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Competition and coevolution drive the evolution and the diversification of CRISPR immunity


The diversity of resistance challenges the ability of pathogens to spread and to exploit host populations. Yet, how this host diversity evolves over time remains unclear because it depends on the interplay between intraspecific competition among host genotypes and coevolution with pathogens. Here we study experimentally the effect of coevolving phage populations on the diversification of bacterial CRISPR immunity across space and time. We demonstrate that the negative-frequency-dependent selection generated by coevolution is a powerful force that maintains host resistance diversity and selects for new resistance mutations in the host. We also find that host evolution is driven by asymmetries in competitive abilities among different host genotypes. Even if the fittest host genotypes are targeted preferentially by the evolving phages, they often escape extinctions through the acquisition of new CRISPR immunity. Together, these fluctuating selective pressures maintain diversity, but not by preserving the pre-existing host composition. Instead, we repeatedly observe the introduction of new resistance genotypes stemming from the fittest hosts in each population. These results highlight the importance of competition on the transient dynamics of host–pathogen coevolution.

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Fig. 1: The three treatments of our coevolutionary experiment.
Fig. 2: Demography of bacteria and phages.
Fig. 3: Dynamics of the diversity of CRISPR immunity.
Fig. 4: Host populations resist phages through the diversification of the CR1 locus.
Fig. 5: Phages induce negative frequency-dependent selection.
Fig. 6: Phage adaptation across space and time.

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Data availability

The sequences of both phages and bacteria of this study have been deposited on National Center for Biotechnology Information under the BioProject of accession number PRJNA843584. Additional data such as the density measurements and the minimal dataset are available at

Code availability

All codes used to process, analyse the data and make the figures are available at


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Sequencing data were obtained through the genotyping and sequencing facilities of the Institut des Sciences de l’Evolution-Montpellier and Labex Centre Méditerranéen Environnement Biodiversité. We thank D. Tremblay, P.-L. Plante and G. Pageau for technical assistance during the sequencing of the bacterial strains. S.M. acknowledges funding from the Natural Sciences and Engineering Research Council of Canada (Discovery program). S.M. holds a T1 Canada Research Chair in Bacteriophages. H.C. was supported by an ETH Zurich Postdoctoral Fellowship. S.G. acknowledges support from a grant on ‘Phylodynamics for experimentally evolving viruses’ funded by the CNRS-MITI (Mission pour les Initiatives Transverses et Interdisciplinaires) and from the grant no. ANR-17-CE35-0012 from the Agence National de la Recherche.

Author information

Authors and Affiliations



S.G., A.N. and H.C. designed the experimental protocol. A.N. carried out the experiment and F.G. carried out phage sequencing. G.M.R. conducted the supplementary experiments for bacterial genomics. E.O.-A. helped with bioinformatics treatment of sequencing data and S.M. participated in the analysis. M.G., H.C., T.B., C.B. and L.H. analysed the data. M.G. and S.G. wrote the manuscript. H.C., T.B. and S.M. revised the manuscript.

Corresponding author

Correspondence to Sylvain Gandon.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Adela Luján, Rachel Whitaker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Fitness distribution of the 16 resistant and the wild-type bacterial strains in the absence of the phage.

The wild-type bacteria is shown in grey and the colors indicate the relative fitness of each of the 16 resistant strains. We used the same color code as the one used in Fig. 4. The fitness of strain i (relative to the wild-type wt) is computed with Wi-Wwt, where W\({}_{{{{\rm{i}}}}}={\log }_{10}(\frac{{f}_{1}(1-{f}_{0})}{{f}_{0}(1-{f}_{1})})\), f0 and f1 are the frequencies of strain i at day 0 and day 1, respectively. Hence, a positive (negative) value means that the strain grows faster (slower) than the wild-type at the beginning of the competition (in the first day of the experiment in treatment A).

Extended Data Fig. 2 Modified Muller plots of the bacterial populations based on the first spacer at the CR1 locus.

Above each graph is the name of the replicate (‘A’ for the no phage control, ‘B’ for the monomorphic phage treatment and ‘C’ for the polymorphic phage treatment). The total height for each day shows the bacterial density (in cfu/ml) on a log scale, and the different colors show the proportion of the strains at each time point on a linear scale. The 17 strains that were added on day 0 (including the phage sensitive strain in grey) are shown in the legend (top-left corner). The blue-to-red color scale ranks the strains according to their initial fitness as detailed in Extended Data Figure 1. We used the same color code as the one used in Fig. 4. The lines are smoothed between each day.

Extended Data Fig. 3 Diversity of the first spacer of resistance in the bacterial population at the CR1 locus.

The diversity is computed as the effective number of host genotypes using only the first spacer from the CR1 locus (compare with Fig. 3 where we used the whole array of new spacers on CR1). Blue points show the data in the absence of phages, orange and red show data for the monomorphic and polymorphic phage treatments, respectively.

Extended Data Fig. 4 Measure of the differentiation of bacterial population between replicates of the same treatment with (a) FST and (b) Jost’s D (see Methods).

As discussed by Jost46, the D statistics may be a more relevant measure of differentiation when the total number of allele varies (see Methods). Blue curves show the values of differentiation in the absence of phages (treatment A), orange and red curves show the values of differentiation in the monomorphic (B) or the polymorphic (C) phage treatments, respectively. The shaded areas show the bootstrap 95% confidence interval and the center of the bands show the mean value.

Extended Data Fig. 5 Phage mutations in (a,c) the monomorphic and (b,d) the polymorphic phage treatments.

The histograms (a,b) show the number of mutations per region of 2-kb in the phage genome. The light colors show mutations that are not located in a protospacer. The black dashed line shows the density of PAM in the genome. The positions of the phage mutations falling inside a protospacer are shown in panels (c,d). The mutations falling into two overlapping protospacers were discarded. The PAM and the seed region of the protospacer are shown.

Extended Data Fig. 6 Measure of phage differentiation among replicate populations of the same treatment using (a) FST and (b) QST (see Methods).

Orange and red curves show the level of differentiation for the monomorphic (treatment B) and the polymorphic (treatment C) phage treatments, respectively. The shaded areas show the bootstrap 95% confidence interval and the center of the bands show the mean value.

Extended Data Fig. 7 Number of phage mutations through time in (a) the monomorphic and (b) polymorphic phage treatments.

The plain line shows the mutation in protospacers, the dashed line shows all of the mutations. Only mutations with frequencies over 0.025 are kept. The shaded areas show the bootstrap 95% confidence interval and the center of the bands show the mean value.

Extended Data Fig. 8 Phage fitness when confronting in silico phages and bacteria of each time points from the same replicate in the monomorphic (a,c) and polymorphic (b,d) phage treatments.

The fitness was computed using equation (1). In panels c and d we try to correct the signal from the CR3 locus. To do this we selected all bacterial genotypes i with a frequency above 0.1 while the corresponding escape mutation i in the phage is at a frequency higher than 0.5. The fact that these host genotypes keep growing (that is their frequency remain > 0.1) even in the presence of escape phages indicates that these host genotypes probably carry an additional resistance on the CR3 locus (see also Table S4). If these host genotypes are resistant to these phages we can correct the measure of mean fitness using hi pi =0 for these host genotypes and this yields figures (c) and (d). Note that this correction only affects measures of phage adaptation at late time points in the experiments (consistent with the emergence of CR3 resistance at the end of the experiment, Table S4).

Extended Data Fig. 9 Phage mutation frequencies correlate with host frequency in the control but not with the protospacer mutation rate.

There is one point for the protospacers targeted by each of the 16 different resistant strains. We show the mean frequency of escape mutations in each of the 16 protospacers (averaged over days 1 to 4 and over the eight replicates) against (a,b) the mean frequency of the corresponding host strain in the treatment without phages (averaged over days 1 to 4 and over the eight replicates) or (c,d) the protospacer mutation rates estimated by Chabas et al.23. The results are shown for the monomorphic phage treatment (a,c) and the polymorphic phage treatment (b,d). Log-linear regression lines (dashed lines) highlight the influence of strain frequencies on the frequencies of escape mutations in the phage population. In panels (c,d), the point on the upper left side was left out of the regression as it may be considered as an outlier (but this point is not left out of the Pearson’s r calculation given in the main text).

Extended Data Fig. 10 The ‘royal family’ model provides a conceptual framework to describe the coevolutionary dynamics in our experiment.

First, selection imposed by phages leads to a diversification of CRISPR immunity. The competitive fitness of distinct resistant strains differ because they carry a variable number of beneficial and deleterious mutations (white and black dots on the bacterial chromosome, respectively). The resistant strain that carries the fewest number of deleterious mutations and the highest number of beneficial mutations is more competitive (that is, the winner in the ‘kill-the-winner’ hypothesis) and constitutes the ‘royal family’ (most future bacteria will derive from this strain). Second, the phage will preferentially adapt to this abundant strain. The acquisition of escape mutations in the phage genome will impose negative-frequency-dependent selection and will contribute to the maintenance of CRISPR diversity. Third, the ‘royal family’ strain will acquire new spacers and become abundant again. Competition will take place, phages will adapt to the ‘royal family’ again and this coevolutionary cycle will continue. Spacers and their corresponding escape mutations in the phage are indicated with the same colors. The ‘royal families’ of bacteria and phages are represented with a crown symbol.

Supplementary information

Reporting Summary.

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Supplementary Tables

Supplementary Table 1. Protospacer sequences of the phage targeted by the 16 resistant strains (CR1 locus). The resistant strains are named according to the middle position (on the phage genome) of the protospacer targeted by the CR1 locus. Supplementary Table 2. Summary of the phages used in the polymorphic phage treatment and their escape mutations. The escape mutations are shown in red in the protospacer sequence. Supplementary Table 3. Summary of mutations in the genome of the 16 starting host strains. We used the same colour code as the one used in Fig. 4. Supplementary Table 4. C3 locus evolution. The black shading indicates the time at which we detected an additional spacers in the CR3 locus by PCR (Methods) for each replicate of both the monomorphic and the polymorphic phage treatments.

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Guillemet, M., Chabas, H., Nicot, A. et al. Competition and coevolution drive the evolution and the diversification of CRISPR immunity. Nat Ecol Evol 6, 1480–1488 (2022).

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