Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Glacier-preserved Tibetan Plateau viral community probably linked to warm–cold climate variations

Nature Geoscience volume 17, pages 912–919 (2024)Cite this article

Subjects

Abstract

Glaciers archive time-structured information on climates and ecosystems, including microorganisms. However, the long-term ecogenomic dynamics or biogeography of the preserved viruses and their palaeoclimatic connections remain uninvestigated. Here we use metagenomes to reconstruct viral genomes from nine time horizons, spanning three cold-to-warm cycles over the past >41,000 years, preserved in an ice core from Guliya Glacier, Tibetan Plateau. We recover genomes of 1,705 approximately species-level viral operational taxonomic units. Viral communities significantly differ during cold and warm climatic conditions, with the most distinct community observed ~11,500 years ago during the major climate transition from the Last Glacial Stage to the Holocene. In silico analyses of virus–host interactions reveal persistently high viral pressure on Flavobacterium (a common dominant glacier lineage) and historical enrichment in the metabolism of cofactors and vitamins that can contribute to host adaptation and virus fitness under extreme conditions. Biogeographic analyses show that approximately one-fourth of Guliya viral operational taxonomic units overlap with the global dataset, primarily with the Tibetan Plateau metagenomes, suggesting regional associations of a subset of the Guliya-preserved viruses over time. We posit that the cold-to-warm variations in viral communities might be attributed to distinct virus sources and/or environmental selections under different temperature regimes.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Glacier ice sampling.
Fig. 2: GPAV number, taxonomy and community clustering.
Fig. 3: Historical changes of virus–host interactions.
Fig. 4: Biogeography of Guliya viruses.

Similar content being viewed by others

Data availability

Metagenomic data of Guliya samples are available to the public via both the Integrated Microbial Genomes (IMG) system (https://genome.jgi.doe.gov/portal/) and NCBI Sequence Read Archive (SRA) database, with an individual accession number for each sample summarized in Supplementary Data 2. The Guliya virus contigs are available via figshare at https://doi.org/10.6084/m9.figshare.24523849 (ref. 99). The accession information of 920 published metagenomes, including the 187 globally available glacier metagenomes, is provided in Supplementary Data 12. Source data are provided with this paper.

Code availability

The custom scripts used for analysing data are available via GitHub at https://github.com/zhiping393/GPAV.

References

  1. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  2. Thompson, L. G. et al. Tropical climate instability: the last glacial cycle from a Qinghai-Tibetan ice core. Science 276, 1821–1825 (1997).

    CAS  Google Scholar 

  3. Rosen, J. L. et al. An ice core record of near-synchronous global climate changes at the Bølling transition. Nat. Geosci. 7, 459–463 (2014).

    CAS  Google Scholar 

  4. Bauska, T. K., Marcott, S. A. & Brook, E. J. Abrupt changes in the global carbon cycle during the last glacial period. Nat. Geosci. 14, 91–96 (2021).

    CAS  Google Scholar 

  5. Priscu, J. C., Christner, B. C., Foreman, C. M. & Royston-Bishop, G. in Encyclopedia of Quaternary Science Vol. 2 (ed. Elias, S. A.) 1156–1166 (Elsevier, 2006).

  6. Karl, D. M. et al. Microorganisms in the accreted ice of Lake Vostok, Antarctica. Science 286, 2144–2147 (1999).

    CAS  Google Scholar 

  7. Suttle, C. A. Marine viruses—major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).

    CAS  Google Scholar 

  8. Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).

    CAS  Google Scholar 

  9. Brum, J. R. & Sullivan, M. B. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat. Rev. Microbiol. 13, 147–159 (2015).

    CAS  Google Scholar 

  10. Anesio, A. M., Mindl, B., Laybourn-Parry, J., Hodson, A. J. & Sattler, B. Viral dynamics in cryoconite holes on a high Arctic glacier (Svalbard). J. Geophys. Res. 112, G04S31 (2007).

    Google Scholar 

  11. Bellas, C. M., Anesio, A. M. & Barker, G. Analysis of virus genomes from glacial environments reveals novel virus groups with unusual host interactions. Front. Microbiol. 6, 656 (2015).

    Google Scholar 

  12. Liu, Y. et al. Diversity and function of mountain and polar supraglacial DNA viruses. Sci. Bull. 68, 2418–2433 (2023).

    CAS  Google Scholar 

  13. Bellas, C. M., Schroeder, D. C., Edwards, A., Barker, G. & Anesio, A. M. Flexible genes establish widespread bacteriophage pan-genomes in cryoconite hole ecosystems. Nat. Commun. 11, 4403 (2020).

    CAS  Google Scholar 

  14. Castello, J. D. et al. Detection of tomato mosaic tobamovirus RNA in ancient glacial ice. Polar Biol. 22, 207–212 (1999).

    Google Scholar 

  15. Zhong, Z. P. et al. Glacier ice archives nearly 15,000-year-old microbes and phages. Microbiome 9, 160 (2021).

    CAS  Google Scholar 

  16. Thompson, L. G. et al. Ice core records of climate variability on the Third Pole with emphasis on the Guliya ice cap, western Kunlun Mountains. Quat. Sci. Rev. 188, 1–14 (2018).

    Google Scholar 

  17. Thompson, L. G. et al. Ice core evidence for an orbital-scale climate transition on the Northwest Tibetan Plateau. Quat. Sci. Rev. 324, 108443 (2024).

    Google Scholar 

  18. Thompson, L. G. et al. Use of δ18Oatm in dating a Tibetan ice core record of Holocene/Late Glacial climate. Proc. Natl Acad. Sci. USA 119, e2205545119 (2022).

    CAS  Google Scholar 

  19. Christner, B. C. Detection, Recovery, Isolation, and Characterization of Bacteria in Glacial Ice and Lake Vostok Accretion Ice. Doctoral thesis, Ohio State Univ. (2002).

  20. Werner, M. et al. Seasonal and interannual variability of the mineral dust cycle under present and glacial climate conditions. J. Geophys. Res. 107, 4744 (2002).

    Google Scholar 

  21. Summerhayes, C. & Charman, D. Introduction to Holocene climate change: new perspectives. J. Geol. Soc. 172, 251–253 (2015).

    Google Scholar 

  22. Chaturvedi, U. C. & Shrivastava, R. Interaction of viral proteins with metal ions: role in maintaining the structure and functions of viruses. FEMS Immunol. Med. Microbiol. 43, 105–114 (2005).

    CAS  Google Scholar 

  23. Yao, T. et al. A review of climatic controls on δ18O in precipitation over the Tibetan Plateau: observations and simulations. Rev. Geophys. 51, 525–548 (2013).

    Google Scholar 

  24. Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).

    CAS  Google Scholar 

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

    Google Scholar 

  26. Bolduc, B. et al. iVirus 2.0: cyberinfrastructure-supported tools and data to power DNA virus ecology. ISME Commun. 1, 77 (2021).

    Google Scholar 

  27. 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).

    CAS  Google Scholar 

  28. Zhong, Z. P. et al. Viral potential to modulate microbial methane metabolism varies by habitat. Nat. Commun. 15, 1857 (2024).

    CAS  Google Scholar 

  29. Liu, Q., Liu, H. C., Zhou, Y. G. & Xin, Y. H. Genetic diversity of glacier-inhabiting Cryobacterium bacteria in China and description of Cryobacterium zongtaii sp. nov. and Arthrobacter glacialis sp. nov. Syst. Appl. Microbiol. 42, 168–177 (2019).

    CAS  Google Scholar 

  30. Liu, Q., Liu, H. C., Zhou, Y. G. & Xin, Y. H. Microevolution and adaptive strategy of psychrophilic species Flavobacterium bomense sp. nov. isolated from glaciers. Front. Microbiol. 10, 1069 (2019).

    Google Scholar 

  31. Liu, Y. et al. Genomic insights of Cryobacterium isolated from ice core reveal genome dynamics for adaptation in glacier. Front. Microbiol. 11, 1530 (2020).

    Google Scholar 

  32. Zhong, Z. P. et al. Clean low-biomass procedures and their application to ancient ice core microorganisms. Front. Microbiol. 9, 1094 (2018).

    Google Scholar 

  33. Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. 40, 1341–1348 (2022).

    Google Scholar 

  34. Trubl, G. et al. Active virus–host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 9, 208 (2021).

    CAS  Google Scholar 

  35. Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature 523, 208–211 (2015).

    CAS  Google Scholar 

  36. Zhao, R., Farag, I. F., Jorgensen, S. L. & Biddle, J. F. Occurrence, diversity, and genomes of ‘Candidatus Patescibacteria’ along the early diagenesis of marine sediments. Appl. Environ. Microbiol. 88, e0140922 (2022).

  37. Debussche, L. et al. Assay, purification, and characterization of cobaltochelatase, a unique complex enzyme catalyzing cobalt insertion in hydrogenobyrinic acid a,c-diamide during coenzyme B12 biosynthesis in Pseudomonas denitrificans. J. Bacteriol. 174, 7445–7451 (1992).

    CAS  Google Scholar 

  38. Rodionov, D. A., Vitreschak, A. G., Mironov, A. A. & Gelfand, M. S. Comparative genomics of the vitamin B12 metabolism and regulation in prokaryotes. J. Biol. Chem. 278, 41148–41159 (2003).

    CAS  Google Scholar 

  39. Dieser, M., Smith, H. J., Ramaraj, T. & Foreman, C. M. Janthinobacterium CG23_2: comparative genome analysis reveals enhanced environmental sensing and transcriptional regulation for adaptation to life in an Antarctic supraglacial stream. Microorganisms 7, 454 (2019).

    CAS  Google Scholar 

  40. Cuffey, K. M. in The Physics of Glaciers (ed. Paterson, W. S. B.) 611–674 (Butterworth-Heinemann/Elsevier, 2010).

  41. Sierra-Hernández, M. R., Beaudon, E., Gabrielli, P. & Thompson, L. 21st-century Asian air pollution impacts glacier in northwestern Tibet. Atmos. Chem. Phys. 19, 15533–15544 (2019).

    Google Scholar 

  42. Beaudon, E. et al. Aeolian dust preserved in the Guliya ice cap (Northwestern Tibet): a promising paleo-environmental messenger. Geosciences 12, 366 (2022).

    CAS  Google Scholar 

  43. Liu, Y. et al. Bacterial responses to environmental change on the Tibetan Plateau over the past half century. Environ. Microbiol. 18, 1930–1941 (2016).

    Google Scholar 

  44. Tung, H. C., Bramall, N. E. & Price, P. B. Microbial origin of excess methane in glacial ice and implications for life on Mars. Proc. Natl Acad. Sci. USA 102, 18292–18296 (2005).

    CAS  Google Scholar 

  45. Miteva, V., Sowers, T., Schupbach, S., Fischer, H. & Brenchley, J. Geochemical and microbiological studies of nitrous oxide variations within the new NEEM Greenland ice core during the last glacial period. Geomicrobiol. J. 33, 647–660 (2016).

    CAS  Google Scholar 

  46. Campen, R., Sowers, T. & Alley, R. Evidence of microbial consortia metabolizing within a low-latitude mountain glacier. Geology 31, 231–234 (2003).

    CAS  Google Scholar 

  47. Price, P. B. A habitat for psychrophiles in deep Antarctic ice. Proc. Natl Acad. Sci. USA 97, 1247–1251 (2000).

    CAS  Google Scholar 

  48. Tung, H. C., Price, P. B., Bramall, N. E. & Vrdoljak, G. Microorganisms metabolizing on clay grains in 3-km-deep Greenland basal ice. Astrobiology 6, 69–86 (2006).

    CAS  Google Scholar 

  49. Barletta, R. E., Jones, W. L., Mader, H. M., Priscu, J. C. & Roe, C. H. Chemical analysis of ice vein microenvironments: II. Analysis of glacial samples from Greenland and Antarctica. J. Glaciol. 58, 1109–1118 (2017).

    Google Scholar 

  50. Price, P. B. & Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Natl Acad. Sci. USA 101, 4631–4636 (2004).

    CAS  Google Scholar 

  51. Doyle, S. M., Montross, S. N., Skidmore, M. L. & Christner, B. C. Characterizing microbial diversity and the potential for metabolic function at −15 °C in the basal ice of Taylor Glacier, Antarctica. Biology 2, 1034–1053 (2013).

    Google Scholar 

  52. Kazlauskas, D., Varsani, A., Koonin, E. V. & Krupovic, M. Multiple origins of prokaryotic and eukaryotic single-stranded DNA viruses from bacterial and archaeal plasmids. Nat. Commun. 10, 3425 (2019).

    Google Scholar 

  53. Dominguez-Huerta, G. et al. Diversity and ecological footprint of Global Ocean RNA viruses. Science 376, 1202–1208 (2022).

    CAS  Google Scholar 

  54. Jang, H. B. et al. Viral tag and grow: a scalable approach to capture and characterize infectious virus–host pairs. ISME Commun. 2, 12 (2022).

    Google Scholar 

  55. Tian, L. et al. Oxygen-18 concentrations in recent precipitation and ice cores on the Tibetan Plateau. J. Geophys. Res. 108, 4293 (2003).

    Google Scholar 

  56. Thompson, L. G. et al. A 1000 year ice-core climate record from the Guliya ice cap, China: its relationship to global climate variability. Ann. Glaciol. 21, 175–181 (1995).

    Google Scholar 

  57. Ohio Supercomputer Center. Ohio Supercomputer Center. Ohio Supercomputer Center http://osc.edu/ark:/19495/f5s1ph73 (1987).

  58. Clum, A. et al. DOE JGI metagenome workflow. mSystems 6, e00804-20 (2021).

    Google Scholar 

  59. Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner (Lawrence Berkeley National Laboratory, 2014).

  60. Zhong, Z. P. et al. Lower viral evolutionary pressure under stable versus fluctuating conditions in subzero Arctic brines. Microbiome 11, 174 (2023).

    CAS  Google Scholar 

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

    Google Scholar 

  62. Ren, J. et al. Identifying viruses from metagenomic data using deep learning. Quant. Biol. 8, 64–77 (2020).

    CAS  Google Scholar 

  63. Amgarten, D., Braga, L. P. P., da Silva, A. M. & Setubal, J. C. MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front. Genet. 9, 304 (2018).

    Google Scholar 

  64. Gregory, A. C. et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell 177, 1109–1123.e14 (2019).

    CAS  Google Scholar 

  65. Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2016).

    Google Scholar 

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

    Google Scholar 

  67. Duhaime, M. B., Deng, L., Poulos, B. T. & Sullivan, M. B. Towards quantitative metagenomics of wild viruses and other ultra-low concentration DNA samples: a rigorous assessment and optimization of the linker amplification method. Environ. Microbiol. 14, 2526–2537 (2012).

    CAS  Google Scholar 

  68. Roux, S., Emerson, J. B., Eloe-Fadrosh, E. A. & Sullivan, M. B. Benchmarking viromics: an in silico evaluation of metagenome-enabled estimates of viral community composition and diversity. PeerJ 5, e3817 (2017).

    Google Scholar 

  69. Roux, S. et al. Optimizing de novo genome assembly from PCR-amplified metagenomes. PeerJ 7, e6902 (2019).

    Google Scholar 

  70. van der Walt, A. J. et al. Assembling metagenomes, one community at a time. BMC Genomics 18, 521 (2017).

    Google Scholar 

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

    Google Scholar 

  72. Lima-Mendez, G., Van Helden, J., Toussaint, A. & Leplae, R. Reticulate representation of evolutionary and functional relationships between phage genomes. Mol. Biol. Evol. 25, 762–777 (2008).

    CAS  Google Scholar 

  73. Skennerton, C. T., Soranzo, N. & Angly, F. MinCED—mining CRISPRs in environmental datasets. GitHub https://github.com/ctSkennerton/minced (2019).

  74. Pratama, A. A. et al. Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation. PeerJ 9, e11447 (2021).

    Google Scholar 

  75. 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).

    CAS  Google Scholar 

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

    Google Scholar 

  77. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).

    CAS  Google Scholar 

  78. Sullivan, M. J., Petty, N. K. & Beatson, S. A. Easyfig: a genome comparison visualizer. Bioinformatics 27, 1009–1010 (2011).

    CAS  Google Scholar 

  79. Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).

    Google Scholar 

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

    CAS  Google Scholar 

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

    CAS  Google Scholar 

  82. Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).

    CAS  Google Scholar 

  83. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).

    CAS  Google Scholar 

  84. 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).

    CAS  Google Scholar 

  85. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    CAS  Google Scholar 

  86. Martin, D. & Rybicki, E. RDP: detection of recombination amongst aligned sequences. Bioinformatics 16, 562–563 (2000).

    CAS  Google Scholar 

  87. Padidam, M., Sawyer, S. & Fauquet, C. M. Possible emergence of new geminiviruses by frequent recombination. Virology 265, 218–225 (1999).

    CAS  Google Scholar 

  88. Salminen, M. O., Carr, J. K., Burke, D. S. & McCutchan, F. E. Identification of breakpoints in intergenotypic recombinants of HIV type 1 by bootscanning. AIDS Res. Hum. Retrovir. 11, 1423–1425 (1995).

    CAS  Google Scholar 

  89. Smith, J. M. Analyzing the mosaic structure of genes. J. Mol. Evol. 34, 126–129 (1992).

    CAS  Google Scholar 

  90. Posada, D. & Crandall, K. A. Evaluation of methods for detecting recombination from DNA sequences: computer simulations. Proc. Natl Acad. Sci. USA 98, 13757–13762 (2001).

    CAS  Google Scholar 

  91. Gibbs, M. J., Armstrong, J. S. & Gibbs, A. J. Sister-scanning: a Monte Carlo procedure for assessing signals in recombinant sequences. Bioinformatics 16, 573–582 (2000).

    CAS  Google Scholar 

  92. Holmes, E. C., Worobey, M. & Rambaut, A. Phylogenetic evidence for recombination in dengue virus. Mol. Biol. Evol. 16, 405–409 (1999).

    CAS  Google Scholar 

  93. Weiller, G. F. Phylogenetic profiles: a graphical method for detecting genetic recombinations in homologous sequences. Mol. Biol. Evol. 15, 326–335 (1998).

    CAS  Google Scholar 

  94. Lam, H. M., Ratmann, O. & Boni, M. F. Improved algorithmic complexity for the 3SEQ recombination detection algorithm. Mol. Biol. Evol. 35, 247–251 (2018).

    CAS  Google Scholar 

  95. Martin, D. P. et al. RDP5: a computer program for analyzing recombination in, and removing signals of recombination from, nucleotide sequence datasets. Virus Evol. 7, veaa087 (2021).

    Google Scholar 

  96. Yang, Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13, 555–556 (1997).

    CAS  Google Scholar 

  97. Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).

    Google Scholar 

  98. Villanueva, R. A. M. & Chen, Z. J. ggplot2: elegant graphics for data analysis (2nd ed.). Meas.-Interdiscip. Res. 17, 160–167 (2019).

    Google Scholar 

  99. Zhong, Z. P. Genomes of glacier-preserved ancient viruses. figshare https://doi.org/10.6084/m9.figshare.24523849 (2024).

Download references

Acknowledgements

This work was supported by a collaborative programme for ice core drilling and analyses between The Ohio State University’s Byrd Polar and Climate Research Center and the Institute of Tibetan Plateau Research of the Chinese Academy of Sciences, funded by the National Science Foundation’s Paleoclimate Program award no. 1502919 and the Chinese Academy of Sciences, respectively, to L.G.T. Partial support was provided by a Gordon and Betty Moore Foundation Investigator Award no. 3790 to M.B.S., the Byrd Postdoc Fellowship to Z.-P.Z. and the Heising-Simons Foundation award no. 2022-4014 to L.G.T., Z.-P.Z., V.I.R. and E.M.-T. A portion of this research was funded by the US Department of Energy Joint Genome Institute CSP project no. 503428 to M.B.S. and was performed under the JGI-EMSL Collaborative Science Initiative and used resources at the DOE Joint Genome Institute and the Environmental Molecular Sciences Laboratory, which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under contract nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). We greatly appreciate the help by M. E. Davis for providing dating and environmental data of the ice, by E. Beaudon, M. R. Sierra-Hernández, D. V. Kenny and P.-N. Lin with ice core sampling, by J. Wainaina, A. Gregory, J. Guo, K. Gerhardt and B. Christner for helpful discussions, by Y. Zhou with figure modifications, by N. E. Solonenko with shipment staff for metagenomic sequencing, by S. Roux with metagenomic processing at JGI and by A. Jermy with manuscript commenting and revising. Bioinformatics were supported by the Ohio Supercomputer Center.

Author information

Authors and Affiliations

  1. Byrd Polar and Climate Research Center, Ohio State University, Columbus, OH, USA

    Zhi-Ping Zhong, Ellen Mosley-Thompson, Virginia I. Rich, Lonnie G. Thompson & Matthew B. Sullivan

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

    Zhi-Ping Zhong, Olivier Zablocki, Yueh-Fen Li, Virginia I. Rich & Matthew B. Sullivan

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

    Zhi-Ping Zhong, Olivier Zablocki, Yueh-Fen Li, Virginia I. Rich & Matthew B. Sullivan

  4. Department of Plant Pathology and Nebraska Center for Virology, University of Nebraska–Lincoln, Lincoln, NE, USA

    James L. Van Etten

  5. Department of Geography, Ohio State University, Columbus, OH, USA

    Ellen Mosley-Thompson

  6. School of Earth Sciences, Ohio State University, Columbus, OH, USA

    Lonnie G. Thompson

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

    Matthew B. Sullivan

Authors
  1. Zhi-Ping Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Olivier Zablocki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yueh-Fen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James L. Van Etten
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ellen Mosley-Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Virginia I. Rich
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lonnie G. Thompson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Matthew B. Sullivan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.-P.Z., J.L.V.E., E.M.-T., V.I.R., L.G.T. and M.B.S. conceived and designed the research. E.M.-T., V.I.R., L.G.T. and M.B.S. supervised this work. E.M.-T., L.G.T. and Z.-P.Z. coordinated the sampling efforts. Y.-F.L. extracted the DNA. Z.-P.Z. performed biological tests before DNA extraction and analysed sequencing data. Z.-P.Z. wrote and O.Z., E.M.-T., V.I.R., L.G.T. and M.B.S. critically revised the manuscript. All authors revised and approved the final manuscript to be published.

Corresponding authors

Correspondence to Zhi-Ping Zhong, Lonnie G. Thompson or Matthew B. Sullivan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks Matthieu Legendre, Wei Li and Ruonan Wu for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of assembly pipelines.

The number of assembled contigs (a) and viral contigs (b) ≥5 kb was compared across 18 assembly pipelines using metagenomes of three glacier-ice samples: A160, A3900, and A11500. Parameters of each pipeline are summarized in legends. These pipelines comprised three groups of comparisons: (i) SPAdes A (in magenta), this group used ‘single-cellSPAdes + six different kmer frequencies’ for assemblies and generated best assemblies; (ii) SPAdes Others (in light blue), this group used metaSPAdes or ‘single-cellSPAdes + three or six different kmer frequencies’ for assemblies and generated significantly fewer contigs than the group SPAdes A; and (iii) MEGAHIT (in green), this group used the tool MEGAHIT for assembly and generated substantially fewer contigs than the group SPAdes A. The two-sided p values are provided for comparisons between groups, with significant difference indicated in red.

Supplementary information

Supplementary Information

Supplementary discussion and Figs. 1–7.

Supplementary Data 1 to 15

Supplementary Data 1: Overall characteristics of the nine samples collected from the GP core of Guliya Glacier. Supplementary Data 2: Metagenomic and virus statistics of the nine ice samples collected from the GP core. Supplementary Data 3: Taxonomic assignments and VC summary. Supplementary Data 4: Relative abundances (%) of the 1,705 vOTUs (≥5 kb) across nine Guliya ice samples. Supplementary Data 5: Statistical relationship between viral communities and environmental parameters. Supplementary Data 6: Putative microbial hosts of Guliya viruses. Supplementary Data 7: Gene annotations of all 1,949 Guliya viral contigs. Supplementary Data 8: Annotations of the putative AMGs and CheckV statistics of viral contigs containing AMGs. Supplementary Data 9: Relative abundances and metabolic categories of virus-encoded AMGs over time. Supplementary Data 10: Tests for selection pressure of cobT gene using site and free-ratio models. Supplementary Data 11: Tests for selection pressure of cysH gene using site and free-ratio models. Supplementary Data 12: Summary of the publicly available datasets for assessing the biogeography of Guliya viruses. Supplementary Data 13: Blastn results by comparing Guliya viruses with the viral genomes of lab contaminants. Supplementary Data 14: Relative abundances of the top eight abundant phyla (via 16S rRNA gene amplicons) and their linked viruses (via vOTU coverage) and the VHR over time. Supplementary Data 15: Genomic features of Patescibacteria and seven other phyla.

Source data

Source Data Fig. 2

Euclidean distance matrix of viral communities generated on the basis of the relative abundances of Guliya vOTUs.

Source Data Fig. 3

Relative abundances of the three most dominant genera (Flavobacterium, Polaromonas and Cryobacterium; via 16S rRNA gene amplicons) and their linked viruses (via vOTU coverage) over time.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, ZP., Zablocki, O., Li, YF. et al. Glacier-preserved Tibetan Plateau viral community probably linked to warm–cold climate variations. Nat. Geosci. 17, 912–919 (2024). https://doi.org/10.1038/s41561-024-01508-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-024-01508-z

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing