Dynamics of immune memory and learning in bacterial communities

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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 address these questions, 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 emerges spontaneously and in tandem, 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.

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  1. eLife assessment

    In this important work, the authors develop a theory for the co-evolutionary dynamics of bacteria and phages, where the major evolutionary pressure comes from CRISPR-Cas adaptive immunity in bacteria. Through extensive stochastic numerical simulations and analytical calculations, the paper presents a compelling analysis of the emergent properties of immune interactions, in the regime of a single proto-spacer and a single spacer. Some of the trends highlighted by the model are recovered from experimental data. The main results concern how diversity in both phage and bacteria population are linked and are shaped by immunity, and should be of broad interest in immunology.

  2. Reviewer #1 (Public Review):

    In this work, the authors set out to understand quantitatively how, in co-existing populations of bacteria and phages, immune diversity emerges and relates to the level of immunity. The work achieves this goal through a number of numerical and theoretical analyses.
    The strength of the work relies in the insight gained via a solvable model, which allows the authors to: discover law-like dependencies between average number of clones and other parameters of the dynamics (e.g. mutation rates); to identify an observable, the average immunity, as a main driver of the coupled phage/bacteria dynamics that can also be measured in real data; to assess the impact of cross-reactivity between CRISPR in bacteria and its targets on phages.

    Claims and conclusions are justified by the simulations and the data presented. The paper is also a useful resource for all readers interested in this type of models, since it proposes an extensive documentation of the field linking the results to other theoretical work and experimental findings. (For this reason, however, the article can be found a bit demanding to fully navigate through).

    To avoid compromising the analytical insight, the model does not include a series of other factors that are likely to impact the dynamics observed in the experiments (spatial structure, niche partitioning), however all these limitations are appropriately mentioned. The agreement to the experimental data, while extremely valuable, remains qualitative, so additional work in the future could be devoted to devising some parameter fitting that allows for a more quantitative fitting to the data.

    The work has the potential to have a broader impact, both by suggesting targets of measurement in experimental settings and by providing insights that could be shared by other types of immune systems, for instance, vertebrate adaptive immunity.

  3. Reviewer #2 (Public Review):

    The reciprocal adaptation of host immune systems and pathogens leads to complex co-evolutionary dynamics. How this dynamical process shapes host and pathogen diversity is a fundamental question in immunology and virology. To study this question experimentally, the CRISPR-Cas system which some prokaryotes use for adaptive immunity against phages has recently emerged as an important model system. In this system both host and viral adaptation can be read out by sequencing, as CRISPR-Cas immunity is guided by genomic spacers incorporated into the host genome. In this context, the current work presents a welcome deep dive into the dynamical regimes predicted by a simple phenomenological model of the co-evolution between CRISPR loci and phages. Among the notable results are the following: First, diversity primarily depends on a single scalar parameter, and does so in a sublinear manner. Second, different types of cross-reactivity are linked to different shapes of viral phylogenies. Third, comparison of spacer turnover and average immunity between theoretical and experimental timeseries data provides hints as to the operative regime in different CRISPR systems. These results together with the extensive discussion should be greatly useful in guiding further work in this field. There are many avenues for further theoretical work generalizing to less simplifying assumptions, and importantly, many concrete suggestions for future experiments.

  4. Reviewer #3 (Public Review):

    CRISPR-Cas immune systems protect bacterial cells from bacteriophages by acquiring DNA-based molecular memories of infection called "spacers". Spacers are transcribed into RNA guides that direct Cas nucleases to cleave matching targets, thereby providing bacterial cells with adaptive immunity. Many studies focus on the mechanisms of CRISPR-Cas immunity, but less is known about how immune diversity emerges and evolves over time in complex populations. Here, the authors develop a computational framework to model stochastic CRISPR-Cas immunization events as well as phage mutations that enable escape. The authors use a rigorous set of parameters and analytical tools to simulate the arms race between bacteria and phage over many generations, allowing them to ask fundamental questions about whether and how host and pathogen are able to coexist. By altering an extensive set of variables, including population size, mutation rates, and spacer uptake and efficacy, the authors show that complex and stochastic dynamics emerge with exciting implications for the effective length of CRISPR arrays. They further show that these dynamics are affected by spacer cross-reactivity, which is likely an important factor in natural settings where distinct phages often share large regions of homology.

    A limitation of the study is that many of the conclusions are drawn from simulations in which each phage contains a single CRISPR-targetable site - or "protospacer", such that a single mutation allows escape from many or all extant spacers in the population. In reality, phages harbor hundreds of protospacers, many of which are sampled by different bacterial cells during immunization. Therefore, bacterial populations encountering a new phage will quickly establish high spacer diversity. In this case, a phage that escapes one spacer by mutating the corresponding protospacer will still be killed efficiently by most other CRISPR immune cells in the population that harbor a different spacer. Nonetheless, the authors establish a rigorous and flexible platform through which existing experimental data can be analyzed, and new hypotheses can be generated. While experiments involving large, complex, and dynamic bacterial and phage populations are challenging, they will be buoyed by recent advances in NGS sequencing depth and complex microbial model systems, as well as the theoretical framework provided by Bonsma-Fisher and Goyal.