TAF6δ orchestrates an apoptotic transcriptome profile and interacts functionally with p53
© Wilhelm et al; licensee BioMed Central Ltd. 2010
Received: 4 August 2009
Accepted: 22 January 2010
Published: 22 January 2010
TFIID is a multiprotein complex that plays a pivotal role in the regulation of RNA polymerase II (Pol II) transcription owing to its core promoter recognition and co-activator functions. TAF6 is a core TFIID subunit whose splice variants include the major TAF6α isoform that is ubiquitously expressed, and the inducible TAF6δ. In contrast to TAF6α, TAF6δ is a pro-apoptotic isoform with a 10 amino acid deletion in its histone fold domain that abolishes its interaction with TAF9. TAF6δ expression can dictate life versus death decisions of human cells.
Here we define the impact of endogenous TAF6δ expression on the global transcriptome landscape. TAF6δ was found to orchestrate a transcription profile that included statistically significant enrichment of genes of apoptotic function. Interestingly, gene expression patterns controlled by TAF6δ share similarities with, but are not equivalent to, those reported to change following TAF9 and/or TAF9b depletion. Finally, because TAF6δ regulates certain p53 target genes, we tested and demonstrated a physical and functional interaction between TAF6δ and p53.
Together our data define a TAF6δ-driven apoptotic gene expression program and show crosstalk between the p53 and TAF6δ pathways.
Apoptosis is an active program of cell death that is required for normal development and tissue homeostasis in metazoans . The deregulation of apoptotic pathways underlies many human diseases . Consequently, apoptotic pathways represent potential targets for therapeutic control of cell death for diseases including neurodegenerative disorders, autoimmune diseases and cancer . Our previous studies have uncovered the existence of an apoptotic pathway termed the TAF6δ pathway that controls cell death [4, 5].
TAF6δ is an inducible splice variant of the TFIID subunit TAF6 (previously termed hTAFII70 or hTAFII80). TFIID is a multiprotein complex containing the TATA-binding protein (TBP) and up to 14 evolutionarily conserved TBP-associated factors (TAFs) [6, 7]. TFIID is the primary core promoter recognition complex for RNA polymerase II (pol II) and thus plays a key role in the regulation of transcription of protein-coding genes . The major TAF6α isoform is ubiquitously expressed  whereas strong expression of the TAF6δ isoform has only been detected in apoptotic conditions (e.g. HL-60 cells undergoing retinoic acid dependent death) . The use of modified antisense RNA oligonucleotides, also termed splice-switching oligonucleotides (SSO), to experimentally direct the expression of endogenous TAF6δ in living cells has recently demonstrated the pro-apoptotic activity of TAF6δ .
The major TAF6α isoform contributes to the stability of core TFIID complexes in part by dimerizing with TAF9 via its histone fold domain [9–13]. Structurally, TAF6δ differs from TAF6α only in that it lacks 10 amino acids within its histone fold domain. These amino acids, however, are critical for the interaction of TAF6α with TAF9 , and as a consequence, TAF6δ cannot interact with TAF9 . As is the case for TAF9, the highly homologous protein TAF9b cannot interact with the pro-apoptotic TAF6δ isoform . TAF6δ does retain the capacity to interact directly with other TFIID subunits including TAF1, TAF5, TBP and TAF12. Consequently, within cells TAF6δ is incorporated into a TFIID-like complex that lacks TAF9 and TAF9b, termed TFIIDπ . Depletion of TAF9 or the highly homologous protein TAF9b in HeLa cells has been shown to alter global gene expression patterns . Presently it is not known whether the transcriptional effects of TAF6δ are related to those resulting from the depletion of TAF9 and/or TAF9b. Our previous work revealed that TAF6δ can alter gene expression , but a physiologically informative definition of the transcriptome impact of TAF6δ is currently lacking.
Data documenting a direct interaction between the major TAF6α isoform with p53 has been shown in vitro using recombinant proteins , in vitro using endogenous human TFIID , and in cultured cells using reporter assays . Furthermore, the interaction of TAF6α with p53 has been shown to be essential for the activation of transcription by p53 in vitro  as well as in vivo in mice bearing point mutations within p53 that block its interaction with TAF6α . Currently it is not known whether the inducible pro-apoptotic TAF6δ isoform can interact with p53. Importantly, TAF6δ induces apoptosis in cell lines that lack p53 expression . Moreover, the induction of TAF6δ produced similar levels of apoptosis in the HCT-116 p53 -/- colon carcinoma cell line as in its p53 positive counterpart . Thus, TAF6δ can induce programmed cell death independently of p53, however the functional relationship between the TAF6δ and p53 pathways requires further clarification.
The TAF6δ pathway represents a tractable experimental paradigm to elucidate the mechanisms by which human cells respond to their environment through subunit changes in the general transcription machinery . Moreover, there is mounting evidence that the TAF6δ pathway may be altered in certain cancers. Aberrant TAF6 expression has been documented in human cancers including lung cancer [22, 23] and breast cancer [24, 25]. The molecular basis for the induction of apoptosis by TAF6δ is currently unknown. In order to shed further light on the impact of TAF6δ on the human transcriptome, here we performed a transcriptome-wide analysis of the impact of endogenous TAF6δ expression in HeLa cervical carcinoma cells. Our data provide the first physiologically coherent transcriptome signature for TAF6δ, establish the relationship of the TAF6δ signature with those of TAF9/TAF9b, and identify a functional and physical interaction of TAF6δ with p53.
The TAF6δ orchestrates a pro-apoptotic gene expression program
To establish the impact of TAF6δ on global gene expression patterns, the expression of the endogenous TAF6δ splice variant was experimentally induced using splice-switching oligonucleotides (SSO) as previously documented . The HeLa cell line was chosen as a model system for transcriptome studies for three principle reasons. Firstly, these cells are readily transfectable and produce a robust apoptotic response to TAF6δ-inducing SSO . Secondly, the TAF6δ cDNA was cloned from a HeLa cell library  and therefore these cells provide a natural cellular context. Thirdly, HeLa cells express no detectable TAF6δ protein under standard culture conditions , thus these cells provide a stringently inducible model for SSO studies. To define the impact of TAF6δ expression on transcriptome dynamics we took advantage of an experimental approach that combines SSO treatment with high sensitivity microarray analysis . We note that to achieve the statistically significant overrepresentation of gene ontology pathways reported here it was necessary to employ optimized SSO sequences designed to more efficiently induce TAF6δ expression than those employed in a previous study . The improved SSO were transfected into HeLa cells and the induction of endogenous TAF6δ mRNA and protein was confirmed, as shown in Additional File 1. Total RNA was isolated 18 hours post-transfection and subjected to microarray analysis as previously detailed . Biological triplicates were performed with SSO T6-1 and, as a control to normalize for any non-specific SSO effects, a scrambled oligonucleotide (SSO ctrol). The statistical analysis and filtering of the raw microarray data was carried out as previously described  to identify significantly (P < 0.05) regulated mRNAs.
To examine the specificity of the TAF6δ-induced transcriptome signature, we compared the microarray data with those obtained when the pro-apoptotic isoform of a distinct gene, Bcl-x, was induced by SSO under identical conditions . As shown in Additional File 2, the transcriptome signatures resulting from the induction of TAF6δ and Bcl-xS are highly distinct. The vast majority of TAF6δ-regulated transcripts (90.5%) were induced while Bcl-xS expression results in a majority of transcripts being repressed (58%). Only a minor fraction (3.4%) of the 870 genes upregulated by TAF6δ was also upregulated by Bcl-xS. Of the small number of transcripts repressed (46) by TAF6δ, 45 are also repressed by Bcl-xS, possibly reflecting a minor subset of genes that are repressed by both of these pro-apoptotic pathways. The portion of Bcl-xS repressed genes also repressed by TAF6δ was minor (15.8%). The fact that genes induced by TAF6δ share little overlap with those induced by Bcl-xS underscores the highly specific impact of the TAF6δ-inducing SSO on the transcriptome.
Changes in mRNA levels detected by microarray analysis can in principle result from a number of effects including alterations in mRNA stability. To obtain evidence that TAF6δ can regulate gene expression in a promoter-dependent and promoter-specific fashion, we tested the ability of endogenous TAF6δ to increase target gene expression in luciferase reporter gene assays. We selected four promoters for analysis. The HES1, DUSP1 and ADM promoters were selected since the endogenous HES1, DUSP1 and ADM mRNA levels are induced in response to TAF6δ expression (Figure 1A & 1B). In the case of HES1, the levels of endogenous HES1 protein were also shown to be induced in response to TAF6δ expression (Figure 3A). The 3 selected genes act in several of the pathways activated by TAF6δ including the Notch (HES1) , angiogenesis (ADM) , oxidative stress and p53 pathways (DUSP1) [31–33]. The Bax promoter was also included because it is a p53-responsive and pro-apoptotic gene , yet is not induced by TAF6δ. The induction of TAF6δ in HeLa cells resulted in increased HES1, DUSP1, and ADM promoter-driven gene expression (Figure 3B). In contrast, the Bax promoter was not induced and even measurably repressed (Figure 3B). These results demonstrate that endogenous TAF6δ can act directly or indirectly to stimulate transcription in a promoter-dependent manner.
The TAF6δ transcriptome signature is not equivalent to those resulting from depletion of TAF9 and/or TAF9b
TAF6δ interacts physically and functionally with p53
To determine whether the TAF6δ-p53 interaction can occur in living cells we performed co-immunoprecipitation assays. As endogenous TAF6δ is highly labile and expressed at very low levels , TAF6δ was expressed by transfection of an expression vector into HCT-116 p53 -/- cells. Exogenous p53 was provided by co-transfection of an expression vector. Immunoprecipitation of TAF6δ resulted in co-immunoprecipitation of p53 (Figure 5C, lane 3), in contrast to the negative control immunoprecipitation of tubulin (Figure 5C, lane 2). In addition, the reciprocal experiment, immunoprecipitation of p53 resulted in recovery of TAF6δ (Figure 5C, lane 4). Together, these data show that the interaction between TAF6δ and p53 can occur in the cellular context.
We next sought to determine whether the interaction between TAF6δ and p53 has functional consequences. We took advantage of the fact that the DUSP1 promoter is activated by TAF6δ (Figure 3B) and is also activated by p53 . A reporter construct expressing firefly luciferase under the control of the DUSP1 promoter was co-transfected with p53 expression vectors, as well as vectors expressing TAF6 variants. TAF6δ transfection resulted in enhanced DUSP1 expression when co-transfected with p53 (Figure 5D). A truncated version of TAF6 that lacks pro-apoptotic activity  failed to show significant co-activation (Figure 5D). The protein levels resulting from transfected plasmids were determined by immunoblotting experiments and are shown in Additional File 5. The data exclude the possibility that higher levels of TAF6δ protein (compared to the truncated negative control TAF6) contribute to the activation levels observed. These data show for the first time that TAF6δ can interact functionally with p53 to co-activate DUSP1 gene transcription.
We also filtered the data to determine the reciprocal influence of p53 status upon previously identified TAF6δ-dependent mRNAs . 51% of the TAF6δ-regulated mRNAs changed significantly only in the absence of p53 (Figure 6E), for example TNFRSF6B (Figure 6F). 9% of the TAF6δ-regulated mRNAs changed independently of p53 status, including ACRC (Figure 6F). 40% of TAF6δ-regulated mRNAs changed significantly only in cells expressing p53, such as TP53I3 (Figure 6F). In general the influence of p53 on TAF6δ-dependent transcription was relatively subtle in magnitude, with at least one exception where the gene LOC342293 displayed opposing regulation in presence or absence of p53 (Figure 6F). Together, the above data establish reciprocal transcriptional crosstalk between the TAF6δ and p53 proteins.
In addition to apoptotic genes the unbiased statistical analysis of our microarray data revealed overrepresentation of genes in the Notch, oxidative stress response, integrin, p53, p53 pathway feedback loops 2, and angiogenesis pathways. The TAF6δ pathway is an orphan pathway whose molecular trigger remains unknown. The novel links between these pathways and TAF6δ expression provide testable hypotheses for the potential physiological triggers and functions of TAF6δ. For example, the identification of p53 target genes prompted us to test and demonstrate a physical interaction between TAF6δ and p53 (see below). Within the TAF6δ-activated transcriptome signature, unbiased statistical approach showed significant overrepresented interconnections between these individual signaling pathways (e.g. integrin and angiogenesis). Additional support for functional interconnections amongst TAF6δ-associated pathways comes from interactions documented in the literature. For example, the Notch  and integrin  pathways both play important roles in angiogenesis. The fact that several of the TAF6δ-induced pathways converge upon the process of angiogenesis implies a physiological coherent impact of TAF6δ on gene expression programs. Interestingly, many of the pathways associated with TAF6δ expression play roles in the tumor progression. For example, angiogenesis is both a key event in tumor progression and a target for anti-cancer therapies . Our study therefore provides the rationale to initiate studies to test the impact of TAF6δ on the process of angiogenesis in vivo in the future.
The incorporation of TAF6δ into TAF-containing complexes results in the formation of TFIIDπ that lacks TAF9 (see Introduction). The currently available evidence is consistent with the lack of TAF9 being the only difference between canonical TFIID complexes and TFIIDπ , however it is conceivable that the inclusion of TAF6δ could cause as yet unknown changes in TFIIDπ subunit composition. TAF6 interacts with TAF9 and the resulting dimeric complex can bind to downstream promoter elements (DPEs) [41, 42]. To date our analysis of TAF6δ-responsive promoters has revealed no statistically significant enrichment of DPEs or any of the known core promoter element within the promoter regions of genes induced by TAF6δ (unpublished data). One mechanistic explanation for the transcriptome impact of TAF6δ could be that the loss of TAF9 or TAF9b from TFIID alone drives transcriptional changes. A prediction of this model is that the transcriptome signatures resulting from depletion of TAF9 and/or TAF9b by small interfering RNAs would be highly similar to that resulting from induction of TAF6δ. The comparative transcriptomic analysis we provide shows both overlapping and unique features of the TAF6δ versus TAF9/TAF9b-dependent transcriptomes. Interestingly, TAF9b-depletion, like TAF6δ induction resulted in transcriptome profile with overrepresentation of genes functioning in angiogenesis pathways. Gene ontology analysis of the genes regulated by all of TAF6δ, TAF9 and TAF9b showed an overrepresentation of a single ontology termed the p53 feedback loops 2 pathway, suggesting overlap in the gene expression programs controlled by these TAFs. A limitation of the current study is that the distinct approaches (siRNA versus SSO RNA) and microarray platforms employed results in the loss of information. Nevertheless, clear differences were observed between the transcriptome profiles of TAF6δ versus TAF9 and TAF9b (Additional File 4). We conclude that exclusion of TAF9 and/or TAF9b from TFIID results in transcriptome changes that share certain targets with, but that do not fully recapitulate the TAF6δ transcriptome signature.
The current transcriptome analysis showed that TAF6δ induces genes in the p53 pathway, a result not revealed by previous transcriptome analysis of TAF6δ in HCT-116 cells . Based on the finding that TAF6δ and p53 can share target genes, we tested and confirmed the direct physical and functional interaction of TAF6δ with p53. The impact of endogenous TAF6δ on p53-dependent gene expression was further demonstrated at the transcriptome-wide level. The reciprocal capacity of endogenous p53 to influence TAF6δ-mediated transcription was also detected, although the magnitude of these effects was globally more modest. Taken together, the data show that there is reciprocal crosstalk between the TAF6δ and p53 pathways. The microarray experiments measure changes in expression resulting from both direct and indirect effects of TAF6δ and p53. Therefore, the crosstalk we have documented includes that resulting from the direct TAF6δ- p53 but also that resulting from indirect transcriptional changes. Given the previous demonstration that TAF6δ can induce apoptosis independent of p53 , we conclude that TAF6δ possesses both p53 independent and p53 dependent activities.
In summary, we report here that the transcriptome landscape orchestrated by TAF6δ includes the induction of apoptotic gene expression. The transcriptome data further uncovered novel links between TAF6δ expression and the Notch, oxidative stress response, integrin, p53, p53 feedback loop 2, and angiogenesis pathways. The TAF6δ-controlled transcriptome landscape was shown not to be equivalent to those resulting from depletion of TAF9 and/or TAF9b. Finally, the data establish a physical and functional interaction between TAF6δ and the p53 tumor suppressor protein.
HeLa cells were grown in DMEM containing 2.5% CS and 2.5% FCS. HCT-116 cells were grown in McCoy's media supplemented with 10% FCS.
2'-O-methyl-oligoribonucleoside phosphorothioate antisense 20-mers were from Sigma-Proligo. "SSO ctrol", "SSO T6-1"  and "SSO Bcl-x"  have been described. "SSO T6-3" 5'-CUGUGCGAUCUCUUUGAUGC-3' targets the 3' part of the alternative exon 2 of TAF6. SSOs were transfected at a final concentration of 200 nM with lipofectamine 2000 (Invitrogen) as a delivery agent (1.6 μl/ml) according to the manufacturer's recommendations. Plasmids were transfected using 1 μl DMRIE-C (Invitrogen) as a delivery agent in a 24 well plate according to the manufacturer's recommendations. All transfections were performed in OptiMEM medium (Invitrogen).
Plasmids expressing firefly luciferase under control of the HES1 , DUSP1 , Bax , and ADM  promoters have been described. Plasmids expressing TAF6α, TAF6δ and TAF6ΔAB  and p53 or its mutated form p53R175H  have been described. To construct vectors for bacterial production of His-tagged TAF6α and TAF6δ proteins, full length cDNAs were excised from pXJ42-TAFII80α and pXJ42-TAFII80δ(ΔA)  respectively, using NotI and XhoI sites. The NotI site was filled in by treatment with Klenow enzyme. The generated fragment was inserted into the SalI and Klenow filled HindIII sites of pQE31 vector (Qiagen), generating pQE31-TAF6α and pQE31-TAF6δ plasmids. pGST-p53Arg  and pGEX4-T-3 (GE Healthcare) were used for expression of GST-tagged p53 and GST proteins respectively.
Monoclonal antibodies directed against TAF6δ (37TA-1 & 37TA-2) , and TBP (3G3)  have been described. The pan-TAF6 monoclonal antibody was purchased from BD Transduction Laboratories. Antibodies against ARNT (sc-17811), JUN (sc-1694), FOS (sc-52), CDKN2B (sc-613) and His probe antibody (sc-803) were purchased from Santa Cruz Biotechnology. Antibodies against HES1 (AB5702), PMAIP1 (Ab13654), alpha-Tubulin (clone B-4-1-2), and p53 (clone PAb1801) were purchased from Millipore, Abcam, Sigma, and Calbiochem respectively.
RT-PCR conditions and primers for amplification of both TAF6α and TAF6δ have been described .
Immunolabelling of TAF6δ in fixed cells was performed as described .
Microarray Analysis of Gene Expression
Transcriptome analysis was performed as we previously detailed , using the NeONORM normalization method with k = 0.20 . The published microarray data for TAF9 and TAF9b depletion by siRNA were generated on the Génopole Genomics Platform Strasbourg using custom technology. In order to be able to compare those data directly to data generated from commercial platforms, the unique probe-set identifiers were mapped to non-redundant NCBI and Ensemble gene IDs. Similarly, the AB1700 data generated for this study or from previous studies on an Applied Biosystems Microarray platform were mapped according to the published procedure  to the same set of gene IDs. After these mapping procedures >87.9% of unique probe-set or probe IDs could be directly compared which corresponds to > 93.2% comparable genes. For comparative pathway inference analyses the TAF9 and TAF9b data were mapped to, and treated as if AB1700 data to avoid any potential bias stemming from the use of different ontology annotation databases.
Gene Ontology (GO) and KEGG annotations were analyzed using the Panther Protein Classification System http://www.pantherdb.org to identify functional annotations that were significantly enriched in the different gene sets when compared to the whole set of genes present on the ABI microarray. Note that a given gene can be assigned to different pathways; in order to reduce multiple probing biases a gene is weighted by the inverse of the number of pathways it can be assigned to, leading to non-natural numbers for the gene counts. P-values are determined using a binominal distribution and a null hypothesis of a random set of genes with identical size. Pathway interconnectivity analysis was performed for the significantly overrepresented pathways based on genes that are annotated to be part of any combination of two of the selected pathways and that were significantly regulated in the subtraction profile analysis. Those numbers were then compared to the entire set of shared genes, and P-values were calculated as above.
Microarray data for the gene sets analyzed herein are provided as Additional Files 3; 6, 7, 8, 9, 10. The transcriptome-wide microarray data for all of the experiments described here were deposited in the M. ACE database http://mace.ihes.fr under accession numbers:
TAF6δ signature: 2937831950; Bcl-x: 2156101006; p53: 2370552334
Real time PCR
Real time PCR was performed as described . RNA was prepared with using an RNeasy mini Kit (Qiagen). 1 μg of total RNA was reverse transcribed using AMV-RT (Roche). Real-time PCR was performed in a final 25 μl reaction on 10 ng of cDNA with 12.5 μl 2× TaqMan® Universal Master Mix (ABI) and 1.25 μl of the following 20× TaqMan® probes: B2M as the internal control (Hs99999907_m1), ACRC (Hs00369516_m1), ADM (Hs00181605_m1), ATF3 (Hs00231069_m1), DDIT3 (Hs00358796_g1), DUSP1 (Hs00610256_g1), EFNA5 (Hs00157342_m1), HES1 (Hs00172878_m1), HOM-TES-103 (Hs00209961_m1), IFRD1 (Hs00155477_m1), IL6 (Hs00174131_m1), NR4A2 (Hs00428691_m1), PFKFβ4 (Hs00190096_m1), PMAIP1 (Hs00560402_m1), or TRIB3 (Hs00221754_m1).
Cells were washed with PBS and lysed with Passive lysis buffer (Promega). The luciferase activity was measured on a Lumistar luminometer (BMG Labtech), after injection of 2× Luciferin reagent; 270 μM CoenzymeA, 470 μM D-Luciferin, 530 μM ATP (all from Sigma-Aldrich) 40 mM Tris-Phosphate pH 7.8, 2.14 mM MgCl2, 5.4 mM MgSO4, 0.2 mM EDTA, 33.3 mM DTT.
Recombinant protein production
Escherichia coli M15 cells were transformed with plasmids expressing His-TAF6α and His-TAF6δ fusion proteins and grown to log-phase before induction of protein expression with 1 mM IPTG (isopropyl-β-d-thiogalactopyranoside) for 18 h at 16.5°C. The cell pellets were resuspended in Ni buffer composed of 1 M NaCl, 30 mM Tris pH8.0 and supplemented with 25% glycerol and 1× Complete protease inhibitor cocktail (PIC) (Roche) and 0.5 mM PMSF (phenylmethyl-sulfonyl fluoride) and sonicated twice for 5 minutes on ice. After clarification, the supernatant was loaded on a His-trap column (GE Healthcare) equilibrated in Ni buffer containing 10 mM imidazole. The column was sequentially washed with Ni buffer containing 60 mM and 100 mM of imidazole and the proteins were finally eluted with 250 mM imidazole. Purified proteins were dialysed against buffer D (20 mM HEPES ph7.9, 100 mM KCl, 20% glycerol, 0.2 mM EDTA).
GST and GST-p53 fusion proteins were produced in Escherichia coli BL-21 cells by IPTG induction (1 mM) of a log-phase culture for 3 h. The cell pellets were resuspended in PBS containing 0.5% Triton X-100 and sonicated twice for 2 minutes. After clarification, the supernatant was incubated with glutathione-sepharose (GE Healthcare) for 1 h 30 at 4°C. The beads were washed three times in PBS and once in buffer D supplemented with 5 mM MgCl2, 0.1% NP40 and EDTA up to 1 mM (buffer D+).
GST "pull-down" assays were performed essentially as previously described . GST- and His-tagged proteins were pretreated with 0.5 U DNase (Promega) for 10 minutes at 37°C before the interaction assay. Equal amounts of GST or GST-p53 linked to sepharose beads were then incubated with His-TAF6α, His-TAF6δ or His-TdT for 1 hour at room temperature in buffer D supplemented with 0.5 μg RNase A (USB) (buffer D+). After four washes with buffer D+ containing 100 mM or 600 mM KCl, bound proteins were analyzed by SDS-PAGE and immunoblotting.
Cells were lysed in RIPA buffer (50 mM Tris pH8, 1% NP40, 0.25% Na deoxycholate, 150 mM KCl, 1 mM EDTA) supplemented with 1× PIC and 0.5 mM PMSF. The lysate was diluted 1/10 with IP100 buffer (25 mM Tris pH8, 5 mM MgCl2, 10% glycerol, 100 mM KCl, 0.1% NP40, 0.3 mM DTT, PIC, PMSF) and precleared with proteinG-sepharose beads for 2 hours at 4°C. The precleared lysate was then incubated overnight at 4°C with anti-p53, anti-TAF6δ or anti-tubulin antibodies immobilized on protein G-sepharose beads. After extensive washes with IP100 buffer, complexes were analyzed by SDS-PAGE and immunoblotting.
We thank Drs. T. Ishimitsu (ADM-luc), C. Prives (BAX-luc), A. Israël (HES1-luc), G. Wu (DUSP1-luc), and L. Banks (pGST-p53Arg) for gifts of plasmids. We thank Drs. R. Day and S. Cagnol for critical comments on the manuscript. L.T.'s group received funding from ANR (05-BLAN-0396-01; Regulome) and European Community (HPRN-CT 00504228 and STREP LSHG-CT-2004-502950; EUTRACC LSHG-CT-2007-037445). A.B.'s group received funds from the European Hematology Association - José Carreras Foundation, and the French Ministry of Research through the "Complexité du Vivant - Action STICS-Santé" program. Work in B.B.'s group was funded through a Discovery grant from Natural Sciences and Engineering Research Council of Canada and the Canada Research Chair program.
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