Operon mRNAs are organized into ORF-centric structures that predict translation efficiency

  1. David H Burkhardt
  2. Silvi Rouskin
  3. Yan Zhang
  4. Gene-Wei Li  Is a corresponding author
  5. Jonathan S Weissman  Is a corresponding author
  6. Carol A Gross  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Howard Hughes Medical Institute, University of California, San Francisco, United States
  3. Massachusetts Institute of Technology, United States

Abstract

Bacterial mRNAs are organized into operons consisting of discrete open reading frames (ORFs) in a single polycistronic mRNA. Individual ORFs on the mRNA are differentially translated, with rates varying as much as 100-fold. The signals controlling differential translation are poorly understood. Our genome-wide mRNA secondary structure analysis indicated that operonic mRNAs are comprised of ORF-wide units of secondary structure that vary across ORF boundaries such that adjacent ORFs on the same mRNA molecule are structurally distinct. ORF translation rate is strongly correlated with its mRNA structure in vivo, and correlation persists, albeit in a reduced form, with its structure when translation is inhibited and with that of in vitro refolded mRNA. These data suggests that intrinsic ORF mRNA structure encodes a rough blueprint for translation efficiency. This structure is then amplified by translation, in a self-reinforcing loop, to provide the structure that ultimately specifies the translation of each ORF.

Data availability

The following data sets were generated

Article and author information

Author details

  1. David H Burkhardt

    Graduate Group in Biophysics, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Silvi Rouskin

    Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Yan Zhang

    Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5440-1414
  4. Gene-Wei Li

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    gwli@mit.edu
    Competing interests
    The authors declare that no competing interests exist.
  5. Jonathan S Weissman

    Department of Cellular and Molecular Pharmacology, Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, United States
    For correspondence
    Jonathan.Weissman@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2445-670X
  6. Carol A Gross

    Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, United States
    For correspondence
    cgrossucsf@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5595-9732

Funding

Howard Hughes Medical Institute

  • Jonathan S Weissman

National Institutes of Health

  • David H Burkhardt
  • Yan Zhang
  • Carol A Gross

Helen Hay Whitney Foundation

  • Gene-Wei Li

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

Copyright

© 2017, Burkhardt 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

  • 7,352
    views
  • 1,429
    downloads
  • 136
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. David H Burkhardt
  2. Silvi Rouskin
  3. Yan Zhang
  4. Gene-Wei Li
  5. Jonathan S Weissman
  6. Carol A Gross
(2017)
Operon mRNAs are organized into ORF-centric structures that predict translation efficiency
eLife 6:e22037.
https://doi.org/10.7554/eLife.22037

Share this article

https://doi.org/10.7554/eLife.22037

Further reading

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease
    Ruihan Dong, Rongrong Liu ... Cheng Zhu
    Research Article

    Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.

    1. Computational and Systems Biology
    2. Neuroscience
    Gabriel Loewinger, Erjia Cui ... Francisco Pereira
    Tools and Resources

    Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.