Abstract

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. Accurate prediction of carbon gas emissions from thawing permafrost is limited by our understanding of these microbial communities. Here we use metagenomic sequencing of 214 samples from a permafrost thaw gradient to recover 1,529 metagenome-assembled genomes, including many from phyla with poor genomic representation. These genomes reflect the diversity of this complex ecosystem, with genus-level representatives for more than sixty per cent of the community. Meta-omic analysis revealed key populations involved in the degradation of organic matter, including bacteria whose genomes encode a previously undescribed fungal pathway for xylose degradation. Microbial and geochemical data highlight lineages that correlate with the production of greenhouse gases and indicate novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in carbon processing, and provide key information for predicting the effects of climate change on permafrost systems.

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References

  1. 1.

    Schuur, E. A. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

  2. 2.

    Roesch, L. F. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).

  3. 3.

    Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).

  4. 4.

    Howe, A. C. et al. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl Acad. Sci. USA 111, 4904–4909 (2014).

  5. 5.

    Johnston, E. R. et al. Metagenomics reveals pervasive bacterial populations and reduced community diversity across the Alaska tundra ecosystem. Front. Microbiol. 7, 579 (2016).

  6. 6.

    Taş, N. et al. Landscape topography structures the soil microbiome in arctic polygonal tundra. Nat. Commun. 9, 777 (2018).

  7. 7.

    Hultman, J. et al. Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).

  8. 8.

    Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31, 533–538 (2013).

  9. 9.

    Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).

  10. 10.

    Johansson, T. et al. Decadal vegetation changes in a northern peatland, greenhouse gas fluxes and net radiative forcing. Glob. Change Biol. 12, 2352–2369 (2006).

  11. 11.

    Jansson, J. K. & Taş, N. The microbial ecology of permafrost. Nat. Rev. Microbiol. 12, 414–425 (2014).

  12. 12.

    Whalen, S. C. Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environ. Eng. Sci. 22, 73–94 (2005).

  13. 13.

    Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, Cambridge, 1995).

  14. 14.

    Kremer, C., Pettolino, F., Bacic, A. & Drinnan, A. Distribution of cell wall components in Sphagnum hyaline cells and in liverwort and hornwort elaters. Planta 219, 1023–1035 (2004).

  15. 15.

    Tveit, A., Schwacke, R., Svenning, M. M. & Urich, T. Organic carbon transformations in high-Arctic peat soils: key functions and microorganisms. ISME J. 7, 299–311 (2013).

  16. 16.

    Ivanova, A. A., Wegner, C. E., Kim, Y., Liesack, W. & Dedysh, S. N. Identification of microbial populations driving biopolymer degradation in acidic peatlands by metatranscriptomic analysis. Mol. Ecol. 25, 4818–4835 (2016).

  17. 17.

    Pankratov, T. A., Ivanova, A. O., Dedysh, S. N. & Liesack, W. Bacterial populations and environmental factors controlling cellulose degradation in an acidic Sphagnum peat. Environ. Microbiol. 13, 1800–1814 (2011).

  18. 18.

    Tveit, A., Urich, T. & Svenning, M. M. Metatranscriptomic analysis of Arctic peat soil microbiota. Appl. Environ. Microbiol. 80, 5761–5772 (2014).

  19. 19.

    Jeffries, T. W. in Pentoses and Lignin 1–32 (Springer, Heidelberg, 1983).

  20. 20.

    Zhang, M. et al. Genetic analysis of d-xylose metabolism pathways in Gluconobacter oxydans 621H. J. Ind. Microbiol. Biotechnol. 40, 379–388 (2013).

  21. 21.

    Kricka, W., Fitzpatrick, J. & Bond, U. Metabolic engineering of yeasts by heterologous enzyme production for degradation of cellulose and hemicellulose from biomass: a perspective. Front. Microbiol. 5, 174 (2014).

  22. 22.

    Kuhn, A., van Zyl, C., van Tonder, A. & Prior, B. A. Purification and partial characterization of an aldo-keto reductase from Saccharomyces cerevisiae. Appl. Environ. Microbiol. 61, 1580–1585 (1995).

  23. 23.

    Sarthy, A. V., Schopp, C. & Idler, K. B. Cloning and sequence determination of the gene encoding sorbitol dehydrogenase from Saccharomyces cerevisiae. Gene 140, 121–126 (1994).

  24. 24.

    Ye, R. et al. pH controls over anaerobic carbon mineralization, the efficiency of methane production, and methanogenic pathways in peatlands across an ombrotrophic–minerotrophic gradient. Soil Biol. Biochem. 54, 36–47 (2012).

  25. 25.

    Horn, M. A., Matthies, C., Küsel, K., Schramm, A. & Drake, H. L. Hydrogenotrophic methanogenesis by moderately acid-tolerant methanogens of a methane-emitting acidic peat. Appl. Environ. Microbiol. 69, 74–83 (2003).

  26. 26.

    Conrad, R. Contribution of hydrogen to methane production and control of hydrogen concentrations in methanogenic soils and sediments. FEMS Microbiol. Ecol. 28, 193–202 (1999).

  27. 27.

    Keller, J. K. & Takagi, K. K. Solid-phase organic matter reduction regulates anaerobic decomposition in bog soil. Ecosphere 4, 54 (2013).

  28. 28.

    Hodgkins, S. B. et al. Changes in peat chemistry associated with permafrost thaw increase greenhouse gas production. Proc. Natl Acad. Sci. USA 111, 5819–5824 (2014).

  29. 29.

    Lipson, D. A., Jha, M., Raab, T. K. & Oechel, W. C. Reduction of iron (iii) and humic substances plays a major role in anaerobic respiration in an Arctic peat soil. J. Geophys. Res. Biogeosci. 115, G00I06 (2010).

  30. 30.

    Klüpfel, L., Piepenbrock, A., Kappler, A. & Sander, M. Humic substances as fully regenerable electron acceptors in recurrently anoxic environments. Nat. Geosci. 7, 195–200 (2014).

  31. 31.

    Christensen, T. R. et al. Thawing sub-arctic permafrost: effects on vegetation and methane emissions. Geophys. Res. Lett. 31, L04501 (2004).

  32. 32.

    Whiticar, M. J. Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane. Chem. Geol. 161, 291–314 (1999).

  33. 33.

    McCalley, C. K. et al. Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514, 478–481 (2014).

  34. 34.

    Mondav, R. et al. Discovery of a novel methanogen prevalent in thawing permafrost. Nat. Commun. 5, 3212 (2014).

  35. 35.

    Stams, A. J. & Plugge, C. M. Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Microbiol. 7, 568–577 (2009).

  36. 36.

    Ishii, S., Kosaka, T., Hori, K., Hotta, Y. & Watanabe, K. Coaggregation facilitates interspecies hydrogen transfer between Pelotomaculum thermopropionicum and Methanothermobacter thermautotrophicus. Appl. Environ. Microbiol. 71, 7838–7845 (2005).

  37. 37.

    Wania, R. et al. Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP). Geosci. Model Dev. 6, 617–641 (2013).

  38. 38.

    Ji, M. et al. Atmospheric trace gases support primary production in Antarctic desert surface soil. Nature 552, 400–403 (2017).

  39. 39.

    Welsh, D. T. Ecological significance of compatible solute accumulation by micro-organisms: from single cells to global climate. FEMS Microbiol. Rev. 24, 263–290 (2000).

  40. 40.

    Rodrigues, D. F. et al. Architecture of thermal adaptation in an Exiguobacterium sibiricum strain isolated from 3 million year old permafrost: a genome and transcriptome approach. BMC Genomics 9, 547 (2008).

  41. 41.

    Maru, B., Bielen, A., Constanti, M., Medina, F. & Kengen, S. Glycerol fermentation to hydrogen by Thermotoga maritima: proposed pathway and bioenergetic considerations. Int. J. Hydrogen Energy 38, 5563–5572 (2013).

  42. 42.

    Vitt, D. H., Halsey, L. A. & Zoltai, S. C. The changing landscape of Canada’s western boreal forest: the current dynamics of permafrost. Can. J. For. Res. 30, 283–287 (2000).

  43. 43.

    Jorgenson, M. T., Racine, C. H., Walters, J. C. & Osterkamp, T. E. Permafrost degradation and ecological changes associated with a warming climate in central Alaska. Clim. Change 48, 551–579 (2001).

  44. 44.

    Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett. 31, L18208 (2004).

  45. 45.

    O’Donnell, J. A. et al. The effects of permafrost thaw on soil hydrologic, thermal, and carbon dynamics in an Alaskan peatland. Ecosystems (N. Y.) 15, 213–229 (2012).

  46. 46.

    Zoltai, S. Cyclic development of permafrost in the peatlands of northwestern Alberta, Canada. Arct. Alp. Res. 25, 240–246 (1993).

  47. 47.

    Quinton, W., Hayashi, M. & Chasmer, L. Permafrost-thaw-induced land-cover change in the Canadian subarctic: implications for water resources. Hydrol. Processes 25, 152–158 (2011).

  48. 48.

    Koven, C. D. et al. Permafrost carbon–climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).

  49. 49.

    Melton, J. et al. Present state of global wetland extent and wetland methane modelling: conclusions from a model intercomparison project (WETCHIMP). Biogeosciences 10, 753–788 (2013).

  50. 50.

    Whiticar, M. J., Faber, E. & Schoell, M. Biogenic methane formation in marine and freshwater environments: CO2 reduction vs. acetate fermentation—isotope evidence. Geochim. Cosmochim. Acta 50, 693–709 (1986).

  51. 51.

    Angly, F. E. et al. CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction. Microbiome 2, 11 (2014).

  52. 52.

    Pielou, E. C. Shannon’s formula as a measure of specific diversity: its use and misuse. Am. Nat. 100, 463–465 (1966).

  53. 53.

    Oksanen, J. et al. The vegan package. Commun. Ecol. Package 10, 631–637 (2007).

  54. 54.

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

  55. 55.

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

  56. 56.

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

  57. 57.

    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).

  58. 58.

    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).

  59. 59.

    Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010).

  60. 60.

    Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science 350, 434–438 (2015).

  61. 61.

    Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).

  62. 62.

    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).

  63. 63.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).

  64. 64.

    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

  65. 65.

    Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).

  66. 66.

    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).

  67. 67.

    R Computing Team R Language Definition (R Foundation for Statistical Computing, Vienna, 2000).

  68. 68.

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

  69. 69.

    Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).

  70. 70.

    Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–W51 (2012).

  71. 71.

    Bairoch, A. The ENZYME database in 2000. Nucleic Acids Res. 28, 304–305 (2000).

  72. 72.

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

  73. 73.

    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).

  74. 74.

    Morozova, D. & Wagner, D. Stress response of methanogenic archaea from Siberian permafrost compared with methanogens from nonpermafrost habitats. FEMS Microbiol. Ecol. 61, 16–25 (2007).

  75. 75.

    Lay, J.-J., Miyahara, T. & Noike, T. Methane release rate and methanogenic bacterial populations in lake sediments. Water Res. 30, 901–908 (1996).

  76. 76.

    Li, Y.-Y. & Noike, T. Upgrading of anaerobic digestion of waste activated sludge by thermal pretreatment. Water Sci. Technol. 26, 857–866 (1992).

  77. 77.

    Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).

  78. 78.

    Costello, A. M., Auman, A. J., Macalady, J. L., Scow, K. M. & Lidstrom, M. E. Estimation of methanotroph abundance in a freshwater lake sediment. Environ. Microbiol. 4, 443–450 (2002).

  79. 79.

    Baani, M. & Liesack, W. Two isozymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp. strain SC2. Proc. Natl Acad. Sci. USA 105, 10203–10208 (2008).

  80. 80.

    Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

  81. 81.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  82. 82.

    Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).

  83. 83.

    Guo, X. & Kristal, B. S. The use of underloaded C(18) solid-phase extraction plates increases reproducibility of analysis of tryptic peptides from unfractionated human plasma. Anal. Biochem. 426, 86–90 (2012).

  84. 84.

    Kim, S. & Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).

  85. 85.

    Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

  86. 86.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

  87. 87.

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

  88. 88.

    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).

  89. 89.

    Racine, J. S. RStudio: A platform-independent IDE for R and Sweave. J. Appl. Econ. 27, 167–172 (2012).

  90. 90.

    Vizcaíno, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44 (D1), D447–D456 (2016).

  91. 91.

    Bertin, P. N. et al. Metabolic diversity among main microorganisms inside an arsenic-rich ecosystem revealed by meta- and proteo-genomics. ISME J. 5, 1735–1747 (2011).

  92. 92.

    Parks, D. H. et al. A proposal for a standardized bacterial taxonomy based on genome phylogeny. Preprint at https://www.biorxiv.org/content/early/2018/01/30/256800 (2018).

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Acknowledgements

This study was funded by the Genomic Science Program of the United States Department of Energy (DOE) Office of Biological and Environmental Research (BER), grants DE-SC0004632, DE-SC0010580 and DE-SC0016440. B.J.W. and P.N.E. are supported by Australian Research Council Discovery Early Career Research Awards #DE160100248 and #DE170100428, respectively. C.M.S. and J.A.B. are supported by Australian Government Research Training Program (RTP) Scholarships, and G.W.T. is supported by Australian Research Council Future Fellowship FT170100070. A portion of the research was performed using Environmental Molecular Sciences Laboratory (EMSL), a DOE Office of Science User Facility, and a portion was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative with resources at both the DOE Joint Genome Institute and EMSL. Both facilities are sponsored by the Office of BER and operated under contracts DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). We thank the IsoGenie 1 and 2 Project Teams and the 2010–2012 field teams for sample collection, particularly T. Logan, as well as the Abisko Scientific Research Station for sampling infrastructure and support. We thank P. Hugenholtz, D. Parks, S. Robbins, B. Kemish, M. Chuvochina, S. Low and M. Butler for helpful discussion and infrastructure support.

Reviewer information

Nature thanks S. Allison, J. Jansson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Joanne B. Emerson

    Present address: Department of Plant Pathology, University of California, Davis, CA, USA

  1. These authors contributed equally: Ben J. Woodcroft, Caitlin M. Singleton

Affiliations

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

    • Ben J. Woodcroft
    • , Caitlin M. Singleton
    • , Joel A. Boyd
    • , Paul N. Evans
    • , Robert D. Hoelzle
    • , Timothy O. Lamberton
    •  & Gene W. Tyson
  2. Department of Microbiology, The Ohio State University, Columbus, OH, USA

    • Joanne B. Emerson
    • , Ahmed A. F. Zayed
    •  & Virginia I. Rich
  3. Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY, USA

    • Carmody K. McCalley
  4. Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA

    • Suzanne B. Hodgkins
    • , Rachel M. Wilson
    •  & Jeffrey P. Chanton
  5. Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA

    • Samuel O. Purvine
    •  & Carrie D. Nicora
  6. Earth Systems Research Center, Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA

    • Changsheng Li
    •  & Steve Frolking
  7. Department of Geological Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden

    • Patrick M. Crill
  8. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    • Scott R. Saleska

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Contributions

C.L., S.F., J.P.C., P.M.C., S.R.S., V.I.R. and G.W.T. designed the overall study of microbial and biogeochemical dynamics of permafrost thaw and procured funding. V.I.R. coordinated sampling efforts, and S.B.H., J.P.C. and G.W.T. collected samples. B.J.W., C.M.S., J.A.B., V.I.R. and G.W.T. designed experiments around specific microbial hypotheses. J.B.E., A.A.F.Z., S.O.P., and C.D.N performed the protein extractions and analyses. C.K.M., S.B.H. and R.M.W. performed geochemical analyses. B.J.W., C.M.S., J.A.B., P.N.E, R.D.H. and T.O.L. carried out microbial experiments and integration of microbial and geochemical data. B.J.W., C.M.S. and G.W.T. wrote the manuscript with contributions from J.A.B., P.N.E. and R.D.H. All authors except C.L. edited, reviewed and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Gene W. Tyson.

Extended data figures and tables

  1. Extended Data Fig. 1 Phylogenetic distribution of MAGs recovered from Stordalen Mire.

    a, Phylogenetic tree of 647 dereplicated MAGs. Numbers in parentheses show total MAGs recovered and phylogenetic gain of Stordalen MAGs compared to publicly available genomes for each phylum. Red text indicates previously poorly represented phyla. b, Acidobacteria subtree showing the Ca. ‘Acidiflorens’ lineages. c, Eremiobacteraeota subtree incorporating the CARN191 MAG. d, Dormibacteraeota subtree, showing Ca. ‘Changshengia’. e, Subtree of Ca. ‘Methanoflorentales’ MAGs, and closest neighbouring orders. In be, pie charts show phylogenetic gain, red lines indicate Stordalen MAGs, black lines indicate public genomes, blue triangles indicate clustered public genomes and red triangles indicate clustered Stordalen MAGs. Black dots indicate bootstrap values 70–100%.

  2. Extended Data Fig. 2 Microbial community profile of the thaw gradient.

    a, Relative abundance of each phylum estimated through the recovery of 16S rRNA gene reads, averaged within each thaw stage. The 15 phyla with the highest relative abundance across all samples are shown. b, Number of MAGs recovered from each of the phyla in a, showing that broadly, MAGs recovered are from lineages highest in abundance. c, Principal coordinates analysis of weighted UniFrac compositional differences between samples, based on average coverage of each recovered genome of reads mapped to the dereplicated genome set. Colours indicate thaw stage: brown = palsa (P), green = sphagnum/bog (S), blue = eriophorum/fen (E). Depth: S = surface, M = mid-depth, D = deep, X = extra-deep. Goodness of fit was 0.57 for PCoA 1 and 0.65 for PCoA 2. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively. d, Quantitative PCR analysis of samples taken in 2012. The number of cells per gram of soil is shown for three depths at the three thaw stages, after correcting for 16S rRNA gene copy number variation (see Methods). Fen samples contained significantly more cells per gram of soil than bog and palsa samples (average 2.6×, P = 7 × 10–8, n = 103, two-sided Mann–Whitney U-test). Sample numbers: n = 8, 9, and 8 for biologically independent samples palsa surface, mid and deep, respectively, n = 9, 8, 9 and 10, 7 and 9 for bog and fen, respectively. e, f, Relative abundances of phyla and classes within the Proteobacteria across the thaw gradient, respectively. The depth of each sample is indicated by the colour of the box (surface: red, mid-depth: green, deep: blue, extra-deep: purple). Each data point is the sum of relative abundances of all lineages assigned to the phylum in a sample after adding a 0.1% pseudocount to all phyla (so the y axis is not dominated by small values visually). Box plots are shown plotted on a log-scale y axis, with phyla and classes ordered by decreasing average relative abundance across all samples. Relative abundance was calculated based on the fraction of the community with recovered genomes (see Methods). Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively.

  3. Extended Data Fig. 3 Prevalence of individual MAGs across the thaw gradient.

    a, Number of samples where each Stordalen MAG is present at >1% relative abundance among each stage of the thaw gradient. Vertical red lines indicate the number of samples sequenced in total from that environment. Only one MAG, ‘Deltaproteobacteria_fen_1087’, was found in a high abundance across fen sites, detected at >1% relative abundance in 96% of fen sites. b, The same information stratified by depth of the sample in the soil column. The specific MAGs prevalent are detailed in Extended Data Table 1, showing that a small number of populations were prevalent at a specific depth of a specific site. c, Stordalen genomes that changed significantly in abundance with depth. For each site, genomes that show the largest absolute difference in abundance between shallow and deep samples are shown. Genomes that are more abundant in shallow samples compared to deep are positive, and those more abundant in deep samples relative to shallow samples are negative. Only those lineages with a mean absolute difference of >1% and that are significantly different (P < 0.05, two-sided Mann–Whitney U-test) are shown. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively. Each bar indicates a 97% dereplicated MAG that changes in relative abundance between surface and deep samples and the colour of each indicates the phylum the genome belongs to. The fen is less stratified between the surface and deep, which is reflected in the fewer population abundances significantly changing in abundance between shallow and deep samples. Recovered congeneric genomes that showed significant but inverse differential abundance between surface and deep samples are shown in Supplementary Data 7. Genomes depicted in c in order are Acidobacteria_palsa_348 = 1, Acidobacteria_palsa_246 = 2, Actinobacteria_palsa_463 = 3, Actinobacteria_palsa_558 = 4, Acidobacteria_palsa_312 = 5, Alphaproteobacteria_palsa_929 = 6, Actinobacteria_palsa_504 = 7, Acidobacteria_palsa_125 = 8, WPS2_palsa_1515 = 9, Acidobacteria_palsa_289 = 10, WPS2_palsa_1516 = 11, Acidobacteria_palsa_310 = 12, Alphaproteobacteria_palsa_913 = 13, Actinobacteria_palsa_693 = 14, Actinobacteria_palsa_465 = 15, Actinobacteria_palsa_691 = 16, Alphaproteobacteria_palsa_895 = 17, Actinobacteria_palsa_505 = 18, Actinobacteria_bog_593 = 19, Actinobacteria_palsa_462 = 20, Acidobacteria_palsa_199 = 21, Acidobacteria_palsa_362 = 22, Acidobacteria_palsa_313 = 23, Gammaproteobacteria_palsa_1209 = 24, Acidobacteria_palsa_267 = 25, Planctomycetes_palsa_1347 = 26, Acidobacteria_palsa_143 = 27, Verrucomicrobia_palsa_1397 = 28, Actinobacteria_palsa_641 = 29, Actinobacteria_palsa_733 = 30, Acidobacteria_palsa_420 = 31, Actinobacteria_palsa_736 = 32, Verrucomicrobia_palsa_1413 = 33, Alphaproteobacteria_palsa_910 = 34, Acidobacteria_palsa_286 = 35, Acidobacteria_palsa_122 = 36, Acidobacteria_palsa_343 = 37, Deltaproteobacteria_palsa_1114 = 38, Gemmatimonadetes_palsa_1248 = 39, Acidobacteria_palsa_340 = 40, Acidobacteria_palsa_141 = 41, Alphaproteobacteria_palsa_922 = 42, WPS2_palsa_1496 = 43, Actinobacteria_bog_635 = 44, Actinobacteria_bog_766 = 45, Actinobacteria_bog_592 = 46, Gammaproteobacteria_bog_1200 = 47, Actinobacteria_bog_594 = 48, Acidobacteria_bog_329 = 49, Verrucomicrobia_bog_1475 = 50, Actinobacteria_bog_723 = 51, Acidobacteria_bog_233 = 52, Verrucomicrobia_bog_1402 = 53, WPS2_bog_1492 = 54, Alphaproteobacteria_bog_899 = 55, WPS2_bog_1527 = 56, Actinobacteria_bog_769 = 57, Acidobacteria_bog_377 = 58, Actinobacteria_bog_637 = 59, FCPU426_bog_1183 = 60, Alphaproteobacteria_bog_900 = 61, Acidobacteria_bog_234 = 62, WPS2_bog_1502 = 63, Verrucomicrobia_bog_1421 = 64, Gammaproteobacteria_bog_1206 = 65, Alphaproteobacteria_bog_908 = 66, Betaproteobacteria_bog_994 = 67, Acidobacteria_fen_416 = 68, Actinobacteria_fen_548 = 69, Acidobacteria_bog_445 = 70, Acidobacteria_bog_96 = 71, Acidobacteria_bog_202 = 72, Actinobacteria_fen_455 = 73, AD3_bog_854 = 74, Acidobacteria_bog_218 = 75, Actinobacteria_bog_806 = 76, Acidobacteria_bog_390 = 77, Actinobacteria_bog_524 = 78, Euryarchaeota_bog_81 = 79, Verrucomicrobia_bog_1459 = 80, AD3_bog_876 = 81, Actinobacteria_bog_808 = 82, Acidobacteria_bog_226 = 83, Actinobacteria_bog_576 = 84, Acidobacteria_bog_406 = 85, Acidobacteria_fen_408 = 86, Deltaproteobacteria_fen_1088 = 87, Nitrospirae_fen_1304 = 88, Bacteroidetes_fen_982 = 89, Bacteroidetes_fen_956 = 90, Acidobacteria_fen_335 = 91, Euryarchaeota_fen_63 = 92, Gammaproteobacteria_fen_1191 = 93, Deltaproteobacteria_fen_1087 = 94, Gammaproteobacteria_fen_1218 = 95, Gammaproteobacteria_fen_1219 = 96, Actinobacteria_fen_730 = 97, Deltaproteobacteria_fen_1138 = 98, Chloroflexi_fen_1050 = 99, Actinobacteria_fen_453 = 100, Acidobacteria_fen_408 = 101, Actinobacteria_fen_548 = 102, Chloroflexi_fen_1019 = 103, Acidobacteria_fen_414 = 104, Actinobacteria_fen_455 = 105.

  4. Extended Data Fig. 4 Cellulase, xylanase and β-glucosidase gene expression across the thaw gradient.

    a, b, Cellulose; c, d, xylanase; e, f, β-glucosidase. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. a, c, e, Relative contribution of each phylum to the total TPM of the enzyme class observed in the metatranscriptomes. b, d, f, Total TPM of all expressed genes in the sample.

  5. Extended Data Fig. 5 Monosaccharide degradation pathway prevalence at Stordalen Mire.

    a, As in Fig. 2, 97% dereplicated MAGs are shown as circles (‘MAG abundance’), where the radius of the circle represents the average relative abundance of that genome in the palsa, bog or fen. b, As in Fig. 2, the total relative abundance of genomes encoding the pathway is shown among the entire community. Sample numbers: n = 53, 65 and 70 biologically independent samples for palsa, bog and fen, respectively.

  6. Extended Data Fig. 6 Xylose degradation pathways at Stordalen Mire.

    a, Diagram of xylose degradation pathways. b, Venn diagram showing how each xylose breakdown pathway is shared among the Stordalen Mire MAGs. Percentages represent the proportion compared to all Stordalen genomes encoding a xylose degradation pathway. In the metaproteomes, genomes Acidobacteria_bog_390, Actinobacteria_fen_455 and Actinobacteria_bog_808 expressed a protein specific to oxidoreductase pathways and a protein specific to the isomerase pathway. In the metatranscriptomes, Acidobacteria_palsa_248, Acidobacteria_bog_370, Acidobacteria_bog_390, Actinobacteria_fen_455, Actinobacteria_bog_586, Actinobacteria_bog_808 and Planctomycetes_fen_1346 expressed a protein specific to oxidoreductase pathways and a protein specific to the isomerase pathway. ch, Gene expression of xylose degradation pathways. Average expression of genes in the canonical bacterial xylose isomerase (c, d), oxidoreductase (e, f) and xylanate dehydratase pathways (g, h) are depicted across the thaw gradient. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. c, e, g, Relative contribution of each phylum to the total TPM of the enzyme class observed in the metatranscriptomes. d, f, h, Total TPM of all expressed genes in the sample.

  7. Extended Data Fig. 7 Gene expression of fermentation pathways.

    Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. a, c, e, g, Total TPM of each fermentation pathway in the metatranscriptomes. b, d, f, h, Relative contribution of each phylum to the total TPM of each pathway.

  8. Extended Data Fig. 8 Correlation of microbial and geochemical data.

    a, CO2 and CH4 concentrations in porewater derived from the bog and fen. The blue line shown is a line of best fit, forced through the origin. Dots indicate the samples, with colours indicating the sample depth. The concentrations are correlated, and the CH4 concentrations are much lower than the CO2 concentrations in both sites. Sample numbers: n = 51 (bog) and 61 (fen) biologically independent samples. b, Methanogenesis versus methanotrophy rates. Each point represents the average relative abundance of methanotrophs and methanogens across all samples in a single core, multiplied by the rate of methane generation or consumption inferred from previous culture-based measurements (2.345 and 20.1 fmol CH4 h–1 per cell of methanogenesis and methanotrophy, respectively, see Methods). The line represents the 1:1 ratio. Inferred fluxes were calculated using relative abundance of methanogenic or methanotrophic lineages so rates are only intended for comparison between the x and axes, rather than as an absolute measure of CH4 flux. Methanotrophy appears to mitigate a significant proportion of the CH4 generated in the bog sites. c, Correlation of the relative abundance of Ca. ‘Methanoflorens stordalenmirensis’ with the isotopic fractionation of methane (αC) dissolved in paired porewater samples taken from the bog. Previously observed in 2011 using 16S rRNA gene amplicon sequencing33, the correlation is confirmed here using genome-centric metagenomic techniques on the 2011 samples, as well as in a new year of sampling in 2012. Sample numbers: n = 23 (2011) and 24 (2012) biologically independent samples. d, e, Expression of methanogenesis marker gene mcrA across the thaw gradient. Samples analysed with metatranscriptomics are described by the date of sampling, core number and depth. d, Relative contribution of each methanogenic order to the total TPM. e, Relative contribution of all mcrA genes in the metatranscriptome. Metaproteomes revealed the expression of 289 hydrogenotrophic McrA proteins across 13 samples, as well as 78 acetoclastic McrA proteins across eight samples (Supplementary Data 2). f, Linear regression analysis for predicting effective fractionation (αc) of CH4 from environmental variables and Ca. ‘Methanoflorens stordalenmirensis’ abundances in the bog. Ca. ‘Methanoflorens stordalenmirensis’ abundance exceeds bulk geochemical parameters in predicting the effective fractionation of CH4. Each line is the result of a linear regression of the specified measurement against the αc of CH4 in bog porewater samples taken in 2011 and 2012 (n = 47 biologically independent samples).

  9. Extended Data Fig. 9 Candidate phylum Dormibacteraeota (AD3) genus Ca. ‘Changshengia’ at Stordalen Mire.

    a, Total relative abundance of the genus Ca. ‘Changshengia’ correlated with the fraction of the concentration of C mineralized to CO2 versus CH4 in the bog porewater samples (R2 = 0.19, P = 0.001, n = 51 biologically independent samples). Each point represents an individual sample from 2012, with its colour representing the depth in the core from which the sample was taken. b, Metabolic reconstruction of genomes belonging to the candidate phylum AD3 genus Ca. ‘Changshengia’ correlating with the CH4:CO2 concentration ratio in porewater from 2012 bog samples. Genomes from four clades within the AD3 were assembled from across Stordalen Mire. Enzyme colour indicates the families that share that metabolic potential, as outlined in the legend on the left. Arrow colouring indicates whether expression was detected (red arrows) or not detected (black arrows) for genes encoding the enzyme in any of the 24 metatranscriptomes. Orange stars indicate detection of protein expression in any of the 22 metaproteomes from the Ca. ‘Changshengia’ and related genomes. All four lineages encode the potential to oxidize glycerol anaerobically through glycerol transporter (glpF), glycerol kinase (glpK) and a membrane-bound glycerol-3-phosphate dehydrogenase (glpABC), entering glycolysis via dihydroxyacetone phosphate processed to glyceraldehyde-3-phosphate by the triosephosphate isomerase (tpiA). Other glycerol derivatives such as glycerol-3-phosphate could be imported (glpT) by this and other family members, and dihydroxyacetone phosphate can also be processed using the PTS-dependent dihydroxyacetone kinase (dhaLMK) complex. Sinks for the electrons generated from the oxidation of glycerol also varied between the different lineages, with Ca. ‘Changshengia’ and clade 1 having a H+-translocating complex I NADH:oxidoreductase, while clade 1 also has a high affinity cytochrome oxidase complex IV, clade 2 genomes encode only a nitrate reductase (narGHI) and clade 4 genomes only a fumarate reductase (sdhABCD). These differences are likely to lead to the differentiation of the niches that each lineage occupies across different sites and depths of the mire. Lineages were considered positive for genes or complexes based on the presence of sequences with 80% homology in 50% of the genomes. c, Phylogenetic subtree showing the family groupings of AD3 for the metabolic analysis. Representative genomes from the 97% average nucleotide identity (ANI) dereplication are indicated in red. Bootstrap support is indicated at the nodes for values over 70% or 90% in grey and black, respectively. Blue clade indicates cluster of seven UBA and RefSeq genomes.

  10. Extended Data Table 1 Genomes with high prevalence in specific sites and depths
  11. Extended Data Table 2 Overview of proteins detected using metaproteomics

Supplementary information

  1. Supplementary Information

    This file contains the Supplementary Notes, Supplementary References and a guide to Supplementary Data files 10–14, which are available on figshare.

  2. Reporting Summary

  3. Supplementary Data 1

    Details about each sample included in this study.

  4. Supplementary Data 2

    Proteins identified as expressed through metaproteomics, including the sample, the name of each protein, where the first 3 elements in snake case refer to the genome, and the final number refers to the protein ID within that genome, peptide and assigned annotation (KEGG orthology group or hydrolysis enzyme annotation).

  5. Supplementary Data 3

    Characteristics of recovered MAGs.

  6. Supplementary Data 4

    Abundances of representative lineages across the thaw gradient. Each column represents the relative abundance of a lineage in a particular sample. The column 'coverage' indicates the trimmed mean pileup (BamM 'tpmean') coverage of reads, 'relative_abundance' represents the fraction of the total community that lineage is estimated to represent, and 'relative_abundance_of_recovered' represents the fraction of the recovered part of the community that genome represents.

  7. Supplementary Data 5

    Pathways encoded by each MAG detected through automated methods.

  8. Supplementary Data 6

    Geochemical characteristics measured at each site.

  9. Supplementary Data 7

    Congeneric genomes which show significant differential abundance between surface and deep samples.

  10. Supplementary Data 8

    Copyrighter database of the GreenGenes 2013 taxonomy.

  11. Supplementary Data 9

    Custom KEGG module definitions used to define metabolic pathways assigned to each MAG.

About this article

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DOI

https://doi.org/10.1038/s41586-018-0338-1

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