In this study, we examined the landscape of transcription factor binding of STAT4 and STAT6 through genome-wide approaches and identified the genes uniquely bound by each STAT, as well as those occupied by both factors. Our analysis revealed a core subset of STAT-occupied genes, whose expression and local epigenetic fingerprint are clearly dependent upon the presence of the given STAT. Using this strategy, a large number of genes were identified, many of which were not previously known to be directly regulated by STATs. Consequently, the direct functional roles of STAT4 and STAT6 are considerably broader than previously appreciated. Furthermore, we found that the two 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-wide, promoting H3K4me3 modifications is a more prominent global feature of STAT4, whereas antagonizing repressive marks (H3K27me3) is a major property of STAT6. Our analysis also uncovered a number of genes for which STAT4 and STAT6 appear to serve as repressors. Among them, a considerable proportion were bound by both factors, and regulated in an opposing manner.
With the ability to measure transcription factor binding on a genome-wide scale, a fundamental issue is the functional relevance of factor binding and how to evaluate it. In this study, we identified over 4000 genes bound by the cognate STAT in Th1 and Th2 conditions. Of these, 15% (684 genes for STAT4) to 29% (1200 genes for STAT6) showed STAT-induced expression changes. An emerging concept coming from genome-scale studies is that only a fraction of factor-bound genes showed clear functional dependence as evaluated by gene expression changes. For example, a recent study enumerating GATA-1 binding showed that ~40% of genes (790 genes out of 1800 bound genes) showed GATA-1 induced expression changes (Yu et al., 2009
). Our study produced similar absolute numbers of functionally relevant transcription factor-bound genes when evaluated by microarray-based expression changes.
By adding analysis of STAT-induced epigenetic changes to gene expression changes, we were able to further separate highly relevant STAT binding that substantially impacted both epigenetic signature and gene transcription, from binding events that had minimal functional outcomes. Our analysis revealed subsets of gene clusters whose epigenetic signature was highly STAT dependent. These genes comprise roughly 1000 genes of total 4000 STAT-occupied genes. Our data firmly establish that there is a substantial subset of genes for which the recruitment of STAT during the course of T helper cell differentiation serves to maintain the distinctive epigenetic pattern of the genomic region. For these STAT-regulated genes, it will be of considerable interest to carefully dissect the kinetics of epigenetic pattern formation during the process of lineage specification.
Also of interest was the identity of the STAT-bound genes whose transcriptional and epigenetic regulation was highly dependent on the factor. The genes identified include phenotype-defining cytokines (Ifng, Il4 and Il24), receptors (Il18rap, Il18r1, Lag3, Il4ra), transcription factors (Gata3, Tbx21) and transcriptional repressor (Id2 and Zbtb32). In addition to these recognized regulators of T helper cell differentiation, we identified a number of previously unrecognized STAT-regulated genes with preferential expression in Th1 and Th2 cells. These include genes that encode transcriptional repressors (Ikzf3 [Aiolos] and Nfil3), and diverse signaling molecules that included kinases, phosphatases, G-proteins and adaptors (Hipk2, Plcd1, Gbp2 and Skap2). Clearly, it will be of interest to define the role of these molecules in T cells and assess how their actions relate to the specification of cognate lineages.
Tfh cells represent a recently recognized subset of helper T cells and we were intrigued to see that a major gene expressed by Tfh cells, Il21
, was also a STAT4-bound and positively regulated gene. Although previous data has argued that IL-6 acting via STAT3 is the main driver of Tfh cell differentiation (Fazilleau et al., 2009
; King et al., 2008
), recent data indicates that in human T cells, IL-12 can also promote Tfh cell specification (Saraiva et al., 2009
). Our data are entirely consistent with this result and suggest that this mechanism is not unique to human T cells. Interestingly, we also found that STAT4 bound the Bcl6
gene, but unlike IL-21, the expression of the former was modest in Th1 cell polarizing conditions. This would argue that IL-12-STAT4 signals alone are not sufficient to drive Bcl6
expression and presumably other signals are required. The data are also consistent with the evolving notions that Tfh cells represent a flexible subset. In related studies, we found that the Bcl6
genes are the targets of STAT3 as well (Durant et al., 2010
) and it will be important to differentiate the roles of different STATs in the transcriptional and epigenetic regulation of these important genes.
An unanticipated finding in our studies became apparent when we compared and contrasted the effects of STAT4 and STAT6 on epigenetic modification of their cognate genes. We found that STAT4 primarily promoted accessible epigenetic marks whereas STAT6 had a more prominent role in antagonizing a repressive mark on its cognate genes. One possibility was that the expression of chromatin modifying enzymes differed in Th1 and Th2 cells such that the balance is shifted toward promoting H3K27me3 modification in the absence of STAT6 in Th2 cell condition. The enhancement of H3K27me3 modifications might be explained by over-expression of the H3K27 methyltransferase, Ezh2, and down-regulation of the H3K27 demethylase, Jmjd3. However, this was not the case. We noted decreased expression of Ezh2 associated with a reciprocal increase of Jmjd3 in STAT6-deficient Th2 cells. Therefore, an alteration in the balance between the “writer” (Ezh2) and “eraser” (Jmjd3) (Wang et al., 2007
) was evidently not the primary cause of increased H3K27me3 mark associated with the absence of STAT6. On the contrary, these enzymes appear to be compensating for STAT6-dependent alteration of H3K27me3 in Th2 cells. At present, we do not have an explanation for these findings, but clearly additional studies are warranted to dissect the molecular mechanisms underlying the differential regulation of H3K27me3 modifications in Th1 and Th2 cells.
The present study also revealed a large number of genes that are bound and negatively regulated by STAT4 and STAT6. Although the ability to concomitantly promote the expression of some genes and repress others is a common feature of transcription factors associated with lineage commitment (Cobaleda et al., 2007
), there is a paucity of circumstances in which STATs have been documented to behave as functional repressors. This was a more prominent feature of STAT4 than STAT6. Clearly, it will be of considerable interest to carefully dissect the molecular basis of the repression of these genes. Particularly interesting were genes bound by both STAT4 and STAT6, for which these factors had opposing functions. In this case, the two STATs appear to function in concert to ensure differential expression of lineage-specifying genes (e.g. Il18r1-l18rap
). These will be an interesting subset of genes to analyze in further detail.
In summary, our data provide a platform for understanding how STATs function to modulate epigenetic events to shape gene expression profile unique to T helper subtype. The emerging picture is that STATs bind to a broad, yet clearly defined set of genes and contribute in a very substantial manner to helper T cell differentiation. They do so in both a positive and negative manner. Investigating the function of newly identified genes as they relate to helper cell function and elucidating the mechanisms by which STATs regulate the expression, both positively and negatively, will clearly be important.