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Gene expression profiling of peripheral blood from patients with untreated new-onset systemic juvenile idiopathic arthritis reveals molecular heterogeneity that may predict macrophage activation syndrome.

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ARTHRITIS & RHEUMATISM
Vol. 56, No. 11, November 2007, pp 3793–3804
DOI 10.1002/art.22981
© 2007, American College of Rheumatology
Gene Expression Profiling of Peripheral Blood
From Patients With Untreated New-Onset
Systemic Juvenile Idiopathic Arthritis Reveals
Molecular Heterogeneity That May Predict
Macrophage Activation Syndrome
Ndate Fall,1 Michael Barnes,1 Sherry Thornton,1 Lorie Luyrink,1 Judyann Olson,2
Norman T. Ilowite,3 Beth S. Gottlieb,4 Thomas Griffin,1 David D. Sherry,5 Susan Thompson,1
David N. Glass,1 Robert A. Colbert,1 and Alexei A. Grom1
expressed genes (P < 0.05) that distinguished patients
with systemic JIA from healthy controls (n ⴝ 30) were
identified. Clustering analysis indicated that expression
patterns correlated with serum ferritin levels. Three
main clusters distinguished systemic JIA patients with
highly elevated ferritin levels (including those with
subclinical macrophage activation syndrome) from
those with normal or only moderately elevated ferritin
levels. The first cluster comprised genes involved in the
synthesis of hemoglobins and structural proteins of
erythrocytes. This transcriptional profile was consistent
with immature nucleated red blood cells, likely reflective
of high red blood cell turnover. Also included were
transcripts indicating immature granulocytes. The second cluster was enriched for genes involved in cell cycle
regulation. The third cluster was enriched for genes
involved in innate immune responses, including those
involved in the negative regulation of Toll-like receptor/
interleukin-1 receptor–triggered inflammatory cascades
and markers of the alternative pathway of macrophage
differentiation. Additional differentially expressed
genes of interest were those involved in the cytolytic
pathway, including SH2D1A and Rab27a.
Conclusion. These data indicate that gene expression profiling can be a useful tool for identifying early
macrophage activation syndrome in patients with systemic JIA.
Objective. Systemic juvenile idiopathic arthritis
(JIA) is frequently associated with the development of
macrophage activation syndrome. This study was undertaken to better understand the relationship between
systemic JIA and macrophage activation syndrome.
Methods. Gene expression profiles were examined
in 17 patients with untreated new-onset systemic JIA, 5
of whom showed evidence of subclinical macrophage
activation syndrome (of whom 2 eventually developed
overt macrophage activation syndrome). Peripheral
blood mononuclear cells (PBMCs) were separated on
Ficoll gradients, and purified RNA was analyzed using
Affymetrix GeneChip expression arrays. A fraction of
the PBMCs were used for flow cytometry to define the
cellular composition of the samples.
Results. Two hundred twenty-five differentially
Supported in part by NIH grant P01-AR-048929 and by a
Translational Research Initiative Grant from the Children’s Hospital
Research Foundation of Cincinnati.
1
Ndate Fall, MS, Michael Barnes, PhD, Sherry Thornton,
PhD, Lorie Luyrink, BA, Thomas Griffin, MD, PhD, Susan Thompson, PhD, David N. Glass, MD, Robert A. Colbert, MD, PhD, Alexei
A. Grom, MD: Children’s Hospital Medical Center, Cincinnati, Ohio;
2
Judyann Olson, MD: Medical College of Wisconsin, Milwaukee;
3
Norman T. Ilowite, MD: Albert Einstein College of Medicine, Bronx,
New York; 4Beth S. Gottlieb, MD, MS: Schneider Children’s Hospital,
New Hyde Park, New York; 5David D. Sherry, MD: Children’s
Hospital of Philadelphia, Philadelphia, Pennsylvania.
Dr. Ilowite has received consulting fees, speaking fees, and/or
honoraria (less than $10,000 each) from Amgen and Xoma.
Address correspondence and reprint requests to Alexei A.
Grom, MD, Children’s Hospital Medical Center, Division of Rheumatology, Pavilion Building, 2-129, 3333 Burnet Avenue, Cincinnati, OH
45229. E-mail: groma0@cchmc.org.
Submitted for publication May 7, 2007; accepted in revised
form July 20, 2007.
At onset, systemic juvenile idiopathic arthritis
(JIA), which constitutes ⬃10% of all cases of JIA, is
distinguished from other forms of JIA by the prominence of extraarticular features, such as spiking fevers,
3793
3794
typical fleeting pink macular rash, generalized lymphadenopathy, hepatosplenomegaly, and occasionally, polyserositis (1). The typical laboratory features include
marked polymorphonuclear leukocytosis and thrombocytosis.
The clinical course during the later stages of
systemic JIA is highly variable (1). The systemic features
tend to subside during the initial months to years of the
disease. Approximately half of children with systemic
JIA recover almost completely. The other half continue
to have progressive involvement of more joints. The
joint disease seen in systemic JIA is in some respects
quite different from that in the other JIA subtypes. The
distinctive features include early destructive changes in
the joints and ankylosis involving the cervical spine,
wrists, and midfoot (2).
The pathogenetic mechanisms underlying the
heterogeneity of systemic JIA are not well understood.
Several lines of evidence suggest that the role of the
adaptive immune response in systemic JIA may be
rather limited compared with its role in other clinical
forms of the disease. In contrast, the contribution of
innate immunity may be much more prominent (3). On
this basis, many view systemic JIA as an autoinflammatory disorder. The relatively good clinical response of
systemic JIA to inhibition of interleukin-6 (IL-6) (4,5)
and/or IL-1 (6–8), therapeutic interventions that are
effective in autoinflammatory diseases with a known
genetic cause (9,10), is certainly consistent with this
view.
A perplexing feature of systemic JIA is its association with macrophage activation syndrome. Macrophage activation syndrome is a severe, potentially lifethreatening complication characterized by the excessive
activation of well-differentiated macrophages, resulting
in fever, hepatosplenomegaly, lymphadenopathy, severe
cytopenias, liver disease, and intravascular coagulation
(11). Most notably, the activated macrophages in this
syndrome exhibit hemophagocytic activity: they phagocytose normal hematopoietic elements, a phenomenon
that leads to the development of life-threatening cytopenias. Macrophage activation syndrome is seen usually
in association with systemic JIA, and very rarely with
other rheumatic diseases. It accounts for much of the
significant morbidity and mortality associated with systemic JIA. At least 10% of patients with systemic JIA
develop macrophage activation syndrome at some time
during the course of the disease (12). Two recent studies
demonstrated the existence of “subclinical macrophage
activation syndrome” in approximately one-third of patients with new-onset systemic JIA (13,14).
FALL ET AL
It is now increasingly recognized that macrophage activation syndrome bears close resemblance to a
group of histiocytic disorders collectively known as hemophagocytic lymphohistiocytosis (15,16). Hemophagocytic lymphohistiocytosis can be primary (familial) or
secondary (associated with infection or malignancy)
(17). One of the early events in hemophagocytic
lymphohistiocytosis/macrophage activation syndrome is
an uncontrolled and persistent expansion of activated T
lymphocytes and hemophagocytic macrophages. These
macrophages express CD163 (13,14,18), a scavenger
receptor that recognizes hemoglobin–haptoglobin complexes (19). CD163 appears to be expressed mainly on
alternatively activated phagocytic macrophages performing “scavenger” functions (20,21). Increased uptake
of hemoglobin–haptoglobin complexes by macrophages
leads to increased synthesis of ferritin, an acute-phase
response protein that binds free iron to prevent oxidative damage (18). A highly elevated level of serum
ferritin is an important diagnostic feature of hemophagocytic syndromes (16,17).
The most consistent underlying immunologic defect reported in patients with hemophagocytic lymphohistiocytosis has been impairment of cytotoxic functions
(16,17). In ⬃40% of patients with primary hemophagocytic lymphohistiocytosis, these immunologic abnormalities have been linked to mutations in the gene encoding
perforin, a protein that mediates cytotoxic activity of
natural killer (NK) and T cells (22). Other mutations
recently implicated in primary hemophagocytic lymphohistiocytosis affect some other proteins involved in the
cytolytic pathway (23). Of note, similar immunologic
abnormalities, i.e., poor NK cell cytolytic activity, often
associated with abnormal levels of perforin expression,
have also been reported to distinguish systemic JIA from
other forms of childhood arthritis (24).
Identification of the immunologic pathways leading to the increased incidence of macrophage activation
syndrome in systemic JIA may help not only with early
diagnosis of macrophage activation syndrome, but also
with identification of children at risk for macrophage
activation syndrome at the time of systemic JIA diagnosis, when different management strategies might avert
the development of the syndrome. The microarray technology that allows simultaneous assessment of the expression of thousands of genes has provided a new
approach to better understand pathogenesis and to
define the molecular basis for clinical heterogeneity of
many diseases, including JIA (25) and systemic lupus
erythematosus (26). We hypothesized that patients with
systemic JIA who have early-stage macrophage activa-
GENE EXPRESSION PROFILING FOR MACROPHAGE ACTIVATION SYNDROME IN SYSTEMIC JIA
3795
Table 1. Clinical characteristics of the patients with new-onset systemic juvenile idiopathic arthritis*
Patient
Characteristic
1†
2
3
4
5†
Age, years
15
4.5 14
4.5
4
Sex
M
F
F
M
M
Disease duration, weeks
12
6
11
3
5
Fever
⫹
⫹
⫹
⫹
⫹
Rash
⫹
⫹
⫹
⫺
⫹
Arthritis
⫹
⫹
⫹
⫹
⫹
Hepatosplenomegaly
⫺
⫹
⫺
⫺
⫺
Lymphadenopathy
⫹
⫹
⫺
⫺
⫺
Serositis
⫺
⫹
⫺
⫺
⫺
9.4 28.9
8.6 19.9 23.2
WBCs, ⫻103/␮l
Hemoglobin, gm/dl
10.8
8.1
7.5
7.6
9.7
Platelets, ⫻103/␮l
304
344
400
364
718
ESR, mm/hour
108
61
79
140
77
CRP, mg/dl
5.2 20.1 16.3 18.9
4.9
Serum ALT, units/liter
NA
16
8
9
29
Serum AST, units/liter
NA
60
43
36
21
D-dimers, ␮g/ml
NA
2.4
2.1 NA
NA
Serum ferritin, ng/ml
3,471 6,228 2,950 1,541 2,470
6
13
F
⬎2
⫹
⫹
⫹
⫺
⫹
⫺
14.4
11.2
453
44
NA
NA
NA
NA
48
7
8
9
10
11
12
13
14
15
16
17
11
2
3
3
14
6
1.5
2
5
3.5
3
F
M
M
F
M
M
F
M
F
F
M
5
12
1
8
16
8 ⬎12
6
9 ⬎12
6
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫹
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫹
⫹
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫹
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
⫺
23.4 18.4 25.5 25.1 27.4
5.4 14.3 13.5 18.5 8.3 20.6
9
8.8 13
9.9 11.2
9.1 10.8
9.2
9.4 10.6 10.5
693
522
835
603
521
670 505
504 677 519
427
140
59
51
98
115 ⬎140
63
NA 109
34
74
23
11.8
2.8 14.4 NA
NA
2.2 NA NA NA NA
NA NA
NA
NA
NA
NA NA NA NA NA NA
NA NA
NA
NA
NA
NA NA NA NA NA NA
NA NA
NA
NA
NA
NA NA NA NA NA NA
938 1,896 1,247 1,124 1,300
84
63
137 1,339
39
209
* WBCs ⫽ white blood cells; ESR ⫽ erythrocyte sedimentation rate; NA ⫽ not available; CRP ⫽ C-reactive protein; ALT ⫽ alanine
aminotransferase; AST ⫽ aspartate aminotransferase.
† Patient later developed macrophage activation syndrome.
tion syndrome could be identified based on their gene
expression profiles in peripheral blood, and the present
study was conducted to investigate this.
PATIENTS AND METHODS
Patients. After written informed consent was provided
by their legal guardians, the patients described herein were
enrolled in an institutional review board–approved prospective
multicenter study of gene expression profiles in childhood
arthritis. Systemic JIA was diagnosed based on the International League of Associations for Rheumatology diagnostic
criteria (27). The clinical phenotypes of the majority of the
patients with systemic JIA have been described previously (13).
Nine of the 17 patients with new-onset JIA were male and 8
were female; the median age was 4.5 years. The duration of
disease at the time of enrollment ranged from 1 week to 16
weeks (Table 1). Peripheral blood samples were obtained
during routine clinic visits prior to the initiation of treatment
with disease modifying antirheumatic drugs or steroids. As
previously described (13), 5 patients (patients 1–5 in Table 1)
had suspected subclinical macrophage activation syndrome
based on mild relative cytopenias, hyperferritinemia, and
increased serum levels of 2 potential macrophage activation
syndrome biomarkers, soluble CD163 (sCD163) and soluble
IL-2 receptor (sIL-2R) ␣-chains (13). Two of these 5 patients
eventually developed overt macrophage activation syndrome.
The control group consisted of 30 healthy children (11 male
and 19 female, median age 11.4 years).
Sample collection. Of the 17 patients with systemic
JIA, 12 were from Cincinnati Children’s Hospital Medical
Center (CCHMC), 2 from Milwaukee, 1 from Children’s
Hospital of Philadelphia, and 2 from Schneider Children’s
Hospital. Whole blood was collected using sodium heparin as
an anticoagulant. In all participating centers, peripheral blood
mononuclear cells (PBMCs) were separated by Ficoll gradient
centrifugation, placed in TRIzol within 4 hours of blood
collection, frozen, and stored at ⫺80°C. Special effort was
made to ensure the uniformity of sample collection and
processing in all participating centers. Personnel from all
participating centers received training at CCHMC, RNA samples were prepared by a single technician at CCHMC, and all
control samples were collected at CCHMC. For immunophenotyping of the samples, a fraction of the PBMCs was placed
in freezing media and stored at ⫺80°C. A separate serum
sample for ferritin assessment was collected and also stored
frozen at ⫺80°C. All samples were shipped overnight on dry
ice to CCHMC.
Microarray analysis. All RNA samples were prepared at the coordinating center (CCHMC) and sent to the
CCHMC Affymetrix core for processing. RNA quality was
assessed on a Bioanalyzer (Agilent, Wilmington, DE) prior
to labeling. For microarray analysis, labeled complementary
DNA (cDNA) was synthesized from total RNA using the
Ovation Biotin RNA Amplification and Labeling System
(NuGen, San Carlos, CA). Labeled cDNA was hybridized to
Affymetrix U133plus2.0 GeneChip. Data quality was assessed using the standard metrics of the CCHMC Affymetrix core, which include assessment of positive and
negative control features on the arrays. Expression values
were derived using the Robust Multiarray Average (RMA)
preprocessing method as implemented in GeneSpring GX
7.3 (Agilent). The RMA preprocessing was performed on a
large data set that included patients with other diseases. The
data discussed in this report have been deposited in NCBI
Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/
geo/ [accession no. GSE7753]).
3796
FALL ET AL
Figure 1. Flow cytometric analysis of peripheral blood mononuclear cells (PBMCs) isolated from patients with
untreated new-onset systemic juvenile idiopathic arthritis (n ⫽ 9) (solid bars) and from healthy controls (n ⫽ 30)
(stippled bars). Samples were stained for the markers of specific cell types present in PBMCs. A, Natural killer
(NK) cells (CD3⫺,CD56⫹), CD56dim NK cells (CD3⫺,CD56⫹dim), CD56bright NK cells (CD3⫺,CD56bright),
NKT cells (CD3⫹,CD56⫹), CD4⫹ T cells (CD4⫹,CD3⫹,CD25⫺), CD8⫹ T cells (CD8⫹,CD3⫹), Treg cells
(CD4⫹,CD3⫹,CD25⫹), B cells (CD19⫹CD3⫺), and mature myeloid cells (mainly monocytes)
(CD45⫹,CD33⫹). B, Immature granulocytes (CD15⫹,CD16⫺), granulocytes (CD15⫹,CD16⫹), and hematopoietic cells (CD34⫹). Values are the mean and SD.夹 ⫽ P ⬍ 0.01; 夹夹 ⫽ P ⬍ 0.05 versus controls, by Student’s
t-test.
Flow cytometric analysis. Single-cell suspensions of
PBMCs from patients with systemic JIA and healthy controls
were stained with fluorescein isothiocyanate (FITC)–
conjugated anti-CD16, phycoerythrin (PE)–conjugated antiCD15, allophycocyanin (APC)–conjugated anti-CD56, peridin
chlorophyll protein (PerCP)–conjugated anti-CD3, FITCconjugated anti-CD8, APC-conjugated anti-CD4, PEconjugated anti-CD25, FITC-conjugated anti-CD34, APCconjugated anti-CD19, PerCP-conjugated anti-CD45, and
APC-conjugated anti-CD33 (all from BD Biosciences, San
Jose, CA). Cells were gated on forward and side scatter
parameters and analyzed on a FACSCalibur flow cytometer
(BD Biosciences) using CellQuest software. Each cell population is presented as percentage of the total PBMCs.
Statistical analysis. Microarray expression values were
analyzed using algorithms available in GeneSpring GX 7.3.
RMA preprocessing was used to control for chip-to-chip
variation. Each probe set was then normalized to the median
expression of that probe set in the control samples. Differential
expression values were identified by analysis of variance and/or
Student’s t-test. In the Affymetrix U133plus2.0 GeneChip, a
number of genes are represented by ⬎1 oligonucleotide probe
set. In some analyses, when the list of differentially expressed
genes included multiple probe sets representing the same gene,
only the probe with the lowest P value was included. Hierarchical clustering using complete linkage was used to group
genes and samples by expression patterns.
RESULTS
PBMC immunophenotyping. When the overall
number of isolated PBMCs was sufficient, a fraction of
the cells was used for flow cytometry to define the
cellular composition of the samples to be used in
microarray experiments. As shown in Figure 1A, the
GENE EXPRESSION PROFILING FOR MACROPHAGE ACTIVATION SYNDROME IN SYSTEMIC JIA
relative cellular composition of PBMC preparations
from patients with systemic JIA (n ⫽ 9) was similar to
that of controls (n ⫽ 30), with the exception of a mild
decrease in the proportion of NK cells in the group of
patients with systemic JIA (P ⬍ 0.05 by Student’s t-test).
This difference was even stronger when the population
of CD56dim NK cells was analyzed separately (P ⬍ 0.01
by Student’s t-test). There was a trend toward an increased proportion of monocytes in patients with systemic JIA, but the difference from controls did not reach
statistical significance. Also worth noting was a small but
statistically significant increase in the proportion of
immature CD34⫹ hematopoietic cells in PBMC preparations from patients with systemic JIA (P ⬍ 0.05 by
Student’s t-test) (Figure 1B). There were no statistically
significant differences between patients and controls in
the numbers of NK T cells, B lymphocytes, CD4⫹ T
lymphocytes, CD8⫹ lymphocytes, or Treg (CD4⫹,
CD25⫹) cells.
Genes differentially expressed in patients with
new-onset systemic JIA compared with healthy controls.
Microarray-generated gene expression levels were analyzed as described in Patients and Methods. To identify
genes that were differentially expressed between healthy
controls and patients with untreated new-onset systemic
JIA, a rather stringent statistical approach (P ⬍ 0.05 by
t-test, with Bonferroni correction for multiple comparisons) yielded a list of 284 differentially expressed probe
sets. Elimination of the redundant probe sets as described in Patients and Methods yielded a list of 225
differentially expressed genes. The expression of 147
genes was increased, and the expression of 78 genes was
decreased, in systemic JIA patients compared with
healthy controls.
The median age of the control group (11.4 years)
was higher than that of the systemic JIA patients (4.5
years). To ensure that the age difference did not contribute to the differences in gene expression, a further
analysis was performed. The control group was divided
into 2 subgroups: those above and those below the
median age. Subgroups were compared to determine
genes whose expression varied with age. This analysis
yielded a list of genes (P ⬍ 0.05 by t-test, with Bonferroni
correction) that did not include any of the genes that had
been found to be differentially expressed in patients with
systemic JIA compared with controls, suggesting that the
age difference did not contribute to the observed differences in gene expression between patients and controls.
Systemic JIA heterogeneity based on clustering
analysis of differentially expressed genes. The 225 differentially expressed genes were analyzed by hierarchi-
3797
cal clustering using the complete linkage clustering
algorithm, in which distance between samples is inversely proportional to similarity (Figure 2). The systemic JIA patients clustered together, with the exception
of 1 individual. Interestingly, this patient (patient 17 in
Table 1) was asymptomatic at the 9-month followup,
after treatment with only nonsteroidal antiinflammatory
drugs. In contrast, all other patients with new-onset
systemic JIA had persistent disease. Although these
systemic JIA patients clustered, heterogeneity in gene
expression was noted, and was reflected in higher-level
“branching” of the clustering tree (Figure 2).
At least 5 distinct gene clusters contributed to the
heterogeneity of systemic JIA (Figure 2). Two clusters
included genes whose expression was lower in systemic
JIA patients compared with healthy individuals. Cluster
1 comprised various genes. Cluster 2 was enriched for
genes involved in cell physiology, genes encoding NK
receptors (including several killer cell lectin- and
immunoglobulin-like receptors), and genes involved in
the regulation of cell cycle and apoptosis.
The genes in the remaining 3 clusters exhibited
higher expression in patients with systemic JIA compared with healthy controls. Cluster 3 was enriched for
genes implicated in the innate immune response to
pathogens, most notably genes involved in negative
regulation of such responses (e.g., SOCS3 [the gene for
suppressor of cytokine signaling protein 3, or SOCS-3]
and ADM [the gene for adrenomedullin]). It also included markers of the monocyte/macrophage pathway of
cell differentiation (CD64, MS4A4A [the gene for
membrane-spanning 4 domain, subfamily A, member 4],
and GPR84 [the gene for orphan G-protein–coupled
receptor 84]). Several genes involved in the regulation of
coagulation (genes for protein S, hemostatic tissue factor inhibitor, and thrombospondin) were also present in
this cluster. Cluster 4 was enriched for genes involved in
the regulation of cell cycle, chromatin/nucleosome assembly, regulation of transcription, and apoptosis. Cluster 5 was highly enriched for genes involved in the
processes of hemoglobin synthesis and oxygen transport,
genes encoding structural proteins of red blood cells
(RBCs), as well as genes encoding rare hemoglobins,
suggesting that this cluster of genes might represent the
signature of the immature nucleated RBCs that can
copurify with PBMCs isolated on Ficoll gradients.
Clinical phenotypes in relation to clustering. To
determine whether clinical phenotypes in these patients
with systemic JIA correlated with the gene clusters, we
compared the position of individual patients in the
clustering tree with their clinical features. All patients
3798
FALL ET AL
Figure 2. Clustering analysis of the genes differentially expressed between patients with systemic JIA (SJIA) and controls. The list of differentially
expressed genes was generated using Student’s t-test, with Bonferroni-corrected P values less than 0.05 considered significant. Redundant probes
were eliminated as described in Patients and Methods. The complete linkage clustering algorithm, in which distance is a measure of similarity, was
used to generate the hierarchical clustering tree. In this tree, each row represents a separate gene and each column represents a separate individual.
The normalized expression level for each gene (rows) in each sample (columns) is indicated by color. Red, yellow, and blue rectangles reflect
expression levels that are greater than, equal to, or less than the mean expression level in healthy controls, respectively. The colored line below the
tree indicates patients (blue) versus controls (yellow). The colored boxes above the tree distinguish patients in the normal-ferritin group (blue) versus
those in the high-ferritin group (red). Patients with subclinical macrophage activation syndrome (MAS) (as defined previously [13]) are indicated
by stars. Note that 1 patient who initially met the diagnostic criteria for systemic JIA, but recovered from the disease within 9 months after being
treated with only nonsteroidal antiinflammatory drugs, clustered with the healthy controls. NK ⫽ natural killer.
had arthritis and active systemic disease (i.e., characteristic spiking fevers and rash) at the time of sampling.
Laboratory findings were reflective of high inflammatory activity in all patients (Table 1) and did not
correlate with patient clustering. The extent of the joint
disease was highly variable, ranging from mild oligoarticular to severe polyarticular disease, and failed to
correlate with the observed clustering (data not shown).
Since one of the major diagnostic criteria of
hemophagocytic syndromes is extreme hyperferritinemia, we assessed ferritin levels in the serum samples
from systemic JIA patients collected at the same time as
the PBMC samples for gene expression profiling. As
shown in Table 1, although the levels were highly
variable, there were 2 distinct subgroups of patients:
those with normal or only slightly elevated levels of
ferritin (ⱕ209 ng/ml) and those with very high ferritin
levels that were comparable with levels observed in
GENE EXPRESSION PROFILING FOR MACROPHAGE ACTIVATION SYNDROME IN SYSTEMIC JIA
3799
Table 2. Innate immune response gene cluster*
Description
Common
name
Probe ID
P
Fold
change†
Solute carrier family 25 member 37
Elastase 2, neutrophil
Resistin
Hypothetical protein MGC17301
Eukaryotic translation initiation factor 2c, 2
Ubiquitin-specific protease 32
Peptidoglycan recognition protein 1
Inhibin ␤A
Bactericidal/permeability increasing protein
Ribonuclease, RNase A family 3
Secretory leukocyte protease inhibitor
Carcinoembryonic antigen–related cell adhesion molecule 6
Myeloperoxidase
Carcinoembryonic antigen–related cell adhesion molecule 1
Haptoglobin
Haptoglobin-related protein
Suppressor of cytokine signaling 3
Adrenomedullin
Family with sequence similarity 20, member A
Membrane-spanning 4 domain, subfamily A, member 4
MSCP
ELA2
RETN
MGC17301
EIF2C2
USP32
PGLYRP1
INHBA
BPI
RNASE3
SLPI
CEACAM6
MPO
CEACAM1
HP
HPR
SOCS3
ADM
FAM20A
MS4A4A
226179_at
206871_at
220570_at
227055_at
225827_at
211702_s_at
207384_at
227140_at
205557_at
206851_at
203021_at
203757_s_at
203949_at
209498_at
206697_s_at
208470_s_at
206359_at
202912_at
241981_at
1555728_a_at
3.41 ⫻ 10⫺8
1.88 ⫻ 10⫺6
1.10 ⫻ 10⫺6
9.92 ⫻ 10⫺5
8.48 ⫻ 10⫺5
3.68 ⫻ 10⫺6
2.33 ⫻ 10⫺7
0.0042
4.82 ⫻ 10⫺5
0.000271
2.82 ⫻ 10⫺5
8.88 ⫻ 10⫺5
3.75 ⫻ 10⫺5
0.000342
4.43 ⫻ 10⫺6
2.02 ⫻ 10⫺6
5.52 ⫻ 10⫺6
5.31 ⫻ 10⫺6
6.26 ⫻ 10⫺9
1.04 ⫻ 10⫺7
1.82
2.81
2.95
2.00
2.00
2.02
2.34
6.49
8.72
5.83
5.08
8.16
4.53
2.68
2.93
2.98
1.26
1.85
2.35
1.71
* Cluster of genes selected from a list of probe sets that passed a one-way analysis of variance with a P value cutoff of 0.01 after Bonferroni correction
for multiple testing (conditions: “normal-ferritin,” “high-ferritin,” and control groups).
† In the “high-ferritin” versus the “normal-ferritin” group, among patients with new-onset systemic juvenile idiopathic arthritis.
hemophagocytic syndromes (ⱖ938 ng/ml). As shown in
Figure 2, there was a trend toward separation of these 2
groups of patients in the clustering tree. The 5 patients
in whom subclinical macrophage activation syndrome
was suspected based on mild relative cytopenias and
increased levels of sCD163 and sIL-2R ␣-chains (2
putative macrophage activation syndrome biomarkers
[13]) clustered within the “high-ferritin” group. Since
separation based on the degree of hyperferritinemia may
reflect important pathogenetic differences, the subsequent analysis included comparison between “normalferritin” and “high-ferritin” groups.
The “innate immune response” cluster. Some of
the genes comprising the innate immune response cluster are listed in Table 2. The list included the monocyte
markers CD64 and MS4A4A. There were several genes
whose expression is strongly up-regulated in the innate
immune response, including those triggered by Toll-like
receptor (TLR)/IL-1R signaling (SOCS3, ADM, BPI
[the gene for bactericidal/permeability increasing protein], INHBA [the gene for activin A, or inhibin ␤A],
SLPI [the gene for secretory leukoprotease inhibitor],
PGLYRP1 [the gene for peptidoglycan recognition protein 1], and RETN [the gene for resistin]). Other genes
in the cluster included solute carrier MSCP (the gene for
solute carrier family 25 member 37 [also known as
mitoferrin]), CEACAM1 (the gene for carcinoembryonic antigen–related cell adhesion molecule 1), and
CEACAM6/6. There were 2 neutrophil-derived proteins
(ELA2 [the gene for elastase 2] and MPO [the gene for
myeloperoxidase]), and FAM20A, a protein that plays a
role in myelocytic lineage commitment (28), was included. The cluster also included the genes for haptoglobin and haptoglobin-related protein. In our data set,
the levels of expression of 12 of the 16 genes in the
cluster were significantly higher in the group of patients
with high ferritin levels (Figure 3A).
Haptoglobin-correlated genes. The gene encoding haptoglobin was one of the most highly overexpressed genes not only in the innate immune response
cluster, but also in the entire data set. Since haptoglobin
is associated with the pathogenetic pathways relevant to
macrophage activation syndrome, we then investigated
for genes with expression patterns closely correlated
with the expression of the haptoglobin gene. In addition
to the entire previously described innate immune response cluster, the group of haptoglobin-correlated
genes included several genes encoding antiinflammatory
molecules, such as IL-1R antagonist and the light chain
of ferritin. Pearson correlation coefficients for these
genes were ⱖ0.7, suggesting that their expression was
highly coregulated.
Cytolytic pathway in systemic JIA patients with
high versus normal ferritin levels. The development of
the primary hemophagocytic syndromes has been linked
to the abnormal cytolytic function of NK cells, NK T
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FALL ET AL
pathway. We focused on the genes that are 1) involved in
the cytolytic pathway and 2) associated with the primary
genetic hemophagocytic syndromes. As shown in Figure
3B, the levels of expression of 2 genes, SH2D1A (Xlinked lymphoproliferative disease) and Rab27a (type 2
Griscelli syndrome) differed significantly between the
normal-ferritin and high-ferritin groups of patients with
systemic JIA (P ⬍ 0.05 by Student’s t-test).
DISCUSSION
Figure 3. Differences in gene expression between systemic juvenile
idiopathic arthritis patients in the high-ferritin group (solid bars) and
those in the normal-ferritin group (stippled bars). Expression levels of
each individual gene for each patient were normalized against the
mean expression level in controls (i.e., fold change, with the mean
expression level in controls set at 1). A, Genes from the innate immune
response cluster. B, Genes involved in the cytolytic pathway and
associated with genetic hemophagocytic syndromes (PRF1 associated
with familial hemophagocytic lymphohistiocytosis [HLH] type 2 [22],
MUNC 13-4 with familial HLH type 3 [51], STX11 with familial HLH
type 4 [52], Rab27a with type 2 Griscelli syndrome [46], SH2D1A with
X-linked lymphoproliferative disease [47], and LYST with ChédiakHigashi syndrome [53]). Values are the mean and SD. 夹 ⫽ P ⬍ 0.01;
夹夹 ⫽ P ⬍ 0.05 versus controls, by Student’s t-test.
cells, and cytotoxic CD8⫹ T cells. Thus, we were interested in whether the 2 groups of systemic JIA patients
could be distinguished based on levels of expression of
the genes whose products are involved in the cytolytic
The current study of gene expression profiles in
the peripheral blood of patients with untreated newonset systemic JIA revealed a strong signature that
distinguished patients from healthy controls. Considering the highly distinct clinical phenotype and the systemic nature of the disease, this was not surprising. The
more intriguing finding was the heterogeneity of patients with systemic JIA, correlating with some clinical
features of macrophage activation syndrome. Thus, hierarchical clustering analysis of the differentially expressed genes revealed at least 2 subgroups of patients
with several clusters of genes contributing to the observed heterogeneity. Overall, the expression patterns
correlated with serum levels of ferritin, and patients with
subclinical macrophage activation syndrome clustered
within the high-ferritin group. Since a very high serum
level of ferritin is an important diagnostic feature of
hemophagocytic syndromes, this distinction may reflect
important differences relevant to the pathogenesis of
macrophage activation syndrome.
We have termed one group of genes that distinguished high-ferritin versus normal-ferritin systemic JIA
an “erythropoiesis” cluster. Based on the genes present
in this cluster, it likely represents immature nucleated
RBCs that are present in PBMC preparations. Since a
fraction of the immature nucleated RBCs are CD34⫹
(29), these cells probably contribute to the small but
statistically significant expansion of CD34 cells in patients with systemic JIA compared with healthy controls,
as determined by flow cytometric analysis (Figure 1).
The increased numbers of these cells in most patients
with systemic JIA probably reflect the highly increased
turnover of RBCs. The fact that this signature is particularly strong in patients with high ferritin levels suggests
that these patients may have increased destruction of
RBCs, perhaps due to subclinical hemophagocytosis.
Consistent with this notion, subclinical macrophage activation syndrome and mild hemophagocytosis have
been demonstrated in approximately one-third of patients with new-onset systemic JIA (13,14).
GENE EXPRESSION PROFILING FOR MACROPHAGE ACTIVATION SYNDROME IN SYSTEMIC JIA
The main trends in another interesting cluster,
designated the “innate immune response” cluster, included 1) increased expression of genes associated with
monocyte/macrophage lineage (CD64, GPR84, and
MS4A4A), 2) increased expression of antimicrobial peptides (e.g., BPI, PGLYRP1), whose expression appears
to be induced by TLR/IL-1R signaling pathways (30),
and 3) increased expression of genes involved in the
negative feedback regulation of innate inflammatory
response (e.g., SOCS3, ADM, INHBA, SLPI, HP).
The presence of MS4A4A in the innate immune
response cluster is intriguing. MS4A4A is a member of
the family of molecules expressed in hematopoietic cells
at different stages of their differentiation (31). Expression of MS4A4A strongly increases in monocytes undergoing differentiation into macrophages, especially those
following the alternative pathway of macrophage differentiation (32). GPR84 is another marker whose expression is markedly induced in monocytes differentiating
into macrophages (33). In our previous study, we noted
highly increased serum levels of sCD163, suggestive of
expansion of CD163⫹ macrophages, in a subgroup of
patients with systemic JIA (13). Surprisingly, in the
current data set the expression of CD163 itself was not
significantly higher in patients with systemic JIA compared with controls. This is likely related to the fact that
by the time macrophages up-regulate the expression of
CD163, they tend to leave the peripheral circulation and
accumulate in tissue. The small expansion of monocytes
in PBMC preparations from patients with systemic JIA
(Figure 1) might have contributed to the increase in the
numbers of transcripts of the monocyte/macrophage
markers. However, a very high degree of overrepresentation of these transcripts (ⱖ1.7-fold change, compared
with only a mild degree of expansion of monocytes)
suggests that the observed expression patterns are related to increased gene expression in these cells, rather
than solely to the increase in their numbers.
The activated status of the myeloid cells in systemic JIA is also evident based on the increased numbers of transcripts whose expression was induced during
the innate immune responses triggered by TLR/IL-1R
signaling. This includes such genes in the immune
cluster as ADM (34,35), BPI (36), INHBA (37,38),
PGLYRP1 (30,39), and SLPI (40). These findings are
consistent with previous reports implicating IL-1 in the
pathogenesis of systemic JIA (6–8). Interestingly, products of many genes in the cluster have been shown to
participate in the negative feedback loops involved in
the down-regulation of the TLR/IL-1R–mediated inflammation. One example is SOCS-3. It belongs to the
3801
SOCS family of proteins, which are up-regulated during
innate immune responses, in which they are key negative
regulators of cytokine signaling (41–43). In our data set,
the innate immune response cluster was much more
prominent in the group of patients with high ferritin
levels (Figure 3A).
The genes for haptoglobin and haptoglobinrelated protein were other genes in the innate immune
response cluster. Since hemoglobin–haptoglobin complexes are recognized by CD163, we identified genes
whose expression patterns closely correlated with the
expression of the haptoglobin gene. In addition to the
entire innate immune response cluster, the list of the
haptoglobin-correlated genes included those for IL-1R
antagonist and the light chain of ferritin. This observation suggests that high levels of ferritin seen in a
subgroup of patients with systemic JIA may be a part of
the antiinflammatory cascade described above. This is
consistent with current understanding of the role of
haptoglobin and ferritin in prevention of oxidative damage induced by iron-derived reactive oxygen species
(Figure 4), a phenomenon that likely accompanies increased RBC turnover.
Results of the clustering analysis, as well as the
analysis of haptoglobin-correlated genes, suggest coregulated expression of the marker of the alternative
pathway of macrophage differentiation (MS4A4A) (32),
haptoglobin, and several negative regulators of innate
immune responses, including SOCS-3. SOCS-3 is induced by a number of proinflammatory cytokines, most
notably, IL-1 (41,42) and IL-6 (43). It interferes with the
signaling pathways of these cytokines, thus providing a
negative feedback loop in inflammatory cascades induced by IL-1/IL-6. SOCS-3 may also alter the responsiveness of monocytes to interferon-␥ (IFN␥) (41) and
induce switching from Th1 to Th2 immune responses
(44).
Coregulated expression of the markers of the
alternative pathway of macrophage differentiation and
several negative regulators of innate immune responses
including SOCS-3 raises the question as to whether in
new-onset systemic JIA, the induced antiinflammatory
cascade might contribute to the skewing of the differentiation of monocytes toward the development of scavenger macrophages, which exhibit increased phagocytic
activity and are CD163⫹. Consistent with this hypothesis is the observation that dendritic cells (DCs) transduced with SOCS3 exhibit a DC type 2 phenotype that
directs Th2 responses (44). Although similar experiments have not been performed on macrophages,
SOCS3 overexpression has been associated with the shift
3802
FALL ET AL
Figure 4. Role of hemoglobin–haptoglobin (Hb–HP) scavenger receptor CD163, heme oxygenases, and ferritin
in adaptation to oxidative stress induced by free heme and iron. Free heme is a source of redox active iron. To
prevent cell damage caused by iron-derived reactive oxygen species, haptoglobin forms a complex with free
hemoglobin. The hemoglobin–haptoglobin complexes then bind to CD163 and are internalized by the
macrophage. Endocytosis of hemoglobin–haptoglobin complexes leads to up-regulation of heme oxygenase
enzymatic activity. Heme oxygenase degrades the heme subunit of hemoglobin into biliverdin, which is
subsequently converted to bilirubin, carbon monoxide, and free iron. The free iron is either sequestered in
association with ferritin within the cell or transported and distributed to red blood cell precursors in the bone
marrow.
toward alternatively activated macrophage responses, at
least in some experimental systems (45). Also in accordance with the hypothesis, increased expression of INHBA, another gene in the cluster, has also been associated with the alternative pathway of macrophage
differentiation (38).
Comparisons between systemic JIA patients
grouped according to high versus normal ferritin levels
also revealed statistically significant differences in the
levels of expression of several genes whose products are
critical to the activation of the cytolytic pathway. This
includes Rab27a and SH2D1A (46,47). Mutations in
these genes have been implicated in the development of
type 2 Griscelli syndrome and X-linked lymphoproliferative disease, both associated with hemophagocytic
syndrome. These observations suggest that the pathways
that distinguish high-ferritin versus normal-ferritin systemic JIA may also be relevant to the development of
the cytolytic abnormalities seen in patients with the
disease. In contrast, perforin expression was low in most
patients with systemic JIA, consistent with the trend
toward lower numbers of NK cells as determined by flow
cytometric analysis. The trend was particularly strong for
CD56dim NK cells, a cell population that has been
associated with particularly high levels of perforin expression (48).
Another interesting finding was the relative paucity of genes whose expression is induced by IFN␥. The
scarcity of IFN-induced genes in the list of genes distinguishing systemic JIA patients from controls is intriguing
since the presence of IFN␥ in the serum of patients with
systemic juvenile idiopathic arthritis has been demonstrated in many studies. One possible explanation is the
altered responsiveness of immune cells to IFN␥. Indeed,
SOCS3, one of the most highly overexpressed genes in
our data set, has been shown to interact with the
JAK/STAT signaling pathways, leading to decreased
responsiveness of monocyte/macrophages to IFN␥ (41).
GENE EXPRESSION PROFILING FOR MACROPHAGE ACTIVATION SYNDROME IN SYSTEMIC JIA
Taken together, these observations support the
hypothesis that the antiinflammatory negative feedback
loops induced by innate immune responses involving
IL-1 and IL-6 may contribute to conditions that favor
the alternative pathway of macrophage differentiation,
leading to the development of the CD163⫹ “scavenger”
phenotype. This phenotype is associated with increased
phagocytic activity and has been implicated in the development of hemophagocytic syndromes. Interestingly,
similar pathways involving mild hemophagocytosis have
been described in septic shock syndrome (49,50), another TLR-triggered phenomenon in which IL-1 and
IL-6 play a major role. In the presence of cytotoxic
abnormalities, this may create a “set-up” for the development of full-blown macrophage activation syndrome.
In summary, gene expression profiling of PBMCs
from patients with untreated new-onset systemic JIA
revealed molecular heterogeneity that corresponded to
serum ferritin levels, a clinical feature that is important
in the diagnosis of hemophagocytic syndromes including
macrophage activation syndrome. The 3 main clusters of
genes that contributed to the heterogeneity are those
involved in erythropoiesis, cell cycle regulation, and
negative regulation of the innate immune response
(particularly those induced by TLR/IL-1R signaling).
Based on these data we propose that the gene expression
signature that includes these clusters may help identify
patients at risk for the development of full-blown macrophage activation syndrome, in whom closer observation as well as timely treatment modifications may be
required.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
AUTHOR CONTRIBUTIONS
Dr. Grom 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. Fall, Barnes, Thornton, Ilowite, Gottlieb, Griffin,
Sherry, Thompson, Glass, Colbert, Grom.
Acquisition of data. Fall, Barnes, Luyrink, Olson, Ilowite, Gottlieb,
Griffin, Sherry.
Analysis and interpretation of data. Fall, Barnes, Thornton, Griffin,
Thompson, Glass, Colbert, Grom.
Manuscript preparation. Fall, Barnes, Thornton, Luyrink, Olson,
Ilowite, Gottlieb, Griffin, Sherry, Thompson, Glass, Colbert, Grom.
Statistical analysis. Fall, Barnes.
16.
17.
18.
19.
20.
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