close

Вход

Забыли?

вход по аккаунту

?

Specific gene expression profiles in systemic juvenile idiopathic arthritis.

код для вставкиСкачать
ARTHRITIS & RHEUMATISM
Vol. 56, No. 6, June 2007, pp 1954–1965
DOI 10.1002/art.22644
© 2007, American College of Rheumatology
Specific Gene Expression Profiles in
Systemic Juvenile Idiopathic Arthritis
Emma Mary Ogilvie, Arshad Khan, Mike Hubank, Paul Kellam, and Patricia Woo
Objective. Patients with systemic juvenile idiopathic arthritis (JIA) have arthritis, quotidian fevers,
and other extraarticular features. This disease often
remains severe and debilitating. The purpose of this
study was to compare gene expression profiles in peripheral blood mononuclear cells (PBMCs) from patients
with active and inactive systemic JIA to define and
better understand the cause of active disease.
Methods. Gene expression profiles of PBMCs
were determined in cells from 9 patients with active
systemic JIA and 8 patients with inactive systemic JIA.
Unsupervised clustering and significance analysis were
performed. We compared the systemic JIA profile with
data from patients with polyarticular JIA, chronic infantile neurologic, cutaneous, articular syndrome, Kawasaki disease, and systemic lupus erythematosus to
identify disease-specific genes. Quantitative reverse
transcription–polymerase chain reaction of selected
genes was performed on negatively selected B cells, T
cells, and monocytes.
Results. Unsupervised clustering of expressed
genes resulted in 2 groups that corresponded to the
clinical status of the patients (active and inactive disease) and was independent of their medications. A total
of 286 genes were identified as significantly upregulated in patients with active disease and 86% of
them were specific to systemic JIA. Interleukin-6 (IL-6)
was expressed in monocytes and B cells, IL-10 in
monocytes, and suppressor of cytokine signaling 3 in
monocytes and T cells from patients with active disease.
Conclusion. Gene expression profiles in PBMCs
identified disease-specific genes in patients with systemic JIA. Cell type analyses should allow further
insight into the mechanisms of the disease.
Juvenile idiopathic arthritis (JIA) is a heterogeneous group of diseases. Systemic JIA, a distinct
subgroup of JIA, consists of a multisystem inflammatory
disease in addition to arthritis. Systemic JIA accounts for
⬃11% of all patients with juvenile arthritis (1). Features
of this subgroup of patients include quotidian fevers,
skin rash, lymphadenopathy, serositis, and hepatosplenomegaly. The more severe spectrum of systemic
JIA has all of these features from the onset of the
disease, and patients often develop severe debilitating
complications. Despite recent therapeutic advances,
children with persistent systemic JIA still experience
early and progressive joint destruction, and ⬃30% have
active disease that continues for 10 years or more after
diagnosis.
The etiology of systemic JIA, as with other complex autoimmune/inflammatory diseases, is thought to
be through the interaction of genetic predisposition with
an environmental trigger in early life. So far, no geographic clustering or any microbial association with this
disease has been found to indicate a dominant environmental etiology. While HLA associations are a significant genetic factor in most of the JIA subtypes, there are
few or no associations with systemic JIA (2,3). However,
non-HLA genes have been reported to be associated
with systemic JIA, for example, the gene for
interleukin-6 (IL-6), where the more highly expressed
IL-6 allele is a susceptibility gene for systemic JIA (4),
and the gene for macrophage inhibitory factor, where a
polymorphism is also found to be associated with systemic JIA (5). Despite these associations, the mechanisms of action of these genes in the pathogenesis of
systemic JIA are not known. Measurements of cytokines
have also shown that serum and synovial levels of IL-6
Supported by the Arthritis Research Campaign (grant 13895)
and the Medical Research Council (grant 69716).
Emma Mary Ogilvie, MSc, Arshad Khan, MSc, Mike Hubank,
PhD, Paul Kellam, PhD, Patricia Woo, PhD, FRCP, FRCPCH:
University College London, London, UK.
Address correspondence and reprint requests to Patricia
Woo, PhD, FRCP, FRCPCH, University College London, 3rd Floor
Windeyer Institute, 46 Cleveland Street, London W1T 4JF, UK.
E-mail: patricia.woo@ucl.ac.uk.
Submitted for publication July 26, 2006; accepted in revised
form February 16, 2007.
1954
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
are increased in active systemic JIA. Whether this is the
consequence of upstream immunopathologic changes is
not clear. Moreover, the main cellular source of the IL-6
in serum and synovial fluid is not known.
Many systemic JIA patients have systemic onset
of symptoms, with little evidence of active synovitis, in
the beginning. Often, persistent arthritis and other
symptoms continue after the fevers subside. The most
severe cases will have all disease-defining symptoms and
frank polyarthritis from the beginning of the illness. It is
thus likely that the pathology is not confined to, or
initiated within, the joints. Peripheral blood mononuclear cells (PBMCs) are therefore likely to reflect
systemic pathologic changes. Up to now, the role of
PBMCs in JIA and the source of genes that lead to the
systemic features during active disease have not been
determined.
In the present study, we compared the gene
expression profiles of PBMCs from patients with active
systemic JIA and patients with inactive systemic JIA in
order to gain insight into which genes are up-regulated
or down-regulated during the active phase of systemic
JIA. We reasoned that healthy children are not necessarily a good control group for identifying the expression
of specific genes responsible for disease activity, especially when the numbers of systemic JIA patients are
small, since the differences observed may simply reflect
variations between healthy individuals and systemic JIA
patients that are not part of the disease process. Rather,
by comparing samples obtained from patients with active
systemic JIA and from patients with inactive systemic
JIA, we control for the genetic variations inherent in
systemic JIA patients in order to find specific genes that
are up-regulated during the active disease process and
thus gain insight into the specific disease pathways.
This study focused on the pathogenesis of active
systemic JIA, but not that of systemic JIA in general, so
any genes involved in both active and inactive systemic
JIA would not be identified. We also attempted to
compare the genes up-regulated in children with active
systemic JIA with the genes up-regulated in children
with other systemic inflammatory diseases in order to
separate genes that are involved in all inflammatory
processes and those that are likely to be more specific to
systemic JIA. We used data from both autoimmune and
autoinflammatory diseases in children (polyarticular
JIA, chronic infantile neurologic, cutaneous, articular
syndrome [CINCA], Kawasaki disease [KD] and systemic lupus erythematosus [SLE]) in order to determine
which genes are likely to be specific to systemic JIA.
Importantly, we also found that there are cell type–
1955
specific expressions of selected genes, suggesting that
the pathology of systemic JIA is manifested through
multiple cell types, which can be observed in the peripheral blood.
PATIENTS AND METHODS
RNA samples. The systemic JIA patients that were
recruited for the study fulfilled the International League of
Associations for Rheumatology classification criteria for
systemic-onset JIA (6). Patients were designated as having
active disease (n ⫽ 9) if they had an elevated erythrocyte
sedimentation rate (ESR) and raised levels of C-reactive
protein (CRP), had synovitis in ⱖ1 joint, and had at least 1
other systemic feature. Patients were designated as having
inactive disease (n ⫽ 8) if they had no joint involvement,
low-to-normal levels of acute-phase proteins, and no features
of systemic disease. Patients were between the ages of 1 year
and 22 years, and all were Caucasian.
Research was performed in compliance with the Declaration of Helsinki, and ethics approval was obtained from the
Great Ormond Street Hospital for Children National Health
Service Trust and the Institute of Child Health, Research
Ethics Committee (reference 02RU06).
Informed consent was obtained, and then 5 ml of blood
was drawn. PBMCs were obtained by density-gradient centrifugation of blood over Lymphoprep (Axis-Shield, Kimbolton,
UK), using endotoxin-free reagents. The PBMCs were resuspended in TRIzol (Invitrogen, Paisley, UK), and RNA was
extracted according to the manufacturer’s protocol.
Enrichment of B cell, T cell, and monocyte fractions. B
cells, T cells, and monocytes were negatively selected from
fresh PBMCs that had been separated with Lymphoprep
(Axis-Shield) using negative isolation kits from Dynal (Wirral,
UK). Lipopolysaccharide was excluded in the separation procedure with the use of endotoxin-free reagents. Purity of the
cell fractions was confirmed using fluorescence-activated cell
sorter analysis with CD19, CD3, and CD14, respectively. We
obtained ⬎90% purity of cells for the B cell fraction and T
lymphocyte cell fractions. For the monocyte fraction, ⬎80%
purity was obtained. The cells were resuspended in TRIzol and
then RNA-extracted according to the manufacturer’s protocol.
Microarray procedures. Samples were prepared using
the GeneChip eukaryotic small sample target labeling assay,
version II, with 100 ng of RNA and an Affymetrix GeneChip
IVT labeling kit (Affymetrix, Santa Clara, CA). Samples were
hybridized to Affymetrix U133 Plus 2.0 arrays.
Analysis of expression data. Data from the arrays were
generated and exported using Affymetrix Microarray Suite
software. Data were normalized according to the median. The
Institute for Genomic Research MultiExperiment Viewer
(available at http://www.tm4.org/mev.html) was used for clustering and significance analysis. Hierarchical clustering (7) was
performed using Pearson’s correlation. Significance Analysis
of Microarrays (SAM) (8) was used to find genes with significantly different levels of expression. SAM was performed with
3 alternative group designations. In the first SAM (SAM1), all
samples from patients with active disease were placed in one
group and all samples from patients with inactive disease in
1956
OGILVIE ET AL
Table 1. Information on samples used for microarrays and for quantitative RT-PCR confirmation of
microarray results*
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
Sample
1
4
5
6I
6A
7
8
12
13
14I
14A
15
16
19
20
25
27
2
3
4(2)
5(2)
8(2)
10
11
26
28
24
29
30
34
35
Disease state
MTX
Steroids
Anti-TNF
Active
Active
Active
Inactive
Active
Inactive
Active
Active
Inactive
Inactive
Active
Inactive
Active
Active
Inactive
Inactive
Inactive
Inactive
Inactive
Active
Active
Active
Inactive
Inactive
Active
Active
Active
Active
Active
Active
Active
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
No
Yes
No
No
No
No
No
Yes
No
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Infliximab
No
No
No
No
No
Etanercept
Infliximab
Infliximab
No
No
No
No
No
No
Etanercept
Etanercept
No
Infliximab
No
No
Etanercept
Infliximab
No
No
No
No
No
No
Sample use
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Microarray
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Cell types
Cell types
Cell types
Cell types
Cell types
RT-PCR
RT-PCR
RT-PCR
RT-PCR
RT-PCR
RT-PCR
RT-PCR
RT-PCR
RT-PCR
* Two patients contributed samples during periods of both active and inactive disease (samples 6A and
14A during active disease; samples 6I and 14I during inactive disease). Patients 4, 5, and 8 contributed
additional samples at a later date; these additional samples are denoted with “(2).” RT-PCR ⫽ reverse
transcription–polymerase chain reaction; MTX ⫽ methotrexate; anti-TNF ⫽ anti–tumor necrosis factor.
another. In SAM2, the samples from active and inactive
disease patients that had partitioned upon hierarchical clustering were placed into 2 groups (and the 2 samples that had not
clustered with other samples from the same disease group were
excluded). In SAM3, the samples were grouped according to
the 2 partitioned clusters obtained from hierarchical clustering.
In each case, a delta value was chosen to obtain a false
discovery rate (FDR) of ⱕ10%. Differentially expressed genes
were functionally annotated using Ensembl v38 software, April
2006 release (available at http://www.ensembl.org/).
Comparison with genes up-regulated in other inflammatory diseases. We compared the list of genes obtained from
our study with the list of up-regulated genes obtained from
published reports on polyarticular JIA (9), CINCA (comparison of CINCA patients who were treated versus those who
were not treated with anakinra [Aksentijevich I: personal
communication]), KD (10), and pediatric SLE (11). Because
the Affymetrix chips that were used were not identical in all
these studies, we reexamined all of the gene annotations.
Genes that could be accurately identified in each study, using
Ensembl identifiers where possible, were used to identify a
common set of genes across all studies and were subsequently
used to determine those that were differentially expressed in
more than one disease.
Quantitative reverse transcription–polymerase chain
reaction (RT-PCR). Quantitative RT-PCR was performed
including a DNA ablation step (QuantiTect Reverse Transcription Kit; Qiagen, Crawley, UK), followed by quantitative
PCR (with QuantiTect Primer assays and QuantiTect SYBR
Green PCR kit; Qiagen) and run on the Applied Biosystems
7000 Sequence Detection System (Applied Biosystems, Warrington, UK). We compared a number of endogenous control
genes since it was recently shown that even some housekeeping
genes vary when analyzed by real-time PCR. We tested
␤-actin, large ribosomal protein P0, eukaryotic translationinitiation factor, and hypoxanthine phosphoribosyltransferase
1. We chose large ribosomal protein P0 as our control gene
because of its consistent expression level across our samples.
Statistical analysis of the quantitative RT-PCR results was
performed using the Mann-Whitney U test.
RESULTS
Characteristics of PBMC samples. PBMCs were
derived from blood samples obtained from 9 patients
during active disease and 8 patients during quiescent
disease. The patients were all treated with 1 of the
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
1957
Figure 1. Clustering of samples according to disease state in patients with active and inactive systemic juvenile idiopathic arthritis. A, Samples
grouped by disease status through hierarchical clustering of all genes. Two patients contributed samples during periods of both active and inactive
disease (samples 6A and 14A during active disease; samples 6I and 14I during inactive disease). The samples clustered according to whether the
disease was active or inactive, with 2 exceptions (sample 6A and sample 7). The sample clustering was not dependent on the medications taken or
the age of the patients. Patients in group 1 were taking methotrexate (MTX) and steroids, those in group 2 were taking MTX and anti–tumor necrosis
factor (anti-TNF), those in group 3 were taking MTX only, those in group 4 were taking anti-TNF and steroids, and those in group 5 were taking
nonsteroidal antiinflammatory drugs only. B, Hierarchical clustering of the 286 genes identified as significantly differentially expressed by
Significance Analysis of Microarrays (SAM), with samples grouped according to disease status. Shown at the right are the functional groups into
which the genes that are up-regulated during active disease were assigned using Ensembl. The colored scale bar at the bottom indicates the fold
change in gene expression across the groups.
1958
following 5 groups of medications: group 1 consisted of
methotrexate (MTX) and steroids (3 patients with active
and 1 with inactive disease), group 2 consisted of MTX
and anti–tumor necrosis factor (anti-TNF) (2 patients
with active and 2 with inactive disease), group 3 consisted of MTX only (3 patients with active and 1 with
inactive disease), group 4 consisted of anti-TNF and
steroids (1 patient with inactive disease), and group 5
consisted of nonsteroidal antiinflammatory drugs only (1
patient with active and 3 with inactive disease). The
patients with active diseases were generally receiving
higher doses of steroids, but similar doses of MTX and
anti-TNF (Table 1). Two individuals contributed samples during both active and inactive stages of disease;
these were samples 6I (inactive) and 6A (active) and
samples 14I (inactive) and 14A (active).
White blood cell (6.69–11.22 ⫻ 109/liter), lymphocyte (1.81–3.2 ⫻ 109/liter), and neutrophil (3.86–
7.22 ⫻ 109/liter) counts were within the normal range for
all patients, except for patient 12, who had active disease
and was receiving no medications (22.58 ⫻ 109/liter,
4.73 ⫻ 109/liter, and 16.78 ⫻ 109/liter, respectively).
There was no statistically significant difference in the
cell counts between the samples obtained from patients
with active disease and those obtained from patients
with inactive disease (data not shown).
Clustering of samples from patients with active
and inactive systemic JIA into 2 groups, as determined
by unsupervised hierarchical clustering. To identify
gene expression differences between active and inactive
systemic JIA, we performed Affymetrix gene expression
profiling. Following data filtering and normalization, we
explored the data structuring using unsupervised hierarchical clustering (7). This showed that the majority of
the samples from patients with clinically active disease
grouped into one cluster, which was distinct from the
cluster for the patients with inactive disease (Figure 1A).
Gene expression profiles from sample 14 during either
active or inactive disease (sample 14A or 14I) clustered
according to the clinically defined disease state, indicating that active and inactive disease differs within as well
as between individual patients. For the other samples
that were taken at 2 time points from the same patient
(samples 6I and 6A), the one obtained during inactive
disease clustered with the other samples from patients
with inactive disease, but the one obtained during active
disease clustered with neither the active nor the inactive
disease samples. This individual is discussed below. The
data have been submitted to ArrayExpress (accession
no. E-MEXP-987).
OGILVIE ET AL
Figure 2. Overlap between genes that were unequivocally upregulated in patients with active systemic juvenile idiopathic arthritis
(JIA) and in patients with other inflammatory diseases. Shown are the
number of overlapping genes between systemic JIA (sJIA) versus
Kawasaki disease (KD) and systemic lupus erythematosus (SLE),
versus polyarticular JIA, versus chronic infantile neurologic, cutaneous, articular syndrome (CINCA), versus KD, and versus SLE. Table
2 lists the individual genes for all comparison groups except for
systemic JIA versus CINCA.
Hierarchical clustering of systemic JIA patients
independent of medications. The location within the
cluster of samples from patients in the various medication groups was examined in order to ensure that
medication was not the cause of the observed pattern of
clustering. The clustering of samples into active and
inactive disease groups was not influenced by the medications they were taking (Figure 1A). Significance analysis of patients in the various medication groups also
showed that there was no significant difference in gene
expression between the different medication groups.
There were 2 exceptions to this robust separation
of disease status by gene expression. Sample 7 did not
cluster as an inactive disease sample. However, the
patient from whom this sample was obtained did not
fulfill all of the clinical criteria for inactive disease; only
clinical measurements were obtained, and the ESR and
CRP levels were not available. Sample 6A was clinically
designated as active disease, but the gene expression
profile clustered with the inactive disease samples. However, it was noted that this sample showed up-regulation
of a subset of the genes associated with active disease
samples, suggesting that sample 6A is at the midpoint of
the spectrum of disease. Importantly, sample 6I, which
was taken from the same individual 21 weeks before
during a clinically inactive phase, did not cluster with
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
1959
Table 2. Genes that overlap between systemic JIA and other inflammatory diseases*
Affymetrix
ID
Overlap groups, gene name
Systemic JIA, KD, and SLE
Adrenomedullin
Cytidine deaminase
Haptoglobin
S100 calcium-binding protein A12
S100 calcium-binding protein A9
S100 calcium-binding protein P
Systemic JIA and polyarticular JIA
Early growth response 1
Glutamate-ammonia ligase (glutamine synthase)
Interleukin-8 receptor ␤
Microtubule-associated monoxygenase, calponin, and LIM domain–containing 2
Serpin peptidase inhibitor, clade B (ovalbumin), member 2
Thrombomodulin
Systemic JIA and KD
Adrenomedullin
Cytidine deaminase
Clusterin
Cytochrome P450 family 1 subfamily B polypeptide 1
Cysteine-rich secretory protein LCCL domain–containing 2
Dysferlin
Early growth response 1
Coagulation factor V
Formyl peptide receptor 1
H2.0-like homeobox 1 (Drosophila)
Haptoglobin
Interleukin-8 receptor ␤
Microtubule-associated monoxygenase, calponin, and LIM domain–containing 2
Membrane-spanning 4-domains subfamily A member 4
S100 calcium-binding protein A12
S100 calcium-binding protein A9
S100 calcium-binding protein P
Systemic JIA and SLE
Adrenomedullin
Cytidine deaminase
Glutamate-ammonia ligase (glutamine synthase)
Haptoglobin
Matrix metalloproteinase 9
S100 calcium-binding protein A12
S100 calcium-binding protein A9
S100 calcium-binding protein P
Gene
symbol
202912_at
205627_at
206697_s_at
205863_at
203535_at
204351_at
ADM
CDA
HP
S100A12
S100A9
S100P
201693_s_at
217202_s_at
207008_at
212473_s_at
204614_at
203887_s_at
EGR1
GLUL
IL8RB
MICAL2
SERPINB2
THBD
202912_at
205627_at
222043_at
202435_s_at
221541_at
218660_at
201693_s_at
204714_s_at
205118_at
214438_at
206697_s_at
207008_at
212473_s_at
219607_s_at
205863_at
203535_at
204351_at
ADM†
CDA†
CLU†
CYP1B1†
CRISPLD2†
DYSF†
EGR1
F5†
FPR1
HLX1†
HP†
IL8RB
MICAL2
MS4A4A†
S100A12†
S100A9†
S100P†
202912_at
205627_at
242281_at
206697_s_at
203936_s_at
205863_at
203535_at
204351_at
ADM
CDA
GLUL
HP
MMP9
S100A12
S100A9
S100P
* JIA ⫽ juvenile idiopathic arthritis; KD ⫽ Kawasaki disease; SLE ⫽ systemic lupus erythematosus.
† One of 13 genes found by Abe et al (10) to be expressed in the monocyte fraction.
sample 6A, but fell into the cluster with the other
inactive disease samples. This result further supports the
fact that sample 6 represented an individual who had an
episode of active disease later, but was disease-free at
the time of this sampling.
Significant up-regulation of many genes in active
systemic JIA. In order to identify genes that were
significantly differentially expressed between active and
inactive systemic JIA, we used SAM (8) and the clinical
classifications of disease status (Figure 1A). The 3
groups of significance analyses (SAM1, SAM2, and
SAM3) showed a total of 286 genes that were significantly up-regulated in patients with active disease (Fig-
ure 1B). SAM1 (clinically defined active disease samples
compared with inactive disease samples) showed 163
genes that were significantly up-regulated in active disease (⌬ ⫽ 0.685, 10% FDR). SAM2 (active disease
samples, excluding sample 6A, and inactive disease
samples, excluding sample 7) showed 203 genes that
were significantly up-regulated in active disease (⌬ ⫽
0.806, 6.5% FDR). SAM3 (samples grouped based on
the 2 clusters into which they were partitioned during
unsupervised hierarchical clustering) showed 129 genes
that were significantly up-regulated in active disease
(⌬ ⫽ 0.79, 5.3% FDR). As expected, there was considerable overlap among the 3 analyses. Nevertheless, 113
1960
genes were identified as being specific to an analysis
group, with 54 unique genes in SAM1, 31 in SAM2, and
28 in SAM3. Of the 286 up-regulated genes, 129 could
be annotated, allowing assignment of genes into functional groups. (Data on genes that were up-regulated in
systemic JIA according to their functional annotation
are available upon request from the corresponding
author.) The main functional groups cover a wide range
of processes. These are listed in Figure 1B.
Likely disease-specific expression of many of the
286 up-regulated genes in active systemic JIA. It is
possible that all studies of inflammatory disease simply
identify sets of genes that are inflammation-specific
rather than disease-type–specific, and since no crosscomparison studies have been undertaken, we wished to
determine whether this was the case. We compared our
systemic JIA profile with the profiles for polyarticular
JIA (9), CINCA (Aksentijevich I: personal communication), KD (10), and SLE (11) by calculating the degree
of overlap between the genes that were assigned as
differentially regulated in these studies and that were
unequivocally the same gene. Nearly all of the genes that
were found to be up-regulated in our active disease
group were specific to systemic JIA. Only 40 of our 286
genes were also up-regulated in a number of other
inflammatory diseases as compared with published data
(Figure 2). Six genes were found to overlap in systemic
JIA, KD, and SLE (Table 2), but not in polyarticular
JIA. Polyarticular JIA and systemic JIA had 6 overlapping genes (Table 2), CINCA and systemic JIA had 35
(data not shown), systemic JIA and KD had 17 (Table
2), and SLE and systemic JIA had 8 (Table 2).
Quantitative RT-PCR confirmation of diseaseassociated gene expression in an independent sample of
systemic JIA patients. To ensure that the gene expression profiles accurately reflected gene expression levels,
we assessed representative genes that were up-regulated
in samples from patients with active systemic JIA by
quantitative RT-PCR, namely, IL-10 and suppressor of
cytokine signaling 3 (SOCS-3). IL-10 was chosen because it is an antiinflammatory gene that may not have
been expected to be more up-regulated in active versus
inactive disease, given that IL-10 is a potent antiinflammatory cytokine and, intuitively, one might expect more
of it in inactive disease. SOCS-3 was chosen because it is
induced by both IL-10– and IL-6–mediated signaling;
the genes for both of these cytokines have been previously shown by us to be associated with systemic JIA
(4,12,13).
OGILVIE ET AL
Figure 3. Relative expression levels of genes for interleukin-10 (IL10) and suppressor of cytokine signaling 3 (SOCS-3). The 17 samples,
9 from patients with active juvenile idiopathic arthritis (JIA) (}) and
8 from patients with inactive JIA (■), used for microarray analysis
were analyzed by quantitative reverse transcription–polymerase chain
reaction for the expression of A, IL-10 and B, SOCS-3. Expression
levels were calculated relative to the control gene large ribosomal
protein P0. Additional samples were obtained from 5 patients with
active JIA (}) and 4 patients with inactive JIA (■), and the expression
of C, IL-10 and D, SOCS-3 was determined. Horizontal bars show the
mean. P values were calculated using the Mann-Whitney U test.
The levels of expression of these genes were
tested by quantitative RT-PCR on the 17 active and
inactive disease samples used for the microarrays and,
importantly, on 9 additional samples. Of these 9 samples, 5 were taken during active disease and 4 during the
quiescent stage (Table 1). Quantitative RT-PCR of the
samples used for gene expression profiling confirmed
the microarray results for IL-10 and SOCS-3, showing
up-regulation in active systemic JIA. Quantitative RTPCR of the additional independent samples from systemic JIA patients further confirmed the up-regulation
of the IL-10 and SOCS-3 genes during active disease
(Figure 3).
To support the findings of our analysis suggesting
that genes from each of the different inflammatory gene
expression patterns are disease-specific, we investigated
the expression of interferon-stimulated transcription
factor 3␥ (ISGF-3␥), which was previously found not to
be differentially expressed in systemic JIA, polyarticular
JIA, CINCA, or KD, but to be up-regulated in SLE (11).
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
We performed quantitative RT-PCR on patients with
active systemic JIA to determine if the expression of
ISGF-3␥ in systemic JIA was consistent with our gene
expression cross-comparisons. The quantitative RTPCR confirmed that there was no difference in expression in our systemic JIA patients (data not shown). The
level of ISGF-3␥ in all of our systemic JIA patients was
low. We believe, therefore, that the interferon response
is not a general inflammatory disease response, but is
specific to SLE.
Expression of IL-6 by B cells and monocytes. IL-6
was not differentially expressed between patients with
active and inactive systemic JIA. There were many
reasons to think that IL-6 expression would not differ in
these samples. First, levels of IL-6 have a circadian
pattern, and not all samples were obtained at the same
time of day. Second, levels of IL-6 rise and fall in parallel
with body temperature; not all of our patients with active
JIA had a fever, and those who did were not all sampled
at the same stage of the fever. Third, it was thought that,
like other acute-phase proteins such as CRP, the IL-6
produced during active systemic JIA may come from the
liver and the reticuloendothelial system including the
synovium, making PBMCs the incorrect cell type to
assess. The possibility exists that IL-6 is expressed by a
subgroup of PBMCs and may be important for some of
the unique systemic features of this disease.
To determine if the lack of differential expression
of IL-6 in PBMCs was due to masking of the signal from
specific and less numerous cell types, we used quantitative RT-PCR to examine IL-6 expression in enriched
fractions of B cells, T cells, and monocytes from 5
systemic JIA patients with clinically active disease (1
individual was not Caucasian) and 5 normal healthy
controls. Levels of expression of IL-6 were assessed to
determine the source of IL-6 in samples obtained from
patients with active disease. IL-6 was not expressed by T
cells. We found that IL-6 was expressed at a very low
level by monocytes in healthy controls and at a significantly higher level in systemic JIA patients with active
disease. B cells in healthy controls expressed higher
levels of IL-6 than did monocytes. Active systemic JIA B
cells from a couple of the patients expressed even higher
levels of IL-6 than did B cells from healthy controls
(Figure 4). Thus circulating mononuclear cell, and perhaps B cell, expression of IL-6 could be important in the
clinical manifestations of systemic JIA.
Up-regulated gene expression in multiple cell
types. The balance of proinflammatory and antiinflammatory cytokines is considered to be in disequilibrium
during active disease in systemic JIA, and it was inter-
1961
Figure 4. Expression of interleukin-6 (IL-6) in B cells and monocytes
from patients with active systemic juvenile idiopathic arthritis (JIA). B
cells (}), T cells (‚), and monocytes (■) from 5 patients with active
systemic JIA (samples 24, 29, 30, 34, and 35 in Table 1) and 5 healthy
control subjects were analyzed by quantitative reverse transcription–
polymerase chain reaction for the expression of IL-6. Expression levels
were calculated relative to the control gene large ribosomal protein P0.
In healthy control subjects, IL-6 is produced by B cells; in patients with
active systemic JIA, IL-6 is produced by monocytes and B cells. The
level of IL-6 in B cells is much higher than that in monocytes.
Horizontal bars show the mean value in monocytes. The P value
represents the comparison of monocyte levels in patients versus
controls, by Mann-Whitney U test.
esting to see the up-regulation of a regulatory and
antiinflammatory cytokine, IL-10, in samples from patients with active disease that were significantly above
the level in samples from patients with inactive disease
(Figure 3). Since dendritic cells and monocytes, as well
as regulatory T cells and B cells, can all produce IL-10,
we performed quantitative RT-PCR to measure the
expression of IL-10 in each cell fraction in order to gain
more insight into the disease mechanisms. We also
examined the expression of SOCS-3, which is known to
be up-regulated by both IL-10 and IL-6 intracellular
signaling. Finding the source of SOCS-3 may help to
indicate the cause of SOCS-3 production and the role of
SOCS-3 in disease.
We found that IL-10 was expressed by the monocyte fraction in samples from patients with active systemic JIA at a much higher level than by the monocyte
fraction in samples from healthy controls. There was a
very low level of IL-10 in the B cell and T cell fractions;
however, since these fractions were only 90% pure, we
cannot rule out the possibility that the presence of IL-10
was due to a small number of monocytes present in these
samples (Figure 5). SOCS-3 was highly expressed by
monocytes and T cells from 4 of 5 samples from systemic
JIA patients with active disease. The levels of SOCS-3 in
B cells from these patients were, however, negligible.
1962
OGILVIE ET AL
Figure 5. Expression of interleukin-10 (IL-10) in monocytes and of
suppressor of cytokine signaling 3 (SOCS-3) in monocytes and T cells.
B cells (}), T cells (‚), and monocytes (■) from 5 patients with active
systemic juvenile idiopathic arthritis (JIA) (samples 24, 29, 30, 34, and
35 in Table 1) and 5 healthy control subjects were analyzed by
quantitative reverse transcription–polymerase chain reaction for the
expression of A, IL-10 and B, SOCS-3. Expression levels were calculated relative to the control gene large ribosomal protein P0. IL-10 is
expressed in monocytes (A), and SOCS-3 is expressed in monocytes
and T cells (B). Horizontal bars show the mean value in monocytes. P
values represent comparisons of monocyte levels in patients versus
controls, by Mann-Whitney U test.
Similarly, there was a very low level of expression of
SOCS-3 in all cell types in samples from the healthy
controls (Figure 5).
DISCUSSION
This is the first study to identify a gene expression
signature that discriminates between active and inactive
systemic JIA. Despite clinical similarities to other diseases, most of these expression differences are specific
to systemic JIA. We have shown that many genes are
up-regulated in active systemic JIA and have implications in the pathology and clinical manifestations of
systemic JIA. Unsupervised clustering of the genes from
all samples resulted in 2 distinct groups that correspond
to their clinical classification as active and inactive
disease. The clinical definitions were such that patients
at opposite ends of the clinical spectrum were selected,
and the gene expression profiles of these samples partitioned into clusters that corresponded to this clinical
definition, rather than the type of medication being
taken. There were no significant differences in white
blood cell, lymphocyte, or neutrophil counts between
patients with inactive and active disease, so the upregulated genes are unlikely to be due to a difference in
cell frequencies within the PBMCs. This suggests, therefore, that the gene expression profiles are indicative of
pathologic processes, rather than a confounding by other
effects.
PBMCs are increasingly being used for staging
diseases (14,15). PBMCs are relatively easy to obtain
and, hence, provide a good starting point for analyses of
gene expression in systemic diseases. There are many
examples in the literature where models of disease have
been determined from PBMC expression data, but we
believe much information is lost when this is not examined in the context of the cell type. Some studies have
observed cell-specific signatures within PBMC expression data and then examined that cell type within disease
groups, but there seems to be no precedent for examining more than one cell type within a disease. It is likely
that a more detailed analysis of the involvement of
discrete cell types is essential before disease mechanisms
can be inferred.
In the case of children, only small amounts of
blood can be obtained, so deriving pure cell fractions
from the same sample is more difficult. Nevertheless, we
have successfully used negative selection of B cell, T cell,
and monocytic cell types to examine the expression of
selected genes that were up-regulated in our gene expression analysis. Of particular interest here is the
increased production of IL-6 by monocytes and B cells in
samples from patients with active disease as compared
with samples from healthy controls. Monocytes and
dendritic cells are a significant source of IL-6, and this is
the first time that circulating monocytic cells have been
shown to produce significant amounts of IL-6 in patients
with systemic JIA. The role of B cells in systemic JIA has
not been considered hitherto, and further work will need
to be done to determine whether the high levels of IL-6
production in a couple of our patients can be replicated
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
in a larger sample. Expression of IL-6 by these circulating white blood cells could be important in the pathogenesis of the multisystem manifestations of systemic
JIA. Neutrophil gene expression was not investigated
here, but should also be considered in future studies to
build a comprehensive picture of integrated cellular
function in systemic diseases.
The finding of an up-regulation of antiinflammatory IL-10 in active systemic JIA as compared with
inactive systemic JIA is counterintuitive. It is possible
that the IL-10 is being expressed in response to the
inflammation but that it is not effective against the
disease. IL-10 is expressed by macrophages and regulatory T cells, among others, and analysis of cell type–
specific expression would inform us of the pathways
involved. We found that IL-10 was expressed mainly by
monocytes from patients with active systemic JIA, but
only at low levels in T cells. This may indicate the need
for further investigation of the state of immune cell
suppression in systemic JIA. Production of SOCS-3 is
stimulated by both IL-10 and IL-6. We found that
SOCS-3 was produced in both monocytes and T cells in
patients with active systemic JIA. Further investigation
of other related genes expressed by each of these cell
types and subsequent pathway analysis will help to
explain the role of these two genes in systemic JIA.
Assignment of our gene list into functional
groups indicated that genes with many diverse functions
were up-regulated in active systemic JIA. Once expression in cell types has been determined, it will be
possible to see which cell types are expressing genes
from which functional groups. This may then inform us
about the pathways involved in the pathogenesis of
active systemic JIA.
Recently, Pascual et al (16) identified an IL-1
signature through gene expression profiling of healthy
PBMCs after in vitro incubation with systemic JIA sera.
Their work subsequently showed up-regulation of IL-1␤
and IL-1 receptor type II expression in their systemic
JIA patients as compared with healthy controls. We
failed to identify an IL-1 signature in the genes upregulated in active PBMCs from our systemic JIA patients. Of the 17 genes Pascual and colleagues found to
be up-regulated in systemic JIA, only 2, the genes for
serpin peptidase inhibitor, clade B (ovalbumin), member
2 (SERPINB2) and for thrombomodulin (THBD), were
found to be up-regulated in our patients with active
systemic JIA. Pascual also showed that sera from febrile
patients were more efficient at inducing IL-1␤ secretion
in PBMCs from healthy individuals than sera from
afebrile patients. This suggests that IL-1 is more related
1963
to fever, rather than to the cause of the full clinical
manifestations of systemic JIA. Furthermore, the positive results from 11 patients treated with IL-1 signaling
blockade (anakinra) have not been universally replicated
by other groups of investigators. For example, in an
ongoing study of our patients, only 1 of 6 patients has
responded to IL-1 blockade (17), and Quartier et al (18)
have presented data showing that only about one-half of
their systemic JIA patients responded to IL-1 blockade.
There are many pediatric systemic inflammatory
diseases in which gene expression studies have been
performed. This allows us to examine whether the genes
that are up-regulated in these diseases are common to
all the diseases or whether there are some diseasespecific genes. For this reason, we compared our data
with other published data. There are differences in the
Affymetrix microarrays used, summarizing methods,
types of analysis, and types of annotation in the different
data sets. Ideally, raw data from these studies would be
compared; however, such data are not publicly available.
It is nevertheless worthwhile to compare what is available, using the Ensembl ID of the published genes and
not the gene names, which can change because the
Ensembl database is regularly updated. By comparing
genes that are unequivocally the same from these data
sets, we have identified 246 genes that are up-regulated
during active systemic JIA as compared with inactive
systemic JIA, but were not apparently up-regulated in
other inflammatory systemic diseases in children. This
indicates there is very likely to be up-regulation of
disease activity–specific genes in systemic JIA.
Like systemic JIA, KD is a multisystem inflammatory illness of the young, with fevers, arthritis, rash,
and coronary artery involvement. Abe et al (10) compared PBMC and monocytic gene expression in patients
with KD before and after intravenous infusion of highdose immunoglobulin, which reduces inflammation and
fever. Thirteen of the genes that were overexpressed in
both systemic JIA and KD were expressed in the monocyte fraction (see Table 2). Comparing the expression in
monocytes and T cells using quantitative RT-PCR
showed that IL-6 was expressed by monocytes and not by
T cells (10). We have shown that in systemic JIA, B cells
produce even more IL-6 than monocytes, suggesting that
B cells may also be important for IL-6 expression in KD.
Abe and colleagues also showed that IL-10 was produced by T cells and monocytes, but T cells from our
patients did not produce IL-10. Together, these data
emphasize the importance of investigating multiple cell
types in complex diseases and suggest that gene expres-
1964
sion and function are not only cell-specific, but also
disease-specific.
Our comparison of the published gene expression
data for polyarticular JIA (9) with our gene expression
data for systemic JIA did not show many overlaps. This
could be due to the use of earlier versions of the
Affymetrix arrays in the polyarticular JIA study, but may
also be due to the fact that this mirrors the clinical
impression that systemic JIA is very different from the
other types of JIA. Since we specifically identified genes
that were differentially up-regulated in active versus
inactive systemic JIA, there may be susceptibility genes
involved in the pathogenesis of systemic JIA that are not
revealed by this comparison. Comparison with other
inflammatory diseases or subtypes of JIA would identify
whether some of the genes are unique to systemic JIA.
We attempted this using currently available information
and found that there is surprisingly little overlap between the up-regulated genes in our systemic JIA patients and those in diseases that have systemic inflammation as a feature and are clinically similar. This is the
first indication in the literature that there is expression
of systemic JIA–specific genes and that they are not
common to all inflammatory diseases.
In another study examining gene expression in
polyarticular JIA (19), an interferon-␥ signature was
found to be present in the cohort; however, the data are
difficult to interpret. The microarrays used contained
probes for only 2,382 genes, and many unconventional
analyses and alternative gene lists were produced. We
have shown that the interferon signaling molecule
ISGF-3␥ is not differentially up-regulated in the expression profiles of patients with active systemic JIA, suggesting that the interferon pathway is not involved in
systemic JIA. Bennett et al (11) also showed that while
the interferon signature was present in their SLE patients, it was absent in their cohort of systemic JIA
patients. We further confirmed by quantitative RT-PCR
the lack of up-regulation of ISGF-3␥ in the arrays of
cells from patients with active systemic JIA, thus lending
support for our method of comparing gene expression
data from PBMCs in different pediatric inflammatory
diseases.
In conclusion, this study is the first to demonstrate that there are disease activity–specific genes in
systemic JIA. We have also shown that analysis of gene
expression in different cell types is important if one is to
infer disease mechanisms from gene expression profiling
data.
OGILVIE ET AL
ACKNOWLEDGMENTS
We would like to thank Ivona Aksentijevich for allowing us to compare our data with her as-yet-unpublished
CINCA data and Virginia Pascual and Jacques Banchereaux
for providing us with the SLE expression data. We would like
to thank Mary Collins for helpful comments on the manuscript, Ana Gutierrez for help with the processing of samples,
Mark Fife for advice on the experiments, and Nipurna Jina for
performing the microarray hybridizations.
AUTHOR CONTRIBUTIONS
Dr. Woo 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. Ogilvie, Kellam, Woo.
Acquisition of data. Ogilvie, Hubank.
Analysis and interpretation of data. Ogilvie, Khan, Hubank, Kellam,
Woo.
Manuscript preparation. Ogilvie, Khan, Kellam, Woo.
Statistical analysis. Ogilvie, Khan, Kellam.
REFERENCES
1. Schneider R, Laxer RM. Systemic onset juvenile rheumatoid
arthritis. Baillieres Clin Rheumatol 1998;12:245–71.
2. Glass DN, Giannini EH. Juvenile rheumatoid arthritis as a complex genetic trait [review]. Arthritis Rheum 1999;42:2261–8.
3. Thomson W, Barrett JH, Donn R, Pepper L, Kennedy LJ, Ollier
WE, et al. Juvenile idiopathic arthritis classified by the ILAR
criteria: HLA associations in UK patients. Rheumatology (Oxford) 2002;41:1183–9.
4. Fishman D, Faulds G, Jeffery R, Mohamed-Ali V, Yudkin JS,
Humphries S, et al. The effect of novel polymorphisms in the
interleukin-6 (IL-6) gene on IL-6 transcription and plasma IL-6
levels, and an association with systemic-onset juvenile chronic
arthritis. J Clin Invest 1998;102:1369–76.
5. Donn R, Alourfi Z, De Benedetti F, Meazza C, Zeggini E, Lunt
M, et al. Mutation screening of the macrophage migration inhibitory factor gene: positive association of a functional polymorphism of macrophage migration inhibitory factor with juvenile
idiopathic arthritis. Arthritis Rheum 2002;46:2402–9.
6. Petty RE, Southwood TR, Baum J, Bhettay E, Glass DN, Manners
P, et al. Revision of the proposed classification criteria for juvenile
idiopathic arthritis: Durban, 1997. J Rheumatol 1998;25:1991–4.
7. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis
and display of genome-wide expression patterns. Proc Natl Acad
Sci U S A 1998;95:14863–8.
8. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci
U S A 2001;98:5116–21.
9. Barnes MG, Aronow BJ, Luyrink LK, Moroldo MB, Pavlidis P,
Passo MH, et al. Gene expression in juvenile arthritis and spondyloarthropathy: pro-angiogenic ELR⫹ chemokine genes relate to
course of arthritis. Rheumatology (Oxford) 2004;43:973–9.
10. Abe J, Jibiki T, Noma S, Nakajima T, Saito H, Terai M. Gene
expression profiling of the effect of high-dose intravenous Ig in
patients with Kawasaki disease. J Immunol 2005;174:5837–45.
11. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau
J, et al. Interferon and granulopoiesis signatures in systemic lupus
erythematosus blood. J Exp Med 2003;197:711–23.
12. Ogilvie EM, Fife MS, Thompson SD, Twine N, Tsoras M,
Moroldo M, et al. The –174G allele of the interleukin-6 gene
GENE EXPRESSION PROFILES IN SYSTEMIC JIA
13.
14.
15.
16.
confers susceptibility to systemic arthritis in children: a multicenter study using simplex and multiplex juvenile idiopathic arthritis families. Arthritis Rheum 2003;48:3202–6.
Fife MS, Gutierrez A, Ogilvie EM, Stock CJ, Samuel JM, Thomson W, et al. Novel IL10 gene family associations with systemic
juvenile idiopathic arthritis. Arthritis Res Ther 2006;8:R148.
Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC,
Relman DA, et al. Individuality and variation in gene expression
patterns in human blood. Proc Natl Acad Sci U S A 2003;100:
1896–901.
Griffiths MJ, Shafi MJ, Popper SJ, Hemingway CA, Kortok MM,
Wathen A, et al. Genomewide analysis of the host response to
malaria in Kenyan children. J Infect Dis 2005;191:1599–611.
Pascual V, Allantaz F, Arce E, Punaro M, Banchereau J. Role of
interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile
1965
idiopathic arthritis and clinical response to IL-1 blockade. J Exp
Med 2005;201:1479–86.
17. Ozen S, Hoffman HM, Frenkel J, Kastner D. Familial Mediterranean fever (FMF) and beyond: a new horizon. Fourth International Congress on the Systemic Autoinflammatory Diseases held
in Bethesda, USA, 6–10 November 2005. Ann Rheum Dis 2006;
65:961–4.
18. Quartier Lequerre T, Rosellini D, Sibilia J, Debandt M, KonePaut I, Job-Deslandre C. Anakinra in systemic-onset juvenile
idiopathic arthritis and adult onset Still’s disease [abstract]. Ann
Rheum Dis 2006;65 Suppl II;107.
19. Jarvis JN, Dozmorov I, Jiang K, Frank MB, Szodoray P, Alex P, et
al. Novel approaches to gene expression analysis of active polyarticular juvenile rheumatoid arthritis. Arthritis Res Ther 2004;61:
R15–32.
Документ
Категория
Без категории
Просмотров
3
Размер файла
242 Кб
Теги
expressions, profiler, idiopathic, specific, systemic, arthritis, genes, juvenile
1/--страниц
Пожаловаться на содержимое документа