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Defining the human gut host–phage network through single-cell viral tagging

Nature Microbiology volume 4, pages 2192–2203 (2019)Cite this article

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Abstract

Viral discovery is accelerating at an unprecedented rate due to continuing advances in culture-independent sequence-based analyses. One important facet of this discovery is identification of the hosts of these recently characterized uncultured viruses. To this end, we have adapted the viral tagging approach, which bypasses the need for culture-based methods to identify host–phage pairings. Fluorescently labelled anonymous virions adsorb to unlabelled anonymous bacterial host cells, which are then individually sorted as host–phage pairs, followed by genome amplification and high-throughput sequencing to establish the identities of both the host and the attached virus(es). We demonstrate single-cell viral tagging using the faecal microbiome, including cross-tagging of viruses and bacteria between human subjects. A total of 363 unique host–phage pairings were predicted, most of which were subject-specific and involved previously uncharacterized viruses despite the majority of their bacterial hosts having known taxonomy. One-fifth of these pairs were confirmed by multiple individual tagged cells. Viruses targeting more than one bacterial species were conspicuously absent in the host–phage network, suggesting that phages are not major vectors of inter-species horizontal gene transfer in the human gut. A high level of cross-reactivity between phages and bacteria from different subjects was noted despite subject-specific viral profiles, which has implications for faecal microbiota transplant therapy.

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Fig. 1: Method development.
Fig. 2: Workflow.
Fig. 3: Examples of viral clusters detected in multiple SAGs.
Fig. 4: Faecal host–phage network obtained from 11 human subjects.
Fig. 5: Host–phage ratios in native faecal metagenomes.
Fig. 6: Crossover single-cell VT between four subjects.

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Data availability

Sequence data (raw reads, MAGs, viral contigs from viromes and single-cell VT) have been deposited in the NCBI Sequence Read Archive under the BioProject number PRJNA492716.

References

  1. Edwards, R. A., McNair, K., Faust, K., Raes, J. & Dutilh, B. E. Computational approaches to predict bacteriophage–host relationships. FEMS Microbiol. Rev. 40, 258–272 (2016).

    CAS  PubMed  Google Scholar 

  2. Martinez-Hernandez, F. et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat. Commun. 8, 15892 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016).

    CAS  PubMed  Google Scholar 

  4. Chehoud, C. et al. Transfer of viral communities between human individuals during fecal microbiota transplantation. mBio 7, e00322-16 (2016).

    PubMed  PubMed Central  Google Scholar 

  5. Kortright, K. E., Chan, B. K., Koff, B. K. & Turner, P. E. Phage therapy: a renewed approach to combat antibiotic-resistant bacteria. Cell Host Microbe 25, 219–232 (2019).

    CAS  PubMed  Google Scholar 

  6. Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).

    CAS  PubMed  Google Scholar 

  7. Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4, e08490 (2015).

    PubMed Central  Google Scholar 

  8. Akhter, S., Aziz, R. K. & Edwards, R. A. PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies. Nucleic Acids Res. 40, e126 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Sorek, R., Kunin, V. & Hugenholtz, P. CRISPR—a widespread system that provides acquired resistance against phages in bacteria and archaea. Nat. Rev. Microbiol. 6, 181–186 (2008).

    CAS  PubMed  Google Scholar 

  10. Burstein, D. et al. Major bacterial lineages are essentially devoid of CRISPR–Cas viral defence systems. Nat. Commun. 7, 10613 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Alrasheed, H., Jin, R. & Weitz, J. S. Caution in inferring viral strategies from abundance correlations in marine metagenomes. Nat. Commun. 10, 501 (2019).

    PubMed  PubMed Central  Google Scholar 

  13. Stepanauskas, R. Wiretapping into microbial interactions by single cell genomics. Front. Microbiol. 6, 258 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. Marbouty, M., Baudry, L., Cournac, A. & Koszul, R. Scaffolding bacterial genomes and probing host–virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci. Adv. 3, e1602105 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. Labonte, J. M. et al. Single-cell genomics-based analysis of virus–host interactions in marine surface bacterioplankton. ISME J. 9, 2386–2399 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Munson-McGee, J. H. et al. A virus or more in (nearly) every cell: ubiquitous networks of virus–host interactions in extreme environments. ISME J. 12, 1706–1714 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Tadmor, A. D., Ottesen, E. A., Leadbetter, J. R. & Phillips, R. Probing individual environmental bacteria for viruses by using microfluidic digital PCR. Science 333, 58–62 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Deng, L. et al. Contrasting life strategies of viruses that infect photo- and heterotrophic bacteria, as revealed by viral tagging. mBio 3, e00373-12 (2012).

    PubMed  PubMed Central  Google Scholar 

  19. Deng, L. et al. Viral tagging reveals discrete populations in Synechococcus viral genome sequence space. Nature 513, 242–245 (2014).

    CAS  PubMed  Google Scholar 

  20. Pinard, R. et al. Assessment of whole genome amplification-induced bias through high-throughput, massively parallel whole genome sequencing. BMC Genomics 7, 216 (2006).

    PubMed  PubMed Central  Google Scholar 

  21. Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).

    CAS  PubMed  Google Scholar 

  22. Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. Cornuault, J. K. et al. Phages infecting Faecalibacterium prausnitzii belong to novel viral genera that help to decipher intestinal viromes. Microbiome 6, 65 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. Rodriguez-Valera, F. et al. Explaining microbial population genomics through phage predation. Nat. Rev. Microbiol. 7, 828–836 (2009).

    CAS  PubMed  Google Scholar 

  25. David, L. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).

    PubMed  PubMed Central  Google Scholar 

  26. Džunková, M. et al. Oxidative stress in the oral cavity is driven by individual-specific bacterial communities. NPJ Biofilms Microbiomes 4, 29 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. De Sordi, L., Lourenço, M. & Debarbieux, L. “I will survive”: a tale of bacteriophage–bacteria coevolution in the gut. Gut Microbes 10, 92–99 (2019).

    PubMed  Google Scholar 

  28. Reyes, A. et al. Viruses in the fecal microbiota of monozygotic twins and their mothers. Nature 466, 334–338 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Minot, S. et al. The human gut virome: inter-individual variation and dynamic response to diet. Genome Biol. 21, 1616–1625 (2011).

    CAS  Google Scholar 

  30. Low, S. J., Džunková, M., Chaumeil, P. A., Parks, D. H. & Hugenholtz, P. Evaluation of a concatenated protein phylogeny for classification of tailed double-stranded DNA viruses belonging to the order Caudovirales. Nat. Microbiol. 4, 1306–1315 (2019).

    CAS  PubMed  Google Scholar 

  31. Rostøl, J. T. & Marraffini, L. (Ph)ighting phages: how bacteria resist their parasites. Cell Host Microbe 25, P184–P194 (2019).

    Google Scholar 

  32. Hjelmsø, M. H. et al. Evaluation of methods for the concentration and extraction of viruses from sewage in the context of metagenomic sequencing. PLoS ONE 12, e0170199 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Simmons, M., Krescher, K., Nadell, C. D. & Bucci, V. Phage mobility is a core determinant of phage–bacteria coexistence in biofilms. ISME J. 12, 531–543 (2018).

    PubMed  Google Scholar 

  35. Nanda, A. M., Thormann, K. & Frunzke, J. Impact of spontaneous prophage induction on the fitness of bacterial populations and host–microbe interactions. J. Bacteriol. 197, 410–419 (2015).

    PubMed  PubMed Central  Google Scholar 

  36. Shkoporov, A. N. et al. ΦCrAss001 represents the most abundant bacteriophage family in the human gut and infects Bacteroides intestinalis. Nat. Commun. 9, 4781 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).

    CAS  PubMed  Google Scholar 

  38. Jiang, X., Hall A. B., Xavier, R. J. & Alm, E. J. Comprehensive analysis of mobile genetic elements in the gut microbiome reveals phylum-level niche-adaptive gene pools. Preprint at https://doi.org/10.1101/214213 (2017).

  39. Enault, F. et al. Phages rarely encode antibiotic resistance genes: a cautionary tale for virome analyses. ISME J. 11, 237–247 (2017).

    CAS  PubMed  Google Scholar 

  40. Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. de Jonge, P. A., Nobrega, F. L., Brouns, S. J. J. & Dutilh, B. E. Molecular and evolutionary determinants of bacteriophage host range. Trends Microbiol. 27, 51–63 (2018).

    PubMed  Google Scholar 

  42. Koskella, B. & Meaden, S. Understanding bacteriophage specificity in natural microbial communities. Viruses 5, 806–823 (2013).

    PubMed  PubMed Central  Google Scholar 

  43. Enav, H., Kirzner, S., Lindell, D., Mandel-Gutfreund, Y. & Béjà, O. Adaptation to sub-optimal hosts is a driver of viral diversification in the ocean. Nat. Commun. 9, 4698 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. Simone, S. & Li, S. et al. Durable coexistence of donor and recipient strains after fecal microbiota transplantation. Science 29, 586–589 (2016).

    Google Scholar 

  45. Reyes, A., Wu, M., McNulty, N. P., Rohwer, F. L. & Gordon, J. I. Gnotobiotic mouse model of phage-bacterial host dynamics in the human gut. Proc. Natl Acad. Sci. USA 50, 20236–20241 (2013).

    Google Scholar 

  46. Norman, J. M. et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell 160, 447–460 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Duerkop, B. A. et al. Murine colitis reveals a disease-associated bacteriophage community. Nat. Microbiol. 3, 1023–1031 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Hannigan, G. D., Duhaime, M. B., Ruffin, M. T., Koumpouras, C. C. & Schloss, P. D. Diagnostic potential and interactive dynamics of the colorectal cancer virome. mBio 9, e02248-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  49. Stepanauskas, R. et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).

    PubMed  PubMed Central  Google Scholar 

  50. Hosokawa, M., Nishikawa, Y., Kogawa, M. & Takeyama, H. Massively parallel whole genome amplification for single-cell sequencing using droplet microfluidics. Sci. Rep. 7, 5199 (2017).

    PubMed  PubMed Central  Google Scholar 

  51. Li, D., Liu, C., Luo, R., Sadakane, K. & Lam, T. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    CAS  PubMed  Google Scholar 

  52. Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).

    PubMed  PubMed Central  Google Scholar 

  53. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  57. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  58. Tange, O. GNU parallel-the command-line power tool. USENIX Mag. 36, 42–47 (2011).

    Google Scholar 

  59. Džunková, M., D’Auria, G. & Moya, A. Direct sequencing of human gut virome fractions obtained by flow cytometry. Front. Microbiol. 6, 955 (2015).

    PubMed  PubMed Central  Google Scholar 

  60. Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).

    CAS  PubMed  Google Scholar 

  61. Popgeorgiev, N. et al. Marseillevirus-like virus recovered from blood donated by asymptomatic humans. J. Infect. Dis. 208, 1042–1050 (2013).

    CAS  PubMed  Google Scholar 

  62. Rinke, C. et al. Validation of picogram- and femtogram-input DNA libraries for microscale metagenomics. PeerJ 4, e2486 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Besemer, J., Lomsadze, A. & Borodovsky, M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 29, 2607–2618 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 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).

    CAS  PubMed  Google Scholar 

  66. Ellis, B. et al. flowViz: visualization for flow cytometry. R package version 1.44.0 (2018).

  67. Ellis, B. et al. flowCore: basic structures for flow cytometry data. R package version 1.46.1 (2018).

  68. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  PubMed  Google Scholar 

  70. Oksanen, J. et al. Vegan: community ecology package. R package version 2.3-2 (2015).

  71. Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).

    CAS  PubMed  Google Scholar 

  72. Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 46, e59 (2018).

    PubMed  PubMed Central  Google Scholar 

  73. DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA cene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Alikhan, N. F., Petty, N. K., Zakour, B. N. L. & Beatson, S. A. BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. BMC Genomics 12, 402 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  77. Bland, C. et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8, 209 (2007).

    PubMed  PubMed Central  Google Scholar 

  78. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    CAS  PubMed  Google Scholar 

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Acknowledgements

We thank the 11 volunteers for providing samples for this study, M. Nefedov for assistance with operation of the FACSAria III, S. Low for creating the Illumina libraries, R. Webb for assistance with transmission electron microscopy and A. Hassan for advice on phage detection. We also thank A. Pribyl Rinke and N. Angel for providing the longitudinal faecal metagenome series, and R. Edwards and J. Barr for useful feedback on the manuscript. The project was supported by an Australian Research Council Laureate Fellowship (FL150100038) awarded to P.H., Australian Research Council (ARC) Future Fellowship (FT170100213) awarded to C.R., fundings from German Research Foundation (DFG Emmy Noether program; DE2360/1-1) and European Research Council Starting (ERC StG 803077) awarded to L.D., and ‘Microbiome Challenge’ strategic funding (DVCR16003A) from the University of Queensland.

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Authors and Affiliations

  1. Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, Australia

    Mária Džunková, Soo Jen Low, Joshua N. Daly, Christian Rinke & Philip Hugenholtz

  2. Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Virology, Neuherberg, Germany

    Li Deng

  3. Technical University Munich, Institute of Virology, Munich, Germany

    Li Deng

Authors
  1. Mária Džunková
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  4. Li Deng
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Contributions

M.D. and P.H. conceived and designed the experiments and analysis. L.D. and C.R. contributed to the method development. M.D. performed the laboratory experiments. M.D., S.J.L and J.N.D. performed bioinformatic analyses. M.D. and P.H. wrote the paper. All co-authors edited the completed draft of the manuscript.

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Correspondence to Mária Džunková or Philip Hugenholtz.

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Džunková, M., Low, S.J., Daly, J.N. et al. Defining the human gut host–phage network through single-cell viral tagging. Nat Microbiol 4, 2192–2203 (2019). https://doi.org/10.1038/s41564-019-0526-2

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