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


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

Article and author information

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
    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


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.

Reviewing Editor

  1. Anne-Florence Bitbol, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland

Publication history

  1. Received: July 7, 2022
  2. Accepted: January 15, 2023
  3. Accepted Manuscript published: January 16, 2023 (version 1)


© 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.


  • 239
    Page views
  • 43
  • 0

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)

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. Madeleine Bonsma-Fisher
  2. Sidhartha Goyal
Dynamics of immune memory and learning in bacterial communities
eLife 12:e81692.
  1. Further reading

Further reading

    1. Physics of Living Systems
    2. Plant Biology
    Lauren Sullivan

    Dandelion seeds respond to wet weather by closing their plumes, which reduces dispersal when wind conditions are poor.

    1. Physics of Living Systems
    Matthew F Lefebvre, Nikolas H Claussen ... Sebastian J Streichan
    Research Article

    The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of largescale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in D. melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static, and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryoscale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.