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Blood First Edition Paper, prepublished online December 20, 2012; DOI 10.1182/blood-2012-05-425769
GENE EXPRESSION SIGNATURES DIFFER BETWEEN DIFFERENT CLINICAL
FORMS OF FAMILIAL HEMOPHAGOCYTIC LYMPHOHISTIOCYTOSIS
SHORT TITLE: gene expression in FHL
Janos Sumegi1, Shawnagay V. Nestheide1, Michael G. Barnes2, Joyce Villanueva1,
Kejian Zhang3, Alexei A. Grom2, and Alexandra H. Filipovich1
1
Division of Bone Marrow Transplantation and Immunodeficiency,
2
Division of
Rheumatology, 3Division of Human Genetics, Cincinnati Children’s Hospital Medical
Center, University of Cincinnati Faculty of Medicine, Cincinnati, Ohio
CORRESPONDING AUTHOR: Janos Sumegi, Division of Bone Marrow Transplantation
and Immunodeficiency, Cincinnati Children’s Hospital Medical Center, 3333 Burnett Ave,
Cincinnati, Ohio 45229, USA, Tel.: 513-636-5976; Fax: 513-636-2880, email:
[email protected]
1
Copyright © 2012 American Society of Hematology
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ABSTRACT
We performed gene expression profiling of peripheral mononuclear cells obtained from
patients with familial hemophagocytic lymphohistiocytosis (FHL) to screen for biologic
correlates with the genetic and/or clinical forms of this disease.
Unsupervised
hierarchical clustering of 167 differentially expressed probe sets, representing 143
genes, identified three groups of patients corresponding to the genetic forms and clinical
presentations of the disease. Two clusters of up- and downregulated genes separated
patients with perforin deficient FHL from patients with unidentified genetic cause(s) of
the disease. The clusters comprised genes involved in defense/immune responses,
apoptosis, zinc homeostasis, and systemic inflammation.
Unsupervised hierarchical
clustering partitioned patients with unknown genetic cause(s) of FHL into two welldistinguished subgroups. Patterns of up- and downregulated genes separated patients
with “late-onset” and “relapsing” forms of FHL from patients with an “early-onset
and
rapidly evolving” form of the disease. A cluster was identified in patients with “late onset
and relapsing” form of FHL related to B and T cell differentiation/survival, T cell
activation and vesicular transport.
The resulting data suggest that unique gene
expression signatures can distinguish between genetic and clinical subtypes of FHL.
These differentially expressed genes may represent biomarkers that can be used as
predictors of disease progression.
2
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INTRODUCTION
Familial hemophagocytic lymphohistiocytosis (FHL) is a collection of autosomal
recessive disorders of the immune system characterized by the uncontrolled activation
of T cells and macrophages, and by the overproduction of inflammatory cytokines
secondary to defects in genes coding for proteins involved in the granule-dependent
cytolytic pathway (1). Linkage studies in patients with FHL have identified a candidate
region containing a still unknown gene on chromosome 9q21 (FHL1; MIM 603552) (2).
In a separate category of patients, termed FHL2 (MIM 603553), defects in PRF1 on
chromosome 10q21 lead to a significant reduction or complete absence of perforin
resulting in impaired cytolytic activity of T cells and NK cells (3–5). Patients with FHL
type 3 (MIM 608898) carry mutations in the UNC13D gene on chromosome 17q25 (6).
Recent studies have identified further mutations in a gene coding for syntaxin 11
(STX11) that define FHL type 4 (MIM 60352) (7).
More recently, mutations in the
STXBP2 gene encoding a syntaxin binding protein have been described in patients with
FHL5 (MIM63101) (8). Mutations in PRF1, UNC13D, STX11, and STXBP2 account for
less than 50% of North American familial hemophagocytic lymphohistiocytosis cases (9).
The principal underlying defect in FHL is impaired T and natural killer (NK) cell
cytotoxicity (1).
Characteristic laboratory findings include elevated serum levels of
ferritin, triglycerides, transaminases, bilirubin, and lactate dehydrogenase, along with
decreased levels of fibrinogen (1). Elevated blood levels of proinflammatory cytokines,
including interleukin-6 (IL-6), IL-8, IL-18, macrophage inflammatory protein-1alpha (MIP1α), macrophage colony-stimulating factor (M-CSF), interferon-gamma (IFNγ), and
tumor necrosis factor-alpha (TNFα), as well as elevated plasma levels of soluble IL-2
receptor (CD25), sCD95-ligand, and sCD163 have also been reported; other studies
have revealed elevated plasma levels of IL-12 and IL-10 (1, 10). Hemophagocytosis is
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an indicator of cytokine-driven macrophages and histiocytes (1, 10). All genetic forms of
FHL can be rapidly fatal if untreated (1, 9, 10). Affected patients die of overwhelming
infections or uncontrolled systemic inflammation and multi-organ failure. Whilst FHL is a
monogenic defect of immune regulation, it is not a homogenous disease in the traditional
sense and is more akin to a syndrome or a broad heterogeneous disease (1, 9). The
concept of FHL subclasses is clinically relevant as it could have significant implications
for the design of therapeutic strategies.
FHL is known to affect children in early
childhood (1, 9). However, exceptions to this general rule have been observed, as there
are an increasing number of reports of familial cases with an age of onset of 18 years
and older (11). The common denominator in FHL is the development of an accelerated
phase,
characterized
hepatosplenomegaly,
by
acute,
central
unremitting
nervous
system
systemic
(CNS)
inflammation,
involvement,
fever,
coagulation
abnormalities, and highly elevated serum levels of proinflammatory cytokines. Marked
hemophagocytosis is usually found in bone marrow and other tissues. Treatment with a
combination of immunosuppressive agents, usually leads to control of manifestations of
accelerated phases and reinduction of remissions.
Although
immune
and
chemotherapeutic
regimens
targeting
activated
macrophages/histiocytes and T cells, are effective in achieving remission of symptoms,
relapse may occur during continuing therapy or after stopping the initial treatment (9, 12,
13). Deterioration of liver function and blood counts along with steady increases in
serum levels of ferritin, soluble CD25 and CD163, may be indicators of relapse (14).
Relapses occur in patients with severe deficiencies of cytotoxic function and the longterm prognosis for patients is death unless hematopoietic stem cell transplantation
(HSCT) is administered. HSCT is currently the only available treatment to cure FHL,
and thus represents the definitive therapy of choice for many patients.
4
It is not
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uncommon, however, for patients to develop recurrence of active disease before a
suitable donor is identified. Physicians are also faced with the task of monitoring the
patient’s response to therapy toward either remission of illness or its progression to CNS
involvement, multisystem organ failure, and eventually death (10). Clinical management
guidelines provide a comprehensive picture of patient status, but fail to provide
prognostic information essential to guide patient stratification and therapeutic
intervention.
The aim of our present study was to utilize genome-wide expression profiling to
identify gene expression signatures, which may be useful to distinguish clinical subtypes
and predict eventual outcomes in cases of familial hemophagocytic lymphohistiocytosis.
MATERIALS AND METHODS
Patients and Sample Preparation.
Patients with active FHL, diagnosed
according to current diagnostic guidelines of the Histiocyte Society (fever, splenomegaly,
cytopenia, hypertrigliceridemia, hemophagocytosis, low or absent NK function, elevated
ferritin and soluble CD25), were included in this study (12, 13). Blood samples from
eleven patients who had been enrolled in an IRB-approved study were collected for
microarray analysis between 2003 and 2005 (GEO Series accession number
GSE26050).
An independent cohort of 21 patients applying the same diagnostic
guidelines, were selected for the validation studies. Blood samples from these patients
were collected between 2008 and 2010. Routine laboratory tests, such as white blood
cell count, hemoglobin level, platelet count, erythrocyte sedimentation rate, and Creactive protein, were available for the majority of patients and were obtained at the time
of sampling. Mutational analysis of PRF1, UNC13D, STX11, STXBP2, and RAB27A
were performed as previously described (11). In patients with the absence of specific
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genetic diagnosis, and involved in this study, the genetic basis was established based
on positive family history with previously affected siblings. The patients’ clinical and
laboratory characteristics are shown in Tables 1 and 2. Twelve controls were recruited,
for the validation studies from the outpatient department of CCHMC, using the following
exclusion criteria: recent febrile, illness and recent use of anti-inflammatory medications.
Clinical, genetic and laboratory data were reviewed by A.H.F., K.Z., and J. V. to select
cases for this study.
There were no significant differences in white blood cell,
lymphocyte, or neutrophil counts between patients.
The ethics committee at the Cincinnati Children’s Hospital Medical Center
approved the studies.
Signed informed consent was obtained from all healthy and
diseased individuals who provided blood samples or from their guardians where relevant
in accordance with the Declaration of Helsinki. Blood samples were collected before the
onset of HLH-specific treatments. Peripheral blood was collected and peripheral blood
mononuclear cells (PBMCs) were isolated over Ficoll; RNA was immediately stabilized in
TRIzol reagent (Invitrogen, Carlsbad, CA) and stored at -80oC.
RNA Isolation and Microarray Procedures. Total RNA was extracted from
PBMCs using TRIzol reagent according to the manufacturer’s instructions, and was
further purified using an RNeasy Mini Kit (Qiagen Inc., Valencia, CA). RNA integrity was
assessed by electrophoresis using an Agilent BioAnalyzer (Agilent Santa Clara, CA).
Gene expression data were obtained using the Affymetrix Human Genome U 133 Plus
2.0 GeneChip® according to the manufacturer’s recommendations (Affymetrix, Santa
Clara, CA) and as described previously (15).
Data quality was assessed using the
standard metrics of the CCHMC Affymetrix Gene Expression Analysis Core, including
assessment of positive and negative controls on the arrays. The expression data that
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passed the quality test were analyzed using SAM (Significant Analysis of Microarray) to
select RNAs that were significantly changed between patients (p < 0.05). These RNAs
were then subjected to the Tukey Honestly Significant Difference (HSD) test (p < 0.05).
The differentially expressed genes were uploaded to Database for Annotation,
Visualization, and Integrated Discovery (DAVID) Bioinformatics Resource where a
Functional Annotation Chart tool was used to generate Gene Ontology (GO) terms (16,
17).
qRT-PCR. Microarray results were validated by qRT-PCR utilizing SYBR-green
based chemistry.
For validation, 21 of the most differentially expressed genes by
microarray, 10 up-regulated and 11 down-regulated, were analyzed on a custom array
made by SABiosciences (Qiagen Inc.) for qRT-PCR analysis. Total RNA (500 ng) was
reverse-transcribed with a SABiosciences RT2 First Strand Kit and applied to the PCR
array plates. These plates were then processed in an Applied Biosystems 7500 RealTime PCR System (Life Technologies Corporation, Carlsbad, CA), using automated
baseline and threshold cycle detection. Data were analyzed using the web-based PCR
array data analysis tool from SABiosciences. Relative changes in the transcript levels of
differentially expressed genes compared with the controls were expressed as ΔΔCt
values (ΔΔCt = ΔCtpatient − ΔCtcontrol) using SABiosciences software. The custom array
contained two housekeeping genes: glyceraldehyde-3-phosphate dehydrogenase
(GAPDH) and β-2 microglobulin (B2M), as mandatory controls for each experiment.
RESULTS
In a previous study, by gene expression profiling patients with FHL and normal
pediatric controls, we identified differentially expressed genes corresponding to various
signaling and metabolic pathways relevant to the pathophysiology of hemophagocytic
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lymphohistiocytosis (15).
expression level.
Data suggested patient heterogeneity at the genomic
In the present study, we have further explored gene expression
analysis of patients with FHL and investigated whether peripheral blood expression
patterns can discriminate among genetic or clinical forms of the disease. Following the
recommendation of Ogilvie et al. (18), we reasoned that healthy children are not
necessarily an appropriate control group for identifying differentially expressed genes in
various forms of FHL, especially when the numbers of patients are small and the
differences observed may simply reflect variations between healthy individuals and
patients with FHL. By directly comparing samples obtained from patients with FHL2 to
patients with positive family history of FHL and not yet identified genetic cause of the
disease, we expected of targeting gene expression pattern differences related to
subtypes of the disease.
Heterogeneity of FHL based on clustering analysis of differentially
expressed genes. PBMCs were derived from whole blood obtained from 11 patients
with FHL.
Three patients carried disease-causing mutations in the gene encoding
perforin and the remaining eight patients had wild type allele(s) of PRF1, UNC13D,
STX11, STXBP2, and RAB27A (Table 1). As previously described these eight patients
did not have EBV infection, malignancy or rheumatologic disorders prior of the onset of
hemophagocytic lymphohistiocytosis (15, 19). They had positive family history of HLH,
suggesting that as yet unidentified gene(s) could account for their disease.
Microarray-generated gene expression levels were analyzed as described in the
Materials and Methods. Gene expression data were subjected to RMA pre-processing
and then normalized to the medium of all samples.
We identified 167 differentially
expressed probe sets (a list of the upregulated and downregulated genes, with fold
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change differences, and P-values is shown in Supplemental Table 1) representing 143
unique and predicted genes (FDR 5%) when patients with FHL2 were compared with
patients with yet unidentified genetic cause(s) of disease. Samples and differentially
expressed probe sets were then ordered using hierarchical clustering and three groups
of patients (ha, hb, and hc) could be distinguished (Figure 1). We next analyzed the
clinical features of the patients in the clustering tree to determine whether their genotype
or clinical phenotypes correlated with the gene expression patterns.
Patients with inactivating mutations of the perforin gene. Of the patients
designated group ha (Figure 1), P35 and P59 harbored the homozygous mutations
148G>A (V50M) and homozygous 50delT (L17fsX50) of PRF1, respectively (15, 19).
Patient P33 had a compound heterozygous mutation of 50delT (L17fsX50) and
1442A>C (Q481P) in the perforin gene (19). The age of patients at onset of the disease
ranged from two months to seven years, with average of 2.4 years. Patients P33, P59,
and P35 formed at least two clusters of differentially expressed genes (Figure 1, gene
cluster A and B), which distinguished them from patients with a yet unidentified genetic
cause(s) of FHL. Cluster A was comprised of down-regulated genes, whilst cluster B
highlighted the genes showing increased expression in comparison with patients with as
yet unknown genetic cause(s) of this disease. Using the DAVID Functional Annotation
Tool (16, 17), the genes in cluster A were shown two major functional categories of
genes coding for proteins related to immune response proteins such as SEMA4D and
DEFB4A, and apoptosis regulatory proteins CDKN2C, and LRDD. Cluster A was also
enriched in genes coding for metal binding proteins (WBSCR17, CCDC72, PRICKLE2,
and ZNF333). Cluster B was found to be enriched in genes coding for proteins involved
in the transmembrane receptor protein tyrosine kinase signaling pathway, enzyme-linked
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receptor protein signaling pathways, cyclic nucleotide phosphodiesterase activities, and
genes involved in regulation of cell cycle and apoptosis.
Patients with unidentified genetic causes of disease.
Based on the
branching patterns of the clustering tree, the eight patients with unidentified genetic
cause(s) of hemophagocytic lymphohistiocytosis formed two groups, hb and hc (Figure
1). Patients P76, P92, P94, P96, and P98 of group hc had an early onset of the disease
with a median of 2.3 years. Patients in group hc had a rapidly evolving form of FHL, as
did the ha patients. These patients developed a rapidly progressive form of FHL, which
was frequently associated with early CNS involvement.
This group of patients
responded to treatment with resolution of symptoms and normalization of inflammatory
markers. As shown in Figure 1, a cluster of down-regulated genes (cluster C) separates
this group of patients from patients in groups ha and hb. Cluster C contains 25 genes
(Figure 1, cluster C), which according to DAVID Functional Annotation Chart analysis
(16, 17) are enriched for genes related to cellular immunity, the myeloid cell lineage, and
apoptosis.
A subset of patients (hb) on the clustering tree is visibly separated from group hc
patients with a rapidly evolving form of FHL (Figure 1). Group hb patients during the
twelve-month follow up period, experienced recurrence, one or more episodes of
accelerated phases. These patients are distinguished from patients in group ha or hc by
two gene clusters (D and E) of up- and downregulated genes (Figure 1). Functional
analysis based on the DAVID bioinformatics tool related the up-regulated genes (cluster
D) to diverse biological functions and the down-regulated genes (cluster E) to
transcription, immunoregulation and differentiation/development of B, T, and dendritic
cells.
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The three patients (P1002, P101, and P66) in group hb, who experienced
relapse were also classified as having a late-onset form of FHL, with a median age of
10.0 years. Variables other than relapse and age-onset were investigated as a possible
explanation for the differentially expressed genes.
Clustering was found not to be
related to the race or gender of the patients.
Validation of gene expression changes by qRT-PCR using an independent
cohort of patients.
Confirmatory analysis of gene expression array study was
performed using an independent method, qRT-PCR, and a new cohort of 21 patients
who had not been included in the microarray analysis (Table 2). Six (LH1, LH6, LH15,
LH17, LH21 and LH26) of the 21 patients had experienced one or more episodes of
disease recurrence during the study period of six month. The remaining 15 patients
responded either to HLH-therapy, or were on “continuation therapy” or received HSCT
(Table 2).
Of the 167 differentially expressed probe sets twenty-one were selected for
validation experiments (Table 3). The most differentially expressed genes from each
gene cluster were selected to compare the two methods. Two control housekeeping
genes, GAPDH and B2M were also included for normalization of the target genes. All
RNA samples from the patients and controls were converted to cDNA using the same
reverse-transcriptase
procedure.
enzyme
from
SABiosciences
following
the
recommended
Comparisons of average fold changes were calculated using the
SABiosciences web-based software, RT2 Profiler PCR Array Data Analysis version 3.5.
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∧
Supplemental Table 2 shows the validated genes, indicating 2 (-Avg(Delta (Ct)),
fold change and the P value and the number of RNA samples that were included in each
comparison. Figures 2-4 shows the up- and downregulated genes and fold regulation
changes when compared to the controls.
Quantitative RT-PCR results of RNA samples obtained from patients with FHL2
(LH2 and LH3) confirmed the previous microarray results for genes, CDKN2C, RNF26,
UQCRH, and MTHFD1L (Figure 2).
The qRT-PCR data confirmed that the expression of GIMAP1, DCKAD, FKBP5
and FLJ20366 was at lower levels in patients with the “late onset” and “recurrent form” of
FHL than in patients without any history of relapse during the defined study period
(Figure 3A).
However, the qRT-PCR results failed to distinguish any significant
correlation between the expression levels of GTPBP2 and relapsing form of FHL. The
qRT-PCR results did reveal that the correlation between the expression levels of
GIMAP1, DCKAD, FKBP5 and FLJ20366 and the emergence of relapse is stronger than
between expression levels of GIMAP1, DCKAD, FKBP5 and FLJ20366 and the age of
onset of FHL.
Genes (LRDD, HLA-DRB4, ADCK2, C18orf8, and HIAT1) from cluster D showed
variable expression according to real time RT-PCR analysis, and failed to recapitulate
the association with relapses in patients with FHL (Figure 3B).
Additional genes with significant differences in expression between the relapsing
and rapidly evolving forms of FHL were ETV3, KIF5C, and DOCK4. These genes were
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found to be upregulated in patients with relapse as compared with patients without an
episode of recurrence of hemophagocytic lymphohistiocytosis during the study period
(Figure 4).
RT-PCR analysis confirmed the microarray data and validated seven genes
GIMAP1, DCKAD, FKBP5, FLJ20366, ETV3, KIF5C, and DOCK4 that show significant
differences in expression between patients with FHL who experienced recurrence of the
acute phase and patients who did not have an incidence of relapse within the study
period (Figure 5).
There was one exception to the association of disease form by gene expression.
Sample 9 did not experienced relapse within the study period but showed suppressed
GIMAP1, DCKAD, FKBP5, and FLJ20366 expression. This sample also had a pattern of
gene expression associated with rapidly evolving form of the disease.
DISCUSSION
FHL is an aggressive and potentially fatal disease presenting significant
challenges for clinicians related to both its diagnosis and treatment. The features of FHL
include altered cytolytic functions, systemic release of proinflammatory cytokines,
persistent activation of macrophages/histiocytes along with T cells, and multi-system
inflammation with characteristic clinical and laboratory findings. The clinical course of
FHL is characterized by prolonged fever and hepatosplenomegaly associated with
anemia
and
thrombocytopenia,
liver
dysfunction,
hypofibrinogenemia,
hypertriglyceridemia, hypoalbuminemia, and hyponatremia (1, 9, 20).
Neurological
symptoms can dominate the early-accelerated phase or may develop later (1, 9, 20). A
significant portion of patients dies at an early stage during treatment from progressive
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disease, recurrent infection, or relapse of the accelerated phase. A definitive treatment
and a possible cure for FHL are only achieved by hematopoietic stem cell
transplantation (1, 9, 20). The clinical outcome in FHL patients depends on how rapidly
and accurately the diagnosis is established.
Appropriate diagnosis, prognostic
information, and prompt treatment with HLH therapy are essential in these cases.
Relapses are the leading cause of poor outcomes prior to HSCT. Hence, the early
prediction of relapses is beneficial. The focus of the present study was to determine
whether there are biologically significant gene expression signatures that distinguish
patients with FHL, which discriminate various genetic or clinical forms of the disease.
Our previous report involving genome-wide expression profiling in children with
FHL focused on the differences between patients and normal controls (15). We thereby
demonstrated sustained repression of genes corresponding to innate/adaptive immunity
and activation of genes encoding inflammatory cytokines and chemokines.
In our
current analyses, we identified expression profiles associated with clinical subgroups of
FHL. The comparison of PBMC gene expression profiles obtained from patients with
FHL identified 167 differentially expressed probe sets (P = 0.005). To group individuals
with similar gene expression profiles, we used unsupervised hierarchical clustering and
demonstrated that the eleven patients with FHL fell into three groups displaying different
patterns of gene expression (Figure 1).
At least 5 distinct gene clusters contributed to the heterogeneity of patients with
FHL. Two clusters of down- (A) and upregulated (B) genes undoubtedly separated
patients (ha) with disease causing PRF1 mutations from patients (hb and hc) with a yet
unknown genetic cause of the disease.
FHL2 patients more frequently show
presentation at an earlier age, they have more severe illness and a higher mortality rate
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compared with cases that were due to other genetic cause(s) (21).
The 24
downregulated genes in Cluster A included genes coding for proteins involved defense
response (DEFB4), in negative regulation of cell proliferation and induction of apoptosis
(CDKN2C), zinc ion binding (RNF26), proteoglycan metabolic processes (COL11A1 and
WBSCR17), mitochondrial electron transport (NDUFB8, UQCRH and NDUFA1) and
transcriptional regulation (RHOJ and GCN5L2).
DEFB4 is a member of the gene-family coding for β-defensins.
β-defensins
consists of a variety of antimicrobial polypeptides that contribute to the immune
response against microbial infections (22). In addition to the antimicrobial effects βdefensins contribute to the adaptive immune response (23). CKDN2C gene encodes the
cyclin-dependent kinase inhibitor p18INK4c (p18). P18 has been shown to be involved
both in early and late B cell differentiation.
Gene cluster B contains genes, which are upregulated in patients with FHL2
(group ha) when compared to patients with unidentified, unknown genetic cause of HLH
(group hb and hc). This gene cluster is enriched in genes implicated the innate immunity
response (AXL, BMPR2, SLC30A7), regulation of transcription (ARID5B, MNAB,
CREM).
The receptor-tyrosine-kinase AXL has a number of cell-type-specific roles,
including growth induction of endothelial cells, antiapoptotic effects on endothelial cells,
the activation of platelets, and the deactivation of antigen-presenting cells (24).
The MTHFD1L gene is involved in the tetrahydrofolate synthesis pathway,
catalyzes folate-cofactor interconversion reactions, which controls the cells to form and
accumulate methotrexate (MTX) polyglutamatase is well recognized as a determinant of
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MTX cytotoxicity (25, 26). MTX is one of the components in contemporary treatment
protocols for hemophagocytic lymphohistiocytosis (9, 13).
Chemotherapy with
epipodophyllotoxin derivatives etoposide, and teniposide combined with corticosteroids
and intrathecal MTX induce remission in 50% of patients (9). Differences in MTHFD1L
expression may point to treatment failures in hemophagocytic lymphohistiocytosis.
The other gene cluster identified (cluster C), designated the “rapidly evolving
form of FHL” included down-regulated genes encoding transcriptional regulators
(ZFYVE9, ETV3, RFXDC2, and JMJD1C), small guanine nucleotide-interacting proteins
(ARF1, TRIO, DOCK4, PDE4B, and PDE8), and membrane proteins (DOCK4 and
C1orf71[consortin]). ZFYVE9 (SARA) encodes a FYVE domain protein, which facilitates
signal transduction by promoting the association of SMAD2 and SMAD3 with receptor
complexes (27). Phosphorylation of the C-terminal domain of ZFYVE9(SARA) increases
the transcriptional activity of target genes and controls TGF-mediated signaling (27).
ZFYVE9(SARA) also plays an important functional role downstream of Rab5-regulated
endosomal trafficking (28). CD55 is markedly downregulated in patients with “rapidly
evolving form of FHL” compared with the patients in groups ha and hc.
The
downregulation of CD55 enhances T cell proliferation and augments the induced
frequency of effector cells (29).
ETV3, a member of the ETS family of transcriptional repressors, is expressed
during macrophage development (30).
ETV3 together with SBNO2 function as
components of the IL-10-regulated pathway that represses inflammatory gene
expression (31). The existence of the IL-10-STAT3-ETV3/SBNO2 pathway is consistent
with the hypothesis that IL10 selectively controls inflammatory gene expression through
a regulated repression mechanism (32). DOCK4 is a member of the DOCK family of
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intracellular signaling proteins, which are recruited to phosphatidylinositol-trisphosphaterich membranes through their Dock homology region 1 (DHR1) domain and serve as
guanine exchange factors for Rho/Rac family GTPases through their DHR2 domain (33).
Guanine nucleotide exchange factors have a significant role in many biological events.
They have the ability to restructure the actin cytoskeleton through the activation of
specific downstream effectors (33). The deficiency of another member of this family,
DOCK8, impairs CD8+ T cell survival and function in humans and mice (34). The role of
DOCK4 in immune cells is unknown. DE4B and PDE8A are the members of cyclic
nucleotide phosphodiesterases, which control the intracellular levels of cAMP and
cGMP.
Cyclic nucleotide phosphodiesterases are involved in the differentiation of
monocytes, dendritic cells, and macrophages (35, 36).
Groups ha and hc represent patients with early onset and rapidly
accelerated form of FHL with a dissimilar pattern of mRNA expression in PBMCs. In ha
patients, there is an evidence of gross PRF1 protein misfolding, protein degradation, and
severely decreased or lack of lytic function (21). Patients in group hc have wild type
PRF1 gene with normal level of mRNA expression and impaired lytic function. Based on
current knowledge, it seems that there are at least two additional FHL-related genes to
be identified (37). The yet unidentified gene(s) in patients of group hc could induce an
early onset, rapidly evolving form of FHL with a distinct mRNA patterns.
Cluster D (Figure 1) contains four known upregulated genes coding for proteins
with a leucine rich repeat and a death domain (LRDD), an aarF domain-containing
protein kinase (ADCK2), a major histocompatibility complex II DR ß 4 (HLA-DRB4), and
a member of superfamily of the Solute Carriers (HIAT1).
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Cluster E represents a 12-gene signature, associated with recurrence of FHL in
patients. This signature is based on genes found to be consistently under-expressed in
disease with relapse compared with FHL2 or the rapidly evolving form of FHL of as yet
unknown genetic cause(s).
Notably, our initial analyses revealed that this gene
signature predicted recurrence in three of the eleven patients analyzed by microarray.
The down-regulation of selected genes from the 12-gene signature was also observed
by qRT-PCR in six patients with a relapse of the accelerated phase of FHL. In addition,
patients in subclass hb were older than patients in subclasses ha and hc. Cluster E was
also enriched in genes related to B and T cell differentiation and survival, T cell
activation as well as intracellular vesicular transport and these patterns of gene
regulation correlate with a distinct and clinically relevant phenotype of FHL.
GIMAP1 (GTPase of the immune-associated protein 1 gene) is a close relative of
GIMAP5 and lies adjacent to it within the GIMAP gene cluster (38). GIMAP5 is a key
regulator of hematopoietic integrity and lymphocyte homeostasis (39).
In GIMAP5
deficient mice, the T and B cells appear to undergo normal development, but they fail to
proliferate upon Ag-receptor stimulation (39). A GIMAP5 deficiency imposes a block in
the NK- and NKT-cell differentiation required for the survival of peripheral T cells, NK-,
and NKT-cells (40). GIMAP1, like GIMAP5, encodes a protein that contains a putative
transmembrane domain at its carboxyl terminus, indicative of the targeting of intracellular
membranes.
GIMAP1 mRNA expression has been found in all stages of T-cell
development and is differentially expressed under conditions of Th1 or Th2 polarization
(41). GIMAP1 protein is required during lymphocyte development in the T cell and B cell
lineages suggesting the involvement of common pathways, such as NF-κB (32).
Homozygous loss of murine Gimap1 leads to a severe deficiency in mature T and B
18
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lymphocytes, indicating the crucial requirement of this gene for mature lymphocyte
development and survival (42).
DCAKD (dephospho-CoA kinase domain-containing protein) is a member of the
CoA biosynthetic pathway and catalyzes the final step of the biosynthesis of mevalonate,
the precursor of cholesterol (43).
Very little is known about the regulation of this
pathway. Dephospho-CoA kinase forms complexes with various src homology-2 domain
proteins in vitro and in vivo (43). Inhibition of the mevalonate biosynthetic pathway
downregulates T cell proliferation and B lymphocyte survival (44).
The protein encoded by FKBP5, is a member of the family of immunophilins, the
FK506 binding proteins (45). The encoded protein is a cis–trans prolyl isomerase that
binds to the immunosuppressants FK506, cyclosporine, and rapamycin (46). FKBPs are
involved in several biochemical processes including protein folding, receptor signaling,
protein trafficking, transcription, and mediating calcineurin inhibition (46). FKBPs also
play important functional roles in T cell activation, when complexed with their ligands
(46). This complex of an immunophilin and cyclosporine A inhibits calcineurin, which
under normal conditions induces the transcription of interleukin-2 (46).
The role of
immunophilins in protein transportation and apoptosis through their molecular
interactions with receptors or proteins has recently emerged (47).
FLJ20366 encodes SYBU, a syntaxin-interacting protein that is expressed in
CD56dimCD16+ and at lower levels in CD56brightCD16− NK cells (42). Cytolytic activity is
mostly confined to CD56dimCD16+ NK cells, whereas cytokine production is generally
assigned to CD56brightCD16+/− NK cells. The expression of SYBU is enhanced after IL2
treatment (48). The FLJ20366 expression pattern is very similar to that of syntaxin 11
19
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(STX11), which is mutated in FHL4 (7). The syntaxins play a role in the intracellular
vesicle transport of the phagocytic system (7). Syntaxin-interacting proteins such as
STXBP2, are important for controlling intracellular granule/membrane trafficking in
polarized epithelial cells, neutrophils, and mast cells (8).
STXBP2 belongs to the
Sec/Munc family of regulatory proteins involved in the assembly and disassembly of
SNARE complexes as well as control of the specificity and timing of membrane fusion.
STXBP2 is also mutated in FHL5 (8). The decreased expression of FLJ20366 coding for
a syntaxin-interacting protein, in patients with recurrent form of FHL, may lower the
threshold for hemophagocytic lymphohistiocytosis by impairing degranulation and NK
cell cytotoxicity.
Based on our study design, the genes identified in the microarray analysis should
be those with the highest differences in expression between the three groups of patients.
Clustering of the genes, which are differently regulated across the three groups,
illustrated, at the genomic level, that gene expression signatures can be used to identify
FHL patients with different clinical characteristics. One limitation of our present study is
the small number of patients, consisting of 11 patients analyzed by microarray and 20
patients assessed by qRT-PCR analysis. Caution must be exercised when interpreting
the results from a small sample set. However, our results do primarily suggest that gene
expression profiling in PBMCs may be used to predict recurrence of FHL.
This
approach, if validated in a larger cohort, could have direct implications for future patient
management.
We speculate that the down-regulation of the genes in cluster E
predisposes patients with FHL to relapse, and that our findings will serve as a foundation
for formulating testable, novel hypotheses regarding the pathophysiology of FHL
outcomes.
20
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An alternative interpretation of our present findings is that the down-regulation of
the identified genes in FHL patients is simply an epiphenomenon of illness severity,
rather than a direct underlying cause of the pathophysiology of the different forms of
FHL. As such, expression differences could still potentially serve as a gene expression
signature for predicting outcomes in FHL patients. PBMC gene expression profiling,
particularly when combined with the clinical assessment of FHL, may provide a better
delineation of a patient’s risk of relapse or other unwanted outcomes of this disease, and
thus be a valuable tool for optimizing the medical management of these cases.
Acknowledgements: This work was supported by grants from the National
Institute of Allergy and Infectious Diseases, NIH (R21 AI079759 and R21 AI076746) and
by the Histiocytosis Association of America.
Authorship Contributions: J. Sumegi conceived and designed the experiments,
analyzed the data, and wrote the manuscript; M. G. Barnes performed the statistical
analyses of expression data and wrote and reviewed the manuscript; S.V. Nestheide
performed the research and was responsible for the qRT-PCR experiments; J.
Villanueva performed the research; K. Zhang performed the genetic analyses; A. A.
Grom contributed vital reagents, wrote and reviewed the manuscript; and A. H. Filipovich
was responsible for the selection and clinical evaluation of patients, and reviewed the
paper.
Conflict of Interest Disclosure: The authors declare no conflicts of interest in
relation to the publication of this study.
21
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Table 1. Clinical and laboratory characteristics of the FHL patients subjected to gene
expression profiling.
No.
patients
1
P33*
age of
onset
2m
NK
function
(LU)
mutation
PRF1, c.[50delT];c.[1442A>C]
(p.L17fsX50);(p.Q481P)
PRF1,
c.[148G>A];c.[148G>A],
p.V50M
siL2R
remarks
28,855
FHL2, consanguinity,
rapidly evolving, early
onset, deceased
0.0
2
P59*
7y
3
P35*
3m
PRF1, c.[50delT];c.[50delT]
(p.L17fsX50),
0.0
4
P1002**
9y
NM
57.4
57,432
5
P101**
6y
NM
15.3
23,563
6
P66**
15y
STXBP2, c.[511G>T];c[?],
p.[168V>L]
0.1
19,743
7
P76**
5y
NM
13.3
NA
8
P92**
1m
NM
1.0
21,030
9
P94**
1m
NM
0.0
16,203
10
P96**
5y
NM
0.0
48,543
11
P98**
16m
NM
7.4
3,904
9.2
69,543
43,042
FHL2,
rapidly evolving, late
onset,
FHL2, consanguinity,
rapidly evolving, early
onset, received HSCT
unknown genetic cause,
late onset, relapse
unknown genetic cause,
consanguinity late
onset, relapse
unknown genetic cause,
late onset, relapse,
received HSCT
consanguinity,rapidly
evolving form, late
onset,
rapidly evolving form,
early onset, received
HSCT
rapidly evolving form,
early onset
rapidly evolving form,
late onset, received
HSCT
rapidly evolving form,
early onset, received
HSCT
* L. Molleran et al., J Med Genet. 2004;41(2):137–144.
**J. Sumegi et al., Blood 2011;117(15):e151-160.
NM=no mutations in PRF1, UNC13D, STX11, STXBP2 and RAB27A were detected
29
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Table 2. Characteristics of FHL patients subjected to quantitative RT-PCR analysis.
NK
function
(LU)
0.0
No.
patients
age of
onset
1
LH1
15 y
2
LH2
2m
3
LH3
3m
4
LH4
2y
5
LH6
12y
6
LH7
15y
7
8
LH8
LH9
2y
2y
9
LH11
13 y
10
11
LH12
LH13
2y
2y
12
LH15
11 y
13
LH16
12 y
14
15
16
17
18
19
20
LH17
LH18
LH19
LH20
LH21
LH22
LH23
14 y
15 y
7y
9y
6y
10 y
5y
NM
NM
NM
NM
NM
NM
NM
0.0
0.1
NA
0.0
0.0
0.0
0.0
119,58
5
11,030
3,016
20,870
NA
87,667
NA
NA
21
LH26
19 y
NM
0.0
NA
mutation
ND
PRF1,
c.[50delT];c.[1442A>C]
p.[L17fsX50[;p.[Q481P]
PRF1,
c.[50delT];c.[1442A>C]
p.[L17fsX50];p.[Q481P]
NM
UNC13D,
g.[1389(+1)G>A];g.[?]
UNC13D,
c.[847A>G];c.[847A>G],
p.I283V
NM
NM
PRF1, c.[272C>T];c.[?],
p.[A91V]
NM
NM
ND
ND
siL2R
remarks
NA
late onset, relapse
0.0
25,564
early onset, FHL2
0.0
35,947
early onset, FHL2
0.0
18,627
early onset
0.0
late onset, relapse
0.0
43,044
late onset, FHL3
4.4
0.3
39,815
51,325
7.9
7704
0.0
0.0
20,240
NA
0.1
15,689
early onset
early onset
late onset, A91V
polymorphism
early onset
early onset
consanguinity, late
onset, relapse
0.0
late onset,
late onset, relapse
late onset,
late onset
late onset,
late onset, relapse
late onset,
late onset,
late onset, relapse,
received HSCT
ND = not determined
NM = no mutations in PRF1, UNC13D, STX11, STXBP2 and RAB27A were detected.
30
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Table 3. Genes selected for validation experiments.
expression
downregulated
in
patients with FHL type
2 (group ha)
upregulated in
patients of group hb
downregulated in
patients of group hb
downregulated in
patients of group hc
upregulated in
patients with FHL type
2 (group ha)
No.
gene symbol
1.
CDKN2C
2.
RNF26
3.
UQCRH
4.
LRDD
5.
6.
7.
8.
HLA-DRB4
ADCK2
C18orf8
HIAT1
protein function
cyclin-dependent kinase 4 inhibitor C, member of the INK4
family of cyclin-dependent kinase inhibitors, interacts with CDK4
or CDK6, prevents the activation of the CDK kinases, functions
as a cell growth regulator, controls cell cycle G1 progression
contains a C3HC5 type of RING finger involved in protein-DNA
and protein-protein interactions
codes for mitochondrial Hinge protein
contains a leucine-rich repeat and a death domain, interacts with
Fas and MAP-kinase activating death domain-containing protein
MADD, functions as an adaptor protein in signaling processes
HLA-DRB4 belongs to the HLA class II beta chain paralogues
uncharacterized aarF domain-containing protein kinase 2
uncharacterized protein C18orf8 of 657 aa
putative tetracycline transporter-like protein
9.
GIMAP1
10.
DCAKD
11.
FKBP5
12.
GTPBP2
13.
FLJ20366
14.
ETV3
15.
KIF5C
16.
RPS27
17.
PDE4B
18.
DOCK4
19.
RFX7
belongs to the GTP-binding immuno-associated nucleotide
subfamily of nucleotide-binding proteins, is involved in the
differentiation of T helper (Th) cells, is critical for the
development of mature B and T lymphocytes
dephospho-CoA kinase domain-containing protein
member of the immunophilin protein family, plays a role in
immunoregulation and basic cellular processes
GTP-binding protein member of superfamily capable of binding
GTP or GDP
syntabulin is a microtubule-associated protein implicated in
syntaxin transport
transcriptional repressor, contributes to growth arrest during
terminal macrophage differentiation by repressing target genes,
microtubule-associated force-producing protein, may play a role
in organelle transport
ribosomal protein
involved in many signal transduction pathways, may play an
important role in platelet aggregation, hormone secretion,
immune cell activation
membrane-associated cytoplasmic protein, functions as a
guanine nucleotide exchange factor and is involved in regulation
of adherens junctions between cells
this gene encodes a ribosomal protein that is a component of
the 40S subunit
20.
KIAA1128
Codes for a hypothetical protein of 588 aa
21.
MTHFD1L
involved in the synthesis of tetrahydrofolate (THF) in the
mitochondrion
31
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Figure 1. Clustering of FHL patient samples in accordance with the disease
phenotype. Patients with FHL type 2 (ha), patients with unknown genetic cause of FHL
and relapse (hb) and patients with rapidly evolving form of FHL (hc) were distinguished
by the hierarchical clustering of all samples. The blue and red boxes on the right side of
the heatmap display down- and upregulated genes, which are clustered according to
disease subtypes.
Figure 2. Real-time quantitative RT-PCR validation of microarray data. Relative fold
changes in the expression of genes selected from gene clusters A and B (Figure 1).
Patients LH1 (1), LH2 (2), LH3 (3), LH4 (4), LH6 (5), LH7 (6), LH8 (7), LH9 (8), LH11 (9),
LH12 (10), LH13 (11), LH15 (12), LH16 (13), LH17 (14), LH18 (15), LH19 (16), LH21
(18) LH22 (19), LH23 (20) and LH26 (21) are represented by bars. Red bars are for
patients with relapse and blue bars indicate patients who did not experience relapse.
Figure 3. Real-time quantitative RT-PCR validation of microarray data. A. Relative
fold changes in the expression of genes selected from gene cluster E. B. Relative fold
changes in the expression of genes selected from gene cluster D. The patients and the
order of patients are as in Figure 2.
Figure 4. Real-time quantitative RT-PCR validation of microarray data. Relative fold
changes in the expression of genes randomly selected from gene cluster C.
Figure 5. Evaluation of GIMPA1, DCAKD, FKBP5, FLJ20366, ETV3, KIF5C and
DOCK4 gene expression in six FHL patients with relapse (red box) and fifteen
patients with FHL, who did not experienced recurrence within the study period
(blue box). The vertical lines indicate the minimum and maximum of all the data, the
horizontal boundaries of the boxes represent the first and the third quartile of data. The
thick black lines in the middle of the boxes indicate the median of the data. Red dotted
line represents median Ct of controls.
32
CDKN2C, NDUFB8, RNF26, UQCRH,
SUB1, WBSCR17, NDUFA1,COL11A1,
DEFB4, LRDD, RY1, CCDC72, KIFC2,
RHOJ, GCN5L2
A
LRDD, HLA-DRB4
ADCK2, C18orf8,
ZFYVE9, C9orf68, ETV3,
HIAT1
D CYP2F1, LOC651619,
KIF5C, NEK8, YTHDC2,
TSC22D2,TRIO, RPS27,
GIMAP1, DCAKD
PDE4B, DOCK4,C1orf71,
FKBP5, FLJ20366,
RFXDC2, KIAA1128,
GTPBP2, SETMAR,
TNFRSF11A, LCMT2, SOAT1, TIPARP, AKR1C2,
PDE8A, CD55, XYLT1,
C1orf95, SLC24A2,
MAX, FBF1
E LMJD1C, CNP, ATP1B3
C
ha
hb
hc
+ + + - - - - - - - 2m 7y 3m 9y 6y 15y 5y 1m 1m 5y 16m
PRF1 mutation
age (onset)
HELB, AGRIN, HSPBAP1,
ADK, FLJ22795, AXL, BMPR22,
HOMER1, FBXW11, MTHFD1L,
TRIO, SAPS3, NPTX1, CREM,
LOC144997, SAMD4A,
ARID58, SMC5, MNAB, ETNK1,
SLC30A7, C10orf70, ST3GAL3,
UBE3C, MYO1E, RP11-82K18,
LRCH1, DIP2B
B
CDKN2C
10
9
5
6
1
-1
3
1
-1
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-10
RNF26
6
UQCRH
25
4
20
3
-15
2
-3
-15
1
-1
-6
-20
-2
-9
-25
-3
MTHFD1L
30
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
35
Fold Regulation (2^-ΔCt)
Fold Regulation (2^-ΔCt)
12
-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-5
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
HLA-DRB4
20
8
10
15
6
8
10
4
6
5
2
4
1
-1
-2
-5
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
1
-1
ADCK2
4
3
2
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-2
-2
-3
-4
-4
-6
-5
-15
-2
HIAT1
5
4
3
2
1
-1
-2
-3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Fold Regulation (2^-ΔCt)
Fold Regulation (2^-ΔCt)
6
26.86
-20
C18orf8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Fold Regulation (2^-ΔCt)
Fold Regulation (2^-ΔCt)
LRDD
T
12
23.47
B
5
KIF5C
15
12
Fold Regulation (2^-ΔCt)
Fold Regulation (2^-ΔCt)
10
9
5
6
1
-1
3
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-10
-15
-3
RPS27
20
2
15
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
5
-3
1
-1
-5
-5
DOCK4
9
RFX7
8
12
6
6
9
3
4
6
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-10
-3
1
-1
-6
-2
-3
-9
-4
-6
-12
-6
3
1
-1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
KIAA1128
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
PDE4B
10
-2
-25
15
25
3
-4
-20
-6
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Fold Regulation (2^-ΔCt)
ETV3
Fold Regulation (2^-ΔCt)
15
20
30
Ct value
25
20
GIMAP1 DCAKD FKBP5 FLJ20366 ETV3
KIF5C DOCK4
From www.bloodjournal.org by guest on June 18, 2017. For personal use only.
Prepublished online December 20, 2012;
doi:10.1182/blood-2012-05-425769
Gene expression signatures differ between different clinical forms of
familial hemophagocytic lymphohistiocytosis
Janos Sumegi, Shawnagay V. Nestheide, Michael G. Barnes, Joyce Villanueva, Kejian Zhang, Alexei A.
Grom and Alexandra H. Filipovich
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