FUBP1 and FUBP2 enforce distinct epigenetic setpoints for MYC expression in primary single murine cells


Physiologically, MYC levels must be precisely set to faithfully amplify the transcriptome, but in cancer MYC is quantitatively misregulated. Here, we study the variation of MYC amongst single primary cells (B-cells and murine embryonic fibroblasts, MEFs) for the repercussions of variable cellular MYC-levels and setpoints. Because FUBPs have been proposed to be molecular “cruise controls” that constrain MYC expression, their role in determining basal or activated MYC-levels was also examined. Growing cells remember low and high-MYC setpoints through multiple cell divisions and are limited by the same expression ceiling even after modest MYC-activation. High MYC MEFs are enriched for mRNAs regulating inflammation and immunity. After strong stimulation, many cells break through the ceiling and intensify MYC expression. Lacking FUBPs, unstimulated MEFs express levels otherwise attained only with stimulation and sponsor MYC chromatin changes, revealed by chromatin marks. Thus, the FUBPs enforce epigenetic setpoints that restrict MYC expression.


From stem cells and proliferation to senescence and cell death, MYC is involved in almost every key decision for every cell1,2,3,4,5,6,7. MYC is an amplifier of almost every active gene and it becomes an oncogene when abnormally upregulated8,9,10,11. On the other hand, although the knockout (KO) of Myc is embryonic lethal in mice and Myc-KO cells have only rarely been obtained (less than a handful of Myc-less cell lines have been established)12,13, very low levels of basal MYC are sufficient to sustain viability13,14,15,16. MYC-haploinsufficient cells are not notably abnormal other than proliferating at a somewhat reduced rate. Haploinsufficient mice display no pathology and show increased life span15,17,18,19,20,21,22. Despite this apparent indifference to normal levels, constraining MYC to a physiological range is crucial for normal development and physiology. In response to a variety of intracellular and extracellular signals and pathological stresses, MYC levels rapidly elevate to enable transcriptional amplification of highly induced genes, as the cell races to confront the challenging insult23,24. Such high-MYC output in cancer may also be enforced by a panoply of pathologically activated super-enhancers14,25,26,27,28.

Myc is embedded in a gene desert and that is studded with cis-elements receiving a plethora of extracellular and intracellular signals that must somehow be integrated at the promoter to set MYC expression levels23,24,29. Of the multiple regions that regulate MYC expression, we have been interested in the far upstream element (FUSE), which is 1.7 kb upstream of the major P2 transcription start site of human MYC gene and binds FUBP123,29,30,31,32,33,34,35,36. The FUSE-binding protein (FUBP) family has three members in human and mouse—FUBP1, FUBP2/KHSRP, and FUBP323,29,30,31,32,33,34,35,37,38—and only one in Drosophila—Psi31,32,37,39. FUBP1 is both prooncogenic and a tumor suppressor37,40,41. The FUBPs all contain four repeated hnRNPK homology motifs in their central regions. Their central regions bind sequence selectively to either single-stranded DNA or RNA and the FUBPs have been reported to interact with various DNA or RNA targets and to participate in transcription, splicing, mRNA degradation, RNA transport, and translational regulation29,31,32,34,35,36,37,38,42,43. After FUSE melts in response to the torsional stress caused by dynamic supercoiling emanating from the Myc promoter, FUBP1 binds and loops to the promoter, interacting with TFIIH through the C-terminal domain of FUBP1 to activate transcription44,45. The FUBP1-interacting repressor (FIR) may then be recruited by FUBP1 to inhibit the helicase activity of TFIIH, attenuating FUBP1 upregulation of Myc transcription43,45,46,47. Thus, the FUSE-FUBP-FIR system has been proposed to comprise a cruise control constraining MYC expression29,31,32,45,48,49,50 by increasing MYC expression in some circumstances and decreasing it in others. Accordingly, when Fubp1 is knocked out in mice, both cell-to-cell and embryo-to-embryo Myc RNA levels fluctuate51.

Intercellular expression variation occurs between different tissues and between individual members of a single-cell type. Almost all types of cells, including stem cells, murine embryonic fibroblasts (MEFs), and cancer cells, are not a homogenous population. They are all heterogeneous at the molecular level. The degree to which this heterogeneity reflects transient stochastic fluctuations (intrinsic noise) vs. biologically distinct, perhaps epigenetic states (extrinsic noise) is not fully resolved52. Such cell-to-cell molecular and functional variation of MYC may be expected to amplify in turn the quantitative or even qualitative diversity of responses to stimuli. Understanding the sources of this variation may provide insights into how cells respond individually or in coordinated groups to physiological and pathological challenges.

Variation in basal MYC levels is well-tolerated in resting and steady-state tissues, yet there are multiple molecular mechanisms that control its expression across its full dynamic range. We set out to examine the precision and variability of MYC regulation and to study the cellular consequences of variation in MYC setpoints both for steady-state growth and in response to cytokine stimulation in primary cells. Primary MEFs and primary naive B-cells were studied to observe Myc regulation in the absence of oncogenic stress. The induction of MYC in B-cells has been well-characterized in vivo and in vitro, and the use of wild-type (WT) vs. FUBP1−/− MEFs, with or without Fubp2 knockdown (KD), provided a simple system to interrogate the roles of these proteins in setting MYC levels, in managing expression noise, and in upregulating MYC in response to cytokine signaling. We found that there is a two-stage upregulation of Myc; in response to low-intensity signals and stress, although MYC is broadly upregulated, it respects a physiological upper limit. With intense signaling, a significant portion of cells breaks the ceiling, with some cells expressing very high-MYC levels. As elevated MYC is seen in almost all types of cancers, neoplastic cells must defeat whatever safeguards enforce physiological MYC levels. These studies may help inform strategies to restore proper regulation on deregulated MYC.


Characterization of Myc mRNA and protein expression in single cells

MYC is predominantly a nuclear protein (Fig. 1a, c). To ensure that MYC levels could be accurately monitored in individual cells microscopically, primary MEFs and primary naive B-cells were fixed, their nuclei segmented based on DNA staining, and then immune-stained for MYC protein. Nuclear size and the mean nuclear fluorescence intensity of MYC were measured in single MEFs (Fig. 1a, b) or naive B-cells (Fig. 1c, d). The total amount of MYC protein was calculated as the product of mean intensity and nuclear size. To validate this measurement, naive B-cells were stimulated with lipopolysaccharide (LPS) and interleukin (IL)-4, and split into two portions: one for flow cytometric analysis and the other for quantitative immunofluorescence microscopy. Comparable results and distributions were obtained with each method, validating both approaches (Fig. 1e). For the determination of Myc RNA levels after in situ hybridization with fluorescent probes for Myc RNA, the probe intensity was measured at the level of individual cells and the distribution of MYC across the full population of MEFs was determined (Fig. 1f, g). Total Myc mRNA intensity was calculated as the product of mean intensity and cell size. The density profiles of MYC protein or mRNA levels of primary MEFs and naive B-cells were plotted (Fig. 1b, d, g); both protein and RNA show similar bell shapes with a long tail extending toward a high expression that approximated a log-normal distribution (Supplementary Fig. 1).

Fig. 1: Distribution of MYC expression in single cells.

a, b MYC protein expression in single MEF by immunostaining. a An immunostaining image of MYC protein in single MEFs, showing that MYC is primarily localized in the nucleus of MEFs. b Histogram (light blue) and density plot (black) of MYC total nuclear protein level determined by immunofluorescence. c, d MYC protein expression in single B-cells by immunostaining. c An immunostaining image of MYC protein in single B-cells, showing that MYC is primarily localized in the nucleus. d Histogram (light blue) and density plot (black) of MYC total nuclear protein level in single naive B-cells are shown. AU: arbitrary unit. e Validation of quantitative immunofluorescence. Primary naive B-cells were stimulated with 25 µg/ml of LPS and 2.5 ng/ml of IL-4, and split into two parts for flow cytometry analysis (FACS) and quantitative immunofluorescence analysis (IF). Comparable results were obtained. f, g MYC mRNA expression in single MEF by in situ hybridization. f An in situ hybridization image of MYC mRNA in single MEFs. g Histogram (light blue) and density plot (black) of Myc RNA level determined by RNA in situ hybridization. AU: arbitrary unit.

Two-stage regulation of MYC expression

To characterize the response of the Myc gene to extracellular signals at both the population and single-cell levels, naive B-cells or MEFs made quiescent by serum starvation were stimulated with different concentrations of IL-4 or fibroblast growth factor (FGF), respectively (Fig. 2). The concentrations of growth factors and cytokines were empirically defined to deliver partial or maximal induction of MYC. These concentrations also approximate levels seen under normal or stressed/pathological concentrations in vivo in various systems. Cumulative distribution functions (CDFs) were used to capture both the single-cell and population properties in a single format amenable to statistical comparison (Fig. 2a). Upon stimulation of B-cells with low concentrations of IL-4, the MYC distribution shifted rightward, without changing the peak values of the MYC expression (Fig. 2b). Thus, although the overall population was MYC responsive, it respected the same expression ceiling as did the unstimulated cells. With high concentrations of IL-4, the CDF shifted further to the right and breached the expression ceiling of unstimulated cells extending to much higher levels of MYC. The MYC CDF curve from MEFs treated with low levels of FGF also shifted rightward and paralleled that from MEFs growing at steady-state with both curves converging to a common upper limit (Fig. 2c). With high FGF, the CDF shifted dramatically rightward with ~20% of cells expressing two to three times the steady-state mean MYC levels. These data suggest that there are two stages of MYC expression as follows: low (Stage I) and high (Stage II). Stage I (which we operationally define as physiological) occurs at a steady state or upon mild stimulation—in Stage I, although Myc is inducible, overall MYC expression is strictly bounded. Stage II (which we operationally define as stressed/pathological) occurs upon intense stimulation with concentrations of growth factors or cytokines that maximize MYC expression, allowing a population of cells to breach the Stage I ceiling and express much higher MYC levels. Plotting MYC distribution as a probability density function of B-cells or MEFs treated with IL-4 or FGF, respectively (Supplementary Fig. 2a, b), confirmed that upon weak stimulation, mean levels shifted to modestly higher values in stage I, but that stage II was not populated without intense stimulation.

Fig. 2: MYC expression has two stages: low (I) and high (II) stages.

a A diagram illustrating the principles and biological implications of the PDF and the CDF plots used in this study. A random log-normal distribution data set were used as an example. b, c The empirical CDF plot of MYC protein level in naive and IL-4-stimulated B-cells (b), and in steady-state and FGF-stimulated MEFs (c) are shown. Low IL-4: 5 ng/ml; high IL-4: 50 ng/ml. Low FGF: 1 ng/ml; high FGF: 4 ng/ml. AU: arbitrary unit. Negative control: nonspecific IgG isotype control. Experiments were repeated with similar results (n = 3 independent experiments).

MYC setpoints are precisely set and fixed in individual cells

Within stage I, the variation in MYC levels between individual cells could reflect stochastic fluctuation with sampling from a single distribution or, alternatively, the superposition of closely nested distributions, each with slightly shifted mean MYC setpoints.

To test whether there is a common setpoint for MYC expression among individual MEFs, fluorescence-activated cell sorting was used to collect a set of low- and a set of high-MYC-expressing cells from an apparently unimodal distribution of fluorescent MEFs (Fig. 3a). These MEFs were homozygous for a fully functional chimeric Myc-Egfp gene expressed at the endogenous Myc locus. Mice homozygous for this fusion allele are phenotypically normal and breed properly. This in-frame chimera of enhanced green fluorescent protein (GFP) at the C terminus of MYC preserves all known features that regulate Myc RNA and protein expression and turnover. We next cultured the low- and high-MYC cells for 1 vs. 7 days (at least two mitotic cycles) and then examined their distributions of MYC protein and mRNA. Myc mRNA and protein are relatively unstable (mRNA half-life is 10–20 min53 and protein half-life is about 20 min54). Surprisingly, both the low- and high-MYC cells still preserved their respective MYC expression levels (Fig. 3b, c). As expected, the high-MYC group doubled slightly faster and were slightly more metabolically active than the low-MYC group (Fig. 3d). These results suggest that MYC setpoints can be finely tuned and transmitted to daughter cells. After 1 week of culture, although the mean MYC levels in the low-MYC cells drifted slightly upward, their mode levels crept downward (Supplementary Fig. 3). The opposite shifts in the mode vs. the mean may be explained by the outgrowth of a subpopulation of cells extending the rightward tail toward higher MYC expression. Whether this subpopulation arises from setpoint reprogramming or the re-emergence of bona fide high-MYC-setpoint cells that factitiously happened to display low GFP fluorescence at the moment of sorting due to stochastic MYC fluctuation is not known.

Fig. 3: MYC setpoints are precisely set and fixed in individual cells.

a Sorting scheme for low- and high-MYC cells by MYC-GFP intensity. b, c The empirical CDF plot of MYC protein (b) and mRNA (c) expression in cells at day 1 (left) or 7 (right) after sorting. d The proliferation of low- and high-MYC cells determined by WST-1 assay. Experiments were performed in triplicate.

We performed mRNA-seq on the low- and high-MYC cells, normalizing the count of transcripts to the count of spiked-in controls. As expected, the volcano plot shows a significant increase in gene expression in high-MYC cells compared to low-MYC cells and almost no downregulated genes as expected for a universal amplifier (Fig. 4a). The expression of Myc, Fubp1, and Fubp2/Khsrp in high-MYC cells is about twofolds of that in low-MYC cells (Supplementary Fig. 4). The mRNAs with the highest fold increase in high-MYC compared to low-MYC cells were highly enriched for genes involved in the inflammatory (9-fold enrichment, GO:0006954, false discovery rate (FDR) q-value < 0.01) and immune responses (5.8-fold enrichment, GO:0006955, FDR q-value < 0.01) and included various cytokines, chemokines, and complement (Supplementary Data 1). This suggests that high-MYC MEFs are primed to participate in immune and inflammatory processes, a major function of fibroblasts. The high-MYC cells were a bit larger than the low-MYC cells as expected55,56. This difference in size was maintained through several divisions of asynchronous culture and so could not be attributed to differences in the cell cycle distribution between the high- and low-MYC cells. Such asynchrony was confirmed by RNA sequencing (RNA-Seq), as the expression of mRNAs of genes expressed at well-defined points in the cell cycle57 was indistinguishable between the high- vs. low-MYC populations (Fig. 4b), indicating that neither population was enriched for cells in particular phases of the cell cycle.

Fig. 4: High-MYC cells are enriched for immune genes and are primed to express even higher MYC.

a The volcano plot of mRNA expression in high-MYC cells vs. low-MYC cells. In high-MYC cells, genes with the highest fold increase (FC ≥ 5) compared to low-MYC cells were enriched for inflammatory and immune response genes (GSEA analysis, FDR < 0.01), which are highlighted in blue. Each of the low-MYC and high-MYC RNA-seq samples has two technical replicates. b Compared with low-MYC cells, high-MYC cells are not enriched for well-defined cell cycle genes at specific stages. In the volcano plots of high-MYC and low-MYC cells, well-defined cell cycle genes at different stages are highlighted in different colors. c Effects of different doses of TNF-α or PDGF on the expression of MYC in low- or high-MYC cells. Unit of TNF-α or PDGF: ng/ml. AU: arbitrary unit. Experiments were repeated with similar results (n = 3 independent experiments).

High-MYC cells are primed to express even higher MYC

After 7 days, low- and high-MYC cells were each serum-starved for 24 h, a condition known to arrest proliferation that puts cells into G0 and depresses MYC levels. Upon serum starvation, both low- and high- MYC cells dropped to the same very low baseline levels of MYC (Fig. 4c). Then cells were re-stimulated with platelet-derived growth factor (PDGF) or tumor necrosis factor-α (TNFα) for 4 h and the level of re-expressed MYC was measured. A greater percentage of high-MYC cells were induced to higher MYC levels than were the low-MYC cells. Thus, following quiescence, high-MYC cells are primed to support a greater MYC response. The results suggest that both the low- and high-MYC cells remember their setpoints through cell division in culture. The fact that high-MYC cells respond more vigorously to signals is expected, as MYC will amplify the outputs of those signals. The genes most expressed in the high-MYC cells encode mediators of inflammation and immunity suggesting that these cells may serve as sentinels that alert tissue to stress or pathological conditions.

To inquire whether the high vs. low-MYC cellular epigenetic memories derive from known epigenetic mechanisms, low- and high-MYC cells were treated with agents that alter epigenetic events, in an attempt to provoke a differential response to reprogram the MYC setpoints. When the low-MYC and high-MYC pools were treated with the DNA methylation inhibitor 5-azacytidine and/or the histone deacetylase (HDAC) inhibitor Trichostatin A, the MYC levels decreased proportionately in both pools, indicating that although DNA methylation and/or histone acetylation levels affected MYC levels, they were most likely not responsible for the memory of different MYC levels between cells (Supplementary Fig. 5). It is noteworthy that whereas for most genes HDAC inhibition and consequent hyperacetylation is associated with increased expression, in the case of Myc, expression falls.

FUBP1 and FUBP2 police the MYC setpoint and enforce stage I in individual cells

To examine the role of FUBPs in regulating MYC setpoints and their relationship to the stage I–II boundary, we examined MYC levels in single cells in four different populations of MEFs: (1) WT, 2) Fubp1 KO51, (3) Fubp2 KD with small interfering RNA (siRNA) (KD), and (4) Fubp1 KO plus Fubp2 KD (KOKD). The KD of Fubp2 in KD and KOKD cells was validated by immunoblotting (Supplementary Fig. 6a). These cells were studied after steady-state growth (Fig. 5a) or after serum starvation followed by stimulation with either PDGF (Fig. 5b), TNFα (Fig. 5c), or FGF (Supplementary Fig. 6b), and KOKD cells become inviable after ~5 days of culture. In MEFs grown at steady state, Fubp2 KD does not perturb the MYC CDF. Although mean MYC levels deviated upwards upon FUBP1 KO, the boundary separating stage I and stage II was not violated. The fact that increased mean MYC level in the KO was not accompanied by increased proliferation (Supplementary Fig. 6c) may be attributable to loss of FUBP1’s direct impact on multiple cell cycle regulators58. The combined loss of Fubp1 and Fubp2 (KOKD) yielded exaggerated and erratic MYC levels that even at steady state cracked the Stage I ceiling to express Stage II levels of MYC. Thus, the FUBPs enforce MYC setpoints and prevent excursions into Stage II. That KOKD cells become inviable after ~5 days suggests that beyond MYC regulation, the FUBPs may contribute to other essential cellular functions.

Fig. 5: Loss of FUBP1 and FUBP2 breaks the boundaries between Stage I and II, and erases MYC setpoints in individual cells.

The empirical CDF plot of MYC protein level in untreated (UT) (a), PDGF-stimulated (b), or TNFα-stimulated (c) MEFs are shown. d Loss of FUBP1 and FUBP2 causes increased MYC protein levels and variation in untreated and stimulated cells. e The mean of MYC protein level of samples in d. *T-test assuming unequal variance, compared with the wild-type sample subjected to the same treatment. f Loss of FUBP1 and FUBP2 causes increased Myc mRNA levels and variation. TNFα or PDGF (2 ng/ml) was used for treatment. AU: arbitrary unit. Experiments were repeated with similar results (n = 3 independent experiments).

When treated with high levels of TNFα or PDGF, WT and Fubp2 KD MEFs responded identically, their almost superimposable CDFs shifting rightward to enter stage II. With stimulation, Fubp1-KO MEFs moved to slightly higher expression than either WT or Fubp2 KD across almost the full range of MYC expression. TNFα or PDGF stimulation of KOKD MEFs elicits the highest and most variable levels of MYC (Fig. 5d, e and Supplementary Fig. 6d, e) seen in these experiments. RNA in situ hybridization confirmed that this increased level of MYC protein in KOKD MEFs reflected a parallel increase of Myc mRNA (Fig. 5f). We conclude that together FUBP1 and FUBP2 help to confine the MYC-setpoint to lower mean values and to limit expression variability within stage I and reduce the range of MYC expression in Stage II.

FUBP1 binds broadly to the Myc gene body and 3′-end in primary MEFs

Although FUBP1 binds to FUSE in human cells, this element is imperfectly conserved in mice and differs in bases that would be predicted to attenuate binding of FUBP1 at the murine homologous location38. Considering the mega-base span of the Myc locus and as no assays have documented the profile of FUBP1 binding across the full locus in either mice or humans, we sought to survey FUBP1 binding in the Myc region using chromatin immunoprecipitation sequencing (ChIP-seq). Here we find that that FUBP1 binds broadly across the first exon and intron, and throughout the transcription termination region and the 3′-untranslated regions of the Myc gene in primary MEFs (Fig. 6a). FUBP1 also binds broadly across many other genes (Supplementary Fig. 7), which will be analyzed and published separately. Notably, the MYC coding region is bracketed by upstream and downstream FUBP1 peaks. Probing for FUBP1 binding at selected sites in MYC by ChIP-PCR yields a higher signal in Fubp1 WT cells than in Fubp1 KO cells (Supplementary Fig. 8a, b) (the residual binding likely represents cross-reaction of anti-FUBP1 with FUBP2 in ChIP (Supplementary Fig. 8c). When stimulated by PDGF or TNFα for 4 h, binding of FUBP1 to the Myc gene body increases compared with serum-starved cells (Fig. 6b), whereas the levels of FUBP1/2 are not notably affected (Supplementary Fig. 8d). Transcription through the Myc gene body would at least transiently expose the entire non-template strand to ssDNA binding proteins such as FUBP1. The frequency, life span, and function of such intragenic binding events remains to be evaluated.

Fig. 6: FUBP1 binds broadly to the Myc gene in parallel to the expression of MYC and loss of FUBPs causes changes in the chromatin at the Myc locus.

a First panel: the FUBP1 ChIP-seq at Myc locus in MEFs. Peaks called by the SICER algorithm against input control are marked with green lines, FDR < 0.01. A mouse FUSE (mFUSE) with a high possibility of melting is marked by a pink line. The positions of the primers used in each panel of b and c were marked with the corresponding letters in this figure. The DNase hypersensitivity sites (DHS) downstream of Myc that were flanked by H3K4me1 peaks and overlapped with p300 peaks84 are marked by a light green rectangle and analyzed in d. Second to the fifth lines: the DHS peaks identified by TACh-seq in WT and KOKD MEFs, biological duplicates. Sixth panel: ENCODE data (ENCFF592HMA) of H3K4me1 ChIP-seq in MEFs. Seventh panel: p300 ChIP-seq in MEFs from published literature. Bottom panel: the transition probabilities calculated by SIDD algorithm as functions of base pair at the Myc locus and regions susceptible to melting are shown. The sequences of 3.7 kb upstream of the transcription start site (TSS) to the 1.2 kb downstream of TTS was analyzed as a superhelical domain, whereas 1.2 to 10 kb downstream of TTS was analyzed as another superhelical domain. Superhelical density = −0.06. The positions of the primers used in each panel of b and c were marked with the corresponding letters. b FUBP1 ChIP-qPCR results of the Myc locus in steady-state and PDGF- or TNFα-stimulated MEFs are shown. The positions of the primers used in each panel are marked with the corresponding letters in a. Experiments were performed in triplicate. **p < 0.01, T-test assuming unequal variance, two-tailed, compared to the serum-starved sample. c The results of H3K4me1 ChIP-qPCR on the Myc gene in WT, KD, KO, and KOKD MEFs are shown. The position of the primers used in this figure is marked with the corresponding letters in Figure a. Experiments were performed in triplicate. *p < 0.05, **p < 0.01, T-test assuming unequal variance, two-tailed, compared to the WT. d The average of the normalized TACh-seq reads density of two biological replicates of WT and KOKD MEFs were plotted on the DHS peak downstream of Myc (the region marked by a light green rectangle in a). DESeq2 analysis showed the signal of this DHS peak in KOKD is significantly higher in KOKD than that in WT (FDR < 0.05), with an average fold increase of 1.6.

A local maximum of FUBP1 binding ~2.5 kb upstream of the TSS was coincident with the strongest upstream predicted peak (mFUSE) of stress-induced duplex destabilization (SIDD)59; coincidence of FUBP1 binding and supercoiled-driven melting is a feature of the human FUSE region (Fig. 6a). Unlike humans, no predicted Z-DNA sites (a left-handed double helix with a 12 bp helical repeat) that would be stabilized by physiological levels of supercoiling were noted in the murine Myc gene. The termination region 3′ of Myc also supports a strong SIDD site that would compete to absorb the torsional stress required for melting with mFUSE when both sequences reside in the same topological domain. During active transcription of Myc, mFUSE and the 3′-SIDD sites will not reside in the same topological domain as the transcribing RNAPII is a node that separates negative supercoiling upstream from positive supercoiling downstream of the enzyme. As the polymerase transits the 3′-SIDD sites, both mFUSE and the 3′-SIDD would become embraced within the same negatively supercoiled domain and compete for melting; most of the time, the 3′-SIDD will win this competition. FUBP1 binding to mFUSE and the 3′-downstream regions of MYC were compared in high- and low-MYC cells during steady-state growth (Supplementary Fig. 8e) and found to be parallel to MYC levels (Supplementary Fig. 8f). Previous studies of the human FUSE during MYC activation revealed that immediate FUBP3 recruitment that peaks 1 h post stimulation is followed at 2 h by FUBP1 that has largely declined by 4 h45,60. The collapse of mFUSE would evict FUBP1 until a subsequent round of activity regenerates enough torsional stress to repeat the mFUSE-FUBP1 regulatory cycle. Although a FUBP1 binding at mFUSE declined 4 h after activation, FUBP1 remained at the coding and 3′-regions (Fig. 6b).

Interrogating MYC for chromatin changes associated with high or low setpoints and/or FUBP action

To assess the possible involvement of processes that might relate to the epigenetic fixation of MYC setpoints, several features of chromatin were interrogated using ChIP-PCR and nuclease hypersensitivity analysis. Multiple changes were noted, mainly in the gene body and 3′-downstream regions of the gene. First, histone H3K4me1, H3K4me3, and H3K27ac ChIP analyses were performed on Fubp1 WT, KD, KO, and KOKD cells (Fig. 6c and Supplementary Fig. 9). PCR probes were selected to examine regions known to be enriched for these features (ENCODE61,62 #ENCSR030BUT). We found that the 3′-region peak of H3K4me1 on the Myc gene in KO and KOKD cells is higher than that in WT cells. A dramatic increase in nuclease hypersensitivity was elicited in KOKD cells at a site 8 kb downstream of the TTS in a region that binds p300 (Fig. 6d). Together, these results indicate that the FUBPs directly or indirectly influence chromatin architecture at multiple locations throughout the MYC locus.


Before the development of single-cell methods, gene expression in tissues and cell lines had been characterized with population-wide measurements—mean levels of protein and mRNA expression, and sometimes their half-lives. The biological interpretations of these measurements have typically assumed that the birth and death of these macromolecules were independent, single, Poisson-distributed stochastic events. With the advent of fluorescence- and -omics-based methods, it has become possible to observe and explain variability in gene expression between cells. Such variability has been considered to reflect either bona fide long-lived biological variation, as similar cells progressively canalize into distinct states and/or microstates63, or, alternatively, the variability may arise due to intrinsic and extrinsic fluctuations within an otherwise homogeneous population64. Interrogating cell-to-cell variation of gene output by flow cytometry or quantitative fluorescence microscopy typically reveals a log-normal distribution of expression (a normal distribution of the logarithm of the measured parameter). Graphically, the log-normal distribution is a bell-shaped distribution with a long tail. The intercellular tallies of most proteins and RNAs exhibit such cell-to-cell variation spanning a range of one–two logs65. For many genes, such variation may be of little physiological or pathologic consequence; for some of these genes, such fluctuations may be simply averaged away over time, whereas for others the fluctuations may simply be tolerated with few lasting untoward consequences for imprecise expression control at the organismal level. With very short protein and mRNA half-lives, MYC would seem to be primed for fluctuating expression levels. Yet, MYC levels matter. Twofold differences in MYC levels have been associated with profound changes in cellular and organismal fates13,19,22,23,29,30,66,67,68.

The plethora of regulatory mechanisms operating on MYC expression at all levels of macromolecular synthesis and degradation would argue that MYC levels are precisely regulated. By distributing regulation across multiple levels of macromolecule synthesis and degradation, expression variation is intrinsically reduced. In addition, there may be mechanisms that actively reduce expression noise. Indeed, it has been shown that FUBP1 can contribute to a feedforward/feedback system that regulates Myc transcription in real-time. FUBP1 has been proposed to be a key effector that can both up- or downregulate MYC and behave as a molecular cruise control that opposes MYC fluctuations29,32,45,69. The superposition of multiple layers on Myc regulation might argue that its expression has been selected to be more tightly controlled than most other genes. Yet, the evidence that MYC levels are indeed held to closer tolerances has been missing.

At first glance, MYC expression at steady state seems to be a typical log-normal-like distribution with protein levels spanning more than a decade. The bulk of cells reside in the narrow low-MYC peak where overall MYC-driven transcription amplification is likely to be modest. Other cells populate a long rightward tail where MYC is expected to be on duty. Yet, sorting and re-culturing the cells from either the low-MYC or the high-MYC sides of the distribution failed to repopulate the parental profile. Thus, the log-normal appearance of the total population belies a more precise setting of MYC expression and indicates that the observed distribution must be a superposition of cell-autonomous subpopulations with differing MYC setpoints. The maintenance of these setpoints through cell division and growth arrest indicates the operation of a somatic epigenetic mechanism. Although inhibitors of DNA methylation and histone deacetylation altered MYC levels, they acted equivalently on high- and low-MYC subpopulations and failed to erode the difference between the high and low cells. We surmise that the heritable fine-tuning of MYC may involve uncharacterized epigenetic mechanisms. The fact that HDAC inhibition, which generally augments transcription, decreases MYC expression (here and as reported previously70) supports the existence of noncanonical epigenetic regulation of Myc.

We find that Myc-stimulation occurs in two stages in non-transformed B-cells and primary fibroblasts. In the first stage following treatment with low levels of growth factors (IL-4, FGF, PDGF, TNFα, etc.) and/or other mitogens (serum), unstressed cells shift mean MYC expression upward while respecting an upper boundary (Fig. 7). Such a regimen would seem to be well-suited for the physiological adjustments as low levels of MYC fine-tune steady-state expression.

Fig. 7: The basal levels of MYC determines the strength of the response to the stimulus.

In response to a weak stimulus (left), the cells in Stage I shift upwards while adhering to the upper limit. In response to a strong stimulus (right), the basal MYC levels determine the number of cells entering into Stage II. The cells with a high-MYC setpoint are more likely to enter Stage II. FUBP1 and FUBP2 play an essential role in limiting the expression of MYC in Stage I.

When the cells are exposed to strong stimuli and express a large amount of MYC, the cells enter a second stage (Fig. 7). In Stage II, MYC expression rises acutely and becomes much more variable with a subfraction of cells displaying very high levels. Such variability has been shown to be functional in a variety of stress responses to environmental, inflammatory, or infectious challenges, where differences from one cell to the next are effectively hedged, allowing subsets of the cell population to find the optimal MYC levels. This variability is not entirely stochastic as the magnitude of the stage II response between subpopulations seems to be primed by their relative MYC setpoints in stage I. As under physiological conditions subpopulations of cells have different stage I setpoints, differing levels of stimulation may be required to drive the different subpopulations across the stage II boundary. Thus, the sensitivity to MYC-inducing signals may be tuned by adjusting the basal level of MYC. Such stable tuning of the Toll-like receptor response by MYC has been noted in mature B-cells71,72. The results here suggest that cells with higher basal MYC are primed to enter stage II and rapidly mount a robust stress response. As MYC amplifier-gain parallels increasing promoter output, these cells will preferentially increase their already higher levels of mediators of inflammation and immunity. High MYC stage I cells may thus serve as sentinels that alert and help to recruit their neighbors when they enter stage II (Fig. 7).

The proto-oncogene, Myc, becomes an oncogene through abnormal upregulation, not requiring changes in its protein sequence. Although coding mutations do occur, they primarily affect MYC stability and turnover, thereby increasing its levels rather than changing its fundamental action73,74,75,76. Much direct and indirect evidence suggests that MYC levels are important in proliferation and immortalization. Small changes in MYC levels prompt cell cycle-arrested cells to proliferate or to cross thresholds that trigger apoptosis68. When normal diploid human fibroblasts are immortalized by retroviral-transduced telomerase, these cells become senescent after knocking out a c-Myc allele so small changes in MYC levels are associated with dramatically different cell fates77. Thus, how much MYC is oncogenic? The two-stage model presented here can help explain. In primary cells, MYC levels are in stage I and are all below the ceiling. When most cells break through the upper limit and enter the Stage II, it raises a hypothesis that this may be a sign of cell state change: cells may convert into another type of cell, or into a more active state, such as become active B-cells from naive B-cells, or become immortalized or carcinogenic. Quantitative and dynamic studies of MYC levels in different contexts are needed to test this hypothesis.

To limit MYC expression in Stage I, Myc is tightly regulated by many signaling pathways at the transcriptional, post-transcriptional and protein regulatory levels, and Myc mRNAs and proteins are relatively unstable. FUBP1/FIR/FUSE system has been proposed to be a molecular cruise control, limiting the upward and downward shift of MYC and possibly other FUBP1 target genes by providing both positive and negative feedback48,49. But this system has been less explored in mice. We have mapped the FUBP1-binding profile across the Myc gene. Here we report a candidate mFUSE ~2.5 kb upstream of the major Myc TSS; this element both melts in response to supercoiling and is a local maximum of FUBP1 binding. mFUSE is predicted to be topologically coupled with a SIDD site at the 3′-end of Myc when both elements reside in the same topological domain. Notably, upon loss of FUBP1 and especially when lacking both FUBP1 and 2, cells spontaneously enter Stage II. After stimulation with PDGF, binding at mFUSE is diminished 4 h after stimulation with PDGF. This finding suggests that the cruise-control action must be disengaged to accelerate and sustain high MYC output.

FUBP1 has been found to associate with a variety of genes that regulate cell growth or death besides MYC, including p21, Ccna1, and USP29, through binding to FUSE-like upstream regulatory sequences78,79,80. FUBP1 also binds to a broadly transcribed region of Myc and along its 3′-end in MEFs. As FUBP1 binds to a range of single-stranded nucleic acids across a hierarchy of affinities, this intragenic and 3′-region binding along with previous works35,36,38,44,45,46,51,69,81,82 suggest that FUBP1 may regulate Myc transcription through conventional and unconventional incompletely characterized mechanisms that operate intragenically and at the 3′-end of Myc.

Among the three members of the FUBP family, FUBP2 shares ~65% sequence similarity with FUBP1 in human and mouse, and is co-expressed in almost every tissue with FUBP1, and thus their function may overlap. This study showed that a shared deficiency of both FUBP1 and FUBP2 dramatically increased both MYC levels and variability. The simultaneous loss of FUBP1 and FUBP2 may render the cells completely unable to recruit FIR (the remaining family member FUBP3 is unable to bind FIR), so that the cells lose both positive and negative feedback at the same time; therefore, the expression level will fluctuate greatly. Whether the overall level of expression rises or falls depends on the sum of the stimulatory or repressive signals of the cells at the time.

Looking ahead, the single-cell microscopy and characterization of MYC expression established in this study should instruct the further study of Myc, as well as of many other genes. Gene expression varies from cell to cell, and pathological deregulation of only a small subpopulation of cells may subsequently elicit disease. Treating gene levels in a cell group as a distribution rather than a single value may be the only way to understand the biology and pathology of these outliers (e.g., drug resistance and tumor relapse).


Cells and reagents

Primary mouse embryonic fibroblasts were prepared as previously described51. Briefly, on gestation Day 13.5, pregnant female mice were killed and embryos were surgically separated, decapitated, and eviscerated. The bodies were minced, digested in trypsin for 10 min, and cells were plated in T-75 flasks. Then MEFs were cultured in Dulbecco’s Modified Eagle Medium (Gibco # 10564) supplemented with 10% fetal bovine serum, 100 units/mL of penicillin, and 100 µg/mL of streptomycin (Gibco #15140) for within three passages. MEFs were 60~70% confluence before 4 h of stimulation or 24 h of treatment, and were 40~50% confluence before siRNA transfection. Primary resting naive mouse B-cells were isolated from splenocytes with anti-CD43 MicroBeads (Miltenyi Biotec) by negative selection. Antibodies to MYC (#ab32072) and FUBP1 (#ab184111) were from Abcam. LPS, mouse IL-4, and mouse FGF were from R&D Systems. Mouse PDGF was from Life Technology.

Quantitative immunofluorescence and imaging acquisition

Primary MEFs or primary naive B-cells were seeded in a fibronectin-coated 96-well glass-bottom plate (Thermo Scientific) and fixed with 2% paraformaldehyde. Cells were incubated with MYC primary antibody and a secondary antibody coupled to Alexa Fluor 488 (Thermo Fisher) and stained with Hoechst 33342 (Thermo Fisher). Cells were imaged on a Nikon Eclipse TiE inverted fluorescence microscope with a ×20 plan apo objective (NA 0.75) using an iXon Ultra-888 camera (Andor), and GFP (450–490/495/500–550 nm) and 4′,6-diamidino-2-phenylindole (DAPI) (325–375/400/435–485 nm) filter sets, to enable the collection of separate data from individual cells, as opposed to values averaged over populations of cells. At least 1000 cells were acquired per condition.

Myc RNA in situ hybridization and imaging acquisition

Primary MEFs were plated in 96-well plate, fixed with 2% paraformaldehyde, and stained with a fluorescent Myc RNA probe following the manufacturer’s instructions (ACD). Cells were imaged on a Nikon Eclipse TiE inverted fluorescence microscope with a ×40 plan apo objective (NA 0.75) using an iXon Ultra-888 camera (Andor), and GFP (450–490/495/500–550 nm) and DAPI (325–375/400/435–485 nm) filter sets, to enable the collection of separate data from individual cells, as opposed to values averaged over populations of cells. At least 100 cells were acquired per condition.

Flow cytometry analysis and cell sorting

Primary MEFs from Myc-Egfp+/+ mice were treated with 20 µM of MG-132 for 6 h, to inhibit proteasome degradation of MYC and therefore enhance the MYC-GFP signal, and then sorted for low- and high-GFP cells. The results were analyzed by Flowjo software.

WST-1 proliferation assay

The low-MYC and high-MYC cells after sorting were seeded in triplicate in a 100 µl volume. Ten microliters of WST-1 (Sigma) was added to the cells and absorbance was measured at 450 nm against 655 nm after 0.5 h.

RNA sequencing

After sorting, the low-MYC and high-MYC cells were seeded overnight. Considering the effects of MYC on global gene expression, external RNA spike-in control (ERCC, Thermo Fisher Scientific) was added to cells in proportion to cell count before total RNA extraction. The mRNA libraries were prepared using TruSeq Stranded mRNA (Illumina) and sequenced on a NextSeq sequencer.

RNA interference

WT and Fubp1 KO MEFs were transfected with 100 nM of mouse Fubp2 or non-target siRNA (Dharmacon) for 48 h using Dharmacon transfection reagents.

ChIP-seq and ChIP-qPCR

ChIP samples were prepared following regular ChIP protocol with minor modifications. Briefly, primary MEFs were cross-linked with 1% formaldehyde for 8 min (FUBP1 ChIP) or 2 min (H3K4me1, H3K4me3, and H3K27ac ChIP), and the cross-linking was terminated by adding glycine to a final concentration of 0.125 M. After collecting cells, the cell pellets were resuspended in RIPA buffer supplemented with protease inhibitors. Samples were sonicated to produce chromatin fragments of 300 bp on average and subjected to immunoprecipitation. Five micrograms of rabbit monoclonal anti-FUBP1 antibody (Abcam, ab181111), anti-H3K4me1 (Abcam, #ab8895), anti-H3K4me3 (Millipore, Sigma, #04-745), and anti-H3K27ac (ActiveMotif #39034) were used for the immunoprecipitation. The immunoprecipitated samples were subjected to library preparation and sequencing (ChIP-seq), or to qPCR quantification with SYBR Green. The ChIP-seq data sets for H3K4me1, H3K4me3, and H3K27ac in primary MEFs are from ENCODE data set ENCSR030BUT. The antibodies listed above for the histone marks ChIP-qPCR are the same as those used for the ENCODE data. The primers for FUBP1, H3K4me1, H3K4me3, and H3K27ac ChIP-qPCR on the Myc gene are listed in Supplementary Table 1.

TACh sequencing

The TACh sequencing (TACh-seq) was performed as previously described with modification83. Briefly, MEFs were lysed in hypertonic lysis buffer and digested with Cyanase (RiboSolutions) for 3 min at 37 °C. Reactions were terminated by the addition of EDTA (10 mM final) and SDS (0.75% final). After RNA and protein digestion, DNA was purified and the fragments of 100–600 bp were selected by gel electrophoresis. The sequencing libraries were prepared using TruSeq Chip Sample Prep Kit (Illumina) and sequenced on a NextSeq sequencer.


Samples were lysed in radioimmunoprecipitation buffer for 30 min and 30 μg of total protein was used for immunoblotting.

Quantitative immunofluorescence and Myc RNA in situ hybridization

Image acquisition, automated segmentation, and intensity measurements were performed using Nikon Elements software version 4.30.02. For MYC protein immunostaining, the nuclear size indicated by Hoechst and the mean nuclear fluorescence intensity of MYC were measured in single cells, and the total amount of MYC protein was calculated as the product of mean intensity and the nuclear size. For Myc RNA in situ hybridization, the cell boundary of the bright-field image was determined by the Contour function provided by the Nikon Elements software and the cell size was determined. Total Myc mRNA intensity was calculated as the product of mean intensity and cell size. Data were analyzed and plotted using custom R software. The analysis codes are available from the corresponding author upon request.

RNA-seq analysis

RNA-seq data were processed using Partek Flow (Partek) and custom R code. After trimming bases on the 3′-end with quality scores below 20, reads were aligned to the mouse transcriptome (mm10) using TopHat. For each ERCC RNA, the number of reads mapping to that transcript was divided by the transcript length to yield Fragments Per Kilobase (FPK). From the top 50 expressed transcripts as measured by average FPK over all samples, a set of 10 highly correlated transcripts was used to represent the quantity of spike-in RNA in each sample. The normalization score for each sample was then calculated as the geometric mean of the expression (FPK) of those ten genes in that sample. Reads mapped to the transcriptome were quantified using the Partek E/M algorithm. For each transcript, the number of reads quantified to that transcript was divided by the transcript length to yield FPK, then divided by the normalization score for that sample to normalize to the number of cells. The normalized FPK of low-MYC and high-MYC cells were compared.

ChIP-seq analysis

After trimming bases on both ends with quality scores below 20, reads were aligned to the mouse genome (mm10) using Bowtie2. ChIP-seq peaks were called using SICER. The input DNA sequenced on the same lane was used as the control.

ChIP-qPCR analysis

The Cq of each ChIP sample was analyzed relative to the input, as this included normalization of background levels and the amount of chromatin for ChIP.

TACh-seq analysis

After trimming bases on both ends with quality scores below 20, reads were aligned to the mouse genome (mm10) using Bowtie2. The TACh-seq peaks were called using SICER. The difference in read density of the TACh-seq peaks between WT and KOKD MEFs was analyzed using DESeq2.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The RNA-seq, ChIP-seq, and TACh-seq sequencing data that support the findings of this study are available in GEO, accession number GSE143800. Source data for the main figures is provided in Supplementary Data 2. All other data that support the findings of this study are available from the corresponding author upon reasonable request.


  1. 1.

    Conacci-Sorrell, M., McFerrin, L. & Eisenman, R. N. An overview of MYC and its interactome. Cold Spring Harb. Perspect. Med. 4, a014357 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  2. 2.

    Dang, C. V. MYC on the path to cancer. Cell 149, 22–35 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Wasylishen, A. R. & Penn, L. Z. Myc: the beauty and the beast. Genes cancer 1, 532–541 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Kim, J., Chu, J., Shen, X., Wang, J. & Orkin, S. H. An extended transcriptional network for pluripotency of embryonic stem cells. Cell 132, 1049–1061 (2008).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Lin, C. H., Lin, C., Tanaka, H., Fero, M. L. & Eisenman, R. N. Gene regulation and epigenetic remodeling in murine embryonic stem cells by c-Myc. PLoS ONE 4, e7839 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. 7.

    Rahl, P. B. et al. c-Myc regulates transcriptional pause release. Cell 141, 432–445 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Lorenzin, F. et al. Different promoter affinities account for specificity in MYC-dependent gene regulation. eLife 5, e15161 (2016).

  10. 10.

    Nie, Z. et al. c-Myc is a universal amplifier of expressed genes in lymphocytes and embryonic stem cells. Cell 151, 68–79 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Wolf, E., Lin, C. Y., Eilers, M. & Levens, D. L. Taming of the beast: shaping Myc-dependent amplification. Trends Cell Biol. 25, 241–248 (2015).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Davis, A. C., Wims, M., Spotts, G. D., Hann, S. R. & Bradley, A. A null c-myc mutation causes lethality before 10.5 days of gestation in homozygotes and reduced fertility in heterozygous female mice. Genes Dev. 7, 671–682 (1993).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Mateyak, M. K., Obaya, A. J., Adachi, S. & Sedivy, J. M. Phenotypes of c-Myc-deficient rat fibroblasts isolated by targeted homologous recombination. Cell Growth Differ. 8, 1039–1048 (1997).

    CAS  PubMed  Google Scholar 

  14. 14.

    Dave, K. et al. Mice deficient of Myc super-enhancer region reveal differential control mechanism between normal and pathological growth. eLife 6, e23382 (2017).

  15. 15.

    Dubois, N. C. et al. Placental rescue reveals a sole requirement for c-Myc in embryonic erythroblast survival and hematopoietic stem cell function. Development 135, 2455–2465 (2008).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Scognamiglio, R. et al. Myc depletion induces a pluripotent dormant state mimicking diapause. Cell 164, 668–680 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Bettess, M. D. et al. c-Myc is required for the formation of intestinal crypts but dispensable for homeostasis of the adult intestinal epithelium. Mol. Cell. Biol. 25, 7868–7878 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    He, C. et al. c-myc in the hematopoietic lineage is crucial for its angiogenic function in the mouse embryo. Development 135, 2467–2477 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Hofmann, J. W. et al. Reduced expression of MYC increases longevity and enhances healthspan. Cell 160, 477–488 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Li, F. et al. Conditional deletion of c-myc does not impair liver regeneration. Cancer Res. 66, 5608–5612 (2006).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Oskarsson, T. et al. Skin epidermis lacking the c-Myc gene is resistant to Ras-driven tumorigenesis but can reacquire sensitivity upon additional loss of the p21Cip1 gene. Genes Dev. 20, 2024–2029 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Trumpp, A. et al. c-Myc regulates mammalian body size by controlling cell number but not cell size. Nature 414, 768–773 (2001).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Liu, J. & Levens, D. Making myc. Curr. Top. Microbiol. Immunol. 302, 1–32 (2006).

    CAS  PubMed  Google Scholar 

  24. 24.

    Wierstra, I. & Alves, J. The c-myc promoter: still MysterY and challenge. Adv. Cancer Res. 99, 113–333 (2008).

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Chapuy, B. et al. Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer Cell 24, 777–790 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Affer, M. et al. Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma. Leukemia 28, 1725–1735 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. 28.

    Schuijers, J. et al. Transcriptional dysregulation of MYC reveals common enhancer-docking mechanism. Cell Rep. 23, 349–360 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Levens, D. Cellular MYCro economics: balancing MYC function with MYC expression. Cold Spring Harb. Perspect. Med. 3, a014233 (2013).

  30. 30.

    Levens, D. You don’t muck with MYC. Genes cancer 1, 547–554 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Zaytseva, O. & Quinn, L. M. Controlling the master: chromatin dynamics at the MYC promoter integrate developmental signaling. Genes 8, 118 (2017).

  32. 32.

    Zaytseva, O. & Quinn, L. M. DNA conformation regulates gene expression: the MYC promoter and beyond. Bioessays 40, e1700235 (2018).

    PubMed  Article  CAS  Google Scholar 

  33. 33.

    Avigan, M. I., Strober, B. & Levens, D. A far upstream element stimulates c-myc expression in undifferentiated leukemia cells. J. Biol. Chem. 265, 18538–18545 (1990).

    CAS  PubMed  Google Scholar 

  34. 34.

    Davis-Smyth, T., Duncan, R. C., Zheng, T., Michelotti, G. & Levens, D. The far upstream element-binding proteins comprise an ancient family of single-strand DNA-binding transactivators. J. Biol. Chem. 271, 31679–31687 (1996).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Duncan, R. et al. A sequence-specific, single-strand binding protein activates the far upstream element of c-myc and defines a new DNA-binding motif. Genes Dev. 8, 465–480 (1994).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Michelotti, G. A. et al. Multiple single-stranded cis elements are associated with activated chromatin of the human c-myc gene in vivo. Mol. Cell. Biol. 16, 2656–2669 (1996).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Zhang, J. & Chen, Q. M. Far upstream element binding protein 1: a commander of transcription, translation and beyond. Oncogene 32, 2907–2916 (2013).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Benjamin, L. R. et al. Hierarchical mechanisms build the DNA-binding specificity of FUSE binding protein. Proc. Natl Acad. Sci. USA 105, 18296–18301 (2008).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Siebel, C. W., Kanaar, R. & Rio, D. C. Regulation of tissue-specific P-element pre-mRNA splicing requires the RNA-binding protein PSI. Genes Dev. 8, 1713–1725 (1994).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Bettegowda, C. et al. Mutations in CIC and FUBP1 contribute to human oligodendroglioma. Science 333, 1453–1455 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Debaize, L. & Troadec, M. B. The master regulator FUBP1: its emerging role in normal cell function and malignant development. Cell Mol. Life Sci. 76, 259–281 (2019).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Braddock, D. T., Louis, J. M., Baber, J. L., Levens, D. & Clore, G. M. Structure and dynamics of KH domains from FBP bound to single-stranded DNA. Nature 415, 1051–1056 (2002).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Cukier, C. D. et al. Molecular basis of FIR-mediated c-myc transcriptional control. Nat. Struct. Mol. Biol. 17, 1058–1064 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Liu, J. et al. Defective interplay of activators and repressors with TFIH in xeroderma pigmentosum. Cell 104, 353–363 (2001).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Liu, J. et al. The FUSE/FBP/FIR/TFIIH system is a molecular machine programming a pulse of c-myc expression. EMBO J. 25, 2119–2130 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Liu, J. et al. The FBP interacting repressor targets TFIIH to inhibit activated transcription. Mol. Cell 5, 331–341 (2000).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Hsiao, H. H. et al. Quantitative characterization of the interactions among c-myc transcriptional regulators FUSE, FBP, and FIR. Biochemistry 49, 4620–4634 (2010).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Kouzine, F. et al. Transcription-dependent dynamic supercoiling is a short-range genomic force. Nat. Struct. Mol. Biol. 20, 396–403 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Kouzine, F., Liu, J., Sanford, S., Chung, H. J. & Levens, D. The dynamic response of upstream DNA to transcription-generated torsional stress. Nat. Struct. Mol. Biol. 11, 1092–1100 (2004).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Quinn, L. M. FUBP/KH domain proteins in transcription: back to the future. Transcription 8, 185–192 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Zhou, W. et al. Far upstream element binding protein plays a crucial role in embryonic development, hematopoiesis, and stabilizing Myc expression levels. Am. J. Pathol. 186, 701–715 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Dani, C. et al. Extreme instability of myc mRNA in normal and transformed human cells. Proc. Natl Acad. Sci. USA 81, 7046–7050 (1984).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Sears, R., Leone, G., DeGregori, J. & Nevins, J. R. Ras enhances Myc protein stability. Mol. cell 3, 169–179 (1999).

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    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).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Johnston, L. A., Prober, D. A., Edgar, B. A., Eisenman, R. N. & Gallant, P. Drosophila myc regulates cellular growth during development. Cell 98, 779–790 (1999).

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Grant, G. D. et al. Identification of cell cycle-regulated genes periodically expressed in U2OS cells and their regulation by FOXM1 and E2F transcription factors. Mol. Biol. Cell 24, 3634–3650 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58.

    Kang, M. et al. Multiple functions of Fubp1 in cell cycle progression and cell survival. Cells 9, 1347 (2020).

  59. 59.

    Zhabinskaya, D., Madden, S. & Benham, C. J. SIST: stress-induced structural transitions in superhelical DNA. Bioinformatics 31, 421–422 (2015).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Chung, H. J. et al. FBPs are calibrated molecular tools to adjust gene expression. Mol. Cell. Biol. 26, 6584–6597 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  62. 62.

    Davis, C. A. et al. The encyclopedia of DNA elements (ENCODE): data portal update. Nucleic Acids Res. 46, D794–d801 (2018).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Waddington, C. H. Canalization of development and genetic assimilation of acquired characters. Nature 183, 1654–1655 (1959).

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Niepel, M., Spencer, S. L. & Sorger, P. K. Non-genetic cell-to-cell variability and the consequences for pharmacology. Curr. Opin. Chem. Biol. 13, 556–561 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Johnston, L. A. Socializing with MYC: cell competition in development and as a model for premalignant cancer. Cold Spring Harb. Perspect. Med. 4, a014274 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Moreno, E. & Basler, K. dMyc transforms cells into super-competitors. Cell 117, 117–129 (2004).

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Murphy, D. J. et al. Distinct thresholds govern Myc’s biological output in vivo. Cancer Cell 14, 447–457 (2008).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Kouzine, F., Sanford, S., Elisha-Feil, Z. & Levens, D. The functional response of upstream DNA to dynamic supercoiling in vivo. Nat. Struct. Mol. Biol. 15, 146–154 (2008).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Collins, I., Weber, A. & Levens, D. Transcriptional consequences of topoisomerase inhibition. Mol. Cell. Biol. 21, 8437–8451 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Heinzel, S. et al. A Myc-dependent division timer complements a cell-death timer to regulate T cell and B cell responses. Nat. Immunol. 18, 96–103 (2017).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Meyer-Bahlburg, A., Bandaranayake, A. D., Andrews, S. F. & Rawlings, D. J. Reduced c-myc expression levels limit follicular mature B cell cycling in response to TLR signals. J. Immunol. 182, 4065–4075 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Spencer, C. A. & Groudine, M. Control of c-myc regulation in normal and neoplastic cells. Adv. Cancer Res. 56, 1–48 (1991).

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Marcu, K. B., Bossone, S. A. & Patel, A. J. myc function and regulation. Annu Rev. Biochem. 61, 809–860 (1992).

    CAS  PubMed  Article  Google Scholar 

  75. 75.

    Sears, R. C. The life cycle of C-myc: from synthesis to degradation. Cell Cycle 3, 1133–1137 (2004).

    CAS  PubMed  Article  Google Scholar 

  76. 76.

    Peter, M. et al. MYC activation associated with the integration of HPV DNA at the MYC locus in genital tumors. Oncogene 25, 5985–5993 (2006).

    CAS  PubMed  Article  Google Scholar 

  77. 77.

    Guney, I., Wu, S. & Sedivy, J. M. Reduced c-Myc signaling triggers telomere-independent senescence by regulating Bmi-1 and p16(INK4a). Proc. Natl Acad. Sci. USA 103, 3645–3650 (2006).

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Debaize, L. et al. Interplay between transcription regulators RUNX1 and FUBP1 activates an enhancer of the oncogene c-KIT and amplifies cell proliferation. Nucleic Acids Res. 46, 11214–11228 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  79. 79.

    Liu, J. et al. JTV1 co-activates FBP to induce USP29 transcription and stabilize p53 in response to oxidative stress. EMBO J. 30, 846–858 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Rabenhorst, U. et al. Single-stranded DNA-binding transcriptional regulator FUBP1 is essential for fetal and adult hematopoietic stem cell self-renewal. Cell Rep. 11, 1847–1855 (2015).

  81. 81.

    He, L. et al. Loss of FBP function arrests cellular proliferation and extinguishes c-myc expression. EMBO J. 19, 1034–1044 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. 82.

    Weber, A., Liu, J., Collins, I. & Levens, D. TFIIH operates through an expanded proximal promoter to fine-tune c-myc expression. Mol. Cell. Biol. 25, 147–161 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Grontved, L. et al. Rapid genome-scale mapping of chromatin accessibility in tissue. Epigenet. Chromatin 5, 10 (2012).

    CAS  Article  Google Scholar 

  84. 84.

    Chronis, C. et al. Cooperative binding of transcription factors orchestrates reprogramming. Cell 168, 442–459.e420 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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We thank Dr. Ferenc Livak in Flow Cytometry Core Facility (NCI, CCR) for assistance with the flow cytometry and sorting. We thank the sequencing facility (NCI, CCR) for assistance with next-generation sequencing. We thank the ENCODE Consortium and the Bing Ren lab (UCSD) generating the ENCSR030BUT data set. This work was supported by the Intramural Research Program of the National Cancer Institute, Center for Cancer Research.

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Y.Z. designed and performed experiments, analyzed results, performed computation, and wrote the manuscript. W.D. designed animal crosses and helped prepare experimental materials. C.J.B. wrote algorithms and analyzed DNA sequences. E.B. designed experiments, analyzed data, and edited the manuscript. D.L. designed experiments, analyzed results, and wrote the manuscript.

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Correspondence to David Levens.

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Zheng, Y., Dubois, W., Benham, C. et al. FUBP1 and FUBP2 enforce distinct epigenetic setpoints for MYC expression in primary single murine cells. Commun Biol 3, 545 (2020). https://doi.org/10.1038/s42003-020-01264-x

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