T cell preparation and analysis

For ChIP-on-chip experiments CD4+ T cells from C57BL/6 mice were activated for three days with anti-CD3 and anti-CD28. Cells were washed and incubated in the presence or absence of 5 ng/ml IL-12 for four hours before being processed for ChIP analysis by Genpathway. For analysis of gene induction and histone modification, wild type andStat4-/- Balb/c mice were activated for three days as above before RNA was isolated from cells that were unstimulated or stimulated with 5 ng/ml IL-12 for 4 or 18 hours. Generation of Th1 cultures and quantitative (real-time) PCR was performed as described (15). ELISA for TNFα was performed using reagents from BD Pharmingen. DAPA and ChIP was performed as described (8). ERK immunoblots were performed using standard methods (8).

FactorPath ChIP-on-Chip

Preparation of cells for ChIP-on-chip was performed at GenPathway (16). Cells prepared as above were fixed with 1% formaldehyde for 15 min and quenched with 0.125 M glycine. Cell lysates were sonicated to an average DNA fragment length of 300-500 bp. Genomic DNA from aliquots was purified for use as input. Chromatin was precleared with protein A agarose beads (Invitrogen) and STAT4-bound DNA sequences were isolated using specific antibodies (Santa Cruz sc-486). Complexes were precipitated with protein A agarose beads, washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65°C, and ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation. Quantitative PCR (Q-PCR) reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad). The resulting signals were normalized for primer efficiency by carrying out Q-PCR for each primer pair using input DNA.

Analysis of ChIP DNA by Affymetrix tiling arrays was carried out by GenPathway. ChIP (multiple reactions) and input DNAs were amplified using LM-PCR. The resulting amplified DNA was purified, quantified, and tested by QPCR at the same specific genomic regions as the original ChIP DNA to assess quality of the amplification reactions. Amplified DNA was fragmented and labeled using the DNA Terminal Labeling Kit from Affymetrix, and hybridized to the Affymetrix GeneChip Mouse Promoter 1.0R Array at 45°C overnight. This array contains over 4.6 million 25-mer oligos tiled to interrogate over 28,000 mouse promoter regions (-7.5 kb to +2.5kb relative to the transcription start site). Arrays were scanned and the resulting CEL files were analyzed using Affymetrix TAS software that computed estimates of fold-enrichment over hybridization with input DNA in linear scale. This metric is referred to as peak binding intensity. Thresholds were selected, and the resulting BED files containing lists of sequence intervals with significant binding detected were analyzed using Genpathway software that provides comprehensive information on genomic annotation, peak metrics and sample comparisons for all peaks (intervals). Graphing of peak binding values were generated using the Affymetrix Integrated Genome Browser. The entire dataset is submitted to the NCBI GEO database (GSE16845;www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16845).

Intersects of our dataset with previously published datasets were determined using Microsoft Excel. Published datasets that were used for analysis of common genes were from naïve Balb/c mouse Th cells cultured under Th1 conditions for 0, 2, 6 and 48 hours (MG-U74Av2 arrays containing 12,488 probes)(12), differentiated C57BL/6 mouse Th1 cells stimulated with IL-12 for 18 hours (custom array with 24,000 probes)(11), differentiated human Th1 cells stimulated with IL-12 for 4 or 24 hours (HuGeneFL array containing 6000 genes)(17), and PHA/IL-2 activated human T cells stimulated with IL-12 for 6 hours (U133A and U133B arrays)(18).

MEME Analysis

The 1111 interval sequences with peak intensity > 4, which were in close proximity to 1540 genes, were screened by MEME (19) to find over-represented motifs. The parameters for MEME included looking at the reverse complement sequence, a maximum motif width of 15, and a cutoff of 30 motifs ordered by increasing E-value. The analysis was run using three different approaches regarding the number of times the motif repeated, any number of repeats, zero or one repeat, and one or more repeats with virtually the same results each time. WebLogo (20) generated the motif logos using the aligned site occurrences for each motif given by MEME.

GO Analysis

The Gene Ontology (GO) analysis was performed by GOStat (21). Using an input list and a selected background, GOStat determines enriched GO terms based on a Fisher exact test. The p-value from the Fisher exact test is altered using a Benjamini correction. Only terms with a corrected p-value less than 0.1 are reported. The analysis was performed three times with the curated gene list as the input and the Affymetrix chip gene list as the background.

Generation and analysis of STAT4 ChIP-on-chip data

To identify STAT4 target genes, CD4+ T cells were activated for three days with anti-CD3 before washed cells were stimulated with IL-12 for 4 hours and chromatin was immunoprecipitated with anti-STAT4 antibody. The pre-activation was performed to allow cells to acquire IL-12 responsiveness (22). IL-12 stimulation of activated cells resulted in 70-80% of cells in the population becoming pSTAT4-positive with a 6-fold increase in mean fluorescence intensity, as assessed by intracellular staining (data not shown). To confirm STAT4 binding to target genes, we testedIl18r1 promoter and theIl2ra PRRIII element DNA in the precipitate following IL-12 stimulation (8,23) and observed a greater than 30-fold induction of STAT4 binding, compared to unstimulated cells (Fig. 1A). DNA from the ChIP was then hybridized to an Affymetrix array containing sequences spanning −7.5kB to +2.5kb of over 28,000 promoters in the mouse genome and enrichment was calculated based on comparison to an array hybridized with input DNA. In replicate experiments, the ChIP-on-chip analysis identified a total of 4,669 genes as near a STAT4 binding sitein vivo. The peak binding intensities (see definition in Materials and Methods) of immunoprecipitated DNA hybridizing to the promoter oligonucleotides showed a wide range (2.2 to 32.4, with the highest value being the most intense binding) indicating the diverse nature of STAT4 binding to target genes. Mean and median of this data set were at 4.4 and 3.3 respectively (Fig. 1B). A manual inspection of the data showed many of the known STAT4 target and Th1-associated genes to have peak intensities of 4 or higher. Thus, we filtered the list of genes using a cutoff peak intensity of 4 (1111 binding sites corresponding to 1540 genes) referred to as the “curated” list and all subsequent analyses were performed on this curated gene set (Table S1). We confirmed binding of STAT4 to genes with a range of peak intensities from 4.5 to 21 using ChIP assay in wild type andStat4-/- activated T cells. The enrichment of percent input between wild type andStat4-/- samples was similar to the enrichment observed between IL-12 stimulated and unstimulated cells (Fig. 1A and2). These results further confirm the identification of STAT4 targets in this dataset.

We then analyzed the frequency of STAT4 binding between 7.5 kb upstream to 2.5 kb downstream of the transcription start site (TSS) to define chromatin location preferences for STAT4. A preponderance of STAT4 binding is in and around the target gene TSS (Fig. 1C). There was no difference in frequency between the curated gene list and a subset of genes that had an exact STAT4 consensus motif (TTCNNNGAA in 660 out of 1111 binding sites or 59.4%)(Fig. 3A) in the association of STAT4 with specific regions in the promoter.

To determine if STAT4 target genes were enriched for any specific category, we defined the enrichment of gene ontology (GO) terms in the curated set compared to an Affymetrix mouse genome background using the GoSTAT program (21). The major statistically significant enrichments (p < 0.05) were in genes involved in cytokine binding, including factors with interleukin receptor activity and TNF receptor binding activity (Fig. 1D). There was also a significant enrichment for nucleic acid binding factors. These analyses demonstrate selectivity in STAT4 binding target genes associated with specific cellular functions.

A number of published reports have examined IL-12-induced gene expression using microarrays from human or mouse cells treated with IL-12 for various times during or after Th1 differentiation (11,12,17,18). We generated a cumulative list of IL-12-induced genes from these studies and compared them to the ChIP-on-chip dataset. The Venn diagram inFig. 1E identifies an overlap of 72 genes (Table S2A) between these two analyses.

Computational identification of the STAT4 consensus motif

To define the proportion of STAT4-bound genes that contained a STAT4 consensus motif, TTCNNNGAA (24) we analyzed the curated gene set by directly examining consensus sites using a motif search algorithm. A Perl script to identify exact matches allowing the ‘NNN’ degeneracy at positions 4 -6 identified motif 1 shown inFig. 3A at a frequency of 59.4% in the curated gene list. In the second approach, we employed the expectation-maximization motif search algorithm, MEME (19) to identify motifs. We observed a number of motifs with low E-values. Sequences containing variants of a STAT4 consensus site were found in all STAT4 bound sequences (Fig. 3A, motif 2), further validating the dataset and suggesting that the false-positive rate is low. The percentage of the genes containing the consensus (motif 1) was highest in sequences that had high peak values, and decreased as peak values decreased (Fig. 3B). While these data shows that some sequences containing the STAT consensus site were not included in the curated dataset (peak value less than 4), the problem of the exclusion of these false-negatives is offset by avoiding the inclusion of large numbers of genes (Fig. 1B) that would lack other identifiable motifs. If all bound genes in the array are considered, the percentage with the consensus falls to 43%, and the MEME algorithm identified less than 10% of the genes as having motif 2 if the cut-off was reduced to a peak value of 3. Thus, our analysis minimizes false-positives at the expense of potentially excluding a limited number of valid targets.

Three additional motifs shown inFig. 3A were obtained from the MEME analysis that were present in 5-10% of the total sequences and had low E-values. Manual and JASPAR-mediated (25) sequence analysis identified a STAT-like site in motif 3, an NF-κB site in motif 4 and a PPARγ/RXR site in motif 5. These motifs were not significantly enriched in any GO categorized gene set. We mapped the frequency of motif 4 and motif 5 occurrences within 100 bp intervals relative to the position of the consensus STAT site (motif 2). Motifs 4 and 5 were enriched in proximity to the STAT site, almost entirely within 1 kb of the consensus sequence (Fig. 3C). We functionally assessed binding to motif 3 using biotinylated oligonucleotides that spanned the motif. We detected STAT4 binding specifically to motif 3 that was competed by STAT consensus oligonucleotides to a greater extent than by non-specific sequences (Fig. 3D and E).

STAT4 binds multiple genes associated with the Th1 phenotype

While the Th1 genetic program is known to be largely dependent on STAT4, and STAT4 binding to a subset of Th1 genes has been demonstrated, all targets of STAT4 required to establish Th1 differentiation are not clear. We examined the curated list for genes demonstrated in the literature to be functionally associated with the Th1 phenotype, or preferentially expressed in Th1 cells, and have demonstrated STAT4- or T-bet-dependence in their expression (Table S3). We identified binding to a set of genes (18/28 Th1 genes searched (Fig. 4A, Table S3); significantly enriched from the genome p < 10^-8) includingFurin,Ifng andIl18r1, genes that are known to be STAT4 targets (5,6,8,26) as well as Th1 genes that are expressed independently of STAT4 includingEgr2 andMs4a4b. Using Affymetrix's Integrated Genome Browser we mapped the peak intensities of binding in a subset of these genes (Fig. 4B). While the microarray in these experiments is focused on promoter sequences, STAT4 peak binding was largely localized to the gene regions immediately upstream of transcription initiation. The exceptions to this wereGadd45g, which had peaks both 5-prime and 3-prime of the gene, andTnf that had intragenic peak binding, closely linked to STAT4 binding at theLta promoter (Fig. 4B).

Several of these genes, while associated with Th1 function, had not been demonstrated to be STAT4-dependent. To test a requirement for STAT4 in Th1 expression of these genes, we differentiated wild type andStat4-/- CD4+ T cells under Th1 conditions and used quantitative PCR to examine relative expression.Gadd45g, which plays a role in IFN-γ production and potentially synergy between IL-12 and IL-18 (27), was decreased more than five-fold inStat4-/- Th1 cultures compared to wild type cultures (Fig. 4C).Myd88, which we have previously shown to be IL-12-inducible in a STAT4-dependent manner (28), also required STAT4 for maximal expression in Th1 cells (Fig. 4C).

STAT4 binds a subset of genes associated with TCR signaling

In a manual examination of the curated gene list we defined subsets of genes that would have specific functional consequences for T cell differentiation or function. Among these clusters we observed a set of genes that are associated with TCR signaling identified from the literature (29) (7/23 TCR signaling genes searched (Table S2B); significant enrichment from the genome p=1 × 10^-4) includingCd3z,Prkcq/PKCθ andLcp2/SLP-76 (Fig. 5A) that are important for the stimulation of T cells in response to specific antigen. To test if expression of these genes was affected by STAT4-deficiency, we differentiated wild type andStat4-/- Th1 cells and used qPCR to examine relative levels of gene expression, usingHlx1 as a STAT4-dependent control. While expression ofPrkcq and a number of other TCR signaling genes was not significantly decreased inStat4-/- Th1 cells,Lcp2 expression inStat4-/- cultures was decreased to one-half the level seen in wild type cultures (Fig. 5B). To determine if there are consequences of this decrease in expression, we analyzed the TCR-induced phosphorylation of the ERK MAPKs and observed decreased ERK phosphorylation inStat4-/- Th1 cells, compared to wild type cells (Fig. 5C). These observations agree with previous data suggesting that strong stimuli minimize the defect in IFN-γ production byStat4-/- Th1 cells (30), though it is difficult to test using IFN-γ as a readout since STAT4 plays an important role in programmingIfng for Th1 expression (31). To avoid this problem, we analyzed TNFα production from wild type andStat4-/- Th1 cultures stimulated with either anti-CD3 or PMA and ionomycin. While TNFα production following anti-CD3 stimulation is decreased inStat4-/- cultures compared to wild type, stimulation with PMA and ionomycin, which bypasses the requirement for proximal signaling molecules such as SLP-76, resulted in similar levels of TNFα production (Fig. 5D). Thus, STAT4 targets genes associated with TCR signaling and contributes to the potency of antigen receptor stimulation.

Distinct patterns of STAT4-dependent gene induction among STAT4 target genes

From the dataset of genes bound by STAT4, we wanted to determine if there was a relationship between the intensity of STAT4 binding to a gene and the level of gene induction particularly since the overlap between our ChIP-on-chip targets and the public domain expressed gene lists was limited (Fig. 1E). To test this, we analyzed mRNA levels of over 30 genes, selected manually based on involvement in Th differentiation, immune responses or gene expression, in wild type andStat4-/- activated T cells stimulated for four or 18 hours with IL-12. InFigure 6A, an analysis of genes that demonstrated STAT4-dependent IL-12 induction indicated that there is no correlation between binding intensity and level of gene induction. This is exemplified byIl18rap, which was one of the highest intensity STAT4 binding genes, but was induced only 2-fold at 4 hours, whileIl18r1, which bound with less intensity, was induced 5-fold. The three genes that showed over 20-fold induction,Furin,Ifng, andIl24, had intensity ranging from 7.5 to 21. We also performed this analysis using an IL-12-induced microarray dataset (18), and observed a similar pattern when the published fold-induction was graphed against our binding intensity values (Fig. 6B).

To further characterize this subset of genes, we divided genes into categories based on the patterns of gene induction by IL-12. “Non-induced” genes had ≤2-fold induction at four or 18 hours; “Transient” genes had >2-fold induction at 4 hours but <2-fold induction at 18 hours, and “sustained” genes had >2-fold induction at 4 and 18 hours. For each subset we graphed the fold IL-12-induced gene expression versus the basal gene expression normalized to the endogenous control β2-microglobulin. We observed a general trend that genes with lower basal expression had greater induction, and observed that those STAT4 target genes not induced by IL-12 were among the most highly expressed genes that we analyzed (Fig. 6C). This suggested that the non-induced genes did not lack induction because they were repressed in T cells, but rather that expression was high and induction by IL-12 might have a more limited effect. To determine if IL-12 was having effects on the non-induced genes we compared histone modifications at the promoters of three sustained genes and four non-induced genes. We observed that IL-12 stimulation increased H3 acetylation and decreased H3K27 methylation at both sets of genes, but had significantly greater effects at the promoters of the induced genes (Fig. 6D). These data, coupled with the demonstration that STAT4 binds to the promoter ofIrf1 (Fig. 2), one of the non-induced genes, suggests that they are bona fide STAT4 targets, but that the effects of IL-12 are limited at these promoters for reasons that may include high basal expression or lack of appropriate promoter context for induction.

Upon examining the genes that were in each group, we observed some interesting patterns (Fig. 6E). Within the non-induced set wereFoxp3 andGzmb, genes not expressed in Th1 cells, andMs4a4b, a STAT4-independent Th1 gene. The transiently induced set included receptors such asIl2ra andCd40l, and transcription factors such asIrf4,Irf8, andBcl6 where transient expression might confer temporary function to the cell. The sustained subset contained many of the genes that require STAT4 to program high expression in Th1 cells includingIfng,Furin,Il18r1 andGadd45g. We analyzed two additional genes that STAT4 is known to program but were not found on the ChIP-on-chip list,Etv5 andHlx1, and found that they also were in this category. Thus, the induction kinetics of STAT4 target genes following IL-12 stimulation of activated T cells is a strong predictor of the requirement for STAT4 in programming expression for Th1 cells.

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