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Complex organisms require tissue-specific transcriptional programs, yet little is known about how these are established. The transcription factor FoxA1 is thought to contribute to gene regulation though its ability to act as a pioneer factor binding to nucleosomal DNA. Through genome-wide positional analyses, we demonstrate that FoxA1 cell type-specific functions rely primarily on differential recruitment to chromatin predominantly at distant enhancers rather than proximal promoters. This differential recruitment leads to cell-type specific changes in chromatin structure and functional collaboration with lineage-specific transcription factors. Despite the ability of FoxA1 to bind nucleosomes, its differential binding to chromatin sites is dependent on the distribution of histone H3 lysine 4 dimethylation. Together, our results suggest that methylation of histone H3 lysine 4 is part of the epigenetic signature that defines lineage-specific FoxA1 recruitment sites in chromatin. FoxA1 translates this epigenetic signature into changes in chromatin structure thereby establishing lineage-specific transcriptional enhancers and programs.
Over the course of development, cells transit from a pluripotent state to one of many committed cell lineages. During this process, transcription factor networks are activated in order to establish cell type-specific transcriptional programs (Son et al., 2005). FoxA1 (Hepatocyte Nuclear Factor 3α), a member of the Forkhead family of winged-helix transcription factors, is involved in the development and differentiation of several organs including liver, kidney, pancreas, lung, prostate and mammary gland (Friedman and Kaestner, 2006; Kouros-Mehr et al., 2006; Spear et al., 2006). In addition, high expression of FoxA1 is commonly observed in tumors arising from these organs, including prostate and estrogen receptor α (ERα)-positive breast tumors (Lacroix and Leclercq, 2004; Lin et al., 2002; Mirosevich et al., 2006). Interestingly, FoxA1 expression is a positive prognostic factor among patients with ERα-positive breast tumors and correlates with sensitivity to endocrine therapy (Badve S, 2007). Consistent with its originally reported role as a pioneer factor involved in liver-specific gene expression (Bossard and Zaret, 2000; Cirillo et al., 1998; Gualdi et al., 1996), FoxA1 acts as a pioneer factor in the recruitment of ERα to several cis-regulatory elements in the genome and subsequent transcriptional induction of target genes such as Cyclin D1 (CCND1) in breast cancer cells (Carroll et al., 2005; Eeckhoute et al., 2006; Laganiere et al., 2005). This is mediated in part through the chromatin remodeling activity of FoxA1 (Cirillo et al., 2002; Eeckhoute et al., 2006), reminiscent of its role in the induction of liver specific gene expression (Friedman and Kaestner, 2006). FoxA1 also interacts with the androgen receptor (AR) in prostate cancer cells where it is thought to impact the regulation of AR target genes (Gao et al., 2003). Hence, FoxA1 appears capable of regulating distinct transcriptional programs in cells of different lineages. However, the molecular bases for the differential transcriptional activities of FoxA1 remain to be established. In the present study, we have investigated FoxA1 differential transcriptional activities in breast and prostate cancer cells and their functional relation with the epigenome of these cells.
Estrogen stimulation leads to the establishment of specific transcriptional programs in ERα-positive breast cancer cells. To address how FoxA1 participates in this process we initially performed an unbiased genome-wide chromatin immunoprecipitation study using tiling-microarrays (ChIP-chip) to define the repertoire of FoxA1 binding sites, which we define as its “cistrome” (http://en.wikipedia.org/wiki/Cistrome), in the MCF7 breast cancer cell-line. A total of 12904 high-confidence FoxA1 recruitment sites were identified in these cells [using a stringent statistical False Discovery Rate (FDR) of 1%] (Fig.S1 and S2). In comparison, the ERα cistrome in MCF7 cells (Carroll et al., 2006) reanalyzed using the MAT algorithm (Johnson et al., 2006) and updated to the most recent human genome sequence (Hg18) revealed 5782 high confidence sites (FDR 1%) (Fig.S3). Interestingly, the genomic distribution of FoxA1 binding sites was reminiscent of that of ERα (Carroll et al., 2005; Lin et al., 2007). Indeed, the majority of the sites (96.9%) were found distant from the proximal 1 kilobase (kb) promoter regions of genes (Fig.S4B). Accordingly, this distribution contrasted with that of RNA Polymerase II (RNA PolII) (Carroll et al., 2005), which is found primarily at proximal promoters (Fig.S4C). Comparing the FoxA1 and ERα cistromes, revealed a highly significant overlap with ~50-60% ERα binding sites occurring on FoxA1 occupied sites (Fig.1A and S5A-B). To determine the functional significance of this co-binding, we subsequently determined the distribution of FoxA1 and ERα binding sites with regards to E2 regulated genes in MCF7 cells (Carroll et al., 2006). Hence, we compared the fraction of E2 regulated versus non-regulated genes in MCF7 cells with at least one binding site specific to ERα, FoxA1 or shared by the two factors (as defined in Fig. S5) within 20 kb of their transcription start site (TSS). Importantly, E2-upregulated genes were significantly enriched compared to non-regulated genes near sites of overlapping ERα/FoxA1 recruitment (Fig.1B). Strikingly, this was also the case for E2-downregulated genes (Fig.1B). These results demonstrate that genes having enhancers within 20 kb of the TSS that bind both ERα and FoxA1 together compared to ERα or FoxA1 separately are much more likely to be regulated in response to E2 treatment in breast cancer cells. A role for FoxA1 in E2-downregulated genes independently of its association with ERα was also revealed through the enrichment for this category of genes near sites recruiting FoxA1 only (Fig.1B). In fact, FoxA1 silencing in MCF7 cells reduced the basal expression of these genes to levels equivalent to the reduction seen after E2 treatment (Fig.S6A-B). This is most likely a consequence of FoxA1 role in allowing for the basal activity of enhancers for those genes (Fig.S6C-D). These data indicate that FoxA1 controls the E2 response in breast cancer cells through a combination of mechanisms consisting of maintaining the basal expression of genes repressed following hormone treatment and allowing for the induction of E2-upregulated genes through a direct collaboration with ERα. Interestingly, genes with FoxA1 binding sites within 20 kb of their TSS also had a greater chance to be expressed together with FoxA1 and ERα in primary breast tumors pointing to the biological relevance of the FoxA1 cistrome beyond the MCF7 cell-line (Fig.1C and S7-8).
Having shown that FoxA1 recruitment to the chromatin within the MCF7 cell-line was correlated with the regulation of the transcriptional program specific to ERα-positive breast tumors, we investigated how FoxA1 binding to the chromatin relates to its cell-specific functions. This was accomplished by comparing the FoxA1 cistromes originating from cell-types of different lineages, namely the MCF7 breast cancer and LNCaP prostate cancer cell-lines. Through genomic-scale studies performed across the non-repetitive regions of human chromosomes 8, 11 and 12 using ChIP-chip assays, we identified over 2000 high confidence sites of FoxA1 recruitment (FDR 1%) in both cell-lines. As in MCF7 cells, these sites were predominantly found at enhancer positions in LNCaP cells (Fig.2A and S9). Importantly, comparison of the FoxA1 partial cistromes in these two cell-lines revealed both a significant number of shared sites and an even greater number of cell type-specific regions (Fig.2B). Indeed, comparisons of the datasets using various cut-offs indicated that the overlap did not exceed 55% and 40% of the MCF7 and LNCaP binding sites, respectively (Fig.S10A-C). Therefore, of all sites identified in both cell lines (3932 sites total), over 65 % of them correspond to regions of cell type-specific recruitment (886 sites specific to MCF7 cells and 1654 sites specific to LNCaP cells). The accuracy of these predictions was validated by ChIP-qPCR experiments (Fig.S10D). Hence, on a genomic-scale the majority of FoxA1 recruitment sites within the chromatin of two distinct cellular lineages are cell-type specific. These results strongly suggested that FoxA1 might regulate differential transcriptional programs as a result of its cell type-specific recruitment pattern in MCF7 and LNCaP cells.
We next investigated the association of FoxA1 binding sites unique to MCF7 or LNCaP, or sites shared between the two cell lines with genes co-expressed with FoxA1 in primary breast or prostate tumors. This revealed a significant enrichment of genes co-expressed with FoxA1 in primary breast tumors over non-co-expressed genes near FoxA1 specific binding sites unique to MCF7 breast cancer cells (Fig.2C and Fig.S11) (van de Vijver et al., 2002; Wang et al., 2005). Reciprocally, genes co-expressed with FoxA1 in primary prostate tumors were significantly enriched over non-co-expressed genes near FoxA1 binding sites unique to LNCaP prostate cancer cells (Fig.2C) (Setlur, SR., Mertz, KD., Hoshida,Y., Demichelis, F., Lupien, M., Perner, S., Sboner, A., Pawitan, Y., Andren, O., Johnson, LA., et al. unpublished results). Altogether, these results demonstrate that differential recruitment is the primary mechanism responsible for the differential function of FoxA1 in these two different cell-lineages.
In order to further characterize the functional mechanisms involved in FoxA1 regulation of the breast and prostate cancer specific transcriptional programs, we monitored the transcription factor binding motifs enriched within the common FoxA1 recruitment sites, as well as those unique to each cell-line. As expected, the Forkhead motif (FKHR) was enriched in all three subsets of FoxA1 binding regions (Fig.3A). Conversely, we found that the recognition motifs for the nuclear receptors ERα (ERE and ERE half-site) and AR (ARE and ARE half-site) were specifically enriched in FoxA1 binding sites unique to MCF7 or to LNCaP cells, respectively (Fig.3A). This suggested that the differential FoxA1 recruitment between MCF7 and LNCaP was correlated with cell-specific transcriptional collaborations with ERα or AR. This hypothesis was tested by comparing the FoxA1 cistrome on chromosomes 8, 11 and 12 from both cell-lines to that of AR in LNCaP cells (Q.W. and M.B. unpublished results) and to that of ERα in MCF7 cells (Carroll et al., 2006). Interestingly, as was the case for ERα, we found that more than half of AR binding sites in LNCaP cells occurred on sites where FoxA1 was also present (Fig.3B). These data strongly suggest that the functional relationship between FoxA1 and AR previously demonstrated at a few model genes (Gao et al., 2003) in fact extend to a large fraction of regions used by this nuclear receptor. Accordingly, FoxA1 silencing modulated the transcriptional response to dihydroxytestosterone (DHT) of several studied target genes (Fig.S12). Importantly, the majority of FoxA1 binding sites overlapping with ERα were sites specific to MCF7 cells, while the majority of FoxA1 binding sites overlapping with AR were sites specific to LNCaP cells (Fig.3B). These data suggest that the cell type-specific recruitment of FoxA1 to the chromatin is linked to breast and prostate cancer transcriptional programs through specific collaborations with ERα in breast cells and AR in prostate cells. Indeed, these nuclear receptors are known to be master regulators of the behavior of a large subset of breast and prostate tumors through transmission of estrogenic and androgenic signals. Hence, we investigated the association of the different classes of sites with genes regulated by E2 in MCF7 cells or those regulated by DHT in LNCaP cells (Carroll et al., 2006; Wang et al., 2007). Only genes regulated by E2 were significantly enriched over non-regulated genes near ERα sites overlapping with FoxA1 in MCF7 cells (Fig.3C). In contrast, genes regulated by DHT were specifically significantly enriched over non-regulated genes near AR sites overlapping with FoxA1 in LNCaP cells (Fig.3C). Importantly, E2 or DHT regulated genes were mostly associated with the cell type-specific FoxA1 binding sites overlapping with ERα or AR and not those common to both cell lines (100% for AR/FoxA1 sites and 70% for ERα˜/FoxA1 sites). Overall, these data clearly implicate a role for FoxA1 in the regulation of breast and prostate specific transcriptional programs through cell-specific recruitment and subsequent differential collaboration with the sex steroid nuclear receptors ERα and AR.
Differential recruitment to the chromatin extends to other transcription factors present in both MCF7 and LNCaP cells. Indeed, AP-1, whose recognition motif was enriched within the FoxA1 binding sites from MCF7 and LNCaP cells (Fig.S13A), was found to be co-recruited together with FoxA1 at a subset of its cell-specific binding sites (Fig.S13B). Hence, these data demonstrate that cell-specific recruitment also extends to ubiquitously expressed transcription factors such as AP-1 and suggest that this differential recruitment could also play an important role in its well-known cell-lineage differential activities (Jochum et al., 2001).
The functional importance of FoxA1 cell-specific recruitment described above raises the question as to how FoxA1 is able to bind to distinct regions within the genome of the MCF7 and LNCaP cells. Accordingly, we first considered the possibility that the sequence recognized by FoxA1 could be different between the two cell-lines. However, de novo motif analysis revealed that the Forkhead factor recognition sequence enriched within the FoxA1 binding sites did not show any significant difference between shared and cell-specific binding regions though it varied somewhat from the previously established consensus motif (Fig.4A). Therefore, we investigated whether the differential FoxA1 binding could rather be linked to specific epigenetic modifications. First, we looked at several repressive histone marks (Bernstein et al., 2007; Kouzarides, 2007) and found that H3K9me2 was more highly enriched on sites not recruiting FoxA1 in both cell lines, although not exclusively found on sites not recruiting FoxA1 (Fig.4B-C and S14A). We then sought to determine if FoxA1 recruitment was on the other hand associated with the presence of active histone marks. Recently, a genomic-scale study demonstrated the occurrence of mono (me1) and dimethylation (me2) of H3K4 at active enhancers (Heintzman et al., 2007). Analyzing the presence of these specific histone modifications at the FoxA1 recruitment sites revealed significant enrichment for H3K4me1 and me2 in a cell type-specific manner (Fig.4D-G). Indeed, in MCF7 cells, FoxA1 binding sites unique to MCF7 cells as well as sites common to both cell lines were significantly mono and dimethylated on H3K4 compared to the LNCaP unique FoxA1 binding sites (Fig.4D,F). On the other hand, in LNCaP cells, the LNCaP specific FoxA1 binding sites together with the common sites were significantly enriched for these histone modifications compared to MCF7 specific sites (Fig.4E-G). To confirm this correlation between H3K4 methylation and FoxA1 occupancy on a genomic scale we performed a ChIP-chip analysis of H3K4me2 levels in MCF7 cells across chromosomes 8, 11 and 12. These data revealed that on a genomic scale levels of H3K4me2 in MCF7 cells were indeed significantly greater on MCF7 specific or shared FoxA1 recruitment sites than on LNCaP specific ones (Fig.4H). H3K4me2 levels were also significantly higher on regions with FoxA1 recognition motifs bound by FoxA1 compared to an equivalent number of randomly selected unbound regions with FoxA1 recognition motifs in MCF7 cells (Fig. 4H). Importantly as less than 3.7% of sites harboring FoxA1 recognition motifs actually recruit FoxA1 in MCF7 cells (Fig. S14C), these data derived from the analysis of thousands of sites reveals a strong correlation between the presence of H3K4me2 and FoxA1 binding. Of the FoxA1 recruitment sites tested, as expected, very few demonstrated enrichment for H3K4me3 in accordance with the predominant occurrence of this modification at promoters rather than enhancers (Heintzman et al., 2007) (Fig.S14B). Overall, these results suggest a link between FoxA1 recruitment with the presence of H3K4me1 and me2.
In MCF7 cells, H3K4me1 and me2 are detected at enhancers prior to E2 stimulation and ERα binding, reminiscent of FoxA1 recruitment (Fig.S15). Accordingly, ERα silencing in these cells did not dramatically affect H3K4 methylation levels or FoxA1 recruitment at most sites where these two factors are recruited (Fig.5A and S16). Moreover, the vast majority (~80%) of FoxA1 sites specific to MCF7 cells do not recruit ERα (Fig.3B). Hence, while we cannot entirely rule out a potential role for ERα in stabilizing FoxA1 binding at a small subset of sites, these results suggest that in general cell-specific FoxA1 recruitment occurs independently of ERα action in MCF7 cells. This raises the issue of whether H3K4me1 and me2 are required for FoxA1 recruitment or are induced as a result of FoxA1 binding to the chromatin. This question was first addressed by investigating whether FoxA1 silencing would affect H3K4 methylation, chromatin remodeling or both in MCF7 and LNCaP cells. Consistent with its cell type-specific recruitment, FoxA1 silencing impacted the DNAse I sensitivity only at those sites to which it was recruited (Fig.5B). Under these conditions, however, these sites did not in general show a significant reduction in the levels of H3K4me1 or me2 in either MCF7 or LNCaP cells (Fig.5C). In fact, a significant increase in H3K4me1 was detectable at most sites tested in LNCaP cells. Similarly, levels of H3K9me2 were unaffected by FoxA1 silencing (Fig.S17). Overall, these data do not favor a model whereby FoxA1 recruitment leads to the induction of these modifications but rather suggest an important contribution of FoxA1 in opening genomic regions marked by H3K4me1 and me2. Accordingly, even though FoxA1 silencing did not modulate H3K4 methylation levels at enhancers (Fig.5D), it affected the transcriptional regulation of their target genes (Fig.5E and Fig.S18). Considering that H3K4me2 is typically associated with gene transcription (Bernstein et al., 2005), these results highlight the critical interplay between the pioneer factor FoxA1 and H3K4me2 at enhancers for efficient gene regulation.
To establish the capacity of H3K4 mono or dimethylation to define the cell type-specific recruitment of FoxA1, we overexpressed the H3K4me1 and me2 specific demethylase KDM1 (also known as LSD1/BHC110) in MCF7 cells and established its impact on FoxA1 recruitment (Shi et al., 2004). Under these conditions, H3K4me1 was slightly reduced (Fig.S19A) and H3K4me2 was significantly lowered on FoxA1 binding sites (Fig.6A). The level of H3K9me2 remained unchanged at these sites (Fig.6C). Although FoxA1 protein levels were unaffected by KDM1 over-expression (Fig.6D), its recruitment to the chromatin was significantly impaired (Fig.6B). Importantly, no global alteration in ChIP efficiency was observed upon KDM1 over-expression (Fig.S20B-C). Hence, these results suggest that H3K4me2 is required to define the cell type-specific regions competent for recruitment of FoxA1. The correlation between the presence of histone marks and FoxA1, ERα or AR recruitment is shown for specific examples of hormone-regulated genes (Fig.6E).
Networks of transcription factors are known to be at the center of cell type-specific transcriptional programs that characterize different cell lineages (Olson, 2006; Schrem et al., 2002). However, how a particular transcription factor manages to regulate gene expression in a cell type-specific fashion within the context of different transcription factor networks is still poorly understood. In particular, it is still elusive how a pioneer factor, such as FoxA1, able to bind condensed chromatin structures in vitro can mediate differential gene regulation in vivo (Cirillo et al., 2002; Eeckhoute et al., 2006). Here, we show that FoxA1 differential transcriptional activities in breast and prostate cells relies primarily on its differential recruitment to the chromatin and alternative collaboration with the lineage-specific factors ERα or AR at cell-specific enhancers (Fig.6E, ,77 and S21). These findings indicate that alternative transcriptional programs depend both on the orchestrated expression of a particular set of collaborating transcription factors together with their ability to bind cell-specific enhancer elements in the vicinity of their target genes. Alternatively, other transcription factor networks may primarily target gene promoters (Bieda et al., 2006; Geles et al., 2006). This may allow for a tight regulation of gene expression both at basal levels and in response to stimuli through combined activities of promoter and enhancer bound regulatory complexes (Hatzis and Talianidis, 2002; Marr et al., 2006). Importantly, we found that even ubiquitous transcription factors, such as AP-1, show differential recruitment to cell type-specific enhancers. Combined with other recent studies (So et al., 2007), this suggests that cell-specific binding to the chromatin represents a general mechanism for differential transcription factor regulatory activities. Cell-specific recruitment of AP-1 to FoxA1 sites could have important functional implications in breast cells especially for E2 downregulated genes where FoxA1 binding sites are enriched for AP-1 and Sp1 motifs (p≤0.05) that can tether ERα to mediate gene repression (Carroll et al., 2006; Stossi et al., 2006). Other important candidates for a global role in control of sex steroid signaling through collaborations with FoxA1 and ERα or AR include GATA family members (Eeckhoute et al., 2007) (Wang et al., 2007), c-myc (Cheng et al., 2006) and NFIC (Eeckhoute et al., 2006).
The occurrence of specific histone modifications at cis-regulatory elements commonly characterizes transcriptionally active or inactive regions (Bernstein et al., 2007; Kouzarides, 2007). Recently, the balance between the presence of active or repressive histone modifications (trimethylation of H3K4 and H3K27) has been shown to correlate with promoter activity (Azuara et al., 2006; Bernstein et al., 2006; Mikkelsen et al., 2007). Here, we show that the cell type-specific activity of enhancers correlates with the presence of the positive mark H3K4me2, previously shown to be distributed in a cell type-specific manner (Bernstein et al., 2005), while inactive enhancers lack H3K4me2 and harbor higher levels of the repressive mark H3K9me2. Interestingly, even though FoxA1 silencing does not modulate levels of H3K4 and K9 methylation at enhancers (Fig.5 and S17) it is required for their activity and consequently for their target gene transcriptional regulation (Fig.5, S6 and S18). Therefore, H3K4me1/2 appear to correlate with competent enhancers but not necessarily with transcriptional activation of target genes that requires factors such as FoxA1 to activate the functionality of these enhancers.
The capacity of FoxA1 to bind unique binding sites in reconstituted chromatin has been studied extensively in vitro (Cirillo et al., 2002; Cirillo et al., 1998; Sekiya and Zaret, 2007). Under these conditions, no histone modifications appear to be required for FoxA1 recruitment. However, our results demonstrate that in vivo FoxA1 actually occupies only a very small fraction of all its potential recognition motifs found in the genome (less than 3.7%). Moreover, this limited number of occupied sites is significantly different between two different cell-types. Therefore, although FoxA1 can act as a pioneer factor able to bind to condensed chromatin, we show here that in vivo its pioneer function is limited to a small subset of sites that are largely cell-type specific. Our data further defines on a genomic-scale the chromatin components involved in directing FoxA1 recruitment to this subset of its potential binding sites. Indeed, our results point to an important role of active and repressive histone marks notably H3K4me2 and H3K9me2, respectively, in guiding FoxA1 recruitment. These data indicate that a better understanding of cell-lineage transcriptional commitment will require the study of how these marks are established and how they regulate recruitment of pioneer transcription factors such as FoxA1. Altogether, our data reveal an additional layer of complexity in the regulation of FoxA1 recruitment to chromatin in vivo that goes beyond the mere presence of its recognition motif. Indeed, FoxA1 translates an epigenetic signature into functional cell-type specific enhancers leading to the establishment of cell type-specific transcriptional programs.
ChIP-chip experiments using Affymetrix Human Tiling 2.0R Array Set were performed as previously described (Carroll et al., 2005; Carroll et al., 2006). For each ChIP-chip experiment, at least three independent assays were performed. Analyses were performed using MAT (Johnson et al., 2006), whose probe mapping had been updated to the latest human genomic sequence (Hg18). We used statistical False Discovery Rate (FDR) as cut-off in those analyses. All ChIP-chip data used in this study can be accessed at http://research.dfci.harvard.edu/brownlab/datasets/. ChIP-qPCR experiments were performed as in (Carroll et al., 2005). Statistical analyses were performed using Student's t-test comparison for unpaired data. Primer sequences can be found in Supplementary Table 1.
Antibodies used for ChIP experiments were FoxA1 (Ab5089 and Ab23738 from Abcam, FOX1 from CeMines) ERα (Ab-10 from Neomarkers, HC-20 from Santa Cruz), pan-jun (D from Santa-Cruz), pan-fos (K-25 from Santa-Cruz) (Schwartz et al., 2007), AR (N20 from Santa-Cruz), H3K4me1, me2, me3, H3K9me1, me2, me3, H4K20me1, me2, me3 (Ab8895, Ab7766, Ab8580, Ab9045 Ab1220, Ab8898, Ab9051, Ab9052 and Ab9053, respectively from Abcam) (Mikkelsen et al., 2007) (Barski et al., 2007), H3K27me1, me2, me3 (07-448, 07-449 and 07-452 from Upstate Biotechnology Inc) (Barski et al., 2007; Mikkelsen et al., 2007; Vakoc et al., 2006) RNA PolII (H-224 from Santa-Cruz and Ab5408 from Abcam), H3 (Ab1791 from Abcam) and AcH4 (from Cell Signaling).
Genomic distribution of binding sites identified by ChIP-chip was performed using cis-regulatory element annotation system (CEAS) (Ji et al., 2006). Two binding sites were considered to overlap as long as they had one base pair in common. The average size of the ChIP-chip regions being 1 kb, this means that the center of the two binding sites had to be in average within 1 kb of each other to be considered overlapping.
Known DNA motifs that are enriched relative to the center of ChIP-chip sites were identified using the following statistic. All sites were trimmed or expanded to 600 bps centered at the middle point of the identified ChIP-enriched regions. All sub-sequences within the trimmed regions were scored by a TRANSFAC motif (Matys et al., 2006) and the genomic background sequence composition to identify hits above certain relative entropy cutoff t. Letting xi, a value between 0 and 1, denote the relative location of motif hit i on the ChIP-regions (0 and 1 representing the center and edge of a ChIP-region, respectively) out of N total motif hits, we define a z-score, to assess the positional bias of a motif towards the center of the regions. Different integer cutoffs t ≥ 3 were tested for each motif, and the cutoff resulting in the highest z was selected. This statistic is based on the assumptions that insignificant DNA motifs will be uniformly distributed across the ChIP-regions and the null distribution of Σxi can be estimated as the N-fold convolution of uniform density functions. In figure 3A, a Gaussian kernel was used to smooth the curves in case too few motif hits appear at particular positions.
Gene expression data was normalized and summarized using RMA (Irizarry et al., 2003) and updated RefSeq probeset definitions (Dai et al., 2005). Where multiple transcripts were associated with a single gene expression index the transcript with the transcription start site closest to a ChIP-enriched region was selected. “Differentially expressed” genes were denoted as those genes with a t-test p-value ≤ 10-3. Genes “close” to a ChIP region were defined as those having such a region within 20 kb of the transcription start site. Fisher's exact test was used to assess the statistical significance of the association between “close” genes and “differentially expressed” genes.
De novo motif searches were performed on sequences ±100 bps from the center of FoxA1 ChIP regions in MCF7 cells or LNCaP cells by using LeitMotif (Song, J. and X.S.L. unpublished results), a modified MDscan (Liu et al., 2002) with ninth-order Markov dependency for the genome background. Motif logos were generated by enoLOGOS (Workman et al., 2005).
FoxA1 was silenced using the following small interfering RNA duplexes: siFoxA1 #1 sense 5′-GAGAGAAAAAAUCAACAGC-3′; antisense 5′-GCUGUUGAUUUUUUCUCUC-3′ (Carroll et al., 2005; Eeckhoute et al., 2006) and siFoxA1 #2 5′-GGACUUCAAGGCAUACGAAUU-3′; 5′-UUCGUAUGCCUUGAAGUCCUU-3′ (Fig.S17). SMARTpool siRNA directed against ERα was purchased from Dharmacon. Small interfering RNA against Luciferase was used as a negative control (Carroll et al., 2005).
DNase I hypersensitivity assays were performed as in (Eeckhoute et al., 2006).
A total of 15mg of pCMX-KDM1 construct or the control empty vector were transfected in MCF7 cells using lipofectamine 2000 (Invitrogen) according to the manufacturers instructions. Following 76 hours of expression, cells were processed for ChIP-qPCR as previously described.
RNA was isolated from MCF7 and LNCaP cells using RNeasy mini kit (Qiagen), with on-column DNase treatment to remove contaminating genomic DNA. Real-time reverse transcription-PCR (RT-PCR) was done as in (Keeton and Brown, 2005). Primers used in RT-qPCR are listed in Supplementary Table 2.
Western blots were processed as described in (Lupien et al., 2007) using antibodies against KDM1 kindly provided by R. Schule (Universitäts-Frauenklinik und Zentrum für Klinische Forschung, Freiburg, Germany), FoxA1 (Abcam) and Calnexin (Stressgen Biotechnologies).
Supplement Data include twenty one figures, two tables and Supplemental References and can be found with this article online at www.cell.com
We thank Dr. Roland Schüle (Universitäts-Frauenklinik und Zentrum für Klinische Forschung, Freiburg, Germany) for pCMX-Flag-KDM1 as well as KDM1 antibodies. We also thank Drs. Mark A. Rubin (Dana Farber Cancer Institute, Massachusetts, USA) and Andrea Sboner (Yale University, Connecticut, USA) for help in analyzing the primary prostate tumor expression datasets. We acknowledge Dr. Shannon T. Bailey for his help with the validation of the siRNA targeting ERα. This work was supported by grants from the NIDDK (R01DK074967 to M.B.), the NCI (P01 CA8011105 and the DF/HCC Breast Cancer SPORE Grant to M.B.), and the DFCI Women's Cancers Program. This study was designed by J.E., M.L., C.A.M, X.S.L and M.B. The experimental procedures were primarily carried out by M.L. and J.E. with assistance from J.S.C. The AR cistrome was provided by Q.W. Biostatistical and computational support and data analysis was provided primarily by C.A.M. with the assistance of Y.Z. and W.L.
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