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Phage resistance mutations in a marine bacterium impact biogeochemically relevant cellular processes

Nature Microbiology volume 11, pages 195–210 (2026)Cite this article

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Abstract

Phage–bacteria interactions shape ecology and biogeochemistry across biomes. Resistance, arising from their evolutionary arms race, is well documented for receptor mutations, but other resistance mechanisms and their ecological implications remain unexplored. Here we isolated, sequenced and characterized 13 phage-resistant mutants of marine Cellulophaga baltica (Flavobacteriia). Mechanistically, mutations in surface proteins provided broad and complete extracellular resistance against multiple phages through decreased adsorption. Intracellular mutations affecting serine, glycine and threonine metabolism produced narrower resistance against a single phage, permitting viral DNA replication, and, in one mutant, were shown to be lipid mediated. Putative ecosystem impacts inferred from in vitro experiments include: (1) altered carbon utilization for all mutants, but especially by surface ones, (2) increased metabolite secretion for one modelled intracellular mutant (including experimentally verified acetate) and (3) increased ‘stickiness’ for all mutants, with surface mutants also sedimenting faster. Our findings highlight new resistance mechanisms and suggest that the phage–host arms race could result in ecosystem-level biogeochemical impacts in marine microorganisms.

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Fig. 1: Thirteen mutants were isolated from Cba18-WT infected with phi18:1 or phi18:4.
Fig. 2: Cba18 mutants have different infection patterns against a collection of 11 phages.
Fig. 3: A single mutation in the serine synthesis pathway reduces l-serine production and alters lipid composition in mutant 184f1.
Fig. 4: Mutation type alters ecologically relevant phenotypes for mutant strains.
Fig. 5: Conceptual model for how phage resistance mutations impact cellular- and ecosystem-scale functions.

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Data availability

The genomes of all previously published viral and wild-type bacterial strains used in this study are publicly deposited in the National Center for Biotechnology Information (Supplementary Table 2), and the raw reads of the 13 mutants were deposited under the accession number PRJNA1261028. The lipidomics data have been deposited at MassIVE under the study identifier MSV000098411 and are available via GitHub at https://github.com/cowusuansah/cba-mutant-fba-analysis.

Code availability

The KBase narrative used for genome annotation, model construction and gap-filling is publicly available at https://narrative.kbase.us/narrative/230154 and https://kbase.us/n/230154/1/. The complete FBA pipeline, including model files, media definitions, simulation scripts and analysis results, is available via GitHub at https://github.com/cowusuansah/cba-mutant-fba-analysis.

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Acknowledgements

We acknowledge A. Arroyo, E. Nitz and A. Fofana for help during laboratory work, M. Gittrich for phage-biology discussions, and G. Smith and D. Segrè for advice on FBA modelling. The University of Arizona Analytical and Biological Mass Spectrometry Facility is acknowledged for analytical chemistry expertise and support with data acquisition for acetate quantification. This work was supported by awards from the US National Science Foundation (awards OCE-2019589 and DBI-2022070) and US Department of Energy, Office of Science, Office of Biological and Environmental Research (awards DE-SC0020173 and DE-SC0023307) to M.B.S. and The Swedish Research Council (2022-04340) to K.H. This is C-CoMP Publication No. 79.

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Author notes

  1. These authors contributed equally: Marion Urvoy, Cristina Howard-Varona.

Authors and Affiliations

  1. Department of Microbiology, Ohio State University, Columbus, OH, USA

    Marion Urvoy, Cristina Howard-Varona, Carlos Owusu-Ansah, Marie Burris, Natalie Solonenko, Karna Gowda & Matthew B. Sullivan

  2. Center of Microbiome Science, Ohio State University, Columbus, OH, USA

    Marion Urvoy, Cristina Howard-Varona, Carlos Owusu-Ansah, Karna Gowda & Matthew B. Sullivan

  3. Biophysics Graduate Program, Ohio State University, Columbus, OH, USA

    Carlos Owusu-Ansah, Karna Gowda & Matthew B. Sullivan

  4. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Andrew J. Stai & Robert L. Hettich

  5. The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN, USA

    Andrew J. Stai

  6. Department of Environmental Science, University of Arizona, Tucson, AZ, USA

    John A. Bouranis & Malak M. Tfaily

  7. Center for Ecology and Evolution in Microbial Model Systems, Department of Biology and Environmental Science, Linnaeus University, Kalmar, Sweden

    Karin Holmfeldt

  8. Department of Chemistry and Biomedical Science, Linnaeus University, Kalmar, Sweden

    Karin Holmfeldt

  9. Department of Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, OH, USA

    Matthew B. Sullivan

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M.U. conceived, designed and performed experiments, analysed the data, and wrote the paper. C.O.-A. and A.J.S. performed the experiments and analysed the data. J.A.B., M.B. and N.S. performed the experiments. R.L.H. and M.M.T. conceived and designed the experiments and contributed materials/analysis tools. K.H. conceived and designed and performed the experiments. C.H.-V., K.G. and M.B.S. conceived and designed the experiments and wrote the paper.

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Correspondence to Matthew B. Sullivan.

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Extended data

Extended Data Table 1 Key values used during the growth simulation of Cba18-WT and 184f1 to assess acetate secretion
Full size table

Extended Data Fig. 1 Glycine complementation partially rescues phi18:4 infection in mutant 184f1.

(A) At 50 mM, glycine rescued the infection of phi18:4 in 184f1, while l-serine and l-threonine did not affect phage production. None of these three amino acids affected phage production in mutants 184d1 and 184e1. Growth of all strains was visibly affected at higher concentrations of glycine, l-serine, and l-threonine and thus concentrations higher than 50 mM could not be tested. (B) Glycine rescued the infection of mutant 184f1 above a threshold concentration of 25 mM, with similar effects between 25 and 50 mM. The titer of phage phi18:4 on Cba18-WT and mutant 184f1 are represented in black and green, respectively.

Extended Data Fig. 2 FBA-derived assessment of metabolic bottlenecks for phage ‘building blocks’.

(A) Amino acid proportion in Cba18-WT and phi18:4, calculated using all coding regions for both organisms. The relative ratio in phi18:4 vs. Cba18-WT is indicated as dots, and amino acids are ranked according to this ratio. l-serine, glycine and l-threonine all have a ratio close to 1 (indicated by the grey vertical bar), suggesting that they would not be limiting for the translation of phi18:4’s proteins. (B-E) FBA-derived production fluxes of amino acids (B), nucleotides (C), cofactors (D), and lipids and lipid precursors (E). l-serine was overall the most impacted metabolites. Black dots represent the ratio of production between 184f1 and Cba18-WT for a given metabolite, and the grey dashed vertical lines represent a ratio of 1.

Extended Data Fig. 3 Experimental ranked doubling times for the mutants relative to Cba18-WT.

Doubling times show that surface mutants (circle) were more impacted than mutants with amino acid-related mutations (square), especially when they were grown on sugars (teal) compared to proteinaceous compounds (red). Doubling times obtained on complex MLB medium are plotted in black. Since the doubling times are relative to Cba18-WT, a doubling time of 1 (horizontal dashed line) indicates no difference between the mutant and Cba18-WT. Darker vertical bars indicate statistical significance (n = 3; two-sample,Welch’s t-test, PBH < 0.05), while lighter ones indicate no significance.

Extended Data Fig. 4 Mutant 184f1’s mutation induces changes in its FBA-modeled secretion profile compared to Cba18-WT, with similar profiles across the 7 modeled carbon sources.

(A) Flux difference between 184f1 and Cba18-WT. A grey square means that neither Cba18-WT nor mutant 184f1 secrete that metabolite in the given medium. (B) Normalized flux difference between 184f1 and Cba18-WT. Normalized flux difference was obtained by dividing the flux difference between Cba18-WT and 184f1 by the sum of their absolute values. Thus, a normalized difference of 1 means that the metabolite is exclusively secreted by mutant 184f1, while a normalized flux difference of –1 means an exclusive Cba18-WT secretion.

Extended Data Fig. 5 Experimental specific acetate ratio between mutant 184f1 and Cba18-WT showed good agreement with the simulated ratio, derived from FBA secretion fluxes.

To compare experimental acetate concentrations to FBA acetate secretion fluxes, we simulated an exponential growth for Cba18-WT and 184f1 –as close as possible to the experimental conditions- and integrated the instantaneous acetate secretion rate over time, assuming no reuptake. Both experiment and simulation were done using D-glucose as a single C source, with key values during the experiment and growth simulation presented in Extended Data Table 1. Simulation of Cba18-WT and 184f1’s exponential growth and acetate concentration.

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Supplementary table legends 1–7, Figs. 1–12, Results and References.

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Supplementary Tables 1–7.

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Urvoy, M., Howard-Varona, C., Owusu-Ansah, C. et al. Phage resistance mutations in a marine bacterium impact biogeochemically relevant cellular processes. Nat Microbiol 11, 195–210 (2026). https://doi.org/10.1038/s41564-025-02202-5

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