Abstract
Bacteriophages-bacteria interactions drive rapid evolution of both partners in laboratory studies. To understand how these dynamics unfold in natural environments, we re-sampled a population of Vibrio crassostreae and their phages in an open, animal-associated marine system four years apart. Analysis of over 1000 predominantly virulent phages revealed rapid change of some lineages, but persistence of others, with genomes highly conserved between years. This pattern is consistent with low substitution rates in persistent lineages and may reflect phages overwintering in wild oysters, slow virion decay, and for temperate phages, lysogeny within hosts. Over 600 V. crassostreae strains recovered at both time points assorted into the same major clades. Oyster-associated vibrios have larger genomes and more abundant and diverse mobile genetic elements suggesting that oysters are hotspots for genetic exchange and horizontal gene transfer. Their genomes encode virulence plasmids, prophages carrying anti-phage systems, phage-plasmids, and phage satellites that persist intracellularly as plasmids. Time series analyses revealed weak correlations between phage and bacterial abundances, a pattern compatible with cryptic population dynamics arising from genetic diversity. Together, these results indicate that natural coevolving phage-bacteria populations can exhibit complex dynamics, with rapid replacement of some lineages alongside multi-year persistence of others.
Data availability
The genome sequences of phages and V. crassostreae isolates generated in this study have been deposited in the European Nucleotide Archive (ENA) under BioProjects PRJEB81325 (phages) and PRJEB67885 (bacterial genomes). Accession numbers are listed in Supplementary Data 1 and 4. Column E of Supplementary Data 1 (“Accession”) refers to the submitted GenBank assembly of each newly isolated phage and refers to newly published data. Column G of Supplementary Data 1 (“Host Accession”) refers to the host strain of V. crassostreae used for the isolation of the phage and as consequence of the experimental design, refers to previously published data12. Column D of Supplementary Data 4 (“Accession”) refers to the submitted Genbank assembly of each newly isolated V. crassostreae strain and refers to newly published data.
The Roscoff Culture Collection (https://roscoff-culture-collection.org/), permits access to the biological material (phages and bacteria isolated in the present study, except when stated otherwise, and can be provided from Le Roux lab at Montreal) upon request, with accession numbers listed in Supplementary Data 1 and 4. Source data are provided with this paper.
Code availability
Custom code used in this study is publicly available on Zenodo (https://doi.org/10.5281/zenodo.18717761)
References
Breitbart, M., Bonnain, C., Malki, K. & Sawaya, N. A. Phage puppet masters of the marine microbial realm. Nat. Microbiol 3, 754–766 (2018).
Brum, J. R. & Sullivan, M. B. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat. Rev. Microbiol. 13, 147–159 (2015).
Koonin, E. V. & Dolja, V. V. A virocentric perspective on the evolution of life. Curr. Opin. Virol. 3, 546–557 (2013).
Rohwer, F. & Thurber, R. V. Viruses manipulate the marine environment. Nature 459, 207–212 (2009).
Marston, M. F. & Martiny, J. B. Genomic diversification of marine cyanophages into stable ecotypes. Environ. Microbiol. 18, 4240–4253 (2016).
Marston, M. F. et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc. Natl. Acad. Sci. USA. 109, 4544–4549 (2012).
Yoshida, T. et al. Cryptic population dynamics: rapid evolution masks trophic interactions. PLoS Biol. 5, e235 (2007).
Kauffman, K. M. et al. Resolving the structure of phage-bacteria interactions in the context of natural diversity. Nat. Commun. 13, 372 (2022).
Hussain, F. A. et al. Rapid evolutionary turnover of mobile genetic elements drives bacterial resistance to phages. Science 374, 488–492 (2021).
Le Roux, F., Wegner, K. M. & Polz, M. F. Oysters and vibrios as a model for disease dynamics in wild animals. Trends Microbiol. 24, 568–580 (2016).
Bruto, M. et al. Vibrio crassostreae, a benign oyster colonizer turned into a pathogen after plasmid acquisition. ISME J. 11, 1043–1052 (2017).
Piel, D. et al. Phage–host coevolution in natural populations. Nat. Microbiol. 7, 1075–1086 (2022).
Bernard, C. et al. Adaptive genomic plasticity in large-genome, broad-host–range vibrio phages. ISME J. 19, 1–14 (2025).
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).
DePaola, A., Motes, M. L., Chan, A. M. & Suttle, C. A. Phages infecting Vibrio vulnificus are abundant and diverse in oysters (Crassostrea virginica) collected from the Gulf of Mexico. App. Environ. Microbiol. 64, 346–351 (1998).
Moraru, C., Varsani, A. & Kropinski, A. M. VIRIDIC—A novel tool to calculate the intergenomic similarities of prokaryote-infecting viruses. Viruses 12, 1268 (2020).
Hockenberry, A. J. & Wilke, C. O. BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains. PeerJ 9, e11396 (2021).
Kupczok, A. & Dagan, T. Rates of molecular evolution in a marine Synechococcus phage lineage. Viruses 11, 720 (2019).
Kupczok, A. et al. Rates of mutation and recombination in Siphoviridae phage genome evolution over three decades. Mol. Biol. Evol. 35, 1147–1159 (2018).
Sanjuán, R. & Domingo-Calap, P. in Encyclopedia of Virology. 53–61 Elsevier, (2021).
Destoumieux-Garzón, D. et al. Antimicrobial peptides in marine invertebrate health and disease. Philos. Trans. R. Soc. B 371, 1–11 (2016).
Camargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat. Biotechnol. 42, 1303–1312 (2024).
Steensen, K. et al. Tailless and filamentous prophages are predominant in marine Vibrio. ISME J. 18, 1–15 (2024).
Garcillán-Barcia, M. P., de la Cruz, F. & Rocha, E. P. C. The extended mobility of plasmids. Nucleic Acids Res. 53, 1–32 (2025).
Pfeifer, E., de Sousa, J. A. M., Touchon, M. & Rocha, E. P. C. Bacteria have numerous distinctive groups of phage-plasmids with conserved phage and variable plasmid gene repertoires. Nucleic Acids Res. 49, 2655–2673 (2021).
Silpe, J. E. & Bassler, B. L. A host-produced quorum-sensing autoinducer controls a phage lysis-lysogeny decision. Cell 176, 268–280 e213 (2019).
Silpe, J. E. et al. Small protein modules dictate prophage fates during polylysogeny. Nature 620, 625–633 (2023).
Penades, J. R., Seed, K. D., Chen, J., Bikard, D. & Rocha, E. P. C. Genetics, ecology and evolution of phage satellites. Nat. Rev. Microbiol. 23, 410–422 (2025).
de Sousa, J. A. M., Fillol-Salom, A., Penadés, J. R. & Rocha, E. P. C. Identification and characterization of thousands of bacteriophage satellites across bacteria. Nucleic Acids Res. 51, 2759–2777 (2023).
Alqurainy, N. et al. A widespread family of phage-inducible chromosomal islands only steals bacteriophage tails to spread in nature. Cell Host Microbe 31, 69–82 e65 (2023).
Fillol-Salom, A. et al. Phage-inducible chromosomal islands are ubiquitous within the bacterial universe. ISME J. 12, 2114–2128 (2018).
Gupta, A. et al. Leapfrog dynamics in phage-bacteria coevolution revealed by joint analysis of cross-infection phenotypes and whole genome sequencing. Ecol. Lett. 25, 876–888 (2022).
Faruque, S. M. et al. Seasonal epidemics of cholera inversely correlate with the prevalence of environmental cholera phages. Proc. Natl. Acad. Sci. USA. 102, 1702–1707 (2005).
Erez, Z. et al. Communication between viruses guides lysis-lysogeny decisions. Nature 541, 488–493 (2017).
Destoumieux-Garzón, D., Duperthuy, M., Vanhove, A., Schmitt, P. & Wai, S. Resistance to antimicrobial peptides in vibrios. Antibiotics 3, 540–563 (2014).
Vanhove, A. S. et al. Copper homeostasis at the host vibrio interface: lessons from intracellular vibrio transcriptomics. Environ. Microbiol. 18, 875–888 (2016).
Rubio, T. et al. Species-specific mechanisms of cytotoxicity toward immune cells determine the successful outcome of Vibrio infections. Proc. Natl. Acad. Sci. USA. 116, 14238–14247 (2019).
Swan, B. K. et al. Prevalent genome streamlining and latitudinal divergence of planktonic bacteria in the surface ocean. Proc. Natl. Acad. Sci. USA. 110, 11463–11468 (2013).
Touchon, M. et al. Phylogenetic background and habitat drive the genetic diversification of Escherichia coli. PLOS Genet. 16, 1–43 (2020).
Giovannoni, S. J. et al. Genome streamlining in a cosmopolitan oceanic bacterium. Science 309, 1242–1245 (2005).
Kauffman, K. M. et al. A major lineage of non-tailed dsDNA viruses as unrecognized killers of marine bacteria. Nature 554, 118–122 (2018).
Eppley, J. M., Biller, S. J., Luo, E., Burger, A. & DeLong, E. F. Marine viral particles reveal an expansive repertoire of phage-parasitizing mobile elements. Proc. Natl. Acad. Sci. USA. 119, 1–10 (2022).
Schmid, N. et al. An autonomous plasmid as an inovirus phage satellite. Appl. Environ. Microbiol. 90, 1–14 (2024).
Rodriguez-Beltran, J. et al. Multicopy plasmids allow bacteria to escape from fitness trade-offs during evolutionary innovation. Nat. Ecol. Evol. 2, 873–881 (2018).
de Lorgeril, J. et al. Immune-suppression by OsHV-1 viral infection causes fatal bacteraemia in Pacific oysters. Nat. Commun. 9, 1–14 (2018).
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).
Kauffman, K. M. & Polz, M. F. Streamlining standard bacteriophage methods for higher throughput. MethodsX 5, 159–172 (2018).
Lomb, N. R. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462 (1976).
Scargle, J. Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853 (1983).
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).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).
Alonge, M. et al. Automated assembly scaffolding using RagTag elevates a new tomato system for high-throughput genome editing. Genome Biol. 23, 258 (2022).
Gilchrist, C. L. M. & Chooi, Y. H. clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics 37, 2473–2475 (2021).
Schwengers, O. et al. Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification: Find out more about Bakta, the motivation, challenges and applications, here. Microb. Genomics 7, 1–13 (2021).
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).
Garcillan-Barcia, M. P., Redondo-Salvo, S., Vielva, L. & de la Cruz, F. MOBscan: Automated annotation of MOB relaxases. Methods Mol. Biol. 2075, 295–308 (2020).
Bouras, G. et al. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics 39, 1–4 (2023).
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, 1–16 (2011).
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).
Steinegger, M. & Soding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).
Pfeifer, E. & Rocha, E. P. C. Phage-plasmids promote recombination and emergence of phages and plasmids. Nat. Commun. 15, 1545 (2024).
Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: Scientific containers for mobility of compute. PLOS ONE 12, 1–20 (2017).
Néron, B. et al. MacSyFinder v2: Improved modelling and search engine to identify molecular systems in genomes. Peer Commun. J. 3, 1–21 (2023).
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).
Gautreau, G. et al. PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph. PLOS Comput. Biol. 16, 1–27 (2020).
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).
Payne, L. J. et al. PADLOC: a web server for the identification of antiviral defence systems in microbial genomes. Nucleic Acids Res. 50, W541–W550 (2022).
Pal, C., Bengtsson-Palme, J., Rensing, C., Kristiansson, E. & Larsson, D. G. BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res. 42, D737–D743 (2014).
Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 51, D690–D699 (2023).
Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).
Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50, D912–D917 (2022).
Perrin, A. & Rocha, E. P. C. PanACoTA: a modular tool for massive microbial comparative genomics. NAR Genomics Bioinforma. 3, 1–12 (2021).
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).
Tonkin-Hill, G. et al. Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe. Genome Res. 33, 129–140 (2023).
Ho, L. & Ane, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).
Revell, L. J. phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ 12, e16505 (2024).
Bruen, T. C., Philippe, H. & Bryant, D. A simple and robust statistical test for detecting the presence of recombination. Genetics 172, 2665–2681 (2006).
Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15–e15 (2015).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).
Pupko, T., Pe’er, I., Shamir, R. & Graur, D. A fast algorithm for joint reconstruction of ancestral amino acid sequences. Mol. Biol. Evol. 17, 890–896 (2000).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLOS Comput. Biol. 15, e1006650 (2019).
Baele, G. et al. Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty. Mol. Biol. Evol. 29, 2157–2167 (2012).
Baele, G., Li, W. L., Drummond, A. J., Suchard, M. A. & Lemey, P. Accurate model selection of relaxed molecular clocks in bayesian phylogenetics. Mol. Biol. Evol. 30, 239–243 (2013).
Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).
Drummond, A. J., Rambaut, A., Shapiro, B. & Pybus, O. G. Bayesian coalescent inference of past population dynamics from molecular sequences. Mol. Biol. Evol. 22, 1185–1192 (2005).
Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
Blum, M. et al. InterPro: the protein sequence classification resource in 2025. Nucleic Acids Res. 53, D444–D456 (2025).
Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).
Fillol-Salom, A. et al. Hijacking the Hijackers: Escherichia coli Pathogenicity Islands Redirect Helper Phage Packaging for Their Own Benefit. Mol. Cell 75, 1020–1030.e1024 (2019).
Acknowledgements
We thank Bruno Petton (Ifremer Brest) for providing oyster juveniles and overseeing in situ experiments, which made the time-series sampling possible. We are grateful to Chloé Berger, Pauline Daszkowski, Justine Groseille, Théo Foutel Rodier, Étienne Levêque, and Mariam Mamba (GV team, Station Biologique de Roscoff, France) for their assistance during sampling and collections, and Carine Diarra (Le Roux lab, Montréal) for technical help. We thank Jorge Moura de Sousa for helpful discussions concerning the biology and classification of phage-satellites and Julien Guglielmini for help running wGRR/GRIS. We thank Léa Verena Zinsli and Otto X. Cordero for valuable comments on the manuscript, and Illumina for reduced pricing of sequencing kits. Editorial support from Stephen Matheson (Life Science Editors) is also warmly acknowledged. This research was enabled in part by the computational resources of Calcul Quebec and the Digital Research Alliance of Canada.
This work was supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 884988, Advanced ERC “DYNAMIC”), the Canada Excellence Research Chairs Program (CERC-2022-00051), and the Fonds de recherche du Québec – Nature et technologies (FRQ-NT, Fonds des leaders John-R.-Evans, grant 44584) awarded to FLR. Substantial support was provided by the Agence Nationale de la Recherche (ANR-20-CE35-0014 “RESISTE”) and the Fonds UdeM-Pasteur pour la découverte de nouveaux antibiotiques et antibactériens, a philanthropic fund at the Courtois Institute in Biomedical Innovation, Faculty of Medicine, University of Montreal, to EPCR and FLR. Biomics Platform, C2RT, Institut Pasteur, Paris, France, is supported by France Génomique (ANR-10-INBS-09) and IBISA (EPCR, LM, and MM).
Author information
Authors and Affiliations
Contributions
F.L.R. conceived the study, F.L.R. and E.P.C.R. supervised the project, and secured funding. K.C., D.P., D.C.G., Y.L., L.M. and F.L.R. conducted the experiments. D.P. and D.C.G. contributed equally. J.L., D.G., C.B. and E.P.C.R. performed the genomic analyses. J.L., KC, DP, DCG, MM, EPCR, and FLR analyzed the data. J.L., E.P.C.R. and F.L.R. wrote the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks William Chang and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Liang, J., Cahier, K., Piel, D. et al. Complex temporal dynamics of phage-bacteria populations in an animal-associated marine system. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71398-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-71398-9