Different promoter affinities account for specificity in MYC-dependent gene regulation
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 .
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 . 10 . 7554/eLife . 15161 . 003Figure 1 . 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 . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 00310 . 7554/eLife . 15161 . 004Figure 1—figure supplement 1 . Effect of different peak calling programs and parameters on peak numbers . ( A ) Number of peaks for endogenous MYC according to different peak calling programs at default parameters . ( B ) Percentage of overlapping MYC peaks called by different peak calling programs . Shown is the percentage of joint peaks relative to the dataset with the smaller number of total peaks . ( C ) Number of peaks for endogenous MYC according to different p-value adjustments in MACS14 . The number of positive and negative peaks is shown with a logarithmic scale . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 004 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 ) . 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 . 10 . 7554/eLife . 15161 . 005Figure 2 . 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 . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 00510 . 7554/eLife . 15161 . 006Figure 2—figure supplement 1 . Quantification of MYC molecules per U2OS cell . ( A ) Coomassie staining of the recombinant MYC fragment used to quantify cellular MYC levels documenting the purity of the protein . ( B ) Coomassie staining of a polyacrylamide gel after the transfer of protein to a PVDF membrane ( used for immunostainings in one of the panels D–F ) documenting complete transfer . ( C ) Plot showing the quantification of signals by recombinant MYC protein in relation its protein amount ( D–F ) . Fitting curves were used for estimating cellular MYC concentrations ( see numbers in Supplementary file 1 ) . ( D–F ) Immunoblots of recombinant MYC protein and MYC in U2OSTet-On cells treated with different doxycycline concentrations , EtOH and a siRNA against MYC . The 9E10 antibody was used to detect both; recombinant and cellular MYC . Immunoblots were analyzed by fluorescence-based quantitative immunoblotting . The absolute cellular MYC levels were calculated as described in Supplementary file 1 based on triplicate experiments ( M: marker ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 00610 . 7554/eLife . 15161 . 007Figure 2—figure supplement 2 . Variation of MYC levels within the cell population demonstrates validity of the model conclusions for the majority of cells . ( A ) Confocal fluorescence showing the variation of MYC expression in U2OSTet-On cells treated with 1 µg/ml doxycycline and ethanol control . Images were taken under the same imaging conditions . The scale bar represents 20 µm ( blue: HOECHST , violet: Y69 ) . ( B , D ) Confocal fluorescence images showing the variation of MYC expression in U2OSTet-On cells treated with 1 µg/ml doxycycline ( D ) and ethanol ( B ) . The scale bar represents 10 µm . ( C , E ) Kernel density plots showing the quantification of MYC staining as shown in B and D . Grey dashed line: log2FC value defining a region containing 80% of all cells , yellow bar: outlier . ( F ) Simulations as in Figure 3G , but yellow areas here indicate the fold variation of MYC expression in the cell population as calculated from panels ( C ) and ( E ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 007 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 . 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 . 10 . 7554/eLife . 15161 . 008Figure 3 . 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 ) . DOI: http://dx . 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 ) . 10 . 7554/eLife . 15161 . 009Figure 4 . 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 . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 00910 . 7554/eLife . 15161 . 010Figure 4—figure supplement 1 . E-box occurrence and level of expression correlate with MYC binding and EC50 values . ( A , B ) Gaussian kernel density estimate of endogenous MYC occupancy stratified by the number of consensus ( A ) or non-consensus ( B ) E-boxes in the promoter . p-Values were calculated with a two-sided Wilcoxon rank-sum test . ( C , D ) Boxplots showing EC50 values of promoters with different numbers of consensus ( C ) or non-consensus ( D ) E-boxes . p-Values were calculated with a two-sided Wilcoxon rank-sum test comparing promoters without E-boxes to the indicated group . Outliers are not shown . ( E , F ) Distribution of consensus and non-consensus E-boxes around the MYC peak center ( E ) in promoter-close peaks ( doxycycline ) and control regions 1 kb upstream ( F ) . ( G ) Binned plot for the number of genes in each bin with a canonical E-box ( CACGTG ) in the MYC peaks versus the EC50 values for MYC binding . Genes were sorted according to their EC50 values and divided into 20 bins . Each dot represents the average of 417 genes . ( H ) Binned plot as in G , but the mRNA expression of the respective genes ( rpkm: reads per kilobase per million ) is shown in the y-axis . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 01010 . 7554/eLife . 15161 . 011Figure 4—figure supplement 2 . Promoters that are bound by WDR5 and contain E-boxes are most strongly occupied by MYC . ( A ) Gaussian kernel density estimate of endogenous MYC occupancy stratified by the number of consensus E-boxes in concert with WDR5 peaks in the promoter . p-values were calculated with a two-sided Wilcoxon rank-sum test . ( B ) Gene ontology analysis of 400 randomly chosen genes with WDR5 peak and consensus E-box ( bottom ) or WDR5 peak but without E-box ( top ) in the promoter . For each class , the top four enriched GO terms are shown . q-values are calculated using a Benjamini-Höchberg correction for multiple testing . ( C ) Box plot showing expression level ( rpkm: reads per kilobase per million ) of MYC-bound genes with WDR5 peak and E-box ( n =2 , 593 ) , WDR5 peak without E-box ( n =4 , 771 ) and MYC-bound genes with neither WDR5 peak nor E-box ( n =699 ) . p-Values were calculated with a two-sided Wilcoxon-rank sum test . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 011 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 with low EC50 values ( Figure 4E ) . We conclude that known interactions of MYC with DNA together with promoter-bound factors can account for the range of promoter occupancies observed in vivo . Since target gene expression , H3K4me3 and the occurrence of E-box sequences stratify high-affinity binding sites ( Figure 4A–C , Figure 4—figure supplement 1G , H ) , we wondered whether this correlation also holds true for MYC that is recruited to promoters upon doxycycline induction . Importantly , we found a strong anti-correlation between the recruitment of exogenous MYC on one hand and the occurrence of E-boxes ( Figure 4F ) , the expression level of target genes ( Figure 4G ) and trimethylation of histone H3K4 ( Figure 4H ) on the other hand . This highlights that the major change in occupancy is due to MYC binding to low-affinity binding sites and to weakly expressed genes when MYC levels increase . Previously , we found that genes encoding ribosomal proteins and proteins involved in ribosomal biogenesis are not regulated in response to doxycycline-mediated MYC overexpression ( Walz et al . , 2014 ) , supporting the hypothesis that MYC binding sites in their promoters are fully occupied by MYC at endogenous levels . To exclude the possibility that these genes are not regulated by MYC in U2OS cells , we depleted endogenous MYC in U2OS cells by siRNA and analyzed gene expression by RNA-sequencing ( Figure 5A–D ) and MYC binding by ChIP-sequencing ( Figure 5—figure supplement 1A–G ) . RNA-sequencing identified 753 genes being down- and 1224 genes being significantly up-regulated ( log2FC < −1 or > 1 , q-value < 0 . 01 , Figure 5B ) . Expression data were analyzed by GSEA to identify functional groups of MYC-regulated genes . Strikingly , ribosomal protein genes and genes encoding proteins involved in ribosomal biogenesis are most strongly down-regulated upon siRNA treatment ( Figure 5C , D ) . Accordingly , ChIP-sequencing upon depletion of MYC ( Figure 5—figure supplement 1A , C ) demonstrated global loss of MYC binding to high-affinity binding sites ( Figure 5—figure supplement 1B , D ) including promoters of ribosomal protein genes ( Figure 5—figure supplement 1E , F ) . Strikingly , genes , which show the highest affinity for MYC , most strongly respond to depletion of endogenous MYC ( Figure 5—figure supplement 1G ) . We confirmed our previous observations that these gene sets are not regulated when MYC is overexpressed ( Figure 5E ) . To test whether similar dose-dependent transcriptional responses to MYC are also observed during tumorigenesis , we utilized data from a murine model for MYC-driven B cell lymphomagenesis ( Figure 5F–H ) ( Sabò et al . , 2014 ) . Expression profiles are published for three different conditions , ( i ) resting B cells expressing only low levels of endogenous MYC ( 'control' ) , ( ii ) proliferating B cells expressing intermediate levels of MYC from the Eµ-MYC transgene ( 'pre-tumor' ) and ( iii ) transformed B cells selected for very high MYC expression ( Sabò et al . , 2014 ) . Strikingly , ribosomal protein genes , hence the genes with lowest EC50 values , are strongly induced when the pre-tumor condition is compared to control cells ( Figure 5F , H ) . In contrast , expression of ribosomal protein genes does not increase further when tumors are compared with pre-tumor cells ( Figure 5G , H ) , although MYC protein levels increase about eight fold ( Sabò et al . , 2014 ) . Conversely , some genes that are regulated by exogenous MYC in U2OS cells and have low-affinity promoters , like those encoding transmembrane transporters , are also regulated when comparing the tumor to pre-tumor condition in B cell lymphomagenesis ( Figure 5—figure supplement 1H , I ) . Therefore , we propose that selective transcriptional responses by MYC at physiological and oncogenic levels are similar in vivo and in vitro . 10 . 7554/eLife . 15161 . 012Figure 5 . Promoter affinity for MYC stratifies functionally different gene sets . ( A ) Immunoblot of MYC upon transfection of U2OSTet-On cells with the indicated siRNA . Vinculin was used as loading control . ( B ) Plot for regulation of genes upon siRNA-mediated depletion of MYC vs relative expression levels ( known as M/A-plot ) . Red dots represent significantly regulated genes ( q-value <0 . 01 , q-values were estimated by Benjamini-Hoechberg-corrected p-values calculated by EdgeR ) . The experiment was performed in biological triplicates . ( C ) GSE analysis comparing gene expression profiles of siCtr- or siMYC-transfected U2OSTet-On cells . NES: normalized enrichment score . ( D ) Enrichment plot of the 'Structural constituent of ribosome' gene set found in C upon depletion of endogenous MYC by siRNA . ( E ) Enrichment plot of the same gene set as in D , but gene expression profiles of U2OSTet-On cells uninduced ( EtOH ) or induced with 1 µg/ml doxycycline were used . ( F-H ) Gene expression analysis comparing the gene expression profile of a ribosomal gene set in the Eµ-MYC model of B cell lymphomagenesis in three different conditions . Differential expression is shown by GSE analysis comparing a pre-tumor condition to control condition ( F ) and a tumor condition to a pre-tumor condition ( G ) or by boxplots ( H ) . Panel F to H are re-analyses of published B cell expression profiles ( Sabò et al . , 2014 ) . ( I ) Boxplots of gene sets found in panel C ( blue ) , genes regulated upon MYC knockout in fibroblasts ( green ) ( Perna et al . , 2012 ) , gene sets regulated by MYC overexpression ( orange ) ( Walz et al . , 2014 ) and selected metabolic processes ( violet ) . The y-axis shows EC50 values calculated as shown in Figure 2 . p-values: One-sided Mann-Whitney-Wilcoxon test comparing the indicated GO terms to the median of 'RNP complex' ( dashed line ) . Outliers not shown . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 01210 . 7554/eLife . 15161 . 013Figure 5—figure supplement 1 . Changes in chromatin binding upon MYC depletion correlate with transcriptional responses . ( A ) Immunoblot of MYC in U2OSTet-On cells tranfected with siRNA targeting MYC and control siRNA . ( B ) Summary of MYC ChIP-sequencing results of MYC-depleted U2OSTet-On cells and control cells . ( C ) List of parameters used for alignment and peak calling in the ChIP-sequencing experiment . ( D ) Heat maps documenting binding of MYC in U2OSTet-On cells transfected with siRNA targeting MYC or control cells . A window of 5 kb around all MYC peaks is shown . Input is used as control and intensity of color indicates binding strength . ( E ) ChIP-sequencing traces for MYC of a large genomic region including the RPL8 gene ( left ) . MYC binding to the RPL8 promoter is shown enlarged on the right side . ( F ) Heat maps documenting binding of MYC to the promoters of ribosomal protein genes ( Structural constituent of ribosome: GO0003735 , see list of genes in Supplementary file 2 ) . ( G ) Binned plot for the comparison of MYC-mediated changes in gene expression ( y-axis ) in U2OSTet-On cells upon siRNA-mediated depletion of MYC versus MYC recruitment ( change in occupancy , x-axis ) . MYC-bound genes were sorted by EC50 values and divided in 30 equally sized bins . Each dot represents the mean value of the bin . The bin containing the RPL8 gene shown in panel E is indicated . The black curve shows quadratic LOESS fitting . ( H ) Gene expression analysis in the Eµ-MYC model of B cell lymphomagenesis ( Sabò et al . , 2014 ) of biological processes with low affinity for MYC . Biological processes with low EC50 values ( as in Figure 2E ) were analyzed by gene set enrichment analysis . The normalized enrichment score ( NES ) indicates regulation of these gene sets between tumors ( T ) and pre-tumors ( P ) in the Eµ-MYC model as in U2OS cells ( positive NES: MYC-activated processes , negative NES: MYC-repressed processes ) . ( I ) Enrichment plot of one gene set from H in the Eµ-MYC model ( Sabò et al . , 2014 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 013 To further explore the correlation between binding affinity and selective transcriptional responses , we compared median EC50 DNA binding values of gene sets that respond to depletion of endogenous MYC to EC50 values of gene sets that are regulated in response to MYC overexpression ( Figure 5I ) . Gene sets , which are strongly regulated upon overexpression , for example , genes involved in angiogenesis and cell migration ( Figure 5I , orange boxplots ) , show a significantly higher median EC50 value than gene sets derived from the depletion of endogenous MYC ( Figure 5I , blue boxplots ) . We noted that also genes involved in circadian rhythm are regulated upon MYC overexpression and showed high EC50 values , consistent with the recent demonstration that disruption of the circadian clock is a critical transforming function of MYC ( Altman et al . , 2015 ) . We concluded that binding affinity determines specific MYC-mediated transcriptional responses at physiological and oncogenic MYC concentrations . To further support this conclusion , we analyzed expression data from embryonic fibroblasts isolated from mice that carry floxed MYC alleles ( Perna et al . , 2012 ) . Strikingly , EC50 values of genes that are down-regulated upon deletion of endogenous MYC are very low ( Figure 5I , green boxplot ) confirming that , as in U2OS cells , at physiological levels MYC regulates genes with the highest binding affinity . In view of reports , which demonstrate that altering cellular metabolism is a transforming mechanism of MYC ( Stine et al . , 2015 ) , we analyzed the affinity of promoters of genes encoding proteins involved in selected metabolic processes and indeed observed elevated EC50 values for lipid- and purine metabolism and potentially glycolysis ( Figure 5I , violet boxplots ) . Our data suggest that MYC regulates different sets of genes at physiological and oncogenic levels and that these gene sets can be stratified by promoter affinity . We wondered , if there are indeed two distinct modes of regulation or if MYC-mediated responses rather vary continually upon changes in cellular MYC concentrations . To test this , we gradually manipulated MYC levels in U2OSTet-On cells by titrating the amount of doxycycline and calculated the absolute number of MYC molecules in triplicate experiments ( Figure 6A , Figure 2—figure supplement 1D–F ) . Analysis of gene expression profiles at five different concentrations of doxycycline revealed different response patterns for different gene sets ( Figure 6B ) , ranging from hardly any response to MYC overexpression ( 'structural constituent of ribosome' ) , medium-level responses ( 'ribonucleoprotein complex biogenesis' ) , to gene sets , which responded predominantly at very high MYC levels ( 'substrate-specific transporter activity' ) . The RNA-sequencing data enabled us to calculate EC50 values for regulation and to correlate them with EC50 values for binding . Importantly , the observed differences in dose response correlate with the promoter affinity of the genes involved in these processes ( Figure 6C ) . We conclude that differences in promoter affinity can account for the distinct transcriptional response programs observed in biological systems exhibiting different MYC expression levels ( Figure 7 ) . 10 . 7554/eLife . 15161 . 014Figure 6 . MYC levels determine the regulation of different transcriptional response programs . ( A ) Bar plot indicating MYC levels in U2OSTet-On cells treated with EtOH or with different concentrations of doxycycline . Data are shown as mean ± standard deviation of three independent biological replicates , which are shown in Figure 2—figure supplement 1D–-F . Table of calculated MYC molecules and concentration per cell ( see Supplementary file 1 for calculation ) . ( B ) Heat maps for regulation of genes belonging to the three gene sets ( Structural constituent of ribosome: GO_0003735; Ribonucleoprotein complex biogenesis: GO_0022613; substrate-specific transporter activity: GO_0022892 ) identified by GSEA in Figure 2D , E and Figure 5C . For all conditions , log2FC was calculated relative to the EtOH sample and only transactivated genes are shown . Supplementary file 2 contains gene set lists used in this study . ( C ) Plot for median EC50 values for binding and regulation of gene sets from Figure 5I . EC50 values for regulation were calculated using RNA-sequencing data from the MYC depletion ( Figure 5A ) and the MYC titration experiments ( Figure 6A ) . ( D ) Heat maps documenting the regulation of genes down-regulated by wild-type MYC and derepressed by MYCV394D or in cells depleted of MIZ1 . Log2FC calculated as in B . ( E ) qRT-PCR analysis from U2OSTet-On induced for MYC overexpression with increasing doxycycline concentrations . Both , MYC mRNA levels ( x-axis ) and mRNA levels of MYC target genes ( y-axis ) are plotted . Dashed lines indicate half-maximal gene regulation . Please note the differences in MYC expression needed for half maximal target gene regulation between transactivated ( orange curve ) and repressed ( blue curve ) genes . The x-axis shows MYC mRNA levels relative to the EtOH control . ( F ) Boxplots of genes repressed by wild-type MYC but not by MYCV394D ( MYCVD , 'Down ( direct ) ' ) or by wild-type MYC when MIZ1 is depleted via shRNA ( shMIZ1 , 'Down ( direct ) ' ) . MYC-activated genes ( 'Up' ) and genes , which are repressed in the absence of a functional MYC/MIZ1 complex ( 'Down ( indirect ) ' ) were used as reference gene sets . p-values ( p ) , One-sided Mann-Whitney-Wilcoxon test . Outliers are not shown in the plot . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 01410 . 7554/eLife . 15161 . 015Figure 7 . Schematic models . ( A ) High-affinity binding sites , for instance characterized by E-boxes and WDR5 binding , are already highly occupied at physiological MYC levels ( medium MYC ) in dividing cells . At oncogenic concentration ( high MYC ) also low-affinity ( low aff . ) binding sites can become occupied by MYC and regulation of these low-affinity genes is indicative for transformation ( ECM: extracellular matrix ) . ( B ) MYC regulates distinct biological processes at different cellular concentrations . DOI: http://dx . doi . org/10 . 7554/eLife . 15161 . 015 Finally , a prominent discrepancy among reports analyzing the transcriptional consequences of MYC level manipulation is the presence or absence of direct MYC-mediated repression ( Lin et al . , 2012; Nie et al . , 2012; Sabò et al . , 2014; Walz et al . , 2014 ) . As direct repression is most apparent at high levels of MYC ( Wiese et al . , 2015 ) , we hypothesized that differences in MYC expression levels might explain this seeming contradiction . Direct MYC-mediated repression largely depends on complex formation with MIZ1 ( Peukert et al . , 1997 ) , which is strongly reduced when expressing a point mutant of MYC ( MYCV394D ) . By using previously published datasets ( Walz et al . , 2014 ) , we identified direct MYC-repressed sets of genes assuming that they are re-expressed if MIZ1 is depleted ( shMIZ1 ) or if MYCV394D instead of wild type MYC is expressed . Interestingly MYC/MIZ1-mediated repression takes place at high levels of MYC , both in a global analysis ( Figure 6D ) as well as for individual genes ( Figure 6E ) . Accordingly , genes directly repressed by MYC ( 'Down ( direct ) ' in Figure 6F ) , show significantly higher EC50 values than transactivated genes ( 'Up' ) , whereas indirectly repressed genes ( 'Down ( indirect ) ' in Figure 6F ) show affinities comparable to that of transactivated genes . We concluded that direct MYC-mediated repression is a predominant form of transcriptional regulation at high ( oncogenic ) levels of MYC , which may not be reached during normal cell growth ( Figure 7A ) .
The aim of this study was to understand whether an analysis of MYC function at different protein levels in combination with mathematical modeling can reconcile two seemingly conflicting observations: Whereas MYC regulates specific genes in some biological settings ( 'specifier model' ) , in others MYC enhances expression of all genes transcribed by RNA polymerase II ( 'amplifier model' ) . Our study has led to two major conclusions . The first conclusion is that direct interaction with DNA alone is not sufficient to account for the observed occupancies of core promoters by MYC/MAX complexes . This result is in agreement with experimental observations showing that the positions occupied by MYC/MAX complexes across the human genome closely correlate with the RNA polymerase II transcription machinery even on promoters , which do not contain E-boxes ( Guccione et al . , 2006; Guo et al . , 2014 ) . The conclusion is based on a modeling approach that assumes two classes of binding sites , a high-affinity class of binding sites with consensus E-boxes and a second class of binding sites containing non-specific DNA . This assumption is an over-simplification , since MYC/MAX complexes have low affinity for binding sites containing non-consensus E-box sequences , which exceeds the affinity for non-specific DNA ( Blackwell et al . , 1993 ) ( Figure 4—figure supplement 1E , F ) . Furthermore , multiple other transcription factors recognize the same consensus sequence as MYC/MAX proteins , suggesting that there is significant competition for E-box binding among these complexes in cells ( Wolf et al . , 2015 ) . As a consequence , the current model overestimates the fraction of MYC/MAX complexes bound to consensus E-boxes . These considerations reinforce the conclusion that DNA binding alone cannot account for the high occupancies of core promoters by MYC/MAX complexes in cells . The mathematical analysis shows that the interaction of MYC/MAX with promoter-bound WDR5 can strongly enhance occupancy of core promoters . Consistent with this finding , a mutant allele of MYC unable to bind to WDR5 shows much reduced binding to high-affinity promoters . MYC does not only interact with WDR5 , but with several components of the transcription machinery including P-TEFb , TBP and TRRAP ( Bouchard et al . , 2004; Eberhardy and Farnham , 2002; Hateboer et al . , 1993; McMahon et al . , 1998; Rahl et al . , 2010 ) . Additionally , we recently identified the interaction of another conserved domain of MYC , MYC BoxI , with the PAF1C complex that is located at core promoters ( Jaenicke et al . , 2016 ) . We restricted our analysis to WDR5 , since no dissociation constants for other protein/protein interactions of MYC have been published . However , MYC high-affinity binding sites can be observed at genomic regions , which are not in proximity to WDR5 binding sites , suggesting that other factors are involved in establishing MYC genomic binding pattern . Future ChIP-sequencing studies of MYC upon depletion of MYC interaction proteins will clarify the impact of individual factors on chromatin binding of MYC . Most likely , therefore , genome-wide occupancies of MYC/MAX complexes result both from interactions of MYC/MAX complexes with DNA and from protein/protein interactions with resident chromatin proteins . We propose that the dependence of MYC/MAX chromatin binding on interactions with proteins that are themselves influenced by existing gene expression patterns at different physiological states of cells , contributes to specific gene regulation by MYC even though MYC´s mechanism of action is the same at all promoters ( as an example , see Liu et al . ( 2015 ) ) . Our analysis leads to the hypothesis that conserved protein/protein interactions of MYC are critical for targeting MYC/MAX complexes to specific classes of binding sites such as core promoters in vivo . The second conclusion that can be drawn from our data is that promoter affinity for MYC confers specificity to gene regulation . Our results show that promoters differ in their apparent affinity for MYC and demonstrate a close correlation between apparent promoter affinity measured by ChIP-sequencing and response to different concentrations of MYC measured by RNA-sequencing at different MYC levels . This correlation leads to the conclusion that a gene is regulated by MYC in a given experimental setting , if the occupancy of its promoter ( or a relevant enhancer ) by MYC changes in this setting . Importantly , measurements of MYC levels and modeling approaches show that promoters can be saturated by levels of MYC that are reached in proliferating cells ( Figure 7A ) . As a result , expression of genes may not respond to MYC in a certain experimental setting ( such as a doxycycline-mediated induction of high MYC levels ) since the promoters are fully occupied before addition of doxycycline . This is likely to be true for strongly transcribed genes as many ribosomal protein genes and genes encoding proteins involved in ribosome biogenesis and translation . The experimentally observed specificity in gene regulation can therefore be explained without the need to invoke productive and non-productive modes of interaction of MYC with chromatin . Consistent with this notion , the analysis of MYC-driven lymphomagenesis shows that MYC can contribute to oncogenic transformation via two different mechanisms: low levels of constitutive MYC enhance expression of genes with high-affinity promoters , such as genes encoding proteins involved in ribosome function . This is seen when pre-tumorigenic B-cells are compared with control lymphocytes . MYC levels further increase at the transition to frank lymphomas and there is clear evidence for further increases in MYC levels during progression of several tumor entities , as for example in APC-mutant colorectal tumors ( Kress et al . , 2011; Myant and Sansom , 2011; Siemens et al . , 2011 ) . This additional increase in MYC levels will enhance occupancy of promoters that are lowly occupied in normal and proliferating cells . Since differences in apparent affinity and in EC50 values stratify classes of promoters of genes that encode proteins that differ in function , we propose that this will alter the physiological and metabolic state ( Stine et al . , 2015 ) of the tumor cells ( Figure 7B ) . Notably , promoters with the lowest apparent affinity are found in genes that encode proteins involved in nutrient transport , G-protein coupled receptors and in the response to hypoxia . Expression of these genes responds to further increases in MYC levels arguing that they are the basis for the selective pressure for increases in MYC levels . Since many tumors depend on ( 'are addicted to' ) high MYC levels , inhibition of proteins encoded by this class of target genes can be a rational strategy for selective inhibition of MYC-dependent tumor growth . Finally , we note that virtually all genes that are repressed by MYC/MAX in complex with MIZ1 display low apparent affinities and high EC50 values , suggesting that MIZ1-mediated repression occurs predominantly at high MYC levels . MIZ1-mediated repression is required for tumor formation in MYC-driven lymphoma ( van Riggelen et al . , 2010 ) and medulloblastoma ( Vo et al . , 2016 ) , suggesting that targeting the complex may provide a second strategy with a significant therapeutic window for MYC-dependent tumors .
U2OS , HeLa , HEK293 and HCT116 cells were cultivated in DMEM ( Sigma-Aldrich , St . Louis , USA ) supplemented with 10% FCS ( EMD Millipore , Billerica , USA ) and 1% penicillin/streptomycin ( Sigma-Aldrich ) . IMECs and HMLE cells were cultivated in DMEM/F12 ( co ) supplemented with 1% penicillin/streptomycin ( Sigma-Aldrich ) , 10 µg/ml insulin ( Sigma-Aldrich ) , 0 . 5 µg/ml hydrocortisone ( Sigma-Aldrich ) and 20 ng/ml EGF ( Life Technologies , Carlsbad , CA ) . MCF10A cells were cultivated in DMEM/F12 ( Thermo Fisher Scientific , Waltham , USA ) supplemented with 5% horse serum ( Sigma-Aldrich ) , 10 µg/ml insulin ( Sigma-Aldrich ) , 0 . 5 µg/ml hydrocortisone ( Sigma-Aldrich ) and 20 ng/ml EGF ( Life Technologies ) and 100 ng/ml choleratoxin ( Sigma-Aldrich ) . Cell lines were tested for mycoplasma and authenticated by STR profiling ( HCT116 , MCF10A , HeLa , U2OS ) . They were not found to be on the list of commonly misidentified cell lines ( International Cell Line Authentication Committee , with the exception of HEK cells ) and received from research labs , companies ( HCT116: LGC genomics ) or public repositories ( HEK293: ATCC ) . For siRNA transfections , the RNAiMAX reagent ( Thermo Fisher Scientific ) and the Opti-MEM medium ( Thermo Fisher Scientific ) were used following the manufacturers’ instructions . A pool of siRNA against MYC ( ON-TARGETplus SMARTpool MYC , L-003282-02-0020 ) and a control pool of siRNA ( ON-TARGETplus Non-targeting Pool , D-001810-10-20 ) were purchased from GE Healthcare . Cells were harvested 48 hr after transfection . The mRNA sequence of the dimerization and DNA-binding domain of human MYC was cloned into the pETM11 ( EMBL-Heidelberg ) vector . The protein was expressed as an N-terminally His-tagged protein in ArcticExpress ( DE3 ) RIL cells ( Agilent Technologies , Santa Clara , USA ) after an OD of 0 . 8 was reached by induction with 0 . 1 mM isopropyl-β-thiogalactoside ( IPTG ) at 14°C for 18 hr . The protein was purified to homogeneity by metal affinity chromatography ( Ni-NTA , Thermo Fisher Scientific ) followed by anion exchange chromatography ( AEC ) . AEC was performed using a MonoQ 10/100 GL column ( GE Healthcare , Chicago , USA ) equilibrated with 20 mM HEPES pH 8 . 0 and 0 . 25 M NaCl . The protein did not bind to the column but eluted in the flow through . Size exclusion chromatography was performed using a HiLoad 16/60 Superdex 200 prep grade column ( GE Healthcare ) to confirm its monomeric state and exclude aggregation . The protein was concentrated after AEC to 0 . 59 mg/ml based on the calculated extinction coefficient using ProtParam ( SwissProt ) and then flash frozen adding 10% glycerol for storage . Cells were either lysed in lysis buffer ( 50 mM HEPES pH 7 . 9 , 140 mM NaCl , 1 mM EDTA , 1% Triton-X-100 , 0 . 1% Sodium deoxycholate , 0 . 1% SDS ) with protease and phosphatase inhibitor cocktails ( Sigma-Aldrich ) or harvested by trypsinization , counted and lysed directly in SDS sample buffer . The same number of cells or total protein amount was loaded for each sample on Bis-Tris or Tris-Glycine gels and transferred to PVDF membranes ( EMD Millipore , Billerica , USA ) . To detect the MYC protein the 9E10 antibody ( amino acids: 410–419 ) or the anti-MYC antibody ( Y69: N-terminus , Abcam , Cambridge , UK ) were used . To detect Vinculin the V9131 anti-Vinculin antibody ( Sigma-Aldrich ) was used . The immunoblots were visualized and quantified using the LAS-4000 mini ( Fujifilm , Tokyo , Japan ) or the Odyssey CLx Infrared Imaging System ( LI-COR , Lincoln , USA ) . The number of MYC molecules/cell was calculated by comparing the signal of recombinant MYC protein with cell lysates from U2OS cells in biological triplicates in quantitative immunoblots ( Odyssey CLx Infrared Imaging System ) . To determine the nuclear MYC concentration , the size of U2OS nuclei was obtained from ( Koch et al . , 2014 ) . Biological replicates are defined by individual U2OS cell harvestings . ChIP and ChIP-sequencing was performed as described previously ( Walz et al . , 2014 ) . Briefly , cells were crosslinked with 1% formaldehyde for 10 min at room temperature . After extraction , nuclei were lysed in lysis buffer ( 50 mM HEPES pH 7 . 9 , 140 mM NaCl , 1 mM EDTA , 1% Triton-X-100 , 0 . 1% Sodium deoxycholate , 0 . 1% SDS ) . The cell lysates were sonicated to reach an average DNA fragment size of 140-300 bp . Immunoprecipitation was performed 6 hours or overnight using Dynabeads ( Thermo Fisher Scientific ) . For MYC and MNT immunoprecipitation , the anti-MYC antibody ( N262 , Santa Cruz , Dallas , USA ) and the anti-MNT antibody ( sc-769 , Santa Cruz , Dallas , USA ) were used , respectively . After washing , chromatin was eluted , de-crosslinked and treated with RNase A and Proteinase K . The DNA was purified by phenol/chloroform extraction and ethanol precipitation and analyzed by qPCR using MX3000P with Sybr green Mix ( Thermo Fisher Scientific , ChIP ) or sequenced on a Next-Seq500 after library preparation ( NEBNext ChIP-Seq Library Prep Master Mix Set , NEB , Ipswich , USA ) . For RT-qPCR analysis , total cellular RNA was extracted using the peqGOLD TriFast ( Peqlab , Erlangen , Germany ) reagent . The first strand synthesis was performed using M-MLV Reverse Transcriptase ( Thermo Fisher Scientific ) and random hexamer primers ( Roche , Basel , Switzerland ) . Results were normalized to β2-microglobulin expression . Primers are listed in Supplementary file 3 . Technical replicates are defined by performing individual qPCR-analyses ( CT-value estimation ) with the same cDNA . Biological replicates are defined by individual U2OS cell harvestings . For RNA-sequencing , total RNA was extracted using the RNeasy mini columns ( Qiagen , Hilden , Germany ) including on-column DNase I digestion . PolyA+-RNA was isolated from total RNA with the NEBNext Poly ( A ) mRNA Magnetic Isolation Module ( NEB , Ipswich , USA ) . The NEBNext Ultra RNA Library Prep Kit for Illumina was used for library preparation following the manufacturer’s instruction . Size selection of the libraries was performed with the AMPure XP Beads ( Beckman Coulter , Brea , USA ) , followed by 12 PCR cycles for amplification . Library quantification and quality was assessed using the Experion Automated Electrophoresis System ( Bio-Rad , Hercules , USA ) . The libraries were sequenced with the Illumina Genome Analyzer IIx ( Illumina , San Diego , USA ) . U2OS cells were treated with doxycycline/ethanol and were fixed with 4% paraformaldehyde . Fixed cells were treated with blocking solution ( 10% horse serum , 2% BSA and 5% sucrose in PBS ) for 45 min after washing . Primary antibodies were incubated overnight at 4°C and incubated for 1 hr with the corresponding fluorescently labeled secondary antibodies at room temperature after washing ( Tris-buffered saline with 0 . 1% Tween-20 ) . Cells were mounted on object glass slides using aqua-fluoromount ( Sigma ) after washing and imaged under a confocal microscope ( Nikon Ti-Eclipse ) . For fluorescent intensity measurement , ImageJ 1 . 50hr ( Schneider et al . , 2012 ) was used . Briefly , z stacked confocal images were converted to 12-bit images via maximum intensity projection and ROI assigned using ROI tool . The intensity was measured per cell nuclei for Hoechst and MYC . The fold change distribution of MYC was calculated by first taking MYC intensity normalized to Hoechst and then further normalizing the values to the median of the set of measurements . Finally Log2 of the median normalized values were displayed as density plots on R using 'sm' library density function . For RNA-sequencing , reads were aligned to the human reference genome ( hg19 ) using BOWTIE v . 0 . 12 . 8 . The obtained BAM files were used for further analysis with R/BioConductor . For differential gene expression analysis EdgeR was employed . The rpkm values were calculated by mapping the read counts to the exons of the corresponding gene . Heat maps showing the gene expression changes were obtained using R . ChIP-sequencing data were obtained from ( Walz et al . , 2014 ) and analyzed similarly . The MYC peaks were visualized using the wig files from MACS v . 1 . 4 . 2 ( Feng et al . , 2012 ) and the Integrated Genome Browser software ( Nicol et al . , 2009 ) . ChIP-sequencing samples from ( Walz et al . , 2014 ) were sequenced deeper for visualizing purposes . Peak calling was performed with MACS v . 1 . 4 . 2 ( parameters: --keep-dup 3 , variable p-value cut-off ) , SICER v . 1 . 1 ( parameters: redundancy 1 , window 200 , fragment size 90 , effective genome fraction 0 . 74 , gap 0 , FDR 0 . 01 ) ( Zang et al . , 2009 ) and CCAT v . 3 . 0 ( parameters: default for transcription factors ) ( Xu et al . , 2010 ) . Overlapping peaks were determined with the 'intersectBed' function from BEDTools v . 2 . 17 . 0 ( Quinlan , 2014 ) . Promoter regions are defined as –1 . 5 kb to +0 . 5 kb relative to the transcriptional start site if not indicated otherwise . To analyze the occurrence of E-boxes in the MYC peak the CentriMo tool from the MEME Suite ( Bailey and Machanick , 2012 ) was used . The empirical probability of consensus and non-consensus E-boxes was plotted with a moving average of 20 bp . To calculate the distribution around the TSSs ( heat maps ) or the center of the MYC peak ( occupancy ) , Seqminer v . 1 . 3 . 3 was used . Heat maps were visualized using Treeview . MYC and H3K4me3 occupancies ( in – and + Dox ) were calculated using a window of ± 100 bp for the center of the MYC peak and used to obtain MYC recruitment . DNA binding of MYC and a mutant of MYC that is compromised in binding to WDR5 , was analyzed similar: 5x107 reads were randomly chosen from fastq files ( GSE60897 ) , mapped with BOWTIE and reads in a window of ± 100 bp around the center of the MYC peak were counted using Seqminer . For Venn diagrams , publicly available peak annotations ( GSE60897 ) were used and the overlap was calculated using Bedtools . For binned plots , genes were sorted and divided in equally sized-groups and the median or mean values are shown . As few genes show very high EC50 values , sometimes one or two bins were not shown in the plots , for clarity . EC50 values for MYC binding at each promoter were calculated using the MYC nuclear concentrations and MYC occupancy in – and +Dox . MYC occupancy was calculated by subtracting the tag values of the input from the tag values of the MYC ChIP and resulting negative values were set to 0 . The Michaelis-Menten model was used to perform the fitting of the data in GraphPad Prism and to calculate maximal occupancy of MYC and the EC50 values . For the estimation of the EC50 values for regulation of MYC target genes , the MYC depletion RNA-sequencing experiment and the Dox titration RNA-sequencing experiment were used . The fold change of expression for each gene was calculated relative to siMYC . Only activated genes were considered . The EC50 values were obtained using the fold change values and the nuclear MYC concentrations . Michaelis-Menten model was used to perform the fitting of the data in GraphPad Prism . To statistically test the difference of the distribution of the EC50 values of different gene sets , the Mann-Whitney-Wilcoxon test was used . For functional analysis of the genes , Gene Set Enrichment Analyses ( GSEA ) were performed with the C5 collection of gene sets from the MSigDB ( http://www . broadinstitute . org/gsea/msigdb ) using the GSEAPreranked tool , where indicated . Gene ontology analysis was performed using DAVID ( Huang et al . , 2009 ) with default settings and gene ontology ( GO ) terms from the domains 'cellular component' , 'molecular function' and 'biological process' as database . Sample size was not estimated by statistical methods but chosen based on common standards . The temporal changes of the system’s components are described by sets of ordinary differential equations ( ODEs ) and algebraic equations describing conservation relations . Model design , the particular equations and parameters are listed in the Appendix 1 . An extended model analysis is provided in Appendix 2 . Steady-state solutions were calculated by setting all time derivatives to zero and solving the resulting algebraic equation system numerically . All calculations were performed using Mathematica 10 . 2 ( Wolfram Research , Inc . ) . The data sets supporting the results of this article are available in the Gene Expression Omnibus repository , GEO: GSE77356 . Data access: http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE77356
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 .
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 .