Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains

  1. Valentina Baldazzi  Is a corresponding author
  2. Delphine Ropers
  3. Jean-Luc Gouzé
  4. Tomas Gedeon
  5. Hidde de Jong  Is a corresponding author
  1. 1Université Côte d'Azur, Inria, INRAE, CNRS, France
  2. Université Grenoble Alpes, France
  3. Montana State University, United States

Abstract

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Models and simulation code are available at https://gitlab.inria.fr/baldazzi/coliallocation. Literature data used for model calibration and validation are included in the manuscript as Supplementary Files S1-S4

Article and author information

Author details

  1. Valentina Baldazzi

    Research Centre Inria Sophia Antipolis - Méditerranée, 1Université Côte d'Azur, Inria, INRAE, CNRS, Sophia Antipolis, France
    For correspondence
    valentina.baldazzi@inria.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9734-9759
  2. Delphine Ropers

    Inria Grenoble - Rhône-Alpes research centre, Université Grenoble Alpes, Grenoble, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2659-3003
  3. Jean-Luc Gouzé

    Research Centre Inria Sophia Antipolis - Méditerranée, 1Université Côte d'Azur, Inria, INRAE, CNRS, Sophia Antipolis, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Tomas Gedeon

    Montana State University, Bozeman, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Hidde de Jong

    Inria Grenoble - Rhône-Alpes research centre, Université Grenoble Alpes, Grenoble, France
    For correspondence
    Hidde.de-Jong@inria.fr
    Competing interests
    The authors declare that no competing interests exist.

Funding

French National Research Agency (Maximic project (ANR-17-CE40-0024))

  • Delphine Ropers
  • Jean-Luc Gouzé
  • Hidde de Jong

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

Reviewing Editor

  1. Petra Anne Levin, Washington University in St. Louis, United States

Version history

  1. Preprint posted: April 27, 2022 (view preprint)
  2. Received: April 27, 2022
  3. Accepted: May 30, 2023
  4. Accepted Manuscript published: May 31, 2023 (version 1)
  5. Version of Record published: June 15, 2023 (version 2)

Copyright

© 2023, Baldazzi 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. Valentina Baldazzi
  2. Delphine Ropers
  3. Jean-Luc Gouzé
  4. Tomas Gedeon
  5. Hidde de Jong
(2023)
Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains
eLife 12:e79815.
https://doi.org/10.7554/eLife.79815

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https://doi.org/10.7554/eLife.79815

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