Mathematical Modeling: Tuning the MYC response

  1. Ying Zheng
  2. David Levens  Is a corresponding author
  1. National Cancer Institute, United States

Cancer develops when cells in the body gain mutations that enable them to divide rapidly and form a tumor. Some of these mutations can increase the expression of particular genes in cells. For example, a gene that encodes a transcription factor protein called MYC is upregulated in most types of tumor, where it supports several aspects of tumor development.

Transcription factors can bind to promoter regions at the start of genes to regulate the first step in gene expression, a process called transcription. MYC can bind to virtually all promoters in the genome and trigger widespread increases in gene activity. However, it is not clear whether MYC binding directly increases the transcription of all the genes (Levens, 2013; Lin et al., 2012; Nie et al., 2012; Wolf et al., 2015), or whether MYC only regulates specific sets of genes that then increase the expression of other genes (Kress et al., 2015).

Previous studies have shown that producing more MYC in cell lines does not increase the expression of all genes equally (Lin et al., 2012; Sabò et al., 2014; Walz et al., 2014). Now, in eLife, Elmar Wolf at the University of Würzburg and colleagues – including Francesca Lorenzin as first author – show that changing the amount of MYC in cells does not change the amount of MYC bound at each gene in the same way (Lorenzin et al., 2016). This prompted the researchers to seek an explanation for the relationship between MYC levels and gene expression across all genes.

Lorenzin et al. – who are based at the University of Würzburg and the Max-Delbrück-Center for Molecular Medicine – manipulated the levels of MYC in a human cell line called U20S, which normally has less of this protein compared to many cancer cell lines. Upregulating MYC in U2OS cells to match the levels seen in cancer resulted in MYC molecules being bound to virtually all promoters.

Further experiments indicated that MYC binding to any promoter can alter the activity of the corresponding gene, but that MYC is better at binding to some promoters than others. At normal levels of MYC, promoters where MYC binds weakly (i.e. those with a low binding affinity) will have few MYC molecules bound to them. However, under the same conditions, high-affinity promoters may already be saturated with MYC molecules. If cells make more MYC, the number of MYC molecules binding to the lower-affinity promoters would increase, but the high-affinity promoters would be largely unaffected. Therefore, the increases in MYC production observed in cancer cells can alter the transcription of some genes more than others. Because every cancer starts with a different pattern of gene expression and a different level of MYC, this may result in each tumor having a distinct transcription profile.

What determines how strongly MYC binds to a promoter? Lorenzin et al. modeled MYC binding across the genome, taking into account that MYC is known to bind strongly to a DNA motif called the E-box (Guo et al., 2014), and less tightly to other DNA sequences (Figure 1). But the model based on E-boxes alone did not explain how MYC is distributed across the genome. Lorenzin et al. then considered the possibility that interactions between MYC and other proteins may help to draw MYC to the promoters of active genes. For example, a protein called WDR5 was speculated to help MYC target genes (Thomas et al., 2015). Indeed, including interactions between MYC and WDR5 markedly improved the model for many (but not all) genes. Moreover, impairing this interaction had a greater impact on the expression of genes with high affinity for MYC than those with low affinity.

The ability of MYC to bind to individual promoters is influenced by interactions with DNA and other proteins.

The MYC protein forms a dimer with another protein called MAX, which allows it to bind to virtually any promoter in the genome and increase gene expression by activating transcription (green arrow). However, the ability of MYC to bind to individual promoters (binding affinity) varies. MYC prefers to bind to DNA motifs called E-boxes, but will also bind to other DNA sequences with lower affinity. A protein called WDR5, which is associated with the DNA of active genes, can help to recruit MYC to particular promoters (beige arrow). MYC also interacts with other DNA-associated proteins – including TRAPP, Paf1, BRD4 and PTEF-b – during transcription, which may also help MYC to bind to certain promoters with higher affinity.

Besides WDR5, MYC interacts with many other proteins and protein complexes during transcription (Figure 1). Do these interactions also help to recruit and retain MYC at promoters, and if so, how might they be incorporated into the model? Perhaps MYC interacts with different partners in different stages of transcription to ensure that the process happens in an efficient and orderly manner. It should be noted that some of these partners might trigger the degradation or modification of MYC leading to its ejection from certain promoters, which would change its apparent binding affinity, and in turn change the outputs of these promoters.

Finally, normal cells contain less MYC than the cells used by Lorenzin et al. and so the majority of promoters in a normal cell are not saturated with MYC molecules. In cancer cells, the genes with the highest affinity for MYC, such as the ribosomal protein genes, are saturated with MYC and are also the most sensitive to its inhibition or reduction. Understanding exactly how MYC binding influences gene expression promises to aid efforts to develop new therapeutic strategies that inhibit its cancer-promoting actions, while preserving its normal responsibilities in cells.

References

Article and author information

Author details

  1. Ying Zheng

    Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0674-2438
  2. David Levens

    Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, United States
    For correspondence
    levensd@mail.nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7616-922X

Publication history

  1. Version of Record published: July 27, 2016 (version 1)

Copyright

© 2016, Zheng et al.

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 1,446
    Page views
  • 324
    Downloads
  • 2
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ying Zheng
  2. David Levens
(2016)
Mathematical Modeling: Tuning the MYC response
eLife 5:e18871.
https://doi.org/10.7554/eLife.18871

Further reading

    1. Cancer Biology
    2. Medicine
    Huan-Huan Chen, Tie-Ning Zhang ... Tao Zhang
    Research Article Updated

    Background:

    Sarcomas comprise approximately 1% of all human malignancies; treatment resistance is one of the major reasons for the poor prognosis of sarcomas. Accumulating evidence suggests that non-coding RNAs (ncRNAs), including miRNAs, long ncRNAs, and circular RNAs, are important molecules involved in the crosstalk between resistance to chemotherapy, targeted therapy, and radiotherapy via various pathways.

    Methods:

    We searched the PubMed (MEDLINE) database for articles regarding sarcoma-associated ncRNAs from inception to August 17, 2022. Studies investigating the roles of host-derived miRNAs, long ncRNAs, and circular RNAs in sarcoma were included. Data relating to the roles of ncRNAs in therapeutic regulation and their applicability as biomarkers for predicting the therapeutic response of sarcomas were extracted. Two independent researchers assessed the quality of the studies using the Würzburg Methodological Quality Score (W-MeQS).

    Results:

    Observational studies revealed the ectopic expression of ncRNAs in sarcoma patients who had different responses to antitumor treatments. Experimental studies have confirmed crosstalk between cellular pathways pertinent to chemotherapy, targeted therapy, and radiotherapy resistance. Of the included studies, W-MeQS scores ranged from 3 to 10 (average score = 5.42). Of the 12 articles that investigated ncRNAs as biomarkers, none included a validation cohort. Selective reporting of the sensitivity, specificity, and receiver operating curves was common.

    Conclusions:

    Although ncRNAs appear to be good candidates as biomarkers for predicting treatment response and therapeutics for sarcoma, their differential expression across tissues complicates their application. Further research regarding their potential for inhibiting or activating these regulatory molecules to reverse treatment resistance may be useful.

    Funding:

    This study’s literature retrieval was supported financially by the 345 Talent Project of Shengjing Hospital of China Medical University (M0949 to Tao Zhang).

    1. Cancer Biology
    James Boot, Gabriel Rosser ... Silvia Marino
    Research Article

    We describe a subset of glioblastoma, the most prevalent malignant adult brain tumour, harbouring a bias towards hypomethylation at defined differentially methylated regions. This epigenetic signature correlates with an enrichment for an astrocytic gene signature, which together with the identification of enriched predicted binding sites of transcription factors known to cause demethylation and to be involved in astrocytic/glial lineage specification, point to a shared ontogeny between these glioblastomas and astroglial progenitors. At functional level, increased invasiveness, at least in part mediated by SRPX2, and macrophage infiltration characterise this subset of glioblastoma.