The c-myc proto-oncogene product, Myc, is a transcription factor that binds thousands of genomic loci1. Recent work suggested that rather than up- and downregulating selected groups of genes1,2,3, Myc targets all active promoters and enhancers in the genome (a phenomenon termed ‘invasion’) and acts as a general amplifier of transcription4,5. However, the available data did not readily discriminate between direct and indirect effects of Myc on RNA biogenesis. We addressed this issue with genome-wide chromatin immunoprecipitation and RNA expression profiles during B-cell lymphomagenesis in mice, in cultured B cells and fibroblasts. Consistent with long-standing observations6, we detected general increases in total RNA or messenger RNA copies per cell (hereby termed ‘amplification’)4,5 when comparing actively proliferating cells with control quiescent cells: this was true whether cells were stimulated by mitogens (requiring endogenous Myc for a proliferative response)7,8 or by deregulated, oncogenic Myc activity. RNA amplification and promoter/enhancer invasion by Myc were separable phenomena that could occur without one another. Moreover, whether or not associated with RNA amplification, Myc drove the differential expression of distinct subsets of target genes. Hence, although having the potential to interact with all active or poised regulatory elements in the genome4,5,9,10,11, Myc does not directly act as a global transcriptional amplifier4,5. Instead, our results indicate that Myc activates and represses transcription of discrete gene sets, leading to changes in cellular state that can in turn feed back on global RNA production and turnover.
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Eilers, M. & Eisenman, R. N. Myc’s broad reach. Genes Dev. 22, 2755–2766 (2008)
Herkert, B. & Eilers, M. Transcriptional repression: the dark side of Myc. Genes Cancer 1, 580–586 (2010)
Dang, C. V. MYC, metabolism, cell growth, and tumorigenesis. Cold Spring Harbor Perspect. Med. 3, http://dx.doi.org/10.1101/cshperspect.a014217 (2013)
Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012)
Nie, Z. et al. c-Myc is a universal amplifier of expressed genes in lymphocytes and embryonic stem cells. Cell 151, 68–79 (2012)
Darzynkiewicz, Z., Traganos, F. & Melamed, M. R. New cell cycle compartments identified by multiparameter flow cytometry. Cytometry 1, 98–108 (1980)
de Alboran, I. M. et al. Analysis of C-MYC function in normal cells via conditional gene-targeted mutation. Immunity 14, 45–55 (2001)
Perna, D. et al. Genome-wide mapping of Myc binding and gene regulation in serum-stimulated fibroblasts. Oncogene 31, 1695–1709 (2012)
Guccione, E. et al. Myc-binding-site recognition in the human genome is determined by chromatin context. Nature Cell Biol. 8, 764–770 (2006)
Soufi, A., Donahue, G. & Zaret, K. S. Facilitators and impediments of the pluripotency reprogramming factors’ initial engagement with the genome. Cell 151, 994–1004 (2012)
Sabo, A. & Amati, B. Genome recognition by MYC. Cold Spring Harbor Perspect. Med. 4, http://dx.doi.org/10.1101/cshperspect.a014191 (2014)
Zhou, V. W., Goren, A. & Bernstein, B. E. Charting histone modifications and the functional organization of mammalian genomes. Nature Rev. Genet. 12, 7–18 (2011)
Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013)
Lin, Y. C. et al. A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate. Nature Immunol. 11, 635–643 (2010)
Lovén, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012)
Iritani, B. M. & Eisenman, R. N. c-Myc enhances protein synthesis and cell size during B lymphocyte development. Proc. Natl Acad. Sci. USA 96, 13180–13185 (1999)
Schuhmacher, M. et al. Control of cell growth by c-Myc in the absence of cell division. Curr. Biol. 9, 1255–1258 (1999)
Yustein, J. T. et al. Induction of ectopic Myc target gene JAG2 augments hypoxic growth and tumorigenesis in a human B-cell model. Proc. Natl Acad. Sci. USA 107, 3534–3539 (2010)
Kouzine, F. et al. Global regulation of promoter melting in naive lymphocytes. Cell 153, 988–999 (2013)
Murn, J. et al. A Myc-regulated transcriptional network controls B-cell fate in response to BCR triggering. BMC Genomics 10, 323 (2009)
Fan, J. et al. Time-dependent c-Myc transactomes mapped by array-based nuclear run-on reveal transcriptional modules in human B cells. PLoS ONE 5, e9691 (2010)
Fernandez, P. C. et al. Genomic targets of the human c-Myc protein. Genes Dev. 17, 1115–1129 (2003)
Guertin, M. J. & Lis, J. T. Mechanisms by which transcription factors gain access to target sequence elements in chromatin. Curr. Opin. Genet. Dev. 23, 116–123 (2013)
Rounbehler, R. J. et al. Tristetraprolin impairs Myc-induced lymphoma and abolishes the malignant state. Cell 150, 563–574 (2012)
Graves, J. A. et al. Mitochondrial structure, function and dynamics are temporally controlled by c-Myc. PLoS ONE 7, e37699 (2012)
Wang, R. et al. The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation. Immunity 35, 871–882 (2011)
Liu, Y. C. et al. Global regulation of nucleotide biosynthetic genes by c-Myc. PLoS ONE 3, e2722 (2008)
Marguerat, S. & Bahler, J. Coordinating genome expression with cell size. Trends Genet. 28, 560–565 (2012)
das Neves, R. P. et al. Connecting variability in global transcription rate to mitochondrial variability. PLoS Biol. 8, e1000560 (2010)
Rahl, P. B. et al. c-Myc regulates transcriptional pause release. Cell 141, 432–445 (2010)
Adams, J. M. et al. The c-myc oncogene driven by immunoglobulin enhancers induces lymphoid malignancy in transgenic mice. Nature 318, 533–538 (1985)
Schmitt, C. A. et al. Dissecting p53 tumor suppressor functions in vivo. Cancer Cell 1, 289–298 (2002)
Gorrini, C. et al. Tip60 is a haplo-insufficient tumour suppressor required for an oncogene-induced DNA damage response. Nature 448, 1063–1067 (2007)
Pajic, A. et al. Cell cycle activation by c-myc in a Burkitt lymphoma model cell line. Int. J. Cancer 87, 787–793 (2000)
Trumpp, A. et al. c-Myc regulates mammalian body size by controlling cell number but not cell size. Nature 414, 768–773 (2001)
Peitz, M., Pfannkuche, K., Rajewsky, K. & Edenhofer, F. Ability of the hydrophobic FGF and basic TAT peptides to promote cellular uptake of recombinant Cre recombinase: a tool for efficient genetic engineering of mammalian genomes. Proc. Natl Acad. Sci. USA 99, 4489–4494 (2002)
Gerondakis, S., Grumont, R. J. & Banerjee, A. Regulating B-cell activation and survival in response to TLR signals. Immunol. Cell Biol. 85, 471–475 (2007)
Morgenstern, J. P. & Land, H. Advanced mammalian gene transfer: high titre retroviral vectors with multiple drug selection markers and a complementary helper-free packaging cell line. Nucleic Acids Res. 18, 3587–3596 (1990)
Littlewood, T. D., Hancock, D. C., Danielian, P. S., Parker, M. G. & Evan, G. I. A modified oestrogen receptor ligand-binding domain as an improved switch for the regulation of heterologous proteins. Nucleic Acids Res. 23, 1686–1690 (1995)
Frank, S. R., Schroeder, M., Fernandez, P., Taubert, S. & Amati, B. Binding of c-Myc to chromatin mediates mitogen-induced acetylation of histone H4 and gene activation. Genes Dev. 15, 2069–2082 (2001)
Blecher-Gonen, R. et al. High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein–DNA interactions and epigenomic states. Nature Protocols 8, 539–554 (2013)
Rabani, M. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nature Biotechnol. 29, 436–442 (2011)
He, H. H. et al. Differential DNase I hypersensitivity reveals factor-dependent chromatin dynamics. Genome Res. 22, 1015–1025 (2012)
Sabo, P. J. et al. Genome-scale mapping of DNase I sensitivity in vivo using tiling DNA microarrays. Nature Methods 3, 511–518 (2006)
Campaner, S. et al. Cdk2 suppresses cellular senescence induced by the c-myc oncogene. Nature Cell Biol. 12, 54–59 (2010)
Geiss, G. K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nature Biotechnol. 26, 317–325 (2008)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008)
Zeitlinger, J. et al. RNA polymerase stalling at developmental control genes in the Drosophila melanogaster embryo. Nature Genet. 39, 1512–1516 (2007)
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009)
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621–628 (2008)
Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)
Shaffer, A. L. et al. XBP1, downstream of Blimp-1, expands the secretory apparatus and other organelles, and increases protein synthesis in plasma cell differentiation. Immunity 21, 81–93 (2004)
Ehrensberger, A. H., Kelly, G. P. & Svejstrup, J. Q. Mechanistic interpretation of promoter-proximal peaks and RNAPII density maps. Cell 154, 713–715 (2013)
Bouchard, C., Marquardt, J., Bras, A., Medema, R. H. & Eilers, M. Myc-induced proliferation and transformation require Akt-mediated phosphorylation of FoxO proteins. EMBO J. 23, 2830–2840 (2004)
We thank A. Piontini, P. Nicoli, A. Gobbi and M. Capillo for their help with the management of mouse colonies; S. Bianchi, L. Rotta and T. Capra for assistance with the Illumina HiSeq and NanoString platforms; S. Barozzi, A. Oldani and D. Parazzoli for assistance with imaging technologies; S. Bonifacio and G. Diaferia for discussions about DNase I sequencing; and G. Natoli for comments on the manuscript. We are grateful to C.-L. Wei and collaborators (Genome Institute of Singapore) for the sequencing of ChIP-seq samples in a preliminary phase of this study. T.R.K. was supported by the Structured International Post Doc program of the European School of Molecular Medicine. This work was supported by funding from the European Community’s Seventh Framework Programme (FP7/2007-2013), project RADIANT (grant agreement no. 305626) to M.P. and projects EuroSystem (grant agreement no. 200720) and MODHEP (grant agreement no. 259743) to B.A., as well as grants from the European Research Council, the Association for International Cancer Research (AICR), the Italian Health Ministry, Fondazione Cariplo and the Italian Association for Cancer Research (AIRC) to B.A.
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 1 Intensity and frequency of Myc binding events increase during tumour progression, along with their distance from promoters.
a, Quantification of c-myc mRNA levels by qRT–PCR (mean ± s.d. of n = 6, 8 and 6 for control (C), pre-tumoral (P) and tumoral (T) mice, respectively; Student’s t-test). b, Myc protein levels were measured by quantitative immunoblotting (mean ± s.d. of n = 4, 4 and 3, for C, P and T mice, respectively; Student’s t-test). c, qPCR analysis of Myc ChIP in C, P and T samples at the indicated promoters. AchR is a negative control and Ncl, Ubfd1 and Dusp6 are known Myc target genes. ChIP data are quantified as percentage of input DNA40. d, ChIP-seq tracks at the same target genes. The maxima of the tracks are 100 for Ncl, 50 for Ubfd1 and 30 for Dusp6. e–g, ChIP-seq quality controls in the Eµ-myc data set. e, ChIP-seq saturation analysis for Myc peaks of different enrichment levels for a representative tumour (T1). In this analysis a random subset of the reads is removed (down-sampling) and the number of identified peaks in the down-sampled data set is compared to the full data set. If most of the peaks are confirmed upon down-sampling, the ChIP-seq is considered to be well saturated. f, Distribution of reads density (library size normalized) for Myc-bound or unbound promoters, as indicated (Myc+, Myc−). 20,000 random 3 kb regions not overlapping with promoters were used to assess background. The Myc signal in unbound promoters (red) is close to the background (grey). g, Validation of ChIP-seq peaks by qPCR. We designed PCR amplicons at 42 sites that correspond to the following categories in Eµ-myc: (1) genes that are either not or barely expressed (RPKM < 4) and show no Myc binding in their promoter according to our ChIP-seq analysis (cyan), (2) genes that are expressed (RPKM > 4) but not bound by Myc (red), (3) genes that are both expressed and bound (black). An IgG antibody was used as negative control: the grey dotted lines indicate the 20th and 80th percentiles of the IgG signal. The left panel shows enrichment determined by ChIP-qPCR relative to that in ChIP-seq. ChIP DNA from C, P and T samples was used to amplify each test site. The right panel shows the distribution of signals obtained in ChIP-qPCR for the different categories of genes along with the signal obtained for all amplicons with the IgG antibody (grey). h, Pie charts showing the number and percentage of promoter, intergenic and intragenic Myc peaks in C, P and T samples (T1, T2, T3), based on the genomic position of each peak’s midpoint. The number above each chart gives the total amount of peaks. i, Overlap between Myc ChIP-seq peaks. For each column the percentage of peaks overlapping (at least 1 bp) with the reference samples is reported. j, Box plot showing the net distance of Myc from the nearest TSS (upstream and downstream sites together). *P < 0.05, **P < 0.001.
a, Percentages of Myc peaks associated with the indicated combinations of chromatin features at promoters. b, Same as panel a but for distal Myc binding sites. c, H3K4me1/H3K4me3 ratios of library size normalized read counts over promoter-proximal (black) and distal (red) Myc-binding sites in a representative tumour (T1). d, Top, Lowess regression between the intensity of Myc binding (x axis) and level of RNAPII, H3K4me1, H3K4me3 and H3K27ac (y axis) at Myc bound promoters for C, P and T samples (T1, T2, T3); on both axes reads density is determined as the number of reads within 6 kb regions centred at the TSS. On the y axis data are divided by the maximum value observed for each mark. Bottom, same as above, for Myc-bound distal sites (6 kb centred at Myc peaks).
a, Representative snapshots of Myc-bound regions in the genome. ChIP-seq tracks are shown for Myc, RNAPII, H3K4me3, H3K4me1 and H3K27ac in the indicated samples (C, P and T1 as a representative tumour). CGI, CpG island. Promoter and enhancer regions, on the basis of annotation and chromatin features, are indicated. b, List of the domains (amplicon) previously tested and validated (last column) as B-cell enhancers14. Bound C, P, T: Myc-binding status in our data set (y, yes; n, no). Note that the two domains listed in bold are also displayed next to the table (Bst1 and Bcl7a loci).
a, Hierarchical clustering of C, P and T samples based on the RNA-seq data shown in Fig. 2a. C and P samples showed relatively homogeneous profiles, whereas T samples were more variable, consistent with the clonal nature of lymphomas. b, Scatter plots with differential gene expression; our calls for up- and downregulated DEGs are reported in red and blue, respectively, and their corresponding numbers reported as inset.
All scatter plots confront NanoString and RNA-seq data on the y and x axes, respectively. a, Absolute expression levels (NanoString counts versus RNA-seq RPKM units). Red and blue dashed lines indicate ± 2 and ± 4 fold differences, respectively. b, The data in panel a expressed as fold-change in the corresponding P and T samples relative to C (log2 FC: log2 of fold-change). c, Same as panel a, normalized to cell equivalents. d, Same as panel b, normalized to cell equivalents.
Extended Data Figure 6 RNA amplification correlates with cell cycle entry and cell growth in P493-6 and primary murine B cells.
a–h, P493-6 cells. Cells were cultured in tetracycline-containing medium (0 h: tet-Myc repressed) or Tet-free medium (tet-Myc on) for 1 h, 24 h or multiple passages (High Myc), as indicated. Low Myc: cells were passaged in Tet- and OHT-containing medium (see text). a, Chromatin profiles of Myc-bound promoters (left panel) and distal sites (right panel) on chromosome 1 (as in Fig. 1). b, Quantification of total RNA per cell (mean ± s.d., n = 6 per group; Student’s t-test). c, Box plot showing gene expression changes relative to 0 h, as measured by NanoString normalized to cell equivalents (n = 3). d, FACS analysis of cell size distribution (mean ± s.d., n = 3 per group; Student’s t-test). e, Percentage of BrdU positive cells (mean of two experiments). f, Measurement of Myc protein levels by quantitative western blot (mean ± s.d., n = 3 per group; Student’s t-test). g, Growth curve (CellTiter-Glo assay; technical triplicate). h, Quantification of total RNA per cell (mean ± s.d., n = 6, 6, 11; Student’s t-test). i–l, Primary murine B cells. Wild-type (wt) or c-mycf/f primary murine B cells were treated with tat-Cre (see Methods) and then with LPS for the indicated periods of time. i, Quantification of total RNA per cell (mean ± s.d., n = 3 pools of mice per genotype; Student’s t-test). j, FACS analysis of cell size distribution (mean ± s.d., n = 3 mice per genotype; Student’s t-test); the bar plot reports the percentage of cells with FSC higher than 600, as illustrated in the representative graph on the right. k, Growth curve, based on direct cell counts (mean ± s.d., n = 3 mice per genotype). l, qRT–PCR analysis of genes regulated during differentiation in plasma cells (mean ± s.d., n = 3 pools of mice per genotype; Student’s t-test). Blimp1 and Xbp1 are induced, whereas Bcl6 is repressed during plasma cell differentiation20,54. The data show that these regulatory patterns are intact in c-myc-deleted cells. P values are shown either relative to 0 h, or between wild type and c-mycf/f cells, as indicated. *P < 0.05, **P < 0.001.
a, ChIP-seq tracks at two representative loci for the indicated proteins and histone marks as well as DNase I hypersensitive sites in 3T9MycER fibroblasts before (0 h OHT, endogenous Myc) and after MycER activation (4 h OHT). Maxima in the Myc tracks are 80 for Ncl, and 20 for Twist2. CGI, CpG island. b, Annotation of Myc binding events, as in Extended Data Fig. 1h. c, Upper panels: chromatin profiles of Myc-bound promoters and distal sites on chromosome 1; lower panels: same at non-bound promoters and enhancers (as in Fig. 1). d, qRT–PCR validation on a per-cell-basis of up-, down- and non-regulated genes after OHT treatment in 3T9MycER fibroblast relative to untreated cells (mean log2 FC, n = 3). RNA was extracted from 50,000 cells at each time-point.
a, Heatmaps showing library size-normalized ChIP-seq read counts for Myc-bound promoters in Eµ-myc and 3T9 samples, as indicated. Promoters are ranked from top to bottom by reads density. b, For all the Eµ-myc samples (C, P, T1, T2, T3) Myc binding events were divided in 5 equally sized groups based on their enrichment. Within each group the graphs report the percentage of Myc peaks overlapping (at least 1 bp) with the indicated histone marks, CpG islands (CGI), canonical and variant E-boxes or only the canonical E-box within a 100 bp window around Myc peak summits. The upper and lower graphs refer to Myc peaks on promoters and enhancers, respectively.
a, GeneOntology (GO) terms enriched in up- and downregulated genes in Eµ-myc lymphomas (T) vs. C, and in OHT-treated (4 h) vs untreated 3T9MycER cells. Genes differentially expressed in at least one tumour were considered for Eµ-myc. GO terms significantly enriched (Fisher test P value < 1 × 10−10) in at least one gene set were selected (rows in the heatmap) and the P values for each GO term in each gene set (columns) were colour-coded as indicated. Only GO terms in the biological process ontology that are assigned to less than 2,000 and more than 10 genes in the mouse genome were considered. b, Venn diagrams showing the overlaps among genes up- and downregulated in Eµ-myc lymphomas and 3T9MycER fibroblasts.
The RNAPII stalling index is defined as the ratio of RNAPII read counts on promoter and gene body for all genes having an RNAPII peak on their promoters30,49. Each row shows the stalling index (left panels), the promoter (central panels) and the gene body (right panels) RNAPII counts for the indicated population of genes in the Eµ-myc and 3T9MycER models. a, b, All genes. c, d, Myc-bound upregulated genes (DEG up). e, f, Myc-bound downregulated genes (DEG down). For 3T9MycER cells, the 4sU-seq data were used, in order to consider only transcriptional changes, but similar results were obtained when using the RNA-seq data set (not shown). These observations can be summarized as follows. First, considering all genes, the stalling index followed different trends after Myc activation in different setting: a decrease along tumour progression in Eµ-myc mice (a) and an increase upon MycER activation in fibroblasts (b). However, the density of RNAPII on the promoter and gene body revealed that both of these measures increased in both systems upon Myc activation, yet in different proportions, resulting in the opposite variations in stalling index. Thus, the stalling index, although capturing the ratio between RNAPII counts on promoter and gene body, conveys no information on the changes of either quantity per se, and may prove mechanistically misleading55. Second, on Myc-induced genes, the increases in RNAPII on promoter and gene body were both larger (c, d), suggesting that Myc can upregulate transcription by increasing both RNAPII recruitment (or re-loading) and elongation30,56. As expected, these effects were lost on Myc-repressed genes (e, f).
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Sabò, A., Kress, T., Pelizzola, M. et al. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature 511, 488–492 (2014). https://doi.org/10.1038/nature13537
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