Different promoter affinities account for specificity in MYC-dependent gene regulation

Overall, this body of work is compelling, although the authors should openly acknowledge that the U2OS cell line is complicated by its intolerance of high expression of MYC (documented in Walz et al. Nature 2014; extended Figure 1), resulting in cell death upon prolonged exposure to high MYC levels (>3days).

We agree and now state: “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 hours after addition of doxycycline.” at the beginning of the Results section.

This nuance could well skew MYC's ectopic activities that are specific for this cell line and hence not generalizable to cancer cells driven by high MYC such as Burkitt's lymphoma or neuroblastoma. For example, the authors should compare the 'ectopic' MYC targets among the different datasets (U2OS, Emu-MYC normal-pretumor-tumor samples, and other datasets referenced in the manuscript). Are there cell-type-independent MYC ectopic target genes similar to cell-type-independent core MYC targets that are involved in ribosome biogenesis?

In reply to this comment, we compared the functional groups of low-affinity target genes defined in the U2OS cell system with those the Eµ-Myc model and describe the data in the new panels H and I of Figure 5—figure supplement 1. Indeed, several functional classes of proteins encoded by low affinity genes, like “transmembrane transporters” are also regulated during lymphomagenesis (subsection “Binding affinity determines MYC-mediated transcriptional responses at different MYC concentrations”, first paragraph).

Essential revisions:

1) The authors use recombinant, purified protein to calibrate immunoblots in order to calculate the absolute number of MYC molecules per cell using cell lysate. This method however will only indicate the average level of MYC for the entire population. It remains possible that some cells express no MYC while others express levels of MYC far exceeding the average. The authors should employ immunofluorescence or a similar technique to demonstrate MYC expression in their system is relatively homogenous.

To address the cell-to-cell variance of MYC levels, we performed immunofluorescence of MYC with three different antibodies at physiological and ectopic MYC concentrations. The new Figure 2—figure supplement 2A–E shows examples and quantification of the immunofluorescence. Of course, there is some variation of MYC levels between cells, but 80% of U2OS cell population differs less than +/– 3.7 fold for endogenous and +/– 2.9 fold for overexpressed MYC. To estimate the impact of this variation, we tested the effect of this maximal variation (80% of the U2OS cells) in the mathematical model. The results are shown as yellow shaded areas in Figure 2—figure supplement 2F. Importantly, high-affinity binding sites (characterized by E-boxes and WDR5 binding) are predicted to be highly occupied for MYC even if protein levels of endogenous MYC are 3.6 fold lower than on average. The new data is described (subsection “Absolute quantification of nuclear MYC allows an estimate of MYC binding affinities”, first paragraph) and interpreted (subsection “Binding to DNA and to WDR5 accounts for high promoter affinity”, third paragraph) in the Results section.

2) In Figure 1A, the authors define the amount of MYC in their U2OS cells −/+ Dox. It appears that the amount of MYC in ETOH treated cells is beyond the range of their lowest standard (compare Figure 1A to Figure 2A). Indeed, if the fold range of expression is truly 14-fold as presented in the manuscript, then standards covering only a four-fold range (2.4 ng to 9.6 ng in Figure 2A) is insufficient. The authors should repeat the experiment with an expanded range of standards to insure that they have assigned the correct number of MYC molecules to the 'control' condition.

In the former version of the manuscript we quantified a small range of defined amounts of recombinant MYC protein. We calibrated the cellular MYC concentration in cells treated with a concentration of doxycycline, which leads to a signal close to that of the recombinant protein. We then estimated concentrations in all other conditions based on relative changes and assumed linearity (fluorescence based immunoblots, Licor).

We agree with the reviewers that an absolute estimation of MYC levels at each condition (siMYC, EtOH, Dox-titration) is more reliable. We therefore repeated the experiment using an extended range of standards covering the signal intensity from all biological conditions analyzed in biological triplicates and calculated a fitting model for the titration of recombinant MYC protein (which indeed does not behave linear). This regression analysis was used to estimate cellular MYC concentrations. The numbers are very similar for low cellular MYC concentrations (siMYC, EtOH) and differ by a maximal factor of three for the highest doxycycline concentration if compared with the former analysis. The new blots are shown in Figure 2—figure supplement 1 and the calculations are presented in Supplementary file 1.

3) The author's analysis of transcription upon knock-down of MYC revealed that high-affinity promoters showed the greatest change in gene expression upon MYC depletion. This finding seems to be in disagreement with the finding from Nie et al. (Nie Z, 2012) in which B cells expressing very low levels of MYC show a proportional decrease of MYC binding to all genomic targets. Can the authors resolve this discrepancy? At the very least, the authors should perform ChIPseq upon MYC knock-down in their U2OS model and determine promoter occupancy to complement their gene expression experiments.

To address this comment, we performed a new MYC ChIP-sequencing experiment upon depletion of MYC by siRNA and a side-by-side ChIP-sequencing experiment with control cells (siCtr). The data is presented in the new Figure 5—figure supplement 1. We observed that (i) endogenous MYC binds to thousands of promoters (siCtr), confirming our previous data (ii) MYC binding is not homogenous but greatly differs for low- and high-affinity binding sites, (iii) binding is abolished (or at least strongly decreased) at the majority of the binding sites, including high-affinity binding sites like the promoters of ribosomal protein genes. Strikingly, the RNA-sequencing experiment and the new ChIP-sequencing experiment upon MYC depletion nicely correlate since the genes that are most strongly affected in expression also show the strongest decrease in MYC binding to their promoters (based on fold change, see Figure 5—figure supplement 1G). The new data is described (subsection “Binding affinity determines MYC-mediated transcriptional responses at different MYC concentrations”, first paragraph) in the Results section.

We think that our data fit well to those reported by Nie et al. For instance, in Figure S2F of Nie et al. (Cell, 2012), MYC binding in T4 (after 4 hours of activation) is not uniform across all genes and MYC binding further increases at T14 on some promoters that are weakly bound at T4 (e.g. group “III”, “VII”), but appears to be saturated at T4 at high affinity binding sites (e.g. group “I”).

4) There appears to be somewhat of a disconnect between the accounting of MYC binding at promoters and the heat map shown in Figure 1C. From the heat map across all genes it appears that MYC follows a monotonic binding pattern all the way to the bottom (I don't see a negative region in the heat map) – in contrast the accounting of MYC positive versus negative promoters is highly dependent on the selection of p-values and/or false discovery rates to create an arbitrary distinction between + and –. Some discussion of this may be warranted. Perhaps the negative peaks simply reflect a continuum of MYC binding as it slides below the level of the technical background.

To address these comments, we include a new supplemental Figure (Figure 1—figure supplement 1), in which we show the analysis with different peak callers and parameters. Importantly, the reported number of MYC binding sites appears to be very robust, as peak calling with different programs using default parameters results in very similar called peaks and peak numbers (Figure 1—figure supplement 1A, B). Panel C illustrates how negative peaks are increasing when decreasing the p-value adjustment in line with increasing positive peak numbers. We describe this analysis (subsection “MYC binding to chromatin appears saturated at certain sites”, second paragraph) in the Results section. We agree with the reviewers that most expressed genes are MYC target genes and added the sentence “In agreement with reports from other systems, we concluded that the promoters of the majority of all expressed genes are bound by MYC.” We reduced the suggested exactness when mentioning the number of MYC bound promoters, first (in the aforementioned paragraph).

5) There are several issues related to the model. The authors only model the influence of WDR in concert with E-boxes; what is the level of binding and the influence of MYC at WDR⁺, E-box^- promoters? This class should be considered, too. What is the extent of MYC binding, expression levels and functional GSEA for this class of promoters?

In response to this comment, we included the group of “WDR5-bound DNA sites without canonical E-boxes” as a fourth class in our model. The reviewers are correct in expecting that this is an interesting group, as binding to those sites is predicted to be high at endogenous MYC concentrations in U2OS cells (see new Figure 3F,G; description in subsection “Binding to DNA and to WDR5 accounts for high promoter affinity”, third paragraph). Subsequently, we characterized these genes in terms of MYC binding status, gene ontology and expression status and present the results in the new Figure 4—figure supplement 2. As one might expect, MYC binding and gene expression of WDR5⁺CACGTG^--genes is higher than WDR5^-CACGTG^--, but lower than WDR5⁺CACGTG⁺- genes (description in the aforementioned paragraph).

6) The model assumes equilibrium (or at least steady state) binding. While this is often a reasonable assumption, at promoters, in vivo, there are many energy-dependent and kinetic steps, that might skew that apparent on and off rates. For example, the authors' own previous work showed that MYC degradation during the transcription-cycle might be required to increase expression. Such degradation will increase the apparent MYC off-rate, artificially depressing apparent affinity of MYC for the promoter. The kinetic limitations of the model should be discussed in the text or in the supplemental material.

As the reviewers point out, binding of MYC to promoters can be affected by several regulated processes that may change the kinetic rates of association (“on-rate”) or of dissociation (“off-rate”). To our knowledge, experimental data on the kinetics of “on-rate” and “off-rate” constants or the respective kinetic rate laws of MYC is not yet available. Moreover, ChIP-sequencing experiments are based on the general notion that the number of DNA-bound molecules (tags) presumably does not change (at least) for the time frame of conducting the experiment. We therefore used the steady state assumption for calculating apparent dissociation constants, which imply ratios on the “on rates” and “off rates”. We mention this limitation in the main text (subsection 2 Binding to DNA and to WDR5 accounts for high promoter affinity”, first paragraph) and refer to appendix 1 for further discussion of this assumption. In order to check for the robustness of the model with respect to the specific values of these dissociation constants, we varied them over a broad range (see Figure 3B,C) and discussed their impact on our conclusions (appendix 2).

7) Some discussion of expression levels for the various functional classes of MYC-target promoters is warranted. It has previously been suggested that MYC binding is related to output levels. It may be that some cellular processes (such as translation) demand high output, and that it is the high level of transcription at these genes that licenses MYC binding, that in turn stimulates more transcription.

Our analysis of MYC binding at endogenous concentrations clearly – and in agreement with the reviewers’ standpoint – demonstrated that target genes are characterized by high expression (Figure 4B) and open chromatin structure (Figure 4C). Accordingly, high affinity targets like ribosomal protein genes (Figure 2D) are known to be among the highest transcribed genes. To make this correlation and the role of existing gene expression as a licensing factor for MYC binding most obvious for the reader, we introduced new statements like “These genes are thought to comprise a core signature of highly expressed MYC target genes (Ji et al., 2011).”, “Since target gene expression, H3K4me3 and the occurrence of E-box sequences stratify high affinity binding sites[…]”, “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, […]” and “This is likely to be true for strongly transcribed genes as many ribosomal protein genes […]” at various sections in the manuscript.

Accompanying Tables of genes in groups identified in the Figures should be provided. (e.g. ribosome, extracellular matrix, solute transporter, etc.).

Gene sets used in the study are now provided in Supplementary file 2.

Further, the authors should provide an analysis of selected metabolic pathways, such as glycolysis and glutaminolysis, that may be regulated in U2OS cells.

We analyzed the EC₅₀ values of selected metabolic pathways and included these analyses in Figure 5I. Indeed, we observed elevated EC₅₀ values for glycolysis, lipid- and purine metabolism (Figure 5I, purple boxplots). The data are described in the last paragraph of the subsection “Binding affinity determines MYC-mediated transcriptional responses at different MYC concentrations” and discussed in the sixth paragraph of the Discussion.