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• W2987486039 abstract "Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods Appendix 1 Appendix 2 Data availability References Decision letter Author response Article and author information Metrics Abstract Enhanced expression of the MYC transcription factor is observed in the majority of tumors. Two seemingly conflicting models have been proposed for its function: one proposes that MYC enhances expression of all genes, while the other model suggests gene-specific regulation. Here, we have explored the hypothesis that specific gene expression profiles arise since promoters differ in affinity for MYC and high-affinity promoters are fully occupied by physiological levels of MYC. We determined cellular MYC levels and used RNA- and ChIP-sequencing to correlate promoter occupancy with gene expression at different concentrations of MYC. Mathematical modeling showed that binding affinities for interactions of MYC with DNA and with core promoter-bound factors, such as WDR5, are sufficient to explain promoter occupancies observed in vivo. Importantly, promoter affinity stratifies different biological processes that are regulated by MYC, explaining why tumor-specific MYC levels induce specific gene expression programs and alter defined biological properties of cells. https://doi.org/10.7554/eLife.15161.001 eLife digest Genes with the potential to cause tumors and cancer are commonly called oncogenes. One example of an oncogene encodes for a protein called MYC and many tumors contain high levels of this protein. MYC is a transcription factor and studies of aggressive tumors suggested that, like most other transcription factors, MYC binds to and regulates the activity of a small number of genes in tumors. However, other studies went on to show that MYC actually binds to thousands of genes and somehow only regulates a subset of them during tumor development. Lorenzin et al. set out to understand how this process works by generating human cells in which the concentration of MYC protein could be altered. In the experiments, the concentration was varied from normal healthy levels to the high levels found in aggressive tumors. The amount of MYC bound to genes and the extent to which it activated the genes inside these cells was also measured. Lorenzin et al. found that increasing MYC levels from normal to tumor-specific levels did not affect MYC binding at genes where the transcription factor was already strongly bound in normal cells. Rather, MYC binding increased only at genes that were weakly bound in normal cells. Consistent with this observation, only genes at which MYC was weakly bound in normal cells were activated by increasing MYC levels. This observation suggests that increasing the concentration of MYC protein from normal to tumor-specific levels “fills up” previously empty binding sites around these genes with the transcription factor. Lorenzin et al. also used mathematical modeling to understand how the concentrations of MYC in normal and tumor cells might explain how MYC behaves in cells. Together, the results imply that the MYC transcription factor regulates distinct sets of genes in normal and tumor cells according to how much MYC is present. Further studies may show that the altered regulation of a tumor-specific set of genes is important for tumor development and could use this new information to identify new targets for treating MYC-driven tumors. https://doi.org/10.7554/eLife.15161.002 Introduction Deregulated expression of one of the three members of the MYC gene family (MYC, MYCN or MYCL) is observed in the majority of human tumors (Dang, 2012; Meyer and Penn, 2008). A broad body of evidence establishes that deregulated MYC expression causally contributes to multiple aspects of tumor development and that tumors depend on enhanced expression of MYC for growth and survival (Casey et al., 2016; Soucek et al., 2013). MYC proteins are transcription factors that bind DNA as part of several protein complexes; best understood is a dimeric complex of MYC with its partner protein MAX that activates transcription via binding to a specific DNA motif termed E-box with a consensus sequence of CACGTG (Conacci-Sorrell et al., 2014). To repress transcription, the binary MYC/MAX complex associates with a zinc finger protein termed MIZ1 (Wiese et al., 2013). ChIP (chromatin-immunoprecipitation) (Fernandez et al., 2003) and ChIP-sequencing experiments (Lin et al., 2012; Nie et al., 2012; Sabò et al., 2014) demonstrate the presence of MYC on virtually all promoters with an open chromatin structure as well as on thousands of enhancers and intergenic sites in multiple cell types, raising the question what the functional relevance of this broad binding might be. Given this global binding pattern, it is surprising that MYC-driven tumors can be recognized by a specific set of up- and down-regulated MYC target genes that holds considerable prognostic and therapeutic value (Sabò et al., 2014; Walz et al., 2014). One hypothesis to explain this observation suggests that MYC proteins globally enhance transcription. This has been termed the general amplifier model (Lin et al., 2012; Nie et al., 2012) and is supported by observations that MYC can cause an increase in total RNA and mRNA levels (Grandori et al., 2005; Hsu et al., 2015; Lin et al., 2012; Nie et al., 2012). In this model, specific gene expression patterns arise indirectly due to feedback and feedforward loops induced by general amplification. The alternative viewpoint suggests that MYC proteins regulate specific genes and that global changes in RNA and mRNA levels occur indirectly as a consequence of MYC-driven cell growth (Sabò and Amati, 2014). To explain the contrast between global binding and specific gene regulation, the latter model proposes that much of MYC binding to chromatin is non-productive in terms of transcriptional regulation (Kress et al., 2015). We show here that the divergent models can be reconciled with experimental observations without the need to invoke productive and non-productive modes of DNA binding. We analyzed U2OS cells that express a doxycycline-inducible allele of MYC (Elkon et al., 2015; Walz et al., 2014) and characterized DNA binding and gene expression patterns at different levels of MYC. We showed previously that doxycycline-induced overexpression of MYC in these cells establishes a gene expression signature, which closely resembles multiple established signatures of MYC target genes and identifies expression signatures of patients with MYC amplification in tumors (Walz et al., 2014). Hence, these cells represent a simple model system, in which the effect of physiological and tumor-specific MYC levels can be compared and provide a tool to elucidate the mechanism(s) by which activation of a globally binding transcription factor can result in regulation of specific and functionally relevant gene expression patterns. Results MYC binding to chromatin appears saturated at certain sites To determine the effect of changes in MYC levels on DNA binding and gene expression, we have previously engineered U2OS cells to express a doxycycline-inducible allele of MYC (Figure 1A). We chose U2OS cells because they have relatively low levels of endogenous MYC, despite being tumor cells. To illustrate this point, we determined MYC levels in a number of normal and transformed cells. Lysates of equal numbers of exponentially growing cells were probed by immunoblotting (Figure 1B). The results show that U2OS cells express levels of MYC that are comparable to non-transformed cells (IMEC, HMLE, MCF10A) and lower than those found in other tumor cell lines (HeLa and HCT116). Notably, prolonged exposure (>3 days) to doxycycline and hence long-term ectopic expression of MYC induces apoptosis in U2OS cells (Walz et al., 2014); therefore, all subsequent analyses were performed 28–30 hr after addition of doxycycline. Figure 1 with 1 supplement see all Download asset Open asset MYC saturates certain binding sites. (A) Immunoblot of MYC and Vinculin in U2OSTet-On cells treated with EtOH or with 1 µg/ml of doxycycline. (B) Immunoblot of MYC and Vinculin in several transformed (U2OS, HeLa, HCT116) and untransformed cell lines (IMECs, HMLE, MCF10A, HEK293). For each sample, 60,000 cells were loaded. A quantification is shown at the bottom. (C) Heat maps for binding of endogenous MYC in U2OSTet-On cells to all UCSC annotated promoters in a window of 5 kb around the transcriptional start site (TSS). Input is shown as control and intensity of color indicates binding strength. (D) ChIP-sequencing traces of MYC for one genomic region as an example. Input is shown as control. (E) ChIP-sequencing traces of MYC for four bound genes. RPL8 is a ribosomal protein, UTP15 and FBL are ribosomal biogenesis factors and VEGFA takes part in cellular signaling. A scale bar is shown at the top of each browser picture. (F) Binned plot for the comparison of MYC recruitment (change in occupancy, x-axis) and MYC occupancy (y-axis) in U2OSTet-On cells expressing endogenous levels of MYC (EtOH, blue dots) or overexpressing MYC (Dox, orange dots). 8,425 genes bound by MYC upon treatment with doxycycline were sorted according to MYC recruitment and divided in 20 equally sized bins. Each dot represents the average value of the bin. The bins containing the genes shown in panel E are indicated. (G) Quantitative ChIP experiments for MYC (left panel) and MXD6 (right panel) at four MYC target genes and a control region. IgG were used as control. U2OSTet-On were treated either with EtOH or with 1 µg/ml doxycycline to induce exogenous MYC expression. Data are shown as mean ± standard deviation of technical triplicates. https://doi.org/10.7554/eLife.15161.003 ChIP-sequencing of MYC in U2OSTet-on cells had previously shown that endogenous MYC binds to about 5,500 promoters and that this number increases to about 8,400 MYC-bound promoters upon addition of doxycycline (Walz et al., 2014). Different peak calling programs (MACS14, SICER, CCAT) with default parameters result in very similar peak numbers (Figure 1—figure supplement 1A,B). Reducing the stringency of peak calling resulted in a moderate increase in peak number but a large increase in the number of negative peaks, suggesting that an analysis using default parameters does not overlook a large number of significant peaks (Figure 1—figure supplement 1C). In agreement with reports from other systems, we concluded that the promoters of the majority of all expressed genes are bound by MYC. Surprisingly, individual promoters showed a wide range of occupancies for endogenous MYC (Figure 1C–E), whereas the differences in occupancy by MYC among promoters were much smaller after induction with doxycycline (Figure 1D,E). Promoters, which are least strongly bound by endogenous MYC recruit most MYC upon overexpression (i.e. VEGFA, Figure 1E), whereas the most strongly bound genes recruit no additional MYC (i.e. RPL8). This prompted us to analyze whether the anti-correlation between occupancy by endogenous MYC and recruitment of exogenous MYC is evident globally (Figure 1F). To this end, we determined the relative MYC recruitment at each bound promoter by calculating the fold-change of MYC occupancy in cells with exogenous and endogenous MYC levels. Genes were ranked according to these MYC recruitment values and plotted against the respective occupancy of endogenous MYC (Figure 1F, blue dots). Strikingly, genes, which are most weakly bound (mean: 89 tags), recruit MYC most strongly (5.3-fold), whereas the most strongly bound genes (mean: 580 tags) show on average no further MYC recruitment. Importantly, when MYC occupancy at exogenous MYC levels is analyzed (Figure 1F, orange dots), all bins of genes are bound to a high extent. One way to explain this observation is the hypothesis that genes strongly bound by endogenous MYC levels are fully occupied ('saturated') and hence exogenous MYC is preferentially recruited to weakly bound genes. To test whether this explanation is correct, we made use of the observation that MYC/MAX heterodimers compete with MXD/MAX heterodimers and MAX homodimers for binding to their target sites (Conacci-Sorrell et al., 2014). If promoters were fully occupied by MYC/MAX heterodimers, one would predict that MXD proteins are completely displaced from these promoters. We tested this prediction by ChIP assays on four genes using an anti-MXD6 (MNT) antibody (Figure 1G). Binding of MXD6 was barely detectable above background for the two genes (NPM1, NCL), which are strongly occupied by endogenous MYC, and did not further decrease upon addition of doxycycline. In contrast, MXD6 occupancy was higher for two genes (HSPBAP1, FBX32), which are poorly bound by endogenous MYC, but strongly decreased upon induction of MYC. Taken together, the easiest model to explain the data is to suggest that a few hundred of promoters are saturated by endogenous MYC in U2OS cells and that overexpression of MYC leads to saturation of the majority of MYC-binding sites in promoters (Figure 1F). Absolute quantification of nuclear MYC allows an estimate of MYC binding affinities To understand whether the number of MYC molecules per cell in U2OS cells is able to saturate the numerous genomic binding sites, we quantified the absolute expression levels of MYC. A carboxy-terminal fragment of human MYC comprising amino acids 353 to 434 was purified to homogeneity and its amount was determined using spectrometric methods (Figure 2—figure supplement 1A). We then used defined quantities of this fragment to calibrate a series of immunoblots. After complete transfer of both, the recombinant protein and cellular MYC (Figure 2—figure supplement 1B), membranes were probed with the 9E10 monoclonal antibody, which recognizes an epitope (EQKLISEEDL) corresponding to amino acids 410 to 419 of human MYC (Figure 2A, Figure 2—figure supplement 1D–F). From triplicate experiments, we estimated that U2OS cells express approximately 100,000 molecules of endogenous MYC per cell and that this number increases to approximately 3x106 molecules of MYC upon induction with 1 μg/ml doxycycline (see calculations in Supplementary file 1). We performed immunofluorescence to estimate the cell-to-cell variation in MYC levels (Figure 2—figure supplement 2). Induction of MYC expression by doxycycline was also observed in situ by immunostaining for MYC and showed that MYC is overexpressed in all cells (Figure 2—figure supplement 2A). Staining and quantification with three different antibodies demonstrated that endogenous MYC levels vary less than +/− 3.7-fold in 80% of all cells (EtOH, Figure 2—figure supplement 2B,C) and less than +/− 2.9-fold upon overexpression (doxycycline, Figure 2—figure supplement 2D,E). Previous estimates found that two human tumor cell lines derived from small cell lung cancer and multiple myeloma express up to 880,000 molecules of MYC per cell (Lin et al., 2012), confirming that upon maximal induction with doxycycline most U2OS cells express MYC levels comparable or slightly higher to levels found in human tumor cells. Figure 2 with 2 supplements see all Download asset Open asset MYC binds with a wide range of affinity (EC50 values) to target genes. (A) Immunoblot of MYC in U2OSTet-On cells treated with 1 µg/ml doxycycline and a recombinant MYC protein fragment, which was used for absolute quantification of cellular MYC levels (M: marker). Absolute quantification is based on biological triplicates shown in Figure 2—figure supplement 1D–F. (B) Diagram of MYC occupancy calculated in ChIP-sequencing experiments of EtOH- or doxycycline-treated U2OSTet-On cells (y-axis) versus the cellular MYC concentration (x-axis). The line was fitted using a Michaelis-Menten model and non-linear regression. (C) Density plot of the distribution of the EC50 values calculated for all MYC-bound genes. Dashed lines indicate the cellular MYC concentration in uninduced (EtOH, blue line), 1 µg/ml doxycycline treated (Dox, dark blue line), or MYC-depleted (siMYC, light blue) U2OSTet-On cells. (D, E) GSE analysis using the MSigDB C5 (GO gene sets) collection, of genes sorted according to EC50 values. Enrichment plots of two gene sets enriched in the GSE analysis are shown as examples. NES: normalized enrichment score. Both, gene sets with very low (D) and very high (E) EC50 values are shown. https://doi.org/10.7554/eLife.15161.005 We used the estimated number of cellular MYC molecules and the nuclear volume of U2OS cells (Koch et al., 2014) to calculate the nuclear concentration of MYC. Knowing both the nuclear MYC concentration and the occupancy of every promoter in cells at endogenous and exogenous MYC levels (ChIP-sequencing +/− doxycycline) allowed us to estimate affinities for all MYC bound promoters. We calculated the concentration of MYC required for half-maximal occupancy of each promoter (EC50) and used it as a measure for the apparent binding affinity (Figure 2B). Promoters with low EC50 values showed a high, and those with high EC50 values a low binding affinity toward MYC. A density plot illustrates that EC50 values of individual promoters vary over a large concentration range (Figure 2C). To better understand the functional significance of the differences in promoter affinity, we used a modified gene set enrichment (GSE) analysis (Subramanian et al., 2005), which uses EC50 rather than expression values to test whether different biological processes can be stratified by promoter affinity towards MYC. In this analysis, promoters of MYC target genes that encode functionally related proteins are enriched if they exhibit similar EC50 values. Notably, genes encoding ribosomal proteins (RPs) showed the lowest EC50 values, followed by genes encoding proteins involved in biosynthetic processes, translation and ribosome biogenesis (Figure 2D; gene sets are shown in Supplementary file 2). These genes are thought to comprise a core signature of highly expressed MYC target genes (Ji et al., 2011). At the other extreme, genes encoding metabolite transporters, G-protein coupled receptors and genes involved in TGF-beta signaling and in the response to hypoxia are among those with the highest EC50 values (Figure 2E). We hypothesized that differences in promoter affinity enable distinct concentrations of MYC to regulate functionally different sets of target genes. Binding to DNA and to WDR5 accounts for high promoter affinity Given the high variation in EC50 values, we wondered which factors account for promoter affinity towards MYC. In a complex with MAX, MYC directly contacts E-box sequences in DNA. We initially tested whether the known DNA binding properties of MYC/MAX heterodimers can explain the EC50 values measured in ChIP-sequencing experiments. The heterodimer makes both base-specific contacts and contacts to the phosphate backbone of DNA and hence binds to canonical E-boxes (CACGTG), non-canonical E-boxes (CANNTG) and DNA with a random sequence (Nair and Burley, 2003). We modeled the binding behavior of MYC assuming canonical E-boxes and unspecific binding sites at random DNA sequences, which are in excess over the canonical E-boxes (model 1, Figure 3A, Appendix 1). This model ignores competition of MYC/MAX heterodimers with other E-box binding proteins with the same binding specificity, such as MXD/MAX complexes, as well as MITF, USF and TFE-3 (Conacci-Sorrell et al., 2014). We explored how the occupancy of canonical E-boxes in the experimentally determined range of cellular MYC concentrations depends on the dissociation constants of MYC reversibly binding to canonical E-boxes (KEbox) or unspecific binding sites (KNNNNNN) (Figure 3B; note that all simulations show steady state solutions discussed in Appendix 1). The simulations demonstrate that occupancies of canonical E-boxes above 90% can be observed for certain combinations of dissociation constants. In contrast, occupancy of unspecific binding sites is much smaller than 1% in the considered parameter space (Figure 3C) illustrating that the number of unspecific binding sites strongly exceeds the number of canonical E-boxes (Appendix 1). The model assumes that the entire genome is accessible and not blocked due to heterochromatin formation. An extended analysis investigating the impact of heterochromatin (Appendix 2) confirms that our assumption on genome accessibility hardly affects our presented results and conclusions. Values of dissociation constants have been previously determined for canonical E-boxes and for DNA with a non-E-box sequence. We fixed the dissociation constants in our subsequent model analyses to one pair of published values (Guo et al., 2014) (see red and blue lines in Figure 3B,C). The respective occupancies of canonical E-boxes and unspecific binding sites in the experimentally determined range of total MYC abundance are shown in Figure 3D (red and blue lines, respectively). Under these assumptions, MYC/MAX complexes are predicted to bind canonical E-boxes with a calculated EC50 value of 1x102 μM (Figure 3D). Comparison with the experimentally determined EC50 values showed that only 363 of 8,425 promoters show this or a higher EC50 value. Figure 3 Download asset Open asset Binding behavior of MYC in U2OS cells analyzed by mathematical modeling. (A) Schematic representation of model 1. For details see Appendix 1. (B, C) Plot illustrating regions of occupancy in the parameter space of dissociation constants KEbox and KNNNNNN as well as total amount of MYC. The grey area indicates the experimentally available concentration range, that is, EtOH to 1 µg/ml doxycycline (Dox) treatment. Regions of 1, 10, 50 and 90% occupancy of E-boxes (B) as well as 0.01% and 0.1% occupancy of NNNNNN sequences (C) are shown. The red line (B) and blue line (C) give the combination of the particular dissociation constants published by Guo et al. (2014). (D) Simulations show that occupancy of E-boxes (red line) by MYC is less than 10% in the measured range of MYC (grey area) while occupancy of NNNNNN (blue line) is below 1%. The EC50, which is the concentration of total MYC to obtain 50% occupancy, is calculated to be 1x102 μM for E-boxes. (E) The EC50 of E-boxes (1x102 μM estimated in D) can be reduced by decreasing the value of KEbox. Simulations predict that a reduction of KEbox (value published by Guo et al., 2014, is indicated by the dashed line) by about one order of magnitude already shifts the EC50 into the measured range of MYC (grey area). (F) Schematic representation of model 2. For details see Appendix 1. (G) In model 2, a reduction of the apparent dissociation constant of MYC and E-boxes as well as that of MYC and unspecific DNA sites are assumed by means of additional regulatory proteins such as WDR5. In the presence of WDR5, occupancy of E-boxes by MYC is above 95% (green line, EC50= 1.4x10-2 μM) and occupancy of unspecific DNA sites by MYC is above 50% (yellow line, EC50 =2.7x10–1 μM). Occupancy of E-boxes or unspecific DNA sites that are not bound by WDR5 (red and blue line, respectively) remain however below 10% and 1%, respectively, in the measured range of MYC (grey area). https://doi.org/10.7554/eLife.15161.008 The majority of E-boxes located in promoters showed considerably lower EC50 values than those predicted by the affinity to E-box-DNA, indicating that binding to DNA alone is not sufficient to account for the chromatin occupancy of MYC observed by ChIP-sequencing. Model simulations predict that a reduction of the dissociation constant of MYC and E-boxes by about one order of magnitude shifts the EC50 value into the measured range of MYC molecules (Figure 3E). To explore potential underlying molecular mechanisms, we searched for features, which identify promoters with high affinity for MYC. Consistent with the DNA binding properties, we found a strong correlation between the occupancy of endogenous MYC and the occurrence of canonical E-box sequences in the binding region (Figure 4A, Figure 4—figure supplement 1A–F). In contrast, non-consensus E-box sequences are only moderately enriched in MYC peaks and their frequency does not positively correlate with MYC binding (Figure 4—figure supplement 1B,D-F). In addition to canonical E-box sequences in the binding region, occupancy by endogenous MYC positively correlated with overall expression of the respective gene (Figure 4B) and with features of open chromatin, such as trimethylation of histone H3K4 (Figure 4C). This observation is in agreement with several previous reports (Guccione et al., 2006; Guo et al., 2014; Lin et al., 2012; Nie et al., 2012). Recent work has identified WDR5, a WD40-repeat-containing protein, which is a part of the MLL/SET methyltransferases that methylate H3K4 and the MOF/NSL histone acetyltransferases that acetylate histone H4, as a direct interaction partner of MYC (Thomas et al., 2015). MYC binds to WDR5 with a KD of 9.3 µM via MYC BoxIII (Thomas et al., 2015), a domain that is not part of the DNA-binding domain, suggesting that binding of MYC to WDR5 occurs independently of binding to DNA. A modified model (model 2; Figure 3F; see also Appendix 1) that assumes (i) that WDR5 is constantly bound to its target sites (ii) that MYC and WDR5 are free to bind to each other when both are bound to chromatin in close proximity predicts an EC50 value of 0.014 µM for MYC occupancy of an E-box in the presence of WDR5 (Figure 3G). This value is lower than the one estimated experimentally for the large majority of promoters, arguing that the MYC/WDR5 interaction is of sufficient high affinity to explain the high occupancy of promoters with low EC50 values (7,963/8,425). Figure 4 with 2 supplements see all Download asset Open asset E-box occurrence, expression level and chromatin status of target genes influence MYC binding. (A) Binned plot for the number of genes in each bin with a canonical E-box (CACGTG) in the MYC peak versus MYC occupancy in U2OSTet-On treated with EtOH. Genes were sorted according to MYC occupancy and divided into 20 bins. Each dot represents the average of 422 genes. (B) Binned plot as in A, but with the mRNA expression of the respective gene. Reads per kilobase per million (rpkm) are shown on the y-axis. (C) Binned plot as in A, but with H3K4me3 status of the respective gene. (D) Venn diagram displaying the promoter-close (+/− 5 kb) binding site overlap of WDR5, wild-type MYC (top), and a MYC mutant, which is compromised in binding to WDR5 ('MYC mut', bottom). Both, wild-type and mutant MYC were fused to a Flag epitope and stably expressed in HEK293 cells by Thomas et al. (2015). (E) Binned plot for MYC binding vs EC50 values. Genes bound by MYC in U2OS cells were sorted according to their EC50 values and correlated to average occupancy of a MYC mutant compromised in binding to WDR5 (blue dots) or wildtype MYC (orange dots). Values for IgG are shown as a background control (grey dots). Panel D and E are re-analyses based on published data (Thomas et al., 2015). (F) Binned plot for the number of genes in each bin having a canonical E-box (CACGTG) in the MYC peak versus MYC recruitment. Genes were sorted according to MYC recruitment and divided in 20 bins. Each bin represents 422 genes. (G) Binned plot as in F, but the mRNA expression of the respective gene. Reads per kilobase per million (rpkm) is shown on the y-axis. (H) Binned plot as in F, but H3K4me3 status of the respective gene was analyzed. https://doi.org/10.7554/eLife.15161.009 The model predicts that occupancy by MYC is strongest at promoters containing both E-boxes and WDR5-binding sites but is also high for promoters bound by WDR5 but lacking E-boxes (EC50 value of 0.27 μM; Figure 3G, yellow curve). This prediction could be confirmed by stratifying all MYC bound promoters in U2OS cells by these features and analyzing the individual groups for occupancy by endogenous MYC, functional annotation and expression (Figure 4—figure supplement 2A–C). Promoters bound by WDR5 and containing consensus E-box sequences are most strongly bound by MYC (Figure 4—figure supplement 2A), enriched in genes encoding for nuclear proteins (Figure 4—figure supplement 2B) and are associated with high expression (Figure 4—-figure supplement 2C). The two central predictions of the model are that (i) binding sites are little occupied at cellular MYC concentrations if only the affinity of MYC to E-boxes is considered (Figure 3G, red curve), and that (ii) the occupancy strongly increases if the interaction of MYC with WDR5 is considered in addition (Figure 3G, green curve). These predictions are valid for at least 80% of cells when the cell-to-cell variation in the population is taken into account (Figure 2—figure supplement 2F). To test the notion that the interaction between MYC and WDR5 is critical for the high occupancy of some promoters, we re-analyzed published ChIP-sequencing data, performed in HEK293 cells, for WDR5, wild-type MYC and a mutant allele of MYC ('MYCWBM'), which is strongly compromised in binding to WDR5 (Thomas et al., 2015). In agreement with the published data, our re-analysis showed that (i) the majority of promoters is bound by WDR5, (ii) almost all binding sites of MYC in promoters overlap with WDR5 binding and (iii) disruption of the interaction between MYC and WDR5 strongly decreases MYC binding to chromatin (Figure 4D). Strikingly, analysis of MYCWBM binding in HEK293 cells showed that eliminating MYC’s ability to interact with WDR5 most strongly affects high-affinity binding sites" @default.
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• W2987486039 title "Author response: Different promoter affinities account for specificity in MYC-dependent gene regulation" @default.
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