1. Physics of Living Systems
Download icon

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

  1. William T Ireland
  2. Suzannah M Beeler
  3. Emanuel Flores-Bautista
  4. Nicholas S McCarty
  5. Tom Röschinger
  6. Nathan M Belliveau
  7. Michael J Sweredoski
  8. Annie Moradian
  9. Justin B Kinney
  10. Rob Phillips  Is a corresponding author
  1. California Institute of Technology, United States
  2. California Institute of Technology, United States
  3. Cold Spring Harbor Laboratory, United States
Research Article
  • Cited 6
  • Views 3,505
  • Annotations
Cite this article as: eLife 2020;9:e55308 doi: 10.7554/eLife.55308

Abstract

Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈ 65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively-parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than 100 E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

Data availability

Sequencing data has been deposited in the SRA under accession no.PRJNA599253 and PRJNA603368Mass spectrometry data is deposited in the CalTech data repository at doi:10.22002/d1.1336Model files and inferred information footprints are deposited in the CalTech data repository at doi:10.22002/D1.1331Processed sequencing data sets and analysis software are available in the GitHub repository available at https://doi.org/10.5281/zenodo.3953312

The following data sets were generated

Article and author information

Author details

  1. William T Ireland

    Physics, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0971-2904
  2. Suzannah M Beeler

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1930-4827
  3. Emanuel Flores-Bautista

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Nicholas S McCarty

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Tom Röschinger

    Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Nathan M Belliveau

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1536-1963
  7. Michael J Sweredoski

    Proteome Exploration Laboratory, Division of Biology and Biological Engineering, Beckman Institute, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0878-3831
  8. Annie Moradian

    Proteome Exploration Laboratory, Division of Biology and Biological Engineering, Beckman Institute, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0407-2031
  9. Justin B Kinney

    Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1897-3778
  10. Rob Phillips

    Department of Bioengineering, California Institute of Technology, Pasadena, United States
    For correspondence
    phillips@pboc.caltech.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3082-2809

Funding

National Institutes of Health (Director's Pioneer Award)

  • Rob Phillips

National Institutes of Health (National Research Service Award,5T32GM007616-38)

  • Suzannah M Beeler

National Institutes of Health (Maximizing Investigators Research Award)

  • Rob Phillips

Howard Hughes Medical Institute (International Student Research Fellowship)

  • Nathan M Belliveau

National Institutes of Health (1S10OD02001301)

  • Annie Moradian

National Institutes of Health (1S10OD02001301)

  • Michael J Sweredoski

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

Reviewing Editor

  1. Armita Nourmohammad, University of Washington, United States

Publication history

  1. Received: January 20, 2020
  2. Accepted: September 18, 2020
  3. Accepted Manuscript published: September 21, 2020 (version 1)
  4. Version of Record published: October 16, 2020 (version 2)

Copyright

© 2020, Ireland 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.

Metrics

  • 3,505
    Page views
  • 484
    Downloads
  • 6
    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)

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

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

Further reading

    1. Physics of Living Systems
    Debasmita Mondal et al.
    Research Article Updated

    Microorganisms swimming through viscous fluids imprint their propulsion mechanisms in the flow fields they generate. Extreme confinement of these swimmers between rigid boundaries often arises in natural and technological contexts, yet measurements of their mechanics in this regime are absent. Here, we show that strongly confining the microalga Chlamydomonas between two parallel plates not only inhibits its motility through contact friction with the walls but also leads, for purely mechanical reasons, to inversion of the surrounding vortex flows. Insights from the experiment lead to a simplified theoretical description of flow fields based on a quasi-2D Brinkman approximation to the Stokes equation rather than the usual method of images. We argue that this vortex flow inversion provides the advantage of enhanced fluid mixing despite higher friction. Overall, our results offer a comprehensive framework for analyzing the collective flows of strongly confined swimmers.

    1. Physics of Living Systems
    Osman Darici, Arthur D Kuo
    Research Article

    The simple task of walking up a sidewalk curb is actually a dynamic prediction task. The curb is a disturbance that could cause a loss of momentum if not anticipated and compensated for. It might be possible to adjust momentum sufficiently to ensure undisturbed time of arrival, but there are infinite possible ways to do so. Much of steady, level gait is determined by energy economy, which should be at least as important with terrain disturbances. It is, however, unknown whether economy also governs walking up a curb, and whether anticipation helps. Here we show that humans compensate with an anticipatory pattern of forward speed adjustments, predicted by a criterion of minimizing mechanical energy input. The strategy is mechanistically predicted by optimal control for a simple model of bipedal walking dynamics, with each leg's push-off work as input. Optimization predicts a tri-phasic trajectory of speed (and thus momentum) adjustments, including an anticipatory phase. In experiment, human subjects ascend an artificial curb with the predicted tri-phasic trajectory, which approximately conserves overall walking speed relative to undisturbed flat ground. The trajectory involves speeding up in a few steps before the curb, losing considerable momentum from ascending it, and then regaining speed in a few steps thereafter. Descending the curb entails a nearly opposite, but still anticipatory, speed fluctuation trajectory, in agreement with model predictions that speed fluctuation amplitudes should scale linearly with curb height. The fluctuation amplitudes also decrease slightly with faster average speeds, also as predicted by model. Humans can reason about the dynamics of walking to plan anticipatory and economical control, even with a sidewalk curb in the way.