1. Physics of Living Systems

Dynamics of immune memory and learning in bacterial communities

  1. Madeleine Bonsma-Fisher
  2. Sidhartha Goyal  Is a corresponding author
  1. University of Toronto, Canada
https://doi.org/10.7554/eLife.81692
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  1. Madeleine Bonsma-Fisher
  2. Sidhartha Goyal
(2023)
Dynamics of immune memory and learning in bacterial communities
eLife 12:e81692.
https://doi.org/10.7554/eLife.81692
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Abstract

From bacteria to humans, adaptive immune systems provide learned memories of past infections. Despite their vast biological differences, adaptive immunity shares features from microbes to vertebrates such as emergent immune diversity, long-term coexistence of hosts and pathogens, and fitness pressures from evolving pathogens and adapting hosts, yet there is no conceptual model that addresses all of these together. To this end, we propose and solve a simple phenomenological model of CRISPR-based adaptive immunity in microbes. We show that in coexisting phage and bacteria populations, immune diversity in both populations is coupled and emerges spontaneously, that bacteria track phage evolution with a context-dependent lag, and that high levels of diversity are paradoxically linked to low overall CRISPR immunity. We define average immunity, an important summary parameter predicted by our model, and use it to perform synthetic time-shift analyses on available experimental data to reveal different modalities of coevolution. Finally, immune cross-reactivity in our model leads to qualitatively different states of evolutionary dynamics, including an influenza-like traveling wave regime that resembles a similar state in models of vertebrate adaptive immunity. Our results show that CRISPR immunity provides a tractable model, both theoretically and experimentally, to understand general features of adaptive immunity.

Data availability

Source code and data is available for all main text figures on GitHub at https://github.com/mbonsma/CRISPR-dynamics-model.Source data for Figures 6E-G and 7C-D is available on GitHub at https://github.com/mbonsma/CRISPR-dynamics-model.Raw simulation data has been uploaded to Dryad: https://doi.org/10.5061/dryad.sn02v6x74.

The following data sets were generated
The following previously published data sets were used

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Author details

  1. Madeleine Bonsma-Fisher

    Department of Physics, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5813-4664

  2. Sidhartha Goyal

    Department of Physics, University of Toronto, Toronto, Canada
    For correspondence
    goyal@physics.utoronto.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7452-892X

Funding

Natural Sciences and Engineering Research Council of Canada (Vanier Canada Graduate Scholarship)

  • Madeleine Bonsma-Fisher

Ministry of Colleges and Universities (Queen Elizabeth II Graduate Scholarship in Science & Technology)

  • Madeleine Bonsma-Fisher

Walter C. Sumner Foundation (Walter C. Sumner Memorial Fellowship)

  • Madeleine Bonsma-Fisher

Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN-2015)

  • Sidhartha Goyal

Natural Sciences and Engineering Research Council of Canada (Discovery Grant and RGPIN-2021)

  • Sidhartha Goyal

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

Copyright

© 2023, Bonsma-Fisher & Goyal

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. Madeleine Bonsma-Fisher
  2. Sidhartha Goyal
(2023)
Dynamics of immune memory and learning in bacterial communities
eLife 12:e81692.
https://doi.org/10.7554/eLife.81692

Share this article

https://doi.org/10.7554/eLife.81692

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