The landscape of transcriptional and 1translational changes over 22 years of bacterial adaptation

  1. John S Favate  Is a corresponding author
  2. Shun Liang
  3. Alexander L Cope
  4. Srujana Samhita Yadavalli
  5. Premal Shah  Is a corresponding author
  1. Rutgers, The State University of New Jersey, United States

Abstract

Organisms can adapt to an environment by taking multiple mutational paths. This redundancy at the genetic level, where many mutations have similar phenotypic and fitness effects, can make untangling the molecular mechanisms of complex adaptations difficult. Here we use the E. coli long-term evolution experiment (LTEE) as a model to address this challenge. To understand how different genomic changes could lead to parallel fitness gains, we characterize the landscape of transcriptional and translational changes across 12 replicate populations evolving in parallel for 50,000 generations. By quantifying absolute changes in mRNA abundances, we show that not only do all evolved lines have more mRNAs but that this increase in mRNA abundance scales with cell size. We also find that despite few shared mutations at the genetic level, clones from replicate populations in the LTEE are remarkably similar in their gene expression patterns at both the transcriptional and translational levels. Furthermore, we show that the majority of the expression changes are due to changes at the transcriptional level with very few translational changes. Finally, we show how mutations in transcriptional regulators lead to consistent and parallel changes in the expression levels of downstream genes. These results deepen our understanding of the molecular mechanisms underlying complex adaptations and provide insights into the repeatability of evolution.

Data availability

Sequencing data have been deposited in GEO under accession code GSE164308.All data generated or analyzed during this study are included in the manuscript and supporting file; Source Data files have been provided for all figures.Code for all data processing and subsequent analysis can be found in a series of R markdown documents uploaded to GitHub https://github.com/shahlab/LTEE_gene_expression_2

The following data sets were generated

Article and author information

Author details

  1. John S Favate

    Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, United States
    For correspondence
    john.favate@rutgers.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6344-4854
  2. Shun Liang

    Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, United States
    Competing interests
    No competing interests declared.
  3. Alexander L Cope

    Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, United States
    Competing interests
    No competing interests declared.
  4. Srujana Samhita Yadavalli

    Waksman Institute of Microbiology, Rutgers, The State University of New Jersey, Piscataway, United States
    Competing interests
    No competing interests declared.
  5. Premal Shah

    Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, United States
    For correspondence
    premal.shah@rutgers.edu
    Competing interests
    Premal Shah, is a scientific advisory board member of Trestle Biosciences and consults for Ribo-Therapeutics. Is also a director at an RNA-therapeutics startup..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8424-4218

Funding

National Institute of General Medical Sciences (ESI-MIRA R35 GM124976)

  • Premal Shah

National Science Foundation (DBI 1936046)

  • Premal Shah

Rutgers, The State University of New Jersey (Start-up funds)

  • Srujana Samhita Yadavalli
  • Premal Shah

National Institutes of Health (IRACDA NJ/NY for Science Partnerships in Research and Education Postdoctoral program NIH PAR-19-366)

  • Alexander L Cope

National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK056645)

  • Premal Shah

National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK109714)

  • Premal Shah

National Institute of Diabetes and Digestive and Kidney Diseases (Subcontract from R01 DK124369)

  • Premal Shah

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

Reviewing Editor

  1. Detlef Weigel, Max Planck Institute for Biology Tübingen, Germany

Version history

  1. Preprint posted: January 13, 2021 (view preprint)
  2. Received: July 19, 2022
  3. Accepted: October 7, 2022
  4. Accepted Manuscript published: October 10, 2022 (version 1)
  5. Version of Record published: November 9, 2022 (version 2)

Copyright

© 2022, Favate 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. John S Favate
  2. Shun Liang
  3. Alexander L Cope
  4. Srujana Samhita Yadavalli
  5. Premal Shah
(2022)
The landscape of transcriptional and 1translational changes over 22 years of bacterial adaptation
eLife 11:e81979.
https://doi.org/10.7554/eLife.81979

Share this article

https://doi.org/10.7554/eLife.81979

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