PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Nature. Author manuscript; available in PMC 2013 September 12.
Published in final edited form as:
PMCID: PMC3771531
NIHMSID: NIHMS507352

Novel Foxo1–dependent transcriptional programs control Treg cell function

Abstract

Regulatory T (Treg) cells, characterized by expression of the transcription factor forkhead box P3 (Foxp3), maintain immune homeostasis by suppressing self-destructive immune responses14. Foxp3 operates as a late-acting differentiation factor controlling Treg cell homeostasis and function5, whereas the early Treg-cell-lineage commitment is regulated by the Akt kinase and the forkhead box O (Foxo) family of transcription factors610. However, whether Foxo proteins act beyond the Treg-cell-commitment stage to control Treg cell homeostasis and function remains largely unexplored. Here we show that Foxo1 is a pivotal regulatorof Treg cell function. Treg cells express high amounts of Foxo1 and display reduced T-cell-receptor-induced Akt activation, Foxo1 phosphorylation and Foxo1 nuclear exclusion. Mice with Treg-cell-specific deletion of Foxo1 develop a fatal inflammatory disorder similar in severity to that seen in Foxp3-deficient mice, but without the loss of Treg cells. Genome-wide analysis of Foxo1 binding sites reveals ~300 Foxo1-bound target genes, including the pro-inflammatory cytokine Ifng, that do not seem to be directly regulated by Foxp3. These findings show that the evolutionarily ancient Akt–Foxo1 signalling module controls a novel genetic program indispensable for Treg cell function.

Among the three Foxo genes expressed in T cells, the transcript of Foxo1 is specifically upregulated in mature thymocytes8. To genetically mark cells expressing Foxo1, we engineered a Foxo1 allele (Foxo1tag) encoding an in-frame fusion protein tag containing green fluorescent protein (GFP), Flag and a biotin-labelling peptide that enables Foxo1 biotinylation in birA-transgenic mice that express the bacterial biotin ligase BirA (Fig. 1a and Supplementary Fig. 1). T-cell expression of CD127 (also known as Il7r), the product of a previously identified Foxo1 target gene11,12, was unaffected in Foxo1tag/tag mice (Supplementary Fig. 1d), indicating that the protein tag does not alter Foxo1 function. Using GFP as a reporter, we found that immature thymocytes expressed low amounts of Foxo1 (Fig. 1a). Foxo1 was more strongly upregulated in Treg cells than in conventional CD4+ T cells in the thymus, whereas the peripheral T cells expressed similar amounts of Foxo1 (Fig. 1a).

Figure 1
Foxo1 expression and TCR-induced Foxo1 nuclear exclusion in Treg cells

Foxo1 resides in the nucleus of quiescent T cells, and relocates to the cytosol after T-cell receptor (TCR) stimulation. To investigate the kinetics of Foxo1 translocation, we performed live-imaging experiments on CD4+ T cells from Foxo1tag/tag mice on a Foxp3-internal ribosome entry site (IRES)-red fluorescent protein (RFP)-transgenic background in which Treg cells are marked by the expression of RFP. Whereas high-dose CD3 antibody induced Foxo1 nuclear clearance in all T cells (Fig. 1b, c and Supplementary Video 1), low-dose CD3 antibody triggered Foxo1 translocation in conventional T cells but not in Treg cells (Fig. 1b, c and Supplementary Video 2). Attenuated Foxo1 nuclear clearance in Treg cells was further revealed by the immunostaining of T cells from wild-type mice (Supplementary Fig. 2a, b). Foxo1 nuclear export is regulated by Akt-induced Foxo1 phosphorylation. Low-dose anti-CD3 induced robust Akt and Foxo1 phosphorylation in conventionalT cells, which was markedly decreased in Treg cells (Fig. 1d). The phosphorylation defects of Foxo1 and Akt were less profound in T cells stimulated with high-dose CD3 antibody (Supplementary Fig. 2c). Anti-CD3-induced Erk1 and Erk2 (also known as Mapk3 and Mapk1, respectively) phosphorylation was not compromised at the late time points in Treg cells (Fig. 1d and Supplementary Fig. 2c), suggesting that the Akt pathway might be specifically modulated, probably through high expression of the Akt phosphatase Phlpp1 (ref. 13). Thus, compared to conventional T cells, Treg cells are resistant to TCR-induced Akt activation and Foxo1 nuclear clearance.

To investigate the function of Foxo1 in Treg cells, we crossed mice carrying floxed Foxo1 alleles (Foxo1fl/fl) with Foxp3cre mice14. Foxo1 protein was barely detectable in Treg cells from Foxp3cre Foxo1fl/fl mice, whereas conventional CD4+ T cells from Foxp3cre Foxo1fl/fl mice expressed comparable amounts of Foxo1 to wild-type T cells (Fig. 2a). In the absence of Foxo1 in Treg cells, mice developed a hunched posture associated with lack of mobility, in addition to crusting of the ears, eyelids and tail (Fig. 2b) before they became moribund within 35 days of birth. Such a fulminant phenotype was comparable in severity to that of mice with the scurfy mutation of the Foxp3 gene (Foxp3sf; Fig. 2b), or mice depleted of Treg cells15,16. In addition, Foxp3cre Foxo1fl/fl mice showed splenomegaly and lymphadenopathy (Fig. 2c), and had a dense infiltrate of leukocytes in the salivary glands, lung interstitia, liver sinusoids, pancreas acini, stomach, and colon mucosa (Fig. 2d).

Figure 2
An essential role for Foxo1 in the control of Treg cell function

The severe immunopathology was associated with the expansion of T-cell populations expressing high amounts of the cell-proliferation marker Ki67 (Fig. 2e and Supplementary Fig. 3). T cells displayed an effector/memory phenotype (Fig. 2f and Supplementary Fig. 4a) and produced increased amounts of the pro-inflammatory cytokine interferon (IFN)-γ (Fig. 2g and supplementary Fig. 4b). Deletion of Foxo1 from CD4+CD8+ immature T cells results in compromised Treg cell differentiation8,10. However, thymic and splenic Treg cell numbers were unaffected in 12-day-old Foxp3cre Foxo1fl/fl mice (Supplementary Fig. 5). By the age of day 20, Treg cell numbers in the peripheral lymphoid tissues were increased in Foxp3cre Foxo1fl/fl mice (Fig. 2h and Supplementary Fig. 4c). These findings imply that the lymphoproliferative disease is caused by a loss of Treg cell function rather than a loss of Treg cells.

The observation that Treg cells were less sensitive to TCR-induced Foxo1 nuclear exclusion implies that Foxo1-dependent transcriptional regulation is crucial for Treg cell function. However, cytosolic Foxo1 regulates autophagy through transcription-independent mechanisms17. To differentiate the activities of nuclear Foxo1 and cytosolic Foxo1 induced by Akt activation, we inserted a complementary DNA fragment coding for a haemagglutinin (HA)-tagged human Foxo1 mutant in which amino acids at the Akt phosphorylation sites were substituted with alanines18 (HA-hFoxo1(AAA)), preceded by a loxP-flanked `neo-STOP' cassette into the Rosa26 locus (Supplementary Fig. 6). Mice carrying the mutant allele (Foxo1AAA) were crossed with oestrogen receptor-Cre (creER) mice. Wild-type T cells or T cells from creER Foxo1AAA/+ mice previously treated with tamoxifen to induce HA-hFoxo1(AAA) expression were stimulated with CD3 and CD28 antibodies. As expected, whereas wild-type Foxo1 translocated from the nucleus to the cytosol, the HA-hFoxo1(AAA)-expressing mutant was defective in nuclear export (Supplementary Fig. 6c). We further crossed Foxo1AAA/+ mice with Foxp3cre mice, and confirmed specific HA-hFoxo1(AAA) expression in Treg cells (Fig. 3a). Ectopic expression of HA-hFoxo1(AAA) did not affect Treg cell differentiation or homeostasis (Supplementary Fig. 7a). Upon breeding the Foxo1AAA allele to Foxp3cre Foxo1fl/fl mice, the lethal inflammatory phenotype was completely rescued (Fig. 3b and supplementary Fig. 7), indicating that the Treg cell defects are caused by a loss of nuclear Foxo1 activity.

Figure 3
Foxo1-dependent transcriptional programs in Treg cells

For global identification of Foxo1 DNA-binding sites in Treg cells, we performed chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-seq) experiments using Foxo1 antibody precipitation of chromatin purified from CD4+CD25+ T cells from C57BL/6 mice, or streptavidin pull-down of chromatin from Treg cells of Foxo1tag/tag birA mice. To define a ChIP-seq peak as significant, we used the model-based analysis of ChIP-seq (MACS) peak calling software and applied an empirical false-discovery rate of 0.01 at which the noise ratio of peak detection was less than 0.005 (Supplementary Fig. 8a). Using this criterion, we identified 3,431 enriched genomic loci that were shared between the antibody and the biotinylated Foxo1 samples (Supplementary Fig. 8b), and were designated as putative Foxo1 binding sites (Supplementary Table 1). Among the Foxo1binding peaks, we could identify previously characterized binding sites of Foxo1 target genes, including an intronic binding site in the Foxp3 locus8, an Il7r enhancer element11,12, as well as binding sites in the proximal promoter regions of the Fbxo32 and Bcl2l11 genes19,20 (Supplementary Fig. 9).

Foxo1 binding sites were mostly enriched in the promoters and the 5′ untranslated regions (UTRs), whereas peaks mapped to the intergenic regions and 3′ UTRs were substantially under-represented (Fig. 3c). Further analysis of the peaks showed that Foxo1 preferentially bound to regions within 500 base pairs of the transcription start sites (Supplementary Fig. 8c). Foxo1 binding peaks were enriched for the high-affinity Foxo1 binding motifs, and de novo motif prediction from the top 500 ranked binding peaks revealed a conserved Foxo1 recognition site (Supplementary Fig. 8d, e). In addition, approximately 73% of Foxo1 binding sites were mapped to the conserved genomic regions of mammalian species (Supplementary Table 1). These observations support an evolutionarily conserved function for Foxo1 in classical transcriptional regulation of target-gene expression.

To identify the cellular functions directly regulated by Foxo1 transcriptional control, we performed gene-expression profiling on Treg cells from wild-type, Foxp3creFoxo1fl/fl, Foxp3creFoxo1AAA/+ and Foxp3creFoxo1fl/fl Foxo1AAA/+ mice. Comparison of data from wild-type and Foxp3creFoxo1fl/fl Treg cells revealed 942 and 1,155 genes downregulated or upregulated, respectively, by more than 1.5 fold, ~80% of which were corrected in Foxp3creFoxo1fl/fl Foxo1AAA/+ Treg cells (Supplementary Table 2). By cross-referencing this data set with the data set of Foxo1-bound genes, we identified 310 putative Foxo1 direct target genes, among which 240 and 70 genes were activated or repressed, respectively, by Foxo1 (Fig. 3d and Supplementary Table 3).

Treg cell homeostasis and function are dependent on the Treg-cell-specific transcription factor Foxp3 (ref. 5). ChIP and genome tiling array studies have revealed Foxp3-bound genes in murine T cells21,22. Approximately 6.8% or 9.6% of Foxo1-bound genes were occupied by Foxp3 in Treg cells or in a T-cell line that overexpressed Foxp3 (Supplementary Fig. 10a). Comparison of the putative direct target genes of Foxo1 and Foxp3 revealed that ~90% of activated genes and ~99% of repressed genes were specifically regulated by Foxo1 or Foxp3 (Supplementary Fig. 10b, c and Supplementary Table 3). Indeed, expression of the Foxp3 target gene Il2ra (coding for CD25) was decreased in T cells transcribing a Foxp3 null allele that encodes a GFP reporter23, but not in Foxo1-deficient Treg cells (Fig. 4a and Supplementary Fig. 10d). A recent study showed that Foxo1 binds to the promoter region of the Foxp3 target gene Ctla4 (ref. 10). ChIP-seq experiments revealed weak Ctla4-promoter binding of Foxo1 in antibody but not biotin samples (data not shown). In the absence of Foxo1, CTLA4 was marginally downregulated, whereas the amount of Ctla4 transcript was approximately fourfold lower in the absence of Foxp3 (Fig. 4a and Supplementary Fig. 10e). Foxp3 and CTLA4 are essential for Treg cell inhibition of conventional T-cell proliferation in vitro23,24. By contrast, as previously reported in ref. 11, Foxo1-deficient Treg cells had normal suppressive activity in such assays (Fig. 4b). These observations imply that the Foxp3-dependent program is largely intact in the absence of Foxo1, and the loss of suppressive activity of Foxo1-deficient Treg cells is probably independent of CTLA4.

Figure 4
Restoration of Foxo1-deficient Treg cell function in the absence of IFN-γ

To gain insights into the general functional features of the Foxo1-regulated program, we analysed the Gene Ontology and BioCarta pathway association of Foxo1 target genes25. The putative Foxo1 direct target genes were strongly associated with cell communication, signal transduction, transcription and metabolism, and were specifically enriched for genes involved in the Jak-STAT, TCR and insulin-signalling pathways (Supplementary Table 4). A salient characteristic of Treg cells is their inability to produce pro-inflammatory cytokines such as IFN-γ. Foxo1 was recruited to a regulatory element located 22 kilobases upstream from the transcriptional start site of the Ifng gene26 in Treg cells (Fig. 4c, d). In the absence of Foxo1, IFN-γ messenger RNA and protein were highly induced in Treg cells (Fig. 4e, f and Supplementary Fig. 11a). However, similar to wild-type Treg cells, Foxo1-deficient Treg cells were unable to produce interleukin (IL)-2 (Supplementary Fig. 11b), which is probably regulated by Foxp3 (ref. 22). To determine whether Foxo1 had a cell-intrinsic role in inhibiting IFN-γ expression, we co-cultured wild-type Treg cells and Treg cells from the disease-free creER Foxo1fl/fl mice previously treated with tamoxifen to acutely delete Foxo1. A sizable fraction of Foxo1-deficient but not wild-type Treg cells produced IFN-γ under the condition of IL-12 and IFN-γ stimulation (Supplementary Fig.12a). Importantly, wild-type and Foxo1-deficient conventional CD4+ T cells produced similar amounts of IFN-γ (Supplementary Fig. 12b), revealing a specific function for Foxo1 in repressing IFN-γ expression in Treg cells.

To investigate whether IFN-γ production by Foxo1-deficient Treg cells contributed to their loss of suppressive function, we crossed Foxp3creFoxo1fl/fl mice to the IFN-γ-deficient background. Ablation of IFN-γ partially corrected the lethal inflammatory phenotype as well as the lymphoproliferative disease of Foxp3creFoxo1fl/fl mice (Supplementary Fig. 13). To assess the specific function of IFN-γ produced by Foxo1-deficient Treg cells, we used a transfer model of colitis. Recipient mice deficient in recombination-activating gene 1 (Rag1−/−) did not develop colitis upon receipt of wild-type, Foxo1−/− or Foxo1−/− Ifng−/− Treg cells (Supplementary Fig. 14). However, recipients of naive T cells developed a wasting disease and colitis within 4 weeks, which was prevented by wild-type but not Foxo1-deficient Treg cells (Fig. 4g, h and Supplementary Fig. 14b). Importantly, the suppressive activity was largely restored in Foxo1−/− Ifng−/− Treg cells (Fig. 4g, h and Supplementary Fig. 14b), supporting Ifng as a critical Foxo1 target gene in the control of Treg cell function.

Foxo proteins control Foxp3 expression and Treg cell differentiation810. Our new data reveal that the Akt–Foxo1 signalling pathway is modulated in mature Treg cells to maintain high nuclear Foxo1 activity; this is crucial for Treg cell function, in part through Foxo1 inhibition of IFN-γ expression. IFN-γ produced by Treg cells may activate conventional T cells or antigen-presenting cells compromising Treg cell function, but the precise mechanisms remain to be determined. Diminished TCR-induced Akt activation has been observed in human Treg cells27, suggesting that Foxo1 controls human Treg cell function. Under conditions of strong TCR stimulation, nuclear Foxo1 is translocated to the cytosol in Treg cells, raising the possibility that the Foxo1-dependent program is subject to regulation. Intriguingly, Treg cells from mice infected with Toxoplasma gondii produce IFN-γ28. Increased frequencies of IFN-γ-secreting Treg cells are also present in multiple sclerosis and diabetes patients29,30. Future studies will determine whether the loss of Foxo1 function accounts for Treg cell plasticity under these pathological conditions, and whether the Akt–Foxo1 signalling pathway can be manipulated for the treatment of Treg-cell-associated immunological disorders.

METHODS

Mice

To generate a Foxo1 knock-in mouse model (Foxo1tag), two genomic DNA fragments of the Foxo1 gene were isolated from a C57BL/6 bacterial artificial chromosome library (Genome Systems). A cDNA coding for GFP, Flag and biotin-labelling peptide was inserted into the genomic DNA fragment containing the second exon of Foxo1 before the stop codon. Targeting vector was constructed by cloning the fragments into pEasy-Flox plasmid. To generate a Rosa26-hFoxo1AAA knock-in mouse model (Foxo1AAA), the cDNA coding for a HA-tagged human Foxo1 (AAA) mutant was inserted into a Rosa26 targeting vector. Linearized targeting vectors were transfected into embryonic stem cells derived from the C57BL/6 strain. Homologous recombinants were identified by Southern blotting analysis, and were implanted into foster mothers. Chimaeric mice were bred to C57BL/6 mice, and the F1 generation was screened for germline transmission. The neo gene in Foxo1tag mice was removed by breeding F1 mice with a strain of cytomegalovirus-promoter-driven cre-transgenic mice (The Jackson Laboratory). Mice containing floxed Foxo1, Foxp3-IRES-RFP, Foxp3Cre, Foxp3gfpko (Foxp3 null allele coding for a GFP reporter) and birA alleles were previously described11,14,23,31,32. C57BL/6, Foxp3sf, Ifng−/−, Rag1−/− and UBC-cre-ERT2 (creER) mice were purchased from the Jackson Laboratory. Mice with intact or floxed Foxo1 alleles were used as wild-type controls. Mice with Treg-cell-specific deletion of Foxo1 were generated by crossing Foxo1 floxed mice with Foxp3cre mice. To mark Treg cells with RFP, mice were bred with Foxp3-IRES-RFP mice. To label Foxo1 with biotin, Foxo1tag mice were bred with birA-transgenic mice. All mice were maintained under specific pathogen-free conditions, and animal experimentation was conducted in accordance with institutional guidelines.

Antibodies and immunoblotting

Anti-p-Foxo1 Thr 24 (no. 9464), anti-Foxo1 (C29H4), anti-p-Akt Ser 473 (D9E), anti-Akt (C67E7), anti-p-Erk1/2, anti-Erk1/2 and anti-HA (6E2) were purchased from Cell Signaling. Anti-β-actin (AC-15) was obtained from Sigma. To determine Foxo1 expression in Treg cells, CD4+Foxp3+ Treg cells were purified by FACS. Total protein extracts were dissolved in SDS sample buffer. To assess Foxo1 translocation, T cells were stimulated with anti-CD3 and anti-CD28, and the cytosolic and nuclear extracts were prepared. To analyse Akt, Foxo1 and Erk phosphorylation, conventional CD4+ T cells and CD4+Foxp3+ Treg cells were purified by FACS. T cells were stimulated with anti-CD3 and anti-CD28, and the total protein extracts were prepared. Protein extracts were separated on 8% SDS–PAGE gels and transferred to polyvinylidene difluoride membrane (Millipore). The membranes were probed with antibodies and visualized with the Immobilon Western Chemiluminescent HRP Substrate (Millipore).

Live imaging of Foxo1 translocation

CD4+ cells were isolated from Foxo1tag/tag Foxp3-IRES-RFP mice, and stimulated with plate-bound anti-CD3 and anti-CD28 on glass surfaces. GFP, RFP and bright-field images were acquired every 2 min after addition of cells using a fluorescence videomicroscope (Olympus IX-81) fitted with a 40X objective lens. Nuclear Foxo1 clearance was quantified by analysing two orthogonal linescans through each cell. For each linescan, the average intensity of three equally spaced pixels inside the cell was divided by the average intensity of the cell edge on each side to determine a `clearance ratio'. Nuclear exclusion leads to a decrease in clearance ratio.

Immunofluorescence microscopy

CD4+ T cells were isolated from spleen and peripheral lymph nodes of C57BL/6 mice with anti-CD4 microbeads (Miltenyi Biotec Inc.), stimulated with plate-bound anti-CD3 (low dose, 0.01 μg ml−1; high dose, 0.1 μg ml−1) and anti-CD28 (1 μg ml−1) on chamber slides at 37 °C for 20 min. After fixation with 4% paraformaldehyde, cells were permeabilized with Foxp3 Fixation/Permeabilization buffer (eBioscience) according to the manufacturer's instructions. After blocking with Permeabilization buffer and 3% BSA, cells were incubated with 1:250 diluted rabbit anti-Foxo1 (C29H4, Cell Signaling) and rat anti-Foxp3 (FJK-16 s, eBioscience), followed by allophycocyanin (APC)-anti-rabbit and fluorescein isothiocyanate (FITC)-anti-rat secondary antibodies in Permeabilization buffer and 1% BSA. Slides were mounted with gold anti-fading mounting buffer (Invitrogen). Images were acquired with a Leica TCS SP5-II confocal microscope. For quantitative analysis, five fields were selected randomly and total cells in the field were manually counted and grouped with Volocity software (PerkinElmer Inc.), on the basis of their Foxo1 nuclear or cytosolic localization and Foxp3 expression.

Flow cytometry

Fluorescent-dye-labelled antibodies against cell-surface markers CD4, CD8, TCR-β, CD44, CD62L, CTLA4 and CD25 were purchased from eBiosciences. Thymic, splenic and lymph node cells were depleted of erythrocytes by hypotonic lysis. Cells were incubated with specific antibodies for 30 min on ice in the presence of 2.4G2 monoclonal antibody to block FcγR binding. All samples were acquired and analysed with an LSR II flow cytometer (Becton, Dickinson) and FlowJo software (TreeStar). Intracellular Foxp3, CTLA4 and Ki67 stainings were carried out with kits from eBiosciences. To determine cytokine expression, splenic and lymph node T cells were stimulated with 50 ng ml−1 phorbol 12-myristate 13-acetate (Sigma), 1 μM ionomycin (Sigma) and GolgiStop (BD Biosciences) for 4 h. After stimulation, cells were stained with cell-surface marker antibodies, fixed and permeabilized, and stained with anti-Foxp3 in combination with specific antibodies against IFN-γ and IL-2 (eBiosciences).

ELISA

Treg cells were purified from wild-type and Foxp3cre Foxo1fl/fl mice by FACS and stimulated with CD3 and CD28 antibodies in the presence of IL-2 for 24 h. Cytokine amounts in tissue-culture supernatants were assayed with ELISA antibody pair for IFN-γ (eBioscience, 88-8312) in accordance with the manufacturer's recommendations.

In vitro T-cell differentiation

CreER-mediated deletion of floxed Foxo1 alleles was induced by intraperitoneal injection of 2 mg of tamoxifen (Sigma) emulsified in 200 ml of corn oil (Sigma) every day for 5 days. CD4+Foxp3+ Treg cells or CD44loCD4+Foxp3 conventional T cells from CD45.1+ wild-type and CD45.2+ tamoxifen-treated creER Foxo1fl/fl (Foxo1−/−) mice were isolated by FACS and were co-cultured at 1:1 ratio with irradiated splenocytes, anti-CD3 (1 μg ml−1), anti-CD28 (2 μg ml−1) and IL-2 (200 U ml−1) in the presence or absence of IL-12 (5 ng ml−1) and IFN-γ (10 ng ml−1) for 3 days.

In vitro Treg cell suppression

CD44loCD4+Foxp3 conventional T cells labelled with CFSE were used as responder cells. Responder T cells (5 × 104) were cultured for 72 h with irradiated splenocytes (1 × 105) and anti-CD3 (2 μg ml−1) in the presence or absence of various numbers of Treg cells. The division of responder T cells was assessed by dilution of CFSE.

Transfer model of colitis

Naive T cells sorted by FACS (CD4+CD44loCD62Lhi, 4 × 105) from CD45.1+ C57BL/6 mice were transferred into Rag1−/− mice alone or in combination with CD45.2+ Treg cells (CD4+Foxp3+, 2 × 105). After T-cell reconstitution, mice were weighed weekly and monitored for signs of disease. Mice were killed at 4 weeks after T-cell transfer and their colons were used for histopathological analysis. The following grades were used to evaluate their disease severity: 0, normal colonic crypt architecture; 1, mild inflammation: slight epithelial cell hyperplasia and increased numbers of leukocytes in the mucosa; 2, moderate colitis: pronounced epithelial hyperplasia, significant leukocyte infiltration, and decreased numbers of goblet cells; 3, severe colitis: marked epithelial hyperplasia with extensive leukocyte infiltration, significant depletion of goblet cells, occasional ulceration, or cryptic abscesses; 4, very severe colitis: marked epithelial hyperplasia with extensive transmural leukocyte infiltration, severe depletion of goblet cells, many crypt abscesses and severe ulceration.

Histopathology

Tissues from killed animals were fixed in Safefix II (Protocol) and embedded in paraffin. 5-μm sections were stained with haematoxylin and eosin.

ChIP-seq

Treg cells were fixed for 10 min at 25 °C with 10% formaldehyde. After incubation, glycine was added to a final concentration of 0.125 M to `quench' the formaldehyde. Cells were pelleted, washed twice with ice-cold PBS and lysed. The lysates were pelleted, re-suspended and sonicated to reduce DNA length to 300–500 base pairs (bp). The chromatin prepared from Treg cells of C57BL/6 mice was incubated with protein-A-anti-Foxo1 (ab39670, Abcam) or an iso-type control antibody overnight. The chromatin prepared from Treg cells of Foxo1tag/tagbirA Foxp3-IRES-RFP or control birA mice was incubated with streptavidin overnight. The immune complexes were washed, and eluted in 500 ml of elution buffer containing 50 μM Tris, 10 mM EDTA and 1.0% SDS. Precipitated ChIP DNA and input DNA were incubated at 65 °C to reverse the crosslinking. After digestion with RNase and proteinase K, the ChIP and input DNA were purified with phenol/chloroform extraction and ethanol precipitation. The purified DNA was repaired, ligated with an adaptor, and amplified by PCR for 15–20 cycles. The amplified DNA was purified by gel extraction and used for sequencing. SR-36 sequencing was done at the Genome Center of Cold Spring Harbor Laboratory.

Visualizing read densities and identifying binding sites

Uniquely mapped sequence reads (36 bp) were aligned to the mouse genome (July 2007, NCBI37/mm9) using the Bowtie33 short-read alignment software (version 0.12.7). Candidate binding sites were predicted using the MACS34 peak detection software (version 1.4.1). In brief, MACS determines regions of enrichment by building sequencing-density profiles for sense and antisense reads, with respect to the reference, and pairing them such that the average distance between pairings can be used to model the approximate library fragment length (d). Reads from both strands were then repositioned by shifting a distance half the estimated fragment length, ½d, downstream to capture the most likely binding location at the fragment midpoint rather than skewing to the 5′ or 3′ ends. Enrichment was then determined by scanning the shifted, genome-wide read profile with a window twice the size of the estimated fragment length, where the number of shifted reads falling within this 2 × d window was assumed to follow a Poisson distribution, with a mean parameter determined from the local background in the input sample. The empirical false-discovery rate (eFDR) was fine-tuned using the noise ratio to maximize the effectiveness of the peak-calling strategy. The noise ratio was defined as the number control IgG peaks overlapping control BirA peaks (control overlaps) divided by the number of Foxo1 antibody peaks overlapping Foxo1 biotin peaks (ChIP overlaps). A noise ratio threshold of 0.005 was chosen as it corresponded to a large reduction in noise. To achieve this ratio, peak-calling was performed at an eFDR of 0.01. As defined in ref. 34, false background peaks were enumerated as the number of peaks called when swapping the input and ChIP sample. The eFDR was then calculated as the number of peaks detected divided by the number of background peaks detected. We, thus, determined a suitable threshold of P < 2.5 × 10−9 and P < 5.0 × 10−6, yielding 19,904 and 61,180 significantly enriched loci for antibody- and biotin-based ChIP-seq of Foxo1, respectively. To visualize read-density profiles, we aggregated counts at each genomic position for uniquely mapped reads that were extended to the estimate d. Counts were then smoothed with a moving average within a 200-bp sliding window across the genome. Smoothed average counts were visualized near to genes of interest using the R software package (http://www.R-project.org).

Associating peaks with gene features

To further refine our enriched loci, we corroborated peaks detected in antibody-based ChIP-seq with those from our biotinylated-Foxo1 tag-based method. To accomplish this, we distinguished high-confidence peaks by selecting for all `antibody' peaks overlapped by one or more `biotin' peaks and extending the 3′ and 5′ most coordinates to encompass all overlaps, so as to reduce any redundancies. The merged coordinates yielded 3,431 high-confidence, unique, merged regions, which we deemed putative Foxo1 binding sites. To associate peaks with functional regions at high precision and resolution, we intersected antibody peak summits (position of highest read density within a candidate peak region) falling within high-confidence corroborated regions with the set of genomic features (for example, 5′ UTR, 3′ UTR, exons, introns, 5-kilobase promoter, intergenic space) obtained from the UCSC Table Browser35 (http://genome.ucsc.edu/). We also compared our regions with conserved phastCons scores and elements (Euarchontoglire subset)36. Over-representation of these features was estimated empirically by generating 1,000 background sets each containing 3,431 random peaks, maintaining the same distribution of chromosomes and widths as our high-confidence set. Peaks from each background set were assigned to appropriate functional categories. The significance of the observed number of each annotation from our high-confidence summits was then inferred using a Z-test with respect to these randomly generated background counts.

DNA motif analysis

We first extracted nucleotide sequences±250 bp flanking all antibody-based ChIP-seq summits associated with peaks corroborated by biotinylated-Foxo1 tag-based ChIP-seq enrichment. These DNA sequences were screened for over-representation of known consensus motifs, using both the JASPAR37 and TRANSFAC38 databases, and the most enriched known motifs were noted. To examine to what extent the consensus Foxo1 TRANSFAC motifs, M00473 and M00474 were enriched, we ranked our putative binding site candidates by P value and assessed the cumulative occurrence rate one peak at a time, from most to least enriched. This was done to illustrate the expectation that the cumulative motif occurrence rate approaches the number of peaks with a motif divided by the total number of peaks but would be significantly increased in more enriched subset of binding candidates. Occurrence was determined using the STORM motif analysis tool available in the CREAD software package39 at a threshold of P<1×10−4. We further substantiated the over-representation of consensus Foxo1 motifs by performing de novo motif prediction from the top 500 ranked candidate binding site sequences using the GADEM40 motif-discovery algorithm, compared to shuffled sequences with similar di-nucleotide composition as background. Statistical similarity of predicted de novo motifs was estimated using the Tomtom41 motif comparison software available as part of the MEME software suite.

Gene-expression profiling

Treg cells were isolated from the spleens and peripheral lymph nodes of Foxp3creFoxo1fl/fl, Foxp3creFoxo1AAA/+, Foxp3creFoxo1fl/fl Foxo1AAA/+ and wild-type mice by FACS. RNA was prepared with the miRNeasy Mini Kit according to the manufacturer's instructions (Qiagen). Two rounds of RNA amplification, labelling and hybridization to M430 2.0 chips (Affymetrix) were done at the Core Facility of Memorial Sloan-Kettering Cancer Center.

Microarray data analysis

Microarray data was analysed using the R statistical software package. To reduce technical variations from array to array, chip normalization was performed using robust multi-array analysis available via the affy BioConductor package and differential gene expression defined by 1.5-fold change with a false-discovery rate of <0.01 was determined using the limma package42,43. When multiple probes for a given gene were significantly differentially expressed, their log2-fold changes were averaged.

qPCR

mRNA amounts of Ifng, Il2ra, Ctla4 and Actb were determined by qPCR with the following primers: Ifng, 5′-gcgtcattgaatcacacctg-3′ and 5′-tgagctcattgaatgcttgg-3′; Il2ra, 5′-gagacttcctgccccataac-3′ and 5′-gccactgctaccttatactcc-3′; Ctla4, 5′-tggactccggaggtacaaag-3′ and 5′-aaacggcctttcagttgatg-3′; Actb, 5′-ttgctgacaggatgcagaag-3′ and 5′-acatctgctggaaggtggac-3′. The mRNA amounts were normalized to those of Actb. Chromatin amounts of the Foxo1 binding site in the Ifng locus were determined by qPCR with the following primers: 5′-gacctgcacttctgtgagca-3′ and 5′-tctccttcctgtggatcacc-3′.

Heat-map

To effectively visualize Foxo1-dependent gene expression, we gated relative expression values on wild-type versus Foxp3creFoxo1AAA/+ values; that is, the log2-fold change between wild-type versus Foxp3creFoxo1AAA/+ was used to define a wild-type value equal to½log2-fold-change and a Foxp3creFoxo1AAA/+ value equal to ½ log2-fold change. The remaining Foxp3creFoxo1fl/fl and Foxp3creFoxo1fl/fl/Foxp3creFoxo1AAA/+ fold-change values were then similarly normalized with respect to this new wild-type value. We focused on the range of genes within±2 log2-fold change; values exceeding this range at the extremes were coloured blue or yellow. A selection of genes was depicted in which the Foxp3creFoxo1fl/fl phenotype was partially or completely rescued by Foxp3creFoxo1AAA/+, and putative Foxo1 binding sites, as determined by ChIP-seq experiments, resided nearby.

Gene-set enrichment analysis

Gene set and pathway analysis was performed using the FatiGO44 tool via the Babelomics25 software suite (http://babelomics.bioinfo.cipf.es) on Foxo1-bound target genes showing differential gene expression rescued by Foxp3creFoxo1AAA/+. Significantly over-represented categories were defined by an adjusted P value of 0.05 or less. We highlighted a selection of Gene Ontology categories and BioCarta pathways made available on the Babelomics web-based tool in our analysis.

Statistical analysis

Student's t-test was used to calculate statistical significance for difference in a particular measurement between groups. A P value of <0.05 was considered statistically significant.

Supplementary Material

Supp Figures

Supp Movie 1

Supp Movie 2

Supp Table 1

Supp Table 2

Supp Table 3

Supp Table 4

Acknowledgements

We thank R. Flavell for the Foxp3-IRES-RFP mouse strain, K. Rajewsky and C. Xiao for the Rosa26 targeting vector and T. Unterman for the HA-hFoxo1(AAA) construct. This work was supported by the Starr Cancer Consortium (13-A123 to M.O.L., M.Q.Z. and G.A.), the Rita Allen Foundation (M.O.L.), National Bio Resource Project (NBRPC) (2012CB316503 to M.Q.Z.) and National Institutes of Health (HG001696 to M.Q.Z.).

Footnotes

Supplementary Information is available in the online version of the paper.

Author Contributions W.O., W.L., C.T.L., N.Y., M.H., G.A., M.Q.Z. and M.O.L. designed the research and analysed the data; W.O., W.L., C.T.L., N.Y., M.H., M.V.K., M.P., P.C., Q.M. and Y.M. did the experiments; D.M. and A.Y.R. provided birA and Foxp3cre mouse strains; K.Z. supervised the ChIP-seq experiment; and W.O., W.L. and M.O.L. wrote the manuscript.

Author Information The expression and ChIP-seq data have been deposited in the NCBI GEO database under accession number GSE40657. Reprints and permissions information is available at www.nature.com/reprints.

The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper.

References

1. Sakaguchi S, Yamaguchi T, Nomura T, Ono M. Regulatory T cells and immune tolerance. Cell. 2008;133:775–787. [PubMed]
2. Tang Q, Bluestone JA. The Foxp3+ regulatory T cell: a jack of all trades, master of regulation. Nature Immunol. 2008;9:239–244. [PMC free article] [PubMed]
3. Feuerer M, Hill JA, Mathis D, Benoist C. Foxp3+ regulatory T cells: differentiation, specification, subphenotypes. Nature Immunol. 2009;10:689–695. [PubMed]
4. Shevach EM. Mechanisms of foxp3+ T regulatory cell-mediated suppression. Immunity. 2009;30:636–645. [PubMed]
5. Rudensky AY. Regulatory T cells and Foxp3. Immunol. Rev. 2011;241:260–268. [PMC free article] [PubMed]
6. Haxhinasto S, Mathis D, Benoist C. The AKT–mTOR axis regulates de novo differentiation of CD4+Foxp3+ cells. J. Exp. Med. 2008;205:565–574. [PMC free article] [PubMed]
7. Sauer S, et al. T cell receptor signaling controls Foxp3 expression via PI3K, Akt, and mTOR. Proc. Natl Acad. Sci. USA. 2008;105:7797–7802. [PubMed]
8. Ouyang W, et al. Foxo proteins cooperatively control the differentiation of Foxp3+ regulatory T cells. Nature Immunol. 2010;11:618–627. [PubMed]
9. Harada Y, et al. Transcription factors Foxo3a and Foxo1 couple the E3 ligase Cbl-b to the induction of Foxp3 expression in induced regulatory T cells. J. Exp. Med. 2010;207:1381–1391. [PMC free article] [PubMed]
10. Kerdiles YM, et al. Foxo transcription factors control regulatory T cell development and function. Immunity. 2010;33:890–904. [PMC free article] [PubMed]
11. Ouyang W, Beckett O, Flavell RA, Li MO. An essential role of the forkhead-box transcription factor Foxo1 in control of T cell homeostasis and tolerance. Immunity. 2009;30:358–371. [PMC free article] [PubMed]
12. Kerdiles YM, et al. Foxo1 links homing and survival of naive T cells by regulating L-selectin, CCR7 and interleukin 7 receptor. Nature Immunol. 2009;10:176–184. [PMC free article] [PubMed]
13. Patterson SJ, et al. Cutting edge: PHLPPregulates the development, function, and molecular signaling pathways of regulatory T cells. J. Immunol. 2011;186:5533–5537. [PubMed]
14. Rubtsov YP, et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity. 2008;28:546–558. [PubMed]
15. Kim JM, Rasmussen JP, Rudensky AY. Regulatory T cells prevent catastrophic autoimmunity throughout the lifespan of mice. Nature Immunol. 2007;8:191–197. [PubMed]
16. Lahl K, et al. Selective depletion of Foxp3+ regulatory T cells induces a scurfy-like disease. J. Exp. Med. 2007;204:57–63. [PMC free article] [PubMed]
17. Zhao Y, et al. Cytosolic FoxO1 is essential for the induction of autophagy and tumour suppressor activity. Nature Cell Biol. 2010;12:665–675. [PubMed]
18. Zhang X, et al. Phosphorylation of serine 256 suppresses transactivation by FKHR (FOXO1) by multiple mechanisms. Direct and indirect effects on nuclear/cytoplasmic shuttling and DNA binding. J. Biol. Chem. 2002;277:45276–45284. [PubMed]
19. Sandri M, et al. Foxo transcription factors induce the atrophy-related ubiquitin ligase atrogin-1 and cause skeletal muscle atrophy. Cell. 2004;117:399–412. [PMC free article] [PubMed]
20. Gilley J, Coffer PJ, Ham J. FOXO transcription factors directly activate bim gene expression and promote apoptosis in sympathetic neurons. J. Cell Biol. 2003;162:613–622. [PMC free article] [PubMed]
21. Zheng Y, et al. Genome-wide analysis of Foxp3 target genes in developing and mature regulatory T cells. Nature. 2007;445:936–940. [PubMed]
22. Marson A, et al. Foxp3 occupancy and regulation of key target genes during T-cell stimulation. Nature. 2007;445:931–935. [PMC free article] [PubMed]
23. Gavin MA, et al. Foxp3-dependent programme of regulatory T-cell differentiation. Nature. 2007;445:771–775. [PubMed]
24. Wing K, et al. CTLA-4 control over Foxp3+ regulatory T cell function. Science. 2008;322:271–275. [PubMed]
25. Medina I, et al. Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Res. 2010;38:W210–W213. [PMC free article] [PubMed]
26. Hatton RD, et al. A distal conserved sequence element controls Ifng gene expression by T cells and NK cells. Immunity. 2006;25:717–729. [PubMed]
27. Crellin NK, Garcia RV, Levings MK. Altered activation of AKT is required for the suppressive function of human CD4+CD25+ T regulatory cells. Blood. 2007;109:2014–2022. [PubMed]
28. Oldenhove G, et al. Decrease of Foxp3+ Treg cell number and acquisition of effector cell phenotype during lethal infection. Immunity. 2009;31:772–786. [PMC free article] [PubMed]
29. Dominguez-Villar M, Baecher-Allan CM, Hafler DA. Identification of T helper type 1-like, Foxp3+ regulatory T cells in human autoimmune disease. Nature Med. 2011;17:673–675. [PMC free article] [PubMed]
30. McClymont SA, et al. Plasticity of human regulatoryT cells in healthy subjects and patients with type 1 diabetes. J. Immunol. 2011;186:3918–3926. [PMC free article] [PubMed]
31. Wan YY, Flavell RA. Identifying Foxp3-expressing suppressor T cells with a bicistronic reporter. Proc. Natl Acad. Sci. USA. 2005;102:5126–5131. [PubMed]
32. Driegen S, et al. A generic tool for biotinylation of tagged proteins in transgenic mice. Transgenic Res. 2005;14:477–482. [PubMed]
33. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25. [PMC free article] [PubMed]
34. Zhang Y, et al. Model-based analysis of ChIP-Seq (MACS) Genome Biol. 2008;9:R137. [PMC free article] [PubMed]
35. Karolchik D, et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 2004;32:D493–D496. [PMC free article] [PubMed]
36. Siepel A, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15:1034–1050. [PubMed]
37. Portales-Casamar E, et al. JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res. 2010;38:D105–D110. [PMC free article] [PubMed]
38. Matys V, et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 2003;31:374–378. [PMC free article] [PubMed]
39. Smith AD, Sumazin P, Xuan Z, Zhang MQ. DNA motifs in human and mouse proximal promoters predict tissue-specific expression. Proc. Natl Acad. Sci. USA. 2006;103:6275–6280. [PubMed]
40. Li L. GADEM: a genetic algorithm guided formation of spaced dyads coupled with an EM algorithm for motif discovery. J. Comput. Biol. 2009;16:317–329. [PMC free article] [PubMed]
41. Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS. Quantifying similarity between motifs. Genome Biol. 2007;8:R24. [PMC free article] [PubMed]
42. Irizarry RA, et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003;31:e15. [PMC free article] [PubMed]
43. López-Romero P. Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library. BMC Genomics. 2011;12:64. [PMC free article] [PubMed]
44. Al-Shahrour F, Diaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics. 2004;20:578–580. [PubMed]