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Polymicrobial infections can select against Pseudomonas aeruginosa mutators because of quorum-sensing trade-offs

Nature Ecology & Evolution volume 6, pages 979–988 (2022)Cite this article

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Abstract

Bacteria with increased mutation rates (mutators) are common in chronic infections and are associated with poorer clinical outcomes, especially in the case of Pseudomonas aeruginosa infecting cystic fibrosis (CF) patients. There is, however, considerable between-patient variation in both P. aeruginosa mutator frequency and the composition of co-infecting pathogen communities. We investigated whether community context might affect selection of mutators. Using an in vitro CF model community, we show that P. aeruginosa mutators were favoured in the absence of other species but not in their presence. This was because there were trade-offs between adaptation to the biotic and abiotic environments (for example, loss of quorum sensing and associated toxin production was beneficial in the latter but not the former in our in vitro model community) limiting the evolvability advantage of an elevated mutation rate. Consistent with a role of co-infecting pathogens selecting against P. aeruginosa mutators in vivo, we show that the mutation frequency of P. aeruginosa population was negatively correlated with the frequency and diversity of co-infecting bacteria in CF infections. Our results suggest that co-infecting taxa can select against P. aeruginosa mutators, which may have potentially beneficial clinical consequences.

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Fig. 1: Mutator fitness is reduced in the presence of the bacterial community.
Fig. 2: Fitness trade-off associated with interspecific interactions is related to QS regulation.
Fig. 3: Relationship between bacterial community diversity and P. aeruginosa mutation frequency in CF sputum samples.
Fig. 4: P. aeruginosa CF populations showing high mutation frequencies accumulate more mutations in QS-related genes.

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Data availability

All data used in this study are available on figshare at https://doi.org/10.6084/m9.figshare.13739452. Genome sequencing reads from P. aeruginosa populations from in vivo and in vitro experiments have been deposited under accession no. PRJEB35620. All other data used in this paper are available in the Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We would like to thank J. P. Pirnay and D. de Vos for providing the bacterial community strains and A. Frias for generating the illustration shown in Extended Data Fig. 1. A.M.L. was supported by FP7 Marie Skłodowska-Curie International Incoming Fellowship (IIF) (no. 331163) and CONICET. E.H. was supported by KRI Future Leaders Fellowship (MR/V022482/1). A.M.S. was funded by ANPCyT (PICT-2016-1545 and PICT-2019-1590). S.M. was supported by Novo Nordisk Foundation Center for Biosustainability (CFB) (NNF10CC1016517). H.K.J. was supported by the Novo Nordisk Foundation as a clinical research stipend (NNF12OC1015920), by Rigshospitalets Rammebevilling 2015–17 (R88-A3537), by Lundbeckfonden (R167-2013-15229), by the Novo Nordisk Foundation (NNF15OC0017444), by RegionH Rammebevilling (R144-A5287) and by Independent Research Fund Denmark/Medical and Health Sciences (FTP-4183-00051). A.B. is supported by NERC (NE/S000771/1 and NE/V012347/1).

Author information

Authors and Affiliations

  1. ESI and CEC, Biosciences, University of Exeter, Penryn, UK

    Adela M. Luján, Elze Hesse, M. D. Sharma, Ellinor O. Alseth & Angus Buckling

  2. Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina

    Adela M. Luján & Andrea M. Smania

  3. Centro de Investigaciones en Química Biológica de Córdoba, CONICET, Córdoba, Argentina

    Adela M. Luján & Andrea M. Smania

  4. Instituto de Investigaciones en Recursos Naturales y Sustentabilidad Jose Sanchez Labrador S.J., IRNASUS-CONICET, Córdoba, Argentina

    Adela M. Luján

  5. Institute of Integrative Biology, University of Liverpool, Liverpool, UK

    Steve Paterson

  6. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

    Lea M. Sommer, Rasmus L. Marvig, Søren Molin & Helle Krogh Johansen

  7. Center for Genomic Medicine, Rigshopitalet, Copenhagen, Denmark

    Rasmus L. Marvig

  8. Costerton Biofilm Center, Department for Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark

    Oana Ciofu

  9. Department of Clinical Microbiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark

    Helle Krogh Johansen

  10. Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Helle Krogh Johansen

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  1. Adela M. Luján
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Contributions

A.M.L. and A.B. conceived and designed the study. A.M.L., L.M.S. and E.O.A. carried out the experiments. S.P., L.M.S., R.L.M. and M.D.S. analysed sequence data. A.M.L. and E.H. analysed experimental data. A.M.L., O.C. and H.K.J. were responsible for the CF sputum sample collection. H.K.J., A.M.S. and S.M. contributed to the design and interpretation of the in vivo analysis. A.M.L. and A.B. wrote the manuscript. All authors provided feedback on the manuscript.

Corresponding author

Correspondence to Adela M. Luján.

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Extended data

Extended Data Fig. 1

Experimental design of the two complementary in vitro and in vivo approaches carried out in the study.

Extended Data Fig. 2 Parameter estimates and diagnostics plots for the most parsimonious LMM testing the effect of co-culturing on relative mutator fitness (v) through time (Fig. 1a main text).

(a) Table with estimated regression parameters ± 95% confidence intervals (CI), t-statistics and p values. Parameter estimates are on log2-scale. The minimal adequate model was arrived at by sequentially deleting terms and comparing model fits using χ2-tests. The most parsimonious models could be best described by a LMM with co-culturing as sole fixed explanatory variable (treatment: χ2(1) = 5.28, p = 0.02, transfer: χ2(3) = 3.94, p = 0.27 and 2-way interaction: χ2(3) = 1.61, p = 0.66) and random intercepts fitted for each population (estimated value for σPopulation is 1.20). Because we computed a single pairwise contrast (monoculture versus co-culture), we did not adjust p values for multiple testing. (b-c) DHARMa simulation-based residual plots indicate no misspecification in the fitted LMM: (b) QQ-plot showing no significant overall deviations from the expected distribution, (c) Boxplot demonstrating homoscedastic dispersion of residuals across community treatments. Boxplots depict the median, the lower and upper quartiles (the 25th and 75th percentiles), with whiskers extending to 1.5x the interquartile range.

Extended Data Fig. 3 Changes in mutator frequency are driven by indirect selection.

Boxplots depicting temporal changes in relative mutator fitness (𝑣) as a function of community absence and presence. We determined relative mutator fitness (𝑣) by comparing mutator frequencies between consecutive time points, using: 𝑣 = x2(1-x1)/x1(1-x2), where x1 x1 is mutator frequency at transfer (tn) and x2 mutator frequency at transfer (tn+1). Boxes depict the upper and lower quartiles of the treatment-specific raw data with the centre line showing the median and whiskers providing a measure of the 1.5x interquartile range. Dots represent individual replicate populations (n = 12 per community treatment). See Extended Data Fig. 4 for associated model predictions and diagnostics.

Source data

Extended Data Fig. 4 Parameter estimates and diagnostics plots of the most parsimonious LMM testing the interactive effect of treatment × transfer (t) on consecutive relative mutator fitness (v) (Extended Data Fig. 3).

(a) Table with estimated regression parameters ± 95% confidence intervals (CI), z- and p values, and pairwise post hoc contrasts. The minimal adequate model was arrived at by sequentially deleting terms and comparing model fits using χ2-tests. The most parsimonious models could be best described by a LMM with transfer as sole fixed explanatory factor (transfer: χ2(3) = 16.75, p < 0.001, treatment: χ2(1) = 1.48, p = 0.22; 2-way interaction: χ2(3) = 0.87, p = 0.83) and random intercepts fitted for each population. Because the estimated value for σPopulation approached zero (0.000049), we fitted the model using the ‘glmmTMB’ R package, using the ‘nlminb’ optimizer and a Gaussian error structure. Note that glmmTMB reports z-statistics (instead of t-statistics) and that parameter estimates are on log2-scale. Pairwise contrasts were corrected for multiple testing using the ‘tukey’ method. (b-c) DHARMa simulation-based residual plots indicate no misspecification in the fitted LMM following model reduction: (b) QQ-plot showing no significant overall deviation from the expected distribution, (c) Boxplot demonstrating homoscedastic dispersion of residuals across time. Boxplots depict the median, the lower and upper quartiles (the 25th and 75th percentiles), with whiskers extending to 1.5x the interquartile range.

Extended Data Fig. 5 Cell densities of P. aeruginosa total population in the absence (monoculture) and in the presence (bacterial community) of the microbial community at the end point of each transfer.

Raw data is shown (n=12). P. aeruginosa population sizes were not affected by the presence of the community (Welch Two Sample two-sided t-test testing the effect of co-culturing on density averaged across time, t = 1.05, df = 22, p = 0.30). Boxplots depict the median, the lower and upper quartiles (the 25th and 75th percentiles), with whiskers extending to 1.5x the interquartile range.

Source data

Extended Data Fig. 6 Parameter estimates and diagnostics plots for the fitted LMM testing the presence of a trade-off between biotic versus abiotic adaptation (Fig. 1b in main text).

(a) Table with estimated regression parameters, 95% confidence intervals (CI), t-statistics and p values. We fitted a LMM with relative fitness of evolved PAO1 populations (v) as a response variable, selection × competition environment as fixed explanatory variables and random intercepts fitted for individual populations (estimated value for σPopulation is 1.68). Again, the minimal adequate model was arrived at by sequentially deleting terms and comparing model fits using χ2-tests. The most parsimonious model included the interaction term (selection × competition environment: χ2(1)= 7.07, p = 0.008). To determine whether populations performed better (that is displayed a higher v) when competing with the ancestor under conditions similar to those they had evolved in (that is with or without the community), we carried out pairwise contrasts between competition treatments within each of the two selection environments. Hence, p values were not adjusted for multiple testing and estimated using the kenward-roger degrees-of-freedom method. Parameter estimates are on log2-scale. (b) We confirmed the robustness of our model predictions using a Bayesian framework (warmup = 1000, post warmup = 4000, chains = 4, thin = 1 and weakly informed priors), which gave very similar parameter estimates. (c-e) DHARMa simulation-based residual plots indicate no misspecification in the fitted LMM (see Fig. 1b for raw data). (c) QQ-plot showing no significant overall deviations from the expected distribution, and (d-e) Boxplots demonstrating homoscedastic dispersion of residuals across selection regimes and competition environments, respectively. Boxplots depict the median, the lower and upper quartiles (the 25th and 75th percentiles), with whiskers extending to 1.5x the interquartile range.

Extended Data Fig. 7 Parameter estimates and diagnostics plots for the fitted GLM testing the effect of community presence on mutant fitness (v) (strains ∆mvaU versus ∆pqsR), relative to the ancestral strain (Fig. 2b in main text).

(a) Table with estimated regression parameters ± 95% confidence intervals (CI), t-statistics and p values. The minimal adequate model was arrived at by sequentially deleting terms and comparing model fits using F-tests. The most parsimonious model included the interaction term (mutant strain × community context: F1, 20 = 13.21, p = 0.002). We carried out pairwise contrasts between strains within each of the two competition environments; hence, p values were not adjusted for multiple testing (see method section in the main text). Parameter estimates are on log2-scale. (b-d) DHARMa simulation-based residual plots indicate no misspecification in the fitted GLM. (b) QQ-plot showing no significant overall deviations from the expected distribution, and (c-d) Boxplots demonstrating homoscedastic dispersion of residuals across community treatments and strains, respectively. Boxplots depict the median, the lower and upper quartiles (the 25th and 75th percentiles), with whiskers extending to 1.5x the interquartile range.

Extended Data Fig. 8 Relation between P. aeruginosa average mutation frequency and the number of CF resistome genes fixed (>0.5) in vivo.

Each point represents data obtained for each P. aeruginosa population obtained from the twenty-four CF patients (two-sided Pearson’s t = 0.98t, df = 22, p-value = 0.3344).

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Luján, A.M., Paterson, S., Hesse, E. et al. Polymicrobial infections can select against Pseudomonas aeruginosa mutators because of quorum-sensing trade-offs. Nat Ecol Evol 6, 979–988 (2022). https://doi.org/10.1038/s41559-022-01768-1

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