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Phage–host coevolution in natural populations

Nature Microbiology volume 7, pages 1075–1086 (2022)Cite this article

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Abstract

Coevolution between bacteriophages (phages) and their bacterial hosts occurs through changes in resistance and counter-resistance mechanisms. To assess phage–host evolution in wild populations, we isolated 195 Vibrio crassostreae strains and 243 vibriophages during a 5-month time series from an oyster farm and combined these isolates with existing V. crassostreae and phage isolates. Cross-infection studies of 81,926 host–phage pairs delineated a modular network where phages are best at infecting co-occurring hosts, indicating local adaptation. Successful propagation of phage is restricted by the ability to adsorb to closely related bacteria and further constrained by strain-specific defence systems. These defences are highly diverse and predominantly located on mobile genetic elements, and multiple defences are active within a single genome. We further show that epigenetic and genomic modifications enable phage to adapt to bacterial defences and alter host range. Our findings reveal that the evolution of bacterial defences and phage counter-defences is underpinned by frequent genetic exchanges with, and between, mobile genetic elements.

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Fig. 1: A modular phage–vibrio infection network.
Fig. 2: Phage adsorption and cell death are not the same.
Fig. 3: Patterns of phage adaptation across space and time.
Fig. 4: Distribution of phage-defence systems in V. crassostreae genomes.
Fig. 5: Anti-phage systems identified in V. crassostreae clade V1.
Fig. 6: Bacterial defence and phage counter-defence.

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Data availability

Sequenced genomes have been deposited under the NCBI BioProject, with accession numbers PRJEB5876 to PRJEB5880, PRJEB5882 to PRJEB5885, PRJNA499864 to PRJNA499870, PRJNA500024 to PRJNA500069, PRJNA538125 to PRJNA538127, PRJNA548064 and PRJNA712984 for vibrios; MW824369 to MW824434, MW865291 to MW865292 and MW865297 to MW865379 for phages. Source data are provided with this paper.

References

  1. Bernheim, A. & Sorek, R. Viruses cooperate to defeat bacteria. Nature 559, 482–484 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Koonin, E. V., Senkevich, T. G. & Dolja, V. V. The ancient Virus World and evolution of cells. Biol. Direct 1, 29 (2006).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Breitbart, M., Bonnain, C., Malki, K. & Sawaya, N. A. Phage puppet masters of the marine microbial realm. Nat. Microbiol. 3, 754–766 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Chevallereau, A., Pons, B. J., van Houte, S. & Westra, E. R. Interactions between bacterial and phage communities in natural environments. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-021-00602-y (2021).

  5. 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 

  6. Hampton, H. G., Watson, B. N. J. & Fineran, P. C. The arms race between bacteria and their phage foes. Nature 577, 327–336 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Hussain, F. A. et al. Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. Science 374, 488–492 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. LeGault, K. N. et al. Temporal shifts in antibiotic resistance elements govern phage-pathogen conflicts. Science https://doi.org/10.1126/science.abg2166 (2021).

  9. Doron, S. et al. Systematic discovery of antiphage defence systems in the microbial pangenome. Science https://doi.org/10.1126/science.aar4120 (2018).

  10. Gao, L. et al. Diverse enzymatic activities mediate antiviral immunity in prokaryotes. Science 369, 1077–1084 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bernheim, A. & Sorek, R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat. Rev. Microbiol. 18, 113–119 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Nurmemmedov, E., Castelnovo, M., Medina, E., Catalano, C. E. & Evilevitch, A. Challenging packaging limits and infectivity of phage lambda. J. Mol. Biol. 415, 263–273 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Hendrix, R. W. Hot new virus, deep connections. Proc. Natl Acad. Sci. USA 101, 7495–7496 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bruto, M. et al. Vibrio crassostreae, a benign oyster colonizer turned into a pathogen after plasmid acquisition. Isme J. 11, 1043–1052 (2017).

    Article  PubMed  Google Scholar 

  15. Lemire, A. et al. Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters. Isme J. 9, 1523–1531 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Piel, D. et al. Selection of Vibrio crassostreae relies on a plasmid expressing a type 6 secretion system cytotoxic for host immune cells. Environ. Microbiol. 22, 4198–4211 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Kauffman, K. M. et al. Resolving the structure of phage-bacteria interactions in the context of natural diversity. Nat. Commun. 13, 372 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Moraru, C., Varsani, A. & Kropinski, A. M. VIRIDIC—a novel tool to calculate the intergenomic similarities of prokaryote-infecting viruses. Viruses https://doi.org/10.3390/v12111268 (2020).

  19. Moura de Sousa, J. A., Pfeifer, E., Touchon, M. & Rocha, E. P. C. Causes and consequences of bacteriophage diversification via genetic exchanges across lifestyles and bacterial taxa. Mol. Biol. Evol. 38, 2497–2512 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gandon, S., Buckling, A., Decaestecker, E. & Day, T. Host-parasite coevolution and patterns of adaptation across time and space. J. Evol. Biol. 21, 1861–1866 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Blanquart, F. & Gandon, S. Time-shift experiments and patterns of adaptation across time and space. Ecol. Lett. 16, 31–38 (2013).

    Article  PubMed  Google Scholar 

  22. Brockhurst, M. A. & Koskella, B. Experimental coevolution of species interactions. Trends Ecol. Evol. 28, 367–375 (2013).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Tesson, F. et al. Systematic and quantitative view of the antiviral arsenal of prokaryotes. Nat. Commun. 13, 2561 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, L. et al. DNA phosphorothioation is widespread and quantized in bacterial genomes. Proc. Natl Acad. Sci. USA 108, 2963–2968 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lopatina, A., Tal, N. & Sorek, R. Abortive infection: bacterial suicide as an antiviral immune strategy. Annu. Rev. Virol. 7, 371–384 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Bazin, A., Gautreau, G., Medigue, C., Vallenet, D. & Calteau, A. panRGP: a pangenome-based method to predict genomic islands and explore their diversity. Bioinformatics 36, i651–i658 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Touchon, M., Bobay, L. M. & Rocha, E. P. The chromosomal accommodation and domestication of mobile genetic elements. Curr. Opin. Microbiol. 22, 22–29 (2014).

    Article  CAS  PubMed  Google Scholar 

  29. Millman, A. et al. Bacterial retrons function in anti-phage defense. Cell 183, 1551–1561.e12 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Alseth, E. O. et al. Bacterial biodiversity drives the evolution of CRISPR-based phage resistance. Nature 574, 549–552 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Seed, K. D. et al. Evolutionary consequences of intra-patient phage predation on microbial populations. eLife 3, e03497 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Thompson, J. R. et al. Genotypic diversity within a natural coastal bacterioplankton population. Science 307, 1311–1313 (2005).

    Article  CAS  PubMed  Google Scholar 

  33. Rocha, E. P. C. & Bikard, D. Microbial defenses against mobile genetic elements and viruses: who defends whom from what? PLoS Biol. 20, e3001514 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Petton, B., Boudry, P., Alunno-Bruscia, M. & Pernet, F. Factors influencing disease-induced mortality of Pacific oysters Crassostreae gigas. Aquac. Environ. Interact. 6, 205–222 (2015).

    Article  Google Scholar 

  35. de Lorgeril, J. et al. Immune-suppression by OsHV-1 viral infection causes fatal bacteraemia in Pacific oysters. Nat. Commun. 9, 4215 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Kauffman, K. M. et al. Viruses of the Nahant Collection, characterization of 251 marine Vibrionaceae viruses. Sci. Data 5, 180114 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vallenet, D. et al. MicroScope–an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data. Nucleic Acids Res. 41, D636–D647 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Miele, V., Penel, S. & Duret, L. Ultra-fast sequence clustering from similarity networks with SiLiX. BMC Bioinformatics 12, 116 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. Gautreau, G. et al. PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph. PLoS Comput. Biol. 16, e1007732 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Abby, S. S., Neron, B., Menager, H., Touchon, M. & Rocha, E. P. MacSyFinder: a program to mine genomes for molecular systems with an application to CRISPR-Cas systems. PLoS ONE 9, e110726 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Cury, J., Abby, S. S., Doppelt-Azeroual, O., Neron, B. & Rocha, E. P. C. Identifying conjugative plasmids and integrative conjugative elements with CONJscan. Methods Mol. Biol. 2075, 265–283 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. van Bloois, L. V. D. G., Wagenaar, J. A. & Zomer, A. L. RFPlasmid: predicting plasmid sequences from short read assembly data using machine learning. Microb. Genom. 7, 000683 (2021).

    Google Scholar 

  46. Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinformatics 70, e102 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Buchfink, B., Reuter, K. & Drost, H. G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mistry, J. et al. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Chan, P. P. & Lowe, T. M. tRNAscan-SE: searching for tRNA genes in genomic sequences. Methods Mol. Biol. 1962, 1–14 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lopes, A., Amarir-Bouhram, J., Faure, G., Petit, M. A. & Guerois, R. Detection of novel recombinases in bacteriophage genomes unveils Rad52, Rad51 and Gp2.5 remote homologs. Nucleic Acids Res. 38, 3952–3962 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Grazziotin, A. L., Koonin, E. V. & Kristensen, D. M. Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation. Nucleic Acids Res. 45, D491–D498 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).

    Article  CAS  PubMed  Google Scholar 

  55. Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Jain, C., Rodriguez, R. L., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Steinegger, M. & Soding, J. Clustering huge protein sequence sets in linear time. Nat. Commun. 9, 2542 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hyman, P. & Abedon, S. T. Practical methods for determining phage growth parameters. Methods Mol. Biol. 501, 175–202 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Le Roux, F., Binesse, J., Saulnier, D. & Mazel, D. Construction of a Vibrio splendidus mutant lacking the metalloprotease gene vsm by use of a novel counterselectable suicide vector. Appl. Environ. Microbiol. 73, 777–784 (2007).

    Article  PubMed  CAS  Google Scholar 

  61. Demarre, G. et al. A new family of mobilizable suicide plasmids based on broad host range R388 plasmid (IncW) and RP4 plasmid (IncPalpha) conjugative machineries and their cognate Escherichia coli host strains. Res. Microbiol. 156, 245–255 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Val, M. E., Skovgaard, O., Ducos-Galand, M., Bland, M. J. & Mazel, D. Genome engineering in Vibrio cholerae: a feasible approach to address biological issues. PLoS Genet. 8, e1002472 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank M. A. Petit, M. Blokesch, M. Sullivan and A. Bernheim for valuable suggestions; M. Touchon and A. Bernheim for assistance with vibrio genome annotation; Z. Chaplain for the illustrations and help during the time series sampling; the staff of the station Ifremer Argenton and Bouin, the ABIMS (Roscoff) and LABGeM (Evry) platforms for technical assistance; Z. Allouche, Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Génomique (ANR-10-INBS-09-09) and IBISA; and G. Riddihough from Life Science Editors for help with the manuscript. This work was supported by funding from the Agence Nationale de la Recherche (ANR-16-CE32-0008-01, REVENGE; ANR-20-CE35-0014, RESISTE), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 884988, Advanced ERC Dynamic) to F.L.R. and Ifremer to D.P. The work was further supported by a grant from the Simons Foundation (LIFE ID 572792) to M.F.P. Part of the Vibrio crassostreae genome sequencing was conducted by the US Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, and is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Ifremer, Unité Physiologie Fonctionnelle des Organismes Marins, ZI de la Pointe du Diable, Plouzané, France

    Damien Piel, Yannick Labreuche, David Goudenège, Adèle James, Bruno Petton & Frédérique Le Roux

  2. Sorbonne Université, UPMC Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, Roscoff cedex, France

    Damien Piel, Maxime Bruto, Yannick Labreuche, David Goudenège, Rubén Barcia-Cruz, Sabine Chenivesse, Sophie Le Panse, Javier Dubert & Frédérique Le Roux

  3. Centre for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, Paris, France

    François Blanquart

  4. Infection Antimicrobials Modelling Evolution, UMR 1137, INSERM, Université de Paris, Paris, France

    François Blanquart

  5. Department of Microbiology and Parasitology, CIBUS-Faculty of Biology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Rubén Barcia-Cruz & Javier Dubert

  6. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Javier Dubert, Fatima A. Hussain, Kathryn M. Kauffman & Martin F. Polz

  7. Eligo Bioscience, Paris, France

    Erica Lieberman

  8. AWI - Alfred Wegener Institut - Helmholtz-Zentrum für Polar- und Meeresforschung, Coastal Ecology, Waddensea Station Sylt, List, Germany

    K. Mathias Wegner

  9. Division of Microbial Ecology, Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria

    Martin F. Polz

  10. Synthetic Biology Group, Department of Microbiology, Institut Pasteur, Paris, France

    David Bikard

  11. CEFE (UMR 5175 CNRS - Université de Montpellier - Université Paul Valéry - EPHE), Montpellier, France

    Sylvain Gandon

  12. Institut Pasteur, Université de Paris, CNRS UMR3525, Microbial Evolutionary Genomics, Paris, France

    Eduardo P. C. Rocha

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  1. Damien Piel
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Contributions

F.L.R. and D.B. conceived the project. F.L.R. wrote the paper with contributions from D.P., M.B., Y.L., F.B., K.M.W., F.A.H., K.M.K., M.F.P., D.B., S.G. and E.P.C.R. D.P. performed phage–vibrio interaction experiments with assistance from S.C., R.B.-C. and E.L. M.B. and D.G. performed the in silico analyses with assistance from K.M.K. and supervision by E.P.C.R. Y.L., F.L.R. and D.P. performed the genetics, and R.B.-C. the epigenetic experiments. S.L.P. performed the electronic microscopy analyses. D.P., Y.L., S.C., A.J., B.P. and F.L.R. established the time-series sampling. K.M.W., F.L.R. and J.D. isolated the phage and vibrio collections from Sylt. F.A.H. and M.F.P. performed and funded part of the vibrio sequencing. F.B. and S.G. designed, and F.B. performed the time-shift analysis. F.L.R. supervised the project and secured funding.

Corresponding author

Correspondence to Frédérique Le Roux.

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Nature Microbiology thanks Edze Westra 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 Dynamics of Vibrio crassostreae.

On each sampling date (May 3rd- September 11th 2017, x-axis), vibrios from seawater (size fraction 1-0.2μm, blue) or five oyster tissues (pink) were selected on TCBS and genotyped to identify V. crassostreae isolates. The y-axis indicates the frequency of V. crassostreae (number of positive isolates out of the randomly picked 48 colonies*100). The arrow indicates the period of oyster mortalities, that is May 29th- August 25th.

Source data

Extended Data Fig. 2 Host range matrix for assay of phages on gyrB-sequenced hosts.

Rows represent vibrio strains (n = 299), columns represent phages (n = 243 phages isolated in Brest and ordered by date or “Pools” when phages were isolated from a mix of viruses from 5 consecutive dates; n = 31 phages isolated in Sylt), red marks indicate infection of host. Pink shade discriminate V. crassostreae isolates from representatives of other species.

Extended Data Fig. 3 Non-overlapping distribution of vibrio clades and phage genus across locations.

The all-by-all host range infection assay (Fig.1) reveals that sympatric killing of vibrio by phages is more frequent than allopatric killing (Fig. 3a). The presence of phage of a given cluster is associated with the presence of the corresponding V. crassostreae clade at each location. Upper pie charts show the number of isolates per clade (V1 to V8) or not in clade among 120 and 33 vibrios from Brest and Sylt respectively. Lower pie charts show the number of isolates per phage cluster among 58 and 17 phages from Brest and Sylt respectively. Phage clusters are indicated as Px_y, where P indicates phage, x the vibrio clade they infect (1 to 8 and N when not in clade), and y is the VIRIDIC genus number (1 to 28).

Extended Data Fig. 4 Core phylogenetic tree and genes synteny for phages from the cluster P1_11.

The three pink (but not the three green) phages are able to neutralize the R-M III defence. Core proteins are indicated in grey (with 30% identity and 80% coverage thresholds), large and small terminase subunit in dark grey and accessory proteins in colors. The tree was built using IQ-TREE2 with 1000 bootstraps and the GTR model. Genes encoding putative recombinases (Rad52/22, RdgC, YejK) and methylases (MTases) were identified using dedicated HMM profiles.

Extended Data Fig. 5 Host-dependent epigenetic modification allows P1_11 podoviruses to neutralize a R-M III system.

Three green and three pink phages were produced in their original host (the strain used to isolate phage from seawater flocculate) or in the strain 46_O_330 (name here 330) that is sensitive to all P1_11 phages. The green phages were 234P8, 219E41.1, 219E41.2 and original hosts were vibrio V1 strains 40_O_234 (234) and 38_P_219 (219). The pink phages were 431E45.1, 431E46.1, 431E48.2 all isolated from 48_O_431 (431). Tenfold dilutions of each progeny were spotted on the strain 7F1_18 wild type and derivatives DR1, DR2 and DR1DR2.

Extended Data Fig. 6 Changes in susceptibility to phage killing observed for V5red specific region deletions.

Single and double deletions of the six regions found in all V5red and absent in all V5blue strains were performed in one strain (29_O_45) of vibrio clade V5. Lawns of bacterial hosts with drop spots of a 1:10 dilution series of phages from cluster P5_14, red (red triangle; phage) and blue (blue triangle). The red phages were 36E38.1, 41E34.2, 44E38.1 and 44E38.2. The blue phages were 24E30.2, 24E35.2, 64E30.1 and 66E30.1.

Extended Data Fig. 7 The Ec48 retron system from V5red strain causes abortive Infection.

Infection dynamics in liquid of the V5red (29_O_45) lacking Dnd (ΔDnd) or both Dnd and the Ec48 retron systems (ΔDndΔRetron) infected by blue P5_14 phage (66E30.1) at MOI 0.2 and 2. Infections were performed in technical triplicate (± SD from three replicates). Data are representative of two independent experiments.

Source data

Extended Data Fig. 8 Core genome phylogenetic tree and gene synteny representation for phage from the cluster P5_14.

Core proteins are indicated in grey (blue/red pairwise identities indicated by a yellow gradiant) large and small terminase subunit in dark grey. P5_14 red (p0020 and p0021 in phage 44E38.1) and blue (p0020 to 23 in phage 66E30.1) specific genes are indicated by red and blue gradient respectively. The tree was built using IQ-TREE2 with 1000 bootstraps and the GTR model. Genes encoding putative recombinases (NinG, Exo/SSPAP) and methylases (MTases) were identified using dedicated HMM profiles.

Extended Data Fig. 9 Gene synteny of P5_14 phages and derivative Blue-PAPS-retron escapers.

Core proteins are indicated in grey, large and small terminase subunit in dark grey, specific P5_14 red proteins in red gradient and specific P5_14 blue proteins in blue gradient and retron-escaping exonuclease in yellow. The number of SNPs and presence of deletion between phages are indicated in white and black circles respectively. SNPs were labeled using HGVS nomenclature.

Extended Data Fig. 10 Genetic mechanisms driving the specificity of the interaction between a natural population of vibrio and their viral predators.

A successful infection requires the phage to adsorb on specific receptor(s) at the surface of the bacterial cell and then bypass the host’s intracellular defences. In Vibrio crassostreae, adsorption governs a first level of specificity of the interaction between a phage cluster and a clade within this bacterial species (1). Defence systems, mostly transmitted by mobile genetic elements, constitute a second barrier to infection (2). The phage can escape these defences by epigenetic or genetic modifications (3). Facing this new threat, a bacterium that has acquired a new defence system can be selected (4) and in the same way, the phage can adapt by acquiring new counter-defences (5). Recombination can allow the exchange of counter-defence systems and generate a genetically diverse progeny (6).

Supplementary information

Source data

Source Data Fig. 3

Cross-infection matrix of 299 vibrio and 274 phages.

Source Data Fig. 4

All data of defence finder with number of infecting phages and genome size for each V. crassostreae strain (all strains and clade-by-clade).

Source Data Fig. 4

Results of statistical analysis in c).

Source Data Fig. 5

Results of two distinct experiments used for statistical analysis.

Source Data Fig. 6

Results of two distinct experiments used for statistical analysis.

Source Data Extended Data Fig. 1

Dynamics of Vibrio crassostreae.

Source Data Extended Data Fig. 7

Results of two distinct experiments used for statistical analysis.

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Piel, D., Bruto, M., Labreuche, Y. et al. Phage–host coevolution in natural populations. Nat Microbiol 7, 1075–1086 (2022). https://doi.org/10.1038/s41564-022-01157-1

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