Download T Cells FOXP3 Target Genes in Natural Regulatory Genome

Genome-Wide Identification of Human
FOXP3 Target Genes in Natural Regulatory
T Cells
This information is current as
of June 18, 2017.
Timothy J. Sadlon, Bridget G. Wilkinson, Stephen Pederson,
Cheryl Y. Brown, Suzanne Bresatz, Tessa Gargett, Elizabeth
L. Melville, Kaimen Peng, Richard J. D'Andrea, Gary G.
Glonek, Gregory J. Goodall, Heddy Zola, M. Frances
Shannon and Simon C. Barry
Supplementary
Material
References
Subscription
Permissions
Email Alerts
http://www.jimmunol.org/content/suppl/2010/06/16/jimmunol.100008
2.DC1
This article cites 85 articles, 35 of which you can access for free at:
http://www.jimmunol.org/content/185/2/1071.full#ref-list-1
Information about subscribing to The Journal of Immunology is online at:
http://jimmunol.org/subscription
Submit copyright permission requests at:
http://www.aai.org/About/Publications/JI/copyright.html
Receive free email-alerts when new articles cite this article. Sign up at:
http://jimmunol.org/alerts
The Journal of Immunology is published twice each month by
The American Association of Immunologists, Inc.,
1451 Rockville Pike, Suite 650, Rockville, MD 20852
Copyright © 2010 by The American Association of
Immunologists, Inc. All rights reserved.
Print ISSN: 0022-1767 Online ISSN: 1550-6606.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
J Immunol 2010; 185:1071-1081; Prepublished online 16
June 2010;
doi: 10.4049/jimmunol.1000082
http://www.jimmunol.org/content/185/2/1071
The Journal of Immunology
Genome-Wide Identification of Human FOXP3 Target Genes
in Natural Regulatory T Cells
Timothy J. Sadlon,* Bridget G. Wilkinson,* Stephen Pederson,* Cheryl Y. Brown,*
Suzanne Bresatz,* Tessa Gargett,* Elizabeth L. Melville,† Kaimen Peng,‡
Richard J. D’Andrea,x,{ Gary G. Glonek,‖ Gregory J. Goodall,{ Heddy Zola,#
M. Frances Shannon,‡ and Simon C. Barry*,†
R
egulatory T cells (Tregs) are essential “master regulators”
of tolerance (1), allowing effective responses to infection
and non-self Ags while preventing autoimmunity and
immune pathology (2). Although there are several subsets of cells
with regulatory capacity (reviewed in Ref. 3), it is clear that the
CD4+CD25+FOXP3+ natural Tregs (nTregs) is a key participant in
the orchestration of tolerance, because without these cells an early
onset aggressive autoimmune condition arises in both mouse and
man (4–6). Furthermore, a number of autoimmune diseases involve a perturbation in Treg numbers or function (reviewed in
Ref. 7), and control of the local response to acute tissue damage
*Molecular Immunology Laboratory and #Leukocyte Biology Laboratory, Women’s
and Children’s Health Research Institute, Women’s and Children’s Hospital, North
Adelaide; †Discipline of Paediatrics and ‖Discipline of Statistics, School of Mathematical Sciences, University of Adelaide; xDepartment of Haematology and Oncology, Queen Elizabeth Hospital; {Centre for Cancer Biology, South Australia
Pathology, Adelaide, South Australia; and ‡Gene Expression and Epigenomics
Group, Genome Biology Program, Biomolecular Resource Facility, John Curtin
School for Medical Research, Australian National University, Canberra, Australian
Capital Territory, Australia
Received for publication January 12, 2010. Accepted for publication May 3, 2010.
The microarray data presented in this article have been submitted to Gene Expression
Omnibus under accession numbers GSE20995 and GSE20934.
Address correspondence and reprint requests to Dr. Simon C. Barry, Molecular
Immunology Laboratory, Discipline of Paediatrics, University of Adelaide, and
Women’s and Children’s Health Research Institute, Women’s and Children’s Hospital, North Adelaide, South Australia 5006, Australia. E-mail address: simon.barry@
adelaide.edu.au
The online version of this article contains supplemental material.
Abbreviations used in this paper: ChIP-on-chip, chromatin immunoprecipitation array
profiling; DE, differentially expressed; FDR, false discovery rate; FKH, forkhead;
miR, microRNA; mRNA, messenger RNA; nTreg, natural regulatory T cell; PI16,
peptidase inhibitor 16; qPCR, quantitative real-time PCR; ROR, retinoic acid receptor-related orphan receptor; Treg, regulatory T cell; TSS, transcription start site.
Copyright Ó 2010 by The American Association of Immunologists, Inc. 0022-1767/10/$16.00
www.jimmunol.org/cgi/doi/10.4049/jimmunol.1000082
appears to require Treg function (8). The ability to monitor and
manipulate Tregs may improve diagnosis or therapeutic intervention for autoimmune diseases. To achieve this, sensitive and specific biomarkers are required, but currently, however, there are
limited markers available. This genome-wide study of human Tregs
takes an unbiased approach for identification of novel markers
and gene regulation networks.
The forkhead (FKH) transcription factor FOXP3 is expressed
constitutively in natural CD4+ Tregs and has been shown to be
essential for nTreg function (9). FOXP3 is a member of the P
subfamily of the FKH family of transcription factors sharing a
number of common features including a C2H2 zinc finger, a leucine zipper, and a FKH DNA-binding domain (10). These domains
are involved in DNA binding, nuclear transport (11), homomeric
and heteromeric complex formation (12), and transcriptional repressor activity (12, 13). Mechanisms of action of FOXP3 in Tregs
have been identified in mouse, but less is known about its role
in the development and function of human Tregs (reviewed in Ref.
12). In particular, the existence of two major human-specific
FOXP3 isoforms (14) with different regulatory capacities (15,
16) and the transient induction of FOXP3 following activation of
CD4+CD252 non-Tregs in humans (17) suggest a degree of species-specific function by, and regulation of, FOXP3.
In mouse and human Tregs, FOXP3 not only represses but also
can induce gene expression, although the mechanism by which
FOXP3 can negatively regulate transcription of some genes, such
as IL2, but positively regulate others, such as CD25 and CTLA4, is
still unclear (reviewed in Ref. 10). FOXP3 interacts with multiple
transcription factors known to be involved in activation, differentiation, and response of CD4+ T cells to TCR stimulation, including NFAT, NF-kB, Runx1, retinoic acid receptor-related
orphan receptors (RORs) (RORa and RORgT), IFN regulatory
factor 4, STAT3, and Jun (15, 16, 18–24). FOXP3 may act as
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
The transcription factor FOXP3 is essential for the formation and function of regulatory T cells (Tregs), and Tregs are essential for
maintaining immune homeostasis and tolerance. This is demonstrated by a lethal autoimmune defect in mice lacking Foxp3 and in
immunodysregulation polyendocrinopathy enteropathy X-linked syndrome patients. However, little is known about the molecular
basis of human FOXP3 function or the relationship between direct and indirect targets of FOXP3 in human Tregs. To investigate
this, we have performed a comprehensive genome-wide analysis for human FOXP3 target genes from cord blood Tregs using
chromatin immunoprecipitation array profiling and expression profiling. We have identified 5579 human FOXP3 target genes and
derived a core Treg gene signature conserved across species using mouse chromatin immunoprecipitation data sets. A total of 739
of the 5579 FOXP3 target genes were differentially regulated in Tregs compared with Th cells, thus allowing the identification of
a number of pathways and biological functions overrepresented in Tregs. We have identified gene families including cell surface
molecules and microRNAs that are differentially expressed in FOXP3+ Tregs. In particular, we have identified a novel role for
peptidase inhibitor 16, which is expressed on the cell surface of >80% of resting human CD25+FOXP3+ Tregs, suggesting that in
conjunction with CD25 peptidase inhibitor 16 may be a surrogate surface marker for Tregs with potential clinical application.
The Journal of Immunology, 2010, 185: 1071–1081.
1072
Materials and Methods
Isolation, in vitro expansion, and characterization of cord
blood T cell populations
Cord blood was obtained with approval from the donor and the Children’s,
Youth and Women’s Health Service Research Ethics Committee. Mononuclear cells were isolated from cord blood postpartum as previously described (29). Briefly, cord blood CD4+CD25+ (Treg) and CD4+CD252 (Th)
cells were isolated from purified mononuclear cells using a Regulatory
CD4+CD25+ T Cell Kit (Dynabeads; Invitrogen, Carlsbad, CA). Ex vivo
expansion of isolated T cell populations (1 3 106 cells per well in a 24-well
plate) were performed in X-Vivo 15 media supplemented with 5% human
AB serum (Lonza, Walkersville, MD), 20 mM HEPES (pH 7.4), 2 mM
L-glutamine, and 500 U/ml recombinant human IL-2 (R&D Systems,
Minneapolis, MN) in the presence of CD3/CD28 T cell expander beads
(Dynabeads; Invitrogen; catalog no. 111-41D) at a bead-to-cell ratio of
3:1. The phenotypes of expanded cells were characterized by surface expression of CD4 (Phycoerythrin-CY5; eBioscience, San Diego, CA; clone
RPA-T4), CD25 (PE; BD Biosciences, San Jose, CA; clone M-A251), and
CD127 (PE; eBioscience; clone ebioRDR73) in combination with intracellular detection of FOXP3 (Foxp3-Alexa Fluor 488; BD Biosciences, San
Jose, CA; clone 259D/C7; Human FOXP3 Buffer Set; BD Biosciences;
catalog no. 560098) by three-color flow cytometry on a Beckman Coulter
Epics Élite ESP flow cytometer (Beckman Coulter, Fullerton, CA). Functional assays using unmatched donor MLR suppressor assays were carried
out as described previously (29).
ChIP, ChIP PCR, and whole-genome array
Expanded cord blood Tregs were cultured overnight in X-Vivo 15 media with
100 U/ml recombinant human IL-2 prior to a 2 h restimulation with 1 mM
ionomycin (Sigma-Aldrich, St. Louis, MO) before cross-linking. Isolated
T cells were subjected to cross-linking for 10 min in 1% formaldehyde
solution (50 mM HEPES KOH [pH 7.5], 100 mM NaCl, 1 mM EDTA,
0.5 mM EGTA, and 11% formaldehyde). Formaldehyde was quenched by
the addition of glycine to a final concentration of 112 mM. Cell lysis, ChIP,
and DNA isolation steps for human FOXP3 ChIP-on-chip experiments were
carried out essentially as described in Ref. 30, using 5 3 107 cells per
immunoprecipitation with either a rabbit anti-Foxp3 IgG (Novus Biochem,
Littleton, CO) or ChIP-grade control rabbit IgG Sera (Abcam, Cambridge,
U.K.). Chromatin shearing conditions consisted of 12 repeats of 30 s pulses
with an amplitude of 90% with 90 s between pulses with a S-4000 ultrasonic
processor (Misonix, Farmingdale, NY) with a microtip. Amplification of
immunoprecipitated and input chromatin (10 ng) for labeling and
hybridization to Affymetrix Human Tiling 2.0R arrays was by wholegenome amplification (Sigma-Aldrich GenomePlex Whole Genome Amplification Kit) (31) adapted for Affymetrix labeling systems by the inclusion of 0.11 mM dUTP in the amplification reaction. Amplified material
was purified using a PCR clean-up kit (Qiagen, Valencia, CA). Data from
two independent ChIP-on-chip experiments were analyzed as replicates
using Model-based Analysis of Tiling-array (32). Labeling and
hybridization of amplified material to Affymetrix human tiling arrays was
carried out at the Biomolecular Resource Facility (John Curtin School of
Medical Research, Australian National University). Data files for this
experiment are lodged on Gene Expression Omnibus (accession no.
GSE20995; www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20995).
ChIP-on-chip data analysis
Data from two independent FOXP3 ChIP whole-genome array sets were
analyzed as replicates using Model-based Analysis of Tiling-array (32).
A false discovery rate (FDR) of 0.5% was used to identify significantly
enriched regions in FOXP3-immunoprecipitated material relative to input
chromatin. Gene accessions were assigned to an individual ChIP region if
the peak of the enriched region was within 20 kb upstream of a transcription start site (TSS) and 20 kb downstream of the transcription end site.
Annotation was performed using gene accessions from both University of
California Santa Cruz and National Center for Biotechnology Information,
encompassing RefSeqs, mRNAs, and miRs, and where more than one
accession could be associated with a ChIP peak, all possible annotations
were recorded. Species conservation was analyzed using VISTA software
(http://genome.lbl.gov/vista/index.shtml) (33).
RNA preparation and expression array
Differential expression analysis was carried out using Affymetrix Human
Exon 1.0 ST arrays. Total RNA was isolated from expanded Th cells and
nTregs that were rested for ∼60 h prior to treatment for 2 h with either
ionomycin or vehicle (DMSO). Total RNA, including small m.w. RNA,
was isolated using QIAshredder and a miRNeasy Mini Kit (Qiagen). RNA
quality was assayed using an Agilent Systems Bioanalyzer (Santa Clara,
CA). Labeling and hybridization were carried out according to the manufacturer’s protocols at the Biomolecular Resource Facility (John Curtin
School of Medical Research, Australian National University). Data files for
this experiment are lodged on Gene Expression Omnibus (accession no.
GSE20934; www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20934).
RNA expression array data normalization
Microarray data were processed using RMA (34) and version 11.0 of an
Entrez Gene-centric cdf (35). All of the processing was conducted using
the statistical software R (R Development Core Team) under the aroma.
affymetrix framework (36). Log fold change in a four-way comparison
between resting and stimulated treatments of nTregs and Th cells was
estimated within individual donors after Loess normalization. Final expression analysis was performed using Limma (37), and raw p values for each
term were adjusted globally to provide an estimate of the FDR (38).
Differential gene expression group assignment and analysis
Differentially expressed genes were divided into seven mutually exclusive
groups (T1–T6 and A1), based upon their behavior across all four comparisons. For genes with a significant adjusted p value in only one comparison, the accepted FDR was raised to p , 0.1 in all of the other
comparisons to enable more accurate group assignment. Clustering within
each subgroup was performed using the AGNES algorithm (M. Maelcher,
unpublished observations), based on log fold-change and whether there
was an associated FOXP3 binding site. Overrepresentation of ChIP hits
within individual groups was assessed by using Fisher’s exact tests (all p
values ,1027).
Pathway mapping
FOXP3 ChIP hits and the sets of differentially expressed genes comprising
groups T1–T6 were assessed for overrepresentation of genes from canonical pathways and known biological functions either individually or
together using Fisher’s exact tests within the Ingenuity Pathways Analysis
software package (Ingenuity, Redwood, CA).
Transcription factor binding motif analysis
Transcription factor binding site analysis was carried out on 400-bp sequences centered on the ChIP peak associated with either the top 1000 FOXP3bound regions (ranked on p value) or genes differentially regulated in
Tregs. Scans for overrepresented motifs were performed on these regions
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
a transcriptional corepressor, because FKH domain-independent
inhibition of the trans-activation activity of NF-kB, CREB, and
RORa has been reported (16, 21). FOXP3 is also likely to modulate gene expression through epigenetic mechanisms, such as
chromatin remodeling and histone deacetylation (25, 26). It is
evident that the composition and activity of the FOXP3 regulatory
complex is dynamic and is influenced by external stimuli acting
partly through the posttranslational modification of FOXP3 (27,
28). These observations demonstrate the complex nature of the
interaction of FOXP3 with its target genes.
In this article, we identify molecular targets of human FOXP3 in
nTregs using a combination of chromatin immunoprecipitation
array profiling (ChIP-on-chip) and expression profiling. Defining
the direct binding sites for FOXP3 reveals target genes that may
be regulated by FOXP3 and allows a detailed analysis of FOXP3bound promoter elements, providing important information relating to FOXP3 transcription factor function. We have also identified
direct targets of FOXP3 that are differentially expressed in human
nTregs, and analysis of the FOXP3 target genes and differentially
regulated genes has uncovered a number of transcription factors,
microRNAs, signaling molecules, and surface molecules that have
potentially important roles in Treg formation or function. In the
search for positive cell surface markers for human Tregs, we report
for the first time that peptidase inhibitor 16 (PI16) is expressed
on the surface of a significant proportion of human Tregs, pointing
to the potential utility of this molecule as a new biomarker for
nTregs.
FOXP3 TARGET GENES IN HUMAN nTregs
The Journal of Immunology
1073
using WEEDER and MatInspector (Genomatix, Munich, Germany) (39–
42). Motifs identified by WEEDER were matched against known transcription factor binding motifs in JASPAR (43). For the 739 genes that were
ChIP hits and members of groups T1–T6, 400-bp genomic sequences
centered at the ChIP peak were scanned for the presence of transcription
factor binding motifs using the default settings of MatInspector. Of these
sequences, the 681 identified as containing one or more binding motifs
belonging to the FKH family were further examined for co-occurrence of
other transcription factor family motifs. A reference set of sequences was
generated by randomly selecting 1000 genes from the Entrez Gene database that were not FOXP3 ChIP hits and selecting 400-bp sequences
around the TSS using the observed distribution for the differentially regulated ChIP hits, as described above. A further set of 701 sequences containing a FKH motif was extracted as a subset of this reference set.
Transcription factor families present in at least 30% of the differentially
expressed ChIP genes with an FDR-adjusted p value , 0.05 were considered significantly overrepresented.
Quantitative real-time PCR
Validation of cell surface molecule expression
For surface molecule analysis, freshly isolated adult peripheral blood CD4+
CD25+ Tregs or CD4+CD252 Th cells were either assayed immediately or
stimulated overnight in the presence of CD3/CD28 beads (bead-to-cell ratio,
1:1) and 100 U/ml IL-2 prior to four-color flow cytometry using Abs against
CD4, CD25, FOXP3, and test Ag. For PI16 detection, a mouse polyclonal Ab
was used (Abnova, Walnut, CA; catalog no. H00221476-B01P)
Results
Isolation and expansion of human cord blood Tregs
We chose to use ex vivo expanded cord blood nTregs to dissect the
FOXP3-driven events within human Tregs because large numbers
can be grown for ChIP-on-chip studies and these cells display
strong FOXP3 expression and a robust suppressive ability in vitro
(29, 46, 47). We routinely obtained 100- to 200-fold expansion of
cord blood CD4+CD25+ Tregs when cultured in vitro using antiCD3/CD28 beads. These cells maintained a Treg phenotype, with
.90% of the expanded cells staining CD4+, CD25hi, and FOXP3+
(Fig. 1A). These cells were also CD127dim (data not shown). The
Tregs retained regulatory function because they were able to robustly suppress the proliferation of CD4+CD252 cells in vitro in
an unmatched donor mixed leukocyte suppression assay (Fig. 1B).
Thus, having demonstrated that we could expand large numbers of
functional Tregs ex vivo, these cells were used for the ChIP-onchip and expression profiling experiments.
FIGURE 1. Expanded cord blood nTregs express FOXP3 and have
suppressive activity. A, Flow cytometric analysis of surface CD4 and CD25
and intracellular FOXP3 expression. Isotype control of CD4+ and CD25+
population (dotted line), FOXP3 expression in CD252 cells (dashed line),
and FOXP3 expression in CD4+ and CD25+ cells (solid line). B, Functional
activity of expanded cells in a MLR. Expanded cord blood nTregs were
cultured for 5 d with unmatched T cells, APCs, and soluble anti-CD3 prior
to a 16 h pulse of [3H]thymidine. Robust suppression of proliferation of
CD4+CD252 responder cells by expanded nTregs was observed from a responder-to-nTreg ratio of 1:1 to 10:1.
Identification of human FOXP3 binding sites using
ChIP-on-chip assays
The human FOXP3 ChIP protocol was established using the
IL7Ralpha (CD127) and IL2 genes (Supplemental Fig. 1), because
both promoters have been previously reported to contain binding
sites for human FOXP3 (23, 48). FOXP3-bound DNA isolated by
ChIP was amplified using a modification of the whole-genome
amplification method (31), and enrichment of the same target
genes postamplification was confirmed (data not shown) prior to
labeling and hybridization to Affymetrix Human Tiling 2.0R
arrays. With a FDR of 0.5%, a total of 8308 unique genomic
regions were determined as significantly enriched in the FOXP3
ChIP material (Supplemental Table I). As a first step in validating
the human FOXP3 ChIP-on-chip data set, a search for the presence of FKH DNA-binding motifs was performed on the top 1000
FOXP3-bound genomic regions (ranked on p value) using MatInspector (Genomatix). FOXP3-bound regions were significantly
enriched for FKH motifs when compared against the Genomatix
promoter database, with 922 of 1000 sequences (92%; p = 2.19 3
10233) containing at least one FKH motif. Using the modeling
algorithm WEEDER, we identified a motif (consensus [(G/A)(T/
A)AAA(C/A)AA]) that was significantly overrepresented in these
human FOXP3 ChIP regions, and comparison with transcription
factor position weight matrices in the JASPAR database indicated
that this represented a FKH binding site [(A/G)(C/T)(A/C)AA(C/
T)A] (49). Our human FOXP3 target sequence (Fig. 2) is similar to
the published mouse Foxp3 binding motif [(A/G)(T/C)AAACA]
(50). This is consistent with localization of human FOXP3 occurring primarily through binding to a FKH-like motif.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
Validation of differentially regulated target genes identified by expression
array analysis was performed on RNA from five donors. Random primed
cDNA was prepared from total RNA using a QuantiTect Reverse Transcription Kit (Qiagen). Relative gene expression analysis was carried out
using SYBR Green quantitative real-time PCR (qPCR) on a Rotor-Gene
6000 PCR machine (Corbet Research, Quiagen, Valencia, CA). Genespecific primers pairs for qPCR were selected from PrimerBank (44). A
primer set specific for RPL13a was used as an internal control (primer
sequences listed in Supplemental Table X). Results were analyzed using
a Rotor-Gene 6000, QGene software (45), and R. Validation of FOXP3
binding to genomic regions was carried out by ChIP qPCR. For ChIP, PCR
primers to amplify target regions were either designed using Primer 3 Plus
(www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi) or purchased
from SABiosciences, Frederick, MD as validated ChIP PCR primers (ChampionChIP PCR assays). Reactions were performed using a RT2 SYBR
Green/ROX qPCR Master Mix (SABiosciences). The relative enrichment
of target regions in FOXP3-immunoprecipitated material relative to input
chromatin analysis was carried out using a ChampionChIP data analysis
spreadsheet (SABiosciences) using the 22ΔΔCT method. MicroRNA expression level comparisons were performed on total RNA (miRNeasy
kits; Qiagen) isolated from freshly purified Tregs or Th cells by real-time
PCR using specific TaqMan miR assays (Applied Biosystems, Carlsbad,
CA). Data were normalized to a housekeeper (RNU6B) and non-FOXP3regulated miRS (miR-16/miR-24). RNA from three independent donors
was used to confirm the differential expression.
1074
FIGURE 2. The human FOXP3 binding motif in the top 1000 human
FOXP3 ChIP hits derived from sequence information using WEEDER.
Location and functional annotation of FOXP3 binding regions
Differential gene expression in resting and activated nTregs
To correlate FOXP3 binding with the transcriptional regulation of
target genes, expression profiling was carried out on expanded cord
blood nTregs and Th cells. Expression profiling of donor-matched
Treg and Th cell populations in both a stimulated and a resting state
(Fig. 4A) identified three classes of genes: those with an nTreg
intrinsic expression pattern, those that responded to stimulation in
a cell type-specific manner, and those that display common responses to T cell activation. Genes displaying an nTreg-specific
response were then clustered into six groups based on patterns of
differential expression by cell type and/or stimulation (Table I). In
total, 1851 genes displayed significant differential expression in
a Treg compared with a Th cell (T1–T6), with a further 746 genes
showing a similar response to activation in both cell types (A1)
(Fig. 4B; described in Table I; gene identifications in Supplemental Table IV). A total of 739 genes in these groups were also
FOXP3 ChIP targets (gray bar in Fig. 4B). These FOXP3 ChIP
hits were statistically overrepresented in all T groups (p , 1 3
1027), but we did not observe any statistically significant difference in the distribution of FOXP3 ChIP hits between the upregulated and downregulated genes.
Of note, groups T1 and T2, which represent the steady-state human nTreg signature, also contained a higher proportion of FOXP3
ChIP hits when compared with those of T3–T6 (50% versus 35%
p = 3.064 3 1029), which is consistent with a role of FOXP3 in
stabilizing and maintaining the Treg phenotype (52). Also, within
the group of genes that were differentially expressed upon stimulation of the cells (T3–T6), there were clear differences, with
a number of genes responding to stimulation in a Th cell but
remaining unchanged in a Treg (T5), whereas a further set of
genes responded to stimulation in an nTreg but remained unchanged in a Th cell (T6). qPCR performed on a group of candidate genes identified in our array confirmed the differential
expression and clustering analysis (Fig. 4C). Similarly, TaqMan
microRNA (miR) assays performed on 20 of the miRs predicted to
be FOXP3 targets in nTregs demonstrated that five were differentially expressed in nTregs (nine miRs shown in Fig. 4D). To identify potential targets regulated by these miRs, a search for miR
binding sites in the 39 UTRs of downregulated FOXP3 target
genes was performed using the microRNA target prediction programs in miRGen (www.diana.pcbi.upenn.edu/miRGen.html). Using stringent criteria in which a miR binding site must be found by
three separate prediction programs (miRanda, PicTar4, and TargetScan), we identified a number of high confidence gene targets
for these validated microRNAs (Supplemental Table V). Many of
these predicted miR target genes (21 of 33) were also FOXP3
ChIP hits, identifying a group of genes that are potentially subjected to multiple FOXP3-dependent mechanisms to ensure their
downregulation in nTregs.
Transcription factor motifs in differentially expressed FOXP3
target genes
The molecular mechanism by which FOXP3-dependent regulation
of gene expression occurs is not well characterized, but a growing list
of transcription factors has been shown to interact with FOXP3. To
identify other potential transcription factors that may coregulate
gene expression with FOXP3, we analyzed sequences flanking the
FOXP3 ChIP peak for the 739 genes that were also differentially
expressed. Position weight matrices from Genomatix corresponding
to the binding sites for transcription factor families AP-1, BCL6,
HAML (Runx family), HNF1, MYBL, NFAT, OVOL, PAX2, PRDF
(PRDM1/Blimp1 family), and STAT transcription factor families
were found to be significantly overrepresented in FOXP3-bound
regions, whereas the CAAT motif only appears significantly overrepresented in groups T1 and T2 (FDR-adjusted p = 0.0462) (Fig. 5A).
In the STAT family (Fig. 5B), the STAT5 consensus binding motif
was the most significantly enriched, suggesting a preferential interaction of FOXP3 with STAT5 within nTregs. These results have
therefore uncovered several new transcription factors that potentially
directly interact with, or coregulate gene expression by, FOXP3.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
We mapped FOXP3-bound regions relative to known and predicted
genes annotated in University of California Santa Cruz and National Center for Biotechnology Information to identify potential
FOXP3 target genes. With a window beginning 20 kb upstream
of annotated TSSs and ending 20 kb downstream of the annotated
transcript end site of a gene, 7012 of the 8308 unique FOXP3bound regions were associated with a gene. The remaining 1296
regions (13%) fell outside this window and were labeled intergenic.
All of the possible gene annotations for a given ChIP peak were
recorded, resulting in 5579 genes associated with at least one
FOXP3-bound region (Supplemental Table I). Within this data
set, FOXP3 binding sites were found in close proximity to 63 loci
encoding miRS, including binding sites near loci encoding mir155, mir-21, mir-22, and mir-142 that were are also mouse Foxp3
targets (50, 51) (Supplemental Table II). Examples of FOXP3bound regions annotated to the human genome are shown in
Fig. 3A. FOXP3 appears to have a preference for engaging in
short-range promoter interactions to regulate the transcription of
target genes because we observed a clear peak of FOXP3-bound
regions starting 2 kb upstream and extending 3 kb downstream of
the TSS (Fig. 3B).
We selected 17 ChIP regions and four control regions for confirmation by ChIP PCR (Fig. 3C). Included in this subset are speciesconserved FOXP3 targets and FOXP3 targets unique to our human
data set. Greater than 2-fold enrichment relative to input chromatin
(normalized for nonspecific chromatin immunoprecipitation) was
detected at the 17 sites tested by qPCR, whereas no enrichment (#1fold) was observed for four different genomic regions predicted not
to bind FOXP3 (intergenic 12p13, AFM intron 1, PDE3B intron 10,
and human IGX1A negative control, SA Biosciences).
Analysis of the potential FOXP3 target genes using Ingenuity
Pathway Analysis software demonstrated a substantial enrichment
for genes involved in the proliferation, activation, and control of
cell death in T lymphocytes in normal immune responses and in
disease states, such as inflammatory and autoimmune diseases.
Molecules involved in the response to TCR signaling and immunoregulatory cytokines/growth factors were significantly
overrepresented in the human FOXP3 target gene data set (Supplemental Table III).
FOXP3 TARGET GENES IN HUMAN nTregs
The Journal of Immunology
1075
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
FIGURE 3. Annotation, validation, and distribution of FOXP3 target genes identified by ChIP-on-chip. A, Annotation of three genes enriched for FOXP3
binding by ChIP showing the mouse/human conserved regions of the locus, the scale in kilobases of the region, the chromosome number and location, the
individual FOXP3 binding regions, and the gene identification and the gene structure. B, Distribution of FOXP3 binding sites annotated to genes in relation to the
distance from the TSS. C, The fold enrichment of target regions in FOXP3-immunoprecipitated material, normalized to control IgG immunoprecipitations,
relative to input chromatin was determined for selected FOXP3 target genes by qPCR. Displayed is the mean fold enrichment 6 SD; n = 3 for each region.
Comparison of human and mouse FOXP3/Foxp3 ChIP data
We next compared our ChIP data set with the previously identified
murine targets (50, 51). Because substantial differences existed
between the two mouse data sets, the mouse data sets were each
compared separately with our human data (Table II). The mouse
data were then combined for clarity (Fig. 6). Comparison of the
human FOXP3 targets with mouse Foxp3 targets indicated that
23% (888 genes) of our human FOXP3 ChIP targets were also
Foxp3 targets in at least one mouse data set (Supplemental Table
VI, Table II) (50, 51), with 107 genes common to all three data
sets. These overlaps were statistically significant (p , 1025), and
a number of genes previously implicated in Treg function were
1076
FOXP3 TARGET GENES IN HUMAN nTregs
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
FIGURE 4. A, Schematic representation of the cells and comparisons used in the expression profiling experiment. B, Heat map representation of significant differentially expressed genes in all four expression array comparisons. The normalized log2 fold changes for repressed (blue) and induced (red)
transcripts across each comparison are shown. FOXP3 target genes identified by ChIP-on-chip analysis are indicated in gray on the right-hand side. C, RTPCR validation of differentially expressed genes in expanded cord blood nTregs versus Th cells. Displayed is the mean log fold change in the relative level
of the genes in expanded nTregs compared with Th cells in stimulated (open bar) and resting (filled bar) cells (n = 6 donors). The log fold change
determined from analysis of the exon arrays for the nTreg versus Th cell comparisons in resting (hatched bar) and stimulated (stripped bar) cells is also
shown. Dotted lines represent fold change of 62. D, RT-PCR validation of potential FOXP3 target miRs. The relative expression of the miR in matched
freshly isolated cord blood nTregs and Th cells was determined on n = 3 donors in triplicate using TaqMan miR assays. Statistical significance (p . 0.05)
using a paired t test was achieved for all but those marked with an asterisk.
identified in these overlapping groups. On the basis of our gene
expression profiling, we found a significantly higher proportion of
differentially expressed genes (20–24%) among the conserved
FOXP3 targets (Table II), suggesting that these genes may represent a core group of FOXP3-dependent genes involved in the
maintenance of the regulatory phenotype in both species. Pathway
and biological function analysis of these 888 species-conserved
FOXP3 target genes using Ingenuity Pathway Analysis software
revealed that members of critical T cell signal transduction pathways including TCR, CD28, and CTLA4 signaling and the intracellular MAPK signaling pathway were overrepresented
among these conserved targets. These pathways were also found
to be significantly overrepresented among the human-specific
ChIP genes (Supplemental Tables VII, VIII), suggesting that although the overall regulation of essential pathways and biological
processes may be conserved in human and mouse Tregs, the genes
within a pathway used to achieve this may vary between species.
Analysis of cell surface molecules in nTregs
When we applied functional annotation to differentially expressed
genes in the data set, we discovered a highly significant (p = 3.36 3
10216) enrichment for surface molecules in groups T1 and T2,
The Journal of Immunology
1077
Table I. Group definitions for the differentially expressed genes
Group
T1
T2
T3
T4
T5
T6
Total
A1
Inclusion Criteria
Effects
Effects
Effects
Effects
Effects
Effects
Number DE
in both stimulated and resting Tregs versus Th cells
only in resting Tregs versus Th cells
only in stimulated Tregs versus Th cells
in stimulated Tregs versus Th cells activation effects in both
in stimulated Tregs versus Th cells and activation effects only in Th cells
in stimulated Tregs versus Th cells and activation effects only in Tregs
Common stimulation effect in both Tregs and Th cells
417
135
176
226
335
562
1851
746
ChIP Hits (% DE)
215
63
65
99
125
172
739
281
(51)
(46)
(37)
(43)
(37)
(30)
(40)
(37)
DE, differentially expressed.
We next examined PI16 expression in adult peripheral blood
CD4+ T cell subsets. PI16 was readily detectable on the surfaces
of resting and stimulated nTregs (Fig. 7A, 7B). In particular, on
resting Tregs, a significant portion (∼85%) of the CD4+CD25bright
FOXP3+ cells were PI16+, and the majority (∼70%) of the PI16+
cells coexpress FOXP3, with only ∼15% FOXP3+PI162 (Fig.
7Aii). In contrast, although ∼50% of the resting CD252 cells are
PI16+, the majority of these (80%) are FOXP32 (Fig. 7Aiii). Upon
isolation of CD4+CD25+ cells and overnight stimulation, ∼80% of
the PI16+ cells coexpress PI16 and FOXP3, although ∼40% of the
activated FOXP3+ cells are PI162 (Fig. 7Bii). Importantly, stimulated CD4+CD252 do not express significant amounts of PI16 or
FOXP3, suggesting that PI16 expression is not linked to activation
but rather to the nTreg phenotype. It is interesting to note that
although the proportion of FOXP3+ PI16+ cells is similar between
resting and stimulated overnight Tregs (Fig. 7Aii, 7Bii) the proportion of FOXP3+PI162 cells increases upon stimulation, suggesting that stimulation alters surface expression of PI16. This is
not due to loss of PI16 transcription, as confirmed by the array and
quantitative RT-PCR data in resting and activated Tregs. There are
also FOXP3+PI162 cells at a lower frequency, which may
represent a distinct Treg subset. In our chip analysis, PI16 was
not a direct target of FOXP3 in human or mouse, suggesting that it
is regulated by genes downstream of FOXP3. Together, these data
indicate that PI16 warrants further investigation as a FOXP3
surrogate marker.
Discussion
FIGURE 5. Overrepresentation of transcription factor binding sites in
FOXP3 target gene promoters. A, Analysis of differentially expressed
genes in the Treg groups that are also ChIP hits for the corepresentation of
transcription factor family binding sites. Red bar, T1 + T2; blue bar, T3–
T6; white bar, control promoters. B, Analysis of the STAT family of
transcription factors demonstrating that STAT5 binding sites are significantly overrepresented in FOXP3 target genes.
Using expanded cord blood nTregs to generate FOXP3 ChIP-onchip and expression profiling data, we show, for the first time, human FOXP3-bound genes and their differential expression profile
in human nTregs. We detected 8308 potential FOXP3 binding sites
in the human genome, which were associated with 5579 genes. Of
these potential FOXP3-regulated genes, 13% (739) displayed differential expression between donor-matched expanded nTregs and
Th cells. We selected an early time point for the expression analysis
to identify genes directly downstream of this altered signaling. Expression profiles at later time points may uncover additional
FOXP3 target genes whose expression level changes more slowly
following stimulation. Nonetheless, our finding that a subset of
FOXP3-bound regions can be linked to the regulation of the neighboring genes is consistent with other genome-wide ChIP studies
where a similar level of transcription factor binding and gene expression changes in nearby genes has been reported (reviewed in
Ref. 53).
The comparison of FOXP3-bound genes from this study in
humans with published studies in mice (50, 51) provided a means
of identifying genes with a conserved function in Tregs. Although
.50% of the FOXP3 targets are bound in a species-specific manner, 23% of FOXP3-bound genes identified in our human study
were present in one of the published mouse Treg studies.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
with 42.6% of genes in these groups being cell surface related
versus 23.8% in groups T3–T6. This included known CD molecules (Supplemental Table IX) as well as less well characterized
surface molecules. However, FACS-based analysis failed to identify a CD molecule uniquely expressed on .50% of the FOXP3+
cells (data not shown). We therefore further mined our expression
array data set to identify additional surface proteins not assigned
a CD designation that were selectively upregulated on the nTregs,
and from this we focused on PI16. The differential expression of
PI16 in resting and stimulated cord blood nTregs versus Th cells
was confirmed by real-time PCR (Fig. 4C).
1078
FOXP3 TARGET GENES IN HUMAN nTregs
Table II. Differential expression of human and mouse FOXP3 target
genes
Comparison
Size
Number
DE
% DE
% DE in
Control
Raw
p Value
Human/either
mouse
Human/Marson
Human/Zheng
Human/mouse
877
175
20.0
16.2
0.0104
662
322
107
120
80
25
18.1
24.8
23.4
16.2
16.2
16.2
0.2240
1.66 3 1024
0.2558
Control, nonconserved Foxp3 target genes.
FIGURE 6. Overlap between human and mouse genes associated with
a FOXP3-bound region. The numbers represent target genes common
(center) and unique to each species (outer). All of the comparisons were
statistically significant (p , 0.005).
FIGURE 7. PI16 and FOXP3 expression in nTregs and Th cells. A,
Validation of PI16 as a surrogate for FOXP3 in resting adult Tregs. When
CD4+ cells were gated on the top 1% CD25+ (i), .85% of the FOXP3+
cells are PI16+ (ii). When gated on CD252, few of the PI16+ cells are
FOXP3+ (iii). B, CD25+ and CD252 CD4 cells were isolated according to
the gates shown (i) and then stimulated overnight with anti-CD3/CD28
beads and stained for PI16 and FOXP3 expression. Sorted stimulated
CD25+ cells stained robustly with the anti-PI16 Ab, and this population
contains .60% of the FOXP3+ cells (ii). The majority of the activated
CD252 cells do not express PI16 or FOXP3 (iii). Representative data from
n = 3 donors.
[DICER (58–60) or DROSHA (61)]. Deletion of either of these
enzymes led to a spontaneous lethal inflammatory disease in mice
due to a severe reduction in the stability and function of nTregs.
MicroRNA signatures for mouse and human nTregs have been
reported (59, 62) and several miRs have been shown to be important in nTreg function, including miR155 in mouse (63, 64) and
miR21 in human cells (62). We have confirmed that both of these
miRs are located near FOXP3 binding sites by ChIP PCR and that
both are differentially upregulated in human nTregs, suggesting
that these are conserved targets for FOXP3 regulation. In addition,
we have identified three further miRs potentially regulated by
Foxp3 (miR-146a, miR-101, and miR-7) that are upregulated in
human nTregs. Thus, in addition to the four miRs also identified
as potential Foxp3 targets in the mouse (miR-7, miR-21, miR-22, and
miR-155) (50, 51) and 10 microRNAs recently identified as
differentially expressed in human Tregs (62), we find that the
FOXP3 targets miR-101 and miR-146a are also significantly upregulated. These results suggest that other miRs are likely to contribute to the Treg gene program and that further work is required to
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
However, upon pooling of the Foxp3-bound genes identified in the
two mouse studies, 45% of FOXP3 targets identified in mouse
Tregs were also bound by FOXP3 in human Tregs. A caveat to
these comparisons is the small overlap of the Foxp3 target genes
between the two mouse studies (7% overlap; data not shown).
However, this degree of conservation of FOXP3 target genes in
human and mouse cells is consistent with other large scale transcription factor binding and expression studies, where a substantial
divergence in transcription factor binding events in human and
mouse cells has been reported (54–57). These species-conserved
FOXP3 targets contained proportionately more differentially regulated genes (20% of conserved targets versus 16.2% in all targets,
p = 0.0104), suggesting that the curation of conserved targets is
a valid approach for identifying proteins associated with the key
role of FOXP3 within the Treg. The proportion of differentially
regulated genes increased to ∼25% (p = 1.66 3 1024) when only
the Foxp3-bound genes from primary mouse Tregs (51) were
compared with our human FOXP3 targets. In contrast, when the
group of mouse Foxp3-bound genes derived using forced expression in hybridoma cells was compared (50), there was no significant enrichment for differentially regulated genes (18%, p =
0.224). This may be a consequence of the differences in intracellular signaling pathways and transcription factor levels in hydridomas compared with those of primary mouse T cells or a result
of overexpression of FoxP3 and highlights the difficulties in comparing transcription factor binding in different cell systems. In
support of this, expression profiling performed by Marson et al.
(50) indicated that Foxp3 overexpression almost overwhelmingly
downregulated gene expression in an activation-dependent manner
in hybridoma cell lines, whereas 30–50% of the Foxp3-bound
genes were upregulated in mouse (50) and human (this study)
primary Tregs.
Our ChIP-on-chip assay also identified FOXP3 binding regions in
proximity to loci encoding 63 microRNAs (miRs), which is consistent with FOXP3 partly exerting its regulatory program by modifying the expression of these posttranscriptional rheostats of
protein expression. In mice, a critical role of miRs in nTreg bio
logy has been demonstrated by the T cell-targeted deletion of either
of the two key enzymes involved in the generation of mature miRs
The Journal of Immunology
FOXP3 expression and the localization of latent TGF-b on the
surface of nTregs upon TCR stimulation (83, 84). Significantly,
we have found that PI16, unlike LRRC32/GARP, is differentially
expressed on resting Tregs. Hence, we propose it as a potential
biomarker for human nTregs, where in conjunction with CD25
and LRCC32/GARP it may be feasible to identify both resting
and stimulated Treg subsets. Little is known about the function
of PI16, which was initially identified as a serum binding partner
of prostate secretory protein 94 (85) and more recently has been
shown to have growth inhibitory properties (86, 87). The Treg
expression profile and the potential growth suppressive activity
of PI16 indicate that the contribution of this surface protein to
Treg function clearly warrants further investigation.
In conclusion, we have generated a gene profile of human Tregs
and identified putative direct targets of FOXP3, which allows us to
model direct and indirect FOXP3 target genes. Pathway mapping
and transcription factor over representation analysis provide novel
information on the mechanisms by which human nTregs function,
and we can now undertake functional studies of the cell surface,
secreted, and other molecules, which may be critical for Treg
function. This may hence provide new leads for diagnosis or
therapeutic intervention.
Acknowledgments
We thank Sylvie Nobbs and Dr. Randall Grose for expert assistance
with flow cytometry and Dr. Doreen Krumbiegel for critical reading of this
manuscript.
Disclosures
The authors have no financial conflicts of interest.
References
1. Tang, Q., and J. A. Bluestone. 2008. The Foxp3+ regulatory T cell: a jack of all
trades, master of regulation. Nat. Immunol. 9: 239–244.
2. Chatenoud, L., B. Salomon, and J. A. Bluestone. 2001. Suppressor T cells—
they’re back and critical for regulation of autoimmunity! Immunol. Rev. 182:
149–163.
3. Bluestone, J. A., and A. K. Abbas. 2003. Natural versus adaptive regulatory
T cells. Nat. Rev. Immunol. 3: 253–257.
4. Bennett, C. L., J. Christie, F. Ramsdell, M. E. Brunkow, P. J. Ferguson,
L. Whitesell, T. E. Kelly, F. T. Saulsbury, P. F. Chance, and H. D. Ochs. 2001.
The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nat. Genet. 27: 20–21.
5. Brunkow, M. E., E. W. Jeffery, K. A. Hjerrild, B. Paeper, L. B. Clark,
S. A. Yasayko, J. E. Wilkinson, D. Galas, S. F. Ziegler, and F. Ramsdell. 2001.
Disruption of a new forkhead/winged-helix protein, scurfin, results in the fatal
lymphoproliferative disorder of the scurfy mouse. Nat. Genet. 27: 68–73.
6. Wildin, R. S., F. Ramsdell, J. Peake, F. Faravelli, J. L. Casanova, N. Buist,
E. Levy-Lahad, M. Mazzella, O. Goulet, L. Perroni, et al. 2001. X-linked neonatal diabetes mellitus, enteropathy and endocrinopathy syndrome is the human
equivalent of mouse scurfy. Nat. Genet. 27: 18–20.
7. Vila, J., J. D. Isaacs, and A. E. Anderson. 2009. Regulatory T cells and autoimmunity. Curr. Opin. Hematol. 16: 274–279.
8. Liesz, A., E. Suri-Payer, C. Veltkamp, H. Doerr, C. Sommer, S. Rivest, T. Giese,
and R. Veltkamp. 2009. Regulatory T cells are key cerebroprotective
immunomodulators in acute experimental stroke. Nat. Med. 15: 192–199.
9. Fontenot, J. D., M. A. Gavin, and A. Y. Rudensky. 2003. Foxp3 programs the
development and function of CD4+CD25+ regulatory T cells. Nat. Immunol. 4:
330–336.
10. Hu, H., I. Djuretic, M. S. Sundrud, and A. Rao. 2007. Transcriptional partners in
regulatory T cells: Foxp3, Runx and NFAT. Trends Immunol. 28: 329–332.
11. Hancock, W. W., and E. Ozkaynak. 2009. Three distinct domains contribute to
nuclear transport of murine Foxp3. PLoS One 4: e7890.
12. Li, B., A. Samanta, X. Song, K. Furuuchi, K. T. Iacono, S. Kennedy,
M. Katsumata, S. J. Saouaf, and M. I. Greene. 2006. FOXP3 ensembles in T-cell
regulation. Immunol. Rev. 212: 99–113.
13. Lopes, J. E., T. R. Torgerson, L. A. Schubert, S. D. Anover, E. L. Ocheltree,
H. D. Ochs, and S. F. Ziegler. 2006. Analysis of FOXP3 reveals multiple
domains required for its function as a transcriptional repressor. J. Immunol. 177:
3133–3142.
14. Allan, S. E., L. Passerini, R. Bacchetta, N. Crellin, M. Dai, P. C. Orban,
S. F. Ziegler, M. G. Roncarolo, and M. K. Levings. 2005. The role of 2 FOXP3
isoforms in the generation of human CD4+ Tregs. J. Clin. Invest. 115: 3276–
3284.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
establish their identity and role. Knockdown of FOXP3 in human
Tregs to assess the role of individual microRNAs underway.
Analysis of the human FOXP3-bound regions that were associated with differentially regulated genes identified a number of transcription factor motifs that appeared to co-occur with FKH motifs,
including AP-1, Runx, NFAT, and STAT binding motifs. The cooccurrence of these motifs is consistent with known roles for these
transcription factors in nTregs (18, 65–69) and, in the case of AP1, NFAT, and Runx family members, with their known physical
interaction with FOXP3 (18, 20, 23). Several of the other transcription factor family motifs that are overrepresented in these
sequences, such as BCL6 and PRDF, also contain members known
to have a role in T cells, such as BCL6, PRDM1/Blimp-1, and cmyb (70–73). The finding of potential PRDM1 motifs within
FOXP3-bound regions is of particular interest given its relatively
high level of expression in natural Tregs and its requirement
for T cell homeostasis, nTreg function, and self-tolerance in the
mouse (74, 75), suggesting that FOXP3 and PRDM1 may cooperate to regulate gene expression in human Tregs. Furthermore,
the PRDM1 gene is a conserved FOXP3/Foxp3 target in humans
and mice (Ref. 51 and this study), suggesting a regulatory loop
involving these two transcription factors.
A major role for FOXP3 is to modulate the response of a T
lymphocyte to external stimuli, such as TCR and cytokine signaling, by altering the level and activity of downstream transcription factors (76). Several of the transcription factors that are
able to bind to sites enriched in FOXP3-bound regions are themselves potential targets of FOXP3 and are differentially expressed.
For example, NFATc2, NFATc3, STAT1, and STAT4 are all potential ChIP targets of FOXP3 and are downregulated in Tregs. A
direct role for FOXP3 downregulating the level of NFAT proteins
has recently been supported by the finding that forced FOXP3
expression in human cell lines inhibits the activation-induced expression of NFAT2 (NFATc/NFATc1) in T cells by competing with
NFAT1 (NFATc2) for binding to a site within the NFAT2 promoter
(77). Among the binding sites identified as belonging to the STAT
family of transcription factors, there was a strong overrepresentation of STAT5 binding sites at FOXP3-bound regions. This enrichment was not observed for the binding sites of other STAT
family members. Thus, the apparent enrichment of STAT5 sites
within FOXP3-bound regions, coupled with a decrease in the expression of other STAT family members, such as STAT1 and
STAT4, suggests an essential requirement for and increased sensitivity to the IL-2/STAT5 signaling pathway for Treg development and homeostasis (66, 67, 69).
Clustering of our gene expression data showed clear differences
in the response to stimulation, with over one third of the differentially expressed genes showing cell type-specific responses to
stimulation (groups T5 and T6; 35% = 897/2597). On top of these
activation-influenced signatures, another group of genes was
revealed that was differentially expressed in both resting and stimulated Tregs, suggesting that these genes represent a steady-state
human nTreg expression profile (T1 + T2; 21% = 552/2597). Together these data may be helpful for the further development of
a gene signature to investigate the relative contributions of nTregs
and other CD4 subsets in disease pathology (78, 79).
We detected a significant enrichment of differentially regulated
cell surface molecules in the steady-state nTreg signature (T1 + T2),
consistent with a role for surface molecules in Treg function, including the homing response (80, 81) and suppressive function (82). In
particular, two molecules, LRRC32/GARP and PI16, were significantly upregulated in human nTregs. LRRC32/GARP has recently
been reported to be specifically upregulated in human nTregs upon
stimulation where it has been proposed to play a role in stabilizing
1079
1080
42. Frisch, M., K. Frech, A. Klingenhoff, K. Cartharius, I. Liebich, and T. Werner.
2002. In silico prediction of scaffold/matrix attachment regions in large genomic
sequences. Genome Res. 12: 349–354.
43. Bryne, J. C., E. Valen, M. H. Tang, T. Marstrand, O. Winther, I. da Piedade,
A. Krogh, B. Lenhard, and A. Sandelin. 2008. JASPAR, the open access database
of transcription factor-binding profiles: new content and tools in the 2008 update.
Nucleic Acids Res. 36(Database issue): D102–D106.
44. Wang, X., and B. Seed. 2003. A PCR primer bank for quantitative gene expression analysis. Nucleic Acids Res. 31: e154.
45. Muller, P. Y., H. Janovjak, A. R. Miserez, and Z. Dobbie. 2002. Processing of
gene expression data generated by quantitative real-time RT-PCR. Biotechniques
32: 1372–1374, 1376, 1378–1379.
46. Godfrey, W. R., D. J. Spoden, Y. G. Ge, S. R. Baker, B. Liu, B. L. Levine,
C. H. June, B. R. Blazar, and S. B. Porter. 2005. Cord blood CD4(+)CD25
(+)-derived T regulatory cell lines express FoxP3 protein and manifest potent
suppressor function. Blood 105: 750–758.
47. Seddiki, N., B. Santner-Nanan, S. G. Tangye, S. I. Alexander, M. Solomon,
S. Lee, R. Nanan, and B. Fazekas de Saint Groth. 2006. Persistence of naive
CD45RA+ regulatory T cells in adult life. Blood 107: 2830–2838.
48. Liu, W., A. L. Putnam, Z. Xu-Yu, G. L. Szot, M. R. Lee, S. Zhu, P. A. Gottlieb,
P. Kapranov, T. R. Gingeras, B. Fazekas de St Groth, et al. 2006. CD127
expression inversely correlates with FoxP3 and suppressive function of human
CD4+ T reg cells. J. Exp. Med. 203: 1701–1711.
49. Carlsson, P., and M. Mahlapuu. 2002. Forkhead transcription factors: key players
in development and metabolism. Dev. Biol. 250: 1–23.
50. Marson, A., K. Kretschmer, G. M. Frampton, E. S. Jacobsen, J. K. Polansky,
K. D. MacIsaac, S. S. Levine, E. Fraenkel, H. von Boehmer, and R. A. Young.
2007. Foxp3 occupancy and regulation of key target genes during T-cell stimulation. Nature 445: 931–935.
51. Zheng, Y., S. Z. Josefowicz, A. Kas, T. T. Chu, M. A. Gavin, and
A. Y. Rudensky. 2007. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature 445: 936–940.
52. Williams, L. M., and A. Y. Rudensky. 2007. Maintenance of the Foxp3dependent developmental program in mature regulatory T cells requires continued expression of Foxp3. Nat. Immunol. 8: 277–284.
53. Farnham, P. J. 2009. Insights from genomic profiling of transcription factors.
Nat. Rev. Genet. 10: 605–616.
54. Odom, D. T., R. D. Dowell, E. S. Jacobsen, W. Gordon, T. W. Danford,
K. D. MacIsaac, P. A. Rolfe, C. M. Conboy, D. K. Gifford, and E. Fraenkel.
2007. Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nat. Genet. 39: 730–732.
55. Vaquerizas, J. M., S. K. Kummerfeld, S. A. Teichmann, and N. M. Luscombe.
2009. A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10: 252–263.
56. Birney, E., J. A. Stamatoyannopoulos, A. Dutta, R. Guigó, T. R. Gingeras,
E. H. Margulies, Z. Weng, M. Snyder, E. T. Dermitzakis, R. E. Thurman, et al;
ENCODE Project Consortium; NISC Comparative Sequencing Program; Baylor
College of Medicine Human Genome Sequencing Center; Washington University Genome Sequencing Center; Broad Institute; Children’s Hospital Oakland
Research Institute. 2007. Identification and analysis of functional elements in 1%
of the human genome by the ENCODE pilot project. Nature 447: 799–816.
57. Wilson, M. D., N. L. Barbosa-Morais, D. Schmidt, C. M. Conboy, L. Vanes,
V. L. Tybulewicz, E. M. Fisher, S. Tavaré, and D. T. Odom. 2008. Speciesspecific transcription in mice carrying human chromosome 21. Science 322:
434–438.
58. Liston, A., L. F. Lu, D. O’Carroll, A. Tarakhovsky, and A. Y. Rudensky. 2008.
Dicer-dependent microRNA pathway safeguards regulatory T cell function. J.
Exp. Med. 205: 1993–2004.
59. Cobb, B. S., A. Hertweck, J. Smith, E. O’Connor, D. Graf, T. Cook, S. T. Smale,
S. Sakaguchi, F. J. Livesey, A. G. Fisher, and M. Merkenschlager. 2006. A role
for Dicer in immune regulation. J. Exp. Med. 203: 2519–2527.
60. Zhou, X., L. T. Jeker, B. T. Fife, S. Zhu, M. S. Anderson, M. T. McManus, and
J. A. Bluestone. 2008. Selective miRNA disruption in T reg cells leads to uncontrolled autoimmunity. J. Exp. Med. 205: 1983–1991.
61. Chong, M. M., J. P. Rasmussen, A. Y. Rudensky, and D. R. Littman. 2008. The
RNAseIII enzyme Drosha is critical in T cells for preventing lethal inflammatory
disease. J. Exp. Med. 205: 2005–2017.
62. Rouas, R., H. Fayyad-Kazan, N. El Zein, P. Lewalle, F. Rothé, A. Simion,
H. Akl, M. Mourtada, M. El Rifai, A. Burny, et al. 2009. Human natural Treg
microRNA signature: role of microRNA-31 and microRNA-21 in FOXP3 expression. Eur. J. Immunol. 39: 1608–1618.
63. Lu, L. F., T. H. Thai, D. P. Calado, A. Chaudhry, M. Kubo, K. Tanaka,
G. B. Loeb, H. Lee, A. Yoshimura, K. Rajewsky, and A. Y. Rudensky. 2009.
Foxp3-dependent microRNA155 confers competitive fitness to regulatory T cells
by targeting SOCS1 protein. Immunity 30: 80–91.
64. Kohlhaas, S., O. A. Garden, C. Scudamore, M. Turner, K. Okkenhaug, and
E. Vigorito. 2009. Cutting edge: the Foxp3 target miR-155 contributes to the
development of regulatory T cells. J. Immunol. 182: 2578–2582.
65. Mantel, P. Y., N. Ouaked, B. Rückert, C. Karagiannidis, R. Welz, K. Blaser, and
C. B. Schmidt-Weber. 2006. Molecular mechanisms underlying FOXP3
induction in human T cells. J. Immunol. 176: 3593–3602.
66. Yu, A., L. Zhu, N. H. Altman, and T. R. Malek. 2009. A low
interleukin-2 receptor signaling threshold supports the development and homeostasis of T regulatory cells. Immunity 30: 204–217.
67. Yao, Z., Y. Kanno, M. Kerenyi, G. Stephens, L. Durant, W. T. Watford,
A. Laurence, G. W. Robinson, E. M. Shevach, R. Moriggl, et al. 2007. Nonredundant roles for Stat5a/b in directly regulating Foxp3. Blood 109: 4368–4375.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
15. Zhou, L., J. E. Lopes, M. M. Chong, I. I. Ivanov, R. Min, G. D. Victora, Y. Shen,
J. Du, Y. P. Rubtsov, A. Y. Rudensky, et al. 2008. TGF-beta-induced Foxp3
inhibits T(H)17 cell differentiation by antagonizing RORgammat function. Nature 453: 236–240.
16. Du, J., C. Huang, B. Zhou, and S. F. Ziegler. 2008. Isoform-specific inhibition of
ROR alpha-mediated transcriptional activation by human FOXP3. J. Immunol.
180: 4785–4792.
17. Allan, S. E., S. Q. Crome, N. K. Crellin, L. Passerini, T. S. Steiner, R. Bacchetta,
M. G. Roncarolo, and M. K. Levings. 2007. Activation-induced FOXP3 in
human T effector cells does not suppress proliferation or cytokine production.
Int. Immunol. 19: 345–354.
18. Ono, M., H. Yaguchi, N. Ohkura, I. Kitabayashi, Y. Nagamura, T. Nomura,
Y. Miyachi, T. Tsukada, and S. Sakaguchi. 2007. Foxp3 controls regulatory
T-cell function by interacting with AML1/Runx1. Nature 446: 685–689.
19. Zheng, Y., A. Chaudhry, A. Kas, P. deRoos, J. M. Kim, T. T. Chu, L. Corcoran,
P. Treuting, U. Klein, and A. Y. Rudensky. 2009. Regulatory T-cell suppressor
program co-opts transcription factor IRF4 to control T(H)2 responses. Nature
458: 351–356.
20. Lee, S. M., B. Gao, and D. Fang. 2008. FoxP3 maintains Treg unresponsiveness
by selectively inhibiting the promoter DNA-binding activity of AP-1. Blood 111:
3599–3606.
21. Bettelli, E., M. Dastrange, and M. Oukka. 2005. Foxp3 interacts with nuclear
factor of activated T cells and NF-kappa B to repress cytokine gene expression
and effector functions of T helper cells. Proc. Natl. Acad. Sci. USA 102: 5138–
5143.
22. Chaudhry, A., D. Rudra, P. Treuting, R. M. Samstein, Y. Liang, A. Kas, and
A. Y. Rudensky. 2009. CD4+ regulatory T cells control TH17 responses in
a Stat3-dependent manner. Science 326: 986–991.
23. Wu, Y., M. Borde, V. Heissmeyer, M. Feuerer, A. D. Lapan, J. C. Stroud,
D. L. Bates, L. Guo, A. Han, S. F. Ziegler, et al. 2006. FOXP3 controls regulatory T cell function through cooperation with NFAT. Cell 126: 375–387.
24. Rudra, D., T. Egawa, M. M. Chong, P. Treuting, D. R. Littman, and
A. Y. Rudensky. 2009. Runx-CBFbeta complexes control expression of the transcription factor Foxp3 in regulatory T cells. Nat. Immunol. 10: 1170–1177.
25. Chen, C., E. A. Rowell, R. M. Thomas, W. W. Hancock, and A. D. Wells. 2006.
Transcriptional regulation by Foxp3 is associated with direct promoter occupancy and modulation of histone acetylation. J. Biol. Chem. 281: 36828–36834.
26. Li, B., A. Samanta, X. Song, K. T. Iacono, K. Bembas, R. Tao, S. Basu,
J. L. Riley, W. W. Hancock, Y. Shen, et al. 2007. FOXP3 interactions with
histone acetyltransferase and class II histone deacetylases are required for repression. Proc. Natl. Acad. Sci. USA 104: 4571–4576.
27. Samanta, A., B. Li, X. Song, K. Bembas, G. Zhang, M. Katsumata, S. J. Saouaf,
Q. Wang, W. W. Hancock, Y. Shen, and M. I. Greene. 2008. TGF-beta and
IL-6 signals modulate chromatin binding and promoter occupancy by acetylated
FOXP3. Proc. Natl. Acad. Sci. USA 105: 14023–14027.
28. Tao, R., E. F. de Zoeten, E. Ozkaynak, C. Chen, L. Wang, P. M. Porrett, B. Li,
L. A. Turka, E. N. Olson, M. I. Greene, et al. 2007. Deacetylase inhibition promotes
the generation and function of regulatory T cells. Nat. Med. 13: 1299–1307.
29. Bresatz, S., T. Sadlon, D. Millard, H. Zola, and S. C. Barry. 2007. Isolation,
propagation and characterization of cord blood derived CD4+ CD25+ regulatory
T cells. J. Immunol. Methods 327: 53–62.
30. Lee, T. I., S. E. Johnstone, and R. A. Young. 2006. Chromatin immunoprecipitation
and microarray-based analysis of protein location. Nat. Protoc. 1: 729–748.
31. O’Geen, H., C. M. Nicolet, K. Blahnik, R. Green, and P. J. Farnham. 2006.
Comparison of sample preparation methods for ChIP-chip assays. Biotechniques
41: 577–580.
32. Johnson, W. E., W. Li, C. A. Meyer, R. Gottardo, J. S. Carroll, M. Brown, and
X. S. Liu. 2006. Model-based analysis of tiling-arrays for ChIP-chip. Proc. Natl.
Acad. Sci. USA 103: 12457–12462.
33. Frazer, K. A., L. Pachter, A. Poliakov, E. M. Rubin, and I. Dubchak. 2004.
VISTA: computational tools for comparative genomics. Nucleic Acids Res. 32
(Web Server issue): W273–W279.
34. Irizarry, R. A., B. Hobbs, F. Collin, Y. D. Beazer-Barclay, K. J. Antonellis,
U. Scherf, and T. P. Speed. 2003. Exploration, normalization, and summaries
of high density oligonucleotide array probe level data. Biostatistics 4: 249–264.
35. Dai, M., P. Wang, A. D. Boyd, G. Kostov, B. Athey, E. G. Jones, W. E. Bunney,
R. M. Myers, T. P. Speed, H. Akil, et al. 2005. Evolving
gene/transcript definitions significantly alter the interpretation of GeneChip data.
Nucleic Acids Res. 33: e175.
36. Bengtsson, H., K. Simpson, J. Bullard, and K. Hansen. 2008. In Tech Report 745.
Department of Statistics, University of California, Berkeley, CA.
37. Smyth, G. 2005. Limma: linear models for microarray data. In Bioinformatics
and Computational Biology Solutions using R and Bioconductor. V. C. R.
Gentleman, W. Huber, R. Irizarry, and S. Dudoit, eds. Springer, New York, p.
397–420.
38. Hochberg, Y., and Y. Benjamini. 1990. More powerful procedures for multiple
significance testing. Stat. Med. 9: 811–818.
39. Pavesi, G., P. Mereghetti, G. Mauri, and G. Pesole. 2004. Weeder Web: discovery
of transcription factor binding sites in a set of sequences from co-regulated
genes. Nucleic Acids Res. 32(Web Server issue): W199–W203.
40. Pavesi, G., and G. Pesole. 2006. Using Weeder for the discovery of conserved
transcription factor binding sites. Curr. Protoc. Bioinformatics Chapter 2: Unit 2.11.
41. Cartharius, K., K. Frech, K. Grote, B. Klocke, M. Haltmeier, A. Klingenhoff,
M. Frisch, M. Bayerlein, and T. Werner. 2005. MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics 21:
2933–2942.
FOXP3 TARGET GENES IN HUMAN nTregs
The Journal of Immunology
78. Pfoertner, S., A. Jeron, M. Probst-Kepper, C. A. Guzman, W. Hansen,
A. M. Westendorf, T. Toepfer, A. J. Schrader, A. Franzke, J. Buer, and
R. Geffers. 2006. Signatures of human regulatory T cells: an encounter with
old friends and new players. Genome Biol. 7: R54.
79. TrehanPati, N., R. Geffers, S. Sukriti, P. Hissar, T. Riese, J. Toepfer, M. Buer,
C. A. Kumar, Guzman, and S. K. Sarin. 2009. Gene expression signatures of
peripheral CD4+ T cells clearly discriminate between patients with acute and
chronic hepatitis B infection. Hepatology 49: 781–790.
80. Huehn, J., and A. Hamann. 2005. Homing to suppress: address codes for Treg
migration. Trends Immunol. 26: 632–636.
81. Walker, L. S. 2009. Regulatory T cells overturned: the effectors fight back.
Immunology 126: 466–474.
82. Shevach, E. M. 2009. Mechanisms of foxp3+ T regulatory cell-mediated suppression. Immunity 30: 636–645.
83. Wang, R., Q. Wan, L. Kozhaya, H. Fujii, and D. Unutmaz. 2008. Identification of
a regulatory T cell specific cell surface molecule that mediates suppressive
signals and induces Foxp3 expression. PLoS One 3: e2705.
84. Tran, D. Q., J. Andersson, R. Wang, H. Ramsey, D. Unutmaz, and
E. M. Shevach. 2009. GARP (LRRC32) is essential for the surface expression of
latent TGF-beta on platelets and activated FOXP3+ regulatory T cells. Proc.
Natl. Acad. Sci. USA 106: 13445–13450.
85. Reeves, J. R., J. W. Xuan, K. Arfanis, C. Morin, S. V. Garde, M. T. Ruiz,
J. Wisniewski, C. Panchal, and J. E. Tanner. 2005. Identification, purification and
characterization of a novel human blood protein with binding affinity for prostate
secretory protein of 94 amino acids. Biochem. J. 385: 105–114.
86. Frost, R. J., and S. Engelhardt. 2007. A secretion trap screen in yeast identifies
protease inhibitor 16 as a novel antihypertrophic protein secreted from the heart.
Circulation 116: 1768–1775.
87. Crawford, N. P., R. C. Walker, L. Lukes, J. S. Officewala, R. W. Williams, and
K. W. Hunter. 2008. The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival. Clin. Exp. Metastasis 25:
357–369.
Downloaded from http://www.jimmunol.org/ by guest on June 18, 2017
68. Strauss, L., M. Czystowska, M. Szajnik, M. Mandapathil, and T. L. Whiteside.
2009. Differential responses of human regulatory T cells (Treg) and effector
T cells to rapamycin. PLoS One 4: e5994.
69. O’Gorman, W. E., H. Dooms, S. H. Thorne, W. F. Kuswanto, E. F. Simonds,
P. O. Krutzik, G. P. Nolan, and A. K. Abbas. 2009. The initial phase of an immune
response functions to activate regulatory T cells. J. Immunol. 183: 332–339.
70. Johnston, R. J., A. C. Poholek, D. DiToro, I. Yusuf, D. Eto, B. Barnett,
A. L. Dent, J. Craft, and S. Crotty. 2009. Bcl6 and Blimp-1 are reciprocal and
antagonistic regulators of T follicular helper cell differentiation. Science 325:
1006–1010.
71. Cimmino, L., G. A. Martins, J. Liao, E. Magnusdottir, G. Grunig, R. K. Perez,
and K. L. Calame. 2008. Blimp-1 attenuates Th1 differentiation by repression of
ifng, tbx21, and bcl6 gene expression. J. Immunol. 181: 2338–2347.
72. Yu, D., S. Rao, L. M. Tsai, S. K. Lee, Y. He, E. L. Sutcliffe, M. Srivastava,
M. Linterman, L. Zheng, N. Simpson, et al. 2009. The transcriptional repressor
Bcl-6 directs T follicular helper cell lineage commitment. Immunity 31: 457–
468.
73. Peng, S., S. Lalani, J. W. Leavenworth, I. C. Ho, and M. E. Pauza. 2007. c-Maf
interacts with c-Myb to down-regulate Bcl-2 expression and increase apoptosis
in peripheral CD4 cells. Eur. J. Immunol. 37: 2868–2880.
74. Kallies, A., E. D. Hawkins, G. T. Belz, D. Metcalf, M. Hommel, L. M. Corcoran,
P. D. Hodgkin, and S. L. Nutt. 2006. Transcriptional repressor Blimp-1 is essential for T cell homeostasis and self-tolerance. Nat. Immunol. 7: 466–474.
75. Martins, G. A., L. Cimmino, M. Shapiro-Shelef, M. Szabolcs, A. Herron,
E. Magnusdottir, and K. Calame. 2006. Transcriptional repressor
Blimp-1 regulates T cell homeostasis and function. Nat. Immunol. 7: 457–465.
76. Gavin, M. A., J. P. Rasmussen, J. D. Fontenot, V. Vasta, V. C. Manganiello,
J. A. Beavo, and A. Y. Rudensky. 2007. Foxp3-dependent programme of
regulatory T-cell differentiation. Nature 445: 771–775.
77. Torgerson, T. R., A. Genin, C. Chen, M. Zhang, B. Zhou, S. Añover-Sombke,
M. B. Frank, I. Dozmorov, E. Ocheltree, P. Kulmala, et al. 2009. FOXP3 inhibits
activation-induced NFAT2 expression in T cells thereby limiting effector cytokine expression. J. Immunol. 183: 907–915.
1081