1. Microbiology and Infectious Disease

Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer

  1. Yang Joon Kim
  2. Kaitlin Rhee
  3. Jonathan Liu
  4. Selene Jeammet
  5. Meghan A Turner
  6. Stephen J Small
  7. Hernan G Garcia  Is a corresponding author
  1. University of California, Berkeley, United States
  2. École Polytechnique, France
  3. New York University, United States
https://doi.org/10.7554/eLife.73395
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Cite this article
  1. Yang Joon Kim
  2. Kaitlin Rhee
  3. Jonathan Liu
  4. Selene Jeammet
  5. Meghan A Turner
  6. Stephen J Small
  7. Hernan G Garcia
(2022)
Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer
eLife 11:e73395.
https://doi.org/10.7554/eLife.73395
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Abstract

A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity capture the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.

Data availability

All data (both input transcription factor concentration and output transcription from all synthetic enhancers, both pre- and post-processed data) have been deposited in Dryad under the doi (https://doi.org/10.5061/dryad.7sqv9s4sv).

The following data sets were generated

Article and author information

Author details

  1. Yang Joon Kim

    Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1742-5657

  2. Kaitlin Rhee

    Department of Chemical Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Jonathan Liu

    Department of Physics, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Selene Jeammet

    Department of Biology, École Polytechnique, Paris, France
    Competing interests
    The authors declare that no competing interests exist.

  5. Meghan A Turner

    Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Stephen J Small

    Department of Biology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Hernan G Garcia

    Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    For correspondence
    hggarcia@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5212-3649

Funding

Burroughs Wellcome Fund (Career Award)

  • Hernan G Garcia

Sloan Research Foundation

  • Hernan G Garcia

Human Frontier Science Program

  • Hernan G Garcia

Searle Scholars Program

  • Hernan G Garcia

Shurl and Kay Curci Foundation

  • Hernan G Garcia

Hellman Foundation

  • Hernan G Garcia

National Institute of Health (DP2 OD024541-01)

  • Hernan G Garcia

National Science Foundation (1652236)

  • Hernan G Garcia

Korea Foundation for Advanced Studies (Graduate Student Fellowship)

  • Yang Joon Kim

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Kim et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Yang Joon Kim
  2. Kaitlin Rhee
  3. Jonathan Liu
  4. Selene Jeammet
  5. Meghan A Turner
  6. Stephen J Small
  7. Hernan G Garcia
(2022)
Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer
eLife 11:e73395.
https://doi.org/10.7554/eLife.73395

Share this article

https://doi.org/10.7554/eLife.73395

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