An integrated genome screen identifies the Wnt signaling pathway as a major target of WT1

  1. Marianne K.-H. Kima,b,
  2. Thomas J. McGarryc,
  3. Pilib Ó Broind,
  4. Jared M. Flatowb,
  5. Aaron A.-J. Goldend and
  6. Jonathan D. Lichta,b,1
  1. aDivision of Hematology/Oncology and
  2. cFeinberg Cardiovascular Research Institute, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611;
  3. bRobert H. Lurie Cancer Center, Northwestern University, Chicago, IL 60611; and
  4. dDepartment of Information Technology, National University of Ireland, Galway, Republic of Ireland

  1. Edited by Peter K. Vogt, The Scripps Research Institute, La Jolla, CA, and approved May 18, 2009 (received for review February 12, 2009)

 
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Abstract

WT1, a critical regulator of kidney development, is a tumor suppressor for nephroblastoma but in some contexts functions as an oncogene. A limited number of direct transcriptional targets of WT1 have been identified to explain its complex roles in tumorigenesis and organogenesis. In this study we performed genome-wide screening for direct WT1 targets, using a combination of ChIP–ChIP and expression arrays. Promoter regions bound by WT1 were highly G-rich and resembled the sites for a number of other widely expressed transcription factors such as SP1, MAZ, and ZNF219. Genes directly regulated by WT1 were implicated in MAPK signaling, axon guidance, and Wnt pathways. Among directly bound and regulated genes by WT1, nine were identified in the Wnt signaling pathway, suggesting that WT1 modulates a subset of Wnt components and responsive genes by direct binding. To prove the biological importance of the interplay between WT1 and Wnt signaling, we showed that WT1 blocked the ability of Wnt8 to induce a secondary body axis during Xenopus embryonic development. WT1 inhibited TCF-mediated transcription activated by Wnt ligand, wild type and mutant, stabilized β-catenin by preventing TCF4 loading onto a promoter. This was neither due to direct binding of WT1 to the TCF binding site nor to interaction between WT1 and TCF4, but by competition of WT1 and TCF4 for CBP. WT1 interference with Wnt signaling represents an important mode of its action relevant to the suppression of tumor growth and guidance of development.

The genetic etiology of Wilms tumorigenesis is heterogeneous including loss-of-imprinting of IGF2 (1), deletions and mutations of WT1 (reviewed in ref. 2) and the recently identified WTX (3) genes. In addition, somatic mutations in β-catenin leading to a stabilized protein are found in 15% of cases and curiously almost all of these mutation cases are found in patients lacking functional WT1 alleles (4–7).

The WT1 tumor suppressor gene encodes a zinc finger transcription factor, possibly yielding up to 32 different isoforms (8). The major isoforms differ in the presence or absence of amino acids KTS in the zinc finger region and the presence or absence of a 17-aa stretch in the middle of the protein. The −KTS isoforms have been linked to DNA binding-mediated transcriptional control whereas the +KTS isoforms have been implicated in RNA processing as well (9). Renal agenesis in the Wt1 knockout mouse and the presence of constitutional mutations in WT1 in a number of renal developmental syndromes indicate a critical role for WT1 in kidney development. WT1 is also involved in the development of other organs including spleen, heart, and liver (10–12). In cell culture systems WT1 generally acts as a growth suppressor (13–18) but its overexpression in a number of tumor types such as colon and thyroid (19, 20) suggests that it may act as an oncogene as well. Accordingly knockdown of WT1 led to decreased growth of a Wilms tumor cell line suggesting a permissive role in growth in some cases of Wilms tumor (21).

WT1 activates or represses gene transcription depending on the cellular, developmental and promoter context. WT1 targets have been sought to explain its function in development and growth control. A first generation of in vitro cotransfection and DNA binding assays have given way to more biologically relevant experiments where manipulation of WT1 levels has been accompanied by examination of candidate or global gene expression. Classes of WT1 targets thus identified consist of inducers of differentiation, regulators of cell growth and modulators of cell death. WT1 target genes include CDKN1A (22), a negative regulator of the cell cycle; AREG (23), a facilitator of kidney differentiation; WNT4 (24), a stimulator of renal development; and SPRY1 (25), a critical intracellular regulator of receptor tyrosine kinase signaling and kidney development. Examination of gene expression in WT1 null and WT1 replete Wilms tumor specimens identified a gene (IFI16) not normally regulated or even expressed in the same subcellular compartment as WT1 but regulated in a pathological manner in tumors (21).

To comprehensively identify WT1 target genes we manipulated WT1 levels in a Wilms tumor cell line by conditional overexpression and shRNA-mediated knockdown. Using gene expression profiling and chromatin immunoprecipitation followed by microarray analysis (ChIP–ChIP) we identified genes bound and regulated by WT1. WT1 targets included genes of the Wnt pathway, which is required for normal renal development (26) and is deregulated in Wilms tumors (5). WT1 inhibited Wnt function during Xenopus development and interfered with Wnt-mediated transcription through the CREB binding protein (CBP) cofactor. Collectively these data suggest that one critical role of WT1 in development and tumorigenesis is to modulate the Wnt signaling pathway.

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Results

Identification of Direct Targets of WT1 by ChIP in Combination with Promoter Microarray Analysis.

ChIP–ChIP was initially performed in Wilms tumor-derived CCG99–11 cells, which express low levels of wild-type WT1 protein. The resulting hybridization signals were low and only 8 promoter regions were identified on all 3 NimbleGen HG18 RefSeq arrays and 57 promoters by 2/3 arrays (Fig. S1A). Previously identified direct targets of WT1 such as IFI16 (21) and MKP3 (13) were not scored by this experiment. To increase the sensitivity of the ChIP–ChIP assay we used CCG-5.1 cells engineered to stably express additional WT1-A in an inducible manner (Fig. S1B). In contrast to the apoptotic phenotype we (27) and others (16) observed upon WT1 induction in Saos2 osteosarcoma cells, further induction of WT1 in CCG-5.1 did not yield apoptosis or cell cycle arrest. ChIP–ChIP analysis of CCG-5.1 cells identified 643 promoters found by 3/3 arrays and 2415 promoters found in 2/3 arrays (Fig. 1A). Hence the higher signal generated by WT1 overexpression identified many more putative WT1 targets but could obscure occasionally, because the enhanced noise caused by WT1 induction led the peak detection program to miss several peaks in this analysis (Fig. S1C). However, some peaks identified in CCG99–11 were absent without any increase in baseline hybridization to other probe sets of the promoter. This could represent redistribution of WT1 among genomic sites upon high-level expression. All 16 tested genomic regions identified by ChIP–ChIP were validated by ChIP-PCR (Fig. S1D). Consistent with the ChIP–ChIP data, peaks in the RHOH and SH3BP5L promoters identified at endogenous levels of WT1 showed significant enrichment without WT1 induction by quantitative PCR. Nevertheless, many genes were identified at both high and low levels of WT1 expression, which suggested that induction of WT1 could be used to identify target genes relevant to Wilms tumors. This was supported by gene ontological analysis of the 2415 genes bound by WT1 from CCG5.1-ChIP–ChIP set. WT1-bound targets were enriched in MAPK signaling, focal adhesion, regulation of actin cytoskeleton and Wnt signaling pathways (Table 1).

Fig. 1.
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Fig. 1.

Identification of WT1-bound promoter regions and motif analysis. (A) Genes identified by ChIP–ChIP at WT1-induced level in CCG-5.1 are shown in Venn diagrams. (B) A total of 199 bound and differentially regulated genes by WT1 (shown in bold) were identified in the comparison from the LOF (by shRNA) and GOF (CCG-5.1) expression array sets and ChIP–ChIP. (C) The representative motif for each cluster was generated using the STAMP platform. The SOMBRERO motifs were created by clustering the original redundant set of 68 motifs produced by the algorithm.

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Table 1.

Functional classification of WT1 targets identified from ChIP-ChiP (2,415 gene symbols)

Motif Analysis on WT1-Bound Regions.

To determine the common motifs among DNA sequences bound by WT1 in vivo, we analyzed the 643 promoters identified after WT1 induction in all 3 ChIP–ChIP arrays (Fig. 1A), using 3 de novo motif prediction tools: AlignACE (28), MEME (29) and SOMBRERO (30). The 627,000 bp from the WT1-bound regions were first filtered using SOMBRERO to increase the signal to noise ratio. All three tools identified highly G-rich sequences in 628 of 643 promoters. When compared against known binding sites in the Transfac (31) and Jaspar (32) databases, the consensus motifs also resembled sites for the SP1, MAZ, and ZNF219 transcription factors. The MatInspector tool (33) revealed 340 overlapping WT1/SP1 sites, 186 overlapping WT1/EGR1 sites and 149 sites where all three factors may compete for binding (examples shown in Fig. S2). There were a total of 675 overlapping sites spread over 437 of the 643 promoters. This information suggests that WT1 target genes may have a complex mode of regulation that depends on the presence and/or activity of WT1, and other transcription factors. Furthermore, among previously identified in vitro WT1 motifs, EGR1 site was more common than others such as WRE and WTE sites (Table S1).

Gene Expression Profiles in Gain-of-Function and Loss-of-Function Systems.

To identify genes differentially regulated by WT1, we performed microarray analysis in both gain-of-function (GOF) (Fig. S1B) and loss-of-function (LOF) (21) systems. In the initial screen for bound and regulated genes, we applied a low stringency cut-off (P < 0.005, ≥1.5-fold change). Microarray profiling showed that 455 genes were regulated in the biological triplicates. Genes activated in this system were enriched in axon guidance, whereas down-regulated genes were enriched in aminoacyl-tRNA synthetases and amino acid metabolism (Table S2). Knockdown of WT1 in the CCG99–11 cells results in apoptosis and cell cycle arrest. The WT1 shRNA targets all 4 major isoforms including WT1-A–D (21). Gene expression profiling was performed when the maximum knock down of WT1 protein was achieved. Many genes (1,129) were differentially regulated after WT1 depletion (P < 0.005, ≥1.5-fold change) in the biological triplicates. Functional classification revealed that up-regulated genes after WT1 knock-down were enriched in axon guidance, ATP synthesis, focal adhesion, and Wnt pathways, whereas down-regulated genes were enriched in cell cycle, pyrimidine metabolism, one carbon pool and amino acid metabolism pathways (Table S3). Furthermore, a total of 81 genes were regulated in both the GOF and LOF systems and of the 81 genes only 9 were identified by ChIP–ChIP, suggesting that the remaining 72 genes might be indirectly regulated by WT1 (Fig. 1B, Table S4). Collectively, these data show that WT1 manipulation affects only a small subset of targets directly at a given time and condition.

To prioritize WT1 targets, we compared genes differentially regulated by WT1 in the either the GOF or LOF systems with genes identified in at least 2/3 ChIP–ChIP arrays, identifying a total of 199 genes both bound and regulated upon manipulation of WT1 levels (Fig. 1B and Table S5). Gene ontological analysis showed that pathways for axon guidance, Wnt signaling, and MAPK signaling were significantly enriched in this gene set (Table 2). For example, ROBO2 (34), a regulator in branching morphogenesis of the kidney, plays a role in axon guidance. We previously showed that WT1 negatively regulates MAPK signaling through activation of SPRY1 (25) and DUSP6 (MKP3) (13), supporting the validity of our system and experimental approach. In the current experiments WT1 was recruited to the DUSP6 promoter but curiously was up-regulated in both the LOF and GOF systems. Thus, our comprehensive approach seemed successful in identifying genes that could be regulated by WT1 but did not necessarily indicate whether they were activated or repressed by WT1. We believe this is due to the G-rich nature of the WT1 binding site, which can also be recognized by other transcription factors such as SP1, MAZ, and EGR1.

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Table 2.

Gene ontology of the genes bound and differentially regulated by WT1

In this study, we further focused on the WT1's effect in the Wnt signaling pathway. The potential interplay between WT1 and Wnt signaling is significant in light of increasing evidence of activationing mutation of Wnt/β-catenin pathways in Wilms tumor, particularly in tumors without functional WT1 (4–7). This led us to hypothesize that WT1 might inhibit Wnt signaling. ChIP–ChIP and expression analysis identified 8 direct targets in the canonical Wnt pathway (Fig. 2) and PPP3CB in the noncanonical Wnt/Ca2+ pathway. Some but not all Wnt-related genes were regulated by WT1 in our systems in a manner indicating that WT1 opposed Wnt signaling. This may be because of cell context and because other transcription factors bind to the GC-rich WT1 motif.

Fig. 2.
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Fig. 2.

Direct targets of WT1 identified in the Wnt signaling pathway. Genes bound and regulated by WT1 are indicated in red bold. PPP3CB, a candidate WT1 direct target, is involved in noncanonical Wnt/Ca2+ pathway (not shown here). Activation and repression by WT1 are shown with up and down arrows, respectively.

WT1 Inhibits the Wnt Signaling in Xenopus Embryonic Development.

We sought to determine how WT1 might affect Wnt signaling in vivo. However, CCG-5.1 and HEK 293–5.1 showed no biological changes in properties such as proliferation or morphology upon Wnt3A-CM treatment, even in the absence of WT1. Therefore, we used the Xenopus system in which exogenous Wnt signals cause axis duplication and the ability of the WTX protein to antagonize the Wnt signaling was shown (3). Four-cell embryos were injected on the ventral side with a mixture of Xwnt8 with either GFP or Xwt1(−KTS) RNAs and WT1 expression was checked (Fig. S3B). Coinjection of Xwt1(−KTS) mRNA with Xwnt8 mRNA dramatically reduced the number of embryos that form a complete secondary axis and most embryos formed only a partial secondary axis, when compared with coinjection of GFP RNA with Xwnt8 as a control (Fig. 3, Fig. S3A). The ability of WT1 to inhibit Xwnt activity was similar in magnitude to that reported for WTX (See SI Text). Injection Xwt1 mRNA by itself had no affect on secondary axis formation. Xwt1(+KTS) also significantly inhibited axis induction by Xwnt8, but the effect was not as pronounced as with the −KTS isoform. Xwt1(−KTS) also inhibited secondary axis formation mediated by Xwnt3a.

Fig. 3.
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Fig. 3.

WT1 negatively affects Wnt signaling in Xenopus embryos. Xwt1 inhibits the secondary axis formation induced by Xwnt8. Xwnt8 (10 pg) was coinjected into the ventral side of 4-cell Xenopus embryos with 500, 2000 pg of GFP or Xwt1(−KTS) mRNA. Xwt1 (2,000 pg) alone was injected as a control. Injected embryos were scored blindly for secondary axis formation when they reached the swimming tadpole stage (Nieuwkoop stage 40). P values from Fisher's exact test (or ∗ from χ2 test) are shown.

WT1 Negatively Affect TCF-Mediated Transcriptional Activity.

WT1 may regulate Wnt signaling in part by binding and regulating genes encoding components and targets of the Wnt pathway. To determine whether other mechanism may be at work as well, we tested the effect of WT1 on TCF-mediated transcription, using an artificial TCF/LEF binding site containing reporter (TOPFLASH) and on the reporter of c-myc promoter, a natural activation target of Wnt signaling (35) and a repression target of WT1 in Wilms tumors (36), in HEK 293–5.1. Transcriptional activities from these promoters were induced by transient transfection of wild-type β-catenin, a hyperactive stabilized β-catenin S37A mutant or Wnt3a and these activities were suppressed ≈50% when WT1 was induced by doxycycline addition (Fig. 4A). Dose-dependent activation of the TOPFLASH reporter by Wnt3a-conditioned media (Wnt3a-CM) was consistently inhibited by WT1 induction as well (Fig. 4B).

Fig. 4.
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Fig. 4.

WT1 negatively regulates TCF-mediated transcription. (A) TCF-mediated transcriptional activity was determined using TOPFLASH construct containing 4 TCF binding sites and the specificity was confirmed using the negative control counterpart FOPFLASH. A c-myc promoter activity was measured as a natural counterpart. Transcriptional activity in 3 replicates was measured after cotransfecting a reporter plasmid (200 ng) with 800 ng of pEGFP-C1 (empty vector), wild-type β-catenin, β-catenin S37A, or WNT3a expressing vector in the absence or presence of WT1 induction in 293–5.1 cells. The luciferase activity was measured at 48 h after transfection. The graphs shown represent 3 independent experiments. (B) Either 200 ng of TOPFLASH or FOPFLASH was cotransfected with 800 ng of pEGFP-C1 in 6-well plates. Transfection mix was removed after 4 h of incubation and fresh medium was added without and with 2 μg/mL doxycycline. After 40 h after transfection, Wnt3a-CM was added to induce the Wnt signaling for 4 h before assays. Twenty microliters of each lysate was used for luciferase assay, and 30 μL was loaded on 10% SDS/PAGE to check expression levels of β-catenin (detected by β-catenin E-5, Santa Cruz) and WT1. GFP was cotransfected to monitor the transfection efficiency and used as a loading control on the Western blot. The graphs shown represent 3 independent experiments.

Next, we investigated how this WT1-mediated transcriptional inhibition occurred. Because we observed that WT1 induction affected neither total nor active β-catenin levels in both 293–5.1 and CCG-5.1 cells, this interference was not due to the decrease in β-catenin level (Fig. 4B and Fig. S4). Electrophoretic mobility shift assays (EMSA) of nuclear extracts from Wilms tumor or 293 cells readily detected a TCF-DNA complex that was completely unaffected by induction of WT1 (Fig. S4).

Because the in vitro EMSA assay may not reflect complex assembly in vivo, we performed ChIP to measure dynamic interactions of WT1, TCF4 (TCF7L2), and the CBP coactivator. β-catenin interacts with CBP to activate transcription and is required for the activation of Wnt-responsive genes in Xenopus (37). CBP also complexes with WT1 to enhance its transcriptional activity (38). Consistent with transient reporter assays, the recruitment of TCF4 to the TOPFLASH promoter was consistently inhibited upon WT1 induction (Fig. 5A). However, WT1 was not recruited to the TOPFLASH promoter, suggesting an indirect mechanism of inhibition, potentially through competition between WT1 and the TCF/β-catenin complex for CBP. Accordingly, the decrease in TCF4 recruitment onto the TOPFLASH promoter upon WT1 induction was abolished when CBP was overexpressed (Fig. 5B). CBP recruitment to the TOPFLASH promoter slightly increased upon CBP cotransfection, although measuring the indirect binding of cofactor on the transiently transfected artificial promoter in a consistent manner may be a technical challenge. Using the same ChIP samples in immunoblotting, we observed that the endogenous CBP interacted with both WT1 and TCF4 (Fig. 5C). Upon WT1 induction, WT1 showed a very weak interaction with TCF4 and conventional immunoprecipitation could not demonstrate a WT1/TCF4 interaction. In addition CBP overexpression, in a dose-dependent manner, reversed the repression activity of WT1 on the TOPFLASH promoter (Fig. 5D). Collectively, these data suggest that the ability of WT1 to interfere with TCF-mediated transcriptional activity is due to its ability to bind to CBP rather than direct interaction with TCF binding sites or TCF4 itself.

Fig. 5.
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Fig. 5.

WT1 inhibited the DNA binding of the TCF complex through cofactor CBP in vivo. (A and B) The 293–5.1 cells were transfected with TOPFLASH (1 μg) and Wnt3a (4 μg) expression vectors (A), and TOPFLASH (1 μg), Wnt3a (4 μg) and mCBP (4 μg) expression vectors (B) in 150-mm plates. Transfection mix was removed after 4 h of incubation and fresh medium was added without and with 2 μg/mL doxycycline. At 48 h after transfection, cells were processed for ChIP. Enrichment was calculated from Ct numbers and shown as relative to % input signal in 2 independent experiments. (C) Western blot analysis of chromatin immunoprecipitated samples. The same amount (5–8 × 106 cell equivalents) of ChIP samples from the same transfection used in Fig. 5A was boiled for 20 min after mixing with SDS-sample loading dye and loaded on the 10% SDS gel. To avoid the signal from IgG heavy chain, True Blot ULTRA HRP-conjugated anti-mouse or anti-rabbit IgG (eBioscience) was used as secondary antibody. (D) TOPFLASH reporter construct was cotransfected with either empty or pSG5-mCBP expression vector into 293–5.1 cells in triplicates. Transfection mix was removed after 4 h of incubation and fresh medium was added without and with 2 μg/mL doxycycline. Wnt3a-CM was added for 4–5 h before harvest. The luciferase assay was done at 48 h after transfection and standard deviation was calculated from 3 independent transfections. The graph represents 5 independent similar experiments.

CBP Is Recruited to the WT1 Targets.

Although CBP was shown to physically interact with WT1 in vitro and contribute to the synergistic activation in reporter assays (38), the importance of CBP for WT1 function in vivo is less certain. ChIP assay showed that WT1 and CBP were present together on genes both up-regulated (ETV5 and NRP1) and down-regulated (ATF3 and PAG1) by WT1 (Fig. 6A). Intriguingly we noted that CBP recruitment to these WT1 target promoters was more evident when cells were grown in the presence of Wnt3a-CM (Fig. 6B). This is more evidence of interplay between WT1 and Wnt signaling. The highly related p300 protein was not recruited to any of the WT1 target promoters.

Fig. 6.
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Fig. 6.

Cofactor CBP is recruited to the WT1-bound regions in Wilms tumor cells. WT1 in CCG5.1 cells was induced for 48 h and Wnt3a-CM treatment was done for 4 h before cross-linking. Polyclonal antibodies of rabbit IgG (Zymed), WT1 and CBP were used for ChIP. Four promoter loci of WT1 candidate genes identified from ChIP–ChIP and expression arrays in this study were tested in the absence (A) and presence (B) of Wnt3a-CM with and without WT1 induction.

TCF4 and WT1 Share Common Targets but Not Binding Sites.

Recently, in vivo binding sites for TCF4 were discovered from 2 genome-wide screens (39, 40). Given our finding that many genes directly regulated by WT1 were also Wnt target genes, we compared the 412 high-confidence TCF4 peaks (40) with WT1 peaks. Of the 412 clusters, as reported previously, nine were found in gene promoters, whereas 213 were found within transcribed regions. Of those 9 genes, 5 genes contain the predicted WT1 binding sites with no overlaps between predicted TCF/LEF and WT1 sites.

However, when we compared TCF peaks (converted to 166 gene symbols) with WT1 peaks (2415 gene symbols) there were 29 genes common even though the TCF4 and WT1 ChIP assays were performed in different cell types and using different techniques, suggesting that there are indeed some WT1 and Wnt genes regulated in common. Both TCF4 studies reported that only ≈15% of TCF binding sites were found in the 5′ flanking sequences of genes (2.5 kb upstream and downstream of the transcriptional start site) and tended to be located at large distances from the body of a gene. Future studies assaying the entire genome for WT1 binding sites could find more overlap with TCF binding regions. Collectively, we conclude that WT1 and TCF4 can share common target genes through distinct DNA binding elements given that there was no overlap between WT1 binding sites and TCF4-GST clusters.

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Discussion

Canonical Wnt/β-catenin signaling and its precise regulation in a temporal and spatial manner are essential for mesenchymal-to-epithelial transition (MET) during kidney development and especially for molecular dynamics of tubule formation (reviewed in refs. 41 and 42). β-catenin has a dual role involved in cell–cell adherent junctions and in gene transcription as a Wnt signaling effector. Identification of Wnt4 as a WT1 target demonstrated a positive role of WT1 on the Wnt signaling during kidney development (24). De-regulation of Wnt/catenin signaling is implicated in the pathogenesis of Wilms tumor (reviewed in ref. 43) and overexpression of activated β-catenin in the developing kidney precludes normal nephron development (44). Interestingly, WT1 null Wilms tumors, frequently containing GOF mutations in β-catenin, have a stromal-predominant histology, whereas WT1 replete tumors have triphasic and blastemal- and epithelial predominant histology (45). One way to explain the occurrence of β-catenin mutation only in WT1 null tumors would be that wild-type WT1 inhibits Wnt signaling and our data support this idea.

First, WT1 directly regulated many Wnt signaling components and Wnt target genes in a Wilms tumor cell line (Fig. 2). We used an integrated genome-wide approach of ChIP–ChIP and microarray analysis to identify WT1 target genes. Because many previously identified WT1 targets contained WT1 responsive elements within 2 kb from transcriptional start sites, we used the promoter arrays. It is possible that more direct targets of WT1 will be identified using genome-wide tiling arrays or the ChIP-seq technique. Nevertheless, WT1 target genes are enriched for those involved in Wnt signaling. This may be one way by which WT1 opposes Wnt signaling. Second, Xwt1 inhibited the Wnt action in the Xenopus system. A recent study showed that WT1 negatively regulates Wnt signaling in other contexts. In Sertoli cells, deletion of WT1 leads to up-regulation of β-catenin itself, and Sertoli-specific Wt1 deletion phenocopied the effect of stabilized β-catenin in the testis (46). Third, WT1 induction inhibited Wnt-mediated transcription by preventing the recruitment of TCF onto a promoter, an effect overcome by overexpression of CBP. Cofactor competition was reported in other systems. For example, nuclear receptors inhibit AP1-dependent transcription by competing for limited amount of CBP (47), whereas the IFN-γ and TGF-β signaling pathways compete for limiting amounts of the CBP-related factor p300 (48). Unlike previous studies showing that WT1 triggered β-catenin degradation in breast cancer cells (15) and Sertoli cells (46), WT1 did not affect β-catenin levels in our system. Last, we discovered that WT1 and TCF4 share common target genes although different cis-acting elements, further supporting the interplay between WT1 and WNT pathways.

The absence of β-catenin mutations in WT1 replete tumors, the ability of WT1 to bind and regulate Wnt pathway genes, to suppress duplex axis formation in Xenopus embryos and to inhibit TOPFLASH activity by wild-type or activated mutant β-catenin, all suggest that inhibition of Wnt pathways is a major tumor suppressor function of WT1. How Wilms tumors develop in the presence of WT1 is still uncertain but may be related to the recently discovered WTX gene that can be mutated in WT1 replete tumors (3).

Although GC-rich and TCC repeat sequences were identified as in vitro binding sites, the nature of the WT1 binding site in vivo has been less clear. Our unbiased searches identified consensus G-rich motifs among WT1-bound regions in vivo. We also observed that WT1 bound G-rich sequences frequently overlapped or coincided with EGR1 and SP1 binding sites. Similarly, WT1 binding sites in the promoters of 11 genes coordinately expressed in prostate cancer cells overlapped with SP1 and EGR1 sites (49). Collectively, these data indicate that G/GC-rich sequences are common WT1 binding sites in vivo. This may explain the finding that at times both induction and knockdown of WT1 activates a WT1 target. In the former case WT1 might occupy the promoter and engage the transcriptional machinery. In the knockdown case, WT1 might represent a repressor or weak activator of the gene and WT1-loss might be accompanied by the binding of EGR1, SP1, or other GC-binding factors of the KLF family. By contrast transcription factors with larger and less degenerate binding sites such as p53 for example might more cleanly bind and activate a set of genes, with less overlap with widely expressed transcription factors. The redundant G/GC rich nature of the WT1 recognition sequence may also explain why microarray studies have identified divergent sets of WT1-regulated genes in different cell types. The nature of the GC sequence binding protein present in the cell likely affects the basal state of gene expression. How readily GC binding factors are released and replaced by WT1 on a promoter could determine the ability of WT1 to regulate the putative target gene. Of note, because the knockdown of WT1 would lead to loss of all WT1 isoforms, whereas only WT1(−KTS) isoform was induced in the GOF system, our study would not score genes whose activation or repression depended on the presence of other alternatively spliced WT1 isoforms and might miss genes that are exclusively bound by those isoforms. In addition, some of the genes maximally activated or repressed in the presence of endogenous levels of WT1 would be missed. Nevertheless, through this genome-wide screen, we found that the action of WT1 in development and growth suppression may be in part explained through its ability to suppress Wnt signaling.

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Materials and Methods

Plasmids and Cell Cultures.

See SI Text.

ChIP and ChIP–ChIP.

ChIP was performed as in ref. 21, using WT1 antibody (C19) (Santa Cruz Biotechnology). Input and WT1-ChIP samples were amplified by ligation-mediated PCR, using JW102 5′-GCGGTGACCCGGGAGATCTGAATTC and JW103 5′-GAATTCAGATC oligos described at http://genomics.ucdavis.edu/farnham/protocol.html. Three biological input replicates were labeled with Cy3 and the corresponding WT1-ChIP samples were labeled with Cy5. Hybridization onto HG18_RefSeq Promoter array was performed by NimbleGen. The peaks were identified with a cut-off of false discovery rate of <0.2 using Nimblegen SignalMap software. For motif analysis genes had to be identified by all 3 ChIP–ChIP experiments. For gene ontology we used a less stringent cut-off and included genes if 2 of 3 replicates detected a significant peak. An independent ChIP was performed for peak validation. Input and ChIP-samples were purified using the Qiagen PCR purification kit. Quantitative PCR was performed using Stratagene Mx3000 machine in a 25-μL reaction volume (21). Antibodies for ChIP include normal rabbit IgG (Santa Cruz, or Zymed in Figs. 5B and 6), WT1(C-19) (Santa Cruz), TCF4 (US Biological) and CBP(A-22) (Santa Cruz). PCR primer sequences are shown in Table S1.

Immunoblotting and EMSA.

See SI Text.

Microarray and Bioinformatic Analysis.

For gain-of-function microarray analysis, RNA was isolated from CCG5.1 cells before and 48 h after WT1-induction, using the RNeasy Mini Kit (Qiagen). For loss-of-function experiments, CCG99–11 cells were transfected with either control shRNA or a WT1 shRNA (21), selected for 4 days with 0.4 μg/mL of puromycin and harvested at day 5 for RNA isolation. After confirmation of RNA quality, using an Agilent 2100 bioanalyzer, RNA was converted to biotin-labeled cDNA, using the GeneChIP expression 3′-amplification reagents one-cycle cDNA synthesis kit (Affymetrix) and hybridized to Affymetrix U133Plus2 chips. Cel files were imported into ArrayAssist software 5.2.2 (Stratagene), and probe levels were normalized by the GC-RMA algorithm. Lists of differentially expressed genes were created using a 1.5-fold cut-off with P < 0.005 by unpaired T test in the biological triplicates. Gene ontology of all differentially expressed genes, and WT1-activated and repressed genes were separately determined using DAVID (http://david.abcc.ncifcrf.gov).

Motif Analysis.

MEME, AlignACE and SOMBRERO algorithms were used to discover motifs of length 8–22 bp in the 643 promoters. Parameters for all algorithms were kept as similar as possible with no predefined expectations on either the number of sites in the dataset or their distribution across the promoters. SOMBRERO motifs were further clustered using the STAMP (50) platform to reduce the redundancy in its predicted motif set. Hierarchical agglomerative clustering with ungapped local alignment was used to produce a reduced set of 5 motifs, each being equivalent to the familial binding profile or average binding specificity for its particular cluster. These 5 representative motifs were compared against the top 5 motifs produce by MEME and AlignACE, and all were queried against the Transfac database to ascertain their similarity to known motifs. Of the resulting matches, a further analysis was carried out to examine the distribution of SP1, EGR1, and WT1 binding sites, using MatInspector. This analysis manually determined loci in which predicted binding sites for 2 or more these factors overlapped. For WT1 binding site analysis in TCF4 peaks, the 412 clusters containing 3 or more GSTs were kindly provided by R. Goodman (Vollum Institute and Department of Medicine, Oregon Health and Science University, Portland, OR) (40). The clusters were mapped as either being within a particular gene (between the transcriptional start and end) or in the gene promoter (2,000 bp upstream of TSS) on build 17 of the human genome. The promoters were defined as a region 2,000 bp upstream to 300 bp downstream of TSS as in the NimbleGen promoter array design.

Xenopus Embryo Injection and Statistical Analysis.

Plasmid DNA was linearized and transcribed in vitro in a 50-μL reaction volume containing 2.5 μg of DNA, 0.5 mM each rNTP, 0.5 mM GpppG cap (Amersham/GE Healthcare), 10 mM DTT, 50 units RNAsin and 40 units SP6 polymerase (Promega). Four-cell Xenopus embryos showing a clear pigmentation difference between the dorsal and ventral side were injected with RNA (10 nL) into the ventral (darker) vegetal blastomeres. Embryos were allowed to develop at room temperature until they reached the swimming tadpole stage (Nieuwkoop stage 40–45) and then were scored blindly for secondary axis formation. If the induced axis contained either cement gland or eye tissue it was scored as complete, otherwise it was scored as partial. Statistical significance was calculated by Fisher's exact test (3 × 2 exact contingency table) except when χ2 test was more appropriate.

Transient Transfections.

See SI Text.

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Acknowledgments

We thank Dr. Greg Khitrov and Jin Chen (Mount Sinai School of Medicine) for microarray hybridization and to Katie Shinnick (Northwestern University) for RNA synthesis for Xenopus embryo injection. This work was supported by National Institutes of Health Grant CA102270 (to J.D.L.), the Northwestern Memorial Foundation (J.D.L.), American Cancer Society Postdoctoral Fellowship PF-05-252-01-MGO (to M.K.-H.K.), Science Foundation Ireland Grant RFP/05/CMS0001 (to P.O. and A.G.) and American Heart Association Grant 0630290N (to T.M.).

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Footnotes

  • 1To whom correspondence should be addressed. E-mail: j-licht{at}northwestern.edu

  • Author contributions: M.K.-H.K. and J.D.L. designed research; M.K.-H.K. and T.J.M. performed research; M.K.-H.K. contributed new reagents/analytic tools; M.K.-H.K., P.O., J.M.F., and A.A.-J.G. analyzed data; and M.K.-H.K. and J.D.L. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.pnas.org/cgi/content/full/0901591106/DCSupplemental.

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  1. Published online before print June 22, 2009, doi: 10.1073/pnas.0901591106 PNAS July 7, 2009 vol. 106 no. 27 11154-11159
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