied to all study subjects to assess the association of
each signature with clinical and biologic features within
the entire population.
Identification of polyarticular JIA subgroups
clustered by PBMC gene expression patterns. For the
first comparison, RF⫹ polyarticular JIA patients were
included, since it was observed that many RF⫹ patients
coclustered with a subset of RF⫺ patients rather than
clustering separately, suggesting that RF status did not
clearly distinguish a subset of polyarticular JIA assessed
by PBMC gene expression profiling. Figure 1 shows an
expression heat map of the 873 starting probe sets (10)
in 61 polyarticular JIA patients (both RF⫹ and RF⫺)
and 59 healthy controls. Subjects were ordered by hierarchical clustering, using Pearson’s correlation with average linkage. Three clusters, predominantly composed
of JIA patients, are shown in the boxed areas of Figure
1. The JIA patients within these 3 clusters comprise
groups A, B, and C.
An alternative approach to clustering was tested
in which 1,523 probe sets identified by ANOVA as being
differentially expressed between all polyarticular JIA
patients (RF⫺ and RF⫹; n ⫽ 61) and controls were
used (data not shown). This approach also demonstrated
3 groups (A, B, and C), with few differences in the group
assignments as compared with the original clustering.
Most importantly, all prototypical patients for each
group remained coclustered, and thus, the prototype
signatures that were ultimately derived from all probe
sets were identical using either approach.
The next round of comparisons identified gene
expression differences between each JIA subgroup
(groups A, B, and C) and the healthy controls. Probe
sets for genes that were identified by t-test using a 5%
false discovery rate (multiple testing correction) as being
differentially expressed comprised 2,111 for group A
(1,025 up-regulated and 1,086 down-regulated), 6,713
for group B (2,900 up-regulated and 3,813 downregulated), and 11,485 for group C (5,319 up-regulated
and 6,166 down-regulated). Notably, comparison of
controls with the 24 polyarticular JIA patients that did
not cluster into any subgroup did not identify any probe
sets for differentially expressed genes, even when using a
20% false discovery rate. Comparisons among the groups
indicated some overlap, particularly between groups B
and C (582 probe sets between groups A and C, 756
Figure 1. Expression heat map for 873 probe sets identified by
analysis of variance as being differentially expressed between rheumatoid factor (RF)–negative patients with polyarticular juvenile idiopathic arthritis (JIA; n ⫽ 45) and healthy controls (n ⫽ 59). The relative intensity of red or blue indicates higher or lower expression as
compared with the mean of all samples for that probe set (yellow). A
total of 61 patients with either RF⫹ or RF⫺ polyarticular JIA and 59
healthy controls are represented. Polyarticular JIA patients are indicated by the solid circles on the left. Subjects are ordered by gene
expression clustering using Pearson’s correlation with average linkage.
Boxed areas indicate the 3 clusters containing predominantly JIA
patients, and the JIA patients from these clusters comprise groups A,
B, and C, as indicated on the right. The JIA patients in groups A, B, and
C who served as prototypes for defining gene expression signatures I, II,
and III, respectively, are indicated by the solid circles on the right.
probe sets between groups A and B, and 2,555 probe sets
between groups B and C).
Table 2. Top 25 probe sets of the 50 probe sets that defined gene expression signatures I, II, and III*
Signature I
Signature II
Signature III
Probe set
Gene symbol
Fold 1
Probe set
Gene symbol
Fold 1
Probe set
Gene symbol
Fold 1
* Fold 1 represents the ratio of the geometric mean of the prototype samples for each group (signatures I, II, and III, corresponding to groups A,
B, and C, respectively) to the geometric mean of the 49 signature-free healthy controls (see Results for details).
Characteristics of polyarticular JIA subgroups.
Table 1 shows a comparison of the JIA patients in
groups A, B, and C, as well as the JIA patients with
polyarticular disease who did not cluster into any subgroup (other polyarticular JIA). Notable characteristics
include a greater proportion of RF⫹ patients and a
trend toward a greater proportion of anti-CCP⫹ patients in group A, a younger age and greater proportion
of ANA⫹ patients in the other polyarticular JIA group,
and a trend toward higher ESR, C-HAQ scores, and
physician’s global assessment scores in groups A and B.
Generally, there was proportionate representation of
patients from each clinical center in groups A and C.
Distribution of the 9 patients in group A from 5 centers
was 1, 2, 3, 1, and 2. Distribution of the 11 patients in
group C was 5, 2, 2, 2, and 0. Conversely, group B had a
disproportionate distribution (3, 11, 1, 1, and 1), with 11
of 17 patients coming from 1 center.
Identification of prototypical patients for each
polyarticular JIA subgroup. For the final round of comparisons, prototypical patients were identified within
each subgroup that maximized the gene expression
signature for that subgroup and minimized overlap with
other subgroups. Patients were included if the geometric
mean of the 50 most up-regulated probe sets in that
subgroup was at least 2-fold greater than that in the
controls. Patients were excluded if they did not remain
coclustered using several alternative hierarchical clustering methods (Pearson’s correlation, standard correlation, and distance correlation) or if the geometric mean
of the 50 most up-regulated probe sets for more than 1
subgroup was increased more than 2-fold over that in the
controls. Using this method, we identified 4 prototypical
patients for group A and 5 prototypical patients each
for groups B and C (solid circles on the right side of
Figure 1).
Determination of probe sets that define subgroupspecific gene expression signatures. Probe sets for genes
differentially expressed between prototypical patients
in each subgroup and the healthy controls were identified using t-tests with a 5% false discovery rate. For this
comparison, 10 controls that had a more than 2-fold
increase in the geometric mean of the 50 most upregulated probe sets for any JIA subgroup (A, B, or C)
were excluded, yielding a cohort of 49 signature-free
controls. This comparison produced lists of 4,098 probe
sets for group A, 7,290 for group B, and 16,462 for group
C (Supplementary Tables 1, 2 and 3, available on the
Arthritis & Rheumatism Web site at http://www3.
Gene expression signatures for each subgroup
(signatures I, II, and III for groups A, B, and C,
respectively) were defined as the 50 most up-regulated
probe sets from these lists, excluding redundant probe
sets for the same genes (using the probe set with the
lowest P value) and excluding probe sets that were
up-regulated more than 1.5-fold in any of the other
prototypical groups. Down-regulated probe sets were
not included, since these probe sets had patterns similar
to those of the up-regulated probe sets in each subgroup,
and inclusion of down-regulated probe sets would not
have significantly changed the discriminatory power of
the signatures, although they may provide important
clues to understanding the processes represented by
each signature and are therefore included in Supplementary Tables 1–3. The top 25 probe sets for signatures
I, II, and III are presented in Table 2, and all 50 probe
sets for each signature are presented in Supplementary
Tables 1–3 (available on the Arthritis & Rheumatism
Web site at
Quantification of subgroup-specific gene expression signatures. Signatures I, II, and III were quantified
for all patients by calculating the average of the geometric mean of the fold increase over control values for the
50 probe sets that comprised each signature. A value of
1 equates to the average signature observed in the 49
signature-free controls. The magnitudes of each signature in every JIA patient are graphed in Figure 2, using
the same order of patients as in Figure 1 for comparison.
As expected, patients in group A predominantly expressed signatures I, and patients in group C predominantly expressed signature III. In contrast, patients in
group B not only expressed signature II, but many
patients in group B also expressed significant amounts of
either signature I or III, suggesting overlap between
groups B and A and between groups B and C. Overall,
the numbers of JIA patients that exhibited at least a
1.5-fold increase in a particular signature were 16
(26.2%) for signature I, 17 (27.9%) for signature II, and
20 (32.8%) for signature III. Quantification of the gene
signatures enabled correlation with the clinical characteristics and the abundance of PBMC subsets determined by flow cytometry.
Correlation of signature I expression with rheumatoid factor and monocytes. Group A contained the
highest proportion of RF⫹ patients (5 of 9 patients)
(Table 1), which suggests that signature I expression
correlated with RF positivity. To test this, the average
Figure 2. Comparison of gene expression signatures I, II, and III in
patients with polyarticular juvenile idiopathic arthritis (JIA) and
healthy controls. The magnitude of each signature in each of the 120
study subjects was calculated as the average fold increase in expression
of the 50 probe sets that defined each signature over the mean of those
50 probe sets in the cohort of 49 signature-free controls. Vertical
broken lines indicate the 2-fold increase over the mean in the controls;
baselines indicate the mean in the controls (value of 1). Subjects are
identical to, and in the same order as, those shown in Figure 1.
Polyarticular JIA patients are indicated by the solid circles on the left:
single symbols are rheumatoid factor (RF)⫺; double symbols are
RF⫹. Boxed areas indicate the 3 clusters containing predominantly
JIA patients (groups A, B, and C, as indicated on the right). The JIA
patients in groups A, B, and C who served as prototypes for defining
gene expression signatures I, II, and III, respectively, are indicated by
the solid circles on the right.
Figure 3. Correlation of gene expression signatures with rheumatoid factor (RF) status, monocyte lineage
markers, and time to freezing of peripheral blood mononuclear cells (PBMCs) from patients with juvenile
idiopathic arthritis (JIA). A, Average magnitude of each gene expression signature (I, II, and III) in RF⫹ (n ⫽ 14)
and RF⫺ (n ⫽ 47) polyarticular JIA patients. Values are the mean and SD. Only signature I gene expression was
statistically significantly different in RF⫹ versus RF⫺ patients, by t-test. B, Correlation coefficients for gene
expression signature I (solid bars), signature II (open bars), and signature III (shaded bars) in relation to monocyte
lineage subsets, as determined by flow cytometry. C, Average time between isolation of PBMCs and freezing (time
to freezing) of samples from polyarticular JIA patients in group B (n ⫽ 16), all other polyarticular JIA patients
(n ⫽ 44), and healthy controls (n ⫽ 59). Time to freezing was not documented for 1 patient in group B. Values
are the mean and SD. D, Plot of time to freezing of PBMC samples from each subject against the magnitude of
signature II gene expression for that subject. Solid circles indicate polyarticular JIA patients in group B; open
circles indicate all other study subjects, including the healthy controls.
magnitude of each signature was compared between
RF⫹ and RF⫺ JIA patients, and indeed, RF⫹ patients
had statistically greater signature I expression than did
RF⫺ patients (Figure 3A). Expression of signature II
did not differ between RF⫹ and RF⫺ patients, and
signature III expression trended toward being greater in
RF⫺ patients, which is consistent with the absence of
any RF⫹ patients in group C (Table 1).
RF⫹ polyarticular JIA is more likely than RF⫺
polyarticular JIA to involve joint damage (14), raising
the possibility that expression of signature I is an
indicator of active joint injury, which is also consistent
with group A trending toward higher ESR and C-HAQ
scores than the other subgroups (Table 1). Along these
lines, the genes that comprise signature I suggest that
this signature comes from monocytes (15), and the
presence of signature I may indicate that these cells are
either mediating or responding to joint damage. In
particular, there was increased expression of monocyte
markers FCGR1A (CD64) (4.09-fold) (Table 2) and
CD14 (2.33-fold) (Supplementary Table 1) in prototypical group A patients (15). While the flow cytometry
analysis did not include CD64 or CD14 markers, other
markers of monocyte lineage subsets (16) correlated
with signature I better than with the other signatures,
demonstrating an association of signature I with increased abundance of monocyte populations (Figure 3B).
Correlation of signature II gene expression with
prolonged sample processing. The biologic basis for the
expression of signature II genes was first suggested by
the observation that patients from 1 clinical center were
overrepresented in this subgroup, which led to the discovery that samples from patients in group B tended to
have had longer processing times, as measured by the
time to freezing. The average time to freezing for group
B samples was significantly greater than that for samples
from other polyarticular JIA patients or healthy controls
(Figure 3C). However, prolonged time to freezing was
Figure 4. Correlation of signature III expression with CD8b expression and with the abundance of CD8⫹ T cells in
patients with juvenile idiopathic arthritis (JIA). A, Relative expression of CD8b mRNA in polyarticular JIA patients in
group C (n ⫽ 11), all other polyarticular JIA patients (n ⫽ 50), and healthy controls (n ⫽ 59). Values are the mean and
SD. B, Percentage of CD3⫹CD8⫹ peripheral blood mononuclear cells (PBMCs) in polyarticular JIA patients in group
C (n ⫽ 8), all other polyarticular JIA patients (n ⫽ 36), and healthy controls (n ⫽ 28), as determined by flow cytometry.
Samples were available for flow cytometry from only 72 of the 120 study subjects. Values are the mean and SD. C, Plot
of the relative expression of CD8b mRNA in each study subject against the magnitude of signature III expression for
that subject. Solid circles indicate polyarticular JIA patients in group C; open circles indicate all other study subjects,
including the healthy controls. D, Plot of the percentage of CD3⫹CD8⫹ PBMCs in each study subject against the
magnitude of signature III expression for that subject. Solid circles indicate polyarticular JIA patients in group C; open
circles indicate all other study subjects, including the healthy controls.
not sufficient to produce signature II expression, since a
number of control and JIA samples did not exhibit this
signature despite having a relatively prolonged time to
freezing (Figure 3D), which suggests differential susceptibility of samples to the length of processing. Interestingly, the data graphed in Figure 3D also suggest that if
all samples were processed promptly, with a time to
freezing of ⬃60 minutes, signature II expression would
not have been observed at all. Signature II gene expression did not correlate with any PBMC subpopulations
analyzed by flow cytometry (all correlation coefficients
⬍0.3), indicating that this signature was not associated
with the abundance of any particular PBMC subset.
Rather, the association with longer time to freezing
suggests that signature II expression was due to activation of PBMCs after phlebotomy.
Correlation of signature III expression with reduced CD8ⴙ T cells and increased plasmacytoid dendritic cells. As noted above, each JIA subgroup had both
up-regulated and down-regulated genes, and up-
regulated genes were sufficient to define the gene expression signatures. Nevertheless, down-regulated genes
are likely to provide valuable information regarding
disease processes, and along these lines, CD8b was noted
to be among the most highly down-regulated genes in
group C patients (0.44-fold as compared with controls)
(Supplementary Table 3). CD8a was also decreased,
although not as strongly as CD8b (0.76-fold as compared
with controls) (Supplementary Table 3).
Since the abundance of CD8⫹ T cells had been
measured by flow cytometry, it was of interest to assess
the correlation of CD8b expression with the proportion
of CD8⫹ T cells in group C patients. Indeed, both CD8b
expression and CD8⫹ T cell abundance were significantly lower in group C patients compared with all other
polyarticular JIA patients or with healthy controls (Figures 4A and B). Additionally, CD8b expression and
abundance of CD8⫹ T cells trended to be lower in
patients who exhibited strong expression of signature III
(Figures 4C and D). The correlation coefficient for the
expression of signature III with the abundance of CD8⫹
T cells was –0.14, which supports an inverse relationship. In contrast, signature III expression exhibited its
strongest positive correlation with Lin–BDCA-4⫹ plasmacytoid dendritic cells (17), with a correlation coefficient of 0.30. These results suggest that reduced CD8⫹
T cells and increased plasmacytoid dendritic cells are
important features of the disease process represented by
signature III expression, which may involve the action of
transforming growth factor ␤ (TGF␤), as discussed
Global gene expression analyses of PBMCs from
a large cohort of patients with polyarticular JIA who had
not been treated with DMARDs or biologic agents
revealed striking subgroups within an otherwise uniform
collection of patients with recent-onset polyarthritis.
These gene expression profiles demonstrated biologically significant heterogeneity within the JIA population, with distinct subgroups of patients expressing
distinct gene expression signatures. These signatures
appeared to be manifestations of biologic processes that
are occurring in some, but not all, patients, and in the
long run, they may prove to be valuable tools for
classifying and managing polyarticular JIA.
Three distinct gene expression signatures with
variable expression among polyarticular JIA patients
were identified. Signature I was strongly expressed in
many RF⫹ JIA patients, but was also expressed in a
number of RF⫺ JIA patients (Figures 2 and 3A). In fact,
signature I may prove useful for identifying a subset of
RF⫺ JIA patients that have a disease phenotype similar
to that in RF⫹ patients, as described by Ravelli and
Martini (3). Signature I contains many genes specifically
expressed in monocytes, and thus, this signature may be
a manifestation of increased abundance and/or activation of peripheral blood monocytes.
A number of genes in signature I have been
identified as being regulated in monocytes from patients
with other rheumatic diseases. For example, FCGR1A
(CD64), the third-ranked gene in signature I, was shown
by Wijngaarden et al (18) to be dramatically downregulated in monocytes from patients with RA following
methotrexate treatment. Likewise, MS4A4A, the topranked gene in signature I, and FCGR1A were the top
2–ranked cell surface marker genes in isolated monocytes that Abe et al (19) found to be significantly
down-regulated in Kawasaki disease following treatment
with intravenous immunoglobulin. Additionally,
MS4A4A, CLU, DYSF, and IL8RB were found by Ogilvie
et al (7) to be up-regulated in PBMCs from patients with
active systemic JIA.
A very interesting comparison with 25 genes that
have been reported to be up-regulated in RA (20)
showed that 16 of these genes were also up-regulated in
group A prototypes, including CD14, AQP9, and several
S100 proteins, while in contrast, 14 of these up-regulated
RA genes were down-regulated in group C prototypes,
emphasizing the contrast between groups A and C
(see Supplementary Tables 1 and 3, available on the
Arthritis & Rheumatism Web site at http://www3. Thus,
signature I may be indicative of monocyte activity in a
number of autoimmune inflammatory diseases, and it
may be useful for assessing disease activity and monitoring response to treatments in these diseases.
Rigorous protocols were followed for isolating
and freezing PBMCs as quickly as possible to minimize
the effects of processing on gene expression. Nevertheless, some of the observed gene expression differences
that contribute to signature II were associated with
prolonged processing times. Still, this signature does not
appear to have arisen solely as a function of processing.
It was also dependent on immunologic differences between subjects, since this signature was observed almost
exclusively in JIA patients and not in healthy controls,
and many of those JIA patients also expressed signature
I and/or signature III genes.
Signature II contains many immediate early
genes, including several FOS-related genes, JUN, and
EGR1 (21), which is consistent with very recent cellular
responses. The association of signatures I and III with
antigen-presenting cells (monocytes and plasmacytoid
dendritic cells, respectively) suggests that signature II
expression may develop after phlebotomy in samples
that have pathologically primed antigen-presenting cells
that are able to readily stimulate other cells in the test
tube environment, leading to rapid induction of immediate early genes. Furthermore, the pathologic relevance
of signature II is supported by the presence of many
genes that have previously been associated with autoimmune arthritis. For example, NR4A2 (also called
NURR1), the first-ranked gene in signature II, is markedly up-regulated in RA synovium (22). Likewise, OSM,
the third-ranked gene in signature II, is detectable in
JIA synovial fluid, and it was shown to induce joint
inflammation in a murine adenoviral gene–transfer
model (23). Thus, signature II may provide valuable
information regarding pathologic processes in the samples subjected to prolonged processing that may not
otherwise have been observed had the processing been
performed more promptly.
Signature III was associated with the absence of
RF (Figure 3) and appears to be distinct from signature
I. In fact, few patients expressed both signatures I and
III together (Figure 2), suggesting that these signatures
are manifestations of independent biologic processes.
Like group A patients, the average age of the group C
patients was greater than that of the entire population of
patients with polyarticular JIA (mean ⫾ SD 9.2 ⫾ 4.6
years), supporting the concept that signature III expression identified a distinct subset of JIA patients. Additionally, group C patients showed a trend toward lower
ESR, lower C-HAQ scores, and lower physician’s global
assessment of disease activity (Table 1), all of which are
consistent with signature III expression identifying a less
inflammatory subset of polyarticular JIA, possibly similar to the “dry synovitis” subset described by Ravelli and
Martini (3).
Moreover, signature III was associated with low
numbers of CD8⫹ T cells (Figure 4) and an increased
abundance of blood dendritic cell antigen 4 (BDCA-4)–
positive plasmacytoid dendritic cells, and it contains
many genes that are inducible by TGF␤ and are potentially involved in mediating or regulating the action of
TGF␤. These include the first-ranked gene, DAPK1,
which is a proapoptotic protein that can be induced by
TGF␤ via SMAD activation (24). Other signature III
genes that are TGF␤-inducible include SMAD3, BCL2,
MAPK1, and FOXO3A (25–28). These observations suggest that TGF␤ may be responsible for reducing levels of
CD8⫹ T cells via a proapoptotic influence and increasing plasmacytoid dendritic cells via an activating effect.
Notably, increased levels of TGF␤ have been detected in
synovial fluid from patients with juvenile arthritis (29),
where it has been conjectured to serve an immunosuppressive role in countering synovial inflammation (30).
Interestingly, while one might conjecture that exposure
of PBMCs to TGF␤ occurs in the inflamed synovium,
another intriguing possibility is that it occurs via regulatory T cells that express TGF␤ (31).
Many of the polyarticular JIA patients did not
express signatures I, II, or III (24 of the 61 patients
evaluated [39%]). This group of patients was statistically
significantly younger and had a higher rate of ANA
positivity than the rest of the polyarticular JIA patients.
It is uncertain whether these patients represent a unified
group, but one can speculate that they do not have the
same disease phenotype as patients that expressed signature I and/or signature III, and given their age and
ANA status, it is likely that many of these patients
comprise a polyarticular JIA subset that closely resembles early-onset ANA-positive oligoarticular JIA, as
described by Ravelli et al (3,32). It is also noted that a
few healthy control subjects clustered into group A, B, or
C. While control subjects were deemed free of inflammatory disease based on a screening questionnaire,
there was no assurance that every control subject was
completely healthy, and this may account for coclustering of some controls with the JIA patients.
In summary, we have demonstrated PBMC gene
expression signatures in a large population of children
with recent-onset polyarticular JIA that correlate with
differences in disease characteristics. These signatures
support the subclassification of polyarticular JIA offered
by Ravelli and Martini (3), with signature I identifying
both RF⫹ and RF⫺ patients with disease similar to that
of adult RA, signature III identifying a less inflammatory disease subset, and patients with ANA⫹ early-onset
disease expressing neither signature. Thus, these signatures offer a molecular classification of polyarticular JIA
and may prove to be valuable tools for assessing disease
activity, predicting response to medications, and forecasting long-term outcomes.
We acknowledge and appreciate the contributions of
the following individuals: Lori Luyrink, Shweta Srivastava,
Ndate Fall, and Sarah Croswell (research assistants, CCHMC);
Wendy Bommer and Anne Johnson (clinical research coordinators, CCHMC); Lukasz Itert (data processing and management, CCHMC); Jesse Gillis (data preprocessing, University
of British Columbia); Marsha Malloy (study coordinator,
Children’s Hospital of Wisconsin); Beth Martin (study coordinator, Toledo Children’s Hospital); Marilyn Orlando (study
coordinator, Schneider Children’s Hospital); Sara Jane Wilson
(study coordinator, Children’s Hospital of Philadelphia); and
Jeremy Zimmermann (data collection, Children’s Hospital of
All authors were involved in drafting the article or revising it
critically for important intellectual content, and all authors approved
the final version to be published. Dr. Griffin 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 conception and design. Griffin, Barnes, Ilowite, Olson, Sherry,
Gottlieb, Aronow, Thompson, Grom, Colbert, Glass.
Acquisition of data. Griffin, Barnes, Ilowite, Olson, Sherry, Gottlieb,
Thornton, Thompson, Grom, Colbert, Glass.
Analysis and interpretation of data. Griffin, Barnes, Aronow, Pavlidis,
Hinze, Thornton, Thompson, Grom, Colbert, Glass.
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expressions, subsets, molecular, polyarticular, signature, disease, idiopathic, heterogeneity, demonstrated, arthritis, classification, genes, juvenile, offer
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