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A genome and gene catalog of glacier microbiomes

Nature Biotechnology volume 40, pages 1341–1348 (2022)Cite this article

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Abstract

Glaciers represent a unique inventory of microbial genetic diversity and a record of evolution. The Tibetan Plateau contains the largest area of low-latitude glaciers and is particularly vulnerable to global warming. By sequencing 85 metagenomes and 883 cultured isolates from 21 Tibetan glaciers covering snow, ice and cryoconite habitats, we present a specialized glacier microbial genome and gene catalog to archive glacial genomic and functional diversity. This comprehensive Tibetan Glacier Genome and Gene (TG2G) catalog includes 883 genomes and 2,358 metagenome-assembled genomes, which represent 968 candidate species spanning 30 phyla. The catalog also contains over 25 million non-redundant protein-encoding genes, the utility of which is demonstrated by the exploration of secondary metabolite biosynthetic potentials, virulence factor identification and global glacier metagenome comparison. The TG2G catalog is a valuable resource that enables enhanced understanding of the structure and functions of Tibetan glacial microbiomes.

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Fig. 1: Geographical distribution of metagenome-assembled genomes.
Fig. 2: TGGs reveal the uniqueness of the microbiome in the glacier ecosystem.
Fig. 3: Functional novelty of the TG2G catalog.
Fig. 4: TGGs harbor a diverse range of potential pathogenic factors.
Fig. 5: Principal component analysis plots of Tibetan, Alps and Arctic glacier microbial communities.

Data availability

The TG2G catalog is organized in three levels: Assembled contigs, Genes and Genomes (Supplementary Fig. 9). The assembled contig section contains the sequence of non-redundant contigs and the coverage of each contig across all samples; the gene section contains the sequences of non-redundant ORFs, the coverage of each gene across all samples and the annotation of all genes using eggNOG, CARD, CAZy, COG, VFDB and KEGG; and the genome section contains the genomes quality assessment file, taxonomic classification, the representative genomes of OTUs and their coverage across all samples, as well as the annotation of genes against the same databases as the gene section of the catalog. The catalog in the structure described is available at the National Omics Data Encyclopedia under project ID OEP003083. Raw reads are also deposited in the NCBI under the project Tibetan Glacier Genome and Gene Catalogue (PRJNA813429) with SRA numbers SRR18576994–SRR18577078 (ref. 69). Additional global glacier metagenomes were downloaded from the NCBI under project IDs PRJNA445613, PRJNA360211, PRJEB12327 and PRJNA283341. The supplementary table file is available at https://doi.org/10.6084/m9.figshare.19766653.

References

  1. Zawierucha, K. & Shain, D. H. Disappearing Kilimanjaro snow—are we the last generation to explore equatorial glacier biodiversity? Ecol. Evol. 9, 8911–8918 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hodson, A. et al. Glacial ecosystems. Ecol. Monogr. 78, 41–67 (2008).

    Article  Google Scholar 

  3. Anesio, A. M., Lutz, S., Chrismas, N. A. M. & Benning, L. G. The microbiome of glaciers and ice sheets. NPJ Biofilms Microbiomes 3, 10 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Stibal, M., Sabacka, M. & Zarsky, J. Biological processes on glacier and ice sheet surfaces. Nat. Geosci. 5, 771–774 (2012).

    Article  CAS  Google Scholar 

  5. Pittino, F. et al. Bacterial communities of cryoconite holes of a temperate alpine glacier show both seasonal trends and year-to-year variability. Ann. Glaciol. 59, 1–9 (2018).

    Article  Google Scholar 

  6. Boetius, A., Anesio, A. M., Deming, J. W., Mikucki, J. A. & Rapp, J. Z. Microbial ecology of the cryosphere: sea ice and glacial habitats. Nat. Rev. Microbiol. 13, 677–690 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. Yarzábal, L. A., Salazar, L. M. B. & Batista-García, R. A. Climate change, melting cryosphere and frozen pathogens: should we worry…? Environ. Sustain. 4, 489–501 (2021).

    Article  Google Scholar 

  8. Knowlton, C., Veerapaneni, R., D’Elia, T. & Rogers, S. O. Microbial analyses of ancient ice core sections from greenland and antarctica. Biology 2, 206–232 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Miteva, V. I. & Brenchley, J. E. Detection and isolation of ultrasmall microorganisms from a 120,000-year-old Greenland glacier ice core. Appl. Environ. Microbiol. 71, 7806–7818 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ma, L.-J., Rogers, S. O., Catranis, C. M. & Starmer, W. T. Detection and characterization of ancient fungi entrapped in glacial ice. Mycologia 92, 286–295 (2000).

    Article  Google Scholar 

  11. Christner, B. C., Mosley-Thompson, E., Thompson, L. G. & Reeve, J. N. Bacterial recovery from ancient glacial ice. Environ. Microbiol. 5, 433–436 (2003).

    Article  CAS  PubMed  Google Scholar 

  12. Santibanez, P. A. et al. Prokaryotes in the WAIS Divide ice core reflect source and transport changes between Last Glacial Maximum and the early Holocene. Glob. Chang. Biol. 24, 2182–2197 (2018).

    Article  PubMed  Google Scholar 

  13. Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Chang. 2, 663–667 (2012).

    Article  Google Scholar 

  14. Bibi, S. et al. Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: a review. Int. J. Climatol. 38, e1–e17 (2018).

    Article  Google Scholar 

  15. Cauvy-Fraunié, S. & Dangles, O. A global synthesis of biodiversity responses to glacier retreat. Nat. Ecol. Evol. 3, 1675–1685 (2019).

    Article  PubMed  Google Scholar 

  16. Stibal, M. et al. Glacial ecosystems are essential to understanding biodiversity responses to glacier retreat. Nat. Ecol. Evol. 4, 686–687 (2020).

    Article  PubMed  Google Scholar 

  17. Souney, J. M. et al. Core handling, transportation and processing for the South Pole ice core (SPICEcore) project. Ann. Glaciol. 62, 118–130 (2021).

    Article  Google Scholar 

  18. Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Mogrovejo-Arias, D. C., Brill, F. H. H. & Wagner, D. Potentially pathogenic bacteria isolated from diverse habitats in Spitsbergen, Svalbard. Environ. Earth Sci. 79, 109 (2020).

    Article  Google Scholar 

  20. Abedon, S. T. & Lejeune, J. T. Why bacteriophage encode exotoxins and other virulence factors. Evol. Bioinform. Online 1, 97–110 (2007).

    PubMed  PubMed Central  Google Scholar 

  21. Casacuberta, E. & Gonzalez, J. The impact of transposable elements in environmental adaptation. Mol. Ecol. 22, 1503–1517 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Soucy, S. M., Huang, J. & Gogarten, J. P. Horizontal gene transfer: building the web of life. Nat. Rev. Genet. 16, 472–482 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cao, S. et al. Structure and function of the Arctic and Antarctic marine microbiota as revealed by metagenomics. Microbiome 8, 47 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Royo-Llonch, M. et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nat. Microbiol. 6, 1561–1574 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. 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 Central  Google Scholar 

  27. Benaud, N. et al. Harnessing long-read amplicon sequencing to uncover NRPS and type I PKS gene sequence diversity in polar desert soils. FEMS Microbiol. Ecol. 95, fiz031 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Kautsar, S. A. et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 48, D454–D458 (2019).

    PubMed Central  Google Scholar 

  29. Vila, E., Hornero-Méndez, D., Azziz, G., Lareo, C. & Saravia, V. Carotenoids from heterotrophic bacteria isolated from Fildes Peninsula, King George Island, Antarctica. Biotechnol. Rep. (Amst). 21, e00306 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Moeller, R., Horneck, G., Facius, R. & Stackebrandt, E. Role of pigmentation in protecting Bacillus sp. endospores against environmental UV radiation. FEMS Microbiol. Ecol. 51, 231–236 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Nupur, L. N. U. et al. ProCarDB: a database of bacterial carotenoids. BMC Microbiol. 16, 96–96 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Anesio, A. M. & Laybourn-Parry, J. Glaciers and ice sheets as a biome. Trends Ecol. Evol. 27, 219–225 (2012).

    Article  PubMed  Google Scholar 

  33. Feller, G. Life at low temperatures: is disorder the driving force? Extremophiles 11, 211–216 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Houwenhuyse, S., Macke, E., Reyserhove, L., Bulteel, L. & Decaestecker, E. Back to the future in a petri dish: origin and impact of resurrected microbes in natural populations. Evol. Appl. 11, 29–41 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 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 

  37. 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 

  38. 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 

  39. 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 

  40. Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. 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 

  42. 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).

    Article  CAS  PubMed  Google Scholar 

  43. 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 

  44. Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nawrocki, E. P., Kolbe, D. L. & Eddy, S. R. Infernal 1.0: inference of RNA alignments. Bioinformatics 25, 1335–1337 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kalvari, I. et al. Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. 46, D335–d342 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. 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 

  48. Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat. Biotechnol. 39, 105–114 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–w259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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 

  52. Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Boeckmann, B. et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B. & Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–d361 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Levasseur, A., Drula, E., Lombard, V., Coutinho, P. M. & Henrissat, B. Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes. Biotechnol. Biofuels 6, 41 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Jia, B. et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).

    Article  CAS  PubMed  Google Scholar 

  63. Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).

    CAS  PubMed  Google Scholar 

  64. Liu, B., Zheng, D., Jin, Q., Chen, L. & Yang, J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 47, D687–D692 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  66. de Nies, L. et al. PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data. Microbiome 9, 49 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Blin, K. et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47, W81–w87 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Navarro-Muñoz, J. C. et al. A computational framework to explore large-scale biosynthetic diversity. Nat. Chem. Biol. 16, 60–68 (2020).

    Article  PubMed  CAS  Google Scholar 

  69. Tibetan Glacier Genome and Gene catalogue Raw sequence reads. Tibetan Glacier Genome and Gene catalogue. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB12327 (2022).

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Acknowledgements

This study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) 2019QZKK0503 (Y. Liu and K.L.) and 2021QZKK0100 (P.L., Y. Luo and T. Yu); the National Key Research and Development Plans 2019YFC1509103 (Y. Liu) and 2021YFC2300904 (M.J.); and the Major Research Plan of the National Natural Science Foundation of China 91851207 (Y. Liu and Y.C.).

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  1. These authors contributed equally: Yongqin Liu, Mukan Ji, Tao Yu.

Authors and Affiliations

  1. Center for Pan-third Pole Environment, Lanzhou University, Lanzhou, China

    Yongqin Liu, Mukan Ji & Pengfei Liu

  2. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Yongqin Liu, Zhihao Zhang, Keshao Liu, Yuying Chen & Tandong Yao

  3. University of Chinese Academy of Sciences, Beijing, China

    Yongqin Liu, Tao Yu, Zhihao Zhang, Songnian Hu, Yuying Chen & Yingfeng Luo

  4. State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China

    Tao Yu, Songnian Hu & Yingfeng Luo

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

    Julian Zaugg & Philip Hugenholtz

  6. Department of Environmental Science, iClimate, Aarhus University, Roskilde, Denmark

    Alexandre M. Anesio

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Contributions

Y. Liu, M.J. and T. Yao designed the study. T. Yu, Z.Z., Y. Luo and Y.C. performed the analyses. Y. Liu and M.J. led the writing of the manuscript, with results interpretations from J.Z., A.M.A., P.H., S.H., P.L. and K.L. All authors contributed to the editing of the text and approved the final version.

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Correspondence to Yongqin Liu or Yingfeng Luo.

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Liu, Y., Ji, M., Yu, T. et al. A genome and gene catalog of glacier microbiomes. Nat Biotechnol 40, 1341–1348 (2022). https://doi.org/10.1038/s41587-022-01367-2

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