1. Ecology
  2. Evolutionary Biology
Download icon

Pleiotropic mutations can rapidly evolve to directly benefit self and cooperative partner despite unfavorable conditions

  1. Samuel Frederick Mock Hart
  2. Chi-Chun Chen
  3. Wenying Shou  Is a corresponding author
  1. Fred Hutchinson Cancer Research Center, United States
  2. University College London, United Kingdom
Research Advance
  • Cited 0
  • Views 294
  • Annotations
Cite this article as: eLife 2021;10:e57838 doi: 10.7554/eLife.57838

Abstract

Cooperation, paying a cost to benefit others, is widespread. Cooperation can be promoted by pleiotropic 'win-win' mutations which directly benefit self ('self-serving') and partner ('partner-serving'). Previously, we showed that partner-serving should be defined as increased benefit supply rate per intake benefit (Hart & Pineda et al., 2019). Here, we report that win-win mutations can rapidly evolve even under conditions unfavorable for cooperation. Specifically, in a well-mixed environment we evolved engineered yeast cooperative communities where two strains exchanged costly metabolites lysine and hypoxanthine. Among cells that consumed lysine and released hypoxanthine, ecm21 mutations repeatedly arose. ecm21 is self-serving, improving self's growth rate in limiting lysine. ecm21 is also partner-serving, increasing hypoxanthine release rate per lysine consumption and the steady state growth rate of partner. ecm21 also arose in monocultures evolving in lysine-limited chemostats. Thus, even without any history of cooperation or pressure to maintain cooperation, pleiotropic win-win mutations may readily evolve.

Article and author information

Author details

  1. Samuel Frederick Mock Hart

    Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5068-2199
  2. Chi-Chun Chen

    Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Wenying Shou

    Genetics, Evolution and Environment, University College London, London, United Kingdom
    For correspondence
    w.shou@ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5693-381X

Funding

National Institutes of Health (DP2 OD006498-01)

  • Samuel Frederick Mock Hart
  • Chi-Chun Chen
  • Wenying Shou

National Institutes of Health (R01GM124128)

  • Samuel Frederick Mock Hart
  • Wenying Shou

W.M. Keck Foundation (Distinguished Young Scholars)

  • Chi-Chun Chen
  • Wenying Shou

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

Reviewing Editor

  1. Kevin J Verstrepen, VIB-KU Leuven Center for Microbiology, Belgium

Publication history

  1. Received: April 20, 2020
  2. Accepted: January 26, 2021
  3. Accepted Manuscript published: January 27, 2021 (version 1)

Copyright

© 2021, Hart 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

  • 294
    Page views
  • 50
    Downloads
  • 0
    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. Ecology
    Chen Chen et al.
    Research Article Updated

    Insect pests negatively affect crop quality and yield; identifying new methods to protect crops against insects therefore has important agricultural applications. Our analysis of transgenic Arabidopsis thaliana plants showed that overexpression of pentacyclic triterpene synthase 1, encoding the key biosynthetic enzyme for the natural plant product (3E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), led to a significant resistance against a major insect pest, Plutella xylostella. DMNT treatment severely damaged the peritrophic matrix (PM), a physical barrier isolating food and pathogens from the midgut wall cells. DMNT repressed the expression of PxMucin in midgut cells, and knocking down PxMucin resulted in PM rupture and P. xylostella death. A 16S RNA survey revealed that DMNT significantly disrupted midgut microbiota populations and that midgut microbes were essential for DMNT-induced killing. Therefore, we propose that the midgut microbiota assists DMNT in killing P. xylostella. These findings may provide a novel approach for plant protection against P. xylostella.

    1. Computational and Systems Biology
    2. Ecology
    Tristan Walter, Iain D Couzin
    Tools and Resources

    Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms' sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.