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Widespread stop-codon recoding in bacteriophages may regulate translation of lytic genes

Abstract

Bacteriophages (phages) are obligate parasites that use host bacterial translation machinery to produce viral proteins. However, some phages have alternative genetic codes with reassigned stop codons that are predicted to be incompatible with bacterial translation systems. We analysed 9,422 phage genomes and found that stop-codon recoding has evolved in diverse clades of phages that infect bacteria present in both human and animal gut microbiota. Recoded stop codons are particularly over-represented in phage structural and lysis genes. We propose that recoded stop codons might function to prevent premature production of late-stage proteins. Stop-codon recoding has evolved several times in closely related lineages, which suggests that adaptive recoding can occur over very short evolutionary timescales.

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Fig. 1: Identification of recoded phage in human and animal microbiomes.
Fig. 2: Phylogeny of recoded phages and suppressor tRNA usage.
Fig. 3: Evolutionary relationships among phages according to genetic code.
Fig. 4: Preferential recoding of lysis-related genes in recoded phages.
Fig. 5: Recoded prophages integrated into bacterial genomes.
Fig. 6: A model for recoding in the phage life cycle.

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Data availability

Accessions for MIUVIG-compliant genomes27 and associated reads for alternatively coded phages and relatives are provided in Supplementary Table 3. Genomes and predicted proteins for alternatively coded phages and relatives, the terminase phylogenetic tree file, closely related Agate and crAss-like genomes, and untrimmed lysogenic contigs are available through Zenodo (10.5281/zenodo.6410225). The UniRef100 database is available through ftp.uniprot.org/pub/databases/uniprot/uniref/uniref100. Source data are provided with this paper.

Code availability

Python script used to analyse coding density and predict genetic code is available on GitHub: https://github.com/borgesadair1/AC_phage_analysis/releases/tag/v1.0.0.

References

  1. Crick, F. H. The origin of the genetic code. J. Mol. Biol. 38, 367–379 (1968).

    Article  CAS  PubMed  Google Scholar 

  2. Knight, R. D., Freeland, S. J. & Landweber, L. F. Rewiring the keyboard: evolvability of the genetic code. Nat. Rev. Genet. 2, 49–58 (2001).

    Article  CAS  PubMed  Google Scholar 

  3. Horowitz, S. & Gorovsky, M. A. An unusual genetic code in nuclear genes of Tetrahymena. Proc. Natl Acad. Sci. USA 82, 2452–2455 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Caron, F. & Meyer, E. Does Paramecium primaurelia use a different genetic code in its macronucleus? Nature 314, 185–188 (1985).

    Article  CAS  PubMed  Google Scholar 

  5. Preer, J. R. Jr, Preer, L. B., Rudman, B. M. & Barnett, A. J. Deviation from the universal code shown by the gene for surface protein 51A in Paramecium. Nature 314, 188–190 (1985).

    Article  CAS  PubMed  Google Scholar 

  6. Keeling, P. J. & Doolittle, W. F. A non-canonical genetic code in an early diverging eukaryotic lineage. EMBO J. 15, 2285–2290 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schneider, S. U., Leible, M. B. & Yang, X. P. Strong homology between the small subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase of two species of Acetabularia and the occurrence of unusual codon usage. Mol. Gen. Genet. 218, 445–452 (1989).

    Article  CAS  PubMed  Google Scholar 

  8. Santos, M. A., Keith, G. & Tuite, M. F. Non-standard translational events in Candida albicans mediated by an unusual seryl-tRNA with a 5’-CAG-3’ (leucine) anticodon. EMBO J. 12, 607–616 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ohama, T. et al. Non-universal decoding of the leucine codon CUG in several Candida species. Nucleic Acids Res. 21, 4039–4045 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Inamine, J. M., Ho, K. C., Loechel, S. & Hu, P. C. Evidence that UGA is read as a tryptophan codon rather than as a stop codon by Mycoplasma pneumoniae, Mycoplasma genitalium, and Mycoplasma gallisepticum. J. Bacteriol. 172, 504–506 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yamao, F. et al. UGA is read as tryptophan in Mycoplasma capricolum. Proc. Natl Acad. Sci. USA 82, 2306–2309 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stamburski, C., Renaudin, J. & Bové, J. M. Mutagenesis of a tryptophan codon from TGG to TGA in the cat gene does not prevent its expression in the helical mollicute Spiroplasma citri. Gene 110, 133–134 (1992).

    Article  CAS  PubMed  Google Scholar 

  13. Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012).

    Article  CAS  PubMed  Google Scholar 

  14. Campbell, J. H. et al. UGA is an additional glycine codon in uncultured SR1 bacteria from the human microbiota. Proc. Natl Acad. Sci. USA 110, 5540–5545 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hanke, A. et al. Recoding of the stop codon UGA to glycine by a BD1-5/SN-2 bacterium and niche partitioning between Alpha- and Gammaproteobacteria in a tidal sediment microbial community naturally selected in a laboratory chemostat. Front. Microbiol. 5, 231 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Shulgina, Y. & Eddy, S. R. A computational screen for alternative genetic codes in over 250,000 genomes. eLife 10, e71402 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zinoni, F., Birkmann, A., Leinfelder, W. & Böck, A. Cotranslational insertion of selenocysteine into formate dehydrogenase from Escherichia coli directed by a UGA codon. Proc. Natl Acad. Sci. USA 84, 3156–3160 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Berry, M. J. et al. Recognition of UGA as a selenocysteine codon in type I deiodinase requires sequences in the 3’ untranslated region. Nature 353, 273–276 (1991).

    Article  CAS  PubMed  Google Scholar 

  19. Hao, B. et al. A new UAG-encoded residue in the structure of a methanogen methyltransferase. Science 296, 1462–1466 (2002).

    Article  CAS  PubMed  Google Scholar 

  20. Sun, J. et al. Recoding of stop codons expands the metabolic potential of two novel Asgardarchaeota lineages. ISME Commun 1, 30 (2021).

    Article  Google Scholar 

  21. Gomes, A. C. et al. A genetic code alteration generates a proteome of high diversity in the human pathogen Candida albicans. Genome Biol. 8, R206 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Ivanova, N. N. et al. Stop codon reassignments in the wild. Science 344, 909–913 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Devoto, A. E. et al. Megaphages infect Prevotella and variants are widespread in gut microbiomes. Nat. Microbiol. 4, 693–700 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Al-Shayeb, B. et al. Clades of huge phages from across Earth’s ecosystems. Nature 578, 425–431 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Yutin, N. et al. Analysis of metagenome-assembled viral genomes from the human gut reveals diverse putative CrAss-like phages with unique genomic features. Nat. Commun. 12, 1044 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Crisci, M. A. et al. Closely related Lak megaphages replicate in the microbiomes of diverse animals. iScience 24, 102875 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Roux, S. et al. Minimum information about an uncultivated virus genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Goltsman, D. S. A. et al. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res. 28, 1467–1480 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Obregon-Tito, A. J. et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nat. Commun. 6, 6505 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Rampelli, S. et al. Metagenome sequencing of the hadza hunter-gatherer gut microbiota. Curr. Biol. 25, 1682–1693 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Lou, Y. C. et al. Infant gut strain persistence is associated with maternal origin, phylogeny, and traits including surface adhesion and iron acquisition. Cell Rep. Med. 2, 100393 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  32. David, L. A. et al. Gut microbial succession follows acute secretory diarrhea in humans. mBio 6, e00381-15 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tung, J. et al. Social networks predict gut microbiome composition in wild baboons. eLife 4, e05224 (2015).

    Article  PubMed Central  Google Scholar 

  34. Munk, P. et al. A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds. J. Antimicrob. Chemother. 72, 385–392 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Andersen, V. D. et al. Predicting effects of changed antimicrobial usage on the abundance of antimicrobial resistance genes in finisher’ gut microbiomes. Prev. Vet. Med. 174, 104853 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Peters, S. L. et al. Validation that human microbiome phages use alternative genetic coding with TAG stop read as Q. Preprint at bioRxiv https://doi.org/10.1101/2022.01.06.475225 (2022).

  38. Osawa, S. & Jukes, T. H. Codon reassignment (codon capture) in evolution. J. Mol. Evol. 28, 271–278 (1989).

    Article  CAS  PubMed  Google Scholar 

  39. Berry, J., Rajaure, M., Pang, T. & Young, R. The spanin complex is essential for lambda lysis. J. Bacteriol. 194, 5667–5674 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Young, R. Phage lysis: three steps, three choices, one outcome. J. Microbiol. 52, 243–258 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Doermann, A. H. The intracellular growth of bacteriophages. I. Liberation of intracellular bacteriophage T4 by premature lysis with another phage or with cyanide. J. Gen. Physiol. 35, 645–656 (1952).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Heagy, F. C. The effect of 2,4-dinitrophenol and phage T2 on Escherichia coli B. J. Bacteriol. 59, 367–373 (1950).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Park, T., Struck, D. K., Dankenbring, C. A. & Young, R. The pinholin of lambdoid phage 21: control of lysis by membrane depolarization. J. Bacteriol. 189, 9135–9139 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hays, S. G. & Seed, K. D. Dominant Vibrio cholerae phage exhibits lysis inhibition sensitive to disruption by a defensive phage satellite. eLife 9, e53200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Cowe, E. & Sharp, P. M. Molecular evolution of bacteriophages: discrete patterns of codon usage in T4 genes are related to the time of gene expression. J. Mol. Evol. 33, 13–22 (1991).

    Article  CAS  Google Scholar 

  46. Yang, J. Y. et al. Degradation of host translational machinery drives tRNA acquisition in viruses. Cell. Syst. https://doi.org/10.1016/j.cels.2021.05.019 (2021).

  47. Durmaz, E. & Klaenhammer, T. R. Abortive phage resistance mechanism AbiZ speeds the lysis clock to cause premature lysis of phage-infected Lactococcus lactis. J. Bacteriol. 189, 1417–1425 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Zeng, L. et al. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141, 682–691 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Erez, Z. et al. Communication between viruses guides lysis–lysogeny decisions. Nature 541, 488–493 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Silpe, J. E. & Bassler, B. L. A host-produced quorum-sensing autoinducer controls a phage lysis–lysogeny decision. Cell 176, 268–280.e13 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Bailly-Bechet, M., Vergassola, M. & Rocha, E. Causes for the intriguing presence of tRNAs in phages. Genome Res. 17, 1486–1495 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Auslander, N., Gussow, A. B., Benler, S., Wolf, Y. I. & Koonin, E. V. Seeker: alignment-free identification of bacteriophage genomes by deep learning. Nucleic Acids Res. 48, e121 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. 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).

    Article  CAS  PubMed  Google Scholar 

  58. Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 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 

  60. Skennerton, C. T. minced: Mining CRISPRs in Environmental Datasets (GitHub, 2019).

  61. 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 

  62. Olm, M. tRep: Quick Get the Taxonomy of a Genome (Github, 2020).

  63. Darling, A. C. E., Mau, B., Blattner, F. R. & Perna, N. T. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bin Jang, H. et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat. Biotechnol. 37, 632–639 (2019).

    Article  CAS  Google Scholar 

  65. Hauser, M., Steinegger, M. & Söding, J. MMseqs software suite for fast and deep clustering and searching of large protein sequence sets. Bioinformatics 32, 1323–1330 (2016).

    Article  CAS  PubMed  Google Scholar 

  66. Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175 (2011).

    Article  PubMed  CAS  Google Scholar 

  67. Söding, J. Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951–960 (2005).

    Article  PubMed  Google Scholar 

  68. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Article  CAS  PubMed  Google Scholar 

  69. Katoh, K., Misawa, K., Kuma, K.-I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 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).

    Article  CAS  PubMed  Google Scholar 

  72. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Zimmermann, L. et al. A completely reimplemented MPI bioinformatics toolkit with a new HHpred server at its core. J. Mol. Biol. 430, 2237–2243 (2018).

    Article  CAS  PubMed  Google Scholar 

  74. 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 

  75. Gilchrist, C. L. M. & Chooi, Y.-H. Clinker & clustermap.js: Automatic generation of gene cluster comparison figures. Bioinformatics https://doi.org/10.1093/bioinformatics/btab007 (2021).

    Article  PubMed  Google Scholar 

  76. Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Y. Song, J. Cate, K. Seed, G. Chadwick, L.-X. Chen, J. West-Roberts and S. Diamond for helpful discussions; K. K. L. Law and J. Hoff for technical support. This work was supported by a Miller Basic Research Fellowship to A.L.B; an NSF Graduate Research Fellowship to B.A-S (No. DGE 1752814); and an NIH award RAI092531A, a Chan Zuckerberg Biohub award and Innovative Genome Institute funding to J.F.B.

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Contributions

A.L.B and J.F.B. developed the project, led analyses and wrote the manuscript with input from all authors. A.L.B, J.F.B, Y.C.L, R.S. and S.L. compiled the phage dataset. B.A-S assembled public metagenome data and provided support for phage genome analyses. Phage genomes were manually curated by J.F.B. P.I.P contributed to phage tRNA analyses. A.L.J. and Y.C.L contributed to design of statistical analyses. J.M.S. contributed DNA samples from animal and arsenic-exposed human gut microbiomes.

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Correspondence to Jillian F. Banfield.

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J.F.B. is a founder of Metagenomi. The other authors declare no competing interests.

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Nature Microbiology thanks Sebastian Pechmann 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 Identification of recoded phages in the Giant Tortoise microbiome.

a. Dereplicated complete or near complete (>=90%) phage genomes from the Giant Tortoise gut microbiome. Phages are plotted by size and coding density (CD) in standard code (Code11) b. Replotting of phage genomes from panel A, but with coding density of the alternatively coded phage calculated with the predicted genetic code instead of standard code. In all plots, phages that have recoded the TGA stop codon are indicated in green, and phages that have recoded the TAG stop codon are indicated in orange.

Source data

Extended Data Fig. 2 True coding density of standard and alternatively coded phages.

a-f. Replotting of phage genomes from Fig. 1 in the main text, but this time the coding density of alternatively coded phage was calculated with their predicted genetic code, not standard code. In all plots, symbol color represents genetic code (TGA recoding = green, TAG recoding = orange, standard code = grey).

Source data

Extended Data Fig. 3 Evolution of alternative coding.

a. Global alignment of an 80 kilobase (kb) partial TGA recoded Agate genome (Cattle_ERR2019405_scaffold_1063) and a close standard code relative (pig_ID_3053_F60_scaffold_12). Homologous collinear sequences are shown with colored blocks (red and green here), where color corresponds to nucleotide alignment between the two genomes and lack of color represents lack of alignment. Genome structure for each phage is shown under the alignment graph, with DNA replication machinery represented as yellow bars and structural and lysis genes with pink bars. TGA stop codons have predominantly arisen in structural and lysis genes (individual recoded genes below in green).

Source data

Extended Data Fig. 4 Genomic maps of Jade, Sapphire, Agate and Topaz phages.

a-d. TGA recoded genomes (A) contain genes with in-frame TGA codons (green) while TAG recoded genomes (B-D) have genes with in-frame TAG codons (orange). Suppressor tRNAs (tRNA TGA or tRNA TAG, red) are predicted to suppress translation termination at TGA and TAG stop codons, respectively. Regions of the genome encoding structural and lysis genes (pink) coincide with high use of alternative code. Contrastingly, genes involved in DNA replication (yellow) are variably encoded in alternative code. Genomes with a GC skew patterns indicative of bidirectional replication and clear origins and termini (C) have unique replichores marked in alternating shades of blue. Genomes with GC skew patterns most consistent with unidirectional replication (A-B,D) have no replication-related annotation. In some cases, unique or interesting genes have been noted with text. Clade representatives: Jade = JS_HF2_S141_scaffold_159238, Sapphire= SRR1747018_scaffold_13, Agate = Cattle_ERR2019359_scaffold_1067472, Topaz = pig_ID_1851_F40_2_B1_scaffold_1589.

Source data

Extended Data Fig. 5 Genomic maps of Lak, Garnet, and Amethyst phages.

a-c. TAG recoded genomes have genes with in-frame TAG codons (orange). Suppressor tRNAs (tRNA TAG, red) are predicted to suppress translation termination at TAG stop codons. Regions of the genome encoding structural and lysis genes (pink) coincide with high use of alternative code. In Lak phage (A), genes involved in DNA replication (yellow) are mostly encoded in alternative code. Origins and termini are unmarked in these genomes as we were unable to define clear replichores for Lak (A) and Garnet and Amethyst (B-C) appear to utilize unidirectional genome replication based on GC skew patterns. In some cases, unique or interesting genes have been noted with text. Clade representatives: Lak = C1–CH_A02_001D1_final, Garnet = pig_ID_3640_F65_scaffold_1252, Amethyst = pig_EL5596_F5_scaffold_275.

Source data

Extended Data Fig. 6 Code change machinery in two TGA-recoded Jade phages.

a. An operon implicated in changing the genetic code from standard code (TGA=Stop) to code 4 (TGA=W) is directly upstream of the lysis cassette. The code change genes themselves are encoded in standard code, while some genes in the lysis cassette have in frame TGA codons (green). TrpRS = Tryptophanyl tRNA synthetase, RF1=Release Factor 1, TM-domain = Transmembrane domain.

Extended Data Fig. 7 Read mapping to Garnet and Topaz lysogens.

a. Reads were mapped against a manually curated Garnet lysogen. Read coverage for the Prevotella DNA is ~2x higher than the read coverage of the Garnet prophage, indicating that the bacterial population in this sample is incompletely lysogenized. Supporting this conclusion are paired reads that span the prophage (not shown), as well as some individual reads which show imperfect mapping to the lysogen consensus sequence (marked with asterisk), which represent the contiguous bacterial sequence. Identical sequence blocks are indicated with color. b. Reads were mapped against a manually curated Topaz lysogen. Read coverage for the integrated Topaz phage genome is ~50x higher than the neighboring Oscillospiraceae sequence. This indicates that the phage is actively replicating in this sample. Supporting this conclusion are paired reads that span the length of the prophage (not shown), as well as individual reads which show imperfect mapping to the lysogen consensus sequence at the 5’ end of the prophage (light blue) and the 3’ end of the prophage (dark blue). The reads correspond to circularized sequences. Identical sequence blocks are indicated with color.

Extended Data Fig. 8 Alignments of free and integrated phage genomes.

a. A 25 kb circular TAG-recoded Garnet phage aligned to a prophage integrated in a Prevotella genome (Prevotella genes = brown). The prophage boundaries are marked by the phage integrase (pink) and the host tRNA Met. b. A 24 kb circular TAG-recoded Topaz phage aligned to a prophage integrated into a Oscillospiraceae genome (Oscillospiraceae genes = blue). The prophage boundaries are marked by the phage integrase (pink) and the host tRNA Thr.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

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Supplementary Tables 1–5

Table 1. Source metagenomes used in this study. Table 2. Clades of alternatively coded phages found in this study. Table 3. Alternatively coded phage genomes and relatives from this study. Table 4. tRNAs for all alternatively coded phage genomes and relatives from this study. Table 5. Table of release factor and tRNA synthetase counts in alternatively coded phage clades.

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Borges, A.L., Lou, Y.C., Sachdeva, R. et al. Widespread stop-codon recoding in bacteriophages may regulate translation of lytic genes. Nat Microbiol 7, 918–927 (2022). https://doi.org/10.1038/s41564-022-01128-6

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