Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3
- Seth Frietze†1,
- Rui Wang†2, 3,
- Lijing Yao1,
- Yu Gyoung Tak1,
- Zhenqing Ye3,
- Malaina Gaddis1,
- Heather Witt1,
- Peggy J Farnham1Email author and
- Victor X Jin3Email author
© Frietze et al.; licensee BioMed Central Ltd. 2012
Received: 9 March 2012
Accepted: 25 May 2012
Published: 5 September 2012
The TCF7L2 transcription factor is linked to a variety of human diseases, including type 2 diabetes and cancer. One mechanism by which TCF7L2 could influence expression of genes involved in diverse diseases is by binding to distinct regulatory regions in different tissues. To test this hypothesis, we performed ChIP-seq for TCF7L2 in six human cell lines.
We identified 116,000 non-redundant TCF7L2 binding sites, with only 1,864 sites common to the six cell lines. Using ChIP-seq, we showed that many genomic regions that are marked by both H3K4me1 and H3K27Ac are also bound by TCF7L2, suggesting that TCF7L2 plays a critical role in enhancer activity. Bioinformatic analysis of the cell type-specific TCF7L2 binding sites revealed enrichment for multiple transcription factors, including HNF4alpha and FOXA2 motifs in HepG2 cells and the GATA3 motif in MCF7 cells. ChIP-seq analysis revealed that TCF7L2 co-localizes with HNF4alpha and FOXA2 in HepG2 cells and with GATA3 in MCF7 cells. Interestingly, in MCF7 cells the TCF7L2 motif is enriched in most TCF7L2 sites but is not enriched in the sites bound by both GATA3 and TCF7L2. This analysis suggested that GATA3 might tether TCF7L2 to the genome at these sites. To test this hypothesis, we depleted GATA3 in MCF7 cells and showed that TCF7L2 binding was lost at a subset of sites. RNA-seq analysis suggested that TCF7L2 represses transcription when tethered to the genome via GATA3.
Our studies demonstrate a novel relationship between GATA3 and TCF7L2, and reveal important insights into TCF7L2-mediated gene regulation.
The TCF7L2 (transcription factor 7-like 2) gene encodes a high mobility group box-containing transcription factor that is highly up-regulated in several types of human cancer, such as colon, liver, breast, and pancreatic cancer [1–4]. Although TCF7L2 is sometimes called TCF4, there is a helix-loop-helix transcription factor that has been given the official gene name of TCF4 and it is important, therefore, to be aware of possible confusion in the literature. Numerous studies have shown that TCF7L2 is an important component of the WNT pathway [3, 5, 6]. TCF7L2 mediates the downstream effects of WNT signaling via its interaction with CTNNB1 (beta-catenin) and it can function as an activator or a repressor, depending on the availability of CTNNB1 in the nucleus. For example, TCF7L2 can associate with the members of the Groucho repressor family in the absence of CTNNB1. The WNT pathway is often constitutively activated in cancers, leading to increased levels of nuclear CTNNB1 and up-regulation of TCF7L2 target genes . In addition to being linked to neoplastic transformation, variants in TCF7L2 are thought to be the most critical risk factors for type 2 diabetes [7–10]. However, the functional role of TCF7L2 in these diseases remains unclear. One hypothesis is that TCF7L2 regulates its downstream target genes in a tissue-specific manner, with a different cohort of target genes being turned on or off by TCF7L2 in each cell type. One way to test this hypothesis is to identify TCF7L2 target genes in a diverse set of cell types.
Previous studies have used genome-wide approaches to identify TCF7L2 target genes in human colon cancer cells [11, 12] and, more recently, chromatin immunoprecipitation sequencing (ChIP-seq) analysis of TCF7L2 was reported in hematopoietic cells . In addition, TCF7L2 binding has been studied in rat islets and rat hepatocytes [14, 15]. However, to date no one study has performed comparative analyses of genome-wide binding patterns of TCF7L2 in diverse human cell types. We have now conducted ChIP-seq experiments and comprehensively mapped TCF7L2 binding loci in six human cell lines. We identified datasets of common and cell-specific TCF7L2 binding loci and a set of predicted TCF7L2-regulated enhancers (by comparing the TCF7L2 peak locations with ChIP-seq data for the active enhancer marks H3K4me1 (histone H3 monomethylated on lysine 4) and H3K27Ac (histone H3 acetylated on lysine 27)). We also predicted bioinformatically and confirmed experimentally that TCF7L2 co-localizes with cell type-specific factors. Finally, we showed that GATA3 (GATA binding protein 3), which co-localizes with TCF7L2 in MCF7 breast cancer cells, is required for recruitment of TCF7L2 to a subset of binding sites. Our studies reveal new insights into TCF7L2-mediated gene regulation and suggest that cooperation with other factors dictates different roles for TCF7L2 in different tissues.
Defining TCF7L2 genomic binding patterns
TCF7L2 binding sites and target genes
Peaks per gene
To determine the potential set of genes regulated by TCF7L2 in each cell type, we identified the closest annotated gene to each TCF7L2 binding site in the six different cell types and the closest annotated gene to the set of 1,864 common TCF7L2 binding sites. The number of target genes (as defined by the nearest gene to a TCF7L2 binding site) ranged from approximately 6,000 to 11,000 in the different cell lines (Table 1). In addition, we also observed that the number of target genes in each cell line was considerably less than the number of TCF7L2 binding sites, demonstrating that TC7L2 binds to multiple locations near each target gene (Table 1). Although less than 2% (1,864 of 116,270 peaks) of the total number of peaks were commonly bound by TCF7L2 in all 6 cell lines, 9% of target genes were common to all 6 cell lines (1,287 of 14,193 genes). This indicates that TCF7L2 regulates certain genes in different cell types using different binding sites. For example, there are 12 TCF7L2 binding sites near the SH3BP4 gene, but these sites are different in MCF7, HCT116, and PANC1 cells (Figure 2c).
TCF7L2 binds to enhancer regions
TCF7L2 binds to enhancer regions
Percentage of TCF7L2 peaks at H3K4me1 sites
Percentage of TCF7L2 peaks at H3K27Ac sites
Percentage of TCF7L2 peaks at active enhancers (H3K4me1 and H3K27Ac)
To further investigate the role of TCF7L2 in cell type-specific enhancers, we determined the percentage of active enhancers in each of the six cell types (that is, genomic regions bound by both H3K4me1 and H3K27Ac) that are also bound by TCF7L2. We found that more than 40% of all enhancers in the different cell lines are occupied by TCF7L2 (Figure 3b). These results indicate that TCF7L2 ChIP-seq data identify many of the active enhancers in a given cell type and suggest that TCF7L2 may play a critical role in specifying the transcriptome in a variety of cancer cells. An example of TCF7L2 binding to sites marked by H3K4me1 and H3K27Ac in HepG2 cells is shown in Additional file 12; TCF7L2 does not bind to this same site in HeLa cells and these sites are also not marked by the modified histones in HeLa cells.
Motif analysis of genomic regions bound by TCF7L2
To investigate the predominant motifs enriched in TCF7L2 binding sites, we applied a de novo motif discovery program, ChIPMotifs [28, 29], to the sets of TCF7L2 peaks in each cell type. We retrieved 300 bp for each loci from the top 1,000 binding sites in each set of TCF7L2 peaks and identified the top represented 6-mer and 8-mer (Additional file 13). For all cell lines, the same 6-mer (CTTTGA) and 8-mer (CTTTGATC) motif was identified (except for HCT116 cells, for which the 8-mer was CCTTTGAT). These sites are almost identical to the Transfac binding motifs for TCF7L2 (TCF4-Q5:SCTTTGAW) and for the highly related family member LEF1 (LEF1-Q2:CTTTGA) and to experimentally discovered motifs in previous TCF7L2 ChIP-chip and ChIP-seq data [11, 30]. These motifs are present in a large percentage of the TCF7L2 binding sites. For example, more than 80% of the top 1,000 peaks in each dataset from each cell type contain the core TCF7L2 6-mer W1 motif, with the percentage gradually dropping to approximately 50% of all peaks (Additional file 14).
TCF7L2 cell type-specific modules
Top four co-localized motifs
AP1, CTCF, NF-E2, SP1
HOXC9, CDX2, PDX1, FOXA1
HNF4α, FOXA2, ERRα, PPARγ
ERG, MAZ, CEBP, NF-E2
GATA3, AP2, TEAD, AP1
CDX2, FOXA1, TEAD, RUNX2
TCF7L2 co-localizes with HNF4α and FOXA2 in HepG2 cells
GATA3 is required for TCF7L2 recruitment to a subset of sites in MCF7 cells
TCF7L2 functions as a repressor when tethered to the genome by GATA3
Genes repressed by TCF7L2 via a GATA motif
Cooperative repression by TCF7L2 and GATA3
TCF7L2 antagonizes GATA3-mediated activation
The TCF7L2 transcription factor has been linked to a variety of human diseases such as type 2 diabetes and cancer [3, 7–9, 35]. To investigate the mechanisms by which this site-specific DNA binding transcriptional regulator can impact on such diverse diseases, we performed ChIP-seq analysis for TCF7L2 in 6 different human cell lines, identifying more than 116,000 non-redundant binding sites, with only 1,864 sites being common to all 6 cell types. Several striking discoveries that came from our ChIP-seq analysis of the 6 different cell lines are: i) TCF7L2 has multiple binding sites near each target gene; ii) TCF7L2 has developed cell type-specific mechanisms for regulating a set of approximately 14,000 genes; iii) TCF7L2 binds to more than 40% of the active enhancers in each of the 6 cancer cell lines; and iv) TCF7L2 functions as repressor when recruited to the genome via tethering by the master regulator GATA3.
By analysis of the TCF7L2 ChIP-seq datasets from 6 different human cancer cell lines, we identified 116,270 TCF7L2 binding sites, with each cell type having approximately 25,000 to 50,000 TCF7L2 peaks. We note that another group has examined TCF7L2 binding in human HCT116 cells , identifying only 1,095 binding sites. It is not clear why Zhao and colleagues  identified such smaller numbers of TCF7L2 binding sites in HCT116 cells, but it is not likely due to the antibody specificity (the antibodies used in both studies give similar patterns on western blots). It is more likely that the 30-fold difference in peak number is due to the ChIP protocol. Zhao et al.  used protein A agarose beads, whereas we used magnetic protein A/G beads; we have found that protein A agarose beads produce low signals in many ChIP assays (unpublished data). Interestingly, the 116,270 TCF7L2 binding sites that we identified correspond to only 14,193 genes, with each target gene having an average of 8.2 TCF7L2 binding sites. Many of these binding sites are cell type-specific, as exemplified by the fact that there are only three to four TCF7L2 binding sites per target gene in any one cell type (Figure 2c).
Cell type-specific binding patterns suggest that TCF7L2 binds cooperatively to the genome along with cell type-specific factors. For example, the AP1 (activator protein 1) motif is enriched in the sets of HCT116-specific and MCF7-specific TCF7L2 binding sites. Interestingly, TCF7L2 has previously been shown to physically interact with JUN (which is one of the heterodimeric components of AP1) and it has been suggested that the JUN and TCF7L2 interaction is a molecular mechanism that integrates the activation of the TCF and CTNNB1 pathway by the JNK (Jun N-terminal kinase) pathway . Although ChIP-seq data for AP1 components is not available for HCT116 or MCF7 cells, there are 7,400 genomic locations that are bound by TCF7L2 in HCT116 cells that are also bound by JUN in HeLa cells ; it is likely that a much larger number of co-localizing regions would be identified if the datasets were from the same cell type. Our detailed bioinformatic analysis of the HepG2-specific TCF7L2 peaks suggested that HNF4α and FOXA2 might be binding partners of TCF7L2 in this cell type. A previous study had shown that FOXA2 and HNF4α colocalize at a subset of sites in mouse liver , but that study did not examine the relationship of these sites with TCF7L2 binding. Therefore, we experimentally validated our bioinformatic prediction by comparing ChIP-seq data for all three factors. We found that greater than 50% of the TCF7L2 HepG2-specific binding sites are also bound by the liver transcription factors HNF4α and FOXA2, suggesting that this trio of factors cooperate in gene regulation. Based on the identification of motifs for all three factors in the TCF7L2 peaks, we suggest that TCF7L2, HNF4α, and FOXA2 all bind directly to the DNA, perhaps with the liver-specific factors helping to stabilize TCF7L2 genomic binding to particular enhancer regions in HepG2 cells. HNF4α and FOXA2 have been shown to be critical determinants of hepatocyte identity; Hnf4α plus Foxa1, Foxa2, or Foxa3 can convert mouse embryonic and adult fibroblasts into cells that closely resemble hepatocytes in vitro . The induced hepatocyte-like cells had multiple hepatocyte-specific features and reconstituted damaged hepatic tissues after transplantation. Future studies should address a potential role of TCF7L2 in hepatocyte identity.
Specification of cell phenotypes is achieved by sets of master transcriptional regulators that activate the genes specific for one cell fate while repressing genes that specify other cell fates. The GATA factors, which include six site-specific DNA binding proteins that bind to the sequence (A/T)GATA(A/G), are master regulators that govern cell differentiation [39–44]. For example, GATA1-3 have been linked to the specification of different hematopoietic cell fates and GATA4-6 are involved in differentiation of cardiac and lung tissues. Also, GATA3 is the most highly enriched transcription factor in the mammary epithelium, has been shown to be necessary for mammary cell differentiation, and is specifically required to maintain the luminal cell fate [43, 44]. Studies of human breast cancers have shown that GATA3 is expressed in early stage, well-differentiated tumors but not in advanced invasive cancers. In addition, GATA3 expression is correlated with longer disease-free survival and evidence suggests that it can prevent or reverse the epithelial to mesenchymal transition that is characteristic of cancer metastasis . Our studies show that TCF7L2 cooperates with the master regulator GATA3 to repress transcription in the well-differentiated MCF7 breast cancer cell line and suggest that a TCF7L2-GATA3 complex may be a critical regulator of breast cell differentiation.
Our finding that TCF7L2 co-localizes and cooperates in gene regulation with a GATA factor in MCF7 breast cancer cells is similar to a recent study of TCF7L2 in hematopoietic cells. Trompouki et al.  showed that in hematopoietic cells, TCF7L2 co-occupies sites with GATA1 and GATA2, which are master regulators of blood cell differentiation. Both the TCF7L2 motif and the GATA motif were found at the co-bound sites (suggesting adjacent binding of the two factors, not tethering) and TCF7L2 functioned as a transcriptional activator at those sites. In contrast, our studies indicate that co-localization of TCF7L2 with GATA3 in MCF7 cells is not due to adjacent binding but rather TCF7L2 is tethered to the genome by interaction with GATA3 binding to a GATA motif and that this tethering results in transcriptional repression. A study of Drosophila TCF binding to the Ugt36Bc upstream region indicated that TCF represses transcription of the Ugt36Bc gene by binding to non-traditional TCF motifs . Interestingly, the three Ugt36Bc TCF sites (AGAAAT, AGATAA, AGATAA) are almost identical to the GATA3 motif. Blauwkamp et al.  suggest that the sequence to which TCF binds has an important function in determining whether a gene will be activated or repressed. Their studies did not address whether TCF bound directly to the GATA-like motifs. However, based on our studies, it would be worthwhile to investigate a possible genomic tethering mechanism of TCF by GATA factors in Drosophila.
Our studies reveal numerous new insights into TCF7L2-mediated gene regulation and suggest that TCF7L2 cooperates with other site-specific DNA binding factors to regulate transcription in a cell type-specific manner. Specifically, we show that TCF7L2 has highly cell type-specific binding patterns, co-localizes with different factors in different cell types, and can be tethered to the DNA by GATA3 in breast cancer cells. Our work, in combination with other studies [13, 47], suggests that TCF7L2 may play a critical role in creating and maintaining differentiated phenotypes by cooperating with cell type-specific master regulators such as HNF4α and FOXA2 in liver cells and GATA3 in breast cells. Both FOXA and GATA family members have been classified as pioneer factors, that is, transcription factors that can access their binding sites when other factors cannot, helping to create open chromatin to enable subsequent binding of other factors . It is possible that FOXA2 and GATA3 serve as pioneer factors that enhance the ability of TCF7L2 to access its sites in liver and breast cells. In addition to having cell type-specific partners, there are many different isoforms of TCF7L2. Although the major isoforms of TCF7L2 are similar in most cell types, it is possible that minor isoforms contribute to the cell type-specificity of TCF7L2 binding via interaction of co-localizing proteins with alternatively encoded exons of TCF7L2. We anticipate that future studies employing isoform-specific antibodies to identify TCF7L2 binding sites in normal and diseased tissues will provide additional insight into the transcriptional networks that are altered in diseases such as type 2 diabetes, pancreatic cancer, and coronary artery disease.
Materials and methods
The human cell lines HCT116 (ATCC #CCL-247), HepG2 (ATCC # HB-8065), HEK293 (ATCC #CRL-1573), MCF7 (ATCC #HTB-22), HeLa (ATCC #CCL-2.) and PANC1 (ATCC #CRL-1469) were obtained from the American Type Culture Collection. HCT116 cells were grown in McCoy's 5A Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin until 80% confluent, whereas HepG2, HEK293, MCF7, HeLa and PANC1 cells were grown in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 2 mM L-glutamine and 1% penicillin/streptomycin) until 75 to 90% confluent.
All siRNAs were purchased from Dharmacon (Thermo Fisher Scientific-Dharmacon Products, Lafayette, CO, USA; ON-TARGET plus SMART pool - Human GATA3, TCF7L2 and Non-Targeting siRNA) and transfected using Lipofectamine™ 2000 Transfection Reagent according to the manufacturer's instructions (Life Technologies, Grand Island, NY, USA). Then, 48 to 56 h following transfection, cells were either crosslinked for ChIP assays or collected for RNA and protein extraction.
The antibodies used for ChIP-seq were: TCF7L2 (Cell Signaling Technology, Danvers, MA, USA; C48H11 #2569), GATA3 (Santa Cruz Biotechnology, Santa Cruz, CA, USA; #sc-268), H3K4me1 (Cell Signaling Technology, Danvers, MA, USA; 9723S lot1), and H3K27Ac (Abcam, Cambridge, MA, USA; Ab4729 lot #GR16377-1). The TCF7L2 antibody will detect both major isoforms of TCF7L2. See Additional file 2 for details of all ChIP-seq experiments. For all factor or histone modification and cell type combination, we performed duplicate ChIP-seq experiments using chromatin from two different cell culture dates. For the TCF7L2 ChIP-seq assays, 500 μg chromatin was incubated with 25 μg of antibody; for the GATA3 experiments, 600 μg chromatin was incubated with 50 μg of antibody; and for the histone ChIP-seq experiments, 10 to 12 μg chromatin and 8 to 10 μg of antibody were used. TCF7L2 and histone ChIP assays were performed as described previously  using protein A/G magnetic beads to collect the immunoprecipitates. GATA3 ChIP-seq experiments were performed using StaphA (Sigma-Aldrich, St. Louis, MO, USA) to collect the immunoprecipitates . After qPCR confirmed enrichment of target sequences in ChIP versus input samples, libraries were created as previously described with minor modifications . Gel size selection of the 200 to 500 bp fraction (TCF7L2 and histones) or the 300 to 600 bp fraction (GATA3) was conducted after the adapter ligation step, followed by 15 amplification cycles. qPCR (see Additional file 17 for a list of primers used in this study) was performed to confirm enrichment of targets in the libraries and then the libraries were analyzed using an Illumina GAIIx. Sequence reads were aligned to the UCSC human genome assembly HG19 using the Eland pipeline (Illumina).
ChIP-seq data processing
The BELT program  and Sole-search [11, 51] were used to identify peaks for TCF7L2 and for modified histones. We used the ENCODE overlap rules to evaluate the reproducibility of the two biological replicates for each factor or histone modification and cell-type combination. For this, we first truncated the peak lists of the two replicates for a given factor/cell-type combination so that both the A and B replicate peak list were the same length. Then, we overlapped the top 40% of the replicate A peak list with the entire replicate B peak list (and vice versa). ENCODE standards state that approximately 80% of the top 40% set should be contained in the larger set. After determining that replicate datasets met this standard (Additional file 4), we merged the two replicates and called peaks on the merged dataset. To determine if we had identified the majority of the TCF7L2 peaks in each cell type, we performed a saturation analysis. We randomly selected different percentages of the reads (10%, 20%, 30%,...,100%) from the merged datasets from the TCF7L2 ChIP-seq experiments for each cell line and called peaks using the BELT program; each merged dataset was analyzed three times. The number of peaks identified in each subset of the total reads was plotted to demonstrate that we had enough reads for each dataset to identify the majority of peaks (Additional file 7).
RNA was extracted using Trizol Reagent (Life Technologies) following the suggested protocol; 2 μg of each RNA sample was used with the Illumina TruSeq RNA Sample Prep Kit (catalogue number RS-122-2001) to make RNA libraries following the Illumina TruSeq RNA Sample preparation Low-Throughput protocol. Briefly, RNA was fragmented, then first-strand cDNA was prepared using the kit-supplied 1st Strand Master Mix and user-supplied Superscript III (Life Technologies, catalogue number 18080-051) followed by second strand cDNA synthesis. The Illumina protocol and reagents were used to complete the library preparation, with 12 cycles of PCR amplification. Libraries were sequenced using an Illumina GAIIx and analyzed as described in Additional file 3.
ChIP assays were performed as described in the ChIP-seq section, except that 30 μg equivalents of DNA was used for each ChIP reaction. The ChIP eluates were analyzed by qPCR using the Bio-Rad SsoFast™ EvaGreen® Supermix (catalogue number 172-5202) according to the manufacturer's instructions (Bio-Rad, Hercules, CA, USA).
Generation of TCF7L2 expression constructs and co-immunoprecipitation assays
TCF7L2 expression constructs were generated by PCR amplification of cDNA prepared from RNA isolated from MCF7 cell cultures and used for GATEWAY cloning into the pTRED-N-FLAG expression vector, which contains an amino-terminal FLAG tag. Control empty vector or an expression construct was transfected into MCF7 cells using Lipofectamine™ 2000 according to the manufacturer's instructions (Life Technologies); 36 h following transfection, cells were harvested and lysed in ice-cold NP-40 lysis buffer (phosphate-buffered saline, 0.25% NP-40, 0.1% sodium-deoxycholate, 2 mM phenylmethylsulfonyl fluoride (PMSF) and 10 μg/ml leupeptin and aprotinin) for co-immunoprecipitation assays. Following extraction on ice for 30 minutes and clarification by centrifugation, soluble protein extracts were diluted 1:10 with lysis buffer and incubated with either an anti-FLAG M2 agarose conjugated antibody (Sigma catalogue number A2220), an anti-GATA3 conjugated antibody (Santa Cruz HG3-31-AC), or a control rabbit IgG agarose conjugated antibody (Sigma catalogue number A2909) for 4 hours at 4°C. The beads were then washed four times and eluted with SDS-PAGE sample buffer prior to SDS-PAGE and western blot analysis using antibodies specific for GATA3 (Santa Cruz HG3-31) or FLAG (Sigma catalogue number A8592).
All data are publicly available via the UCSC Genome Preview Browser and/or has been submitted to the Gene Expression Omnibus (information concerning how to access the data is provided in Additional file 2).
activator protein 1
catenin beta 1
GATA binding protein
histone H3 acetylated on lysine 27
histone H3 monomethylated on lysine 4
hepatocyte nuclear factor
polymerase chain reaction
small interfering RNA
transcription factor 7-like 2.
The work was supported in part by 1U54HG004558 as a component of the ENCODE Project and by P30CA014089 from the National Cancer Institute. The HepG2 and HeLa H3K4me1 and H3K27Ac ChIP-seq data were generated at the Broad Institute and in the Brad Bernstein lab at the Massachusetts General Hospital/Harvard Medical School; the HNF4α and FOXA2 ChIP-seq data were produced in the lab of Rick Myers at the HudsonAlpha Institute for Biotechnology; the MCF7 H3K4me3 and RNA polymerase II ChIP-seq data were generated by the Iyer lab at UT-Austin. For these ChIP-seq datasets, data generation and analysis was supported, in part, by funds from the NHGRI as part of the ENCODE project. We thank Drs Bernstein, Myers, and Iyer and the Data Coordination Center at UCSC for providing access to these data. All other ChIP-seq data and the RNA-seq data from control and siRNA-treated MCF7 cells were generated by the Farnham lab at the University of Southern California; libraries were sequenced at the USC Epigenome Data Production Facility and at Stanford University.
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