Immunity Resource

Discrete Roles of STAT4 and STAT6 Factors in Tuning Epigenetic Modifications and Transcription during Differentiation

Lai Wei,1,5 Golnaz Vahedi,1,5 Hong-Wei Sun,2 Wendy T. Watford,1 Hiroaki Takatori,1 Haydee L. Ramos,1 Hayato Takahashi,1 Jonathan Liang,1 Gustavo Gutierrez-Cruz,3 Chongzhi Zang,4 Weiqun Peng,4 John J. O’Shea,1 and Yuka Kanno1,* 1Molecular Immunology and Inflammation Branch 2Biodata Mining and Discovery Section 3Laboratory of Muscle Stem Cells and Regulation NIAMS, National Institutes of Health, Bethesda MD 20892, USA 4Department of Physics, George Washington University, Washington, DC 20052, USA 5These authors contributed equally to this work *Correspondence: [email protected] DOI 10.1016/j.immuni.2010.06.003

SUMMARY constrain immune-mediated damage (Sakaguchi et al., 2008). Thus, the attainment of these cell fates is critical not only for Signal transducer and activator of transcription 4 proper elimination of pathogens, but also for avoidance of (STAT4) and STAT6 are key factors in the specifica- damage to the host (Reiner, 2007). tion of helper T cells; however, their direct roles in The microenvironment of the naive CD4+ greatly influ- driving differentiation are not well understood. Using ences its specification, with a key factor being the chromatin immunoprecipitation and massive parallel milieu. A major means by which exert their effect is sequencing, we quantitated the full complement of through activation of signal transducer and activator of transcrip- tion (STAT) family transcription factors, which translocate to the STAT-bound , concurrently assessing global nucleus, bind to the regulatory region of genes and induce tran- STAT-dependent epigenetic modifications and scription (Levy and Darnell, 2002; Schindler and Darnell, 1995). gene transcription by using cells from cognate Among the 7 STAT family members, STAT4, in conjunction STAT-deficient mice. STAT4 and STAT6 each bound with STAT1, plays pivotal role in Th1 cell differentiation by trans- over 4000 genes with distinct binding motifs. Both mitting IL-12 signals to produce IFN-g and enhancing expression played critical roles in maintaining chromatin config- of T-box 21 (T-bet) (Thieu et al., 2008). STAT6, activated by IL-4, uration and transcription of a core subset of genes is the key player in Th2 cell differentiation and serves to enhance through the combination of different epigenetic expression of IL-4, IL-13, and GATA binding 3 (Gata3) patterns. Globally, STAT4 had a more dominant role (Zhu and Paul, 2008). Among the STATs, STAT4 and STAT6 in promoting active epigenetic marks, whereas stand out as having highly specific functions because the pheno- STAT6 had a more prominent role in antagonizing type of gene-targeted mice is largely confined to Th1 or Th2 cell defects, respectively, and the mice develop otherwise normally repressive marks. Clusters of genes negatively regu- with normal numbers of T cells (Wurster et al., 2000). lated by STATs were also identified, highlighting Transcriptional profiling has helped to clarify genes whose previously unappreciated repressive roles of STATs. expression is controlled by STAT4 and STAT6, and these data Therefore, STAT4 and STAT6 play wide regulatory argue that these transcription factors are clearly important for roles in T helper cell specification. regulating a wide array of genes (Chen et al., 2003; Hoey et al., 2003; Lund et al., 2004; Lund et al., 2007; Rogge et al., 2000). However, this technology does not permit the discrimination of INTRODUCTION genes that are directly versus indirectly regulated. Chromatin immunoprecipitation (ChIP) can identify genes bound by tran- In mounting effective immunity toward diverse microbial patho- scription factors, but relatively few genes have been confirmed gens, naive CD4+ T helper cells adapt distinct fates, becoming to be occupied by STATs. In the case of STAT4, known targets T helper 1 (Th1), Th2 or Th17 cells, and thereby orchestrating include Ifng, Il18r1, Il12rb2, Il2ra, and Furin (Letimier et al., effector responses (Abbas et al., 1996; Murphy and Reiner, 2007; O’Sullivan et al., 2004; Pesu et al., 2006; Thieu et al., 2002; Weaver et al., 2006). These T helper cell effector subtypes 2008), whereas STAT6-bound genes include Il4, Gata3, and are defined by their signature cytokines, namely interferon-g Ccl17 (Kubo et al., 1997; Wirnsberger et al., 2006). Therefore, it (IFN-g) for Th1 cells, interleukin-4 (IL-4) and IL-13 for Th2 cells, was far from evident what proportion of genes are directly occu- and IL-17A for Th17 cells (Zhou et al., 2009). CD4+ T cells can pied by STAT4 or STAT6 and ultimately contribute to Th1 or Th2 also differentiate to become regulatory T cells, which serve to cell phenotypes or whether STATs work predominantly in an

840 Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

indirect manner. ChIP coupled with hybridization to arrays (ChIP- identified wide regulatory roles for STAT4 and STAT6 in regu- on-Chip) or massive parallel sequencing (ChIP-seq) provides the lating epigenetic state and transcription in their cognate T helper opportunity to identify binding sites on lineages. a broader scale (Good et al., 2009; Jothi et al., 2008). This has expanded our understanding of STAT-bound genes; however, RESULTS relating transcription factor binding to functional consequence, i.e., transcriptional control, has been difficult (Beima et al., Genomic Distribution of STAT Binding Sites and 2006; Good et al., 2009; Wunderlich and Mirny, 2009). Identification of Consensus Binding Motifs Although transcription factors like STATs play a critical role in To identify genes directly bound by STAT4 or STAT6, we polar- regulating differentiation, is also dependent on ized naive CD4+ T cells under Th1 or Th2 cell conditions, and the status of chromatin structure, which is regulated by DNA reactivated the cells with anti-CD3 and anti-CD28 methlylation, ATP-dependent remodeling of nucleosomes, and the respective cytokines for 2 hr prior to ChIP-seq analysis. incorporation of histone variants, and posttranslational modifica- The appropriate polarization of Th1 and Th2 cells was confirmed tion of histone tails (Bernstein et al., 2007). As STATs play a crit- by intracellular staining (data not shown) and mRNA expression, ical role in initiating a defined gene transcription program, it is of as determined by microarray analysis. A total of 7 to 8 million great interest to determine how the distribution of histone epige- nonredundant reads were uniquely aligned onto mouse genome netic marks and recruitment of STAT transcription factors (mm9 Build 37 assembly by NCBI). We used CisGenome to coordinate to orchestrate gene regulation in differentiating identify peaks (Ji et al., 2008) and used preimmune serum as T cells (Wei et al., 2009; Wilson et al., 2009). Whether histone a negative control to define nonspecific binding. Genome- modifications are prerequisite events for STAT recruitment or wide, we identified 10,831 STAT4-occupied peaks and 8,434 vice versa has not been resolved. In the case of the Ifng and STAT6-bound peaks in wild-type cells cultured under Th1 and Il4, Il13, and Il5 loci, there is evidence that epigenetic modifica- Th2 cell conditions, respectively (Table S1 available online). tions and higher-order structural remodeling are STAT depen- To examine the comparative distribution of STAT4 and STAT6 dent (Bernstein et al., 2007; Lee et al., 2006; Wilson et al., binding sites, we divided the mouse genome into four regions: 2009; Zhang and Boothby, 2006). However, other than the (transcriptional start site [TSS] to upstream 10 kb), signature cytokine genes, there is a paucity of information on exonic, intronic, and intergenic regions. As shown in Figure 1A, the epigenetic control of genes that participate in T helper cell 31% of STAT4-occupied sites and 36% of STAT6-occupied differentiation and the involvement of STATs in the process sites localized to promoters. This represents a statistically signif- (Thieu et al., 2008; Yu et al., 2007). Whether signature cytokine icant (p < 10À6) enrichment, given that promoter regions consti- genes (Ifng and Il4) represent a very limited subset of STAT- tute only 2% of the total mouse genome (Figure S1). In contrast, dependent T helper cell genes with respect to epigenetic control only 5% of STAT4 and STAT6 binding sites localized to introns or whether they are prototypical of a large number of similarly and less than 1% of the binding sites localized to exons regulated genes has not been determined; only very limited (Figure 1A), with intronic and exonic sequences accounting for segments of the genome have previously been interrogated. 37% and 0.2% of the total mouse genome (Figure S1), respec- By contrast, recent genome-wide mapping of STAT1 binding tively. Notably, 63% of STAT4 binding sites and 59% of STAT6 sites in HeLa cells indicated that epigenetic modifications binding sites localized to intergenic regions, where they could preceded IFN-g-triggered STAT1 binding (Robertson et al., potentially function as distal enhancers or promoters of unanno- 2008; Robertson et al., 2007). Thus, the exact interplay between tated transcripts (Figure 1A). transcription factors and epigenetic modifications is still an open To enumerate STAT-bound genes, we defined a gene question. as a region encompassing potential promoter elements (10 kb We set out to address these questions by simultaneously upstream from TSS), as well as the exonic and intronic regions measuring genome-wide STAT recruitment, histone modifica- of the gene itself (TSS to transcription termination site). tions, and mRNA expression during differentiation of Th1 and Using this definition, we identified 2,334 genes uniquely bound Th2 cells in wild-type and STAT-deficient cells. By mapping by STAT4 in Th1 cells and 1,984 genes selectively bound by the distribution of H3K4me3, H3K27me3, and H3K36me3 along STAT6 in Th2 cells. Interestingly, 2,152 genes were bound by with STAT4 and STAT6 binding, we determined the entire both factors (Figure 1B). spectrum of specific STAT4- and STAT6-occupied genes and Previous in vitro studies have indicated that the STAT family linked STAT binding to corresponding gene transcription and bind to a GAA palindromic motif (Decker et al., 1997). epigenetic modifications. For the majority of STAT-bound genes, To determine whether such an element was identifiable in our the absence of the cognate STAT had little effect on epigenetic genome-wide analysis, we carried out de novo searches of modifications and gene transcription. However, we identified consensus motifs of STAT4- and STAT6-bound regions by using a core subset of STAT-occupied genes whose gene expression MEME (Bailey and Elkan, 1994). We identified the consensus and chromatin configuration was highly STAT dependent. motif for STAT4 as a GAA palindrome with a three-nucleotide Furthermore, we found that the global effects of STAT4 and spacer. In contrast, STAT6 preferentially bound to a palindrome STAT6 are distinct in that STAT4 primarily promoted accessible with a four-base spacer (Figure 1C). Of the total of 4486 STAT4- epigenetic marks whereas STAT6 had a prominent role in bound genes, 100% exhibited at least one copy of the motif that antagonizing repressive marks. This analysis also allowed the was depicted in Figure 1C. However, only 30% of 4136 STAT6- identification of subset of genes for which STAT4 and STAT6 ap- bound genes contained at least one copy of the four-space motif peared to serve as repressors. Therefore, our global analysis that was identified in Figure 1C. Among 2152 genes that were

Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. 841 Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

Figure 1. Genomic Distribution of STAT Binding Sites and Identification of Consensus Binding Motifs (A and B) ChIP-seq analysis was used to map STAT4 and STAT6 binding from polarized Th1 and Th2 cells. The distribution of (A) STAT4 and STAT6 binding sites was analyzed on the basis of location: promoter (within 10 kb upstream from the transcription start site), exon, intron, and intergenic regions. (B) Venn diagram showing the number of genes uniquely bound by STAT4, STAT6, or genes bound by both STATs. STAT-bound genes were identified if at least one peak of binding was present within the gene region defined as 10 kb upstream of TSS to the transcription end site. (C) A parallel version of MEME (Bailey and Elkan, 1994) was used to perform a de novo search of consensus binding motifs for STAT4 and STAT6 and resulted in distinctive GAA palindromes for STAT4 with 3 bp spacer and STAT6 with 4 bp spacer.

localization of H3K4me3, H3K27me3, and H3K36me3 marks for all genes, relating this to their TSS and transcriptional end site (TES) to visualize tag density profile for each mark (Figures 2C and 2D). As previously reported, H3K4me3 (in red) was highly enriched around the TSS (Roh et al., 2006). STAT4 and STAT6 binding sites (in blue) were also enriched around TSS; however, the precise peak of STAT binding coincided with a valley of H3K4me3 binding (Figures 2C and 2D, lower panels). This suggests that maximal STAT binding probably occurs in a relatively nucleosome-poor region in the midst of abundant H3K4me3 modification encompass- ing several nucleosomes. Of note, H3K36me3 modifications (in purple) increased along gene bodies and peaked at the transcription end sites (Figures 2C and 2D, upper panels), whereas bound by both STAT4 and STAT6, 800 genes (37%) contained H3K27me3 marks (in green) were relatively depleted in regions in both three-space and four-space consensus motifs. which STATs were bound (Figures 2C and 2D, lower panels).

Colocalization of STAT Binding with Histone ChIP-Seq Provides a Comprehensive View Modifications of STAT-Occupied Genes that Can Participate Other genome-wide surveys of transcription factor binding have in Helper Cell Function suggested that in many cases, binding does not necessarily corre- The ChIP-seq data set generated allowed us to assess global late with regulation of gene expression, particularly in eukaryotes quantitative views of transcription factor binding and STAT- (Wunderlich and Mirny, 2009). The interplay between transcription dependent epigenetic modifications, but its precision and factor binding and epigenetic events has been equally unclear resolution also provided a refined view at the gene level. There- (Robertson et al., 2008; Zheng et al., 2007). To begin to relate fore, we next evaluated localized STAT-dependent epigenetic epigenetic changes and occupancy of STAT transcription factors, modification of STAT-occupied genes that contribute to the Th we also mapped the distribution of H3K4me3, H3K27me3, and phenotype. Figure 3 depicts a compilation of browser views of H3K36me3 using ChIP-seq (Table S1). We found 60% of selected genes showing relevant marks (STAT binding, STAT4 and STAT6 binding sites colocalized with H3K4me3 islands H3K4me3, and H3K36me3) in wild-type and STAT-deficient (Figures 2A and 2B). Only 0.3% of STAT4 binding sites colocalized cells. The complete list of genes occupied by STAT4 and with H3K27me3 islands, whereas 5% of STAT6 binding sites colo- STAT6 are provided (Table S2) and genome-wide STAT binding calized with this modification. Of note, under the culture conditions and STAT-dependent epigenetic modifications can be visualized used, 1.7-fold more total H3K27me3 islands were present in Th2 via the genome browser: http://www.niams.nih.gov/Research/ cells than in Th1 cells, whereas the numbers of total H3K4me3 Ongoing_Research/Branch_Lab/Molecular_Immunology_and_ islands were similar (Figures 2A and 2B). We next compiled the Inflammation/Supplemental/OShea/KannoImmunity10/.

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Figure 2. Global STAT Binding and Epigenetic Modifications (A and B) ChIP-seq was performed to map histone epigenetic modification in Th1 cells and Th2 cells. The concordance between H3K4me3 and H3K27me3 modifications and (A) STAT4 binding sites in Th1 cells and (B) STAT6 binding sites in Th2 cells is shown. (C and D) Compiled tag density profiles for H3K4me3, H3K27me3, H3K36me3, and (C) STAT4 binding in Th1 cells, and (D) STAT6 in Th2 cells are shown. The diagrams represent all genes that showed positive signals for the respective marks. The upper panel shows a tag density profile across gene body ±5 kb flanking regions with 200 bp resolution. The lower panel shows tag density profiles across promoter ±5 kb flanking regions with 25 bp resolution. All data including STAT bindings were processed by SICER for peak calling.

Among selected Th1 cell genes shown in Figure 3A, the Ifng is throughout the Tbx21 gene were highly STAT4-dependent. We well known to be regulated by STAT4 (Xu et al., 1996). In addition found that STAT4 bound many other Th1 cell-expressed genes to STAT4 binding in the promoter, we identified multiple peaks and influenced permissive epigenetic marks (see below). across the extended Ifng locus (Figure S2). Consistent with A key feature of lineage commitment is the repression of alter- previous work (Wilson et al., 2009), permissive H3K4me3 and native fates in concert with promotion of the desired lineage. H3K36me3 modifications were identified throughout the entire Zbtb32, which encodes repressor of GATA3, is a factor that Ifng locus and nearly all permissive marks were STAT4 depen- attenuates Th2 cell responses (Hirahara et al., 2008; Miaw dent (Figure S2). The Tbx21 gene encodes the key transcription et al., 2000). It was also a STAT4-occupied gene that exhibited factor T-bet, which is regulated by both IFN-g-STAT1 and IL-12- STAT4-dependent epigenetic modifications. Thus, STAT4 not STAT4 (Afkarian et al., 2002; Lighvani et al., 2001; Thieu et al., only directly regulates Ifng itself but also regulates transcription 2008). STAT4 binding localized to an upstream region, which factors that promote Th1 cell differentiation and repressors included a previously described enhancer at À13 kb (Yang that inhibit Th2 cell differentiation (Figure 3A). Specific STAT4 et al., 2007). Of note, H3K4me3 and H3K36me3 marks binding of selected genes was confirmed by ChIP-qPCR

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Figure 3. STAT-Bound Genes Represent Multiple Aspects of Th Cell Differentiation ChIP-seq signal profile maps are shown as a genome browser view. (A) shows Th1 cell genes, Ifng (chr10:117,875,074-117,885,977), Tbx21 (chr11:96,947,000- 96,996,774), Lag3 (chr6:124,848,219-124,863,009), Zbtb32 (chr7:31,374,500-31,385,000), and Il21 (chr3:37,119,708-37,136,092). (B) shows Th2 cell genes, Il4 (chr11:53,418,425-53,444,775), Gata3 (chr2:9,770,098-9,802,379), Il24 (chr1:132,778,117-132,786,123), Plcd1 (chr9:118,980,999-119,011,401), and Hipk2 (chr6:38,641,845-38,850,017). STAT4 and STAT6 binding in wild-type is shown in red upward peaks. Epigenetic marks (H3K4me3, H3K36me3) in wild-type cells are depicted as red upward peaks, whereas the accompanying blue colored downward peaks depict the binding in STAT4-deficient cells. Where no signal was detected, the corresponding columns are blank.

(Figure S3 and Table S3). By contrast, a number of other genes from other polarized subsets. Recently, it has been shown that preferentially expressed in Th1 cells were not directly bound by IL-12 can promote the generation of human Tfh cell (Schmitt STAT4 (e.g., Cdh3, Cxcr3, Etv5, and Cebpb)(Rogge et al., 2000). et al., 2009). We found that the Il21 gene was a very prominent IL-21-producing follicular T helper (Tfh) cells have recently STAT4-bound gene whose epigenetic regulation was strongly been recognized as a new subset of T cells; however, their STAT4 dependent (Figure 3A). These data argue that STAT4 is origins are complex (Fazilleau et al., 2009; King et al., 2008). a factor that can regulate Th1 cell differentiation but can also They can arise separately as a lineage, but can also be generated contribute to Tfh cell differentiation.

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Figure 4. STAT-Bound Genes Form Clusters that Share Common Epigenetic Signatures (A and B) The total tag count of each epigenetic modification (H3K4me3, H3K27me3, and H3K36me3) was computed across the body of each STAT-occupied gene in wild-type versus STAT-deficient cells. The ratio of tag counts for three epigenetic modifications was used to cluster STAT-occupied genes by applying the K-means clustering method with squared Euclidean distance with 1000 iterations. (A) STAT4-bound genes in Th1 cells clustered in 6 patterns based on the ratio of wild-type versus STAT4-deficient cells: H3K4me3(K4)-high, H3K36me3 (K36)-high, H3K27me3 (K27)-low, H3K27me3 (K27)-high, H3K36me3 (K36)-low, and an indeterminate pattern. (B) STAT6-bound genes in Th2 cells showed 5 distinct epigenetic patterns similar to (A) but with- out the K4-high cluster. The accompanying tables list the number and percentage of genes in each cluster.

with high amounts of H3K4me3 (denoted ‘‘K4 high,’’ 5.7%) and H3K36me3 (‘‘K36 high,’’ 4.3%) that were clearly STAT4 dependent (Figure 4A). In addition, the third cluster showed STAT-dependent inhibition of H3K27me3 marks (‘‘K27 low,’’ 7.0%). Analysis of STAT6-occupied genes revealed similar but not identical clusters (Figure 4B). Like STAT4, roughly Previous studies have argued that STAT6 directly contributes 25% of STAT-occupied genes showed strong dependency on to the regulation of the Il4 and Gata3 genes, although limited STAT6 for their distinct epigenetic patterns. However, a discrete regions of the genes were analyzed (Ansel et al., 2006; Pai cluster of genes in which STAT6 induced high levels of et al., 2003; Zheng and Flavell, 1997). As shown in Figure 3B, H3K4me3 was not apparent. Although analysis of selected genes STAT6 bound to both the Il4 and Gata3 loci, which were associ- showed that STAT6 can positively regulate H3K4me3 (Figure 3B), ated with strong permissive epigenetic modifications. Other these genes did not cluster as a discrete group. Rather, the STAT6-bound genes whose permissive epigenetic modifications proportion of genes for which STAT6 reduced H3K27me3 was showed dependency on STAT6 included Il24 (Schaefer et al., greatly expanded compared to the corresponding cluster of 2001; Wang and Liang, 2005), Plcd1, and Hipk2 (Chen et al., STAT4-occupied genes (11.8% versus 7.0%). 2003)(Figure 3B). To confirm the clustering analysis, we plotted the global distri- bution of epigenetic marks on STAT-occupied genes in wild-type Differential Patterns of Epigenetic Regulation and STAT-deficient cells. In the absence of STAT4, H3K4me3 by STAT4 and STAT6 modifications on STAT-bound genes were markedly reduced Having established that the epigenetic modifications of many Th (Figure 5A, upper panels), but as expected from the previous cell-defining genes are highly STAT dependent, we sought to analysis, the absence of STAT6 did not affect the global pattern evaluate the impact of the cognate STAT on the global epigenetic of H3K4me3 marks on the bound genes. The global distribution patterns of occupied genes. To this end, we calculated the of H3K36me3 modifications (Figures 5A and 5B, middle panels) total tag counts of epigenetic modifications (K4me3, K27me3, showed minimum changes in the absence of STAT4 or STAT6. In and K36me3) encompassing each occupied gene. Using the contrast, STAT4 and STAT6 showed notably different influences K-means algorithm, we clustered STAT-bound genes on the basis on H3K27me3 marks. Although deficiency of STAT4 had little of the ratio of tag counts in wild-type and STAT-deficient cells for effect on the global pattern of H3K27me3 marks, the absence each of three epigenetic marks (Figure 4 and Table S4). From this of STAT6 resulted in a significant, global increase of analysis, six epigenetic patterns for STAT4-occupied genes H3K27me3 marks (p = 8E-42, Student’s t test) (Figure 5B, lower emerged (Figure 4A). For the largest cluster (75% of the total panels). Taken together, our results suggested that STAT4 and STAT4 bound genes), the absence of STAT4 had minimal effects STAT6 differentially affected distinct epigenetic modifications on epigenetic modifications, suggesting that other factors were of their cognate genes. Specifically, STAT4 primarily promoted the major regulators (denoted as ‘‘indeterminate pattern’’). Five H3K4me3 marks, whereas STAT6 predominantly acted to antag- other clusters of genes were also apparent. We identified clusters onize H3K27me3 modifications of its bound genes.

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Figure 5. Differential Regulation of Epigenetic Marks by STAT4 and STAT6 on STAT-Bound Genes (A and B) Compiled tag density profiles for histone modification marks (H3K4me3, H3K36me3, and H3K27me3) were calculated from all STAT4-bound genes in Th1 cells (A) and STAT6-bound genes in Th2 cells (B) in wild-type cells (blue) and in STAT-deficient cells (red). The tag density profiles across promoter±5kb flanking regions are shown for H3K4me3 (top panels) and H3K27me3 (bottom panels). The tag density profiles across gene body ±5 kb flanking regions for H3K36me3 are shown in middle panels.

Correlations between STAT-Dependent Epigenetic By comparing STAT4- and STAT6-regulated genes, we were Regulation and Gene Expression particularly interested in a group of genes bound by both factors Having identified distinct clusters of STAT-dependent epigenetic and how STATs regulated their function in their corresponding modifications, we next assessed how these patterns correlated lineages. For 5% of the genes bound by both STATs, both with regulation of gene expression. Transcriptional profiling was factors served to positively regulate these genes in the respec- performed in Th1 and Th2 cells (Table S5) so that STAT-depen- tive subsets. Examples of these genes include Il10 (Saraiva dent genes could be identified (R2-fold difference in expression et al., 2009), Il7r (Kallies, 2008), Socs3 (Dimitriou et al., 2008), between wild-type versus STAT-deficient cells). Among them, and Id2 (Kusunoki et al., 2003). As shown in Figure 7A, permis- clusters of genes that exhibited strong STAT-dependent sive epigenetic marks on those genes, as well as gene expres- H3K4me3 or K36me3 marks were overwhelmingly positively sion, were dependent upon the cognate STAT. regulated by STAT in terms of mRNA expression (Figure 6). Genes assigned to these clusters for STAT4 include Ifng, Genes Regulated by Both STAT4 and STAT6: Opposing Tbx21, and Il21. Interestingly for STAT6, the gene cluster for Function on Key Genes which this factor inhibited H3K27me3 marks included the major Although ample evidence points to roles of STAT in driving gene lineage-specifying genes such as Il4, Gata3, Il24, and Il4ra. The expression, there are remarkably few examples in which STAT entire lists of genes included in each gene cluster are presented serve as direct functional repressors. In this regard, we were in Table S4 (also see Figure S4 for summary). For both STAT4 intrigued by clusters of genes in which STAT apparently inhibited and STAT6, STAT-dependent positive epigenetic changes H3K36me3 (K36 low) or increased H3K27me3 (K27 high), both (K4 high, K36 high, and K27 low) correlated well with the indicative of repressive epigenetic changes (Figure 4). Indeed STAT-dependent positive gene expression (Figure 6). for the genes included in these clusters, STAT-induced gene

846 Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

Figure 6. STAT-Dependent Epigenetic Signature Correlates with Gene Expression (A and B) STAT-dependent gene expression change was evaluated by microarray for each epigenetic cluster as described in Figure 4. Two- fold change was used as a cut off to sort genes into those positively (red bars) and nega- tively (blue bars) regulated by STAT4 (A) and by STAT6 (B). Genes whose expression was not affected by STAT4 or STAT6 are not depicted in the graphs.

STATs differ in their ability to influence global epigenetic profiles. On the level of each bound gene, STATs induce a variety of localized positive or negative epigenetic patterns. However genome- inhibition was a more prominent feature than activation, particu- wide, promoting H3K4me3 modifications is a more prominent larly for STAT4 bound genes (Figure 6). One example of such global feature of STAT4, whereas antagonizing repressive marks a gene is Ccr8 (Figure 7B). STAT4 binding in the upstream region (H3K27me3) is a major property of STAT6. Our analysis also and adjacent H3K27me3 marks were present in wild-type cells, uncovered a number of genes for which STAT4 and STAT6 but absent in STAT4-deficient cells. This was of interest because appear to serve as repressors. Among them, a considerable Ccr8 is a gene that preferentially expressed in Th2 cells (Gonzalo proportion were bound by both factors, and regulated in an et al., 2007) and that is bound by STAT6. Thus, it appears that opposing manner. STAT4 actively promotes repression of this gene in Th1 cell With the ability to measure transcription factor binding on conditions. Globally, we identified 265 genes bound and nega- a genome-wide scale, a fundamental issue is the functional tively regulated by STAT4, among which 150 (57%) were prefer- relevance of factor binding and how to evaluate it. In this study, entially expressed in Th2 cell condition (Figure S5A). Therefore, we identified over 4000 genes bound by the cognate STAT in Th1 a prominent portion of STAT4-repressed genes was preferen- and Th2 cell conditions. Of these, 15% (684 genes for STAT4) to tially expressed in Th2 cells. Conversely there were 213 STAT6- 29% (1200 genes for STAT6) showed STAT-induced expression bound and negatively regulated genes, of which 40% were pref- changes. An emerging concept coming from genome-scale erentially expressed in Th1 cells (Figure S5B). One such example studies is that only a fraction of factor-bound genes showed though, is the region encompassing Il18r1-Il18rap, for which clear functional dependence as evaluated by gene expression STAT6-dependent H3K27me3 marks were present in Th2 cell changes. For example, a recent study enumerating GATA-1 conditions, indicative of STAT6-induced epigenetic repression binding showed that 40% of genes (790 genes out of 1800 of the loci (Figure 7C). As such, Il18r1-l18rap is a provocative bound genes) showed GATA-1-induced expression changes example for which STAT4 and STAT6 induce opposing epige- (Yu et al., 2009). Our study produced similar absolute numbers netic changes: STAT4 promotes permissive marks, whereas of functionally relevant transcription factor-bound genes when STAT6 induce repressive marks (Figure 7C). Therefore, a critical evaluated by microarray-based expression changes. subset of genes exists to which both STAT4 and STAT6 bind, and By adding analysis of STAT-induced epigenetic changes to these factors act in an opposing manner to modulate epigenetic gene expression changes, we were able to further separate changes and associating gene expression. This divergent action highly relevant STAT binding that substantially impacted both presumably provides insurance of preferential, lineage-specific epigenetic signature and gene transcription, from binding events expression (Figure S4). that had minimal functional outcomes. Our analysis revealed subsets of gene clusters whose epigenetic signature was highly DISCUSSION STAT dependent. These genes comprise roughly 1000 genes of total 4000 STAT-occupied genes. Our data firmly establish that In this study, we examined the landscape of transcription factor there is a substantial subset of genes for which the recruitment binding of STAT4 and STAT6 through genome-wide approaches of STAT during the course of T helper cell differentiation serves and identified the genes uniquely bound by each STAT, as well to maintain the distinctive epigenetic pattern of the genomic as those occupied by both factors. Our analysis revealed a region. For these STAT-regulated genes, it will be of consider- core subset of STAT-occupied genes, whose expression and able interest to carefully dissect the kinetics of epigenetic pattern local epigenetic fingerprint are clearly dependent upon the pres- formation during the process of lineage specification. ence of the given STAT. Using this strategy, we identified a large Also of interest was the identity of the STAT-bound genes number of genes, many of which were not previously known to whose transcriptional and epigenetic regulation was highly be directly regulated by STATs. Consequently, the direct func- dependent on the factor. The genes identified include pheno- tional roles of STAT4 and STAT6 are considerably broader than type-defining cytokines (Ifng, Il4, and Il24), receptors (Il18rap, previously appreciated. Furthermore, we found that the two Il18r1, Lag3, and Il4ra), transcription factors (Gata3, Tbx21) and

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Figure 7. STAT4 and STAT6 Work in Concert and in Opposition to Influence Gene Expression (A) ChIP-seq profiles for binding of STAT4 and STAT6, as well as epigenetic modifications in Th1 and Th2 cells. Illustrative genes include: Il10 (chr1: 132,911,300- 132,926,671), Il7r (chr15: 9,430,427-9,470,839), Socs3 (chr11:117,821,153-117,836,725) and Id2 (chr12: 25,769,886-25,787,125). (B and C) ChIP-seq data illustrating STAT binding and epigenetic modifications of (B) Ccr8 (chr 9: 119,988,491-120,013,456) and (C) Il18r1-Il18rap (chr1: 40,521,609-40,620,416).

transcriptional repressor (Id2 and Zbtb32). In addition to these An unanticipated finding in our studies became apparent when recognized regulators of T helper cell differentiation, we identi- we compared and contrasted the effects of STAT4 and STAT6 on fied a number of previously unrecognized STAT-regulated genes epigenetic modification of their corresponding genes. We found with preferential expression in Th1 and Th2 cells. These include that STAT4 primarily promoted accessible epigenetic marks, genes that encode transcriptional repressors (Ikzf3 [Aiolos] and whereas STAT6 had a more prominent role in antagonizing Nfil3), and diverse signaling molecules that included kinases, a repressive mark on its cognate genes. One possibility was phosphatases, G-proteins and adaptors (Hipk2, Plcd1, Gbp2 that the expression of chromatin-modifying enzymes differed in and Skap2). Clearly, it will be of interest to define the role of these Th1 and Th2 cells such that the balance is shifted toward molecules in T cells and assess how their actions relate to the promoting H3K27me3 modification in the absence of STAT6 in specification of cognate lineages. Th2 cell condition. The enhancement of H3K27me3 modifica- Tfh cells represent a recently recognized subset of T helper tions might be explained by overexpression of the H3K27 cells and we were intrigued to see that a major gene expressed methyltransferase Ezh2 and downregulation of the H3K27 deme- by Tfh cells, Il21, was also a STAT4-bound and positively regu- thylase Jmjd3. However, this was not the case. We noted lated gene. Although previous data have argued that IL-6 acting decreased expression of Ezh2 associated with a reciprocal via STAT3 is the main driver of Tfh cell differentiation (Fazilleau increase of Jmjd3 in STAT6-deficient Th2 cells. Therefore, an et al., 2009; King et al., 2008), recent data indicate that in human alteration in the balance between the ‘‘writer’’ (Ezh2) and ‘‘eraser’’ T cells, IL-12 can also promote Tfh cell specification (Saraiva (Jmjd3) (Wang et al., 2007) was evidently not the primary cause of et al., 2009). Our data are entirely consistent with this result and increased H3K27me3 mark associated with the absence of suggest that this mechanism is not unique to human T cells. Inter- STAT6. On the contrary, these enzymes appear to be compen- estingly, we also found that STAT4 bound the Bcl6 gene, but sating for STAT6-dependent alteration of H3K27me3 in Th2 cells. unlike IL-21, the expression of the former was modest in Th1 At present, we do not have an explanation for these findings, but cell polarizing conditions. This would argue that IL-12-STAT4 clearly additional studies are warranted to dissect the molecular signals alone are not sufficient to drive Bcl6 expression and mechanisms underlying the differential regulation of H3K27me3 presumably other signals are required. The data are also consis- modifications in Th1 and Th2 cells. tent with the evolving notions that Tfh cells represent a flexible The present study also revealed a large number of genes that subset. In related studies, we found that the Bcl6 and Il21 genes are bound and negatively regulated by STAT4 and STAT6. are the targets of STAT3 as well (Durant et al., 2010), and it will Although the ability to concomitantly promote the expression be important to differentiate the roles of different STATs in the of some genes and repress others is a common feature of tran- transcriptional and epigenetic regulation of these important scription factors associated with lineage commitment (Cobaleda genes. et al., 2007), there is a paucity of circumstances in which STATs

848 Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

have been documented to behave as functional repressors. This version of MEME (Bailey and Elkan, 1994) was used for performing a de was a more prominent feature of STAT4 than STAT6. Clearly, it novo search of consensus binding motifs for STAT4 and STAT6. will be of considerable interest to carefully dissect the molecular basis of the repression of these genes. Particularly interesting Quantitative Calculation of STAT Binding and H3K4me3, H3K27me3, and H3K36me3 Levels for All Genes were genes bound by both STAT4 and STAT6, for which these A list of 24,946 unique Refseq genes was obtained from UCSC Genome Table factors had opposing functions. In this case, the two STATs Browser (mm9). A gene is considered to be bound by STAT if at least one peak appear to function in concert to ensure differential expression is found with the region between 10 kb upstream of the transcription start site of lineage-specifying genes (e.g., Il18r1-l18rap). These will be and the end of the transcript by the two-sample CisGenome analysis (Ji et al., an interesting subset of genes to analyze in further detail. 2008). Epigenetics data were analyzed on the basis of the island approach as In summary, our data provide a platform for understanding described before (Zang et al., 2009). The tag count for each island was first how STATs function to modulate epigenetic events to shape normalized to get a tag count per 1 million of total tag counts in that library. gene expression profile unique to T helper subtype. The Profiles of Tag Density across Genes emerging picture is that STATs bind to a broad, yet clearly For each gene, uniquely mapped tags were summarized in 200 bp windows defined set of genes and contribute in a very substantial manner for the regions from 5 kb upstream of the TSS to the TSS and from the TES to T helper cell differentiation. They do so in both a positive and to 5 kb downstream of TES. Within the gene bodies, tags were summarized negative manner. Investigating the function of newly identified in windows equal to 4% of the gene length. All window tag counts were genes as they relate to helper cell function and elucidating the normalized by the total number of bases in the windows and the total read mechanisms by which STATs regulate the expression, both number of the given sample. positively and negatively, will clearly be important. Microarray Data Collection and Analysis Total cellular RNA from cells cultured under Th1 and Th2 cell conditions was extracted with mirVana (Applied Biosystems/Ambion) in accordance with EXPERIMENTAL PROCEDURES the manufacturer’s instructions. Approximately 10 mg of RNA was labeled and hybridized to GeneChip Mouse Genome 430 2.0 arrays (Affymetrix) in Mice, Isolation of Cells, and Cell Culture accordance with the manufacturer’s protocols. Expression values were C57BL/6J and STAT6-deficient mice were purchased from Jackson Labora- determined with GeneChip Operating Software (GCOS) v1.1.1 software. All tory. STAT4-deficient mice were provided by M. Kaplan at Indiana University. data analysis was performed with Affymetrix Power Tools (APT) software Animals were handled and housed in accordance with the guidelines of the package 1.10.2 (Affymetix). NIH Animal Care and User Committee. Splenic and lymph node T cells were obtained by disrupting organs of 8- to 10-week-old mice. All cell cultures were performed in RPMI supplemented with 10% fetal calf serum, 2 mM ChIP-qPCR glutamine, 100 IU/mL penicillin, 0.1 mg/mL streptomycin, and 2 mM b-mer- In order to confirm STAT-binding peaks detected by ChIP-seq, PCR primers captoethanol. T cells were enriched with a CD4+ T cell kit and AutoMacs were designed (Table S3) and qPCR was performed from the chromatin that isolator (Miltenyi Biotec, Auburn CA). Naive CD4+ T cells were further isolated was prepared similarly as ChIP-seq. by flow cytometry, staining with CD4, CD62L, CD44, and CD25 antibodies. Naive CD4+ T cells were first cultured in the presence of plate-bound anti- ACCESSION NUMBERS CD3 (10 mg/mL) and anti-CD28 (10 mg/mL), IL-12 (20 ng/mL) and anti-IL-4 (10 mg/mL) for 3 days and then cultured in IL-2 (50 U/mL) and IL-12 (10 ng/ Sequencing and gene expression data are available in the Gene Expression mL) for 4 days (Th1 cell conditions). Alternatively, such cells were cultured in Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) under the acces- the presence of plate-bound anti-CD3 and anti-CD28, plus IL-4 (10 ng/mL) sion number GSE22105. and anti-IFN-g (10 mg/mL) for 3 days and then cultured in IL-2 (50 U/mL) and IL-4 (10 ng/mL) for 4 days (Th2 cell conditions). Before harvesting, cells were SUPPLEMENTAL INFORMATION restimulated with plate-bound anti-CD3 and anti-CD28 and respective cytokines for 2 hr. Cytokines were from R&D Systems and antibodies were Supplemental Information includes five figures and five tables and can be from BD Phamingen. found with this article online at doi:10.1016/j.immuni.2010.06.003.

ACKNOWLEDGMENTS ChIP-Seq Analysis ChIP-Seq experiments and data processing were performed as described We thank K. Zhao, J. Bream, and V. Sartorelli for critical reading of the manu- previously (Wei et al., 2009; Zang et al., 2009). In brief, T cells (2 3 107) were script, W. Resch for help in data analysis, and J. Simone for technical help with treated with MNase to generate mononucleosomes fraction to analyze histone cell sorting. This research was supported by the Intramural Research modifications with following antibodies (anti-H3K4me3, ab8580; anti- Programs of NIAMS. H3K36me3, ab9050, Abcam; and anti-H3K27me3, 07-449, Millipore). For STAT-ChIP, we chemically crosslinked and sonicated cells to generate frac- tionated genomic DNA. The DNA was immunoprecipitated with anti-STAT4 Received: September 18, 2009 (sc486) or anti-STAT6 (sc981, Santa Cruz Biotechnology). The DNA fragments Revised: April 20, 2010 were blunt-end ligated to the Illumina adaptors, amplified, and sequenced Accepted: May 6, 2010 with the Illumina Genome Analyzer II (Illumina). Sequenced reads of 25 bp Published online: June 17, 2010 were obtained with the Illumina Analysis Pipeline. All reads were mapped to the mouse genome (mm9), and only uniquely matching reads were retained. REFERENCES Unique read numbers for each library are listed in Table S1. Significant peaks (islands) were identified on the basis of window tag-count threshold deter- Abbas, A.K., Murphy, K.M., and Sher, A. (1996). Functional diversity of helper T mined from a p value of 0.05 (defined by Poisson background model) for lymphocytes. Nature 383, 787–793. histone modifications (Zang et al., 2009). For STAT bindings, CisGenome Afkarian, M., Sedy, J.R., Yang, J., Jacobson, N.G., Cereb, N., Yang, S.Y., (Ji et al., 2008) was used for determining significant peaks compared to the Murphy, T.L., and Murphy, K.M. (2002). T-bet is a STAT1-induced regulator negative control of normal rabbit serum IP with the FDR of 1%. A parallel of IL-12R expression in naive CD4+ T cells. Nat. Immunol. 3, 549–557.

Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. 849 Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

Ansel, K.M., Djuretic, I., Tanasa, B., and Rao, A. (2006). Regulation of Th2 Lee, G.R., Kim, S.T., Spilianakis, C.G., Fields, P.E., and Flavell, R.A. (2006). differentiation and Il4 locus accessibility. Annu. Rev. Immunol. 24, 607–656. T helper cell differentiation: regulation by cis elements and epigenetics. Bailey, T.L., and Elkan, C. (1994). Fitting a mixture model by expectation Immunity 24, 369–379. maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Letimier, F.A., Passini, N., Gasparian, S., Bianchi, E., and Rogge, L. (2007). Mol. Biol. 2, 28–36. Chromatin remodeling by the SWI/SNF-like BAF complex and STAT4 activa- Beima, K.M., Miazgowicz, M.M., Lewis, M.D., Yan, P.S., Huang, T.H., and tion synergistically induce IL-12Rbeta2 expression during human Th1 cell Weinmann, A.S. (2006). T-bet binding to newly identified target gene differentiation. EMBO J. 26, 1292–1302. promoters is cell type-independent but results in variable context-dependent Levy, D.E., and Darnell, J.E., Jr. (2002). Stats: transcriptional control and functional effects. J. Biol. Chem. 281, 11992–12000. biological impact. Nat. Rev. Mol. Cell Biol. 3, 651–662. Bernstein, B.E., Meissner, A., and Lander, E.S. (2007). The mammalian Lighvani, A.A., Frucht, D.M., Jankovic, D., Yamane, H., Aliberti, J., Hissong, epigenome. Cell 128, 669–681. B.D., Nguyen, B.V., Gadina, M., Sher, A., Paul, W.E., and O’Shea, J.J. Chen, Z., Lund, R., Aittokallio, T., Kosonen, M., Nevalainen, O., and Lahesmaa, (2001). T-bet is rapidly induced by interferon-gamma in lymphoid and myeloid R. (2003). Identification of novel IL-4/Stat6-regulated genes in T lymphocytes. cells. Proc. Natl. Acad. Sci. USA 98, 15137–15142. J. Immunol. 171, 3627–3635. Lund, R.J., Chen, Z., Scheinin, J., and Lahesmaa, R. (2004). Early target genes Cobaleda, C., Schebesta, A., Delogu, A., and Busslinger, M. (2007). Pax5: The of IL-12 and STAT4 signaling in th cells. J. Immunol. 172, 6775–6782. guardian of identity and function. Nat. Immunol. 8, 463–470. Lund, R.J., Loytomaki, M., Naumanen, T., Dixon, C., Chen, Z., Ahlfors, H., Decker, T., Kovarik, P., and Meinke, A. (1997). GAS elements: A few nucleo- Tuomela, S., Tahvanainen, J., Scheinin, J., Henttinen, T., et al. (2007). tides with a major impact on cytokine-induced gene expression. J. Interferon Genome-wide identification of novel genes involved in early Th1 and Th2 cell Cytokine Res. 17, 121–134. differentiation. J. Immunol. 178, 3648–3660. Miaw, S.C., Choi, A., Yu, E., Kishikawa, H., and Ho, I.C. (2000). ROG, repressor Dimitriou, I.D., Clemenza, L., Scotter, A.J., Chen, G., Guerra, F.M., and of GATA, regulates the expression of cytokine genes. Immunity 12, 323–333. Rottapel, R. (2008). Putting out the fire: Coordinated suppression of the innate and adaptive immune systems by SOCS1 and SOCS3 proteins. Immunol. Rev. Murphy, K.M., and Reiner, S.L. (2002). The lineage decisions of helper T cells. 224, 265–283. Nat. Rev. Immunol. 2, 933–944. Durant, L., Watford, W.T., Ramos, H.L., Laurence, A., Vahedi, G., Wei, L., Ta- O’Sullivan, A., Chang, H.C., Yu, Q., and Kaplan, M.H. (2004). STAT4 is required kahashi, H., Sun, H.W., Kanno, Y., Powrie, F., et al. (2010). Diverse targets of for interleukin-12-induced chromatin remodeling of the CD25 locus. J. Biol. the transcription factor STAT3 contribute to T cell pathogenicity and homeo- Chem. 279, 7339–7345. stasis. Immunity 32, 605–615. Pai, S.Y., Truitt, M.L., Ting, C.N., Leiden, J.M., Glimcher, L.H., and Ho, I.C. Fazilleau, N., Mark, L., McHeyzer-Williams, L.J., and McHeyzer-Williams, M.G. (2003). Critical roles for transcription factor GATA-3 in thymocyte develop- (2009). Follicular helper T cells: Lineage and location. Immunity 30, 324–335. ment. Immunity 19, 863–875. Gonzalo, J.A., Qiu, Y., Lora, J.M., Al-Garawi, A., Villeval, J.L., Boyce, J.A., Pesu, M., Muul, L., Kanno, Y., and O’Shea, J.J. (2006). Proprotein convertase Martinez, A.C., Marquez, G., Goya, I., Hamid, Q., et al. (2007). Coordinated furin is preferentially expressed in T helper 1 cells and regulates interferon involvement of mast cells and T cells in allergic mucosal inflammation: critical gamma. Blood 108, 983–985. role of the CC chemokine 1:CCR8 axis. J. Immunol. 179, 1740–1750. Reiner, S.L. (2007). Development in motion: Helper T cells at work. Cell 129, Good, S.R., Thieu, V.T., Mathur, A.N., Yu, Q., Stritesky, G.L., Yeh, N., O’Malley, 33–36. J.T., Perumal, N.B., and Kaplan, M.H. (2009). Temporal induction pattern of Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao, Y., Zeng, T., STAT4 target genes defines potential for Th1 lineage-specific programming. Euskirchen, G., Bernier, B., Varhol, R., Delaney, A., et al. (2007). Genome- J. Immunol. 183, 3839–3847. wide profiles of STAT1 DNA association using chromatin immunoprecipitation Hirahara, K., Yamashita, M., Iwamura, C., Shinoda, K., Hasegawa, A., and massively parallel sequencing. Nat. Methods 4, 651–657. Yoshizawa, H., Koseki, H., Gejyo, F., and Nakayama, T. (2008). Repressor of Robertson, A.G., Bilenky, M., Tam, A., Zhao, Y., Zeng, T., Thiessen, N., GATA regulates TH2-driven allergic airway inflammation and airway hyperres- Cezard, T., Fejes, A.P., Wederell, E.D., Cullum, R., et al. (2008). Genome- ponsiveness. J. Allergy Clin. Immunol. 122, 512–520, e511. wide relationship between histone H3 lysine 4 mono- and tri-methylation Hoey, T., Zhang, S., Schmidt, N., Yu, Q., Ramchandani, S., Xu, X., Naeger, and transcription factor binding. Genome Res. 18, 1906–1917. L.K., Sun, Y.L., and Kaplan, M.H. (2003). Distinct requirements for the naturally Rogge, L., Bianchi, E., Biffi, M., Bono, E., Chang, S.Y., Alexander, H., Santini, occurring splice forms Stat4alpha and Stat4beta in IL-12 responses. EMBO J. C., Ferrari, G., Sinigaglia, L., Seiler, M., et al. (2000). Transcript imaging of the 22, 4237–4248. development of human T helper cells using oligonucleotide arrays. Nat. Genet. Ji, H., Jiang, H., Ma, W., Johnson, D.S., Myers, R.M., and Wong, W.H. (2008). 25, 96–101. An integrated software system for analyzing ChIP-chip and ChIP-seq data. Roh, T.Y., Cuddapah, S., Cui, K., and Zhao, K. (2006). The genomic landscape Nat. Biotechnol. 26, 1293–1300. of histone modifications in human T cells. Proc. Natl. Acad. Sci. USA 103, Jothi, R., Cuddapah, S., Barski, A., Cui, K., and Zhao, K. (2008). Genome-wide 15782–15787. identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Sakaguchi, S., Yamaguchi, T., Nomura, T., and Ono, M. (2008). Regulatory Acids Res. 36, 5221–5231. T cells and immune tolerance. Cell 133, 775–787. Kallies, A. (2008). Distinct regulation of effector and memory T-cell differentia- Saraiva, M., Christensen, J.R., Veldhoen, M., Murphy, T.L., Murphy, K.M., and tion. Immunol. Cell Biol. 86, 325–332. O’Garra, A. (2009). Interleukin-10 production by Th1 cells requires interleukin- King, C., Tangye, S.G., and Mackay, C.R. (2008). T follicular helper (TFH) 12-induced STAT4 transcription factor and ERK MAP kinase activation by high cells in normal and dysregulated immune responses. Annu. Rev. Immunol. antigen dose. Immunity 31, 209–219. 26, 741–766. Schaefer, G., Venkataraman, C., and Schindler, U. (2001). Cutting edge: FISP Kubo, M., Ransom, J., Webb, D., Hashimoto, Y., Tada, T., and Nakayama, T. (IL-4-induced secreted protein), a novel cytokine-like molecule secreted by (1997). T-cell subset-specific expression of the IL-4 gene is regulated by Th2 cells. J. Immunol. 166, 5859–5863. a silencer element and STAT6. EMBO J. 16, 4007–4020. Schindler, C., and Darnell, J.E., Jr. (1995). Transcriptional responses to Kusunoki, T., Sugai, M., Katakai, T., Omatsu, Y., Iyoda, T., Inaba, K., Nakahata, polypeptide ligands: The JAK-STAT pathway. Annu. Rev. Biochem. 64, T., Shimizu, A., and Yokota, Y. (2003). TH2 dominance and defective develop- 621–651. ment of a CD8+ dendritic cell subset in Id2-deficient mice. J. Allergy Clin. Schmitt, N., Morita, R., Bourdery, L., Bentebibel, S.E., Zurawski, S.M., Immunol. 111, 136–142. Banchereau, J., and Ueno, H. (2009). Human dendritic cells induce the

850 Immunity 32, 840–851, June 25, 2010 ª2010 Elsevier Inc. Immunity Genome-wide Profiling of STAT4 and STAT6 Targets

differentiation of interleukin-21-producing T follicular helper-like cells through Yang, Y., Ochando, J.C., Bromberg, J.S., and Ding, Y. (2007). Identification of interleukin-12. Immunity 31, 158–169. a distant T-bet enhancer responsive to IL-12/Stat4 and IFNgamma/Stat1 Thieu, V.T., Yu, Q., Chang, H.C., Yeh, N., Nguyen, E.T., Sehra, S., and Kaplan, signals. Blood 110, 2494–2500. M.H. (2008). Signal transducer and activator of transcription 4 is required for Yu, Q., Thieu, V.T., and Kaplan, M.H. (2007). Stat4 limits DNA methyltransfer- the transcription factor T-bet to promote T helper 1 cell-fate determination. ase recruitment and DNA methylation of the IL-18Ralpha gene during Th1 Immunity 29, 679–690. differentiation. EMBO J. 26, 2052–2060. Wang, M., and Liang, P. (2005). Interleukin-24 and its receptors. Immunology 114, 166–170. Yu, M., Riva, L., Xie, H., Schindler, Y., Moran, T.B., Cheng, Y., Yu, D., Hardison, Wang, G.G., Allis, C.D., and Chi, P. (2007). Chromatin remodeling and cancer, R., Weiss, M.J., Orkin, S.H., et al. (2009). Insights into GATA-1-mediated gene Part I: Covalent histone modifications. Trends Mol. Med. 13, 363–372. activation versus repression via genome-wide chromatin occupancy analysis. Mol. Cell 36, 682–695. Weaver, C.T., Harrington, L.E., Mangan, P.R., Gavrieli, M., and Murphy, K.M. (2006). Th17: An effector CD4 T cell lineage with regulatory T cell ties. Immunity Zang, C., Schones, D.E., Zeng, C., Cui, K., Zhao, K., and Peng, W. (2009). 24, 677–688. A clustering approach for identification of enriched domains from histone Wei, G., Wei, L., Zhu, J., Zang, C., Hu-Li, J., Yao, Z., Cui, K., Kanno, Y., Roh, modification ChIP-Seq data. Bioinformatics 25, 1952–1958. T.Y., Watford, W.T., et al. (2009). Global mapping of H3K4me3 and H3K27me3 Zhang, F., and Boothby, M. (2006). T helper type 1-specific Brg1 recruitment reveals specificity and plasticity in lineage fate determination of differentiating and remodeling of nucleosomes positioned at the IFN-gamma promoter are CD4+ T cells. Immunity 30, 155–167. Stat4 dependent. J. Exp. Med. 203, 1493–1505. Wilson, C.B., Rowell, E., and Sekimata, M. (2009). Epigenetic control of T-helper-cell differentiation. Nat. Rev. Immunol. 9, 91–105. Zheng, W., and Flavell, R.A. (1997). The transcription factor GATA-3 is Wirnsberger, G., Hebenstreit, D., Posselt, G., Horejs-Hoeck, J., and Duschl, A. necessary and sufficient for Th2 cytokine gene expression in CD4 T cells. (2006). IL-4 induces expression of TARC/CCL17 via two STAT6 binding sites. Cell 89, 587–596. Eur. J. Immunol. 36, 1882–1891. Zheng, Y., Josefowicz, S.Z., Kas, A., Chu, T.T., Gavin, M.A., and Rudensky, Wunderlich, Z., and Mirny, L.A. (2009). Different gene regulation strategies A.Y. (2007). Genome-wide analysis of Foxp3 target genes in developing and revealed by analysis of binding motifs. Trends Genet. 25, 434–440. mature regulatory T cells. Nature 445, 936–940. Wurster, A.L., Tanaka, T., and Grusby, M.J. (2000). The biology of Stat4 and Zhou, L., Chong, M.M., and Littman, D.R. (2009). Plasticity of CD4+ T cell Stat6. Oncogene 19, 2577–2584. lineage differentiation. Immunity 30, 646–655. Xu, X., Sun, Y.L., and Hoey, T. (1996). Cooperative DNA binding and sequence-selective recognition conferred by the STAT amino-terminal Zhu, J., and Paul, W.E. (2008). CD4 T cells: Fates, functions, and faults. Blood domain. Science 273, 794–797. 112, 1557–1569.

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