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Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan

Nature Microbiology volume 8, pages 1064–1078 (2023)Cite this article

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Abstract

Distinct gut microbiome ecology may be implicated in the prevention of aging-related diseases as it influences systemic immune function and resistance to infections. Yet, the viral component of the microbiome throughout different stages in life remains unexplored. Here we present a characterization of the centenarian gut virome using previously published metagenomes from 195 individuals from Japan and Sardinia. Compared with gut viromes of younger adults (>18 yr) and older individuals (>60 yr), centenarians had a more diverse virome including previously undescribed viral genera, such as viruses associated with Clostridia. A population shift towards higher lytic activity was also observed. Finally, we investigated phage-encoded auxiliary functions that influence bacterial physiology, which revealed an enrichment of genes supporting key steps in sulfate metabolic pathways. Phage and bacterial members of the centenarian microbiome displayed an increased potential for converting methionine to homocysteine, sulfate to sulfide and taurine to sulfide. A greater metabolic output of microbial hydrogen sulfide in centenarians may in turn support mucosal integrity and resistance to pathobionts.

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Fig. 1: Tandem analysis of bacteria and viruses in centenarian microbiomes.
Fig. 2: Healthy centenarians display a more diverse and rich virome compared with young and older individuals.
Fig. 3: Phage signatures correlate with the unique centenarian bacterial communities.
Fig. 4: Lysogeny in the phageome dominates from young age to centenarian in the healthy microbiome.
Fig. 5: The centenarian virome supports a greater host-metabolic conversion of microbial sulfide.
Fig. 6: The centenarian gut microbiome configuration is primed for effective utilization of taurine, sulfate and methionine.

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Data availability

This paper analyses existing, publicly available data. The raw sequencing files used in this study for Centenarian, Sardinian, Tanzanian 300FG and EDIA microbiome datasets are available at the NCBI database under accession numbers PRJNA675598, PRJEB25514, PRJNA686265 and PRJNA707065, respectively. The viral genomes used to establish previously undescribed viral biodiversity are available from the Metagenomic Viruses Database (https://portal.nersc.gov/MGV). Additional files including vOTU genomes, phylogenetic tree, VOG marker table and master annotation table for vOTUs can be accessed at Zenodo (https://doi.org/10.5281/zenodo.6579480). Source data are provided in Supplementary Information.

Code availability

All original code has been deposited at GitHub and is publicly available as of the date of publication. Workflows and supporting code can be accessed at the following repository: https://github.com/RasmussenLab/vCentenarian. Any additional information required to reanalyse the data reported in this paper is available from the lead contact (D.R.P.) upon request.

References

  1. Odamaki, T. et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 16, 90 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Goronzy, J. J. & Weyand, C. M. Understanding immunosenescence to improve responses to vaccines. Nat. Immunol. 14, 428–436 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Adiliaghdam, F. & Jeffrey, K. L. Illuminating the human virome in health and disease. Genome Med. 12, 66 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sutton, T. D. S. & Hill, C. Gut bacteriophage: current understanding and challenges. Front. Endocrinol. 10, 784 (2019).

    Article  Google Scholar 

  7. Liang, G. et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature 581, 470–474 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shamash, M. & Maurice, C. F. Phages in the infant gut: a framework for virome development during early life. ISME J. 16, 323–330 (2022).

    Article  PubMed  Google Scholar 

  9. Gogokhia, L. et al. Expansion of bacteriophages is linked to aggravated intestinal inflammation and colitis. Cell Host Microbe 25, 285–299.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Franceschi, C., Garagnani, P., Parini, P., Giuliani, C. & Santoro, A. Inflammaging: a new immune–metabolic viewpoint for age-related diseases. Nat. Rev. Endocrinol. 14, 576–590 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kieft, K. et al. Ecology of inorganic sulfur auxiliary metabolism in widespread bacteriophages. Nat. Commun. 12, 3503 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kieft, K. et al. Virus-associated organosulfur metabolism in human and environmental systems. Cell Rep. 36, 109471 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Mayneris-Perxachs, J. et al. Caudovirales bacteriophages are associated with improved executive function and memory in flies, mice, and humans. Cell Host Microbe 30, 340–356.e8 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Nissen, J. N. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotechnol. 39, 555–560 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Johansen, J. et al. Genome binning of viral entities from bulk metagenomics data. Nat. Commun. 13, 965 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in sardinian centenarians. mSystems 4, e00325-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nayfach, S. et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat. Microbiol. 6, 960–970 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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).

    Article  CAS  PubMed  Google Scholar 

  20. Kieft, K. & Anantharaman, K. Deciphering active prophages from metagenomes. mSystems 7, e0008422 (2022).

    Article  PubMed  Google Scholar 

  21. Vatanen, T. et al. Mobile genetic elements from the maternal microbiome shape infant gut microbial assembly and metabolism. Cell 185, 4921–4936.e15 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Stražar, M. et al. Gut microbiome-mediated metabolism effects on immunity in rural and urban African populations. Nat. Commun. 12, 4845 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Murphy, J., Mahony, J., Ainsworth, S., Nauta, A. & van Sinderen, D. Bacteriophage orphan DNA methyltransferases: insights from their bacterial origin, function, and occurrence. Appl. Environ. Microbiol. 79, 7547–7555 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gulyaeva, A. et al. Discovery, diversity, and functional associations of crAss-like phages in human gut metagenomes from four Dutch cohorts. Cell Rep. 38, 110204 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Shkoporov, A. N. et al. The human gut virome is highly diverse, stable, and individual specific. Cell Host Microbe 26, 527–541.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Roux, S. et al. Minimum Information about an Uncultivated Virus Genome (MIUViG). Nat. Biotechnol. 37, 29–37 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Santoro, A. et al. Gut microbiota changes in the extreme decades of human life: a focus on centenarians. Cell. Mol. Life Sci. 75, 129–148 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Redgwell, T. A. et al. Prophages in the infant gut are largely induced, and may be functionally relevant to their hosts. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2021.06.25.449885v1 (2021).

  31. Sovran, B. et al. Age-associated impairment of the mucus barrier function is associated with profound changes in microbiota and immunity. Sci. Rep. 9, 1437 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Diard, M. et al. Inflammation boosts bacteriophage transfer between Salmonella spp. Science 355, 1211–1215 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Biagi, E. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Heidelberg, J. F. et al. The genome sequence of the anaerobic, sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough. Nat. Biotechnol. 22, 554–559 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Buret, A. G., Allain, T., Motta, J.-P. & Wallace, J. L. Effects of hydrogen sulfide on the microbiome: from toxicity to therapy. Antioxid. Redox Signal. 36, 211–219 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Stacy, A. et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell 184, 615–627.e17 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  39. Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26, 680–682 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Plaza Oñate, F. et al. MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data. Bioinformatics 35, 1544–1552 (2019).

    Article  PubMed  Google Scholar 

  42. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).

    PubMed  PubMed Central  Google Scholar 

  47. Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).

    Article  CAS  PubMed  Google Scholar 

  48. 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 

  49. Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kirchberger, P. C., Martinez, Z. A. & Ochman, H. Organizing the global diversity of microviruses. mBio 13, e0058822 (2022).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  52. Russel, J., Pinilla-Redondo, R., Mayo-Muñoz, D., Shah, S. A. & Sørensen, S. J. CRISPRCasTyper: automated identification, annotation, and classification of CRISPR-Cas loci. CRISPR J. 3, 462–469 (2020).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hockenberry, A. J. & Wilke, C. O. BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains. PeerJ 9, e11396 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 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  Google Scholar 

  59. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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 

  61. Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank E. Heppenheimer for editorial assistance during text and figure preparation.

The study was funded in part by the Novo Nordisk Foundation (NNF21SA0072102); the Center for Microbiome Informatics and Therapeutics (R.J.X.); NIH grant DK043351 (R.J.X.); and NIH grant DK120485 (R.J.X.). J.J. and S.R. were supported by the Novo Nordisk Foundation (grant NNF14CC0001). K.H., K.A. and Y.A. were supported by Moonshot Research and Development Program (JP22zf0127007) and NeDDTrim (JP21ae0121041) from AMED Japan. K.H. and K.A. were supported by MEXT Japan World Premier International Research Center Initiative (WPI). The EDIA study was supported by NIH grant 1DP3DK094338-01 (M.K.) and by the Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research No. 250114 (M.K.).

Author information

Authors and Affiliations

  1. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Joachim Johansen, Tommi Vatanen, Ramnik J. Xavier & Damian R. Plichta

  2. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Joachim Johansen & Simon Rasmussen

  3. Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo, Japan

    Koji Atarashi & Kenya Honda

  4. Center for Supercentenarian Medical Research, Keio University School of Medicine, Tokyo, Japan

    Yasumichi Arai & Nobuyoshi Hirose

  5. Section of Microbiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark

    Søren J. Sørensen

  6. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Tommi Vatanen & Mikael Knip

  7. New Children’s Hospital, Helsinki University Hospital, Helsinki, Finland

    Mikael Knip

  8. Tampere Center for Child Health Research, Tampere University Hospital, Tampere, Finland

    Mikael Knip

  9. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Simon Rasmussen

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Contributions

J.J., D.R.P., S.R. and R.J.X. conceptualized the project. J.J. and D.R.P. developed the methodology. J.J. developed software. J.J. and D.R.P. conducted formal analysis. J.J. conducted the investigations. R.J.X. and S.R. procured resources. J.J., D.R.P., K.H., K.A., Y.A., N.H., T.V. and M.K. curated data. J.J. performed visualization. J.J. wrote the original draft. J.J., D.R.P., R.J.X., S.R., K.H., S.J.S. and K.A. reviewed the draft. D.R.P., R.J.X. and S.R. supervised the project.

Corresponding authors

Correspondence to Ramnik J. Xavier, Simon Rasmussen or Damian R. Plichta.

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Competing interests

R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics, a member of the Scientific Advisory Board of Nestle, and a member of the Board of Directors at Moonlake Immunotherapeutics. These organizations had no role in supporting this study. The other authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–5.

Reporting Summary

Supplementary Tables

Table 1. vOTU genus counts (known and new) with number of vOTUs in each. Bacterial host annotation at each taxonomic lineage level. Table 2. Integrated prophage summary table of prophages identified in bacterial isolates of centenarians. Contains isolate ID, length and integration coordinates. Table 3. Test statistics for viral bacterial abundance ratios between age groups of prevalent sulfate genes. Table 4. Test statistics for dissimilatory sulfate microbiome abundance between age groups. Table 5. CE91 isolates information including taxonomy and genome quality.

Supplementary File 1

MSP MAG taxonomy.

Supplementary File 2

Virus host annotation.

Supplementary File 3

Virus master table.

Supplementary File 4

Figures 2 and 3 viral abundance matrix.

Supplementary File 5

Figures 3 and 4 MSP (bacterial) abundance matrix.

Supplementary File 6

Figure 4 viral to bacterial ratios between bacteria and species at host level.

Supplementary File 7

Figures 5 and 6 auxiliary metabolic gene measurements.

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Johansen, J., Atarashi, K., Arai, Y. et al. Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan. Nat Microbiol 8, 1064–1078 (2023). https://doi.org/10.1038/s41564-023-01370-6

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