About half of all bacteria carry genes for CRISPR–Cas adaptive immune systems1, which provide immunological memory by inserting short DNA sequences from phage and other parasitic DNA elements into CRISPR loci on the host genome2. Whereas CRISPR loci evolve rapidly in natural environments3,4, bacterial species typically evolve phage resistance by the mutation or loss of phage receptors under laboratory conditions5,6. Here we report how this discrepancy may in part be explained by differences in the biotic complexity of in vitro and natural environments7,8. Specifically, by using the opportunistic pathogen Pseudomonas aeruginosa and its phage DMS3vir, we show that coexistence with other human pathogens amplifies the fitness trade-offs associated with the mutation of phage receptors, and therefore tips the balance in favour of the evolution of CRISPR-based resistance. We also demonstrate that this has important knock-on effects for the virulence of P. aeruginosa, which became attenuated only if the bacteria evolved surface-based resistance. Our data reveal that the biotic complexity of microbial communities in natural environments is an important driver of the evolution of CRISPR–Cas adaptive immunity, with key implications for bacterial fitness and virulence.
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All data used in this study are available on figshare at https://doi.org/10.6084/m9.figshare.9752903.
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We thank A. Buckling for critical reading of the manuscript, J. Common, E. Hesse and S. Meaden for comments on the manuscript, and J. P. Pirnay and D. de Vos for sharing clinical isolates of S. aureus, A. baumannii and B. cenocepacia. This work was supported by grants from the ERC (ERC-STG-2016-714478 - EVOIMMECH) and the NERC (NE/M018350/1), which were awarded to E.R.W.
The authors declare no competing interests.
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Extended data figures and tables
Phage levels (in p.f.u. ml−1) in minutes after infection of P. aeruginosa PA14 and three other bacterial species (n = 84 biologically independent replicates). Controls were carried out in the absence of bacteria. Here, the lines are regression slopes with shaded areas corresponding to 95% confidence intervals. Linear model: effect of P. aeruginosa on phage titre over time; t = −3.37, P = 0.0009; S. aureus; t = 1.63, P = 0.11; A. baumannii; t = 1.20, P = 0.23; B. cenocepacia; t = −0.27, P = 0.79; overall model fit; F9,235 = 4.33, adjusted R2 = 0.11, P = 3.17 × 10−5Source Data.
Proportion of P. aeruginosa that acquired surface modification or CRISPR-based immunity (or remained sensitive) 3 d.p.i. with phage DMS3vir when grown in ASM (6 replicates per treatment, with 24 colonies screened from each replicate, n = 30 biologically independent replicates). Deviance test: relationship between community composition and CRISPR; residual deviance (25, n = 30) = 1.26, P = 2.2 × 10−16; Tukey contrasts: monoculture versus mixed; z = −5.30, P = 1 × 10−4; monoculture versus A. baumannii; z = −5.60, P = 1 × 10−4; monoculture versus B. cenocepacia; z = −2.80, P = 0.02; monoculture versus S. aureus; z = −0.76, P = 0.93. Data are mean ± s.e.mSource Data.
Extended Data Fig. 3 Increased evolution of CRISPR-based resistance across a range of microbial community compositions over time.
Proportion of P. aeruginosa that acquired surface modification or CRISPR-based immunity (or remained sensitive) at up to 3 d.p.i. with phage DMS3vir when grown either in monoculture (100%) or in polyculture mixtures consisting of the mixed microbial community but with varying starting percentages of P. aeruginosa based on volume (6 replicates for most samples, with 24 colonies per replicate, n = 42 biologically independent replicates for a, n = 32 biologically independent replicates for b, and n = 42 biologically independent replicates for c). a, Resistance evolution at 1 d.p.i. Data are mean ± s.e.m. Deviance test: relationship between CRISPR and P. aeruginosa starting percentage at time point 1; residual deviance (35, n = 42) = 4.42, P = 0.004; 1%; z = −3.27, P = 0.002; 10%; z = 1.21, P = 0.23; 25%; z = 1.62, P = 0.11; 50%; z = 2.20, P = 0.034; 90%; z = 2.07, P = 0.046; 99%; z = 0.47, P = 0.65; 100%; z = 1.47, P = 0.15. b, Resistance evolution at 2 d.p.i. Data are mean ± s.e.m. Deviance test: relationship between CRISPR and P. aeruginosa starting percentage at time point 2; residual deviance (25, n = 32) = 3.86, P = 2.51 × 10−6; 1%; z = −2.14, P = 0.04; 10%; z = 1.19, P = 0.25; 25%; z = 2.07, P = 0.049; 50%; z = 1.89, P = 0.07; 90%; z = 1.12, P = 0.27; 99%; z = 1.21, P = 0.24; 100%; z = 1.11, P = 0.28. c, Resistance evolution at 3 d.p.i. Data are mean ± s.e.m. Deviance test: relationship between CRISPR and P. aeruginosa starting percentage at time point 3; residual deviance (35, n = 42) = 8.24, P = 0.0004; 1%; z = −3.38, P = 0.002; 10%; z = 2.12, P = 0.04; 25%; z = 2.77, P = 0.009; 50%; z = 3.07, P = 0.004; 90%; z = 2.46, P = 0.019; 99%; z = 1.55, P = 0.13; 100%; z = 0.87, P = 0.39Source Data.
The DMS3vir phage titres (in p.f.u. ml−1) over time up to 3 d.p.i. of P. aeruginosa grown either in monoculture (100%) or in polyculture mixtures as shown in Extended Data Fig. 3. Each data point represents the mean, error bars denote s.e.m. (n = 171 independent biological samples). Two-way ANOVA: overall effect of P. aeruginosa starting percentage on phage titre; F6,105 = 14.84, P = 1.1 × 10−12Source Data.
The correlation between the proportion of evolved phage-resistant clones with CRISPR-based resistance and the phage epidemic sizes (in p.f.u. ml−1) in the presence of other bacterial species, using data taken from experiments shown in Fig. 1, Extended Data Figs. 2, 3c and 6 (n = 137 biologically independent samples per time point). Correlations are separated by day, as phage titres were measured daily. Here, the lines are regression slopes, with shaded areas corresponding to 95% confidence intervals. Pearson’s product–moment correlation tests between phage titres (at each day after infection) and levels of CRISPR-based resistance: T = 1; t136 = −0.02, P = 0.98, R2 = −0.002; T = 2; t136 = 0.59, P = 0.55, R2 = 0.05; T = 3; t136 = −0.90, P = 0.37, R2 = −0.08Source Data.
Extended Data Fig. 6 Starting phage titre does not affect CRISPR evolution in the presence of a microbial community.
Proportion of P. aeruginosa that acquired CRISPR-based resistance at 3 d.p.i. with varying starting titres of phage DMS3vir when grown in polyculture (6 replicates per treatment, with 24 colonies per replicate, n = 24 biologically independent replicates). Deviance test: start phage and CRISPR; residual deviance (20, n = 24) = 2.00, P = 0.13; Tukey contrasts: 102 versus 104; z = −1.52, P = 0.42; 104 versus 106; z = −0.76, P = 0.87; 106 versus 108; z = 1.31, P = 0.56; 102 versus 106; z = −2.24, P = 0.11; 102 versus 108; z = −0.99, P = 0.75; 104 versus 108; z = 0.56, P = 0.94. Data are mean ± s.e.mSource Data.
Time until death (given as median ± one standard error) for G. mellonella larvae infected with PA14 clones that evolved phage resistance by LPS modification, compared to the phage-sensitive ancestral (n = 209 biologically independent samples). Cox proportional hazards model with Tukey contrasts: sensitive (ancestral) versus LPS; z = 4.81, P = 1.49 × 10−6. overall model fit; LRT3 = 44.94, P = 1 × 10−9Source Data.
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Alseth, E.O., Pursey, E., Luján, A.M. et al. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance. Nature 574, 549–552 (2019) doi:10.1038/s41586-019-1662-9
Trends in Microbiology (2019)