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Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria

Nature Microbiology volume 7, pages 1210–1220 (2022)Cite this article

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Abstract

Vibrio cholerae biotype El Tor is perpetuating the longest cholera pandemic in recorded history. The genomic islands VSP-1 and VSP-2 distinguish El Tor from previous pandemic V. cholerae strains. Using a co-occurrence analysis of VSP genes in >200,000 bacterial genomes we built gene networks to infer biological functions encoded in these islands. This revealed that dncV, a component of the cyclic-oligonucleotide-based anti-phage signalling system (CBASS) anti-phage defence system, co-occurs with an uncharacterized gene vc0175 that we rename avcD for anti-viral cytodine deaminase. We show that AvcD is a deoxycytidylate deaminase and that its activity is post-translationally inhibited by a non-coding RNA named AvcI. AvcID and bacterial homologues protect bacterial populations against phage invasion by depleting free deoxycytidine nucleotides during infection, thereby decreasing phage replication. Homologues of avcD exist in all three domains of life, and bacterial AvcID defends against phage infection by combining traits of two eukaryotic innate viral immunity proteins, APOBEC and SAMHD1.

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Fig. 1: AvcD-induced filamentation is inhibited by sRNA AvcI.
Fig. 2: AvcD is a DCD.
Fig. 3: AvcI–AvcD is a toxin–antitoxin system.
Fig. 4: avcID homologues provide phage defence.
Fig. 5: AvcD mediates nucleotide pool depletion.
Fig. 6: Model for AvcID-based anti-phage activity in bacteria.

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Data availability

Data supporting the findings of this study are available here: https://figshare.com/articles/dataset/AvcID_NatureMicrobio_Full_Datasets_zip/19746904. Source data are provided with this paper.

References

  1. Dziejman, M. et al. Comparative genomic analysis of Vibrio cholerae: genes that correlate with cholera endemic and pandemic disease. Proc. Natl Acad. Sci. USA 99, 1556–1561 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Hu, D. et al. Origins of the current seventh cholera pandemic. Proc. Natl Acad. Sci. USA 113, E7730–E7739 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. O’Shea, Y. A. et al. The Vibrio seventh pandemic island-II is a 26.9 kb genomic island present in Vibrio cholerae El Tor and O139 serogroup isolates that shows homology to a 43.4 kb genomic island in V. vulnificus. Microbiology 150, 4053–4063 (2004).

    Article  PubMed  CAS  Google Scholar 

  4. Nusrin, S. et al. Peruvian Vibrio cholerae O1 El Tor strains possess a distinct region in the Vibrio seventh pandemic island-II that differentiates them from the prototype seventh pandemic El Tor strains. J. Med. Microbiol. 58, 342–354 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. Davies, B. W., Bogard, R. W., Young, T. S. & Mekalanos, J. J. Coordinated regulation of accessory genetic elements produces cyclic di-nucleotides for V. cholerae virulence. Cell 149, 358–370 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Murphy, S. G. et al. Endopeptidase regulation as a novel function of the zur-dependent zinc starvation response. MBio 10, e02620-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jaskólska, M., Adams, D. W. & Blokesch, M. Two defence systems eliminate plasmids from seventh pandemic Vibrio cholerae. Nature 604, 323–329 (2022).

    Article  PubMed  CAS  Google Scholar 

  8. Beyhan, S., Tischler, A. D., Camilli, A. & Yildiz, F. H. Differences in gene expression between the classical and El Tor biotypes of Vibrio cholerae O1. Infect. Immun. 74, 3633–3642 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cohen, D. et al. Cyclic GMP–AMP signalling protects bacteria against viral infection. Nature 574, 691–695 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Severin, G. B. et al. Direct activation of a phospholipase by cyclic GMP–AMP in El Tor Vibrio cholerae. Proc. Natl Acad. Sci. USA 115, E6048–E6055 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yen, M., Cairns, L. S. & Camilli, A. A cocktail of three virulent bacteriophages prevents Vibrio cholerae infection in animal models. Nat. Commun. 8, 14187 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. O’Hara, B. J., Barth, Z. K., McKitterick, A. C. & Seed, K. D. A highly specific phage defense system is a conserved feature of the Vibrio cholerae mobilome. PLoS Genet. 13, e1006838 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Silva-Valenzuela, C. A. & Camilli, A. Niche adaptation limits bacteriophage predation of Vibrio cholerae in a nutrient-poor aquatic environment. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1810138116 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Severin, G. B. et al. A broadly conserved deoxycytidine deaminase protects bacteria from phage infection. bioRxiv https://doi.org/10.1101/2021.03.31.437871 (2021).

  15. Waldron, E. J. et al. Structural basis of DSF recognition by its receptor RpfR and its regulatory interaction with the DSF synthase RpfF. PLoS Biol. 17, e3000123 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Leipe, D. D., Koonin, E. V. & Aravind, L. Evolution and classification of P-loop kinases and related proteins. J. Mol. Biol. 333, 781–815 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Handee, W. et al. An energy-independent pro-longevity function of triacylglycerol in yeast. PLoS Genet. 12, e1005878 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Boussardon, C. et al. The cytidine deaminase signature HxE(x) n CxxC of DYW1 binds zinc and is necessary for RNA editing of ndhD-1. New Phytol. 203, 1090–1095 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Iyer, L. M., Zhang, D., Rogozin, I. B. & Aravind, L. Evolution of the deaminase fold and multiple origins of eukaryotic editing and mutagenic nucleic acid deaminases from bacterial toxin systems. Nucleic Acids Res. 39, 9473–9497 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vértessy, B. G. & Tóth, J. Keeping uracil out of DNA: physiological role, structure and catalytic mechanism of dUTPases. Acc. Chem. Res. 42, 97–106 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Khan, S. R. & Kuzminov, A. Thymineless death in Escherichia coli is unaffected by chromosomal replication complexity. J. Bacteriol. 201, e00797-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Itsko, M. & Schaaper, R. M. dGTP starvation in Escherichia coli provides new insights into the thymineless-death phenomenon. PLoS Genet. 10, e1004310 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Short, F. L., Akusobi, C., Broadhurst, W. R. & Salmond, G. P. C. The bacterial type III toxin–antitoxin system, ToxIN, is a dynamic protein–RNA complex with stability-dependent antiviral abortive infection activity. Sci Rep. 8, 1013 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Guegler, C. K. & Laub, M. T. Shutoff of host transcription triggers a toxin–antitoxin system to cleave phage RNA and abort infection. Mol. Cell 81, 2361–2373.e9 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Krishnan, A., Iyer, L. M., Holland, S. J., Boehm, T. & Aravind, L. Diversification of AID/APOBEC-like deaminases in metazoa: multiplicity of clades and widespread roles in immunity. Proc. Natl Acad. Sci. USA 115, E3201–E3210 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Lada, A. G. et al. Mutator effects and mutation signatures of editing deaminases produced in bacteria and yeast. Biochemistry 76, 131–146 (2011).

    CAS  PubMed  Google Scholar 

  28. Conticello, S. G., Thomas, C. J. F., Petersen-Mahrt, S. K. & Neuberger, M. S. Evolution of the AID/APOBEC family of polynucleotide (deoxy)cytidine deaminases. Mol. Biol. Evol. 22, 367–377 (2005).

    Article  CAS  PubMed  Google Scholar 

  29. Dy, R. L., Przybilski, R., Semeijn, K., Salmond, G. P. C. C. & Fineran, P. C. A widespread bacteriophage abortive infection system functions through a type IV toxin–antitoxin mechanism. Nucleic Acids Res. 42, 4590–4605 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Koga, M., Otsuka, Y., Lemire, S. & Yonesaki, T. Escherichia coli rnlA and rnlB compose a novel toxin–antitoxin system. Genetics 187, 123–130 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pecota, D. C. & Wood, T. K. Exclusion of T4 phage by the hok/sok killer locus from plasmid R1. J. Bacteriol. 178, 2044–2050 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Makarova, K. S., Wolf, Y. I., Snir, S. & Koonin, E. V. Defense islands in bacterial and archaeal genomes and prediction of novel defense systems. J. Bacteriol. 193, 6039–6056 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tal, N. et al. Antiviral defense via nucleotide depletion in bacteria. bioRxiv https://doi.org/10.1101/2021.04.26.441389 (2021).

  34. Harms, A., Brodersen, D. E., Mitarai, N. & Gerdes, K. Toxins, targets, and triggers: an overview of toxin–antitoxin biology. Mol. Cell 70, 768–784 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Ayinde, D., Casartelli, N. & Schwartz, O. Restricting HIV the SAMHD1 way: through nucleotide starvation. Nat. Rev. Microbiol. 10, 675–680 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Stavrou, S. & Ross, S. R. APOBEC3 proteins in viral immunity. J. Immunol. 195, 4565–4570 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Helene Thelin, K. & Taylor, R. K. Toxin-coregulated pilus, but not mannose-sensitive hemagglutinin, is required for colonization by Vibrio cholerae O1 El Tor biotype and O139 strains. Infect. Immun. 64, 2853–2856 (1996).

    Article  Google Scholar 

  38. Skorupski, K. & Taylor, R. K. Positive selection vectors for allelic exchange. Gene 169, 47–52 (1996).

    Article  CAS  PubMed  Google Scholar 

  39. Bose, J. L., Rosenberg, C. S. & Stabb, E. V. Effects of luxCDABEG induction in Vibrio fischeri: enhancement of symbiotic colonization and conditional attenuation of growth in culture. Arch. Microbiol. 190, 169–183 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Fürste, J. P. et al. Molecular cloning of the plasmid RP4 primase region in a multi-host-range tacP expression vector. Gene 48, 119–131 (1986).

    Article  PubMed  Google Scholar 

  41. Dunn, A. K., Millikan, D. S., Adin, D. M., Bose, J. L. & Stabb, E. V. New rfp- and pES213-derived tools for analyzing symbiotic Vibrio fischeri reveal patterns of infection and lux expression in situ. Appl. Environ. Microbiol. 72, 802–810 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Edelheit, O., Hanukoglu, A. & Hanukoglu, I. Simple and efficient site-directed mutagenesis using two single-primer reactions in parallel to generate mutants for protein structure–function studies. BMC Biotechnol. 9, 61 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Kim, P.-J. & Price, N. D. Genetic co-occurrence network across sequenced microbes. PLoS Comput. Biol. 7, e1002340 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28, 1947–1951 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lüdeke, C. H. M., Kong, N., Weimer, B. C., Fischer, M. & Jones, J. L. Complete genome sequences of a clinical isolate and an environmental isolate of Vibrio parahaemolyticus. Genome Announc. 3, e00216–15 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Lutgring, J. D. et al. FDA–CDC antimicrobial resistance isolate bank: a publicly available resource to support research, development, and regulatory requirements. J. Clin. Microbiol. 56, e01415-17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Evans, D. J. & Evans, D. G. Three characteristics associated with enterotoxigenic Escherichia coli isolated from man. Infect. Immun. 8, 322–328 (1973).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Madeira, F., Madhusoodanan, N., Lee, J., Tivey, A. R. N. & Lopez, R. Using EMBL-EBI Services via Web Interface and Programmatically via Web Services. Curr. Protoc. Bioinforma. 66, e74 (2019).

    Article  Google Scholar 

  49. Will, S., Joshi, T., Hofacker, I. L., Stadler, P. F. & Backofen, R. LocARNA-P: accurate boundary prediction and improved detection of structural RNAs. RNA 18, 900–914 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Burke, J. T., Chen, S. Z., Sosinski, L. M., Johnston, J. B. & Ravi, J. MolEvolvR: a web-app for characterizing proteins using molecular evolution and phylogeny. bioRxiv https://doi.org/10.1101/2022.02.18.461833 (2022).

  51. Lassmann, T. Kalign 3: multiple sequence alignment of large data sets. Bioinformatics https://doi.org/10.1093/bioinformatics/btz795 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bonatesta, E., Horejs-Kainrath, C. & Bodenhofer, U. Multiple sequence alignment. https://www.bioinf.jku.at/software/msa/ (2020)

  54. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Rambaut, A. FigTree. https://tree.bio.ed.ac.uk/software/figtree/ (2009).

  56. Paradis, E. et al. ape: Analyses of phylogenetics and evolution. https://ape-package.ird.fr/ (2020).

  57. Fernandez, N. L. et al. Vibrio cholerae adapts to sessile and motile lifestyles by cyclic di-GMP regulation of cell shape. Proc. Natl Acad. Sci. USA 117, 29046–29054 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  59. Dryselius, R., Izutsu, K., Honda, T. & Iida, T. Differential replication dynamics for large and small Vibrio chromosomes affect gene dosage, expression and location. BMC Genomics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Koirala, D., Lewicka, A., Koldobskaya, Y., Huang, H. & Piccirilli, J. A. Synthetic antibody binding to a preorganized RNA domain of hepatitis C virus internal ribosome entry site inhibits translation. ACS Chem. Biol. 15, 205–216 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Kao, C., Rüdisser, S. & Zheng, M. A simple and efficient method to transcribe RNAs with reduced 3′ heterogeneity. Methods 23, 201–205 (2001).

    Article  CAS  PubMed  Google Scholar 

  62. Dong, H. et al. An enzymatic assay for high-throughput screening of cytidine-producing microbial strains. PLoS ONE 10, e0121612 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank S. Manning (STEC Center, Michigan State University), J. Jones (US FDA), A. Brown (US CDC) and M. Hao Kuo (Michigan State University) for providing us with E. coli ETEC, P. mirabilis, V. parahaemolyticus and S. cerevisiae strains, respectively, and M. Laub (Massachusetts Institute of Technology) for providing us with coliphages. We thank K. Yu and D. Pyeon for valuable suggestions and D. Jones and L. Chen from the MSU RTSF mass spectrometry facility core for their technical support. Figure 6 was generated using software from Biorender.com. This work was supported by National Institutes of Health (NIH) grants GM109259, GM110444 and AI158433 to C.M.W. and M.B.N., GM139537 and AI143098 to C.M.W., National Science Foundation (NSF) grant DBI-0939454 to C.M.W. and E.M.T., NIH grant GM110185 and the NSF CAREER Award 1750125 to K.N.P. and NSF Graduate Research Fellowship grant no. 1842399 to C.A.E. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Author notes

  1. Geoffrey B. Severin

    Present address: Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA

  2. Evan J. Waldron

    Present address: Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

  3. These authors contributed equally: Brian Y. Hsueh, Geoffrey B. Severin.

Authors and Affiliations

  1. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI, USA

    Brian Y. Hsueh, Alex J. Wessel, Christopher R. Rhoades, Janani Ravi & Christopher M. Waters

  2. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

    Geoffrey B. Severin, John A. Dover & Kristin N. Parent

  3. Department of Biological Sciences, Institute for Interdisciplinary Data Sciences, Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID, USA

    Clinton A. Elg & Eva M. Top

  4. Department of Microbiology, Biochemistry, and Molecular Genetics, New Jersey Medical School, Rutgers University, Newark, NJ, USA

    Evan J. Waldron, Abhiruchi Kant & Matthew B. Neiditch

  5. Department of Mathematics and Statistical Sciences, University of Idaho, Moscow, ID, USA

    Benjamin J. Ridenhour

  6. Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, USA

    Janani Ravi

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Extended data

Extended Data Fig. 1 VSP-1 and VSP-2 schematic and predicted gene networks (MRS).

Cartoon of VSP-1 (A) and VSP-2 (B) from El Tor V. cholerae N16961 and gene network predictions from GeneCoOccurrence. Arrows indicate the highest partial correlation Wij each gene has to another (ovals). Two arrows are presented pointing in opposing directions where the highest correlation Wij is reciprocal between two genes. MRS = maximum relatedness subnetwork.

Extended Data Fig. 2 Complementation of various ig222 constructs to prevent AvcD induced cell filamentation.

Cell length distributions of Δig222 V. cholerae expressing pAvcD. All cell length distributions represent ~750-1000 cells measured per strain (n = 3 biological samples), with summary statistics: mean (diamonds), median (horizontal black line), interquartile range (box), and data below and above the interquartile range (vertical lines). Different letters indicate significant differences at p < 0.05, according to Two-way ANOVA with Tukey’s multiple comparison post-hoc test.

Extended Data Fig. 3 AvcD C-terminal 6x Histidine fusion maintains the same activity as the WT AvcD enzyme and the presence of avcI does not reduce the abundance of AvcD.

(A) Representative images of WT V. cholerae and Δig222 cultures maintaining an empty vector plasmid (pVector1) or Ptac-inducible avcD-6xHIS plasmid (pAvcD6xHis) grown in the presence of 100 µM IPTG for 2 h. Cells were stained with FM4-64 prior to imaging and performed in biological triplicate. (B) Representative coomassie stained PAGE gel (top) and matched anti-6x His antibody Western blot (bottom) of whole cell lysates normalized to total protein from V. cholerae WT and Δig222 cultures maintaining pVector1 or pAvcD6xHis. Black triangles correspond to AvcD6xHis (60.6 kDa). Analysis was performed in biological triplicate and the relative signal intensity (C) was the determined by comparing the intensities of AvcD6xHis from paired WT and Δig222 lysates probed on the same blots. Data represent the mean ± SEM of three biological replicate. Statistical significance was determined using two-sided Student’s t-test. P values between WT and Δig222 is 0.185. ns indicate not significant.

Extended Data Fig. 4 AvcD-AvcI complex formation in solution and Denaturing urea PAGE analysis of AvcI and AvcI-RC.

(A) AvcD forms a complex with AvcI in an AvcD concentration-dependent manner as determined by EMSA. Trace quantities of AvcI reverse complement (AvcI-RC) binding to AvcD is observed. (B) AvcI and AvcI-RC run at essentially equivalent molecular weights on a 7 M urea denaturing PAGE. Low range ssRNA ladder (NEB). This was performed at least three times, yielding similar results.

Source data

Extended Data Fig. 5 multiple sequence alignment of AvcD homologs and AvcI homologs explored in this study.

(A) Amino acid alignment of the V. cholerae AvcD and three homologs using EMBL-EBI ClustalW48. ‘*’ indicates 100% identity, ‘:’ indicates >75%, and ‘.’ Indicates >50% similarity. Black triangles indicate conserved residues in V. cholerae AvcD targeted for site-directed mutagenesis. (B) Nucleotide alignment of V. cholerae AvcI and three homologs using LocARNA49. The average secondary structure is indicated in dot-bracket notation (top). Consensus identities are correlated with the height of the bars below the corresponding nucleotide. Compatible base pairs are colored according to the number of different types C-G (1), G-C (2), A-U (3), U-A (4), G-U (5) or U-G (6) of compatible base pairs in the corresponding columns. The color saturation decreases with the number of incompatible base pairs.

Extended Data Fig. 6 Cross-species inhibition of avcD and avcI homologs.

Cell length distributions of E. coli co-expressing various combinations of Ptac-inducible plasmids encoding homologs of avcD and avcI. All cell length distributions represent ~1000-3000 cells measured per strain (n = 3 biological samples), with summary statistics: mean (diamonds), median (horizontal black line), interquartile range (box), and data below and above the interquartile range (vertical lines). Different letters indicate significant differences at p < 0.05, according to Two-way ANOVA with Tukey’s post-hoc test. VC = Vibrio cholerae, VP = Vibrio parahaemolyticus, PM = Proteus mirabilis, ETEC = E. coli ETEC.

Extended Data Fig. 7 Phylogenetic analysis and domain architectures of the six AvcD query proteins.

(A) Phylogenetic tree of AvcD homologs from representative phyla across the tree of life. Stars indicate the six proteobacterial starting points for the homology search, as well as the eukaryotic Saccharomyces cerevisiae dcd1 (triangle). (B) Domain architecture and secondary structure predictions for the six proteobacterial starting points (query proteins) were predicted using InterProScan (Methods). Results from six main analyses are shown here for the query proteins: Gene3D (including CATH structure database), Pfam, ProSiteProfiles, PANTHER, and SUPERFAMILY protein domain profile databases, and MobiDBLite for disorder prediction. No transmembrane regions (using TMHMM) or membrane/extracellular localization were predicted for any of the proteins (using Phobius); hence not shown. Numbers (bottom) indicate the amino acid position of predicted domains and features. (C) Cell length distributions of E. coli expressing pAvcD, a Ptac-inducible plasmid encoding dcd1 from S. cerevisiae (pDcd1Sc), or pVector1. All cell length distributions represent ~1000-3000 cells measured per strain (n = 3 biological samples), with summary statistics: mean (diamonds), median (horizontal black line), interquartile range (box), and data below and above the interquartile range (vertical lines). Different letters indicate significant differences at p < 0.05, according to Two-way ANOVA with Tukey’s post-hoc test.

Extended Data Fig. 8 Mutations in conserved residues of AvcD do not affect the stability or function of the protein.

(A) Phyre2 (ref. 18) predicted structure of AvcD from V. cholerae El Tor. Insets highlight conserved residues of the PLN (top) and DCD (bottom) domains selected for mutagenesis. (B) Representative Coomassie stained gel (top) and anti-6x His antibody Western blot (bottom) of whole cell lysates from E. coli BL21(DE3) cells maintaining an empty vector (pVector6xHis), inducible C-terminal 6x histidine tagged avcD (WT) or avcD variants (S52K, D162A + Q163A, E384A, and C411A + C414A) grown in the presence of 1 mM IPTG for 3 h. Sample inputs were normalized by culture OD600 and resolved by SDS-PAGE. Three biological replicates of each strain were analyzed with similar results. Black triangles correspond to the predicted molecular weight of the AvcD tagged fusions (60.6 kDa). M = molecular weight marker. (C) V. cholerae mutant expressing the indicated AvcD variants. ori/ter ratios of Chromosome 1 in Δig222 V. cholerae strains expressing the indicated pAvcD construct and quantified using qRT-PCR. Each bar represents the mean ± SEM, n=3. Different letters indicate significant differences (n=3) at p < 0.05, according to Two-way ANOVA with Tukey’s post-hoc test. (D) Representative images of Δig222 cultures maintaining an empty vector plasmid pVector 1 or pAvcD grown in the presence of 100 µM IPTG for 8 h. Cells were stained with FM4-64 prior to imaging and performed in biological triplicate. (E) Relative difference in avcD expression between Δig222 and WT V. cholerae at three different growth phases using qRT-PCR and an endogenous gyrA control. Data represent the mean ± SEM of three biological replicates.

Source data

Extended Data Fig. 9 Cessation of global translation, by treatment with spectinomycin, does not liberate AvcD enzymatic activity.

Intracellular abundance of dCTP (A), dCMP (B), dUTP (C), and dUMP (D) of WT and ΔavcD V. cholerae during spectinomycin treatment (200 μg/mL) measured by UPLC-MS/MS. Data represent the mean ± SEM of three biological replicate cultures. No statistically significant differences in nucleotide concentrations were observed between strains at any time point as determined by Two-way ANOVA with Two-way ANOVA with Šídák’s multiple-comparison test.

Extended Data Fig. 10 Ectopic expression of DncV and AvcD does not lead to filamentation in the ΔcapV mutant of V. cholerae.

Cell length distributions measured from three biological replicates of ΔcapV V. cholerae cultures co-expressing either two empty vectors, pDncV and an empty vector, pAvcD and an empty vector, or pDncV and pAvcD grown in the presence of 100 µM IPTG for 8 h. Distributions represent ~1200-1700 cells measured per strain (n=3 biological samples). Different letters indicate significant differences at p < 0.05, according to Two-way ANOVA with Tukey’s post-hoc test.

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Source data

Source Data Fig. 1

Unprocessed EMSA.

Source Data Fig. 3

Unprocessed agarose gel of the PCR to determine operon structure, and a Coomassie stain and western blot.

Source Data Extended Data Fig. 4

Unprocessed EMSA and a denatured acrylamide gel to show that the probes are the same size.

Source Data Extended Data Fig. 8

Unprocessed Coomassie stain and western blot.

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Hsueh, B.Y., Severin, G.B., Elg, C.A. et al. Phage defence by deaminase-mediated depletion of deoxynucleotides in bacteria. Nat Microbiol 7, 1210–1220 (2022). https://doi.org/10.1038/s41564-022-01162-4

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