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Host diversity slows bacteriophage adaptation by selecting generalists over specialists

Nature Ecology & Evolution volume 5, pages 350–359 (2021)Cite this article

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Abstract

Most viruses can infect multiple hosts, yet the selective mechanisms that maintain multi-host generalists over single-host specialists remain an open question. Here we propagate populations of the newly identified bacteriophage øJB01 in coculture with many host genotypes and find that while phage can adapt to infect any of the new hosts, increasing the number of hosts slows the rate of adaptation. We quantify trade-offs in the capacity for individual phage to infect different hosts and find that phage from evolved populations with more hosts are more likely to be generalists. Sequencing of evolved phage reveals strong selection and the genetic basis of adaptation, supporting a model that shows how the addition of more potential hosts to a community can select for low-fitness generalists over high-fitness specialists. Our results show how evolution with multiple hosts alters the rate of viral adaptation and provides empirical support for an evolutionary mechanism that promotes generalists over specialists.

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Fig. 1: Adaptation of the novel phage øJB01 to a community of permissive and non-permissive E. coli hosts.
Fig. 2: Increased host diversity slows phage adaptation.
Fig. 3: Evolution with multiple hosts is shaped by trade-offs for infectivity on each host.
Fig. 4: Host identity and host diversity impacts the evolution of generalists.
Fig. 5: Parallel evolution of the tail fibre protein gp17.
Fig. 6: Community diversity and trade-offs predict the relative fitness of generalists and specialists.

Data availability

Raw sequencing reads used to generate the data in Fig. 4a and Extended Data Fig. 4 have been deposited in GenBank under the Bioproject ID PRJNA673261.

References

  1. Koskella, B. & Brockhurst, M. A. Bacteria–phage coevolution as a driver of ecological and evolutionary processes in microbial communities. FEMS Microbiol. Rev. 38, 916–931 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Fernandez, L., Rodriguez, A. & Garcia, P. Phage or foe: an insight into the impact of viral predation on microbial communities. ISME J. 12, 1171–1179 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Zimmerman, A. E. et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat. Rev. Microbiol. 18, 21–34 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Gordillo Altamirano, F. L. & Barr, J. J. Phage therapy in the postantibiotic era. Clin. Microbiol. Rev. 32, e00066-18 (2019).

  5. Flores, C. O., Valverde, S. & Weitz, J. S. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 7, 520–532 (2013).

    Article  PubMed  Google Scholar 

  6. Koskella, B. & Meaden, S. Understanding bacteriophage specificity in natural microbial communities. Viruses 5, 806–823 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Law, R. Optimal life histories under age-specific predation. Am. Nat. 114, 399–417 (1979).

    Article  Google Scholar 

  9. Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).

    Article  Google Scholar 

  10. Woolhouse, M. E., Taylor, L. H. & Haydon, D. T. Population biology of multihost pathogens. Science 292, 1109–1112 (2001).

    Article  CAS  PubMed  Google Scholar 

  11. MacLean, R. C., Bell, G. & Rainey, P. B. The evolution of a pleiotropic fitness tradeoff in Pseudomonas fluorescens. Proc. Natl Acad. Sci. USA 101, 8072–8077 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhong, S., Khodursky, A., Dykhuizen, D. E. & Dean, A. M. Evolutionary genomics of ecological specialization. Proc. Natl Acad. Sci. USA 101, 11719–11724 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bennett, A. F. & Lenski, R. E. An experimental test of evolutionary trade-offs during temperature adaptation. Proc. Natl Acad. Sci. USA 104, 8649–8654 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lee, M. C., Chou, H. H. & Marx, C. J. Asymmetric, bimodal trade-offs during adaptation of Methylobacterium to distinct growth substrates. Evolution 63, 2816–2830 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Bono, L. M., Smith, L. B. Jr., Pfennig, D. W. & Burch, C. L. The emergence of performance trade-offs during local adaptation: insights from experimental evolution. Mol. Ecol. 26, 1720–1733 (2017).

    Article  PubMed  Google Scholar 

  16. Duffy, S., Turner, P. E. & Burch, C. L. Pleiotropic costs of niche expansion in the RNA bacteriophage phi 6. Genetics 172, 751–757 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Duffy, S., Burch, C. L. & Turner, P. E. Evolution of host specificity drives reproductive isolation among RNA viruses. Evolution 61, 2614–2622 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Buckling, A. & Rainey, P. B. Antagonistic coevolution between a bacterium and a bacteriophage. Proc. Biol. Sci. 269, 931–936 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Burch, C. L. & Chao, L. Evolvability of an RNA virus is determined by its mutational neighbourhood. Nature 406, 625–628 (2000).

    Article  CAS  PubMed  Google Scholar 

  20. Crill, W. D., Wichman, H. A. & Bull, J. J. Evolutionary reversals during viral adaptation to alternating hosts. Genetics 154, 27–37 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Brockhurst, M. A., Buckling, A. & Rainey, P. B. The effect of a bacteriophage on diversification of the opportunistic bacterial pathogen, Pseudomonas aeruginosa. Proc. Biol. Sci. 272, 1385–1391 (2005).

    PubMed  PubMed Central  Google Scholar 

  22. Gomez, P. & Buckling, A. Bacteria–phage antagonistic coevolution in soil. Science 332, 106–109 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Paterson, S. et al. Antagonistic coevolution accelerates molecular evolution. Nature 464, 275–278 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pal, C., Macia, M. D., Oliver, A., Schachar, I. & Buckling, A. Coevolution with viruses drives the evolution of bacterial mutation rates. Nature 450, 1079–1081 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Bono, L. M., Gensel, C. L., Pfennig, D. W. & Burch, C. L. Competition and the origins of novelty: experimental evolution of niche-width expansion in a virus. Biol. Lett. 9, 20120616 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Bono, L. M., Gensel, C. L., Pfennig, D. W. & Burch, C. L. Evolutionary rescue and the coexistence of generalist and specialist competitors: an experimental test. Proc. Biol. Sci. 282, 20151932 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Petrie, K. L. et al. Destabilizing mutations encode nongenetic variation that drives evolutionary innovation. Science 359, 1542–1545 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Dennehy, J. J., Friedenberg, N. A., Holt, R. D. & Turner, P. E. Viral ecology and the maintenance of novel host use. Am. Nat. 167, 429–439 (2006).

    Article  PubMed  Google Scholar 

  29. Heineman, R. H., Springman, R. & Bull, J. J. Optimal foraging by bacteriophages through host avoidance. Am. Nat. 171, E149–E157 (2008).

    Article  PubMed  Google Scholar 

  30. Jerison, E. R., Nguyen, Ba,A. N., Desai, M. M. & Kryazhimskiy, S. Chance and necessity in the pleiotropic consequences of adaptation for budding yeast. Nat. Ecol. Evol. 4, 601–611 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Remold, S. Understanding specialism when the jack of all trades can be the master of all. Proc. Biol. Sci. 279, 4861–4869 (2012).

    PubMed  PubMed Central  Google Scholar 

  32. Betts, A., Gray, C., Zelek, M., MacLean, R. C. & King, K. C. High parasite diversity accelerates host adaptation and diversification. Science 360, 907–911 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Betts, A., Rafaluk, C. & King, K. C. Host and parasite evolution in a tangled bank. Trends Parasitol. 32, 863–873 (2016).

    Article  PubMed  Google Scholar 

  34. Iguchi, A. et al. Complete genome sequence and comparative genome analysis of enteropathogenic Escherichia coli O127:H6 strain E2348/69. J. Bacteriol. 191, 347–354 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11, 754 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lanfear, R., Kokko, H. & Eyre-Walker, A. Population size and the rate of evolution. Trends Ecol. Evol. 29, 33–41 (2014).

    Article  PubMed  Google Scholar 

  37. Li, Y., Petrov, D. A. & Sherlock, G. Single nucleotide mapping of trait space reveals Pareto fronts that constrain adaptation. Nat. Ecol. Evol. 3, 1539–155 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Bachmann, H. et al. Availability of public goods shapes the evolution of competing metabolic strategies. Proc. Natl Acad. Sci. USA 110, 14302–14307 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Keen, E. C. Tradeoffs in bacteriophage life histories. Bacteriophage 4, e28365 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Gonzalez-Garcia, V. A. et al. Conformational changes leading to T7 DNA delivery upon interaction with the bacterial receptor. J. Biol. Chem. 290, 10038–10044 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Holtzman, T. et al. A continuous evolution system for contracting the host range of bacteriophage T7. Sci. Rep. 10, 307 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. McDonald, M. J. Microbial experimental evolution—a proving ground for evolutionary theory and a tool for discovery. EMBO Rep. 20, e46992 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Whitlock, M. C. The red queen beats the jack-of-all-trades: the limitations on the evolution of phenotypic plasticity and niche breadth. Am. Nat. 148, S65–S77 (1996).

    Article  Google Scholar 

  44. Zhao, J. et al. Characterizing the biology of lytic bacteriophage vB_EaeM_phiEap-3 infecting multidrug-resistant Enterobacter aerogenes. Front. Microbiol. 10, 420 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Buttimer, C. et al. Pectobacterium atrosepticum phage vB_PatP_CB5: a member of the proposed genus ‘Phimunavirus’. Viruses 10, 394 (2018).

    Article  PubMed Central  Google Scholar 

  46. Van Twest, R. & Kropinski, A. M. Bacteriophage enrichment from water and soil. Methods Mol. Biol. 501, 15–21 (2009).

    Article  PubMed  Google Scholar 

  47. Bonilla, N. et al. Phage on tap—a quick and efficient protocol for the preparation of bacteriophage laboratory stocks. PeerJ 4, e2261 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jakociune, D. & Moodley, A. A rapid bacteriophage DNA extraction method. Methods Protoc. 1, 27 (2018).

  49. Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Deatherage, D. E. & Barrick, J. E. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Stecher, G., Tamura, K. & Kumar, S. Molecular evolutionary genetics analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Dereeper, A. et al. Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. 36, W465–W469 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Whelan, S. & Goldman, N. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Mol. Biol. Evol. 18, 691–699 (2001).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

M.J.M. was supported by ARC Discovery Grant (grant no. DP180102161) and ARC Future Fellowship (grant no. FT170100441).

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Authors and Affiliations

  1. School of Biological Sciences, Monash University, Monash, Victoria, Australia

    Duhita G. Sant, Laura C. Woods, Jeremy J. Barr & Michael J. McDonald

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  1. Duhita G. Sant
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Contributions

M.J.M. undertook the conceptualization, methodology, resources and funding acquisition. M.J.M. and J.J.B. supervised the work. M.J.M. and D.G.S. were responsible for project administration. M.J.M. and D.G.S. conducted the formal analysis. M.J.M., D.G.S. and J.J.B. undertook validation of results. D.G.S. was involved in investigation and visualization. L.C.W. provided software and data curation. M.J.M. and D.G.S. prepared the original draft manuscript. J.J.B. then reviewed and edited the manuscript.

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Correspondence to Michael J. McDonald.

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The authors declare no competing interests.

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Peer review information Nature Ecology & Evolution thanks Britt Koskella and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Growth of E. coli EPEC and phage øJB01 in coculture.

The number of øJB01 plaque-forming units were monitored over a period of 90 min in coculture with exponential phase E. coli EPEC at MOI 0.1(A). The growth of E. coli EPEC was assayed using a plate reader. E. coli EPEC was propagated either in monoculture, or in coculture with phage øJB01 added at two different MOIs (1 and 0.1) (B). Error bars represent standard error of the mean (SEM) (n = 3).

Extended Data Fig. 2 Two-way plots showing data used for simplex plots in Fig. 4.

Panels a-c correspond to three-host treatment (EPEC, MG1655, BL21). Panels d-f correspond to the three-host treatment (EPEC, MG1655, REPEC). Measurements of infectivity (PFU) for phage clones isolated from MG1655 (blue circles), EPEC (orange triangles), BL21(red squares) and REPEC (green diamonds). Filled markers show the measurements for phage clones obtained at the end point of the experiment (Day 10). Open markers show phage clones sampled at early timepoints (day 4 - day 9). Error bars are SEM, n = 3.

Extended Data Fig. 3 CLUSTAL multiple sequence alignment of tail fibre protein gp17.

The amino acid sequence of Escherichia øJB01 phage, gp17 was aligned with homologous sequences from Escherichia phage N30 and T7 phage. Identical amino acids are marked with asterisks and non-identical are marked by dots. The gp17 conserved region is from position 1- 250 and the hypervariable region from 300–554.

Extended Data Fig. 4 Phylogenetic tree based on the gp17 sequences of 26 phage clones from the three-host treatment (EPEC, MG1655, REPEC).

Sequences were aligned with MUSCLE (v3.8.31) in MEGA X51,52. The phylogenetic tree was reconstructed using the maximum likelihood method as implemented in PhyML (v3.1/3.0)53,54. The WAG substitution model55 was selected assuming an estimated proportion of invariant sites (of 0.951) and 4 gamma-distributed rate categories to account for rate heterogeneity across sites. The gamma shape parameter was estimated directly from the data (gamma=91.073). Reliability of internal branches was assessed using the aLRT test (SH-Like). Final Log-Liklihood: −1839.18. Phage clones labelled by phenotype and day of generation. ‘S’ or ‘G’ refers to specialist or generalist according to phenotypic measurements (Figs. 3–4) and the number refers to the day of isolation. C8, D8, C9, D9, E8, F8 refer to the specific three-host experimental population from which the phage clone was isolated. Each phage clone was obtained by plating the whole population lysate onto an individual host (Figs. 3–4). The strain name refers to that host of isolation. For example, ‘G10 C8 MG1655’ refers to a phage clone with a generalist phenotype isolated from a day 10 population C8, isolated from a plaque on MG1655. Numbers on branches show bootstrapping support for that branch (percent, 100 bootstraps). Strains are clustered based on those clades with greater than 80% support. Dashed lines are for labels and are not part of the tree.

Supplementary information

Supplementary Information

Supplementary Tables 1–7.

Reporting Summary

Supplementary Data

The genetic variants discovered in gp17 in all sequenced clones. For example, Day 4, Generalist, C8 REPEC is a clone that originated from Day 4 of the evolution experiment in the ‘C8’ replicate population within the three-host treatment with EPEC, MG1655 and REPEC. The clone itself was isolated as a plaque from a lawn of E. coli REPEC on an agar plate.

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Sant, D.G., Woods, L.C., Barr, J.J. et al. Host diversity slows bacteriophage adaptation by selecting generalists over specialists. Nat Ecol Evol 5, 350–359 (2021). https://doi.org/10.1038/s41559-020-01364-1

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